Biology subjects

Zhai, J.

Publications and source records attributed to Zhai, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Convergent genome- and gene-level constraints shape repeated environmental adaptation in grasses

Grasses (Poaceae) dominate terrestrial ecosystems and sustain global food security, yet the genomic principles enabling their repeated adaptation to extreme environments remain unresolved. Combining dense phylogenomic sampling, global environmental data, and genomic large language models (gLLMs), we characterize the mutational targets underlying environmental adaptation across 707 genomes from 569 species spanning 17 climate zones. We identify 19-30 phylogenetically independent transitions into extreme temperature, water, and soil environments, accompanied by convergent shifts in genome-scale molecular properties, including the Nitrogen-to-Carbon balance and the biosynthetic cost of the proteome. Our gLLMs-informed phylogenetic mixed modeling framework identifies 330 genes that repeatedly underlie distinct axes of environmental adaptation, highlighting the importance of protein modification and localization within extracellular and organellar compartments. Overlaying independent convergent adaptation tests identifies 17 high-confidence candidates for further characterization. Together, our results show that grass adaptation is canalized by layered constraints at genome-wide and gene-specific scales, producing predictable evolutionary trajectories.

evolutionary biology↗

bioRxiv2026

A transcriptomic atlas of grass senescence reveals divergent underground sink networks limit nitrogen recycling in annuals

Senescence enables plants to remobilize and recycle nutrients from aging organs to support growth, reproduction, and survival. In annual crops like maize, nitrogen remobilization from leaves to grain is incomplete, with 30-50% of nitrogen stranded in aboveground tissues and subject to environmental loss. Mitigating nitrogen loss in annual crops could be achieved by leveraging the physiological strategies of perennial grasses, which remobilize nitrogen and other nutrients into underground organs at the end of the growing season, thereby preventing environmental leakage. To uncover the molecular basis of perennial nitrogen recycling to underground organs, we built a transcriptomic atlas from field-grown plants, comprising 2,685 RNA-seq libraries from 14 grass species within the Panicoideae (Poaceae), utilizing maize and sorghum as annual references for comparative analyses. The atlas spans leaves, roots, stalks, and rhizomes across two seasons, from mid-growing season to senescence. Using a photosynthetic index to align the leafs transition from nitrogen sink to source across species, co-expression network analysis revealed that the subnetworks driving leaf nitrogen recycling are preserved across annuals and perennials. However, we discovered that the subnetworks associated with underground sink establishment, specifically those associated with seed-like dormancy and desiccation tolerance pathways, have diverged among annual crop accessions. Our work identifies conserved gene candidates and networks that could be used to reintroduce perennial-like nutrient recycling into annual crops to enhance long-term nutrient retention in the field.

plant biology↗

bioRxiv2026

Interactions between the myosin Dachs, the adaptor Dlish, and the palmitoyltransferase Approximated mediate Fat-Dachsous signaling

Bidirectional signaling mediated by heterophilic binding between the giant Drosophila protocadherins Fat and Dachsous (Ds) limits growth through the Hippo pathway and, via patterned binding and cell-by-cell polarization, influences planar cell polarity (PCP) and fate choices along major tissue axes. Prior work showed that the signal transduction initiated by the intracellular domains (ICDs) of Fat and Ds changes the localization and levels of three unusual binding partners: the type XX atypical myosin Dachs, the SH3 domain containing adaptor Dlish, and the DHHC palmitoyltransferase Approximated (App). However, the complex interactions between the three proteins and Fat and Ds are less well understood. Our evidence shows that these proteins play overlapping but distinct roles regulating each other and mediating signaling. Dlish localization and activity requires App binding and palmitoylation. Palmitoylated Dlish helps tether Dachs to the cortex but is also needed to couple Dachs to stabilization by the Ds ICD and destabilization by the Fat ICD. In contrast, Dachs does not localize Dlish but protects it from degradation. Our results also indicate that Fat does not act by changing App levels or localization. We discuss an alternative model based on Dlishs proposed role as an adaptor for E3 ubiquitin ligases.

