Biology subjects

Yao, V.

Publications and source records attributed to Yao, V..

17 recordsLinked to original sources

bioRxiv2026

A network of bZIP transcription factors and inhibitors enforces differentially proliferative and cytotoxic states in T cells

The bZIP family of transcription factors (TFs) plays a central role in regulating T-cell exhaustion. Their complex dimeric interactions obscure the roles of individual members, which genetic knockout or overexpression alone poorly resolve. Here, we use overexpression of full-length bZIP family TFs alongside genetically encoded, protein-based inhibitors to probe the effect of increased or decreased bZIP TF activity on CAR T-cell expansion under exhausting conditions. Enrichment of inhibitors of pro-exhaustion TFs (CREM, NFIL3) illustrated the utility of this approach. The screen enriched full-length TFs of established anti-exhaustion relevance and identified underappreciated bZIP TFs such as BATF2. Detailed comparison of several bZIP TFs and inhibitors revealed distinct effects on proliferation, persistence, and cytotoxicity. We investigated combinations of pro-cytotoxic JUN, pro-proliferative BATF2, and a novel inhibitor (AEL-CREM) of pro-dysfunction CREM. Distinct pairs were more advantageous for maintenance of cytotoxicity or proliferation, revealing relationships between BATF2, JUN-, and CREB1-family bZIPs. Using a combination of epistasis, transcriptome, proteome, and AP-MS interactome analyses of BATF2 and AEL-CREM, we discovered functional overlap in their inhibition of pro-dysfunction CREB1-family activity, potentially driven by unexpected binding of CREB1-family members by overexpressed BATF2. Their function diverged in BATF2's early promotion of pro-proliferation AP-1 activity and later physical and functional interactions with BACH2, contributing to persistence. These findings were mirrored in murine chronic viral infection and syngeneic tumor models, where BATF2 or AEL-CREM overexpression were notably pro-proliferative and anti-exhaustion. Together, these results illuminate new connections and functional roles enabled by bZIP family perturbations, with implications for modulating T-cell state.

immunology↗

bioRxiv2026

Reference-guided pseudotime inference across species and biological contexts

Cells collected at the same chronological age can vary substantially in biological age due to the heterogeneity in the timing of differentiation, speed of maturation, and degeneration. However, existing pseudotime inference methods either disregard chronological time information, or rely on accurate time-series labels within similar species or biological conditions of interest. As a result, both types of strategies often fail to faithfully order cells from biological contexts without reliable time labels, along the desired axis of interest such as human embryonic development or disease progression. Here, we propose Cavebear, a machine learning framework that enables pseudotime inference in a query species or condition guided by scRNA-seq time-series profiles from a reference species or condition. Cavebear achieves more accurate developmental pseudotime inference than existing methods and provides in vivo temporal mapping for in vitro experiments. Furthermore, we illustrate the potential of Cavebear to study cellular-level disease progression in human patients using mouse cancer development models as references. By transferring temporal information across species and conditions, Cavebear enables systematic investigation of biological variation in contexts where such annotations were previously unattainable.

bioinformatics↗

bioRxiv2026

Alternative splicing reshapes protein interaction networks through structured, directional rewiring across cancers

Alternative splicing generates extensive transcript diversity in cancer, but whether the resulting changes to protein-protein interaction (PPI) networks are structured across tumors or idiosyncratic to individual patients remains unresolved. Existing analyses catalog splicing events and their effects on individual interactions, but do not reconstruct patient-specific interaction networks at scale, leaving the systems-level architecture of splicing-driven rewiring uncharacterized. Here, focusing on exon skipping, the most prevalent form of alternative splicing in humans, we analyze 7,950 tumors across 28 cancer types to reconstruct patient-specific rewired PPI networks relative to matched normal tissues, distinguishing interaction gains from losses. We identify widespread and structured remodeling of protein connectivity, including a unique pattern of interaction gains in brain cancers, a conserved pan-cancer axis of interaction loss that converges on GTPase and Ras signaling, and a subset of genes that function as bidirectional switches, where rewiring direction varies across cancer types. At the patient level, tumors exhibiting extreme rewiring, especially pronounced interaction gains, are associated with significantly poorer survival, independent of tumor stage and mutational burden. These results delineate systems-level patterns through which alternative splicing reshapes protein interaction networks and contributes to tumor heterogeneity across cancers.

