Genome-wide and evolutionary analysis of the class III peroxidase gene family in wheat and Aegilops tauschii reveals that some members are involved in stress responses

Background

The class III peroxidase (PRX) gene family is a plant-specific member of the PRX superfamily that is closely related to various physiological processes, such as cell wall loosening, lignification, and abiotic and biotic stress responses. However, its classification, evolutionary history and gene expression patterns are unclear in wheat and Aegilops tauschii.

Results

Here, we identified 374, 159 and 169 PRXs in Triticum aestivum, Triticum urartu and Ae. tauschii, respectively. Together with PRXs detected from eight other plants, they were classified into 18 subfamilies. Among subfamilies V to XVIII, a conserved exon-intron structure within the “001” exon phases was detected in the PRX domain. Based on the analysis, we proposed a phylogenetic model to infer the evolutionary history of the exon-intron structures of PRX subfamilies. A comparative genomics analysis showed that subfamily VII could be the ancient subfamily that originated from green algae (Chlamydomonas reinhardtii). Further integrated analysis of chromosome locations and collinearity events of PRX genes suggested that both whole genome duplication (WGD) and tandem duplication (TD) events contributed to the expansion of T. aestivum PRXs (TaePRXs) during wheat evolution. To validate functions of these genes in the regulation of various physiological processes, the expression patterns of PRXs in different tissues and under various stresses were studied using public microarray datasets. The results suggested that there were distinct expression patterns among different tissues and PRXs could be involved in biotic and abiotic responses in wheat. qRT-PCR was performed on samples exposed to drought, phytohormone treatments and Fusarium graminearum infection to validate the microarray predictions. The predicted subcellular localizations of some TaePRXs were consistent with the confocal microscopy results. We predicted that some TaePRXs had hormone-responsive cis-elements in their promoter regions and validated these predicted cis-acting elements by sequencing promoters.

Conclusion

In this study, identification, classification, evolution, and expression patterns of PRXs in wheat and relative plants were performed. Our results will provide information for further studies on the evolution and molecular mechanisms of wheat PRXs.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-6006-5) contains supplementary material, which is available to authorized users.

Systems Biology and Machine Learning in Plant-Pathogen Interactions

Systems biology is an inclusive approach to study the static and dynamic emergent properties on a global scale by integrating multiomics datasets to establish qualitative and quantitative associations among multiple biological components. With an abundance of improved high throughput -omics datasets, network-based analyses and machine learning technologies are playing a pivotal role in comprehensive understanding of biological systems. Network topological features reveal most important nodes within a network as well as prioritize significant molecular components for diverse biological networks, including coexpression, protein-protein interaction, and gene regulatory networks. Machine learning techniques provide enormous predictive power through specific feature extraction from biological data. Deep learning, a subtype of machine learning, has plausible future applications because a domain expert for feature extraction is not needed in this algorithm. Inspired by diverse domains of biology, we here review classic systems biology techniques applied in plant immunity thus far. We also discuss additional advanced approaches in both graph theory and machine learning, which may provide new insights for understanding plant-microbe interactions. Finally, we propose a hybrid approach in plant immune systems that harnesses the power of both network biology and machine learning, with a potential to be applicable to both model systems and agronomically important crop plants.

Genome-wide identification, classification, evolutionary analysis and gene expression patterns of the protein kinase gene family in wheat and Aegilops tauschii

In this study we systematically identified and classified PKs in Triticum aestivum, Triticum urartu and Aegilops tauschii. Domain distribution and exon-intron structure analyses of PKs were performed, and we found conserved exon-intron structures within the exon phases in the kinase domain. Collinearity events were determined, and we identified various T. aestivum PKs from polyploidizations and tandem duplication events. Global expression pattern analysis of T. aestivum PKs revealed that some PKs might participate in the signaling pathways of stress response and developmental processes. QRT-PCR of 15 selected PKs were performed under drought treatment and with infection of Fusarium graminearum to validate the prediction of microarray. The protein kinase (PK) gene superfamily is one of the largest families in plants and participates in various plant processes, including growth, development, and stress response. To better understand wheat PKs, we conducted genome-wide identification, classification, evolutionary analysis and expression profiles of wheat and Ae. tauschii PKs. We identified 3269, 1213 and 1448 typical PK genes in T. aestivum, T. urartu and Ae. tauschii, respectively, and classified them into major groups and subfamilies. Domain distributions and gene structures were analyzed and visualized. Some conserved intron-exon structures within the conserved kinase domain were found in T. aestivum, T. urartu and Ae. tauschii, as well as the primitive land plants Selaginella moellendorffii and Physcomitrella patens, revealing the important roles and conserved evolutionary history of these PKs. We analyzed the collinearity events of T. aestivum PKs and identified PKs from polyploidizations and tandem duplication events. Global expression pattern analysis of T. aestivum PKs revealed tissue-specific and stress-specific expression profiles, hinting that some wheat PKs may regulate abiotic and biotic stress response signaling pathways. QRT-PCR of 15 selected PKs were performed under drought treatment and with infection of F. graminearum to validate the prediction of microarray. Our results will provide the foundational information for further studies on the molecular functions of wheat PKs.