Genomics, Proteomics & Bioinformatics

WD40 domains share a widespread {beta}-propeller fold, and often act as versatile scaffold proteins. Despite their central role in organizing dynamic cellular complexes, the molecular and structural mechanisms of many WD40 proteins remain poorly understood. Among them, DCAF7, an ubiquitously expressed and essential gene in human, also encodes a highly conserved WD40 protein in eukaryotic organisms. It is known to interact with multiple and functionnally diverse partners to coordinates cellular activity of several protein kinases as well as transcriptional regulators, thereby modulating key cellular processes such as cell growth, differentiation, and transcriptional regulation. However, the precise mode of action of DCAF7 is unknown and its important divergence in sequence from better characterize WD40 prevent information transfer by similarity. Structural interactomic can reveal how protein-protein interactions (PPIs) occur within an organism and are essential for understanding biological functions and developing new therapeutic strategies. Using SLiMAn2, AlphaFold2/3 and PSSMsearch, we identified a conserved -helical short linear motif (SLiM) in several well known DCAF7 partners that binds to the top surface of its {beta}-propeller. This motif was subsequently used to generate a regular expression, to identify potential new direct binders across the DCAF7 meta-interactome and the human proteome. Domain-domain interactions were also predicted for some other partners. Finally, modeling of oligomeric complexes with such new hits reveals the structural basis of DCAF7 scaffolding, with links to neurodevelopmental disorders such as autism.

Single-cell omics and spatial omics technologies are nowadays widely used in biological and medical research. In both single-cell and spatial omics data analysis, accurate cell type annotation is a key step for downstream analysis and scientific discoveries. However, high-quality cell annotation usually requires multiple rounds of manual analysis for result refinement, which poses great challenges to most researchers. Here, we present CellClick, an interactive platform for convenient and accurate cell type annotation in single-cell and spatial omics data. CellClick provides Data Preprocessing, Data Visualization, Cell Annotation, Annotation Validation, and Cell Reannotation modules, which facilitate automatic or user-guided cell selection and annotation. The feasibility of using CellClick to generate more accurate cell annotation results was exemplified by both scRNA-seq and spatial transcriptomics data.

Agarikon (Laricifomes officinalis syn. Fomitopsis officinalis) is an endangered fungus belonging to a unique lineage in the Polyporales (Basidiomycota) with a growing body of evidence supporting its medicinal value. In this study, we report the hybrid de novo assembly and annotation of the first L. officinalis nuclear and mitochondrial genome sequences, with a nuclear genome size of 28.76 Mb assembled across 66 scaffolds (51.96% GC content; BUSCO completeness of 99.4%), and a complete core mitochondrial genome size of 197.67 kb. Structural and functional annotation of the nuclear genome yielded 8,717 predicted genes including 8,604 protein-coding genes, with 310 genes in 27 biosynthetic gene clusters. We characterized the mating type loci matA and matB, consistent with a tetrapolar mating system, and identified genes encoding key enzymes involved in triterpenoid and polyketide biosynthetic pathways that lead to the production of a diverse array of secondary metabolites. Additionally, we conducted maximum likelihood phylogenomic analysis to confirm the taxonomic position of L. officinalis among 21 species in Polyporales using protein sequences for 860 shared BUSCO genes. This high-quality annotated genome of L. officinalis will serve as a foundation for further investigations into the evolutionary history of this distinct fungal lineage, provide a reference for future population genomic analyses, and elucidate mechanisms underlying the synthesis of the bioactive compounds responsible for agarikons wide-ranging medicinal benefits.

Bifidobacteriaceae and Lactobacillaceae are key probiotic families and widely used in food production, yet a comprehensive understanding of strain functions and their gut microbial interactions based on complete genomes remain understudied. Here we constructed a complete-genome dataset of 3,300 strains from these two families, including 1,151 newly isolated from China. Compared with draft assemblies, complete genomes substantially recovered a gene functional landscape encompassing stress tolerance, surface exopolysaccharide synthesis, nutrient utilization, and mobile genetic elements. Major species from both families exhibited a prevalence >60% in the Chinese population, far higher than that in US/Dutch cohorts. Notably, as a core probiotic species with remarkable genomic plasticity and gut-adaptive potential, Lactiplantibacillus plantarum stood out in our dataset for its enriched functional profile and was particularly abundant in the Chinese population. Moreover, compared with non-Chinese genomes, our isolates of key species displayed less metabolic complementarity and stronger competition with potentially pathogenic keystone species in the gut, thereby linking strain origin to enhanced probiotic potential and ecological fitness to benefit human gut health.

