Biology subjects

Mathews, J.

Publications and source records attributed to Mathews, J..

15 recordsLinked to original sources

bioRxiv2026

Membrane voltage and connexin expression work together to enhance tumor growth and metastasis in cancer

There is strong evidence of tumors manipulating their resting membrane potential (Vmem). While most fully-differentiated cells have a Vmem of roughly -70mV, tumor cells are generally depolarized, with Vmem {approx}-30mV, which more closely resembles the Vmem of stem cells. This is often believed to serve the purpose of accelerating the cell cycle and hence advantaging tumor proliferation. But when the tumor becomes invasive, its cells sometimes revert to a hyperpolarized Vmem with no obvious reason why. Separately, it is well accepted that solid tumors that are not yet invasive greatly underexpress connexins relative to healthy tissue; connexins, for our purpose, form gap junctions (GJs), small connecting tubes between nearby cells. Tumors that are invasive, by contrast, overexpress connexins. There is very little explanation for the paradox that connexins are first underexpressed and then overexpressed. However, it has long been known that Vmem electrically gates GJs; specifically, that homotypic GJs conduct best when the two cells they connect have a similar Vmem. Our in-silico model results explain this phenomenon, showing that when considered together, tumors' electrical and connexin-expression behaviors form a unified and effective strategy to control communication between the tumor and its healthy neighbor cells. This has implications for the emerging field of cancer bioelectrics, potentially leading to more precisely-targeted therapies.

cancer biology↗

bioRxiv2026

A platform for automated training of mammalian cell physiology

Controlling cell physiology is difficult, not only because of cells complexity, but also their capacity for real-time adaptation to interventions, leading to challenges such as drug resistance and transgene silencing. Accumulating evidence suggests that this adaptivity resembles classical forms of learning defined in behavioral science. However, a lack of appropriate platforms has led to gaps in our understanding of cells capacity for adaptive problem-solving in physiological and transcriptional space. Here, we present a device, the Cell Trainer, capable of performing a wide variety of automated training experiments on non-neural mammalian cells, using timed drug pulses as the stimulus, and a mobile fluorescence microscope to capture images of responses, across replicate cultures. The Cell Trainer can operate in either an open-loop (feedforward) or closed-loop (feedback-controlled) mode, and our image analysis pipeline can report the behaviors of individual cells throughout each experiment and quantify population heterogeneity. We showcase the ability of the Cell Trainer to execute experimental protocols and perform single-cell analyses in both modes. We first demonstrate with a feedforward experiment in which myoblasts are repeatedly pulsed with dimethyl sulfoxide (DMSO) and their discrete calcium responses are analyzed, revealing sensitization-like dynamics. Next, we demonstrate a feedback control scheme wherein the fluorescence of a pH/voltage reporter in kidney cells is maintained below a threshold level with controlled pulses of acid. To accelerate research in the field of cell training, learning, and memory, we are openly sharing the Cell Trainer schematics and software with the research community. This platform provides a flexible tool for studying how cellular physiological states can be shaped by patterned stimulation and feedback control through approaches that work with the native adaptive competencies of cells.

bioengineering↗

bioRxiv2026

Ionic Exposure History Shapes Inner Nuclear Membrane Voltage and Chromatin Texture Responses

While bioelectricity is increasingly recognized as an important regulator of cell function and morphogenesis, the field has almost exclusively focused on plasma membrane states. Voltage across the inner nuclear membrane (INM) has been proposed as a potential regulator of nuclear function, but how it responds to extracellular ionic perturbations and whether it relates to chromatin organization remain unclear. Here, we targeted the ratiometric genetically encoded voltage indicator ASAP3-R3 to SUN2-associated nuclear membranes in intact NRK cells and combined INM voltage measurements with Gray-Level Co-Occurrence Matrix (GLCM)-based chromatin texture analysis. Reporter localization was confirmed by fluorescence imaging and electron microscopy, and functional validation in isolated nuclei showed that sodium-potassium pump inhibition produced INM depolarization consistent with Goldman-Hodgkin-Katz (GHK)-based prediction. We then used our validated construct to determine the response of Vnuc and chromatin texture to changing ionic conditions via two exposure methods, gradual (ramped) exposure or direct application. In intact cells, ramping different sets of ionic solutions of decreasing sodium/increasing potassium, decreasing sodium, increasing potassium, or decreasing chloride induced INM hyperpolarization and coordinated changes in chromatin texture, including increased contrast and entropy, reduced homogeneity, and reduced nuclear area. These effects were strongly path-dependent, with nuclear responses shaped by the history and order of ionic exposure: sodium and potassium responses emerged most clearly during ramping exposure, whereas reducing chloride by direct exposure showed a more pronounced response profile. Direct changes in sodium exposure produced limited electrical and chromatin-texture effects, while direct potassium exposure altered chromatin texture and nuclear area without significantly changing VNuc. Importantly, shifting baseline chromatin state in either direction, through Trichostatin-A (TSA)-induced chromatin relaxation or sodium azide/2-deoxy-D-glucose-induced compaction, blunted ion-associated Vnuc and chromatin responses across sodium, potassium, and chloride conditions. Together, these findings identify the nucleus as a dynamic, ion-responsive electro-structural system in which INM voltage and chromatin organization are functionally coupled, and in which both ionic trajectory and pre-existing chromatin state shape the magnitude of the nuclear response.