Cancer Biology↗

bioRxiv2026

ARUNA: Slice-based self-supervised imputation for upscaling DNA methylation sequencing assays

Whole-genome bisulfite sequencing (WGBS) can provide near-comprehensive, base-resolution maps of DNA methylation, transforming our understanding of epigenetic regulation in development and disease, but its cost is often prohibitive for many studies. Reduced representation bisulfite sequencing (RRBS) offers a cost-effective alternative that profiles a CpG-enriched subset of the genome at base resolution. Similar sequencing protocols for both assays pose an opportunity for cross-assay integration, presenting an opportunity for massively increasing sample sizes at whole-genome resolution. However, existing imputation methods are designed for within-assay scenarios and cannot handle the substantial CpG coverage differences between WGBS and RRBS. We introduce ARUNA, a self-supervised denoising convolutional autoencoder that predicts genome-wide CpG-level methylation using only a small subset of observed methylation values and CpG coordinates. By modeling methylation "slices," spatially stacked windows that preserve local correlation structure, ARUNA captures biologically meaningful covariation while avoiding representation collapse. In simulation studies using the GTEx dataset, ARUNA successfully upscales RRBS-scale sparse methylomes (80-95% missingness) to whole-genome resolution, consistently outperforming baselines and maintaining robust performance across donor and tissue holdouts. When applied to real RRBS data from the ENCODE dataset, ARUNA outperformed state-of-the-art methods, with performance validated by matching upscaled RRBS samples to isogenic WGBS replicates. Source code for ARUNA can be found at https://github.com/ylaboratory/ARUNA.

bioinformatics↗

bioRxiv2026

MONTE: Methylation-based Observation Normalization and Tumorpurity Estimation

Bulk DNA methylation profiling is widely used to study cancer epigenomics in clinical settings, but these measurements aggregate signals from malignant and non-malignant cells, introducing composition-dependent confounding that complicates tumor-intrinsic interpretation and cross-cohort analyses. While existing methods can estimate tumor purity and, in some cases, correct methylation measurements, they typically require cancer-specific reference models, matched normal samples, or predefined probe sets, limiting their applicability to rare cancers, different clinical cohorts, and cross-dataset comparisons. We present MONTE (Methylation-based Observation Normalization and Tumor purity Estimation), a unified, cancer label-free framework for tumor purity inference and CpG-resolved methylation correction from bulk DNA methylation data. MONTE learns probe-wise relationships between methylation and tumor purity using an empirical Bayes-moderated linear model and infers purity in new samples via signal-to-noise weighted aggregation, without requiring matched normals, cancer labels, or predefined probe sets. A single pan-cancer MONTE model outperforms existing cancer-specific methods for purity estimation across 21 cancer types, generalizes across purity references, and runs orders of magnitude faster on full-dataset analyses. MONTE also introduces Bayesian transfer learning, which enables efficient recalibration to alternative purity definitions, validated on three independent external cohorts. Methylation correction with MONTE further amplifies tumor-relevant regulatory signal and improves the reproducibility of differential methylation analyses. By unifying purity estimation and correction in a single flexible, scalable, and interpretable framework, MONTE broadens the accessibility of tumor-intrinsic methylation analysis across cancer types and datasets.

bioinformatics↗

bioRxiv2025

Ontology-aware DNA methylation classification with a curated atlas ofhuman tissues and cell types

AO_SCPLOWBSTRACTC_SCPLOWDNA methylation (DNAm) is a core gene regulatory mechanism that captures cellular responses to short- and long-term stimuli such as environmental exposures, aging, and cellular differentiation. Although DNAm has proven valuable as a baseline biomarker for aging by enabling robust characterization of disease-associated methylation shifts associated with age, its potential to reveal analogous shifts in the context of tissue remains underex-plored. A major obstacle has been the absence of comprehensive, curated reference atlases spanning diverse normal human tissues, limiting most existing work to disease-subtype differentiation or localized tissue comparisons. To bridge this gap, we assemble the largest and most diverse atlas of exclusively healthy human tissue and cell samples profiled by 450K arrays, comprising of 16,959 samples across 86 tissues and cell types. Leveraging this resource, we introduce an ontology-aware classification framework that identifies robust CpG features associated with tissue and cell identity while integrating known anatomical and functional relationships (e.g., prefrontal cortex in the brain, leukocytes in blood). Our novel application of Minipatch learning distills a set of 190 CpG sites that can accurately support multi-label classification. We further validate our approach through an ontology-based label transfer task, demonstrating the effectiveness of ontology-informed learning to accurately predict relevant labels for 31 tissues and cell types not seen during training. These findings underscore the potential of our framework to enhance our understanding of healthy methylation landscapes and facilitate future applications in disease detection and personalized medicine.

