Biology subjects

Yue, Z.

Publications and source records attributed to Yue, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

Cellular context shapes the transcriptomic impact of somatic variants

Somatic SNVs are commonly prioritized by recurrence or sequence annotation, leaving the molecular consequences of private variants unresolved. We developed PrismSNV and Ligo-Seq, a single-cell full-length transcriptome sequencing platform, to quantify transcriptomic displacement associated with within-cell SNV-state toggling. Across 26 samples from three cancer types, more than 60% of candidate SNVs showed significant cell-type-specific displacement with over 30% variation in magnitude. Sequence-based deleteriousness was poorly aligned with cellular displacement, and fewer than 3% of top-displacement SNVs across six tumor types were catalogued in ClinVar. Displacement was strongest in transcriptionally plastic, less differentiated states. Analysis of 655,349 CD4+ T-cell transcriptomes further showed that SNV-gene associations changed in magnitude and sometimes direction during activation. Lack of recurrence or clinical annotation does not imply transcriptomic inertness; variant impact is shaped by cellular context rather than sequence alone, establishing the variant-cell pair as a critical unit for interpreting somatic variation.

genetics↗

bioRxiv2026

Neutrophils orchestrate pre-metastatic immunosuppressive bone marrow remodeling during prostate cancer metastasis

The dynamic reprogramming of the bone marrow (BM) microenvironment during metastatic progression is poorly defined. Through single-cell profiling of BM samples spanning benign conditions, high-risk bone metastatic potential localized prostate cancer (HMP) and established bone metastatic prostate cancer (PCa), we uncovered a primary tumor-to-BM signaling axis that orchestrates niche formation through neutrophil-mediated remodeling. Tumor-associated neutrophils (TANs) emerge during the HMP stage, persist throughout metastatic progression, correlate with poor patient prognosis, and exhibit direct immunosuppressive activity that contributes to a BM environment permissive to metastatic colonization. Functional and genetic studies showed that tumor-associated systemic IGF1 reprogrammed BM neutrophils through IGF1R-MAPK/ERK-KLF4 signaling, with IL-1{beta}, reactive oxygen species and nitric oxide contributing to TAN-mediated T cell suppression. Tumor-cell Igf1 perturbation, IGF1R blockade and neutrophil-specific Igf1r deletion attenuated TAN-associated BM remodeling, and reduced subsequent bone metastatic progression. In bone-metastasis models, combined IGF1R and PD-1 blockade suppressed bone-metastatic progression. Translationally, our investigator-initiated clinical trial (NCT06866548) of teprotumumab plus tislelizumab in patients with established metastatic castration-resistant prostate cancer (mCRPC) provided initial safety data and exploratory clinical observations. Together, these findings identify IGF1R-dependent neutrophil reprogramming as a functionally important component of the evolving PCa bone metastatic microenvironment and provide a rationale for therapeutic targeting of IGF1R signaling together with immune checkpoint blockade.

cancer biology↗

bioRxiv2026

siDiff: De Novo siRNA Design via Efficacy-Guided Discrete Masked Diffusion

Target-conditioned de novo siRNA design requires capturing both target mRNA binding context and internal siRNA sequence-activity rules. Traditional computational pipelines predominantly rely on discriminative prediction models that rank pre-filtered candidate pools. However, these approaches struggle with generalization on novel target genes and often overlook potent candidates due to dataset size constraints and motif overfitting. To reconcile generation precision with sequence diversity, we propose siDiff, an efficacy-guided discrete diffusion framework for target-conditioned de novo siRNA design. siDiff pairs a discrete-masked diffusion transformer--which models the underlying sequence distribution over functional duplexes--with a mask-robust efficacy guidance model. During inference, we introduce a biology-aware, three-stage sampling mechanism that performs structural candidate filtering, dynamic unmasking guidance, and cluster-aware redundancy mitigation. This dual mechanism enables the diffusion process to explore broad sequence spaces while the efficacy model prevents distributional shift toward non-functional candidates. Extensive experiments across four datasets, including the public Takayuki benchmark and three curated patent datasets, demonstrate that siDiff significantly outperforms state-of-the-art discriminative baselines and discrete diffusion models, achieving relative hit-rate improvements of over 30% and demonstrating superior generalization on out-of-distribution gene targets. The source code and related materials are available at https://github.com/cybericha/siDiff.