cell biology↗

bioRxiv2025

MECP2 Duplication Uncouples Mitochondrial and Purine Metabolism During neuronal maturation

Mitochondria and nucleotide metabolism are critical for cellular and developmental homeostasis, yet their potential interdependence and role in neurodevelopmental disease remain unclear. In MECP2 Duplication Syndrome (MDS), we identify a conserved correlation between mitochondrial function and purine metabolism that is disrupted across human, organoid, and mouse models. Multiomics integration reveals Complex III as the focal point of mitochondrial collapse, leading to redox stress, DNA damage, and hyperactivation of the de novo purine biosynthesis via purinosome assembly. The breakdown of mitochondria-purinosome coupling compromises genome stability, impairs radial glia proliferation, and delays neuronal maturation. By linking a defined genetic dosage imbalance to metabolic network failure, our study positions the mitochondria-purinosome coordination as a fundamental control axis for neurodevelopment and a therapeutic entry point across metabolic and neurodevelopmental disorders. Metabolic control is fundamental to cellular function, influencing energy production, signaling, epigenetic regulation, and tissue homeostasis1. Nowhere is this more critical than in the brain, where tightly regulated metabolic networks sustain high energetic demands and support neuronal development, synaptic plasticity, and circuit formation2. Disruptions in these networks are increasingly implicated across a spectrum of neurodevelopmental disorders3-5, yet their precise metabolic signatures and mechanistic contributions remain poorly understood.

neuroscience↗

bioRxiv2025

Multiplex-edited mice recapitulate woolly mammoth hair phenotypes

The woolly mammoth (Mammuthus primigenius) possessed a thick woolly coat and other cold-adaptive traits that enabled survival in harsh arctic environments. Current de-extinction efforts focus on genetically modifying the closely related Asian elephant to express woolly mammoth traits. In this study, we establish a multiplex-edited mouse model with modifications in genes associated with hair morphology and lipid metabolism, enabling insights into traits involved in developing woolly hair textures. Our optimized workflows achieved high editing efficiencies and produced genetically modified mice with simultaneous editing of up to seven different genes. Selected modifications include loss-of-function mutations in Fgf5, Tgm3, and Fam83g, among others. The resulting mice display exaggerated hair phenotypes including curly, textured coats, and golden-brown hair. This study advances methods of rapid establishment of complex genetic models. These approaches inform de-extinction efforts and research involving the genetic basis of mammalian hair development.

genetics↗

bioRxiv2023

Computing the inducibility of B cell lineages under a context-dependent model of affinity maturation: Applications to sequential vaccine design

A key challenge in B cell lineage-based vaccine design is understanding the inducibility of target neutralizing antibodies. We approach this problem through the use of detailed stochastic modeling of the somatic hypermutation process that occurs during affinity maturation. Under such a model, sequence mutation rates are context-dependent, rendering standard probability calculations for sequence evolution intractable. We develop an algorithmic approach to rapid, accurate approximation of key marginal sequence likelihoods required to inform modern sequential vaccine design strategies. These calculated probabilities are used to define an inducibility index for selecting among potential targets for immunogen design. We apply this approach to the problem of choosing targets for the design of boosting immunogens aimed at elicitation of the HIV broadly-neutralizing antibody DH270min11. Author summaryVaccine effectiveness relies on inducing the immune system to generate protective antibodies. Because antibodies are generated by random processes coupled to positive selection, the ability to induce certain rare but desirable antibodies can be limited by the inherent probability of occurrence. We use computational modeling to estimate the probability of antibody occurrence, and demonstrate the use of these estimates in designing vaccine regimens which maximize the probability of induction of broadly neutralizing antibodies.