Magnaporthe oryzae, the rice blast fungus, plays a role as a model organism for molecular plant-microbe interaction research. Studies on the pathogenic mechanism of this fungus revealed many genes involved in signaling pathways. As multi-omics data are being available, genomic-level researches have been conducted to uncover the underlying biological processes during the pathogenesis of M. oryzae. Identifying the genome-wide protein-protein interaction (PPI) network is one of the omics-level approaches, which helps to understand signaling and regulatory pathways. However, existing biological network resources of M. oryzae are not sufficient to decipher pathogenesis mechanisms due to the abundance of false positives/negatives. In this study, a reliable PPI network database of M. oryzae, MagNet, was constructed with three methods, including homology-based Interolog search, co-expression network construction, and domain-domain interaction (DDI)-based prediction. With three approaches altogether, the pan-network with 5,600,976 interactions was generated, including 217,531 highly confident interactions supported by all three methods. Experimental data on M. oryzae PPIs supported that our PPI network can predict PPIs with higher accuracy compared to the previously constructed databases. MagNet would provide integrated biological network data, which can help to understand the molecular mechanisms of the rice blast fungus. The PPI data can be accessed via https:/magnet.scnu.ac.kr.

TAR DNA-binding protein 43 (TDP-43) is a multifunctional DNA/RNA-binding protein implicated in transcriptional and post-transcriptional regulation. Dysregulation of TDP-43 is closely correlated with human diseases such as cancer and neurodegenerative diseases. Although its roles in RNA metabolism are well characterized, its function in transcriptional regulation remains largely underexplored. DNA G-quadruplexes (dG4s) are non-canonical nucleic acid structures enriched at gene promoters and regulatory elements, where they facilitate chromatin looping and gene transcription. Here, we investigated the transcriptional regulatory role of TDP-43 by integrating multi-omics datasets, including Hi-C, dG4 ChIP-seq, TDP-43 ChIP-seq, RNA-seq and ATAC-seq from K562 and HepG2 cells. Our analyses demonstrate TDP-43 binding and dG4s formation are highly colocalized at chromatin loop anchors, particularly at promoter and enhancer regions. TDP-43 occupancy at these anchors correlates with increased dG4 stability, chromatin loop interaction frequency, elevated chromatin accessibility, and upregulated gene expression. Morover, TDP-43 knockdown in HepG2 cells revealed a significant reduction in dG4 formation and loop interaction strength, accompanied by widespread transcriptional dysregulation. Collectively, our findings highlight a novel regulatory role of TDP-43 in facilitating long-range chromatin interactions and transcriptional activation through binding to and stabilizing dG4 structures, providing a mechanistic basis for gene dysregulation driven by TDP-43 dysfunction in diseases.

A plethora of studies have identified shared molecular mechanisms involved in tumor development across humans and other mammalian species. While these two-species analyses advance understanding of human disease, extending them across many species would provide evolutionary insight into molecular mechanisms driving human cancers. However, this expansion requires knowledge transfer and harmonization across species. Genomic differences between species, including variation in genome annotation quality, have historically hindered multi-species large-scale atlas creation. To overcome these challenges, we present Paipu, a comprehensive pipeline designed to streamline querying, preprocessing, harmonization, and retrieval of large-scale RNA-seq data and associated metadata from the NCBI Sequence Read Archive (SRA). Paipu facilitates multi-species analysis by creating a harmonized atlas from user-defined search terms and species. It consists of three components: reference genome preparation, SRA metadata retrieval, and RNA-seq data processing. We apply Paipu to 188 cancer-related terms in 239 non-human mammalian species, creating a harmonized atlas of 3,484 RNA-seq samples spanning 17 species and 35 cancers. This pan-mammalian pan-cancer atlas enables myriad comparative genomics analyses that leverage genetic variation to better understand rare human cancers. As such, Paipu serves as a resource for cross-species cancer genomics and supports atlas creation for any set of species and search terms. Graphical Abstract

