Biology subjects

Newton, M. A.

Publications and source records attributed to Newton, M. A..

10 recordsLinked to original sources

bioRxiv2026

CD3, CD28, TCRαβ expression and IL-2 production in a spontaneous glycosylphosphatidylinositol-deficient Jurkat T cell line

Glycosylphosphatidylinositol (GPI) anchors are involved in the organization of membrane microdomains that support T cell receptor (TCR) signaling. However, their role in regulating expression of TCR-related proteins and downstream functional output remains unclear. This study aimed to characterize the effects of GPI-deficiency on TCR, cluster of differentiation 3 (CD3), and CD28 expression as well as interleukin-2 (IL-2) production using a GPI-deficient Jurkat T cell line (S12). Flow cytometry confirmed the complete loss of GPI anchors and GPI-anchored proteins (GPI-APs) in the S12 cell line. Compared to GPI-producing parental Jurkat, S12 had significantly higher expression of CD3 and TCR{beta} while CD28 had similar expression. IL-2 production by S12 was assessed following stimulation with anti-CD3/anti-CD28 beads and following stimulation with phorbol 12-myristate 13-acetate (PMA) and ionomycin. Neither S12 nor parental Jurkat produced detectable IL-2 in response to anti-CD3/anti-CD28 bead-mediated stimulation. Both parental Jurkat and S12 produced IL-2 following PMA/ionomycin-mediated stimulation. No significant difference in IL-2 production was observed between S12 and parental Jurkat following PMA/ionomycin-mediated stimulation. These findings demonstrate that GPI-deficiency influences surface receptor expression but does not significantly impair downstream IL-2 production under PMA/ionomycin stimulation. This finding suggests that GPI anchors and GPI-APs contribute to proximal signaling organization but are not required for cytokine production when downstream pathways are directly activated.

immunology↗

bioRxiv2026

Spatial Autocorrelation Aware Resampling Improves Cell-Cell Interaction Inference in Spatial Transcriptomics Data

Spatial transcriptomics has enabled finer-grained analyses of cell-cell interactions through the co-expression of ligands and their cognate receptors, thereby accounting for the spatial constraints of signaling. However, existing methods employ random permutations or analytic calculations to assess statistical significance, neither of which accounts for spatial autocorrelation, a common property of spatially resolved data. Here, we introduce SOAAR (Spatial Omics Autocorrelation-Aware Resampling), a statistical method for testing gene-gene correlations in spatial data that maintains spatial gene-level autocorrelation in resampled datasets used to generate null distributions. SOAAR uses spatial map patterns to decompose autocorrelation. The associations between gene expression and autocorrelation patterns are then randomized to construct resampled datasets used for evaluating significance testing. We showed that SOAAR maintains gene-level spatial autocorrelation and yields a lower false-positive rate than random permutations across varying degrees of gene-level spatial autocorrelation in simulation studies. In a 10X Visium dataset from 10 HNSCC patients treated with immunotherapy, SOAAR filters out low-confidence interactions that were present in only individual samples or in fewer than 3 samples. That led to the identification of a consistent signature of T-cell recruitment in Responder patients and a resistance signature driven by angiogenesis and tumor cell proliferation in Non-Responders. Similar trends were observed in a larger cohort of 23 patients profiled with single-cell spatial CosMX SMI data, revealing immune cell interactions in response to immunotherapy. Overall, SOAAR provides a more calibrated framework for testing spatial correlation, grounded in spatial statistics. Future developments will seek to link localized correlation patterns to downstream changes in biological pathways, thereby informing biomarker and therapeutic target discovery.

bioinformatics↗

bioRxiv2025

Thioredoxin interacting protein (TXNIP), a redox regulator, mediates the RAPGEF3/4 signaling dependency in primary melanoma

Progression of cutaneous primary melanoma that arises from melanocytes leads to lethal metastatic disease. Molecular mechanisms that control the growth of primary melanoma in the skin and promote progression are not fully understood. Previously we showed that RAP guanine exchange factors EPAC1/2 (Exchange Protein Activated by cyclic AMP) promote the growth of primary melanoma and loss of dependency on EPACs is associated with metastatic progression. In this study, we show that EPACs are activated during malignant transformation of melanocytes, and chemical inhibition or genetic deletion of EPAC inhibits melanomagenesis in Braf/Pten mice. Low expression of EPAC mRNA and its effector RAP1-GTP protein in primary melanoma correlate with better recurrence-free survival. RNAseq analysis of matched primary and metastatic melanoma cells treated with an EPAC inhibitor showed that TXNIP, an important regulator of redox homeostasis, is a downstream effector of EPAC signaling. We also show that EPACs promote melanoma growth by regulating redox homeostasis and mitochondrial ROS through activation of mechanistic target of rapamycin complex 1 (mTORC1) that stabilizes hypoxia-inducible factor 1-alpha (HIF-1), a transcriptional activator of redox regulator TXNIP and glycolytic enzymes. Our data suggest that targeting mechanisms that melanoma cells employ to bypass EPAC dependency is a potential therapeutic approach. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/696903v2_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1967afaorg.highwire.dtl.DTLVardef@63b907org.highwire.dtl.DTLVardef@1089da4org.highwire.dtl.DTLVardef@a5b2ef_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Invasive cancer and spontaneous regression two weeks after papillomavirus infection