immunology↗

bioRxiv2023

Robust collection and processing for label-free single voxel proteomics

With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk tissues. However, such bulk measurement lacks spatial resolution and obscures important tissue heterogeneity, which make it impossible for proteome mapping of tissue microenvironment. Here we report an integrated wet collection of single tissue voxel and Surfactant-assisted One-Pot voxel processing method termed wcSOP for robust label-free single voxel proteomics. wcSOP capitalizes on buffer droplet-assisted wet collection of single tissue voxel dissected by LCM into the PCR tube cap and MS-compatible surfactant-assisted one-pot voxel processing in the collection cap. This convenient method allows reproducible label-free quantification of [~]900 and [~]4,600 proteins for single voxel from fresh frozen human spleen tissue at 20 m x 20 m x 10 m (close to single cells) and 200 m x 200 m x 10 m ([~]100 cells), respectively. 100s-1000s of protein signatures with differential expression levels were identified to be spatially resolved between spleen red and white pulp regions depending on the voxel size. Region-specific signaling pathways were enriched from single voxel proteomics data. Antibody-based CODEX imaging was used to validate label-free MS quantitation for single voxel analysis. The wcSOP-MS method paves the way for routine robust single voxel proteomics and spatial proteomics.

molecular biology↗

bioRxiv2023

LipidSIM: inferring mechanistic lipid biosynthesis perturbations from lipidomics with a flexible, low-parameter, systematic Markov Modeling framework

Lipid metabolism is a complex and dynamic system involving numerous enzymes at the junction of multiple metabolic pathways. Disruption of these pathways leads to systematic dyslipidemia, a hallmark of many pathological developments, such as nonalcoholic steatohepatitis and diabetes. Recent advances in computational tools can provide insights into the dysregulation of lipid biosynthesis, but limitations remain due to the complexity of lipidomic data, limited knowledge of interactions among involved enzymes, and technical challenges in standardizing across different lipid types. In this study, we present a low-parameter, biologically interpretable framework named Lipid Synthesis Investigative Markov model (LipidSIM), which models and predicts the source of perturbations in lipid biosynthesis from lipidomic data. LipidSIM achieves this by accounting for the interdependency between the lipid species via the lipid biosynthesis network and generates testable hypotheses regarding changes in lipid biosynthetic reactions. This feature allows the integration of lipidomics with other omics types, such as transcriptomics, to elucidate the direct driving mechanisms of altered lipidomes due to treatments or disease progression. To demonstrate the value of LipidSIM, we first applied it to hepatic lipidomics following Keap1 knockdown and found changes in mRNA expression of the lipid pathways were consistent with the LipidSIM-predicted fluxes. Second, we used it to study lipidomic changes following intraperitoneal injection of CCl4 to induce fast NAFLD/NASH development and the progression of fibrosis and hepatic cancer. Finally, to show the power of LipidSIM for classifying samples with dyslipidemia, we used a Dgat2-knockdown study dataset. Thus, we show that as it demands no a priori knowledge of enzyme kinetics, LipidSIM is a valuable and intuitive framework for extracting biological insights from complex lipidomic data.

bioinformatics↗

bioRxiv2022

Cerebral Small Vessel Disease Burden is Associated with Decreased Abundance of Gut Barnesiella intestinihominis Bacterium in the Framingham Heart Study

