Biology subjects

Chen, S.

Publications and source records attributed to Chen, S..

At least 253 records · Page 14Linked to original sources

bioRxiv2025

ECCFP: a consecutive full pass based bioinformatic analysis for eccDNA identification using Nanopore sequencing data

It is commonly known that extrachromosomal circular DNA (eccDNA) has the potential as a molecular marker because of its close relationship with cancer progress and its prevalent existence in eukaryotic organisms. The mainstream technique of eccDNA detection is using high-throughput sequencing supported by bioinformatics analysis. Although these have various analysis pipelines for sequencing data, they are restricted by sequencing platforms or have shortcomings in accuracy and efficiency. To address these limitations, we design ECCFP, a bioinformatic analysis pipeline that detects eccDNAs amplified by rolling circle amplification (RCA) from long-read sequencing data and outputs eccDNA genomic coordinates and consensus sequences. This pipeline proposes a rigorous algorithm to retain all consecutive full passes derived from individual reads to obtain candidate eccDNAs, followed by systematic consolidation of candidate eccDNAs to detect unique eccDNAs. Using simulated datasets and experimental eccDNA sequencing datasets, we estimated ECCFP in several aspects and compared it with other existing pipelines. It exhibits a marked reduction in false positive rates compared with eccDNA_RCA_nanopore and superior sensitivity relative to CReSIL and FLED. Besides, inverse PCR and Sanger sequencing further validated the existence and accuracy of the position of detected eccDNAs by ECCFP. Collectively, ECCFP provides a more efficient choice for eccDNA detection from long-read sequencing data.

bioinformatics↗

bioRxiv2025

Sequential Amino Acid Mutagenesis-Driven De Novo Evolution of Adenine Deaminases Enables Efficient In Vivo Base Editing in Primate

Base editing allows for the precise modification of genetic information, providing new avenues for treating diseases1. The adenine base editor ABE8e is currently the most efficient and widely used tool for adenine base editing. ABE8e was developed through multiple rounds of directed evolution of Escherichia coli tRNA adenine deaminase, including phage-assisted continuous evolution (PACE)2,3. While PACE is highly effective, it is a complex system that poses challenges for implementation4. Despite its high efficiency, ABE8e is associated with limitations such as relatively higher bystander editing effects and elevated off-target activity5, which need to be addressed to further enhance its precision and safety. Here, we developed a novel method for the de novo discovery of evolved ABE components, particularly adenine deaminases. This process involves identifying candidate proteins through AI-based structural prediction and clustering, followed by the enhancement of deaminase editing activity through screening libraries created by sequential amino acid saturation mutagenesis. This evolutionary strategy simplifies the approach by employing saturation mutagenesis libraries tailored to specific segments, thereby enabling exploration of an expanded sequence space and increasing the likelihood of discovering adenine deaminases with superior capabilities. The newly developed hpABE5.20 here demonstrates a more refined editing window, reduced DNA off-target effects that are both sgRNA-dependent and -independent, and minimized RNA off-target activity, while maintaining robust editing efficiency relative to ABE8e. Furthermore, hpABE5.20 has been successfully applied for precise and effective therapeutic adenine base editing in cellular disease models, humanized mice, and non-human primates.

bioengineering↗

bioRxiv2025

Pangenome analysis provides insights into the evolution of mango (Mangifera spp.)

