MGWAS of Gln-related traits reveals an unexpected seed-specific interaction between glutamine and glucosinolates and in shaping the metabolic homeostasis in Ara

Maize (Zea mays) seeds are a good source of protein, despite being deficient in several essential amino acids. However, eliminating the highly abundant but poorly balanced seed storage proteins has revealed that the regulation of seed amino acids is complex and does not rely on only a handful of proteins. In this study, we used two complementary omics-based approaches to shed light on the genes and biological processes that underlie the regulation of seed amino acid composition. We first conducted a genome-wide association study to identify candidate genes involved in the natural variation of seed protein-bound amino acids. We then used weighted gene correlation network analysis to associate protein expression with seed amino acid composition dynamics during kernel development and maturation. We found that almost half of the proteome was significantly reduced during kernel development and maturation, including several translational machinery components such as ribosomal proteins, which strongly suggests translational reprogramming. The reduction was significantly associated with a decrease in several amino acids, including lysine and methionine, pointing to their role in shaping the seed amino acid composition. When we compared the candidate gene lists generated from both approaches, we found a nonrandom overlap of 80 genes. A functional analysis of these genes showed a tight interconnected cluster dominated by translational machinery genes, especially ribosomal proteins, further supporting the role of translation dynamics in shaping seed amino acid composition. These findings strongly suggest that seed biofortification strategies that target the translation machinery dynamics should be considered and explored further.

Advanced publicly available sequencing data from large populations have enabled informative genome-wide association studies (GWAS) that associate SNPs with phenotypic traits of interest. Many publicly available tools able to perform GWAS have been developed in response to increased demand. However, these tools lack a comprehensive pipeline that includes both pre-GWAS analysis such as outlier removal, data transformation, and calculation of Best Linear Unbiased Predictions (BLUPs) or Best Linear Unbiased Estimates (BLUEs). In addition, post-GWAS analysis such as haploblock analysis and candidate gene identification are lacking. Here, we present HAPPI GWAS, an open-source GWAS tool able to perform pre-GWAS, GWAS, and post-GWAS analysis in an automated pipeline using the command-line interface. HAPPI GWAS is written in R for any Unix-like operating systems and is available on GitHub (https://github.com/Angelovici-Lab/HAPPI.GWAS.git). Supplementary data are available at Bioinformatics online.

Accurate cell type annotation remains a major bottleneck in plant single-cell RNA sequencing (scRNA-seq), where existing tools are often adapted from animal studies and perform sub-optimally on plant data. The lack of plant-specific computational frameworks limits the construction of plant cell atlases and downstream biological discovery. We develop and evaluate scPlantAnnotate, a Transformer-based reference annotation framework tailored for plant scRNA-seq data, and benchmark it against state-of-the-art deep learning and conventional methods across multiple plant species. Species-specific scPlantAnnotate models were trained using curated datasets from Arabidopsis thaliana, Zea mays, Oryza sativa, and Glycine max. We compared scPlantAnnotate with leading baselines under both standard random-split evaluation and a more stringent leave-one-dataset-out setting, which tests robustness to completely unseen datasets and tissue types. scPlantAnnotate consistently outperforms existing approaches across all four species under random-split evaluation. In the leave-one-dataset-out setting for A. thaliana, where performance drops markedly for all methods due to strong batch effects and dataset heterogeneity, scPlantAnnotate nonetheless achieves the highest Accuracy, Macro-F1, Balanced Accuracy, and Macro-AUROC on average and ranks first on most held-out datasets. These results demonstrate improved robustness to dataset shifts, a critical yet underexplored challenge in plant scRNA-seq analysis. A freely accessible web server enables users to annotate their own datasets using pretrained models. scPlantAnnotate provides a plant-specific, Transformer-based framework for single-cell annotation that delivers state-of-the-art performance and enhanced robustness to unseen datasets. By addressing limitations of existing tools and enabling scalable reference-based annotation, scPlantAnnotate supports the development of comprehensive plant cell atlases and facilitates broader use of single-cell genomics in plant biology.

Monocarboxylate transporter 2 (MCT2; SLC16A7) is a high-affinity pyruvate transporter implicated in cancer metabolism. However, its role in lung cancer progression and the tumor microenvironment remains unclear. This study examined the effects of MCT2 reduction on tumor growth and cell-type-specific transcriptional changes within the tumor microenvironment. MCT2 loxP/loxP mice were crossed with mCre-Tg mice, and MCT2 deletion was induced by tamoxifen. Control (CO) mice received vehicle treatment. TC1 cells (100,000 cells/mouse) were injected subcutaneously, and tumors were harvested after 24 days. Single-nucleus RNA sequencing (snRNA-seq) was performed on isolated tumor nuclei (4000 nuclei/sample; n = 3 per group) using the 10x Genomics Chromium platform. Data were processed with Cell Ranger v3.0.2 and Seurat v5.2.1, followed by differential expression and pathway enrichment analyses integrated with macrophage bulk RNA-seq data. Tumors in mice with systemic MCT2 reduction grew significantly faster than those in control mice, demonstrating an association between host MCT2 reduction and increased tumor growth. Transcriptomic analysis generated high-quality profiles from 6864 CO and 10,055 KO nuclei. Clustering identified 12 cellular populations and cell types. MCT2 reduction altered pathways involved in glycolysis, the tricarboxylic acid cycle, oxidative phosphorylation, and fatty acid metabolism across multiple populations. Macrophages showed prominent transcriptional changes, including enrichment of MAPK, PI3K-Akt, IgSF-CAM, ECM, and cytokine–cytokine signaling pathways. These findings were supported by macrophage bulk RNA-seq data. Systemic MCT2 reduction was associated with increased tumor growth and broad transcriptional alterations within the tumor micro-environment. Differences in metabolic and immune-related transcriptional programs, particularly in macrophages, identify potential mechanisms associated with tumor progression that warrant further functional investigation.