bioinformatics↗

bioRxiv2026

RiboRep: Replicate-Aware Cross-Modal Transformers for Codon-Resolved Ribosome Density Prediction

Ribosome profiling enables genome-wide measurement of translation at nucleotide resolution and provides a dynamic view of cellular protein synthesis under diverse biological conditions. Existing computational approaches primarily operate on codon-level representations, potentially losing fine-grained translational signals critical for modeling context-dependent cellular responses. Such predictive translational modeling is increasingly important for emerging biological digital twins, where accurate simulation of molecular-state dynamics is required to characterize cellular adaptation, perturbation response, and phenotype progression. We present RiboRep, a replicate-aware cross-modal transformer for codon-resolved ribosome density prediction. RiboRep jointly models nucleotide-resolution RNA sequences and reference ribosome occupancy signals using dual-stream convolutional encoders, RoPE-based self-attention, asymmetric cross-attention, and replicate-aware conditioning tokens. By explicitly modeling replicate-specific variation and integrating sequence context with experimentally observed translational activity, RiboRep provides a framework for reconstructing and simulating translational states across biological conditions. Across bacterial, yeast, and plant ribosome profiling datasets, RiboRep achieves competitive or improved performance compared with existing baselines, with particularly strong gains on replicate-rich plant datasets. Ablation studies further demonstrate the importance of local codon-aware feature extraction, replicate-aware conditioning, and gated readout. Beyond predictive performance, the proposed framework establishes a foundation for translation-aware molecular digital twins capable of modeling ribosome occupancy landscapes, perturbation-induced translational responses, and condition-specific regulatory programs at codon resolution. 1

bioinformatics↗

bioRxiv2026

MondrianMap: Navigating Gene Set Hierarchies with Multi-Resolution Enrichment Maps

Gene Ontology encodes genes as a hierarchy, yet every enrichment visualization flattens it into a ranked list, discarding the ability to view the same process at different levels of abstraction. We present MondrianMap, a free interactive web application (https://mondrianmap.smartdrugdiscovery.org/) that organizes enrichment results into 13 semantically principled layers derived from the GOALS framework and renders them as color-encoded rectangular maps where block area reflects significance, color encodes effect direction, and spatial proximity preserves semantic relations, all within an interactive interface. Three case studies across the NIH Common Fund Data Ecosystem demonstrate that visualization facilitates recognition of patterns that are difficult to discern in flat outputs: (1) LINCS CRISPR perturbations reveal that TP53 and KRAS knockouts produce opposite color maps at a single semantic layer, the same immune recruitment processes suppressed by TP53 loss are activated by KRAS disruption; (2) GTEx aging signatures expose the inflammaging paradox as an immediate visual phenomenon, identical antimicrobial defense programs appear uniformly upregulated in aging blood yet uniformly downregulated in aging liver at matched semantic resolution; and (3) MoTrPAC exercise data capture temporal dynamics as color transitions where brown adipose tissue undergoes a threshold switch from two enriched terms to thirty-four at a single molecular layer, while cardiac tissue reverses from uniformly activated to uniformly suppressed glycolytic metabolism as adaptation progresses. MondrianMap facilitates hierarchical visual reasoning, complementing statistical enrichment reporting for biological discovery. HIGHLIGHTSO_LIMondrianMap provides multi-resolution enrichment visualization for Gene Ontology C_LIO_LILayer-specific views reveal directional oppositions difficult to discern in flat term lists C_LIO_LINavigating layers organizes one enrichment into a multi-scale biological narrative C_LIO_LIDemonstrated on cancer, aging, and exercise data across three CFDE programs C_LI IN BRIEFMondrianMap is a web application that transforms gene set enrichment results into layered visualizations encoding regulation, significance, and semantic hierarchy. Across cancer, aging, and exercise datasets, viewing enrichment at defined semantic layers exposes directional inversions, temporal switches, and multi-scale biological narratives that are difficult to extract from conventional flat enrichment outputs. THE BIGGER PICTUREWhen researchers measure gene expression changes in disease, aging, or drug response, they rely on enrichment analysis to translate thousands of molecular measurements into interpretable biological themes. The standard output is a ranked list of processes sorted by statistical significance. This format served the field well when studies examined a single condition; however, modern genomics routinely compares dozens of tissues, timepoints, and perturbations, each generating hundreds of enriched terms. The critical limitation is not statistical power; however, interpretive structure: a flat list cannot show whether two conditions activate the same biological process in opposite directions, whether a process visible at one level of abstraction disappears or transforms at another, or how a tissues functional response evolves across time. These are precisely the questions that define contemporary systems biology. Does a tumor suppressor gene silence the same immune program that an oncogene activates? Does aging drive the same defense pathway upward in the blood and downward in the liver? Does an exercise response flip from activation to suppression as the tissue adapts? Answering these questions requires a visualization framework that preserves hierarchy, encodes direction, and enables comparison at matched levels of biological resolution. MondrianMap provides this framework by organizing Gene Ontology terms into quantitatively defined semantic layers and rendering enrichment as color-encoded rectangular maps navigable from molecular mechanism to system-level theme. The result is a tool that extends enrichment analysis from a reporting step into an interactive framework for biological reasoning, hypothesis generation, and cross-dataset discovery.

