Genome Biology最新文献

Background: The viticulture has long suffered from the downy mildew caused by Plasmopara viticola, a strictly obligate biotrophic oomycete. Numerous studies have been performed to reveal how grapevine defends against Plasmopara viticola, but they mainly investigate the plant defense responses on the whole tissue level, not on the cellular level.

Results: Here we employ single-cell RNA sequencing and spatial RNA sequencing to profile approximately 100,000 individual cells (~ 89,000 from scRNA-seq and ~ 11,000 from spRNA-seq), generating the first single-cell transcriptome atlas of grapevine leaves during Plasmopara viticola infection. This high-resolution atlas reveals the dynamic and distinct defense responses of plant cells at early stages of oomycete infection. Notably, we find that Plasmopara viticola reprograms the guard cell transcriptome to facilitate successful invasion, likely by altering the expression of ABA negative regulators and modulating a potassium channel regulatory pathway to influence stomatal movement.

Conclusions: Overall, our work reveals differential and dynamic responses of grapevine to the Plasmopara viticola infection at a single-cell level, providing valuable clues for dissecting the interaction between plants and oomycetes.

Deep mutational scanning (DMS) coupled with fluorescence-activated cell sorting (FACS) provides a high-throughput method to link genetic variants with quantitative molecular phenotypes. Analysis of these experiments is challenging due to measurement variance and the multidimensional FACS readout. However, no statistical method has yet been developed to address these challenges. Here we present Lilace, a Bayesian statistical model to estimate variant effects with uncertainty quantification from FACS-based DMS experiments. We validate Lilace's performance and robustness using simulated data and apply it to OCT1 and Kir2.1 DMS datasets, demonstrating an improved false discovery rate while largely maintaining sensitivity.

Background: Whole genome sequence (WGS) data in multi-ancestry samples supports discovery of low-frequency or population-specific genetic variants associated with chronic obstructive pulmonary disease (COPD) and lung function.

Results: We performed single variant, structural variant, and gene-based analysis of pulmonary function (FEV₁, FVC and FEV₁/FVC) and COPD case-control status in 44,287 multi-ancestry participants from the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. We validated findings using the UK Biobank and assessed implicated genes using lung single-cell RNA-seq (scRNA-seq) data sets. Applying a genome-wide significance threshold (P < 5 × 10^-9), we replicated known loci and identified novel associations near LY86, MAGI1, GRK7, and LINC02668. Colocalization with gene expression quantitative trait loci (eQTL) from the Lung Tissue Research Consortium highlighted known candidate genes including ADAM19, THSD4, C4B, and PSMA4, which were not identified through other eQTL sources. Multi-ancestry analysis improved fine-mapping resolution (e.g., HTR4 and RIN3). Gene-based analysis identified and replicated HMCN1. In human lung scRNA-seq data sets, lung epithelial cells and immune cell types showed enriched expression, while fibroblasts showed higher expression for HMCN1. CRISPR targeting HMCN1 in IMR90 demonstrated reduced expression of collagen genes.

Conclusions: Large-scale multi-ancestry WGS analysis improves variant discovery and fine-mapping resolution for lung function and COPD and highlights biologically relevant genes and pathways.

Background: Non-negative matrix factorization is a powerful linear algebra tool used in multiple areas of data analysis, including computational biology. Despite numerous optimization methods devised for non-negative matrix factorization, our understanding of the inherent topological structure within factorizable matrices remains limited.

Results: This study reveals the topological properties of linear mixture data, leading to a remarkable reduction of the non-negative matrix factorization optimization problem to a search for K(K-1) variables, where K represents the number of pure components, regardless of the initial matrix size. This is achieved by revealing complementary simplex structures existing in both feature and sample spaces and leveraging the Sinkhorn transformation to find the relationship between these simplexes. We validate this approach in the context of an unconstrained mixed images scenario and achieve a significant improvement in decomposition accuracy. Furthermore, we successfully applied the proposed approach in the biological context of bulk RNA-seq gene expression deconvolution.

Conclusions: The Dual Simplex unified analytical framework improves robustness to noise and enhances optimization stability, enabling accurate recovery of component proportions and expression profiles. Importantly, the framework naturally accommodates both reference-free and marker-based deconvolution settings, providing a general and efficient solution for analyzing complex biological mixtures such as bulk RNA-seq and single-cell derived data.

Background: The NONO protein plays a crucial role in RNA metabolism and DNA repair. It undergoes various post-translational modifications, including phosphorylation, ubiquitination, acetylation and methylation, all of which regulate its diverse cellular functions. However, the role of O-GlcNAcylation in regulating NONO's function in DNA damage repair is not well understood.

