Biology subjects

Pyne, S.

Publications and source records attributed to Pyne, S..

18 recordsLinked to original sources

bioRxiv2026

Single-Cell Omics to Uncover Cell-Type-Specific Gene Regulatory Networks Driving Mucilage Production in Specialized Roots

Aerial roots of certain maize (Zea mays) and sorghum (Sorghum bicolor) genotypes secrete a carbohydrate-rich mucilage that sustains nitrogen-fixing bacteria, and the underground roots of most plants secrete mucilage at their tips. In both settings, border cells released from the root cap produce this mucilage, yet the genes controlling their formation remain largely unknown. We sequenced the RNA of about 23,000 nuclei from mucilage-secreting roots of maize, sorghum, and Medicago truncatula, sampled before and after secretion. Each species yielded 10 to 15 clusters, which we annotated with published markers and with new markers that we validated by in situ hybridization in the two cereals and by promoter-reporter fusions in M. truncatula. We then inferred modular and cell type-specific gene regulatory networks. Transcriptional programs diverged across the three species except in the quiescent center and the meristem, and in the clusters containing border cells, where a shared set of regulators emerged. Silencing three of five candidate regulators reduced border cell number, while silencing a partially overlapping set of three regulators reduced root diameter in M. truncatula. Silencing the C2H2 zinc finger protein MtrunA17_Chr1g0148421 also reduced border cell viability. A maize insertion mutant of bzip89 released fewer border cells than the wild type. These networks offer entry points for tuning mucilage production in cereal and legume crops.

plant biology↗

bioRxiv2026

Loss of PTPRB function remodels VEGFR1 activation in tumors overexpressing the receptor tyrosine kinase

The VEGF Receptor-1 (VEGFR1) is a deceptive receptor tyrosine kinase (RTK). In early embryonic development, VEGFR1 negatively regulates angiogenesis by acting like a decoy receptor. Ligand binding transiently phosphorylates the receptor and induces a weak activation, even at high receptor density. Yet, in multiple cancers, overexpression of VEGFR1 plays a central role in tumor vascularization and growth. Unlike many pro-oncogenic RTKs, extensive patient data analysis revealed no somatic mutation in VEGFR1 that may spontaneously activate the tyrosine kinase. The mechanism by which VEGFR1 is activated in cancers has remained an open question for more than two decades. Here, we evaluated the multi-omics profiles of VEGFR1 and its regulators in a pan-cancer database. We observed an inverse correlation between VEGFR1 and PTPRB phosphatase expression in KIRC patients and disease outcome. We observed that patients overexpressing VEGFR1 and deficient in PTPRB expression have a lower likelihood of survival. Using super-resolution single-cell imaging, we discovered that inhibiting PTPRB spontaneously activates VEGFR1 by inducing ligand-independent dimerization, possibly by shifting the equilibrium toward the active state. PTPRB inhibition induces sustained, ligand-dependent phosphorylation of VEGFR1, which may promote tumor vascularization. We conclude that a subtle phosphatase imbalance is fundamental in determining VEGFR1s role in pathological angiogenesis in tumors.

biochemistry↗

bioRxiv2026

An automated platform for spatial functional modeling and fingerprint analysis of tissue molecular landscapes