Systems Biology↗

bioRxiv2026

PAG-Agent: a biologist-oriented research assistant for context-aware pathway-level analysis and interpretation

Pathway analysis is a critical step for translating gene-level omics results into biological mechanisms, yet existing workflows often leave researchers with long lists of statistically significant pathways that are difficult to interpret, validate, and connect to experimental context. We developed PAG-Agent, a biologist-oriented virtual research assistant that integrates pathway-level statistical analysis, context-aware biological interpretation, literature-supported reasoning, and scientific writing support within a unified workflow. PAG-Agent supports bulk and single-cell transcriptomic data and enables users to perform data preprocessing, differential expression analysis, pathway analysis, pathway-level consensus analysis, and pathway-level meta-analysis through click-based and chat-based interactions. Unlike conventional pathway-analysis tools that analyze gene sets largely in isolation, PAG-Agent incorporates experimental conditions and research objectives to prioritize biologically relevant pathways and generate interpretable hypotheses. The system also provides gene and pathway annotation, citation retrieval, visualization, and writing refinement functions. In Alzheimers disease case studies using three transcriptomic datasets, PAG-Agent consistently identified neurodegeneration-related pathways across multiple analysis methods and datasets. In citation-retrieval benchmarking, PAG-Agent outperformed six competing LLMs across five common literature-support scenarios, demonstrating improved ability to provide contextually relevant and valid references. Overall, PAG-Agent lowers technical barriers for pathway-level analysis and helps researchers move from transcriptomic data to biologically grounded interpretation, hypothesis generation, and scientific communication.