The rapid explosion of large-scale, high-throughput biological data has created an urgent demand for efficient analysis pipelines. Traditional bioinformatics approaches, while powerful, often require specialized computational expertise, placing them out of reach for bench biologists. Large Language Models (LLMs) offer new possibilities for automating complex reasoning and tool integration, yet existing LLM-based solutions have not sufficiently lowered this barrier, and expert-level analysis remains inaccessible to most nonexperts. Here, we present BioGAIP, an LLM-powered agent that integrates expert-level reasoning within an end-to-end platform for bioinformatics tasks. By coupling optimized autonomous agents with full graphical interfaces, BioGAIP transforms complex analytical workflows into an automated, user-friendly, and low-intervention process with natural language input. Key features of BioGAIP include dynamic information retrieval, automatic environment configuration, and self-directed design of analysis pipelines, making large-scale multi-omics analysis highly accessible. Built on agent-based client-server architecture, BioGAIP ensures secure resource management and supports heavy computational demands. Extensive evaluations on diverse published datasets demonstrate that BioGAIP reliably recapitulates established biological insights and shows strong potential for novel discovery. By democratizing complex bioinformatics workflows, BioGAIP accelerates accessible data-driven discovery for both experts and nonexperts.

Voltage-gated potassium channels (Kv) are a large family of potassium channels composed of 40 members across 12 subtypes. The KCNH genes encode 3 subfamilies of voltage-gated potassium channels: Kv10 (EAG, ether a go go), Kv11 (ERG, EAG-related gene), and Kv12 (ELK, EAG-like K). Kv channels play prominent roles in the neuronal and cardiovascular systems. Mutations in Kv channels have been linked to many human diseases, such as epilepsy, heart arrhythmias, and cancers. Significant progress has been made in understanding protein structures, physiological functions, and the pharmacological modifiers. However, the evolutionary history and gene expression of vertebrate KCNH genes during embryonic development remain largely unknown. We systematically identified and cloned 14 kcnh genes in zebrafish. Then, we examined vertebrate KCNH channel evolution by phylogenetic and syntenic analyses. Our data revealed that the three subtypes of the KCNH gene family have already evolved in invertebrates, long before the emergence of vertebrates. The number of vertebrate KCNH genes increased, most likely due to whole-genome duplications (WGDs). In addition, we examined zebrafish kcnh gene expression during early embryogenesis by in situ hybridization. Each subgroups genes showed similar but distinct gene expression domains with some exceptions. Most of them were expressed in neural tissues. Notably, kcnh6a showed robust expression in the developing heart, consistent with its conserved role in cardiac repolarization. Additionally, a few kcnh genes were transiently expressed in nonneural tissues, such as somites and the notochord, suggesting they may have a unique role in embryonic development. Our phylogenetic and developmental analyses of KCNH channels shed light on their evolutionary history and potential roles during embryogenesis, in line with their physiological functions and human channelopathies.

Aging is one of the primary drivers for the decline of female fertility, and oocyte quality is the main cause for ovary aging, which is related with infertility. Although some effective anti-aging natural components have been reported, highly efficient strategies for reversing ovary aging remain lacking. In this study, we reported that peptide elamipretide reserved ovary function for female fertility during maternal aging. Our findings demonstrated that elamipretide improved aged human oocyte maturation and fertilization. Elamipretide injection increased the litter size of aged mice, and improved oocyte quality with the reverse of follicle and embryo development defect. Metabolomic and transcriptomic analyses demonstrated that multiple biological processes in oocytes were significantly reserved. Both nuclear maturation and cytoplasmic maturation of aged oocytes were improved, showing with enhancing cytoskeletal dynamics, mitochondrial metabolism and organelle rearrangement. In vitro supplementation during culture also restored oocyte developmental competence in both mouse and porcine oocytes. Mechanistic analysis suggested that elamipretide reversed age-related ovarian damage via synergistic activation of the Vitamin B6-VEGF axis. Therefore, our study proposed a new peptide therapy for aging-induced infertility, showing that elamipretide reverses aged oocyte quality for fertility by promoting both nuclear and cytoplasmic maturation through the coordination with VEGF signaling pathway.