Mango (Mangifera spp.) is a major tropical fruit crop of global economic importance, but advanced genomic resources are needed to support its breeding, conservation, and sustainable cultivation. In this study, a mango pangenome was constructed using high-quality, telomere-to-telomere genomes of four cultivated mango (M. indica) accessions representing distinct genetic origins, and one wild relative, M. odorata. Genome-wide analyses revealed a significant reduction in heterozygosity among elite commercial cultivars, indicating a genetic bottleneck resulting from long-term artificial selection. Core genes were enriched in fundamental biological pathways, including primary metabolism, photosynthesis, transcriptional regulation, and cellular signaling. Variable genes were primarily associated with secondary metabolite biosynthesis, reflecting local environmental adaptations. Pangenome and comparative genomic analyses identified structural variations among accessions. Additionally, 10,482 high-confidence single nucleotide polymorphisms (SNPs) were detected and utilized for population genomic analysis of 197 mango accessions, delineating four genetically distinct groups. Southeast Asian accessions exhibited unique genetic diversity and divergence from Caribbean, Indian, and U.S. groups. Comparative analyses revealed differentiation in specialized metabolic pathways, particularly alkaloid and diterpenoid biosynthesis, likely reflecting adaptive responses to the complex ecological interactions and high biodiversity of Southeast Asian tropical rainforests. Genomic analysis of the MiRWP gene, associated with apomixis, provided comprehensive insights into this important mango trait, demonstrating the potential of the pangenome for future mango breeding efforts. The genomic resources generated in this study establish a critical foundation for advancing mango genetic research, facilitating trait improvement, and informing conservation strategies.

genomics↗

bioRxiv2025

Data Heterogeneity Limits the Scaling Effect of Pretraining Neural Data Transformers

A key challenge in analyzing neuroscience datasets is the profound variability they exhibit across sessions, animals, and data modalities-i.e., heterogeneity. Several recent studies have demonstrated performance gains from pretraining neural foundation models on multi-session datasets, seemingly overcoming this challenge. However, these studies typically lack finegrained data scaling analyses. It remains unclear how different sources of heterogeneity influence model performance as the amount of pretraining data increases, and whether all sessions contribute equally to downstream performance gains. In this work, we systematically investigate how data heterogeneity impacts the scaling behavior of neural data transformers (NDTs) in neural activity prediction. We found that explicit sources of heterogeneity, such as brain region mismatches among sessions, reduced scaling benefits of neuron-level and region-level activity prediction performances. For tasks that do exhibit consistent scaling, we identified implicit data heterogeneity arising from cross-session variability. Through our proposed session-selection procedure, models pretrained on as few as five selected sessions outperformed those pretrained on the entire dataset of 84 sessions. Our findings challenge the direct applicability of traditional scaling laws to neural data and suggest that prior reports of multi-session scaling benefits may need to be re-examined in the light of data heterogeneity. This work both highlights the importance of incremental data scaling analyses and suggests new avenues toward optimally selecting pretraining data when developing foundation models on large-scale neuroscience datasets.

neuroscience↗

bioRxiv2025

miR-486-3p ameliorates aging-related impaired angiogenesis in adult moyamoya disease by targeting Forkhead Box O4

ObjectiveInvestigate miR-486-3ps role in alleviating age-related angiogenic decline in Moyamoya disease (MMD) by regulating senescent endothelial cells. MethodsClinical analysis of 151 MMD patients correlated age with postoperative angiogenesis (DSA grading). Senescent HUVECs (SA-{beta}-gal>80%) exhibited elevated SASP factors (IL-6/IL-8/MCP-1). Functional assays (EdU/Transwell/Matrigel) and bioinformatics identified miR-486-3p targets, validated via luciferase/Western blot. ResultsPatients [≥]35 years had 3.67-fold higher risk of poor angiogenesis (OR=3.67). Senescent HUVECs showed 10-16-fold higher SASP secretion (p<0.01). miR-486-3p overexpression increased proliferation (EdU+25%), migration (1.7-fold), and tube formation (+40% branches) in senescent cells (p<0.01) and enhanced angiogenesis in vivo (p<0.001). miR-486-3p directly targeted FOXO4, reducing its luciferase activity (-35%) and protein levels (-37%) (p<0.01), suppressing SASP. ConclusionmiR-486-3p enhances post-revascularization angiogenesis in MMD by inhibiting FOXO4-mediated SASP, offering a therapeutic target and predictive biomarker.

molecular biology↗

bioRxiv2025

Statistical learning drives anticipatory micro-saccades toward suppressed distractor locations