developmental biology↗

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

Microglia Rank signaling regulates GnRH function and the Hypothalamic-Pituitary-Gonadal axis

The hypothalamic-pituitary-gonadal axis (HPG) controls pubertal development, sexual maturation, and fertility. We hereby demonstrate a new role of hypothalamic microglia in controlling the HPG axis through Rank signaling, a pathway known for its role in bone and mammary gland biology. Whole-body and microglia Rank depletion leads to hypogonadotropic hypogonadism (HH) resulting from an alteration in gonadotropin-releasing hormone (GnRH) function. In addition, we identify rare gene variants of RANK in patients with HH. Transcriptional profiling upon pubertal Rank loss reveals defective microglia activation and morphological alterations in the median eminence (ME), decreasing the contacts and engulfment of GnRH terminal projections and impairing GnRH responses to kisspeptin. Overall, our data uncovers the crucial role of microglia in regulating GnRH function through Rank signaling, with implications for reproductive maturation and fertility. One-Sentence SummaryMicroglia regulates GnRH function through RANK signaling

cell biology↗

bioRxiv2025

Unimeth: A unified transformer framework for accurate DNA methylation detection from nanopore reads

Nanopore sequencing enables direct detection of DNA modifications from native DNA. However, accurate methylation calling across species, sequence contexts, modification types and chemistries remains challenging. We present Unimeth, a transformer-based framework that jointly processes raw signals and basecalled sequences in read patches and predicts all target methylation sites within each patch. Unimeth uses a three-phase training strategy that combines signal pre-training, methylation fine-tuning and site-level calibration using methylation frequency information. We evaluated Unimeth for 5mC and 6mA detection using public and in-house datasets spanning 14 species, three nanopore chemistries and wild-type, mutant and enzyme-treated samples. Unimeth improved plant 5mC detection in non-CpG contexts, reduced false-positive calls in low-methylation samples and maintained high 5mCpG performance in mammalian datasets. For 6mA, Unimeth reduced background calls while preserving signals for Fiber-seq nucleosome and gene-level analyses. Unimeth provides a unified framework for nanopore-based methylation detection across methylation types and biological contexts.

bioinformatics↗

bioRxiv2025

Full-length structure of CPR-containing self-sufficient cytochrome P450

Cytochrome P450 (P450s) are heme-thiolate monooxygenases that exploit electrons sourced from pyridine nucleotide to reduce the oxygen and heme iron to catalyze hydroxylation of inactivated C-H bond1-4. Self-sufficient P450s that contain the substrate-binding heme-domain and the electron-donating NADPH:cytochrome P450 reductase (CPR) domain in the same polypeptide chain are highly effective and have been engineered to catalyze various challenging reactions. The high efficacy attributes to the effective electron transfer rate, but how the electrons travel among the redox centers remains elusive owing to the lack of structural information of the full-length protein. Here, we report the structure of a homologue of the most extensively studied P450BM3 from Shimazuella soli (SoP450) resolved by single particle cryo-electron microscopy (cryo-EM) and X-ray crystallography. SoP450 primarily exists as a homodimer formed via the intertwined CPR-domains. The spatial alignment of the heme-domain that is linked via an extensive loop was also determined. Notably, a class of structure that lacks one heme-domain was identified from the cryo-EM analyses, indicating that the heme-domain is mobile. We suspected that the heme-domain could move to reach the CPR-domain for electron acquisition and built a model of putative catalytic state to reveal how the electrons are relayed from NADPH to heme. These results are of fundamental importance to understand the catalytic reaction of CPR-containing self-sufficient P450s, which shall provide critical information to the engineering and applications of these enzymes.

synthetic biology↗

bioRxiv2025

SPARP-seq reveals poly(A) tail shortening as a hallmark of the active human translatome

