A LysM Receptor Heteromer Mediates Perception of Arbuscular Mycorrhizal Symbiotic Signal in Rice

Symbiotic microorganisms improve nutrient uptake by plants.To initiate mutualistic symbiosis with arbus-cular mycorrhizal(AM)fungi,plants perceive Myc factors,including lipochitooligosaccharides(LCOs)and short-chain chitooligosaccharides(CO4/CO5),secreted by AM fungi.However,the molecular mechanism of Myc factor perception remains elusive.In this study,we identified a heteromer of LysM receptor-like kinases consisting of OsMYR1/OsLYK2 and OsCERK1 that mediates the perception of AM fungi in rice.CO4 directly binds to OsMYR1,promoting the dimerization and phosphorylation of this receptor complex.Compared with control plants,Osmyr1 and Oscerk1 mutant rice plants are less sensitive to Myc factors and show decreased AM colonization.We engineered transgenic rice by expressing chimeric receptors that respectively replaced the ectodomains of OsMYR1 and OsCERK1 with those from the homologous Nod factor receptors MtNFP and MtL YK3 of Medicago truncatula.Transgenic plants displayed increased cal-cium oscillations in response to Nod factors compared with control rice.Our study provides significant mechanistic insights into AM symbiotic signal perception in rice.Expression of chimeric Nod/Myc recep-tors achieves a potentially important step toward generating cereals that host nitrogen-fixing bacteria.

Comprehensive relative quantitative metabolomics analysis of lycopodium alkaloids in different tissues of Huperzia serrata

Qian ceng Ta,the whole plant of Huperzia serrata,is an important landscape and medicinal herbs and contains abundant bioactive lycopodium alkaloids.Although the structures of more than 100 lycopodium alkaloids in Huperzia serrata have been isolated and identified,the content and distribution of these alkaloids in different tissues are still unclear.In current study,an ultra-performance liquid chromatography-mass spectrometry based comprehensive metabolomics strategy was developed,including the extraction,separation,identification,and statistical analysis.The results showed that different types lycopodium alkaloids could be separated at different time-windows,which was helpful for further metabolite identification.Peak4388 and peak3954 were metabolite biomarkers for the different tissues according to the principle component analysis and partial least squares-discriminant analysis model.A computational tool based in-house database was also built up and used for putative identification.Of the 2354 true peaks after four-step filtration,118 peaks were putatively identified as lycopodium alkaloids by using in-house database,and four of which was identified by authentic standards.Alternatively,another computational software was used to predict the fragmentation pattern,to dereplicate the structure of identified peaks,and identified the peak3585 to N-methylhuperzine A.The integration of both computational tools could be used for more metabolites identification.