PDF(Pubmed)
Abstract：
UNASSIGNED: Systemic dimorphic fungi pose a significant public health challenge, causing over one million new infections annually. The dimorphic transition between saprophytic mycelia and pathogenic yeasts is strongly associated with the pathogenesis of dimorphic fungi. However, despite the dynamic nature of dimorphic transition, the current omics studies focused on dimorphic transition primarily employ static strategies, partly due to the lack of suitable dynamic analytical methods.
UNASSIGNED: We conducted time-course transcriptional profiling during the dimorphic transition of Talaromyces marneffei, a model organism for thermally dimorphic fungi. To capture non-uniform and nonlinear transcriptional changes, we developed DyGAM-NS (dynamic optimized generalized additive model with natural cubic smoothing). The performance of DyGAM-NS was evaluated by comparison with seven other commonly used time-course analysis methods. Based on dimorphic transition induced genes (DTIGs) identified by DyGAM-NS, cluster analysis was utilized to discern distinct gene expression patterns throughout dimorphic transitions of T. marneffei. Simultaneously, a gene expression regulatory network was constructed to probe pivotal regulatory elements governing the dimorphic transitions.
UNASSIGNED: By using DyGAM-NS, model, we identified 5,223 DTIGs of T. marneffei. Notably, the DyGAM-NS model showcases performance on par with or superior to other commonly used models, achieving the highest F1 score in our assessment. Moreover, the DyGAM-NS model also demonstrates potential in predicting gene expression levels throughout temporal processes. The cluster analysis of DTIGs suggests divergent gene expression patterns between mycelium-to-yeast and yeast-to-mycelium transitions, indicating the asymmetrical nature of two transition directions. Additionally, leveraging the identified DTIGs, we constructed a regulatory network for the dimorphic transition and identified two zinc finger-containing transcription factors that potentially regulate dimorphic transition in T. marneffei.
UNASSIGNED: Our study elucidates the dynamic transcriptional profile changes during the dimorphic transition of T. marneffei. Furthermore, it offers a novel perspective for unraveling the underlying mechanisms of fungal dimorphism, emphasizing the importance of dynamic analytical methods in understanding complex biological processes.
UNASSIGNED: We conducted time-course transcriptional profiling during the dimorphic transition of Talaromyces marneffei, a model organism for thermally dimorphic fungi. To capture non-uniform and nonlinear transcriptional changes, we developed DyGAM-NS (dynamic optimized generalized additive model with natural cubic smoothing). The performance of DyGAM-NS was evaluated by comparison with seven other commonly used time-course analysis methods. Based on dimorphic transition induced genes (DTIGs) identified by DyGAM-NS, cluster analysis was utilized to discern distinct gene expression patterns throughout dimorphic transitions of T. marneffei. Simultaneously, a gene expression regulatory network was constructed to probe pivotal regulatory elements governing the dimorphic transitions.
UNASSIGNED: By using DyGAM-NS, model, we identified 5,223 DTIGs of T. marneffei. Notably, the DyGAM-NS model showcases performance on par with or superior to other commonly used models, achieving the highest F1 score in our assessment. Moreover, the DyGAM-NS model also demonstrates potential in predicting gene expression levels throughout temporal processes. The cluster analysis of DTIGs suggests divergent gene expression patterns between mycelium-to-yeast and yeast-to-mycelium transitions, indicating the asymmetrical nature of two transition directions. Additionally, leveraging the identified DTIGs, we constructed a regulatory network for the dimorphic transition and identified two zinc finger-containing transcription factors that potentially regulate dimorphic transition in T. marneffei.
UNASSIGNED: Our study elucidates the dynamic transcriptional profile changes during the dimorphic transition of T. marneffei. Furthermore, it offers a novel perspective for unraveling the underlying mechanisms of fungal dimorphism, emphasizing the importance of dynamic analytical methods in understanding complex biological processes.
摘要：
■系统性双态真菌构成了重大的公共卫生挑战，每年造成超过一百万的新感染。腐生菌丝体和致病性酵母之间的双态转变与双态真菌的发病机理密切相关。然而,尽管二态转变具有动态性，当前的组学研究侧重于二态转变，主要采用静态策略，部分原因是缺乏合适的动态分析方法。
■我们在马尔尼菲塔拉酵母的双态转变过程中进行了时程转录分析，热二态真菌的模型生物。为了捕获非均匀和非线性的转录变化，我们开发了DyGAM-NS（具有自然三次平滑的动态优化广义加法模型）。通过与其他七种常用的时程分析方法进行比较，评估了DyGAM-NS的性能。基于DyGAM-NS鉴定的二态转变诱导基因(DTIG)，利用聚类分析来辨别马尔尼菲双态转变过程中不同的基因表达模式。同时,构建了一个基因表达调控网络，以探测控制二态转换的关键调控元件。
■通过使用DyGAM-NS，模型，我们确定了5,223种马内菲的DTIG。值得注意的是,DyGAM-NS模型展示了与其他常用模型相当或优于其他常用模型的性能，在我们的评估中获得了最高的F1分数。此外，DyGAM-NS模型还显示了在整个时间过程中预测基因表达水平的潜力。DTIG的聚类分析表明菌丝体到酵母和酵母到菌丝体转换之间的基因表达模式不同，表示两个过渡方向的不对称性质。此外,利用已识别的DTIG，我们构建了二态转变的调节网络，并确定了两个含锌指的转录因子，它们可能在马尔尼菲T.
■我们的研究阐明了马尔尼菲双态转变过程中动态转录谱的变化。此外，它提供了一个新的视角来揭示真菌双态的潜在机制，强调动态分析方法在理解复杂生物过程中的重要性。
■我们在马尔尼菲塔拉酵母的双态转变过程中进行了时程转录分析，热二态真菌的模型生物。为了捕获非均匀和非线性的转录变化，我们开发了DyGAM-NS（具有自然三次平滑的动态优化广义加法模型）。通过与其他七种常用的时程分析方法进行比较，评估了DyGAM-NS的性能。基于DyGAM-NS鉴定的二态转变诱导基因(DTIG)，利用聚类分析来辨别马尔尼菲双态转变过程中不同的基因表达模式。同时,构建了一个基因表达调控网络，以探测控制二态转换的关键调控元件。
■通过使用DyGAM-NS，模型，我们确定了5,223种马内菲的DTIG。值得注意的是,DyGAM-NS模型展示了与其他常用模型相当或优于其他常用模型的性能，在我们的评估中获得了最高的F1分数。此外，DyGAM-NS模型还显示了在整个时间过程中预测基因表达水平的潜力。DTIG的聚类分析表明菌丝体到酵母和酵母到菌丝体转换之间的基因表达模式不同，表示两个过渡方向的不对称性质。此外,利用已识别的DTIG，我们构建了二态转变的调节网络，并确定了两个含锌指的转录因子，它们可能在马尔尼菲T.
■我们的研究阐明了马尔尼菲双态转变过程中动态转录谱的变化。此外，它提供了一个新的视角来揭示真菌双态的潜在机制，强调动态分析方法在理解复杂生物过程中的重要性。