bioinformatics↗

bioRxiv2025

Interpretable phenotype decoding from multi-condition sequencing data with ALPINE

As sequencing techniques advance in precision, affordability, and diversity, an abundance of heterogeneous sequencing data has become available, encompassing a wide range of phenotypic features and biological perturbations. Unfortunately, increased resolution comes with a cost of increased complexity of the biological search space, even at the individual study level, as perturbations are now often examined across many dimensions simultaneously, including different: donor phenotypes, anatomical regions and cell types, and time points. Furthermore, broad integration across studies promise unique opportunity to explore the molecular underpinnings of distinct healthy and disease states, larger than the original scope of the individual study. To fully realize the promise of both individual higher resolution studies and large cross-study integrations we need a robust methodology that can disentangle the influence of technical and non-relevant phenotypic factors, isolating relevant condition-specific signals from shared biological information while also providing interpretable insights into the genetic effects of these conditions. Current methods typically excel in only one of these areas. To address this gap, we developed ALPINE, a supervised non-negative matrix factorization (NMF) framework that effectively separates both technical and non-technical factors while simultaneously offering direct interpretability of condition-associated genes. Through simulations across 4 different scenarios, we demonstrate that ALPINE outperforms existing methods in both isolating the effect of different phenotypic conditions and prioritizing condition-associated genes. Furthermore, ALPINE has favorable performance in batch effect removal compared with state-of-the-art integration methods. When applied to real-world case studies, we showcase how ALPINE can be used to extract insights into the biological mechanisms that underlie differences between phenotypic conditions.

bioinformatics↗

bioRxiv2025

Benchmarking gene embeddings from sequence, expression, network, and text models for functional prediction tasks

Accurate, data-driven representations of genes are critical for interpreting high-throughput biological data, yet no consensus exists on the most effective embedding strategy for common functional prediction tasks. Here, we present a systematic comparison of 38 gene embedding methods derived from amino acid sequences, gene expression profiles, protein-protein interaction networks, and biomedical literature. We benchmark each approach across three classes of tasks: predicting individual gene attributes, characterizing paired gene interactions, and assessing gene set relationships while trying to control for data leakage. Overall, we find that literature-based embeddings deliver superior performance across prediction tasks, sequence-based models excel in genetic interaction predictions, and expression-derived representations are well-suited for disease-related associations. Interestingly, network embeddings achieve similar performance to literature-based embeddings on most tasks despite using significantly smaller training sets. The type of training data has a greater influence on performance than the specific embedding construction method, with embedding dimensionality having only minimal impact. Our benchmarks clarify the strengths and limitations of current gene embeddings, providing practical guidance for selecting representations for downstream biological applications.

bioinformatics↗

bioRxiv2025

Biophysical Basis of the in vivo Electroretinogram of the Mouse: 4-Shell Current Source Density Model

A goal of contemporary physiology is to translate the knowledge obtained ex vivo of the molecular structure and function of ionic mechanisms into tools for quantitative, in vivo measurement of that function in humans, and in animal models of disease and therapeutic intervention. Non-invasive field potentials such as ECGs, EMGs, EEGs, and ERGs hold great promise in efforts to achieve this goal, but their full translation is challenging due to the multiplicity of cells with distinct ionic mechanisms and distributions of membrane current sources and sinks, and the requirement of adequate characterization of volume conduction in the relevant tissue(s). The molecular identities and subcellular distributions of the ionic mechanisms of mouse rod photoreceptors and the adjacent retinal pigment epithelium (RPE) have been thoroughly characterized, and are associated with two major components of the ERG, the a-wave and the c-wave, respectively. To develop a molecular-biophysical description of these components of the ERG, we have pharmacologically and genetically isolated rod photoreceptor-driven currents, created a 3D "4-shell" model of volume conduction in the mouse eye and extraocular tissue, and solved the Equation of Continuity for trans-photoreceptor layer and trans-RPE layer sources. Corneal and intraocular measurements of a- and c-waves are shown to reject the classic Rodieck-Ford electrical circuit model of the ERG, but found consistent with a 4-shell model having realistic values for extracellular conductivity in the eye and extraocular tissues. Our results and analysis explain the large variation across studies in maximal a-wave amplitudes and show how a-wave amplitudes exceeding 1 mV can be achieved.

