Cong Li

Plant pathogens are one of the main factors hindering the breeding of cash crops. Pathogens, including oomycetes, fungus, and bacteria, secrete effectors as invasion weapons to successfully invade and propagate in host plants. Here, we review recent advances made in the field of plant-pathogen interaction models and the action mechanisms of phytopathogenic effectors. The review illustrates how effectors from different species use similar and distinct strategies to infect host plants. We classify the main action mechanisms of effectors in plant-pathogen interactions according to the infestation process: targeting physical barriers for disruption, creating conditions conducive to infestation, protecting or masking themselves, interfering with host cell physiological activity, and manipulating plant downstream immune responses. The investigation of the functioning of plant pathogen effectors contributes to improved understanding of the molecular mechanisms of plant-pathogen interactions. This understanding has important theoretical value and is of practical significance in plant pathology and disease resistance genetics and breeding.

Deciphering universal gene regulatory mechanisms in diverse organisms holds great potential for advancing our knowledge of fundamental life processes and facilitating clinical applications. However, the traditional research paradigm primarily focuses on individual model organisms and does not integrate various cell types across species. Recent breakthroughs in single-cell sequencing and deep learning techniques present an unprecedented opportunity to address this challenge. In this study, we built an extensive dataset of over 120 million human and mouse single-cell transcriptomes. After data preprocessing, we obtained 101,768,420 single-cell transcriptomes and developed a knowledge-informed cross-species foundation model, named GeneCompass. During pre-training, GeneCompass effectively integrated four types of prior biological knowledge to enhance our understanding of gene regulatory mechanisms in a self-supervised manner. By fine-tuning for multiple downstream tasks, GeneCompass outperformed state-of-the-art models in diverse applications for a single species and unlocked new realms of cross-species biological investigations. We also employed GeneCompass to search for key factors associated with cell fate transition and showed that the predicted candidate genes could successfully induce the differentiation of human embryonic stem cells into the gonadal fate. Overall, GeneCompass demonstrates the advantages of using artificial intelligence technology to decipher universal gene regulatory mechanisms and shows tremendous potential for accelerating the discovery of critical cell fate regulators and candidate drug targets.