Spatial transcriptomics (ST) enables high-resolution molecular profiling while preserving tissue architecture, creating new opportunities to investigate how disease-associated pathways are organized within tissues. However, existing analytical approaches largely focus on individual pathways or cell types and do not provide a unified framework for modeling spatially varying pathway interactions across tissue sections and anatomical planes. We present an integrative framework, Spatial Fingerprints Analytics (SFinx), that introduces the concept of a spatial fingerprint for representing patterns of molecular signatures, and Spatial Functional Data Analysis for spatial regression and mapping of localized pathway activity and pathway-phenotype interactions in complex tissues. Applying SFinx to ST datasets on murine lupus nephritis, we reconstructed continuous spatial landscapes of pathway activity and disease-associated phenotypes across kidney sections. This approach identified anatomically restricted inflammatory domains characterized by coordinated activation of immune pathways and revealed substantial spatial heterogeneity in pathway crosstalk across renal compartments. Using generalized additive models with tensor-product splines, we quantified spatially varying associations between lupus nephritis and neutrophil activation pathways across tissue sections, uncovering regions with both positive and negative relationships that would be obscured by conventional bulk analyses. Multi-slice integration further demonstrated reproducible spatial interaction patterns while accounting for section-specific variability. Together, SFinx transforms mixed-spot transcriptomic measurements into interpretable spatial pathway landscapes and interaction maps, providing a general framework for identifying localized disease mechanisms. SFinx revealed previously unrecognized spatial organization of inflammatory signaling in lupus nephritis and presents a broadly applicable strategy for studying spatially coordinated biological processes in cancers, autoimmune and neurodegenerative diseases.

bioinformatics↗

bioRxiv2026

Generative AI Models Reveal Dynamic Views of Aging (DyViA) Phenotypes in Healthy Individuals

Background and objectivesIn recent years, the need to develop analytical strategies for healthy aging has assumed great importance. In this study, we introduce DyViA, a generative artificial intelligence (genAI) platform that can construct personalized trajectories capable of predicting the plausible progression of selected phenotypes with advancing age. Research design and methodsDyViA presents a suite of deep learning models covering two major GenAI approaches: DyViA-Diff, a new diffusion model; and DyViA-mGAN, an improved version of a recent Generative Adversarial Network model. It demonstrated the dynamic progression of femoral neck bone mineral density (BMD) using data from a longitudinal cohort study of women in the U.S. of age 65 years or above. ResultsUsing very few initial measurements, DyViA generated individual-specific continuous trajectories of BMD, with a corresponding region of acceptable predictions, from 66 to 89 years. The results were subjected to rigorous quality-control and comparative analysis across multiple methods. While DyViA-Diff is the superior model with more coherent and accurate predictions, DyViA-mGAN allows for encoding population- and individual-level effects with a better control. Discussion and implicationsGiven the prevalence of osteoporosis in the aging population, the main impact of DyViAs genAI-driven contribution in the form of personalized, plausible models of BMD progression with age lies in the systematic yet rigorous transition from otherwise static models of inference about a clearly dynamic phenomenon to a continuous one. The foresight offered by DyViAs outputs empowers an individual by conferring a certain degree of strategic preparedness in the course of aging.

bioinformatics↗

bioRxiv2026

Multimodal Fusion of Circular Functional Data on High-resolution Neuroretinal Phenotypes

Progressive optic neuropathies, particularly glaucoma, represent a significant global health challenge, and the need for precise understanding of heterogeneous neurodegenerative phenotypes cannot be overstated. Here, we brought together two complementary sources of unstructured yet clinically relevant information about neuroretinal rim (NRR) thinning, a common clinical marker of such decay. These are based on a new dataset of fundus digital images and a corresponding dataset of optical coherence tomography, both collected from a large clinical cohort of healthy eyes. First, we represented them using a common data structure that imposed a high-resolution scale of 180 equally spaced and registered measurements on a 360{degrees} circular axis. We modeled the NRR measurements of each eye as circular curves and aligned these multimodal curves to obtain a fused NRR curve for each eye. Unsupervised clustering of these fused curves identified four clusters of eyes with structural heterogeneity, which were also found to have distinctive clinical covariates. Computation of functional derivatives revealed troughs in the curves of each cluster. Using circular statistics, we estimated the directional distributions of these troughs as potentially clinically relevant regions of NRR degeneration. A comparative study using landmark registration based on functional canonical correlation analysis demonstrated that our curve-alignment-based multimodal fusion is superior. Moreover, it improves the robustness of baseline NRR data obtained from fundus imaging.

bioinformatics↗

bioRxiv2026

Predicting Clinical Phenotypes by Growth Curve Modeling of Transcriptomic Signatures during Disease Progression

