Unified Transcriptomic Signature of Arbuscular Mycorrhiza Colonization in Roots of Medicago truncatula by Integration of Machine Learning, Promoter Analysis, and Direct Merging Meta-Analysis - Mohammadi-Dehcheshmeh, Manijeh; Niazi, Ali; Ebrahimi, Mansour; Tahsili, Mohammadreza; Nurollah, Zahra; Ebrahimi Khaksefid, Reyhaneh; Ebrahimi, Mahdi; Ebrahimie, Esmaeil (Frontiers in Plant Science, 2018)
Plant root symbiosis with Arbuscular mycorrhiza improves uptake of water and mineral nutrients and improves plant development under stressful conditions. Unravelling the unified transcriptomic signature of a successful colonisation provides a better understanding of symbiosis. We developed a framework for finding the transcriptomic signature of Arbuscular mycorrhiza colonisation and it’s regulating transcription factors in roots of Medicago truncatula. Expression profiles of roots in response to Arbuscular mycorrhiza species were collected from four separate studies and were combined by direct merging meta-analysis. Batch effect, the major concern in expression meta-analysis, was reduced by three normalisation steps: Robust Multi-array Average algorithm, Z-standardisation, and quartiling normalisation. Then, expression profile of 33685 genes in 18 root samples of Medicago as numerical features as well as study ID and Arbuscular mycorrhiza type as categorical features were mined by seven models: RELIEF, UNCERTAINTY, GINI INDEX, CHI SQUARED, RULE, INFO GAIN, and INFO GAIN RATIO. In total, 73 genes selected by machine learning models were up-regulated with the Z-value difference > 0.5 in response to Arbuscular mycorrhiza. Feature weighting models also documented that this signature is independent from study (batch) effect. The developed AM inoculation signature was able to efficiently differentiate between AM inoculated and non-inoculated samples. AP2 domain class transcription factor, GRAS family transcription factors, and cyclin-dependent kinase were among the highly expressed meta-genes in signature. Promoter analysis of upregulated genes in transcriptomic signature led to 13 transcription factor matrix families, as master regulators of AM colonisation, including the essential transcription factors for endosymbiosis establishment and development such as NF-YA factors. The developed approach in this study offers three distinctive features: (I) it improves direct merging meta-analysis by integrating supervised machine learning models and normalisation steps to reduce study-specific batch effects; (II) seven attribute weighting models assessed the suitability of each gene for transcriptomic signature which contributes to robustness of signature (III) the approach is justifiable, easy to apply, and useful in practice. Our integrative framework of meta-analysis, promoter analysis, and machine learning provides a foundation to reveal the transcriptomic signature and regulatory circuits governing Arbuscular mycorrhizal symbiosis and is transferable to the other biological settings.