Meta-analysis of fungal plant pathogen infection-related gene profiles using transcriptome datasets.

is a serious soil-borne fungal pathogen that affects the production of many economically important crops worldwide. Recent reports suggest that this fungus is becoming the dominant species in soil and could become the main infectious fungus in the future. However, the infection mechanisms employed by are poorly understood. In the present study, using a network meta-analysis technique and public transcriptome datasets for different and plant interactions, we aimed to explore the common molecular infection strategy used by this fungus and to identify vital genes involved in this process. Principle component analysis showed that all the fungal culture samples from different datasets were clustered together, and were clearly separated from the infection samples, suggesting the feasibility of an integrated analysis of heterogeneous datasets. A total of 335 common differentially expressed genes (DEGs) were identified among these samples, of which 262 were upregulated and 73 were downregulated significantly across the datasets. The most enriched functional categories of the common DEGs were carbohydrate metabolism, amino acid metabolism, and lipid metabolism. Nine co-expression modules were identified, and two modules, the turquoise module and the blue module, correlated positively and negatively with all the infection processes, respectively. Co-expression networks were constructed for these two modules and hub genes were identified and validated. Our results comprise a cross fungal-host interaction resource, highlighting the use of a network biology approach to gain molecular insights.