Class III peroxidases (CIII Prxs) play critical roles in plant immunity by scavenging reactive oxygen species (ROS). However, the functions of CIII Prxs in rice (Oryza sativa L.) immunity are largely unexplored. Here, we report a Prx precursor, OsPrx30, that is responsive to the bacterial blight Xanthomonas oryzae pv. oryzae (Xoo). OsPrx30 was primarily expressed in rice roots, leaves, and stems,

Although adenosine monophosphate (AMP) binding domain is widely distributed in multiple plant species, detailed molecular functions of AMP binding proteins (AMPBPs) in plant development and plant-pathogen interaction remain unclear. In the present study, we identified an AMPBP OsAAE3 from a previous analysis of early responsive genes in rice during Magnaporthe oryzae infection. OsAAE3 is a homolog of Arabidopsis AAE3 in rice, which encodes a 4-coumarate-Co-A ligase (4CL) like protein. A phylogenetic analysis showed that OsAAE3 was most likely 4CL-like 10 in an independent group. OsAAE3 was localized to cytoplasm, and it could be expressed in various tissues. Histochemical staining of transgenic plants carrying OsAAE3 promoter-driven GUS (β-glucuronidase) reporter gene suggested that OsAAE3 was expressed in all tissues of rice. Furthermore, OsAAE3-OX plants showed increased susceptibility to M. Oryzae, and this finding was attributable to decreased expression of pathogen-related 1a (PR1) and low level of peroxidase (POD) activity. Moreover, OsAAE3 over-expression resulted in increased content of H2O2, leading to programmed cell-death induced by reactive oxygen species (ROS). In addition, OsAAE3 over-expression repressed the floret development, exhibiting dramatically twisted glume and decreased fertility rate of anther. Meanwhile, the expressions of lignin biosynthesis genes were significantly decreased in OsAAE3-OX plants, thereby leading to reduced lignin content. Taken together, OsAAE3 functioned as a negative regulator in rice blast resistance, floret development, and lignin biosynthesis. Our findings further expanded the knowledge in functions of AMBPs in plant floret development and the regulation of rice-fungus interaction.

Flowering or heading is one of most important agronomic traits in rice. It has been characterized that CONSTANS (CO) and CONSTANS-like (COL) proteins are critical flowering regulators in response to photoperiodic stress in plants. We have previously identified that the COL family member OsCOL9 can positively enhance the rice blast resistance. In the present study, we aimed to explore the functional role of OsCOL9 in modulating the photoperiodic flowering. Our data showed that overexpression of OsCOL9 delayed the flowering time under both short-day (SD) and long-day (LD) conditions, leading to suppressed expressions of EHd1, RFT and Hd3a at the mRNA Level. OsCOL9 expression exhibited two types of circadian patterns under different daylight conditions, and it could delay the heading date by suppressing the Ehd1 photoperiodic flowering pathway. In contrast, the expressions of previously reported flowering regulators were not significantly changed in OsCOL9 transgenic plants, indicating that OsCOL9 functioned independently of other flowering pathways. In addition, OsCOL9 served as a potential yield gene, and its deficiency reduced the grain number of main panicle in plants. Furthermore, yeast two-hybrid assay indicated that OsCOL9 physically interacted with Receptor for Activated C-kinase 1 (OsRACK1). Rhythmic pattern analysis suggested that OsRACK1 responded to the change of daylight, which was regulated by the circadian clock. Taken together, our results revealed that OsCOL9 could delay the flowering time in rice by repressing the Ehd1 pathway.

The regulation of innate immunity and plant growth, along with the trade-off between them, affects the defense and recovery mechanisms of the plant after it is attacked by pathogens. Although it is known that hormonal crosstalk plays a major role in regulating interaction of plant growth and PAMP-triggered immunity, the relationship between plant growth and effector-triggered immunity (ETI) remains unclear. In a large-scale yeast two-hybrid screening for Pik-H4-interacting proteins, a homeodomain transcription factor OsBIHD1 was identified, which is previously known to function in biotic and abiotic stress responses. The knockout of OsBIHD1 in rice lines carrying Pik-H4 largely compromised the resistance of the rice lines to Magnaporthe oryzae, the fungus that causes rice blast. While overexpression of OsBIHD1 resulted in enhanced expression of the pathogenesis-related (PR) and ethylene (ET) synthesis genes. Moreover, OsBIHD1 was also found to directly bind to the promoter region of ethylene-synthesis enzyme OsACO3. In addition, OsBIHD1 overexpression or deficiency provoked dwarfism and reduced brassinosteroid (BR) insensitivity through repressing the expression of several critical genes involved in BR biosynthesis and BR signaling. During M. oryzae infection, transcript levels of the crucial BR catabolic genes (CYP734A2, CYP734A4, and CYP734A6) were significantly up-regulated in OsBIHD1-OX plants. Furthermore, OsBIHD1 was found to be capable of binding to the sequence-specific cis-elements on the promoters of CYP734A2 to suppress the plant growth under fungal invasion. Our results collectively suggest a model that OsBIHD1 is required for Pik-H4-mediated blast resistance through modulating the trade-off between resistance and growth by coordinating brassinosteroid-ethylene pathway.

Conventional supervised binary classification algorithms have been widely applied to address significant research questions using biological and biomedical data. This classification scheme requires two fully labeled classes of data (e.g. positive and negative samples) to train a classification model. However, in many bioinformatics applications, labeling data is laborious, and the negative samples might be potentially mislabeled due to the limited sensitivity of the experimental equipment. The positive unlabeled (PU) learning scheme was therefore proposed to enable the classifier to learn directly from limited positive samples and a large number of unlabeled samples (i.e. a mixture of positive or negative samples). To date, several PU learning algorithms have been developed to address various biological questions, such as sequence identification, functional site characterization and interaction prediction. In this paper, we revisit a collection of 29 state-of-the-art PU learning bioinformatic applications to address various biological questions. Various important aspects are extensively discussed, including PU learning methodology, biological application, classifier design and evaluation strategy. We also comment on the existing issues of PU learning and offer our perspectives for the future development of PU learning applications. We anticipate that our work serves as an instrumental guideline for a better understanding of the PU learning framework in bioinformatics and further developing next-generation PU learning frameworks for critical biological applications.