Statistical learning enables individuals to suppress locations associated with salient distractors, yet the mechanisms underlying this suppression remain unclear. Proactive accounts propose that suppression operates without prior attentional allocation to distractor locations, whereas reactive accounts suggest that suppression requires covert attention to the distractor location before it can be engaged. To address this ongoing debate, the current study recorded micro-saccades--an index of covert attention--during the pre-stimulus interval, alongside electroencephalogram (EEG) data collected during a visual search task. Participants were instructed to ignore a salient distractor that occurred more frequently at a specific location. Statistical learning reduced attentional capture for distractors presented at this high-probability location, and this effect was accompanied by oculomotor markers: micro-saccade rates decreased before stimulus onset relative to a control condition. Strikingly, these anticipatory micro-saccades were more often directed toward high-probability distractor locations than away from them, consistent with a reactive suppression mechanism. In parallel, alpha-band activity (8-14 Hz) carried decodable representations of high-probability distractor locations, indicating preparatory neural tuning. Overall, these findings provide evidence that the oculomotor system is closely involved in encoding and responding to learned spatial regularities.

neuroscience↗

bioRxiv2025

UNCOVERseq Enables Sensitive and Controlled Gene Editing Off-Target Nomination Across CRISPR-Cas Modalities and Systems

The rapid development of CRISPR-Cas gene editing technologies has revolutionized genetic medicine, offering unprecedented precision and potential for treating a wide array of genetic disorders. However, assessing the risks of unintended gene editing effects remains critical, and is complicated by new editing modalities and unclear analytical guidelines. We present UNCOVERseq (Unbiased Nomination of CRISPR Off-target Variants using Enhanced RhPCR), an improved in cellulo off-target nomination workflow designed to sensitively nominate off-target sites (<0.01% editing) with defined input requirements and analytical process controls to provide empirical performance evidence across diverse circumstances. Using this workflow, we nominated off-targets across 192 guide RNAs (gRNAs) and demonstrated superior performance compared to existing methodologies. We identified a subset of six gRNAs with a dynamic range of specificity and confirmed the relevance and high true positive rate of our nomination method, providing relative risk assessments for multiple modalities (S.p. Cas9 and derived high-fidelity variants / base editors) in a translational system involving hematopoietic stem and progenitor cells (HSPCs). Additionally, we established that double-strand break (DSB) editing retains a strong, positive rank correlation to single-strand break (SSB)-mediated base editing, highlighting the importance of DSB nomination sites as candidate loci for base editing. Overall, UNCOVERseq improves informed risk assessment of gene editing in translational systems by enhancing the quality of off-target nomination.

molecular biology↗

bioRxiv2025

3D Printed Nerve Guidance Conduit for Biologics-Free Nerve Regeneration and Vascular Integration

There is a clinical need for an effective nerve guidance conduit to treat peripheral nerve injuries. Many studies have explored different materials and active cues to guide neural regeneration, with some success. However, none have demonstrated a comparable or better functional recovery than the clinical standard autograft. Autografts are often insufficient for reconstruction of an injury to long nerves such as the sciatic or brachial plexus. Synthetic nerve guidance conduits (NGCs) have been investigated for these injuries to guide axonal regeneration and lead to functional recovery. We have designed a biologics-free hydrogel-based multi-channel conduit with defined microscale features to guide axonal outgrowth. To investigate extraneural vascular infiltration and its effects on functional recovery, we also designed a multi-microchannel conduit with defined regularly spaced micropores, orthogonal to the axon guidance channels. Using our custom-built Rapid Projection, Image-guided, Dynamic (RaPID) bioprinting system, we are able to fabricate each hydrogel conduit within minutes from a milliliter-volume prepolymer vat. With our state-of-the-art printing platform, we have achieved NGCs with a consistent channel wall width of 10 microns. We implanted the NGCs for 17 weeks in a murine sciatic nerve transection injury model. We assessed the functional recovery by dynamic gait analysis throughout the recovery period and by compound muscle action potential (CMAP) electrophysiology before NGC harvesting. Both the non-porous and micro-porous conduit groups led to functional nerve regeneration on par with the autograft group. Further, both conduit groups resulted in restoration of bulk motor function to pre-injury performance. Translational Impact StatementWe have developed a synthetic therapeutic that can be implanted to aid nerve regeneration after a debilitating injury that performs similarly to the current clinical standard treatment. One major hurdle to clinically translating an implantable therapeutic is needing to include biologics such as cells or active biomolecules, which increases the regulatory burden. With this in mind, we systematically developed a biologics-free synthetic hydrogel nerve guidance conduit, composed of polymer backbones that have been used in many FDA-approved applications, capable of restoring motor function.