biophysics↗

bioRxiv2024

Disentangling associations between complex traits and cell types with seismic

AO_SCPLOWBSTRACTC_SCPLOWIntegrating single-cell RNA sequencing (scRNA-seq) with Genome-Wide Association Studies (GWAS) can help reveal GWAS-associated cell types, furthering our understanding of the cell-type-specific biological processes underlying complex traits and disease. However, current methods have technical limitations that hinder them from making systematic, scalable, interpretable disease-cell-type associations. In order to rapidly and accurately pinpoint associations, we develop a novel framework, seismic, which characterizes cell types using a new specificity score. We compare seismic with alternative methods across over 1,000 cell type characterizations at different granularities and 28 traits, demonstrating that seismic both corroborates findings and identifies trait-relevant cell groups which are not apparent through other methodologies. Furthermore, as part of the seismic framework, the specific genes driving cell type-trait associations can easily be accessed and analyzed, enabling further biological insights. The advantages of seismic are particularly salient in neurodegenerative diseases such as Parkinsons and Alzheimers, where disease pathology has not only cell-specific manifestations, but also brain region-specific differences. Interestingly, a case study of Alzheimers disease reveals the importance of considering GWAS endpoints, as studies relying on clinical diagnoses consistently identify microglial associations, while GWAS with a tau biomarker endpoint reveals neuronal associations. In general, seismic is a computationally efficient, powerful, and interpretable approach for identifying associations between complex traits and cell type-specific expression.

bioinformatics↗

bioRxiv2023

Enhancing gene set analysis in embedding spaces: a novel best-match approach

AO_SCPLOWBSTRACTC_SCPLOWEmbedding methods have emerged as a valuable class of approaches for distilling essential information from complex high-dimensional data into more accessible lower-dimensional spaces. Applications of embedding methods to biological data have demonstrated that gene embeddings can effectively capture physical, structural, and functional relationships between genes. However, this utility has been primarily realized by using gene embeddings for downstream machine learning tasks. Much less has been done to examine the embeddings directly, especially analyses of gene sets in embedding spaces. Here, we propose ANDES, a novel best-match approach that can be used with existing gene embeddings to compare gene sets while reconciling gene set diversity. This intuitive method has important downstream implications for improving the utility of embedding spaces for various tasks. Specifically, we show how ANDES, when applied to different gene embeddings encoding protein-protein interactions, can be used as a novel overrepresentation-based and rank-based gene set enrichment analysis method that achieves state-of-the-art performance. Additionally, ANDES can use multi-organism joint gene embeddings to facilitate functional knowledge transfer across organisms, allowing for phenotype mapping across model systems. Our flexible, straightforward best-match methodology can be extended to other embedding spaces with diverse community structures between set elements.

bioinformatics↗

bioRxiv2023

Splitpea: uncovering cancer patient-specific protein interaction network rewiring

Protein-protein interactions play an essential role in nearly all biological processes, and it has become increasingly clear that in order to better understand the fundamental processes that underlie disease, we must develop a strong understanding of both their context specificity (e.g., tissue-specificity) as well as their dynamic nature (e.g., how they respond to environmental changes). While network-based approaches have found much initial success in the application of protein-protein interactions (PPIs) towards systems-level explorations of biology, they often overlook the fact that large numbers of proteins undergo alternative splicing. Alternative splicing has not only been shown to diversify protein function through the generation of multiple protein isoforms, but also remodel PPIs and affect a wide range diseases, including cancer. Isoform-specific interactions are not well characterized, so we develop a computational approach that uses domain-domain interactions in concert with differential exon usage data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression project (GTEx). Using this approach, we can characterize PPIs likely disrupted or possibly even increased due to splicing events for individual TCGA cancer patient samples relative to a matched GTEx normal tissue background.

systems biology↗

bioRxiv2022

The proto-oncogene DEK regulates neuronal excitability and tau accumulation in Alzheimer's disease vulnerable neurons

Neurons from layer II of the entorhinal cortex (ECII) are the first to accumulate tau protein aggregates and degenerate during prodromal Alzheimers disease. Here, we use a data-driven functional genomics approach to model ECII neurons in silico and identify the proto-oncogene DEK as a potential driver of tau pathology. By modulating DEK levels in EC neurons in vitro and in vivo, we first validate the accuracy and cell-type specificity of our network predictions. We then show that Dek silencing changes the inducibility of immediate early genes and alters neuron excitability, leading to dysregulation of neuronal plasticity genes. We further find that loss of function of DEK leads to tau accumulation in the soma of ECII neurons, reactivity of surrounding microglia, and eventually microglia-mediated neuron loss. This study validates a pathological gene discovery tool that opens new therapeutic avenues and sheds light on a novel pathway driving tau pathology in vulnerable neurons.