High-throughput transcriptomic analysis has benefited from many statistical tests of differential gene expression across two or more groups such as t tests, ANOVA, etc. Yet, in complex transcriptomic datasets such as multi-group longitudinal measures, few studies have addressed such key issues as group effects and temporal dependency in expression profiles with a single model that is both practically effective and theoretically grounded. In this study, we used Growth Curve Model (GCM), as a generalization of MANOVA, to identify differentially expressed longitudinal profiles of genes, and thus predicted the associated clinical phenotypes, of pediatric lupus during the progressions of the disease across two different racial groups. In particular, we detected a module of histone genes which was shown to be linked with lupus.

bioinformatics↗

bioRxiv2025

Generative Reconstruction of Unobserved Cellular Dynamics using Single-Cell Transcriptomic Trajectories

The primary challenge in cellular dynamics is to capture the intermediate states between two distinct biological stages. This is because of the technical constraints in capturing transient states or the nature of single-cell sequencing protocols. To address this challenge, we introduce a new computational framework, GRAIL (Generative Reconstruction of Artificial Intermediate Lineages), which aims to bridge these missing transitions. GRAIL reconstructs biologically plausible intermediate cell states between two distinct developmental stages (A and B) through a Locality Sensitive Hashing (LSH) based cell selection strategy followed by a smooth interpolation in learned latent space. The interpolated latent representations are subsequently decoded into gene expression profiles using a trained generator from a Generative Adversarial Network (GAN). The framework consists of three components: (a) a pretrained autoencoder that learns latent representations of stage specific transcriptomes, (b) an LSH-guided interpolation procedure that identifies anchor cells and performed interpolation in latent space, and (c) a GAN generator that extrapolates realistic intermediate expression from the interpolated samples consistent with the underlying trajectory. We benchmarked GRAIL with different state-of-the-arts (SOTA) in diverse setup of simulated dataets as well as in real life scRNA-seq datasets. Generated samples from GRAIL preserve expected marker gene expression patterns and also improve downstream analyses, including differential expression and cell clustering. Our method addresses the critical gap in studying cellular transitions when experimental intermediate samples are unavailable.

bioinformatics↗

bioRxiv2025

Automated generation of personalized trajectories of aging phenotypes with DyViA-GAN

With a general increase in human lifespan, the need for technological advances to develop strategies for healthy aging has assumed great importance. In the present study, our goal is to predict the progression of selected aging phenotypes in a given healthy individual as one continues aging past 65 years. Therefore, we developed a novel framework called Dynamic Views of Aging with conditional Generative Adversarial Networks (or DyViA-GAN) which is capable of predicting the plausible personalized trajectories of a selected aging phenotype conditioned on the available measurements of the phenotype at a few initial time instances, and additional covariates. Given the prevalence of osteoporosis in the aging population, we selected femoral neck Bone Mineral Density (BMD) of a healthy individual as the phenotype of interest, and baseline individual Body Mass Index (BMI) as covariate. We trained DyViA-GAN on a publicly available longitudinal dataset of a large cohort of mostly white women in the United States of age 65 years or above. Thus, it generated, for each individual, continuous phenotype trajectories, along with a corresponding region of acceptable predictions, for an age range of 66 to 89 years, for eight different combinations both with and without involving the covariate. The prediction results were subjected to rigorous quality-control and multiple comparative analyses. Our results clearly demonstrate the potential of generative deep learning frameworks in healthspan research.

bioinformatics↗

bioRxiv2025

A mathematical framework for human neutrophil state transitions inferred from single-cell RNA sequence data

Neutrophils, the most abundant immune cells in the human circulation, play a central role in the innate immune system. While neutrophil heterogeneity is a topic of increasing research interest, few efforts have been made to model the dynamics of neutrophil population subsets. We develop a mathematical model to describe the dynamics that characterizes the states and transitions involved in the maturation of human neutrophils. We use single-cell gene expression data to identify five clusters of healthy human neutrophils, and pseudo-time analysis to inform model structure. We find that precursor neutrophils transition into immature neutrophils, which then either transition to an interferon-responsive state or continue to mature through two further states. The key model parameters are the transition rates (the inverse of a transition rate is the mean waiting time in one state before transitioning to another). In this framework, the transition from the precursor to immature state (mean time less than an hour) is more rapid than subsequent transitions (mean times more than 12 hours). Approximately a quarter of neutrophils are estimated to follow the interferon-responsive path; the remainder continue along the standard maturation pathway. We use Bayesian inference to describe the variation, between individuals, in the fraction of cells within each cluster.

