Biology subjects

Verner, E.

Publications and source records attributed to Verner, E..

5 recordsLinked to original sources

bioRxiv2023

Selective inhibition of interleukin-2 inducible T cell kinase (ITK) enhances anti-tumor immunity in association with Th1-skewing, cytotoxic T cell activation, and reduced T cell exhaustion

ITK is a tyrosine kinase expressed predominantly by T lymphocytes. In mice, selective knock-out of the ITK gene produces Th1 skewing of T helper cell differentiation. We synthesized a covalent ITK inhibitor, soquelitinib, that binds ITK with greater than 100-fold selectivity compared to binding to resting lymphocyte kinase (RLK). In vitro studies with normal or malignant T cells demonstrated that soquelitinib suppresses Th2 cytokine production preferentially with relative sparing of Th1 cytokines. Soquelitinib inhibits the in vivo growth of several syngeneic murine tumors including those that do not express ITK. Treatment with soquelitinib leads to increased tumor infiltration of normal CD8+ cells that possess enhanced T effector function. Soquelitinib inhibited expression of T cell exhaustion markers and was able to restore T effector function to exhausted cells. Pharmacologic selective ITK inhibition may represent a novel approach to cancer immunotherapy.

cancer biology↗

bioRxiv2022

Machine Learning Predicts Treatment Response in Bipolar & Major Depressive Disorders

Diagnosis of bipolar disorder (BD) patients with complex symptoms presents a challenge to clinicians. Patients tend to spend more time in a depressive state than a manic state. In such complex cases, the current Diagnostic and Statistical Manual (DSM), which is not based on pathophysiology, can lead to misdiagnosis as major depressive disorder (MDD) and an imperfect or even harmful medication response. A biologically-based classification algorithm is needed to improve the accuracy of diagnosis. Osuch et al. (2018) presented a kernel support vector machine (SVM) algorithm to predict the medication-class of response from new patient samples whose diagnoses were unclear. Here we also utilize the kernel support vector machine (SVM) algorithm but with a few novel contributions. We applied the robust, fully automated neuromark independent component analysis (ICA) framework to extract comparable features in a multi-dataset setting and learn a kernel function for support vector machine (SVM) on multiple feature subspaces. The neuromark framework successfully replicates the prior result with 95.45% accuracy (sensitivity 90.24%, specificity 92.3%). To further evaluate the generalizability of our approach, we incorporated two additional datasets comprising bipolar disorder (BD) and major depressive disorder (MDD) patients. We validated the trained algorithm on these datasets, resulting in a testing accuracy of up to 89% (sensitivity 0.88, specificity 0.89) without using site or scanner harmonization techniques. We also translated the model to predict improvement scores of major depressive disorder (MDD) with up to 70% accuracy. This approach reveals some salient biological markers of medication-class of response within mood disorders. HighlightsO_LIWe demonstrate a DSM-free approach for predicting treatment response from resting-state functional magnetic resonance imaging (fMRI) data. C_LIO_LIWe identify several replicable biomarkers using the approach. C_LIO_LIOur work has potential for clinical application by replacing trial-and-error in treating complex psychiatric disorders. C_LI

neuroscience↗

bioRxiv2022

Federated analysis in COINSTAC reveals functional network connectivity and spectral links to smoking and alcohol consumption in nearly 2,000 adolescent brains

With the growth of decentralized/federated analysis approaches in neuroimaging, the opportunities to study brain disorders using data from multiple sites has grown multi-fold. One such initiative is the Neuromark, a fully automated spatially constrained independent component analysis (ICA) that is used to link brain network abnormalities among different datasets, studies, and disorders while leveraging subject-specific networks. In this study, we implement the neuromark pipeline in COINSTAC, an open-source neuroimaging framework for collaborative/decentralized analysis. Decentralized analysis of nearly 2000 resting-state functional magnetic resonance imaging datasets collected at different sites across two cohorts and co-located in different countries was performed to study the resting brain functional network connectivity changes in adolescents who smoke and consume alcohol. Results showed hypoconnectivity across the majority of networks including sensory, default mode, and subcortical domains, more for alcohol than smoking, and decreased low frequency power. These findings suggest that global reduced synchronization is associated with both tobacco and alcohol use. This work demonstrates the utility and incentives associated with large-scale decentralized collaborations spanning multiple sites.

bioinformatics↗

bioRxiv2021

Decentralized Brain Age Estimation using MRI Data

Recent studies have demonstrated that neuroimaging data can be used to predict brain age, as it captures information about the neuroanatomical and functional changes the brain undergoes during development and the aging process. However, researchers often have limited access to neuroimaging data because of its challenging and expensive acquisition process, thereby limiting the effectiveness of the predictive model. Decentralized models provide a way to build more accurate and generalizable prediction models, bypassing the traditional data-sharing methodology. In this work, we propose a decentralized method for brain age estimation and evaluate it on three different feature sets, including both volumetric and voxelwise structural MRI data as well as resting functional MRI data. The results demonstrate that a decentralized brain age model can achieve similar performance compared to the models trained with all the data in one location.

neuroscience↗

bioRxiv2019

Decentralized multi-site VBM analysis during adolescence shows structural changes linked to age, body mass index, and smoking: A COINSTAC analysis

In the recent past, there has been an upward trend in developing frameworks that enable neuroimaging researchers to address challenging questions by leveraging data across multiple sites all over the world. One such framework, Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC), provides a platform to analyze neuroimaging data stored locally across multiple organizations without the need for pooling the data at any point during the analysis. In this paper, we perform a decentralized voxel-based morphometry analysis of structural magnetic resonance imaging data across two different sites to understand the structural changes in the brain as linked to age, body mass index and smoking. Results produced by the decentralized analysis are contrasted with similar findings in literature. This work showcases the potential benefits of performing multi-voxel and multivariate analyses of large-scale neuroimaging data located at multiple sites.

neuroscience↗