Biology subjects

Craddock, T. J. A.

Publications and source records attributed to Craddock, T. J. A..

4 recordsLinked to original sources

bioRxiv2025

Computational modeling-directed combination treatment with etanercept and mifepristone mitigates neuroinflammation in a mouse model of Gulf War Illness.

Gulf War Illness is a chronic multi-symptom disorder experienced by over 30% of veterans from the 1990-1991 Gulf War and is increasingly recognized to be driven by underlying persistent neuroinflammation resulting from chemical and physiological exposures experienced during deployment. Despite significant advances in identifying Gulf War-relevant exposures and underlying pathobiology, effective treatment strategies for Gulf War Illness are still largely lacking. Many studies that have evaluated potential therapies for Gulf War Illness have primarily focused on a single treatment. However, through a mechanistically informed computational evaluation of blood biomarkers and gene expression in veterans with Gulf War Illness, we identified that a combination of anti-inflammatory and anti-glucocorticoid treatment may prove effective in treating Gulf War Illness. Here, we have evaluated combined treatment with the anti-TNF drug, etanercept, and anti-glucocorticoid, mifepristone, in an established long-term mouse model of Gulf War Illness of combined physiological stress and nerve agent exposure. Supporting results from the computational modeling of this treatment, we found that this drug combination significantly alleviates the underlying neuroinflammation associated with Gulf War Illness. The fusion of computational and in vivo preclinical treatment evaluation may provide a highly useful and translationally relevant means by which to identify successful treatment paradigms for Gulf War Illness.

neuroscience↗

bioRxiv2025

Magnetic Field-dependent Isotope Effect Supports Radical Pair Mechanism in Tubulin Polymerization

Weak magnetic fields and isotopes have been shown to influence biological processes; however, the underlying mechanisms remain poorly understood, particularly because the corresponding interaction energies are far below thermal energies, making classical explanations challenging or impossible. Microtubules, dynamic cytoskeletal fibers, offer an ideal system to test weak magnetic field effects due to their self-assembling capabilities, sensitivity to magnetic fields, and their central role in cellular processes. In this study, we use a combination of experiments and simulations to explore how nuclear spin dynamics affect microtubule polymerization by examining interactions between magnesium isotope substitution and weak magnetic fields. Our experiments reveal an isotope-dependent effect, which can be explained via a radical pair mechanism, explicitly arising from nuclear spin properties rather than isotopic mass differences. This nuclear spin-driven isotope effect is notably enhanced under an applied weak magnetic field of approximately 3 mT. Our theoretical model based on radical pairs achieves quantitative agreement with our experimental observations. These results establish a direct connection between quantum spin dynamics and microtubule assembly, providing new insights into how weak magnetic fields influence cellular and biomolecular functions.

biophysics↗

bioRxiv2024

Application of Supervised Machine Learning Models for Drug-Action Prediction Towards Nuclear Type I Receptors

1.Interactions between drugs can lead to adverse side effects for patients taking combination therapies to treat complex diseases such as cancer. Knowledge of drug-action towards a receptor would allow these drug-drug interactions to be predicted, and in this study, we trained a total of 5 different machine learning models to classify whether a given drug was an agonist (activator), antagonist (blocker), or a decoy (non-binder) to each of the androgen, estrogen, glucocorticoid, and progesterone receptors. The classification performance and efficiency, measured in training time, of the decision tree, naive Bayes, neural network, random forest, and support vector machine models for each receptor were then compared. The results showed that the decision tree and naive Bayes models were best suited for drug-action prediction across all receptors while only requiring minutes of training time at most. Future work will focus on increasing the prediction accuracy of antagonist drugs, integrating experimental data during training, and using other targets outside of nuclear type I receptors.

bioinformatics↗

bioRxiv2024

Supervised Machine Learning for Bioelectrical Cellular Networks

1.Cells utilize bioelectricity to form networks as well as regulate and control a variety of processes such as apoptosis, tumor suppression, and voltage-gated ion channels. In-silico modeling of bioelectrical networks can be performed using BETSE, an application that models gap junctions and ion channel activity of networked cells, but its usage of matrix-based differential equations to estimate these properties limits simulations based on the amount of computational resources available. To alleviate this issue, we trained a total of 8 machine learning models to replace three core functions of BETSE, that is, 1) predicting the average transmembrane potential (Vmem) of an entire cellular network, 2) predicting the Vmem of each individual cell within the network, and finally, 3) predicting the average ion concentrations of sodium, potassium, chloride, and calcium within the cell network. For objective 1, the random forest model was shown to be most performant over all 4 scoring metrics, in objective 2 both the decision tree and k-nearest neighbors models scored best in half of all metrics, and for objective 3 the super learner, a meta-learner comprised of multiple base learners, scored best among all scoring metrics. Overall, these models provide a more resource efficient method of predicting properties of bioelectric cellular networks, and future work will include further properties such as temperature and pressure.

bioinformatics↗