Principal component analysis (PCA) is widely used in mass spectrometry-based metabolomics for exploratory data mining. Statistical testing of loading values can extract metabolite features associated with score patterns, but this approach requires principal components (PCs) to remain orthogonal while loadings are defined as correlation coefficients between PC scores and variables. Adjustment for Confounding PCA (AC-PCA) was previously developed to explore biologically meaningful components from data matrices affected by biological and technical confounders. However, AC-PCA does not simultaneously ensure PC orthogonality and a correlation-coefficient definition of loadings, limiting the statistical interpretation of its loadings. Here, we reformulated AC-PCA as Orthogonal Adjustment for Confounding effects in PCA (OAC-PCA). In OAC-PCA, PCs remain orthogonal, and loadings retain this correlation-coefficient interpretation. These properties enable statistical testing of metabolite associations while accounting for confounding effects.

Dietary intervention is an effective way to alter the gut microbiome to promote human health. Yet, due to our limited knowledge of diet-microbe interactions and the highly personalized gut microbial compositions, an efficient method to prescribe personalized dietary recommendations to achieve desired gut microbial compositions is still lacking. Here, we propose a machine learning framework to resolve this challenge. Our key idea is to implicitly learn the diet-microbe interactions by training a machine learning model using paired gut microbiome and dietary intake data from a population-level cohort. The well-trained machine learning model enables us to predict the microbial composition of any given species collection and dietary intake. Next, we prescribe personalized dietary recommendations by solving an optimization problem to achieve the desired microbial compositions. We systematically validated this Machine learning-based Personalized Dietary Recommendation (MPDR) framework using synthetic data generated from an established microbial consumer-resource model. We then validated MPDR using real data collected from a diet-microbiome association study. The presented MPDR framework demonstrates the potential of machine learning for personalized nutrition.

Background.Mycoviruses infect fungal cells and represent important components of the global virome with potential biological control applications. The rice blast pathogen Pyricularia oryzae causes devastating crop losses worldwide, yet its mycovirus diversity remains understudied. While traditional dsRNA extraction remains a standard method for virus discovery, recent advancements, such as monoclonal antibody (mAb)-based dsRNA enrichment, offer improved specificity and sensitivity for viral detection. Methods.We developed the monoclonal anti-dsRNA antibody-based metagenomics (MADAM) approach, integrating dsRNA enrichment using 2G4 monoclonal antibody, sequence-independent reverse transcription-PCR with random octamer primers, and Oxford Nanopore Technologies sequencing. Total RNA was extracted from four P. oryzae isolates collected from Yuanyang rice terraces (Yunnan, China). After nuclease treatment, dsRNA was enriched using anti-dsRNA antibodies, followed by strand-switching cDNA synthesis, PCR amplification, and MinION sequencing. Genome gaps and terminal sequences were resolved through targeted RT-PCR and modified 3' RACE approaches. Results.MADAM achieved high viral read recovery rates (46.9-72.7%) and identified 18 P. oryzae-associated RNA viruses across seven families: Botourmiaviridae, Deltaormycoviridae, Mymonaviridae, Partitiviridae, Polymycoviridae, Splipalmiviridae, and Ambiguiviridae. Nearly complete to complete genomes (ranging from 1,226 to 6,085 nucleotides) were recovered, with sequence coverage spanning 88-100%. Co-infections occurred in three out of four isolates. Notable discoveries included the first deltaormycovirus in P. oryzae, a putative novel Botourmiaviridae member, and an additional genomic segment of a polymycovirus. The method detected positive-sense, negative-sense ssRNA, and dsRNA viruses, demonstrating broad applicability.