bioinformatics↗

bioRxiv2026

A Multi-Agent RAG Framework for Biomedical Literature Analysis

BackgroundThe biomedical literature is expanding at an unprecedented rate, with over 4,000 new articles indexed on PubMed each day. Clinicians and researchers frequently lack the time to review this volume before making decisions. Retrieval-Augmented Generation (RAG) systems attempt to bridge this gap by grounding language model responses in relevant documents, but standard implementations rank all retrieved passages solely by semantic similarity, treating a case report and a meta-analysis as equally authoritative. ObjectiveWe aimed to develop and pilot-evaluate a RAG variant that incorporates evidence quality and publication recency into the retrieval scoring function, and to determine whether these signals improve answer quality on biomedical questions compared with standard cosine similarity RAG and a full-context baseline. MethodsWe developed ET-RAG (Evidence-Temporal RAG), which scores each retrieved chunk using a weighted combination of cosine similarity (50%), evidence quality based on the GRADE hierarchy (30%), and temporal recency (20%). We evaluated ET-RAG alongside two baselines: a full context agent powered by Gemini 2.0 Flash and a standard cosine RAG agent using GPT-4o-mini. All agents were tested on 40 benchmark questions (10 single-choice, 10 multiple-choice, 10 short answer, and 10 long answer) drawn from 10 peer-reviewed Alzheimers disease papers published between 2021 and 2025. ResultsET-RAG achieved the highest scores across all four question categories: single choice (0.90), multiple choice (0.74), short answer (0.92), and long answer (0.89), with a combined average of 0.86. Cosine RAG scored 80%, 0.48, 0.82, and 0.69, respectively (average 0.70), while the full context agent scored 0.60, 0.59, 0.71, and 0.53 (average 0.61). The full context agent, despite having access to the entire corpus through Geminis large context window, struggled with consistent answer extraction and was prone to rate limiting under heavy query loads. A control question on forestry was correctly rejected by all three agents, suggesting no hallucination on this control item. ConclusionsIn this pilot Alzheimers disease benchmark, incorporating evidence quality and recency into RAG retrieval improved answer quality relative to pure cosine similarity retrieval and full-corpus prompting. The evidence-temporal scoring function is lightweight to implement and adds minimal computational overhead to existing vector search pipelines, but broader validation across domains, evidence levels, and stronger retrieval baselines are required before claims of generalizable biomedical reliability can be made.

bioinformatics↗

bioRxiv2026

Eleven deep-sea coral genome assemblies unveil insights into evolution, adaptation, and coral biodiversity

Deep-sea corals are vital in maintaining coral ecosystem biodiversity, yet their genetic characteristics remain largely unexplored. Here, we present 11 deep-sea coral genome assemblies, including four Hexacorallia and seven Octocorallia species, significantly contributing new genomic information across two orders. Our analysis reveals the historical dynamics of coral speciation and the influence of environmental factors on the evolution of coral reef ecosystems.Total of 126 horizontal gene transfer (HGT) events were detected, among which genes from the ancestor of symbiodiniaceae indicate that the ancestors of deep-sea corals may have inhabited shallow-sea environments. Notably, several of these HGTs are involved in phosphorus (PhnX/PhnW) and cholesterol (DHCR7) metabolisms within corals, indicating that HGTs may serve as an adaptive survival strategy for the coral holobionts. Deep-sea corals also rely on symbiotic bacteria to synthesize 10 essential amino acids (such as valine and tyrosine), retaining only partial amino acid synthesis capacity. In addition, we investigated the evolution of key biological rhythm genes and temperature adaptation in corals. The loss of key rhythm genes (e.g., clock and cry) in deep-sea corals and copy number difference of genes related to heat stress (e.g., Cbl-b and Rchy) revealed genetic difference between deep-sea and shallow-sea corals. Our new genome assemblies enhance the understanding of deep-sea coral evolution, biodiversity, and adaptation, providing a genetic foundation for coral conservation.

genomics↗

bioRxiv2026

ExMODE: A Multi-Omics Repository for Extremophile Adaptation and Bioprospecting

Extreme environments, though hostile to most life forms, host specialized extremophile communities that have redefined biological cognition and emerged as vital biotechnological resources, with their unique adaptive traits and bioactive molecules driving advances in multiple scientific and industrial fields. However, research on extremophiles is hindered by limitations in culture-based methods, fragmented multi-omics data with non-uniform annotation standards across repositories, the lack of cross-extreme comparative research in existing resources, and the singularity of data dimensionality that neglects key structural information, all of which restrict the functional interpretation of extremophile microbes and the exploitation of their bioprospecting potential. To tackle these challenges, we developed ExMODE (https://db.genomics.cn/exmode/), a comprehensive multi-omics database platform dedicated to extremophiles. It centrally integrates multi-omics data from diverse extreme habitats with a standardized annotation framework, resolving data fragmentation and enabling systematic cross-environment comparative analyses to elucidate extremophile adaptive mechanisms. Moreover, ExMODE aggregates multi-dimensional datasets including genes, genomes, secondary metabolite sequences and protein structures, overcoming the constraints of single-dimensional data and significantly improving the efficiency of biotechnological resource discovery from extreme microorganisms.

