Biology subjects

Yang, Y. T.

Publications and source records attributed to Yang, Y. T..

8 recordsLinked to original sources

bioRxiv2026

Dietary landscapes shape genotype- and sex-specific responses to insecticides

Insecticide resistance is typically studied as a response to chemical toxicity, yet in natural and agricultural systems insecticides are embedded within food resources. How resistance alleles interact with nutritional environments to shape fitness remains largely unknown. Here we combine nutritional geometry approaches to test how variation at a major resistance locus, Cyp6g1, modifies sex-specific reproductive performance across dietary landscapes in Drosophila melanogaster. We show that a resistance allele does more than increase survival: it profoundly reshapes reproductive allocation in both sexes. Resistant females exhibited up to a two-fold increase in ovariole number, with benefits amplified in protein-rich, high-calorie diets. In contrast, resistant males displayed increased testis size but reduced seminal vesicle and accessory gland size, revealing sex-specific trade-offs. Critically, contaminating doses of imidacloprid shifted nutritional optima according to genotype, in some cases enhancing reproduction in susceptible flies, consistent with diet-dependent hormesis. Thus, resistance, nutrient availability and toxin exposure jointly determine fitness outcomes. Our findings demonstrate that resistance evolution is embedded within dietary landscapes rather than driven by toxicity alone, highlighting the need to integrate nutritional ecology into predictions of resistance dynamics in human-modified environments.

ecology↗

bioRxiv2025

Augmented prediction of multi-species protein--RNA interactions using evolutionary conservation of RNA-binding proteins

AbstractRNA-binding proteins (RBPs) play critical roles in gene expression regulation. Recent studies have begun to detail the RNA recognition mechanisms of diverse RBPs. However, given the array of RBPs studied so far, it is implausible to experimentally profile RBP-binding peaks for hundreds of RBPs in multiple non-model organisms. Here, we introduce MuSIC (Multi-Species RBP-RNA Interactions using Conservation), a deep learning-based framework for predicting cross-species RBP-RNA interactions by leveraging label smoothing and evolutionary conservation of RBPs across 11 diverse species ranging from human to yeast. MuSIC outperforms state-of-the-art computational methods, and provides predicted RBP-binding peaks across species with high accuracy. The prediction confidence is higher in the closely related species, partially due to the RBP conservation patterns. Finally, the effects of homologous genetic variants on RBP binding can be computationally quantified across species, followed by experimental validations. The target transcripts with disrupted binding events are enriched with the ubiquitination-associated pathways. To summarize, MuSIC provides a useful computational framework for predicting RBP-RNA interactions cross-species and quantifying the effects of genetic variants on RBP binding, offering novel insights into the RBP-mediated regulatory mechanisms implicated in human diseases. HighlightsO_LIMuSIC integrates RBP-binding peaks with conservation-weighted label smoothing to predict RBP-RNA interactions across eleven species C_LIO_LIMuSIC outperforms state-of-the-art computational methods in predicting cross-species RBP-RNA interactions C_LIO_LICross-species prediction accuracy of RBP-binding peaks correlates with the conservation of RBPs C_LIO_LIMuSIC quantifies the effects of homologous SNVs on RBP binding with experimental validation in mouse C_LI

bioinformatics↗

bioRxiv2025

Comprehensive analysis of pseudogene expression in human and macaque brains compared with other tissues

Although gene expression in the brain has been extensively investigated and compared to other tissues, the activity of pseudogenes has not been comprehensively surveyed. Here, leveraging large-scale RNA-seq data, we construct consistent pseudogene expression profiles in human and macaque brains and compare them to 29 other tissues. We further annotate pseudogenes with potential cellular roles based on co-clustering them with protein-coding genes. Notably, the majority of the expressed pseudogenes show elevated expression in the brain relative to other tissues, and these pseudogenes show broad and consistent expression patterns across brain subregions. Furthermore, spatiotemporal analyses reveal that pseudogenes in different brain subregions have greatly varying temporal trajectories (e.g., increasing vs. decreasing), in contrast to protein-coding genes that tend to be more uniform. Finally, we identify a set of pseudogenes exhibiting significant changes in neuropsychiatric disorders, some of which overlap with known brain eGenes (genes whose expression is associated with expression quantitative trait loci, eQTLs), as well as with genes implicated by genome-wide association studies (GWAS). Together, our study provides a public resource of pseudogene expression in human and macaque brains (in comparison to other tissues) and highlights aspects of pseudogene expression in brain development and pathogenesis.

bioinformatics↗

bioRxiv2025

An expanded reference catalog of translated open reading frames for biomedical research

Non-canonical (i.e., unannotated) open reading frames (ncORFs) have until recently been omitted from reference genome annotations, despite evidence of their translation, limiting their incorporation into biomedical research. To address this, in 2022, we initiated the TransCODE consortium and built the first community-driven consensus catalog of human ncORFs, which was openly distributed to the research community via Ensembl-GENCODE. While this catalog represented a starting point for reference ncORF annotation, major technical and scientific issues remained. In particular, this initial catalogue had no standardized framework to judge the evidence of translation for individual ncORFs. Here, we present an expanded and refined catalog of the human reference annotation of ncORFs. By incorporating more datasets and by lifting constraints on ORF length and start-codon, we define a comprehensive set of 28,359 ncORFs that is nearly four times the size of the previous catalog. Furthermore, to aid users who wish to work with ncORFs with the strongest and most reproducible signals of translation, we utilized a data-driven framework (i.e. translation signature scores) to assess the accumulated evidence for any individual ncORF. Using this approach, we derive a subset of 7,888 ncORFs with translation evidence on par with canonical protein-coding genes, which we refer to as the Primary set. This set can serve as a reliable reference for downstream analyses and validation, with a particular emphasis on high quality. Overall, this update reflects continual community-driven efforts to make ncORFs accessible and actionable to the broader research public and further iterations of the catalog will continue to expand and refine this resource.