bioengineering↗

bioRxiv2025

Induction of cervical dysfunction associated with preterm birth by IL-1 and dysbiotic microbiome revealed in human endocervix chips

Cervical dysfunction, a major contributor to preterm labor and neonatal mortality, remains poorly understood due to the absence of physiologically relevant human models. Here, we show that a microfluidic human Endocervix Chip lined by primary endocervical epithelium interfaced with stromal cells and cultured under pregnancy-like hormonal conditions recapitulates key aspects of the biology of the endocervix, including formation of a mucus plug-like structure with antimicrobial properties. Culturing a dysbiotic cervico-vaginal microbiome on-chip increased secretion of pro-inflammatory cytokines observed in patients with preterm labor and enhanced production of matrix metalloproteinases (MMPs) that degrade stromal extracellular matrix (ECM). Perfusion with inflammatory cytokines at clinically relevant concentrations altered cervical mucus composition, upregulated prostaglandin-endoperoxide synthase 2 expression, increased MMP secretion, and reduced collagen production, which together drive dissolution of the stromal ECM and promote cervical ripening. Addition of circulating peripheral blood mononuclear cells (PBMCs) amplified these effects. Administration of a clinically approved drug for prevention of preterm labor that the Food and Drug Administration (FDA) recently deemed ineffective was also found to be inactive in the chip, while an approved therapeutic antagonist of the IL-1 receptor successfully protected against cervical dysfunction in this model. These findings demonstrate that IL-1 acts directly on human cervical tissues to promote changes associated with initiation of labor and that primary human endocervix chips may represent a useful preclinical model for studies on cervical dysfunction associated with preterm birth. One Sentence SummaryHuman endocervix chip modeling of cervical dysfunction suggests that IL-1 receptor antagonists may prevent preterm labor.

bioengineering↗

bioRxiv2025

GenVS TBDB: An Open AI-Generated andVirtual Screened Small-Molecule Database for Tuberculosis Drug Discovery

Tuberculosis (TB) remains a leading global health threat, with over 10 million new cases and 1.25 million deaths reported in 2023. Current TB therapies rely on a limited drug repertoire and prolonged treatment courses that have changed little in four decades. Here, we present GenVS-TBDB, a target-aware AI-generated and virtual-screened small-molecule database, which expands the chemical space against Mycobacterium tuberculosis (M.tb) essential proteins. We first identified 460 probable small-molecule binding pockets across 377 essential M.tb proteins by integrating multiple sources. Then, by leveraging the target-aware molecule generative model, over 1.2 million novel small molecules tailored to these pockets were produced. The key physicochemical properties were computed for all compounds to ensure medicinal chemistry tractability. These compounds were also evaluated using molecular docking and anti-TB specific graph neural network model, yielding binding propensity ranking and whole-cell activity prioritization for each target. To substantiate the integrated AI-driven workflow for anti-TB discovery, 30 compounds were obtained, including 22 AI-designed molecules synthesized de novo and 8 commercially available analogs. In validation, 2 synthesized compounds demonstrated significant thermal stabilization of FtsZ, confirming target engagement. 6 compounds exhibited cellular inhibition below 50 {micro}M, the most potent at 12 {micro}M. Furthermore, GDI-11785 showed binding to the cell wall biosynthesis pathway with 35 {micro}M cellular activity, establishing a promising starting point for tuberculosis drug discovery. Our findings contribute a significant library of bioactive molecules which may hasten the preliminary phase of tuberculosis drug discovery. The GenVS-TBDB small library is publicly accessible and can be downloaded freely at https://datascience.ghddi.org/database/view.