Phyllanthus niruri (Phyllanthaceae) is a medicinally important herb known for producing phyllanthin, a bioactive dibenzylbutane lignan with reported hepatoprotective and antioxidant properties. However, the biosynthetic basis of phyllanthin production remains unresolved, largely due to the absence of a reference genome for the species. We report a Chromosome-Scale Assembly of P. niruri generated by integrating PacBio HiFi long reads and Illumina short reads, followed by reference-guided scaffolding against Phyllanthus cochinchinensis. The assembly has an L50 of 7 and 97.6% BUSCO completeness. Annotation predicted 19,254 protein-coding genes (91.1% functionally annotated), with phenylpropanoid biosynthesis emerging as the most enriched specialized-metabolism pathway in the genome. Using pathway-guided genome mining, structural similarity analysis, and comparative metabolic reconstruction, we propose a putative biosynthetic pathway for phyllanthin originating from the phenylpropanoid-lignan branch through secoisolariciresinol-like intermediates, followed by terminal O-methylation reactions. A total of 305 unique candidate genes associated with the proposed pathway were identified, including expanded families of dirigent proteins, peroxidases, secoisolariciresinol dehydrogenases, and O-methyltransferases. Comparative transcriptomic analyses across related Phyllanthus species further supported the proposed pathway through coordinated expression of lignan-associated genes and tissue-specific enrichment of O-methyltransferases. This work provides the first reference genome for P. niruri and a prioritized candidate gene set for functional characterization of phyllanthin biosynthesis.

The Japanese quail (Coturnix japonica) is a widely used model organism in developmental biology, genetics, and agriculture. Here, we present new, haplotyped, high-quality genome assemblies of the Japanese quail, generated using a combination of state-of-the-art sequencing technologies, including PacBio HiFi long reads, Oxford Nanopore sequencing, and Hi-C scaffolding. This assembly has a total length of 1.19 Gb, 80% of which is included in chromosomes, and is highly complete (BUSCO score aves_odb10: 97.3). Assembly metrics show a marked improvement in contiguity, with a significantly higher scaffold N50 and a lower number of contigs compared to the reference genome assembly. Remarkably, the assembly extends previously truncated chromosome ends, with 31 telomeres detected. In addition, we merged the existing Ensembl and Refseq annotations and obtained a combined set of 26,102 genes, of which 25,038 genes were successfully mapped on the improved assembly haplotype 1 (Cjap1.hap1). Together, these new genome assemblies and their enriched annotation provide a robust genomic framework for future research. They enhance our ability to investigate developmental processes, genetic and epigenetic inheritance, and host-pathogen interactions. Furthermore, they offer valuable insights for conservation genetics and sustainable breeding programs. This resource represents a critical step forward in leveraging the full potential of the Japanese quail as a model species in both basic and applied research.

H3K27ac and H3K4me3 are enriched at transcriptional start sites and have been implicated in transcription. However, how these marks concertedly regulate transcription is not fully understood. Here, we developed a dual chemically inducible CRISPR/dCas9-based epigenome editing system that enables independent, temporal and transcription stage-specific modulation of H3K27ac and H3K4me3 at a specific gene locus. Stage-specific removal of H3K4me3 impaired RNA polymerase II recruitment, increased promoter-proximal pausing, reduced productive elongation, and accelerates mRNA decay via increased m6A deposition. Losing both H3K27ac and H3K4me3 rapidly abolished transcriptional activity, while preserving H3K4me3 without H3K27ac can partially sustain transcription. These findings revealed a functional hierarchy and interdependence between H3K27ac and H3K4me3 in different transcription stages and the established versatile tool will contribute to the functional dissection of the temporal dynamics of chromatin modifications in gene regulation.