genomics↗

bioRxiv2021

A community-driven roadmap to advance research on translated open reading frames detected by Ribo-seq

Ribosome profiling (Ribo-seq) has catalyzed a paradigm shift in our understanding of the translational vocabulary of the human genome, discovering thousands of translated open reading frames (ORFs) within long non-coding RNAs and presumed untranslated regions of protein-coding genes. However, reference gene annotation projects have been circumspect in their incorporation of these ORFs due to uncertainties about their experimental reproducibility and physiological roles. Yet, it is indisputable that certain Ribo-seq ORFs make stable proteins, others mediate gene regulation, and many have medical implications. Ultimately, the absence of standardized ORF annotation has created a circular problem: while Ribo-seq ORFs remain unannotated by reference biological databases, this lack of characterisation will thwart research efforts examining their roles. Here, we outline the initial stages of a community-led effort supported by GENCODE / Ensembl, HGNC and UniProt to produce a consolidated catalog of human Ribo-seq ORFs.

genomics↗

bioRxiv2020

Predicting changes in protein thermostability upon point mutation with deep 3D convolutional neural networks

Predicting mutation-induced changes in protein thermodynamic stability ({triangleup}{triangleup}G) is of great interest in protein engineering, variant interpretation, and understanding protein biophysics. We introduce ThermoNet, a deep, 3D-convolutional neural network designed for structure-based prediction of {triangleup}{triangleup}Gs upon point mutation. To leverage the image-processing power inherent in convolutional neural networks, we treat protein structures as if they were multi-channel 3D images. In particular, the inputs to ThermoNet are uniformly constructed as multi-channel voxel grids based on biophysical properties derived from raw atom coordinates. We train and evaluate ThermoNet with a curated data set that accounts for protein homology and is balanced with direct and reverse mutations; this provides a framework for addressing biases that have likely influenced many previous {triangleup}{triangleup}G prediction methods. ThermoNet demonstrates performance comparable to the best available methods on the widely used Ssym test set. However, ThermoNet accurately predicts the effects of both stabilizing and destabilizing mutations, while most other methods exhibit a strong bias towards predicting destabilization. We further show that homology between Ssym and widely used training sets like S2648 and VariBench has likely led to overestimated performance in previous studies. Finally, we demonstrate the practical utility of ThermoNet in predicting the {triangleup}{triangleup}Gs for two clinically relevant proteins, p53 and myoglobin, and for pathogenic and benign missense variants from ClinVar. Overall, our results suggest that 3D convolutional neural networks can model the complex, non-linear interactions perturbed by mutations, directly from biophysical properties of atoms. Author SummaryThe thermodynamic stability of a protein, usually represented as the Gibbs free energy for the biophysical process of protein folding ({triangleup}G), is a fundamental thermodynamic quantity. Predicting mutation-induced changes in protein thermodynamic stability ({triangleup}{triangleup}G) is of great interest in protein engineering, variant interpretation, and understanding protein biophysics. However, predicting {triangleup}{triangleup}Gs in an accurate and unbiased manner has been a long-standing challenge in the field of computational biology. In this work, we introduce ThermoNet, a deep, 3D-convolutional neural network designed for structure-based {triangleup}{triangleup}G prediction. To leverage the image-processing power inherent in convolutional neural networks, we treat protein structures as if they were multi-channel 3D images. ThermoNet demonstrates performance comparable to the best available methods. However, ThermoNet accurately predicts the effects of both stabilizing and destabilizing mutations, while most other methods exhibit a strong bias towards predicting destabilization. We also demonstrate that the presence of homologous proteins in commonly used training and testing sets for {triangleup}{triangleup}G prediction methods has likely influenced previous performance estimates. Finally, we highlight the practical utility of ThermoNet by applying it to predicting the {triangleup}{triangleup}Gs for two clinically relevant proteins, p53 and myoglobin, and for pathogenic and benign missense variants from ClinVar.

bioinformatics↗

bioRxiv2019

An integrative ENCODE resource for cancer genomics

ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.

genomics↗

bioRxiv2018

A Computational Framework for DNA Sequencing-Based Microscopy

Barcoded DNA polony amplification techniques provide a means to impart a unique sequence identity onto specific locations of a surface wafer or chip. We describe a method whereby micro-scale spatial information such as the relative positions of biomolecules on a surface can be transferred to a sequence-based format and reconstructed into images without the use of conventional optics. The principle is based on the pair-wise association of uniquely tagged and spatially adjacenct polonies. The network of polonies connected by shared borders forms a graph whose topology can be reconstructed from a set of edges derived from pairs of barcodes associated with each other during a polony crosslinking phase, the sequences of which could be determined by isolation of the DNA and next-gen sequencing. We developed a mathematical and computational framework for this principle called Polony Adjacency Reconstruction for Spatial Inference and Topology and show that Euclidean spatial data may be partially stored and transmitted in the form of untethered graph topology. This effect may be exploited to form images by transferring molecular information from a surface of interest, which we demonstrated in silico by reconstructing images formed from stochastic transfer of hypothetical red, green, and blue molecular markers. The theory developed here could serve as a guide for a highly automated, multiplexable, and potentially super resolution imaging method based on molecular information encoding and transmission.

biophysics↗