pharmacology and toxicology↗

bioRxiv2025

Normal breast tissue classifiers assess large-scale tissue compartments with high accuracy

Cancer research emphasises early detection, yet quantitative methods for normal tissue analysis remain limited. Digitised haematoxylin and eosin (H&E)-stained slides enable computational histopathology, but artificial intelligence (AI)-based analysis of normal breast tissue (NBT) in whole slide images (WSIs) remains scarce. We curated 70 WSIs of NBTs from multiple sources and cohorts with pathologist-guided manual annotations of epithelium, stroma, and adipocytes (https://github.com/cancerbioinformatics/OASIS). We developed robust convolutional neural network (CNN)-based, patch-level classification models, named NBT-Classifiers, to tessellate and classify NBTs at different scales. Across three external cohorts, NBT-Classifiers trained on 128{square}x{square}128{square}{micro}m and 256{square}x{square}256{square}{micro}m patches achieved AUCs of 0.98-1.00. The model learned independent normal features different from those of precancerous and cancerous epithelium, which were further visualised using two explainable AI techniques. When integrated into an end-to-end preprocessing pipeline, NBT-Classifiers facilitate efficient downstream analysis within peri-lobular regions. NBT-Classifiers provide robust compartment-specific analytical tools and enhance our understanding of NBT appearances, which serve as valuable reference points for identifying premalignant changes and guiding early breast cancer prevention strategies.

pathology↗

bioRxiv2025

Characterization of the functional and clinical impacts of CACNA1A missense variants found in neurodevelopmental disorders

CACNA1A encodes the P/Q-type CaV2.1 calcium channels whose function underlies neuronal excitability, presynaptic neurotransmitter release, and Ca2+ signaling in neurons. Pathogenic variants in CACNA1A have been found in individuals with various neurological conditions, including hemiplegic migraine, epilepsy, developmental delay, and ataxia. Clinical presentations can vary significantly between patients, with limited information known about the underlying neurobiology of these different clinical patterns. Adding further complication, prior work on pathogenic missense variants has demonstrated variable impacts on CaV2.1 channel function, sometimes in opposite directions. As such, the relationships between specific coding variants, electrophysiological properties, and clinical phenotypes remain elusive. In this study, we determined the biophysical properties of an allelic series of 42 de novo missense CACNA1A variants discovered in a neurodevelopmental disorder cohort of more than 31,000 individuals, together with the most common eight coding variants found in the general population. We found that all but one de novo variant altered at least one aspect of the channel properties examined, and the majority (70%) of the variants reduced the channel current density. In addition, for variants that encode human CaV2.1 channels (hCaV2.1) with detectable currents, nearly 50% altered how channels respond to membrane potential, while common variations did not significantly change any channel biophysical properties. Coupled with our functional analyses and AlphaMissense prediction, we showed that CaV2.1 missense variants significantly underlie the risk of developmental epileptic encephalopathy. Subsequently, we examined the physiological impact of variant hCaV2.1 using NEURON simulations as an omnibus output of neuronal function and found that abnormal biophysical channel properties have a profound impact on Purkinje cell excitability. Most interestingly, we correlated the clinical phenotype with molecular consequences of missense variants provided by our comprehensive functional analyses and found that distinct CaV2.1 channel molecular function is significantly associated with different clinical outcomes. By analyzing an entire allelic series of CACNA1A de novo changes in a large cohort of individuals with neurodevelopmental disorders, we provide a powerful approach to dissecting the role of missense variants in CACNA1A channelopathy, which in turn may help pave the way for future precision medicine initiatives.