cell biology↗

bioRxiv2025

Uncovering Functional Gene Regulatory Networks in Bulk and Single-Cell Data through Robust Transcription Factor Activity Estimation and Model-Guided Experimental Validation

Reconstructing genome-scale gene regulatory networks (GRNs) remains a difficult problem in systems biology, and many experimental and computational methods have been developed to address this problem. Recent computational methods have aimed to more accurately model GRNs by estimating the hidden Transcription Factor Activity (TFA) from prior knowledge of TF target regulatory connections, encoded as an input directed graph, to relax the assumption that mRNA level of the regulator correlates with the protein activity of the regulator. However, the noise in the prior knowledge can adversely affect the estimated TFA levels and the quality of the downstream inferred GRNs. Here, we present a new approach, MERLIN+P+TFA, that uses prior knowledge-guided sparsity regularization to robustly and accurately estimate TFA and downstream GRNs. We apply our method to simulated and real expression data in yeast and mammalian systems and show improved quality of inferred GRNs for both bulk and single-cell datasets. Regularized TFA offers benefits to a variety of other GRN inference algorithms, including those that have traditionally been used with expression alone, in both bulk and scRNA-seq settings. We used the inferred GRN to prioritize key regulators for the mouse Embryonic Stem Cell (mESC) state and validate 58 regulators experimentally. We identified both known and novel regulators of the mESC state and further validate the targets of 4 known and novel regulators. Our validation experiments suggest that computationally inferred networks can capture functional targets of TFs with higher precision than estimated in current benchmarks, however, it is important to generate context-specific gold standards.

bioinformatics↗

bioRxiv2025

Stability and Performance of Linear Combination Tests of Gene Set Enrichment for Multiple Covariance Estimators in Unbalanced Studies

Gene set analysis (GSA) is essential for understanding coordinated gene expression changes within biological pathways, especially in high-dimensional data generated by platforms such as RNA-seq and microarrays. This study focuses on the linear combination test (LCT), a GSA method that combines multiple genelevel statistics into a powerful test statistic to assess the association between a gene set and outcomes of interest in a given set of samples. We evaluated the performance and stability of LCT using different covariance matrix estimators, including ridge, graphical lasso, and adaptive lasso, which are known for their effectiveness in high-dimensional data analysis. In addition, we assessed the robustness of LCT in the face of unbalanced study designs, which are typical in biomedical research due to limited sample availability and the high cost of data generation. We conducted a simulation study and applied LCT to publicly available gene expression datasets comparing patients with systemic lupus erythematosus (SLE) to healthy controls, where the number of controls is significantly lower than the number of cases. Our findings demonstrate that while LCTs default shrinkage estimator shows limitations in highly correlated and unbalanced designs, ridge estimation provides a more reliable alternative for unbalanced scenarios. Researchers can optimize LCTs performance by selecting appropriate covariance estimators based on their data structure. These results suggest that LCT is a reliable and powerful tool for GSA in unbalanced studies, identifying SLE-relevant gene sets more effectively than other GSA methods and showing validation against clinical phenotypes, offering valuable insights into the underlying mechanisms of complex diseases such as SLE.

bioinformatics↗

bioRxiv2023

A network-based model of Aspergillus fumigatus elucidates regulators of development and defensive natural products of an opportunistic pathogen

