Aeromonas hydrophila is a globally occurring, potentially virulent, gram-negative opportunistic pathogen that is known to cause water and food-borne diseases around the world. In this study, we use whole genome sequencing and in silico analyses to identify 14 putative O antigen gene clusters (OGCs) located downstream of the housekeeping genes acrB and/or oprM. We have also identified 7 novel OGCs by analyzing 15 publicly available genomes of different A. hydrophila strains. From the 14 OGCs identified initially, we have deduced that O antigen processing genes involved in the wzx/wzy pathway and the ABC transporter (wzm/wzt) pathway exhibit high molecular diversity among different A. hydrophila strains. Using these genes, we have developed a multiplexed Luminex-based array system that can identify up to 14 A. hydrophila strains. By combining our other results and including the sequences of processing genes from 13 other OGCs (7 OGCs identified from publicly available genome sequences and 6 OGCs that were previously published), we also have the data to create an array system that can identify 25 different A. hydrophila serotypes. Although clinical detection, epidemiological surveillance, and tracing of pathogenic bacteria are typically done using serotyping methods that rely on identifying bacterial surface O antigens through agglutination reactions with antisera, molecular methods such as the one we have developed may be quicker and more cost effective. Our assay shows high specificity, reproducibility, and sensitivity, being able to classify A. hydrophila strains using just 0.1 ng of genomic DNA. In conclusion, our findings indicate that a molecular serotyping system for A. hydrophila could be developed based on specific genes, providing an important molecular tool for the identification of A. hydrophila serotypes.

The phosphatidylethanolamine-binding protein (PEBP) gene family is critical for regulating plant growth, development, and flowering. Sunflower (Helianthus annuus L.) is the fourth most important oilseed crop globally. However, the genomic structure and functional diversity of PEBP genes in sunflower remain unexplored. Leveraging the recently assembled telomere-to-telomere (T2T) sunflower genome, a genome-wide analysis of the HaPEBP family was carried out. A total of 12 PEBP genes were identified in sunflower and categorized into three subfamilies: TFL1-like, FT-like, and MFT-like. Phylogenetic and synteny analyses revealed that tandem duplication events have substantially contributed to the evolution and expansion of the HaPEBP gene family. Furthermore, the analysis of the promoter regions revealed 77 distinct cis-acting elements, including 35 related to light signaling and growth regulation, highlighting their potential involvement in the regulation of flowering and development in sunflower. Expression profile analysis using RNA-seq data across various tissues indicated that FT-like and TFL1-like HaPEBP genes may be the key regulators of flowering time and plant architecture in sunflower varieties. This study offers valuable insights into the structural, evolutional, and functional dynamics of the HaPEBP gene family and holds significant implications for sunflower breeding strategies aimed at optimizing flowering time and plant architecture traits.

Sesame (Sesamum indicum L., 2n = 26) is a crucial oilseed crop cultivated worldwide. The ancient evolutionary position of the Sesamum genus highlights its value for genomics and molecular genetics research among the angiosperms of other genera. However, Sesamum is considered a small orphan genus with only a few genomic databases for cultivated sesame to date. The urgent need to construct comprehensive, curated genome databases that include genus-specific gene resources for both cultivated and wild Sesamum species is being recognized. In response, we developed Sesamum Genomics Database (SesamumGDB), a user-friendly genomic database that integrates extensive genomic resources from two cultivated sesame varieties (S. indicum) and seven wild Sesamum species, covering all three chromosome groups (2n = 26, 32, and 64). This database showcases a total of 352 471 genes, including 6026 related to lipid metabolism and 17 625 transcription factors within Sesamum. Equipped with an array of bioinformatics tools such as BLAST (basic local alignment search tool) and JBrowse (the Javascript browser), SesamumGDB facilitates data downloading, screening, visualization, and analysis. As the first centralized Sesamum genome database, SesamumGDB offers extensive insights into the genomics and genetics of sesame, potentially enhancing the molecular breeding of sesame and other oilseed crops in the future. Database URL: http://www.sgbdb.com/sgdb/

Single-cell RNA sequencing (scRNA-seq) enables high-resolution transcriptome analysis, enabling the study of cellular heterogeneity beyond bulk transcriptomics. However, plant science lags behind animal science in this field largely due to limited marker genes. In this study, we developed Plant single-cell Original Analyses (PscOA) (http://sdau.biodb.com.cn/pscoa/), a plant scRNA-seq marker gene database with 39 347 marker genes. PscOA integrates Basic Local Alignment Search Tool (BLAST) for homology-based marker gene mining, and A Cell Type Annotation Tool for Single-Cell RNA-seq Data (SCSA) for cell type annotation, complemented by visualization tools. Case studies in Arabidopsis thaliana and Populus alba L. demonstrate the potential of cell type annotation in PscOA. Leveraging A. thaliana marker genes from PscOA, we predict 258 potential markers in Nicotiana tabacum, showcasing its marker gene discovery potential for specific species. Differential expression analysis under stress reveals common and diverse strategies at the single-cell level, offering insights into plant cell type diversity and function. Altogether, PscOA serves as a valuable repository for original scRNA-seq analysis in plant science, deepening our understanding of the plant cellular transcriptome.