Development of invasive cancer in mammals is thought to require months or years after initial events such as mutation or viral infection. Rarely, invasive cancers regress spontaneously. We show that cancers can develop and regress on a timescale of weeks, not months or years. Invasive squamous cell carcinomas developed in normal adult, immune-competent mice as soon as 2 weeks after infection with mouse papillomavirus MmuPV1. Tumor development, regression or persistence was tissue- and strain-dependent. Cancers in infected mice developed rapidly at sites also prone to papillomavirus-induced tumors and cancers in humans - the throat, anus, and skin - and their frequency was increased in mice constitutively expressing the papillomavirus E5 oncogene, which MmuPV1 lacks. Cancers and dysplasia in the throat and anus regressed completely within 4-8 weeks of infection; however, skin lesions in the ear persisted. T-cell depletion in the mouse showed that regression of throat and anal tumors requires T cells. We conclude that papillomavirus infection suffices for rapid onset of invasive cancer, and persistence of lesions depends on factors including tissue type and host immunity. The speed of these events should promote rapid progress in the study of viral cancer development, persistence, and regression. Summary Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/611275v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1da9642org.highwire.dtl.DTLVardef@1bef109org.highwire.dtl.DTLVardef@c2ba0eorg.highwire.dtl.DTLVardef@451baf_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Intermittent energy restriction inhibits tumor growth and enhances paclitaxel response in a transgenic mouse model of endometrial cancer

ObjectiveOverweight/obesity is the strongest risk factor for endometrial cancer (EC), and weight management can reduce that risk and improve survival. We aimed to establish the differential abilities of intermittent energy restriction (IER) and low-fat diet (LFD), alone and in combination with paclitaxel, to reverse the procancer effects of high-fat diet (HFD)-induced obesity in a mouse model of EC. MethodsLkb1fl/flp53fl/fl mice were fed high-fat diet (HFD) or LFD to generate obese and lean phenotypes, respectively. Obese mice were maintained on HFD or switched to LFD (HFD-LFD) or IER (HFD-IER). Ten weeks after induction of endometrial tumor, mice in each group received paclitaxel or placebo for 4 weeks. Body and tumor weights; tumoral transcriptomic, metabolomic and oxylipin profiles; and serum metabolic hormones and chemocytokines were assessed. ResultsHFD-IER and HFD-LFD, relative to HFD, reduced body weight; reversed obesity-induced alterations in serum insulin, leptin and inflammatory factors; and decreased tumor incidence and mass, often to levels emulating those associated with continuous LFD. Concurrent paclitaxel, versus placebo, enhanced tumor suppression in each group, with greatest benefit in HFD-IER. The diets produced distinct tumoral gene expression and metabolic profiles, with HFD-IER associated with a more favorable (antitumor) metabolic and inflammatory environment. ConclusionIn Lkb1fl/flp53fl/fl mice, IER is generally more effective than LFD in promoting weight loss, inhibiting obesity-related endometrial tumor growth (particularly in combination with paclitaxel), and reversing detrimental obesity-related metabolic effects. These findings lay the foundation for further investigations of IER as a EC prevention strategy in women with overweight/obesity.

cancer biology↗

bioRxiv2023

Surrogate selection oversamples expanded T cell clonotypes

Inference from immunological data on cells in the adaptive immune system may benefit from modeling specifications that describe variation in the sizes of various clonal sub-populations. We develop one such specification in order to quantify the effects of surrogate selection assays, which we confirm may lead to an enrichment for amplified, potentially disease-relevant T cell clones. Our specification couples within-clonotype birth-death processes with an exchangeable model across clonotypes. Beyond enrichment questions about the surrogate selection design, our framework enables a study of sampling properties of elementary sample diversity statistics; it also points to new statistics that may usefully measure the burden of somatic genomic alterations associated with clonal expansion. We examine statistical properties of immunological samples governed by the coupled model specification, and we illustrate calculations in surrogate selection studies of melanoma and in single-cell genomic studies of T cell repertoires. FundingThis research was supported in part by the National Science Foundation (grant 2023239-DMS), and by grants from the National Institutes of Health: R01 GM102756, P01 CA022443, P01 CA250972, P50 CA278595, UL1 TR002373, P50 CA269011, and P30 CA014520. This work was also supported by resources at the William S. Middleton Memorial Veterans Hospital, Madison, WI, USA, and the UW Carbone Comprehensive Cancer Center. Additional support was provided by Anns Hope Foundation, Taking on Melanoma, the Tim Eagle Memorial, and the Jay Van Sloan Memorial from the Steve Leuthold Family Foundation, philanthropic support in the USA. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the views of the Dept. of Veterans Affairs or the United States Government.