A bidirectional communication exists between the brain and the gut, in which the gut microbiota influences cognitive function and vice-versa. Gut dysbiosis has been linked to several diseases, including Alzheimers disease and related dementias (ADRD). However, the relationship between gut dysbiosis and markers of cerebral small vessel disease (cSVD), a major contributor to ADRD, is unknown. In this cross-sectional study, we examined the connection between the gut microbiome, cognitive, and neuroimaging markers of cSVD in the Framingham Heart Study (FHS). Markers of cSVD included white matter hyperintensities (WMH), peak width of skeletonized mean diffusivity (PSMD), and executive function (EF), estimated as the difference between the trail-making tests B and A. We included 972 FHS participants with MRI scans, neurocognitive measures, and stool samples and quantified the gut microbiota composition using 16S rRNA sequencing. We used multivariable association and differential abundance analyses adjusting for age, sex, BMI, and education level to estimate the association between gut microbiota and WMH, PSMD, and EF measures. Our results suggest an increased abundance of Pseudobutyrivibrio and Ruminococcus genera was associated with lower WMH and PSMD (p-values < 0.001), as well as better executive function (p-values < 0.01). In addition, in both differential and multivariable analyses, we found that the gram-negative bacterium Barnesiella intestinihominis was strongly associated with markers indicating a higher cSVD burden. Finally, functional analyses using PICRUSt implicated various KEGG pathways, including microbial quorum sensing, AMP/GMP-activated protein kinase, phenylpyruvate, and {beta}-hydroxybutyrate production previously associated with cognitive performance and dementia. Our study provides important insights into the association between the gut microbiome and cSVD, but further studies are needed to replicate the findings.

neuroscience↗

bioRxiv2019

Optimal Mass Transport for Robust Texture Analysis

The emerging field of radiomics, which consists of transforming standard-of-care images to quantifiable scalar statistics, endeavors to reveal the information hidden in these macroscopic images. This field of research has found different applications ranging from phenotyping and tumor classification to outcome prediction and treatment planning. Texture analysis, which often consists of reducing spatial texture matrices to summary scalar features, has been shown to be important in many of the latter applications. However, as pointed out in many studies, some of the derived texture statistics are strongly correlated and tend to contribute redundant information; and are also sensitive to the parameters used in their computation, e.g., the number of gray intensity levels. In the present study, we propose first to consider texture matrices, with an emphasis on gray-level co-occurrence matrix (GLCM), as a non-parametric multivariate objects. The proposed modeling approach avoids evaluating redundant and strongly correlated features and also prevents the feature processing steps. Then, via the Wasserstein distance from optimal mass transport theory, we propose to compare these spatial objects to identify computerized tomography slices with dental artifacts in head and neck cancer. We demonstrate the robustness of the proposed classification approach with respect to the GLCM extraction parameters and the size of the training set. Comparisons with the random forest classifier, which is constructed on scalar texture features, demonstrates the efficiency and robustness of the proposed algorithm.

bioengineering↗

bioRxiv2019

Hierarchical Network Exploration using Gaussian MixtureModels

We present a framework based on optimal mass transport to construct, for a given network, a reduction hierarchy which can be used for interactive data exploration and community detection. Given a network and a set of numerical data samples for each node, we calculate a new computationally-efficient comparison metric between Gaussian Mixture Models, the Gaussian Mixture Transport distance, to determine a series of merge simplifications of the network. If only a network is given, numerical samples are synthesized from the network topology. The method has its basis in the local connection structure of the network, as well as the joint distribution of the data associated with neighboring nodes.\n\nThe analysis is benchmarked on networks with known community structures. We also analyze gene regulatory networks, including the PANTHER curated database and networks inferred from the GTEx lung and breast tissue RNA profiles. Gene Ontology annotations from the EBI GOA database are ranked and superimposed to explain the salient gene modules. We find that several gene modules related to highly specific biological processes are well-coordinated in such tissues. We also find that 18 of the 50 genes of the PAM50 breast-tumor prognostic signature appear among the highly coordinated genes in a single gene module, in both the breast and lung samples. Moreover these 18 are precisely the subset of the PAM50 recently identified as the basal-like markers.

bioinformatics↗

bioRxiv2019

A Novel Integrative Multiomics Method Reveals a Hypoxia-Related Subgroup of Breast Cancer with Significantly Decreased Survival

The remarkable growth of multi-platform genomic profiles has led to the multiomics data integration challenge. The effective integration of such data provides a comprehensive view of the molecular complexity of cancer tumors and can significantly improve clinical out-come predictions. In this study, we present a novel network-based integration method of multiomics data as well as a clustering technique involving the Wasserstein (Earth Movers) distance from the theory of optimal mass transport. We applied our proposed method of integrative Wasserstein-based clustering (iWCluster) to invasive breast carcinoma from The Cancer Genome Atlas (TCGA) project. The subtypes were characterized by the concordant effect of mRNA expression, DNA copy number alteration, and DNA methylation as well as the interaction network connectivity of the gene products. iW-Cluster is substantially more effective in distinguishing clusters with different survival rates as compared to isolated one-dimensional conventional omics analysis. Applying iWCluster to breast cancer TCGA data successfully recovered the known PAM50 molecular subtypes. In addition, iWCluster preserves the gene-specific data, which enables us to interpret the results and perform further analysis of significant genes for a specific cluster. The gene ontology enrichment analysis of significant genes in our substantially low survival sub-group leads to the well-known phenomenon of tumor hypoxia and the transcription factor ETS1 whose expression is induced by hypoxia. Increased expression of ETS1 is associated with an increased risk of recurrence and worse prognosis in breast cancer. Consequently, we believe iWCluster has the potential to discover novel subtypes by accentuating the genes that have concordant multiomics measurements in their interaction network, which are challenging to find without the network inference or with single omics analysis.

