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Heterosis utilization is very important in hybrid seed production. An AL-type cytoplasmic male sterile (CMS) line has been used in wheat-hybrid seed production, but its sterility mechanism has not been explored. In the present study, we sequenced and verified the candidate CMS gene in the AL-type sterile line (AL18A) and its maintainer line (AL18B). In the late uni-nucleate stage, the tapetum cells of AL18A showed delayed programmed cell death (PCD) and termination of microspore at the bi-nucleate stage. As compared to AL18B, the AL18A line produced 100% aborted pollens. The mitochondrial genomes of AL18A and AL18B were sequenced using the next generation sequencing such as Hiseq and PacBio. It was found that the mitochondrial genome of AL18A had 99% similarity with that of Triticum timopheevii, AL18B was identical to that of Triticum aestivum cv. Chinese Yumai. Based on transmembrane structure prediction, 12 orfs were selected as candidate CMS genes, including a previously suggested orf256. Only the lines harboring orf279 showed sterility in the transgenic Arabidopsis system, indicating that orf279 is the CMS gene in the AL-type wheat CMS lines. These results provide a theoretical basis and data support to further analyze the mechanism of AL-type cytoplasmic male sterility in wheat.

Abstract Background The exploration of gene-disease associations is crucial for understanding the mechanisms underlying disease onset and progression, with significant implications for prevention and treatment strategies. Advances in high-throughput biotechnology have generated a wealth of data linking diseases to specific genes. While graph representation learning has recently introduced groundbreaking approaches for predicting novel associations, existing studies always overlooked the cumulative impact of functional modules such as protein complexes and the incompletion of some important data such as protein interactions, which limits the detection performance. Results Addressing these limitations, here we introduce a deep learning framework called ModulePred for predicting disease-gene associations. ModulePred performs graph augmentation on the protein interaction network using L3 link prediction algorithms. It builds a heterogeneous module network by integrating disease-gene associations, protein complexes and augmented protein interactions, and develops a novel graph embedding for the heterogeneous module network. Subsequently, a graph neural network is constructed to learn node representations by collectively aggregating information from topological structure, and gene prioritization is carried out by the disease and gene embeddings obtained from the graph neural network. Experimental results underscore the superiority of ModulePred, showcasing the effectiveness of incorporating functional modules and graph augmentation in predicting disease-gene associations. This research introduces innovative ideas and directions, enhancing the understanding and prediction of gene-disease relationships.