Aspergillus fumigatus is a notorious pathogenic fungus responsible for various harmful, sometimes lethal, diseases known as aspergilloses. Understanding the gene regulatory networks that specify the expression programs underlying this fungus diverse phenotypes can shed mechanistic insight into its growth, development, and determinants of pathogenicity. We used eighteen RNA-seq datasets (seventeen publicly available and one previously unpublished) of Aspergillus fumigatus to construct a comprehensive gene regulatory network resource. Our resource, named GRAsp (Gene Regulation of Aspergillus fumigatus), was able to recapitulate known regulatory pathways such as response to hypoxia, iron and zinc homeostasis, and secondary metabolite synthesis. Further, GRAsp was experimentally validated in two cases: one in which GRAsp accurately identified an uncharacterized transcription factor negatively regulating the production of the virulence factor gliotoxin and another where GRAsp revealed the bZip protein, AtfA, as required for fungal responses to microbial signals known as lipo-chitooligosaccharides. Our work showcases the strength of using network-based approaches to generate new hypotheses about regulatory relationships in Aspergillus fumigatus. We also unveil an online, user-friendly version of GRAsp available to the Aspergillus research community.

systems biology↗

bioRxiv2022

Geographically Weighted Linear Combination Test for Gene Set Analysis of a Continuous Spatial Phenotype as applied to Intratumor Heterogeneity.

BackgroundThe impact of gene-sets on phenotype is not necessarily uniform across different locations of a cancer tissue. This study introduces a computational platform, GWLCT, for combining gene set analysis with spatial data modeling to provide a new statistical test for association of phenotypes and molecular pathways in spatial single-cell RNA-seq data collected from an input tumor sample. MethodsAt each location, the most significant linear combination is found using a geographically weighted shrunken covariance matrix and kernel function. Whether a fixed or adaptive bandwidth is determined based on a cross validation procedure. Our proposed method is compared to the global version of linear combination test (LCT), bulk and random-forest based gene-set enrichment analyses using data created by the Visium Spatial Gene Expression technique on an invasive breast cancer tissue sample, as well as 144 different simulation scenarios. ResultsIn an illustrative example, the new geographically weighted linear combination test, GWLCT, identifies the cancer hallmark gene-sets that are significantly associated at each location with the five spatially continuous phenotypic contexts in the tumors defined by different well-known markers of cancer-associated fibroblasts. Scan statistics revealed clustering in the number of significant gene-sets. A spatial heatmap of combined significance over all selected gene-sets is also produced. Extensive simulation studies demonstrate that our proposed approach outperforms other methods in the considered scenarios, especially when the spatial association increases. ConclusionsOur proposed approach considers the spatial covariance of gene expression to detect the most significant gene-sets affecting a continuous phenotype. It reveals spatially detailed information in tissue space and can thus play a key role in understanding contextual heterogeneity of cancer cells.

bioinformatics↗

bioRxiv2022

Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation.

bioinformatics↗

bioRxiv2021

Opioid withdrawal abruptly disrupts amygdala circuit function by reducing peptide actions

Opioid withdrawal drives relapse and contributes to compulsive drug use through disruption of endogenous opioid dependent learning circuits in the amygdala. Normally, endogenous opioids control these circuits by inhibiting glutamate release from basolateral amygdala principal neurons onto GABAergic intercalated cells. Using patch-clamp electrophysiology in rat brain slices, we reveal that opioid withdrawal dials down this endogenous opioid inhibition of synaptic transmission. Peptide activity is dialled down due to a protein kinase A dependent increase in the activity of the peptidase, neprilysin. This disrupts peptidergic control of both GABAergic and glutamatergic transmission through multiple amygdala circuits, including reward-related outputs to the nucleus accumbens. This likely disrupts peptide-dependent learning processes in the amygdala during withdrawal. and may direct behaviour towards compulsive drug use. Restoration of endogenous peptide activity during withdrawal may be a viable option to normalise synaptic transmission in the amygdala and restore normal reward learning. In BriefWe find that opioid withdrawal dials down inhibitory neuropeptide activity in the amygdala. This disrupts both GABAergic and glutamatergic transmission through amygdala circuits, including reward-related outputs to the nucleus accumbens. This likely disrupts peptide-dependent learning processes in the amygdala during withdrawal and may direct behaviour towards compulsive drug use. HighlightsO_LIDuring opioid withdrawal, peptidase activity is upregulated in an amygdala circuit C_LIO_LIPeptidase upregulation occurs via a PKA-dependent mechanism C_LIO_LIIncreased peptidase activity limits peptidergic control of synaptic transmission C_LIO_LIOpioid withdrawal disrupts the balance of excitation and inhibition in the amygdala C_LI

neuroscience↗

bioRxiv2019

Rapid Reconstruction of Time-varying Gene Regulatory Networks with Limited Main Memory