The poly(A) tail at the 3 end of mRNAs plays a critical role in the synthesis of proteins, but its dynamic relationship with the translation process has been unclear. Here, using a novel single-molecule approach SPARP-seq that integrates poly(A) assessment with ribosome profiling, we demonstrate that poly(A) tail shortening is a universal hallmark of active translation in human cells. We find that most mRNAs begin translation on single ribosome (monosome) with long tails that progressively shorten as additional ribosomes are recruited. In contrast, highly expressed housekeeping genes are translated efficiently on monosomes and possess intrinsically short poly(A) tails. Furthermore, we find that translationally repressed transcripts, such as those retaining introns, are trapped on monosomes with long tails. Pharmacological inhibition of translation induces global poly(A) tail lengthening, confirming that deadenylation is coupled to ongoing translation. These findings reveal a dynamic system where the poly(A) tail integrates mRNA status with translational fate, defining the monosome as a central hub for post-transcriptional control.

molecular biology↗

bioRxiv2025

GeneCAD: Plant Genome Annotation with a DNA Foundation Model

Accurate genome annotation is fundamental to biological discovery, yet identifying gene structures directly from DNA sequence remains a major challenge in complex genomes. We introduce GeneCAD, a sequence-only framework that predicts biologically coherent gene models without requiring species-matched transcriptomic or proteomic evidence. GeneCAD integrates lineage-specific DNA representations from the PlantCAD2 foundation model with a transformer encoder and a chromosome-scale conditional random field (CRF) to enforce structural constraints, such as splice-phase and feature order. To ensure high-quality supervision, we implement a curation strategy using a sequence-based masked-motif score to filter reference transcripts. As a primary validation across diverse angiosperms, including a complex allotetraploid, GeneCAD improves transcript F1 by approximately 9% over current tools like Helixer and BRAKER3, while sharpening boundary precision and achieving a best-in-class recovery of 86% of classical coding sequences. Furthermore, we demonstrate the frameworks modularity by adapting it to animal lineages through the substitution of the underlying DNA foundation model. While the long introns of vertebrates challenge full transcript reconstruction, the model remains highly effective at identifying individual exons. By connecting evolutionary signals with structured decoding, GeneCAD provides a versatile and scalable solution for high-fidelity genome annotation across the Tree of Life. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/685877v3_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@5dd348org.highwire.dtl.DTLVardef@1da37daorg.highwire.dtl.DTLVardef@188ef17org.highwire.dtl.DTLVardef@108da0c_HPS_FORMAT_FIGEXP M_FIG C_FIG Lay SummaryIdentifying where genes are located within a genome is a major challenge in biology, especially in plants with large and repetitive DNA. Current methods often rely on expensive laboratory data or struggle to find genes in "noisy" regions. We developed GeneCAD, a deep-learning tool that identifies genes using only the raw DNA sequence. By using AI models that recognize patterns shaped by millions of years of evolution, GeneCAD predicts gene structures with high accuracy and consistency. Our tests show that GeneCAD outperforms existing tools in plants and can be easily adapted for use in other species, including animals. By removing the need for costly lab experiments, GeneCAD provides an affordable way for researchers to quickly map the genomes of everything from rare wild plants to essential food crops.

bioinformatics↗

bioRxiv2025

PlantCAD2: A Long-Context DNA Language Model for Cross-Species Functional Annotation in Angiosperms

