Research in microbiology · 2025
Co-expression network insights into Leptospira interrogans pathogenesis.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Greco João Pedro Gomes, de Oliveira Natasha Rodrigues, Dellagostin Odir Antônio, Kremer Frederico Schmitt
Аннотация
Leptospira interrogans, the agent of leptospirosis, employs complex virulence mechanisms that are not fully understood at a systems level. To elucidate the regulatory landscape of its pathogenicity, we used Weighted Gene Co-expression Network Analysis (WGCNA) on a comprehensive transcriptomic dataset to map the architecture of its virulence programs. Our analysis revealed that the L. interrogans transcriptome is organized into distinct, functionally coherent modules. We discovered that known virulence factors are significantly concentrated in two key modules: a "lightgrey" module that orchestrates host colonization and immune evasion, containing genes for surface adhesion (loa22, ompL1) and defense (lipL21); and a "black" module that functions as an arsenal for tissue invasion (colA), stress adaptation (clpB), and cytotoxicity (sph2). Furthermore, by contextualizing genes within this network, our approach implicated numerous uncharacterized genes (e.g., from the PF07598 family) in pathogenesis due to their strong co-expression with established virulence factors. These findings provide a systems-level blueprint of the regulatory networks driving leptospirosis, offering a rich resource of functionally validated gene modules and novel targets for the development of next-generation vaccines and therapeutics. These findings not only deepen the understanding of L. interrogans virulence regulation but also provide a conceptual framework for integrating transcriptomic data into systems-level models of bacterial pathogenesis, paving the way for translational applications in diagnostics and vaccine design.