microbiology↗

bioRxiv2026

OneGenomeRice (OGR): A Genomic Foundation Model for Rice

The transition of genomics to a predictive intelligence discipline is driven by the advent of genomic foundation models. While substantial progress has been observed in all-life and human-centric models, plant species, particularly for the staple crops, remains hindered by a lack of models. Here we introduce OneGenome-Rice (OGR), a rice (Oryza sativa) genomic foundation model pre-trained on a genomic dataset comprising 422 high-quality genomes of cultivated and wild rice. OGR is engineered upon a Mixture of Experts (MoE) transformer architecture with 1.25-billion parameters and supports an ultra-long context window of up to 1 million base (Mb) pairs at single-nucleotide resolution. A comprehensive benchmark demonstrated that OGR significantly outperforms existing state-of-the-art multi-plant or all-life genome models in 11 categories (e.g. motif identification, sweep detection, etc). We further demonstrated the utility of OGR in several downstream applications, such as indica-japonica subspecies introgression analysis, identification of agronomy trait-associated functional loci and prediction of gene expression from DNA sequences. These results establish OGR as a promising foundational computational infrastructure for rice functional genomics and precision breeding. The OGR and its fine-tuned models, including pretrained weights, training code and the rice genomic benchmark suit, have been fully opened.

genomics↗

bioRxiv2026

Isoform-resolved spatial transcriptomics on a lab-made high-density array via a single-chip NGS-TGS workflow

High-resolution spatial transcriptomics is still cost-prohibitive and dominated by short-read sequencing, limiting in situ detection of transcript structures. Here we present a low-cost, lab-made, high-density bead-in-microwell chip assembled by routine centrifuge and decoded by a tri-part combinatorial long-barcode strategy. The design provides 5.6x107 barcode combinations across 4.1x10{square} capture sites, reducing collisions while remaining tolerant to third-generation sequencing raw errors. Using a single-chip, dual-platform workflow, we split full-length cDNA for next-generation and third-generation sequencing within the same spatial coordinates. In an incompatible tomato-pepper graft, we identified interface-associated splicing reprogramming, including thousands of unannotated isoforms, enriched novel CaGRP1 isoforms, and SlPIP2 intron retention, supported by individual long-read sequences. In mouse embryos, we further demonstrate cross-species applicability by resolving unannotated Col1a2 isoforms in oligodendrocyte progenitors and spatially restricted Dalrd3 intron retention. This cost-efficient system broadens access to spatial full-length transcriptomics and may support diverse biological studies.

plant biology↗

bioRxiv2026

Structural Basis for Dual Peptidoglycan Hydrolysis by an E. faecium Minhovirus Tail Spike Lysin

Bacteriophages rely on breaching the bacterial cell wall as the first step of infection. We characterise ORF11, a putative tail-spike lysin from the 19 kbp Minhovirus SHEF14, a podovirus infecting Enterococcus faecium. Bioinformatic analyses indicate that ORF11 comprises four domains: a predicted glycosyl hydrolase (D1) a cysteine, histidine-dependent amidohydrolases/peptidases (CHAP, D4), separated by a helical linker (D2) and a CHAP-like domain (D3). This modular organisation is conserved among Enterococcus minhoviruses but differs markedly from analogous proteins in Copernicusvirus phage and related staphylococcal phage. ORF11 2.1 [A] crystal structure reveals an unusual dimeric assembly. The predicted glycosyl hydrolase and CHAP peptidase domains occupy opposite ends of the protein, bridged by the two other domains positioned at the dimer interface. Biochemical assays using recombinant ORF11 and LC-MS/MS confirmed dual peptidoglycan-degrading activity. ORF11 functions as both an N-acetylglucosaminidase and a D, D-endopeptidase, cleaving the bond between the D-alanine in position-4 and the D-aspartate residue at the end of the side chain. Together, these results provide the first structural description of a podovirus tail-spike lysin and demonstrate its bifunctional enzymatic activity. This dual action likely facilitates initial surface recognition and localised peptidoglycan degradation during infection of E. faecium, offering new insights into how minimal-genome phage target this clinically significant antimicrobial-resistant pathogen.

