Genome Biology最新文献

Antifungal peptides (AFPs) are crucial for plant defense against biotic stress. Yet, no artificial intelligence tool specifically classifies plant AFPs. To fill this gap, we develop FungiGuard, which integrates Random Forest, Long Short-Term Memory, and attention mechanisms to identify AFPs using functionally annotated plant small peptides. FungiGuard outperforms existing generalized AFP model in classifying plant AFPs, and detects candidate AFPs in Arabidopsis, wheat, rice, and maize. It also discovers novel AFPs through randomly generated sequences. Experimental validation confirms the antifungal activity of candidate AFP against Botrytis cinerea. This tool deepens plant AFP understanding and facilitates novel AFP discovery.

Background: Glucocorticoids are corticosteroid hormones that are commonly used for treating systemic inflammatory diseases and acute infections. Immunosuppressive effects of glucocorticoids have been studied in many cell types, particularly macrophages and T cells. Despite the importance and abundance of neutrophils in the human immune system, glucocorticoid responses remain understudied in neutrophils.

Results: Here, we perform quantitative mass spectrometry-based proteomics of primary neutrophils and neutrophil-like cells differentiated from human HL-60 promyelocyte cells. Primary neutrophils exhibited CK2 kinase activation and increase phosphorylation of HSP90 following 2-h incubation, highlighting potential effects of short-term ex vivo handling. Proteome and flow cytometry analysis show that neutrophil-like cells share features of neutrophils. Quantitative proteomics and phosphoproteomics of neutrophil-like cells treated with two synthetic glucocorticoid compounds, the clinical drugs dexamethasone and prednisolone, identify higher numbers of significantly regulated proteins and phosphosites compared to parental HL-60 cells. Glucocorticoid treatments modulated toll-like receptor signaling and CXCR4 serine phosphorylation. In addition, we identify RIPOR2 as a glucocorticoid-regulated protein associated with Rho GTPase signaling networks and actin cytoskeletal remodeling in neutrophils and neutrophil-like cells, though its exact functional role requires further investigation.

Conclusions: Our results not only reveal unconventional regulatory mechanisms of glucocorticoids in the human immune system but also provide valuable resources for discovering novel glucocorticoid-responsive protein targets in neutrophils.

Background: Aging is a multi-modal process, leaving distinct molecular signatures across the epigenome. DNA methylation is among the most robust biomarkers of biological aging, yet most studies assume linear age relationships and analyze mixed-sex cohorts, overlooking known sex differences. Such approaches risk obscuring critical nonlinear transitions and sex-specific trajectories.

Results: We develop SNITCH, a computational framework to detect complex nonlinear methylation trajectories and disentangle shared from sex-divergent patterns. Applied to the array-derived whole-blood methylomes from 252 females and 246 males (ages 19-90 years), SNITCH reveals convergent and divergent epigenetic aging pathways independent of immune cell composition. Nonlinear trajectories are enriched for developmental transcription factor motifs, including NF1/CTF and REST, with known oncogenic roles. Importantly, a female-specific nonlinear cluster is prospectively associated with cancer onset and systemic inflammation in an independent cohort, nominating clinically relevant biomarkers. We replicate the analysis in an additional cohort and highlight consistent nonlinear trajectories.

Conclusions: Our results uncover sex-specific, nonlinear aging programs that capture the dynamics of epigenetic change beyond linear models. These findings provide potential candidate biomarkers for early disease risk and advance understanding of how aging trajectories diverge between sexes.

Dimensionality reduction is routinely applied to single-cell transcriptomic data to improve interpretability, remove noise and redundancy, and enable visualization. Most existing methods aim at preserving the most prominent data properties, which can lead to omission of rare but important signals. Here we propose a novel framework, SAKURA, that uses knowledge-derived genes of interest to guide dimensionality reduction, which can help cluster rare cells and separate highly similar cell subpopulations. We demonstrate the utility of our framework in identifying endocrine cell subtypes in the pancreatic islet, highly similar hematopoietic subpopulations, and rare senescent cells.

Drug combinations can improve cancer therapy by boosting efficacy, limiting dose-related toxicity, and delaying resistance. We present UniSyn, an interpretable multi-modal deep learning framework that transfers knowledge from monotherapy responses to enhance drug-synergy prediction. Through hybrid attention-based integration of drug and cell-line features, UniSyn supports multi-task learning and yields mechanistic insights. It generalizes robustly to unseen drug pairs and cell types, maintaining consistent performance across multiple synergy scoring metrics. Applied at scale to tumor cell lines, UniSyn captures context-specific synergy signals and prioritizes therapeutic combinations with translational potential.

Nuclear morphology encodes rich phenotypic information critical for understanding cellular states, yet its full potential remains untapped in biomedical analysis. This study introduces NuSPIRe, a self-supervised deep learning model designed to analyze nuclear morphology using DAPI-stained images. Pretrained on 15.52 million cell nucleus images, NuSPIRe performs robustly in cell type identification and perturbation detection, even with limited annotations. Moreover, NuSPIRe integrates nuclear morphology with spatial omics data, uncovering significant correlations between cellular structure and gene expression. Notably, NuSPIRe further enables AI-driven experimental optimization for region-of-interest identification and field-of-view selection, enhancing data efficiency in spatial omics and molecular cell biology.