Understanding how DNA sequence encodes biological function remains a fundamental challenge in biology. Flowering plants (angiosperms), the dominant terrestrial clade, exhibit maximal biochemical complexity, extraordinary species diversity (over 100,000 species), relatively recent origins ([~]160 million years), [~]200-fold variation in genome size and relative compact coding regions compared with other eukaryotes. These features present both a unique challenge and opportunity for pre-training DNA language models to understand plant-specific evolutionary conservation, regulatory architectures and genomic functions. Here, we introduce PlantCAD2, an extended context, plant-specific DNA language model with single-nucleotide resolution, pre-trained on 65 angiosperm genomes, together with a series of public benchmarks for evaluation. Comprehensive zero-shot testing shows that PlantCAD2 (676 million parameters) efficiently captures evolutionary conservation, surpassing the 7-billion-parameter Evo2 model in 10 of 12 tasks. With parameter-efficient fine-tuning, PlantCAD2 also outperforms the 1-billion-parameter AgroNT across seven cross-species tasks including chromatin accessible region, gene expression and protein translation. Moreover, its 8,192bp context window substantially improves accessible chromatin prediction in large genomes such as maize (AUPRC increasing from 0.587 to 0.711), underscoring the importance of long-range context for modeling distal regulation. Together, these results establish PlantCAD2 as a powerful, efficient, and versatile foundation model for plant genomics, enabling accurate genome annotation across diverse species.

bioinformatics↗

bioRxiv2025

A conserved small RNA-generating gene cluster undergoes sequence diversification and contributes to plant immunity

Small RNA-mediated gene silencing contributes to plant immunity. The secondary small interfering RNA (siRNA) pathway promotes defense by silencing target genes in invading fungal and oomycete pathogens. Many secondary siRNAs derive from transcripts potentially encoding pentatricopeptide repeat (PPR) proteins. Here, we report that siRNA production is an ancient function of an evolutionarily conserved clade of PPR genes that undergo extensive within-species diversification. In Arabidopsis thaliana, siRNA-source PPRs are physically clustered in one locus on Chromosome 1. These sequences are diversified through gene duplication followed by sequence diversification as well as accumulation of high-impact variations including pseudogenization. This diversity leads to the accumulation of a diverse PPR-siRNA pool, consistent with an engagement in a co-evolutionary arms race with the pathogens. This study defines siRNA-producing PPRs as a family of defense genes and highlights the potential of PPR-siRNA-based engineering for enhancing broad-spectrum disease resistance.

plant biology↗

bioRxiv2025

PHbinder and PSGM: A Cascaded Framework for Epitope Prediction and HLA-I Allele Identification

The presentation of antigens by Human Leukocyte Antigen class I (HLA-I) molecules is a cornerstone of adaptive immunity. Although existing prediction tools such as NetMHCpan and MHCflurry exhibit high accuracy in predicting binding affinity between peptides and specific HLA-I alleles, they are constrained to a preset set of alleles. Consequently, they can neither directly determine whether a peptide is an epitope nor provide a holistic binding profile across the entire HLA-I allelic landscape. To overcome these challenges, we introduce two synergistic models: PH-binder (Peptide-HLA-I Binder) and PSGM (Pseudo Sequence Generation and Mapping). PHbinder integrates features from a fine-tuned ESM2 language model with Low-Rank Adaptation (LoRA), processing them through parallel CNN and Transformer branches to capture local and global patterns, which are then fused using a Cross-Multi-Head Attention mechanism. In the epitope prediction task, PHbinder achieved an accuracy of prediction of 85. 12%, significantly exceeding established benchmark models. Complementing this, the PSGM model employs a Generative Adversarial Network(GAN) architecture to generate the corresponding HLA-I pseudo sequences. These are then mapped to the known alleles using a Hamming distance-based nearestneighbor search. PSGM achieved 49.26% average coverage in its predictions of the Top-50 alleles. Furthermore, orthogonal validation with MHCflurry revealed that 63% of the highest affinity binding partners within its Top-50 list were new experimentally unverified HLA-I alleles. Together, PHbinder and PSGM establish a cascaded framework that enables a precise "Peptide [->] Epitope Determination [->] HLA-I Alleles List" pipeline. This work accelerates the screening of immunogenic epitopes and provides a powerful upstream preprocessor for traditional prediction tools.