biochemistry↗

bioRxiv2026

Super-resolution single-cell spatial atlas of plant de novo regeneration

Plant de novo regeneration, a capacity absent in animals, relies on stem-cell-niche formation rather than pre-existing reservoirs. Growth hormones such as auxin and cytokinin, together with numerous genetic regulators, participate in regeneration, but the architectural principles coordinating extensive cellular reprogramming at the individual-cell scale remain elusive. Using super-resolution multimodal spatial transcriptomics to 1.16 million cells, we tracked tomato regeneration across temporal intervals from wounding to organogenesis. Datasets are available at http://www.single-cell-spatial.com.

cell biology↗

bioRxiv2026

Abalone genomics reveals an ancient asymmetry axis and the multifunctionality of the mantle

Gastropod diversification represents one of the most spectacular evolutionary radiations, underpinned by a fundamentally asymmetric body plan and diverse pigmentation patterns. To understand the genetic underpinnings of these cardinal traits, we constructed a chromosome-level genome assembly for the rainbow abalone, Haliotis iris. Integrating systematic genomics, histoembryology, and molecular assays, we uncover an ancient regulatory axis, conserved for over 544 million years, in which the long non-coding RNA lncRNA1 functions as a competing endogenous RNA to shield pitx transcripts from miRNA-mediated degradation, thereby driving the establishment of asymmetric body patterning, including the mantle. Furthermore, we identify a wnt-mitf-tyr cascade as the driver of melanogenesis and demonstrate that the mantle is the primary site of melanin synthesis. Concurrently, we reveal the mantle as a multifunctional organ possessing mechanosensory capabilities. These findings elucidate the ancient genetic toolkit governing trait evolution in gastropods. TeaserA deeply conserved RNA switch directs gastropod body plans and reveals the mantles hidden talents.

genomics↗

bioRxiv2025

Sleep controls peroxisomal abundance to reduce wake-induced brain oxidation

Sleep is increasingly linked to the regulation of Reactive Oxygen Species (ROS) and lipid metabolism. However, the mechanisms underlying this interaction are underexplored. Here, we use Drosophila melanogaster to report a bidirectional relationship between sleep and peroxisomes, cellular organelles that process lipids and alleviate ROS. Of the genes that change expression after sleep deprivation in the dorsal fan-shaped body, knockdown of the peroxisomal biogenesis factor Pex16 results in decreased sleep. Pex16 acts in several brain regions to modulate sleep amount, with ellipsoid body neurons (EB) producing the highest sleep reduction of the sleep-promoting regions. Consistent with a general role for peroxisomes, knockdown of other peroxisomal enzymes relevant for lipid import and synthesis also decreases sleep. Whole-brain peroxisomal numbers increase with wake, which is supported by lipidomic analysis indicating that peroxisomal-derived phospholipids are the major contributors to phospholipid changes after wake or sleep deprivation. Peroxisomal proliferation in the EB is driven by neuronal activity and increased oxidation, suggesting that these mediate the effect of wake/sleep loss. In turn, peroxisomes alleviate the oxidation accumulated during wake, such that loss of Pex16 in the EB works non-cell autonomously to increase lipid peroxidation brain-wide. This likely contributes to sleep loss, as sleep is rescued with an antioxidant. Together, these results position peroxisomes as key players in sleep, regulating ROS and thereby maintaining normal cycles.

neuroscience↗

bioRxiv2025

Senescent astrocytic deposits drive cognitive decline by disrupting tripartite synapse in the aging brain