neuroscience↗

bioRxiv2022

Joint embedding of biological networks for cross-species functional alignment

Model organisms are widely used to better understand the molecular causes of human disease. While sequence similarity greatly aids this transfer, sequence similarity does not imply functional similarity, and thus, several current approaches incorporate protein-protein interactions (PPIs) to help map findings between species. Existing transfer methods either formulate the alignment problem as a matching problem which pits network features against known orthology, or more recently, as a joint embedding problem. Here, we propose a novel state-of-the-art joint embedding solution: Embeddings to Network Alignment (ETNA). More specifically, ETNA generates individual network embeddings based on network topological structures and then uses a Natural Language Processing-inspired cross-training approach to align the two embeddings using sequence orthologs. The final embedding preserves both within and between species gene functional relationships, and we demonstrate that it captures both pairwise and group functional relevance. In addition, ETNAs embeddings can be used to transfer genetic interactions across species and identify phenotypic alignments, laying the groundwork for potential opportunities for drug repurposing and translational studies.

bioinformatics↗

bioRxiv2021

Neuron-Glia Signaling Regulates the Onset of the Antidepressant Response

Commonly prescribed antidepressants, such as selective serotonin reuptake inhibitors (SSRIs) take weeks to achieve therapeutic benefits1, 2. The underlying mechanisms of why antidepressants take weeks or months to reverse depressed mood are not understood. Using a single cell sequencing approach, we analyzed gene expression changes in mice subjected to stress-induced depression and determined their temporal response to antidepressant treatment in the cerebral cortex. We discovered that both glial and neuronal cell populations elicit gene expression changes in response to stress, and that these changes are reversed upon treatment with fluoxetine (Prozac), a widely prescribed selective serotonin reuptake inhibitor (SSRI). Upon reproducing the molecular signaling events regulated by fluoxetine3 in a cortical culture system, we found that these transcriptional changes are serotonin-dependent, require reciprocal neuron-glia communication, and involve temporally-specified sequences of autoregulation and cross-regulation between FGF2 and BDNF signaling pathways. Briefly, stimulation of Fgf2 synthesis and signaling directly regulates Bdnf synthesis and secretion cell-non-autonomously requiring neuron-glia interactions, which then activates neuronal BDNF-TrkB signaling to drive longer-term neuronal adaptations4-6 leading to improved mood. Our studies highlight temporal and cell type specific mechanisms promoting the onset of the antidepressant response, that we propose could offer novel avenues for mitigating delayed onset of antidepressant therapies.

neuroscience↗

bioRxiv2018

Selective neuronal vulnerability in Alzheimer’s disease: a network-based analysis

A major obstacle to treating Alzheimers disease (AD) is our lack of understanding of the molecular mechanisms underlying selective neuronal vulnerability, which is a key characteristic of the disease. Here we present a framework to integrate high-quality neuron-type specific molecular profiles across the lifetime of the healthy mouse, which we generated using bacTRAP, with postmortem human functional genomics and quantitative genetics data. We demonstrate human-mouse conservation of cellular taxonomy at the molecular level for AD vulnerable and resistant neurons, identify specific genes and pathways associated with AD pathology, and pinpoint a specific functional gene module underlying selective vulnerability, enriched in processes associated with axonal remodeling, and affected by both amyloid accumulation and aging. Overall, our study provides a molecular framework for understanding the complex interplay between A{beta}, aging, and neurodegeneration within the most vulnerable neurons in AD.

neuroscience↗

bioRxiv2017

Transcriptome Analysis of Adult C. elegans Cells Reveals Tissue-specific Gene and Isoform Expression

The biology and behavior of adults differ substantially from those of developing animals, and cell-specific information is critical for deciphering the biology of multicellular animals. Thus, adult tissue-specific transcriptomic data are critical for understanding molecular mechanisms that control their phenotypes. We used adult cell-specific isolation to identify the transcriptomes of C. elegans four major tissues (or \"tissue-ome\"), identifying ubiquitously expressed and tissue-specific \"super-enriched\" genes. These data newly reveal the hypodermis metabolic character, suggest potential worm-human tissue orthologies, and identify tissue-specific changes in the Insulin/IGF-1 signaling pathway. Tissue-specific alternative splicing analysis identified a large set of collagen isoforms and a neuron-specific CREB isoform. Finally, we developed a machine learning-based prediction tool for 70 sub-tissue cell types, which we used to predict cellular expression differences in IIS/FOXO signaling, stage-specific TGF-b activity, and basal vs. memory-induced CREB transcription. Together, these data provide a rich resource for understanding the biology governing multicellular adult animals

genomics↗