bioinformatics↗

bioRxiv2025

Super-giga and tiny orchid genomes illuminate evolution of Orchidaceae

Orchidaceae (orchids) is commonly known as one of the largest families of seed plants, and grow in an extensive range of habitats worldwide. In the present study, we generated chromosome-level reference genomes for two orchids using a combination of PacBio, Illumina, and Hi-C sequencing, Cypripedium singchii has the largest genome and chromosomes among the sequenced species so far, with a genome size of 43.19 Gb (1C) with ten chromosomes, and Apostasia fujianica has the smallest known genome and chromosomes in Orchidaceae, with a genome size of 340.90 Mb (1C) with 35 chromosomes. We predicted a total of 32,412 and 21,724 protein-coding genes for C. singchii and A. fujianica, respectively. The overall BUSCO score was 85.01% for C. singchii and 91.80% in A. fujianica. Based on protein-coding sequences from 55 conserved single-copy families across 21 plant species, we constructed a high-confidence phylogenetic tree and estimated the divergence times. The high-quality genomes of super-giga and tiny orchids offer key insight for future evolutionary researches.

genomics↗

bioRxiv2025

Grass Rhizome Proteomics Reveals Convergent Freezing-Tolerance Strategies

O_LIGrasses in the PACMAD clade independently colonized cold environments from warm-climate ancestors, but whether their molecular responses to freezing reflect shared evolutionary solutions or lineage-specific innovations remains unknown. We used comparative proteomics to test whether protein-level cold responses show stronger cross-species conservation than previously observed at the transcript level. C_LIO_LIWe quantified seasonal rhizome proteomes (winter vs summer) from five PACMAD species grown in a common garden exposed to sustained sub-zero temperatures, identified differentially abundant proteins, and compared fold-change magnitudes across species using orthogroup-based correlation analyses. We further examined LEA3 protein structure through hydropathy profiling and motif analysis. C_LIO_LIShared cold-responsive proteins showed higher cross-species fold-change correlation ({rho} = 0.80) than background proteins ({rho} = 0.45), despite greater divergence in baseline abundance. LEA3 was the only ortholog elevated across all five species. Cold-tolerant species contained more tandem 11-mer repeats than the cold-sensitive maize, and two species accumulated multiple LEA3 paralogs, increasing total LEA3 abundance. C_LIO_LIIndependent evolution of freezing tolerance in PACMAD grasses is governed by evolutionary constraints on protein-level response magnitude, reflecting the retention of an ancestral protective capacity. Structural divergence of LEA3 in maize suggests that transcriptional induction alone does not ensure freezing tolerance; functional protection likely requires intact motif architecture. C_LI

evolutionary biology↗

bioRxiv2025

Extensive modulation of a conserved cis-regulatory code across 589 grass species

The growing availability of genomes from non-model organisms offers new opportunities to identify functional loci underlying trait variation through comparative genomics. While cis-regulatory regions drive much of phenotypic evolution, linking them to specific functions remains challenging. We identified 514 cis-regulatory motifs enriched in regulatory regions of five diverse grass species, with 73% consistently enriched across all, suggesting a deeply conserved regulatory code. We then quantified conservation of specific motif instances across 589 grass species, revealing widespread gain and loss over evolutionary time. Conservation declined rapidly over the first few million years of divergence, yet [~]50% of motif instances were conserved back to the origin of grasses [~]100 million years ago. Conservation patterns varied by gene class, with modestly higher conservation at transcription factor genes. To test for adaptive cis-regulatory changes, we used phylogenetic mixed models to identify motif gains and losses associated with ecological niche transitions. Our models revealed polygenic adaptation across 810 motif-orthogroup combinations, including convergent gains of HSF/GARP motifs at an Alpha-N-acetylglucosaminidase gene associated with adaptation to temperate environments. Our results support a "stable code, variable sites" model in which cis-regulatory evolution involves extensive turnover of individual binding site instances while largely preserving transcription factors binding preferences. Cis-regulatory changes at hundreds to thousands of genes appear to contribute to environmental adaptation. Our results highlight the potential of comparative genomics and phylogenetic mixed models to reveal the genetic basis of complex traits.

genomics↗

bioRxiv2025

deepTFBS: Improving within- and cross-species prediction of transcription factor binding using deep multi-task and transfer learning