Brain aging involves synapse decline, with astrocytes playing a key role in synapse homeostasis. However, the impact of astrocyte senescence on synaptic dysfunction and cognitive decline remains unclear. Here, we identified a hallmark of aging astrocytes--Senescent Astrocytic Deposits (SAD) observed at aged rodents, macaques, and human hippocampal astrocytic processes --that is associated with tripartite synapse dysfunction and memory decline. Laser capture microdissection-coupled mass spectrometry (LCM-MS), spatial transcriptome analysis and 3D electron microscopy revealed that SAD are abnormal protein deposits at the processes of ApoE-high expression astrocyte subtype and associated with dysfunctional tripartite synapses. Using a transgenic mouse (Nrbf2 knockout) with accelerated SAD formation as a tool for genetic manipulation, we clearly demonstrated that age-dependent defect of phagocytosis at maturation stage in astrocytic drives SAD accumulation, synaptic injury and cognitive deficits. Collectively, our findings establish SAD as a mechanistic link between astrocyte senescence and synaptic damage, underscoring the critical role of astrocytic phagocytic function in preserving synaptic homeostasis and cognitive function during aging. Significance StatementThis study reveals that impaired phagocytic maturation in senescent astrocytes leads to formation of SAD and impaired synaptic plasticity, identifying hippocampal astrocyte senescence as a key contributor to age-related cognitive decline.

neuroscience↗

bioRxiv2025

ctsGRN: Inferring cell type-specific gene regulatory networks in Arabidopsis

Gene regulatory networks (GRNs) govern life processes. And exploring GRNs is of great value for understanding biological functions and developing species resources. At present, studies on plant GRNs are limited, mostly focusing on the tissue level, which hinders the discovery of cell type-specific regulatory networks and mechanisms. To address this, we have proposed a GRN research framework named ctsGRN (CellTypeSpecGRN) for conducting cell-specific GRN research at the cell type resolution. In this framework, we employed three representative algorithms from statistics (e.g., correlation analysis), traditional machine learning (e.g., random forest) and deep learning (e.g., neural networks) to construct GRNs for different cell types in Arabidopsis roots. Among these, traditional machine learning methods showed the best performance. Experimental validation through yeast one-hybrid (Y1H) assays and ChIP-seq data confirmed the high reliability and predictive power of these inferred GRNs. By analyzing these networks, we mined novel cell type-specific regulatory relationships, identified core transcription factor (TF) AT5G08790 involved in root development, discovered 249 cell-type-specific hub TFs, and 136 key functional modules were found to be enriched in biological processes. Additionally, we revealed significant heterogeneity of TF-target gene interactions across various cell types. This comprehensive analysis offers a detailed overview of TF-mediated cell type-specific transcriptional regulation in Arabidopsis roots, providing new insights into the molecular mechanisms of root development and establishing a guideline framework for GRN research at the cell-type level in plants.

bioinformatics↗

bioRxiv2025

A deep learning and co-conservation framework enable discovery of non-canonical Cas proteins

CRISPR-Cas systems are central to prokaryotic adaptive immunity, widely harnessed for biotechnology. Yet, their vast and uncharacterized diversity, especially non-canonical variants, impedes full exploitation. Here we present BioPrinCRISPR, a class-agnostic computational framework leveraging gene co-conservation, protein domain co-occurrence, and embedding similarity to identify and characterize CRISPR-Cas systems across prokaryotic genomes. Applying BioPrinCRISPR to over one million bacterial genomes, we uncovered extensive canonical and uncharacterized systems, revealing a rich landscape of atypical Cas proteins and novel domain architectures. Notably, we identified recurrent fusion proteins with unique enzymatic combinations, suggesting roles in regulatory control or nucleic acid remodeling. Experimental validation of two divergent Cas13an-like effectors demonstrated RNA knockdown capacity in human cells, confirming our frameworks predictive power. These findings expand the functional repertoire of CRISPR-associated proteins and highlight unexplored modes of microbial immunity. BioPrinCRISPR thus stands as a powerful tool for comprehensively mapping CRISPR-Cas diversity, offering new insights into prokaryotic defense and facilitating discovery of novel candidates for next-generation genome engineering. An accompanying interactive web platform was also developed to facilitate data exploration.

synthetic biology↗