Polysome profiling is a powerful technique used to analyze the association of mRNA with ribosomes, providing insights into the translational status of a cell. It relies on the separation of ribosome-bound mRNAs through sucrose density gradient centrifugation, where the number of ribosomes on an mRNA correlates with its sedimentation rate. While numerous studies have successfully applied this method to cell line and mouse tissue, application to the human postmortem brain remains scarce due to challenges related to sample quality and low concentration of recoverable material. To overcome these challenges, we: O_LIImplemented a protocol specifically optimized for low-concentration human post-mortem brain tissue. C_LIO_LIImplemented a gradient-maker-free method to manually prepare sucrose gradients with tunable sensitivity for low-input samples. C_LIO_LIAdapted the human brain tissue protocol for neuronal cell lines and mouse brain with minimal modification. C_LI

The axon initial segment (AIS) undergoes structural plasticity and refines neuronal excitability, yet the underlying mechanisms remain unclear. We here developed an in vivo CRISPR/Cas9 knockout platform using an all-in-one triple-guide RNA vector introduced via electroporation and employed this approach to seek molecules that regulate the developmental shortening of AIS in the chicken nucleus magnocellularis. We have targeted fourteen molecules associated with microtubules and found that knockouts of glycogen synthase kinase 3{beta} (GSK3{beta}) and Tau disabled the AIS shortening. Conversely, overexpression of constitutively active form of GSK3{beta} facilitated the AIS shortening in vivo. This extensive shortening was replicated in slice cultures, which was occluded by stabilization of microtubules. These results suggested that microtubule remodeling by GSK3{beta} activity contributed to the AIS shortening. This study thus provides a genetic approach suitable for genetic screening that allows identifying regulators of the AIS plasticity in the chicken brain.

Schizophrenia is a severe neuropsychiatric disorder that affects the behavioral, emotional and cognitive state of patients. Despite its substantial heritability, the molecular etiology of the disease remains poorly understood. Many schizophrenia-associated genetic variants reside in non-coding regions, and exert their effects through distal regulatory elements of the genome. In this context, the three-dimensional organization of the genome is expected to play a decisive role in establishing contacts between these regulatory elements and their target genes, thereby mediating schizophrenia-associated dysregulation of gene expression. Here, we present a novel Hi-C dataset providing an unprecedented view of three-dimensional genome organization in post-mortem schizophrenia brain samples. Our findings indicate that most changes occur at long-range genomic distances while local architecture of topologically-associated domains remains largely intact. However, neurons display localized and functionally relevant loop differences, particularly in regulatory regions associated with neurodevelopmental processes. Global characteristics of higher-order chromatin organization show accelerated aging alteration pattern in schizophrenia, and downstream analysis of transcriptomic data in schizophrenia brain samples further confirms that schizophrenia is associated with accelerated aging.

Achieving high-throughput and precise phenotypic quantification and imaging modalities of stomatal and epidermal cells across diverse species remains a primary bottleneck in elucidating the mechanisms of stomatal dynamics, epidermal patterning, and environmental adaptation of plants. Here, we developed EpiReasoner, an artificial intelligence framework comprising a vision module, EpiVision, and a knowledge-based reasoning module, EpiBrain, for the quantitative phenotypic analysis and domain-specific knowledge reasoning of stomatal complexes and pavement cells in plants. Operating across bright-field, scanning electron microscopy, and differential interference contrast modalities, EpiVision achieves precise instance segmentation in various monocotyledonous, dicotyledonous, and fern species. Its performance significantly surpasses current state-of-the-art models. Moreover, we defined 23 quantitative indices describing stomatal cell morphology and spatial distribution. For domain-specific tasks such as phenotype prediction, genotype deduction, and molecular mechanism reasoning, EpiBrain demonstrates a human preference rate significantly higher than that of general-purpose large language models, including GPT-5 and Claude Sonnet 4. The application of EpiReasoner to phenotypic data of stomatal density derived from a tomato natural population of 170 accessions successfully identified a major quantitative trait locus on chromosome 8. The candidate gene, SKP1-interaction partner 19L (SKIP19L), encoding an F-box family protein, exhibited severe allele frequency drift during tomato domestication, which is highly consistent with the adaptive trend of reduced stomatal density under artificial selection. EpiReasoner provides a novel paradigm that unifies visual phenomics and knowledge-driven reasoning for the biology of stomata and pavement cells, thereby significantly accelerating scientific discovery in plant science.