The precise prediction of transcription factor binding sites (TFBSs) is crucial in understanding gene regulation. In this study, we present deepTFBS, a comprehensive deep learning (DL) framework that builds a robust DNA language model of TF binding grammar for accurately predicting TFBSs within and across plant species. Taking advantages of multi-task DL and transfer learning, deepTFBS is capable of leveraging the knowledge learned from large-scale TF binding profiles to enhance the prediction of TFBSs under small-sample training and cross-species prediction tasks. When tested using available information on 359 Arabidopsis TFs, deepTFBS outperformed previously described prediction strategies, including position weight matrix, deepSEA and DanQ, with a 244.49%, 49.15%, and 23.32% improvement of the area under the precision-recall curve (PRAUC), respectively. Further cross-species prediction of TFBS in wheat showed that deepTFBS yielded a significant PRAUC improvement of 30.6% over these three baseline models. deepTFBS can also utilize information from gene conservation and binding motifs, enabling efficient TFBS prediction in species where experimental data availability is limited. A case study, focusing on the WUSCHEL (WUS) transcription factor, illustrated the potential use of deepTFBS in cross-species applications, in our example between Arabidopsis and wheat. deepTFBS is publically available at https://github.com/cma2015/deepTFBS. One sentence summaryThe high-performing deep learning framework deepTFBS can flexibly perform within- and cross-species prediction of TF binding, transferring the transcription regulation from model to non-model species.

bioinformatics↗

bioRxiv2025

Extensive genome evolution distinguishes maize within a stable tribe of grasses

Over the last 20 million years, the Andropogoneae tribe of grasses has evolved to dominate 17% of global land area. Domestication of these grasses in the last 10,000 years has yielded our most productive crops, including maize, sugarcane, and sorghum. The majority of Andropogoneae species, including maize, show a history of polyploidy - a condition that, while offering the evolutionary advantage of multiple gene copies, poses challenges to basic cellular processes, gene expression, and epigenetic regulation. Genomic studies of polyploidy have been limited by sparse sampling of taxa in groups with multiple polyploidy events. Here, we present 33 genome assemblies from 27 species, including chromosome-scale assemblies of maize relatives Zea and Tripsacum. In maize, the after-effects of polyploidy have been widely studied, showing reduced chromosome number, biased fractionation of duplicate genes, and transposable element (TE) expansions. While we observe these patterns within the genus Zea, 12 other polyploidy events deviate significantly. Those tetraploids and hexaploids retain elevated chromosome number, maintain nearly complete complements of duplicate genes, and have only stochastic TE amplifications. These genomes reveal variable outcomes of polyploidy, challenging simple predictions and providing a foundation for understanding its evolutionary implications in an ecologically and economically important clade.

evolutionary biology↗

bioRxiv2024

PhosF3C: A Feature Fusion Architecture with Fine-Tuned Protein Language Model and Conformer for prediction of general phosphorylation site

Protein phosphorylation, a key post-translational modification (PTM), provides essential insight into protein properties, making its prediction highly significant. Using the emerging capabilities of large language models (LLMs), we apply LoRA fine-tuning to ESM2, a powerful protein large language model, to efficiently extract features with minimal computational resources, optimizing task-specific text alignment. Additionally, we integrate the conformer architecture with the Feature Coupling Unit (FCU) to enhance local and global feature exchange, further improving prediction accuracy. Our model achieves state-of-the-art (SOTA) performance, obtaining AUC scores of 79.5%, 76.3%, and 71.4% at the S, T, and Y sites of the general data sets. Based on the powerful feature extraction capabilities of LLMs, we conduct a series of analyses on protein representations, including studies on their structure, sequence, and various chemical properties (such as Hydrophobicity (GRAVY), Surface Charge, and Isoelectric Point). We propose a test method called Linear Regression Tomography (LRT) which is a top-down method using representation to explore the models feature extraction capabilities, offering a pathway to improved interpretability.

bioinformatics↗