immunology↗

bioRxiv2022

SpatialCorr: Identifying Gene Sets with Spatially Varying Correlation Structure

Recent advances in spatially resolved transcriptomics technologies enable both the measurement of genome-wide gene expression profiles and their mapping to spatial locations within a tissue. A first step in spatial transcriptomics data analysis is identifying genes with expression that varies spatially, and robust statistical methods exist to address this challenge. While useful, these methods do not detect spatial changes in the coordinated expression within a group of genes. To this end, we present SpatialCorr, a method for identifying sets of genes with spatially varying correlation structure. Given a collection of gene sets pre-defined by a user, SpatialCorr tests for spatially induced differences in the correlation of each gene set within tissue regions, as well as between and among regions. An application to cutaneous squamous cell carcinoma demonstrates the power of the approach for revealing biological insights not identified using existing methods.

bioinformatics↗

bioRxiv2020

EBSeq: improving mixing computations for multi-group differential expression analysis

EBSeq is a Bioconductor package designed to calculate empirical-Bayesian inference summaries from sequence-based gene-expression (RNA-Seq) data. It produces gene or isoform-specific scores that measure various patterns of differential expression among a set of sample groups, and is most commonly deployed to measure differential expression between two groups. Its use of local posterior probabilities from a fitted mixture model provides the data analyst a direct way to score the false discovery rate of any reported list of genes, and it is one of the only tools that can address local false discovery rates when analyzing multiple sample groups. Contemporary applications have increasing numbers of sample groups, and the algorithms deployed in EBSeq are neither space nor time efficient in this important case. We describe a version update utilizing code improvements and novel pruning and clustering algorithms in order to reduce the complexity of mixture computations. The algorithms are supported by a theoretical analysis and tested empirically on a variety of benchmark and synthetic data sets.

bioinformatics↗

bioRxiv2019

A Compositional Model To Assess Expression Changes From Single-Cell RNA-Seq Data

On the problem of scoring genes for evidence of changes in the distribution of single-cell expression, we introduce an empirical Bayesian mixture approach and evaluate its operating characteristics in a range of numerical experiments. The proposed approach leverages cell-subtype structure revealed in cluster analysis in order to boost gene-level information on expression changes. Cell clustering informs gene-level analysis through a specially-constructed prior distribution over pairs of multinomial probability vectors; this prior meshes with available model-based tools that score patterns of differential expression over multiple subtypes. We derive an explicit formula for the posterior probability that a gene has the same distribution in two cellular conditions, allowing for a gene-specific mixture over subtypes in each condition. Advantage is gained by the compositional structure of the model, in which a host of gene-specific mixture components are allowed, but also in which the mixing proportions are constrained at the whole cell level. This structure leads to a novel form of information sharing through which the cell-clustering results support gene-level scoring of differential distribution. The result, according to our numerical experiments, is improved sensitivity compared to several standard approaches for detecting distributional expression changes.

bioinformatics↗

bioRxiv2019

Predicting kinase inhibitors using bioactivity matrix derived informer sets

Prediction of compounds that are active against a desired biological target is a common step in drug discovery efforts. Virtual screening methods seek some active-enriched fraction of a library for experimental testing. Where data are too scarce to train supervised learning models for compound prioritization, initial screening must provide the necessary data. Commonly, such an initial library is selected on the basis of chemical diversity by some pseudo-random process (for example, the first few plates of a larger library) or by selecting an entire smaller library. These approaches may not produce a sufficient number or diversity of actives. An alternative approach is to select an informer set of screening compounds on the basis of chemogenomic information from previous testing of compounds against a large number of targets. We compare different ways of using chemogenomic data to choose a small informer set of compounds based on previously measured bioactivity data. We develop this Informer-Based-Ranking (IBR) approach using the Published Kinase Inhibitor Sets (PKIS) as the chemogenomic data to select the informer sets. We test the informer compounds on a target that is not part of the chemogenomic data, then predict the activity of the remaining compounds based on the experimental informer data and the chemogenomic data. Through new chemical screening experiments, we demonstrate the utility of IBR strategies in a prospective test on two kinase targets not included in the PKIS. Using limited training data in both retrospective and prospective tests, bioactivity fingerprints based on chemogenomic data outperform chemical fingerprints in predicting active compounds in both standard virtual screening metrics and accurate identification of hits from novel chemical classes. Author SummaryIn the early stages of drug discovery efforts, computational models are used to predict activity and prioritize compounds for experimental testing. New targets commonly lack the data necessary to build effective models, and the screening needed to generate that experimental data can be costly. We seek to improve the efficiency of the initial screening phase, and of the process of prioritizing compounds for subsequent screening. We choose a small informer set of compounds based on publicly available prior screening data on distinct (though related) targets. We then use experimental data on these informer compounds to predict the activity of other compounds in the set against the target of interest. Computational and statistical tools are needed to identify informer compounds and to prioritize other compounds for subsequent phases of screening. Using limited training data, we find that selection of informer compounds on the basis of bioactivity data from previous screening efforts is superior to the traditional approach of selection of a chemically diverse subset of compounds. We demonstrate the success of this approach in retrospective tests on the Published Kinase Inhibitor Sets (PKIS) chemogenomic data and in prospective experimental screens against two additional non-human kinase targets.

pharmacology and toxicology↗