Reconstruction of time-varying gene regulatory networks underlying a time-series gene expression data is a fundamental challenge in the computational systems biology. The challenge increases multi-fold if the target networks need to be constructed for hundreds to thousands of genes. There have been constant efforts to design an algorithm that can perform the reconstruction task correctly as well as can scale efficiently (with respect to both time and memory) to such a large number of genes. However, the existing algorithms either do not offer time-efficiency, or they offer it at other costs - memory-inefficiency or imposition of a constraint, known as the smoothly time-varying assumption. In this paper, two novel algorithms - an algorithm for reconstructing Time-varying Gene regulatory networks with Shortlisted candidate regulators - which is Light on memory (TGS-Lite) and TGS-Lite Plus (TGS-Lite+) - are proposed that are time-efficient, memory-efficient and do not impose the smoothly time-varying assumption. Additionally, they offer state-of-the-art reconstruction correctness as demonstrated with three benchmark datasets.\n\nSource Codehttps://github.com/sap01/TGS-Lite-supplem/tree/master/sourcecode

bioinformatics↗

bioRxiv2019

High-Speed Automatic Characterization of Rare Events in Flow Cytometric Data

A new computational framework for FLow cytometric Analysis of Rare Events (FLARE) has been developed specifically for fast and automatic identification of rare cell populations in very large samples generated by platforms like multi-parametric flow cytometry. Using a hierarchical Bayesian model and information-sharing via parallel computation, FLARE rapidly explores the high-dimensional marker-space to detect highly rare populations that are consistent across multiple samples. Further it can focus within specified regions of interest in marker-space to detect subpopulations with desired precision.

bioinformatics↗

bioRxiv2018

Rapid Reconstruction of Time-varying Gene Regulatory Networks

--Rapid advancements in high-throughput technologies has resulted in genome-scale time series datasets. Uncovering the temporal sequence of gene regulatory events, in the form of time-varying gene regulatory networks (GRNs), demands computationally fast, accurate and scalable algorithms. The existing algorithms can be divided into two categories: ones that are time-intensive and hence unscalable; others that impose structural constraints to become scalable. In this paper, a novel algorithm, namely an algorithm for reconstructing Time-varying Gene regulatory networks with Shortlisted candidate regulators (TGS), is proposed. TGS is time-efficient and does not impose any structural constraints. Moreover, it provides such flexibility and time-efficiency, without losing its accuracy. TGS consistently outperforms the state-of-the-art algorithms in true positive detection, on three benchmark synthetic datasets. However, TGS does not perform as well in false positive rejection. To mitigate this issue, TGS+ is proposed. TGS+ demonstrates competitive false positive rejection power, while maintaining the superior speed and true positive detection power of TGS. Nevertheless, main memory requirements of both TGS variants grow exponentially with the number of genes, which they tackle by restricting the maximum number of regulators for each gene. Relaxing this restriction remains a challenge as the actual number of regulators is not known a priori.\n\nReproducibilityThe datasets and results can be found at: https://github.com/aaiitg-grp/TGS. This manuscript is currently under review. As soon as it is accepted, the source code will be made available at the same link. There are mentions of a supplementary document throughout the text. The supplementary document will also be made available after acceptance of the manuscript. If you wish to be notified when the supplementary document and source code are available, kindly send an email to saptarshipyne01@gmail.com with subject line TGS Source Code: Request for Notification. The email body can be kept blank.

bioinformatics↗