genomics↗

bioRxiv2018

Topological Data Analysis of PAM50 and 21-Gene Breast Cancer Assays

We introduce a classification of breast tumors into 7 classes which are more clearly defined by interpretable mRNA signatures along the PAM50 gene set than the 5 traditional PAM50 intrinsic subtypes. Each intrinsic subtype is partially concordant with one of our classes, and the 2 additional classes correspond to division of the classes concordant with the Luminal B and the Normal intrinsic subtypes along expression of the Her2 gene group. Our Normal class shows similarity with the myoepithelial mammary cell phenotype, including TP63 expression (specificity: 80.8% and sensitivity: 82.8%), and exhibits the best overall survival (89.6% at 5 years). Though Luminal A tumors are traditionally considered the least aggressive, our analysis shows that only the Luminal A tumors which are now classified as myoepithelial have this phenotype, while tumors in our luminal class (concordant with Luminal A) may be more aggressive than previously thought. We also find that patients with Basal tumors surviving to 48 months exhibit favorable survival rates when certain markers for B-lymphocytes are present and poor survival rates when they are absent, which is consistent with recent findings.

cancer biology↗

bioRxiv2018

Molecular phenotyping using networks, diffusion, and topology: soft tissue sarcoma

Many biological datasets are high-dimensional yet manifest an underlying order. In this paper, we describe an unsupervised data analysis methodology that operates in the setting of a multivariate dataset and a network which expresses influence between the variables of the given set. The technique involves network geometry employing the Wasserstein distance, global spectral analysis in the form of diffusion maps, and topological data analysis using the Mapper algorithm. The prototypical application is to gene expression profiles obtained from RNA-Seq experiments on a collection of tissue samples, considering only genes whose protein products participate in a known pathway or network of interest. Employing the technique, we discern several coherent states or signatures displayed by the gene expression profiles of the sarcomas in the Cancer Genome Atlas along the p53 signaling network. The signatures substantially recover the leiomyosarcoma, dedifferentiated liposarcoma (DDLPS), and synovial sarcoma histological subtype diagnoses, but they also include a new signature defined by simultaneous activation and inactivation of about a dozen genes, including activation of fibrinolysis inhibitor SERPINE1/PAI and inactivation of p53-family tumor suppressor gene P73 along with cyclin dependent kinase inhibitor 2A CDKN2A/P14ARF.

cancer biology↗

bioRxiv2017

Characterizing Cancer Drug Response andBiological Correlates: A Geometric NetworkApproach

In the present work, we consider a geometric network approach to study common biological features of anticancer drug response. We use for this purpose the panel of 60 human cell lines (NCI-60) provided by the National Cancer Institute. Our study suggests that utilization of mathematical tools for network-based analysis can provide novel insights into drug response and cancer biology. We adopted a discrete notion of Ricci curvature to measure the robustness of biological networks constructed with a pre-treatment gene expression dataset and coupled the results with the GI50 response of the cell lines to the drugs. The link between network robustness and Ricci curvature was implemented using the theory of optimal mass transport. Our hypothesis behind this idea is that robustness in the biological network contributes to tumor drug resistance, thereby enabling us to predict the effectiveness and sensitivity of drugs in the cell lines. Based on the resulting drug response ranking, we assessed the impact of genes that are likely associated with individual drug response. For important genes identified, we performed a gene ontology enrichment analysis using a curated bioinformatics database which resulted in very plausible biological processes associated with drug response across cell lines and cell types from the biological and literature viewpoint. These results demonstrate the potential of using the mathematical network analysis in assessing drug response and in identifying relevant genomic biomarkers and biological processes for precision medicine.

cancer biology↗