Modern wheat production comes from two polyploid species, Triticum aestivum and Triticum turgidum (var durum), which putatively arose from diploid ancestors Triticum urartu, Aegilops speltoides, and Aegilops tauschii. How gene expression during embryogenesis and grain development in wheats has been shaped by the differing contributions of diploid genomes through hybridization, polyploidization, and breeding selection is not well understood. This study describes the global landscape of gene activities during wheat embryogenesis and grain development. Using comprehensive transcriptomic analyses of two wheat cultivars and three diploid grasses, we investigated gene expression at seven stages of embryo development, two endosperm stages, and one pericarp stage. We identified transcriptional signatures and developmental similarities and differences among the five species, revealing the evolutionary divergence of gene expression programs and the contributions of A, B, and D subgenomes to grain development in polyploid wheats. The characterization of embryonic transcriptional programming in hexaploid wheat, tetraploid wheat, and diploid grass species provides insight into the landscape of gene expression in modern wheat and its ancestral species. This study presents a framework for understanding the evolution of domesticated wheat and the selective pressures placed on grain production, with important implications for future performance and yield improvements.

Orchestration of an effective T lymphocyte response at infection sites is critical for protection against Mycobacterium tuberculosis (Mtb) infection. However, the local T cell immunity landscape in human tuberculosis is poorly defined. Tuberculous pleural effusion (TPE), caused by Mtb, is characterized by an influx of leukocytes to the pleural space, providing a platform suitable for delineating complex tissue responses to Mtb infection. Using single-cell transcriptomics and T cell receptor sequencing, we analyzed mononuclear cell populations in paired pleural fluid and peripheral blood of TPE patients. While all major cell clusters were present in both tissues, their relative proportions varied significantly by anatomic location. Lineage tracking analysis revealed subsets of CD8 and CD4 T cell populations with distinct effector functions specifically expanded at pleural sites. Granzyme K–expressing CD8 T cells were preferentially enriched and clonally expanded in pleural fluid from TPE, suggesting that they are involved in the pathogenesis of the disease. The findings collectively reveal the landscape of local T cell immunity in tuberculosis.

Type III secreted effector (T3SE) prediction remains a big computational challenge. In practical applications, current software tools often suffer problems of high false-positive rates. One of the causal factors could be the relatively unitary type of biological features used for the design and training of the models. In this research, we made a comprehensive survey on the sequence-based features of T3SEs, including signal sequences, chaperone-binding domains, effector domains, and transcription factor binding promoter sites, and assembled a unified prediction pipeline integrating multi-aspect biological features within homology-based and multiple machine learning models. To our knowledge, we have compiled the most comprehensive biological sequence feature analysis for T3SEs in this research. The T3SEpp pipeline integrating the variety of features and assembling different models showed high accuracy, which should facilitate more accurate identification of T3SEs in new and existing bacterial whole-genome sequences.

Gram-negative bacteria harness multiple protein secretion systems and secrete a large proportion of the proteome. Proteins can be exported to periplasmic space, integrated into membrane, transported into extracellular milieu, or translocated into cytoplasm of contacting cells. It is important for accurate, genome-wide annotation of the secreted proteins and their secretion pathways. In this review, we systematically classified the secreted proteins according to the types of secretion systems in Gram-negative bacteria, summarized the known features of these proteins, and reviewed the algorithms and tools for their prediction.