Background: Potato (Solanum tuberosum) breeding is severely hindered by its highly heterozygous autotetraploid genome, where complex allelic interactions impede precise trait selection. Reconstructing complete haplotype-resolved assemblies is crucial for genome-assisted breeding. However, current assembly methods for autopolyploids often generate fragmented sequences, haplotype-switch errors, and gaps in complex regions such as centromeres.

Results: To address these challenges, we develop PHap, a haplotype assembly pipeline tailored for autopolyploids, using only standard sequencing data, including long-reads and Hi-C. Applying PHap to the autotetraploid potato cultivar HuaShu4, we generate a haplotype-resolved, near telomere-to-telomere assembly of 3.12 Gb with an N50 of 32.7 Mb and 99.7% haplotype accuracy. Comparisons with alternative methods and existing assemblies highlight PHap's advantages in assembly quality and cost-effectiveness. Integration of transcriptomic and epigenomic data demonstrates that the genomic and methylation divergence across haplotypes drives substantial allelic expression differentiation. Time-course RNA-seq further reveals, for the first time, that 55% of genes exhibit divergent allelic expression, with dynamic shifts in dominant or suppressed alleles during tuber development. Additionally, our assembly resolves high-resolution haplotype-specific structures in centromeres and subtelomeres, as well as haplotype divergence of structural rearrangements. It also shows neocentromere formation via the expansion of megabase-scale satellite arrays.

Conclusions: These findings provide insights into the architecture of autopolyploid genomes and establish a foundation for genomics-assisted breeding of polyploid potatoes.

Background: ALK gene fusions are key oncogenic drivers in cancers such as non-small cell lung cancer, where they define a molecular subtype responsive to ALK tyrosine kinase inhibitors (TKIs). However, resistance commonly arises due to single nucleotide variants (SNVs) within the ALK tyrosine kinase domain, many of which remain variants of uncertain significance (VUSs).

Results: To systematically profile resistance, we use prime editing to generate and assess 3,208 ALK variants covering 99% of all possible SNVs across exons 20-28, along with intronic variants. We evaluate drug resistance across three generations of ALK TKIs: alectinib, lorlatinib, and zotizalkib. These high-resolution resistance landscapes validate known resistance mutations (e.g., G1202R, L1196M), identify previously uncharacterized resistance-associated VUSs, and reveal distinct patterns of drug-specific and shared resistance across inhibitors. Structural mapping further contextualizes resistance-associated variants relative to the ATP-binding pocket and distal regions associated with resistance.

Conclusions: This study provides a comprehensive functional atlas of ALK tyrosine kinase domain variants under TKI selection, offering a valuable experimental framework for interpreting resistance-associated variants. Although derived from in vitro models and therefore context dependent, this resource complements existing clinical and genomic knowledge and may aid in the functional interpretation of ALK variants observed in ALK-driven cancers.

Spatial omics technologies localize individual molecules at subcellular resolution, yet growing numbers of molecules, features and replicates set analysis challenges. We present smoppix, a nonparametric analysis method based on the probabilistic index, to test for several uni- and bivariate localization patterns. It exploits the high-dimensionality of the data for variance weighting and for providing a background null distribution, unique for every molecule. Moreover, smoppix sidesteps segmentation, edge correction, warping and density estimation, and is scalable thanks to an exact permutation null distribution. We unearth spatial patterns in datasets from four kingdoms, and validate some findings experimentally on spikemoss roots.

Background: The field vole, an abundant and widespread microtine rodent, is a complex comprised of three cryptic species: the short-tailed field vole (Microtus agrestis) which is present over much of Eurasia, the Mediterranean field vole (Microtus lavernedii) in southern Europe, and the Portuguese field vole (Microtus rozianus) in western Spain and Portugal. Previous research has shown high genomic differentiation of these three lineages. However, the details of the process underlying their divergence remain unknown.

Results: We analyse 70 mitogenomes and 16 nuclear genomes of modern specimens, and 83 mitogenomes and 12 nuclear genomes of ancient specimens spanning the last 75 thousand years (ka). We estimate the divergence of Portuguese from short-tailed and Mediterranean field voles to be ca. 220 ka ago and of the latter two species to be ca. 110 ka ago, earlier than previous estimates involving only modern sequences. The divergence times we obtain match those between major mitochondrial lineages of cold-adapted and steppe rodents in Europe. We find signatures of gene flow within and between field vole lineages, with some analyses suggesting a hybrid origin of the Mediterranean lineage. Ancient specimens from the Italian Peninsula reveal a previously unrecognised lineage that show evidence of genetic exchange with other populations.

Conclusions: The pattern of genetic variation in the field vole species complex demonstrates the impact of stadial-interstadial cycles in generating recurrent episodes of allopatry and connectivity of populations, a situation which could only be revealed by our dense genomic sampling over time.