neuroscience↗

bioRxiv2025

Discovery of a tRNA-regulatory transcription factor that suppresses breast cancer metastasis

Transfer RNAs (tRNAs), once viewed as static adaptors in translation, are now recognized as dynamic regulators of gene expression. While recent studies have illuminated roles for tRNA stability, the upstream mechanisms governing tRNA transcription remain poorly understood. To address this gap, we generated the EXpression atlas of tRNA (EXTRNA), a high-resolution tRNA expression dataset spanning 24 cell lines across 9 human tissues. EXTRNA revealed both tissue-type-specific expression programs ("tRNAomes") and unexpected intra-tissue heterogeneity across breast cancer samples. Integrating EXTRNA with computational network analysis and data from other publicly available datasets, we identified Zinc Finger ZZ-Type And EF-Hand Domain Containing 1 (ZZEF1) as the first sequence-specific transcription factor of a particular tRNA. ZZEF1 promoted tRNA-LysUUU transcription by partnering with the ATP-dependent chromatin remodeler Chromodomain Helicase DNA Binding Protein 6 (CHD6), enhancing chromatin accessibility at tRNA-Lys-TTT-3 loci. ZZEF1 deficiency reduced tRNA-LysUUU abundance, decreased the translational efficiency of AAR codon-enriched mRNAs--including the tumor suppressor Serine/Threonine Kinase 3 (STK3)--and promoted metastatic progression in breast cancer in vivo. Together, our findings establish a previously unrecognized mechanism for RNA polymerase III-mediated tRNA transcription and define a regulatory circuit linking chromatin remodeling, codon-specific translation, and tumor suppression. More broadly, this work introduces a framework for dissecting the regulatory logic of the tRNAome and highlights tRNA expression control as a promising avenue for therapeutic intervention.

genomics↗

bioRxiv2025

Trans-ancestry Mendelian Randomization Discovers Novel Causal Genes for Autoimmune Disease Traits

Mendelian randomization (MR) using summary statistics from genome-wide association studies (GWAS) has become a powerful tool for dissecting the causal relationships between exposure and outcomes. As GWAS begins to incorporate samples of diverse ancestries, the optimal strategy to perform MR analysis integrating these GWAS results remains to be determined. To fill in this gap, we proposed a robust method that aggregates the genetic variant-risk factor association summary statistics of multiple ancestries by modelling the genetic effects of the exposure as a function of the principal components of genome-wide allele frequency. This allows borrowing information from different ancestry groups and accounting for potential between-ancestry heterogeneities. The method is general and can be used with different MR methods. We demonstrated that our method improves accuracy and power compared to the inverse-variance weighting method based on only ancestry-stratified samples, with additional benefits in correcting winners curse bias. We also illustrated the flexibility and potential of discovering novel causal genes for autoimmune disease traits of our method in practice.

genetics↗

bioRxiv2025

SAI: A Python Package for Statistics for Adaptive Introgression

Adaptive introgression is an important evolutionary process, yet widely used summary statistics--such as the number of uniquely shared sites and the quantile of the derived allele frequencies in such sites--lack accessible implementations, limiting reproducibility and methodological clarity. Here, we present SAI, a Python package for computing these statistics, and apply it to three datasets. First, using the 1000 Genomes Project data, we replicated previously reported candidate regions and identified additional ones, including a region detected by studies using supervised deep learning. Second, reanalysis of a Lithuanian genome dataset revealed no candidates in the HLA region. Finally, we investigated bonobo introgression into central chimpanzees and identified a candidate region that overlaps a high-frequency Denisovan-introgressed haplotype block reported in modern Papuans--an intriguing co-occurrence across divergent lineages. Discrepancies with prior results highlight the importance of transparent and reproducible analysis workflows, especially as machine learning becomes increasingly prevalent in evolutionary genomics.