Results: This study demonstrates that O-GlcNAcylation of NONO at Serine 147 (Ser147) is essential for its recruitment to DNA damage sites. Specifically, O-GlcNAcylation at Ser147 reduces NONO ubiquitination and stabilizes its interaction with SFPQ, regulating the alternative splicing of the histone methyltransferase SETMAR. A deficiency in O-GlcNAcylation at Ser 147 impairs NONO's binding to SETMAR pre-mRNA, leading to an increased production of the truncated isoform of SETMAR (SETMAR-S). The resulting SETMAR-S suppresses the generation of H3K36me2 and inhibits the recruitment of Ku70 at DNA damage sites, ultimately impairing non-homologous end joining (NHEJ) repair. Furthermore, the disruption of O-GlcNAcylation at Ser147 sensitizes liver cancer cells to ionizing radiation treatment, both in vitro and in vivo.

Conclusions: O-GlcNAcylation at Ser 147 of NONO mediates the alternative splicing of SETMAR and facilitates NHEJ repair. Collectively, our findings suggest that targeting NONO O-GlcNAcylation may provide a novel therapeutic strategy for cancer treatment.

The increasing availability of multi-omics data is promising in enhancing genomic prediction in breeding and human genetics. However, integrating multi-omics data into genomic prediction models remains challenging due to complex relationships between omics layers and phenotypic outcomes. We propose Fusion Similarity Best Linear Unbiased Prediction (FSBLUP), a novel strategy that integrates genomic and intermediate omics data using a unified similarity matrix approach. FSBLUP systematically estimates how different omics layers contribute to phenotypic variation via machine-learning-optimized parameters that capture underlying genetic architecture of complex traits. FSBLUP demonstrates greater predictive accuracy than existing methods, as validated through theoretical and practical evaluations.

Background: The regulation of gene expression in plants is governed by complex interactions between cis-regulatory elements and epigenetic modifications such as histone marks. While deep learning models have achieved success in predicting regulatory features from DNA sequence, their cross-species generalizability in plants remains largely unexplored.

Results: We systematically evaluate the ability of deep learning models to predict histone modifications across plant species using a multi-stage framework based on the Sei architecture. We train species-specific models for Arabidopsis (Arabidopsis thaliana), rice (Oryza sativa), and maize (Zea mays), achieving high within-species predictive performance and strong agreement between predictions and experimental ChIP-seq profiles. However, cross-species predictions show reduced performance with increasing phylogenetic distance, highlighting limited model transferability between monocots and dicots. To improve generalization, we construct a Poaceae family-level model by jointly training on rice and maize, and an Arabidopsis-trained model based solely on Arabidopsis. These models demonstrate robust predictive power in completely unprofiled species that are not used in training set, highlighting the model's adaptability to novel plant genomes based solely on conserved regulatory syntax. In contrast, cross-family models produce less consistent results, with reliable performance only in species sharing conserved regulatory features. We also develop an easy-to-use pipeline that predicts genome-wide chromatin signals directly from DNA sequences.

Conclusions: Our findings demonstrate that phylogenetically informed model training significantly improves cross-species epigenomic prediction, offering a scalable computational strategy for functional annotation in non-model and agriculturally important plants.

Background: Root rot disease caused by fungal pathogens of wine grapevines poses a serious threat to their growth and results in a substantial economic impact on grape industry. The rhizosphere microbiome recruited to plants is critical for mitigating soil-borne pathogens. However, how beneficial microbes influence disease resistance remains unclear.

Results: We investigate the composition and gene functions of microorganisms in wine grapevines with root rot disease and healthy controls by amplicon and metagenomic sequencing. We use culturomics and in vivo experiments to verify the pathogen and beneficial strains to improve plant health. We find that root rot disease in grapevines significantly affects rhizosphere microbiome diversity and composition. The microbial interkingdom network indicates that the disease destabilizes the bacteria-fungi co-occurrence network. We find that plants recruit the potentially beneficial bacteria Pseudomonas, Bacillus and Streptomyces in healthy rhizosphere soil. By culturomics, we confirm that Fusarium solani is the main pathogen causing root rot disease. We further observe that these three key beneficial bacteria from the co-occurrence networks enhance the resistance of grapevines to pathogens. Furthermore, metagenomic analysis reveals that beneficial bacterial strains suppress pathogens by enriching potential functional genes in pathways involved in disease resistance.

Conclusions: Our findings highlight the critical role of disease resistance pathways of potentially beneficial microorganisms in fighting disease and supporting plant health, offering new insight for the exploration of beneficial microbial resources and providing a basis for the development of biological control of grape root rot disease.

There is an urgent need to increase sustainable crop production. The application of molecular marker technologies such as genomic selection and machine learning based approaches are aiding accelerated crop improvement. Conventional molecular marker technologies use single nucleotide polymorphisms to predict traits, however these do not capture local epistasis and can be challenging for machine learning applications. With the growth of genome sequence data, it is possible to define haplotypes that can account for local epistatic effects and are more suitable for machine learning models. This review discusses the different methods for defining haplotype blocks and their application in plant breeding.