evolutionary biology↗

bioRxiv2025

A unified eIF1A+ luminal cells-centered hypoxic and "cold" tumor microenvironment promotes PCa progression among different subtypes

PCa is a malignancy with high heterogeneity arises from both tumor microenvironment (TME) and histological subtypes. To achieve superior clinical efficacy, it is imperative to identify unified progression drivers within such heterogeneity in PCa, thereby enabling the development of more consistent and effective therapeutic strategies. In this study, we applied imaging mass cytometry to stain 39 proteins on 71 tissues comprising para-cancer, low-grade acinar adenocarcinoma (LgPAC), high-grade PAC (HgPAC), intraductal carcinoma (IDC) and ductal adenocarcinoma (DAC) tissues, obtaining the spatial proteomic landscape of 345,233 single cells. We discovered a hypoxic eIF1A+ luminal epithelial (LE) cluster exhibiting both translational activation and immunosuppressive properties, which was enriched in high-risk PCa including HgPAC, IDC and DAC, and associated which tumor progression and unfavorable prognosis. Additionally, eIF1A+ LE orchestrates the formation of an immunologically "cold" TME, characterized by the low infiltration of anti-cancer immune cells including PD1- T cells CD163- macrophages. Through in vivo validation and investigator-initiated study, we further found that Homoharringtonine, an inhibitor targeting protein translation process, effectively inhibited PCa growth, alleviated hypoxic microenvironment, and enhanced immune cell infiltration. This study is the first to apply spatial proteomics to delineate consistent molecular features across histological subtypes, providing transformative insights for clinical management of PCa.

cancer biology↗

bioRxiv2025

Inferring non-coding RNA regulatory network from transcriptomic data and curated databases

Non-coding RNAs (ncRNAs) have played an indispensable role in regulating gene expression and cellular processes. However, the complex regulatory circuits between ncRNAs and coding genes remain understudied. Major challenges for inferring ncRNA regulatory network (NRN) on a transcriptome-wide scale include high dimensionality of both ncRNA and coding gene expression data, their context-dependent interaction, and lack of validation. We hereby propose a comprehensive analytical framework, namely Construction and Analysis of non-coding RNA regulatory NETwork (CAR-NET), specifically designed to infer NRN from transcriptomic data and curated databases on experimentally validated ncRNA-gene interactions and disease-related ncRNAs. At its core is a novel computationally efficient Bayesian network structure learning method that leverages the semi-bipartite graph structure of NRN for dimension reduction, edge orientation and global network search. We also developed and applied algorithms to perform differential network, subnetwork and pathway analysis of the inferred network. We showed the strength of CAR-NET in simulations and three case studies, encompassing lncRNA regulation in brain development, miRNA differential regulation in renal cell carcinoma and regulation by different classes of ncRNAs in different cell lines using bulk or single cell RNA-seq data. An R-shiny app (https://github.com/kehongjie/CAR-NET) was developed to implement the method(s) with interactive GUI.

bioinformatics↗

bioRxiv2025

TIR immune signaling is blocked by phosphorylation to maintain plant growth

Toll/interleukin-1 receptor (TIR) domain proteins are immune signaling components and function as NAD+-cleaving enzymes to activate defense responses. Activation of TIRs represses growth and drives cell death in plants and promotes axon degeneration in animals, but how plant TIRs are repressed remains unclear. Here, we show that TIR NADase activity requires a conserved serine residue spatially close to the catalytic glutamate. The plant Ca2+-dependent protein kinases (CPKs), the mammalian Ca2+/calmodulin-dependent protein kinase II delta (CAMK2D) and TANK binding kinase 1 (TBK1) phosphorylate TIR domains at this conserved serine, which blocks TIR NADase activities and functions and thus maintains growth in plants and suppresses SARM1 TIR signaling in animals, respectively. Our findings define a fundamental molecular mechanism by which phosphorylation at a conserved serine residue blocks TIR signaling to balance growth and defense trade-offs.

plant biology↗