For various sexually mature insects, including the brown planthopper (BPH, Nilaparvata lugens), the abdominal vibration (AV) signal is the initiation of the mating process, and it is critical to the success of mating. Currently, there are few studies on the genetic and molecular mechanisms of AV regulation. Our previous AV-related transcriptomic study in female BPH identified myoinhibitory peptide (NlMIP) as a gene that potentially affects AV status in females, but how NlMIP affects AV status remains unknown. In this study, we confirmed that NlMIP regulates AV production and mating behavior in female BPH. When the RNAi knockdown efficiency of NlMIP was 59.00%, the probability of females producing AV and the mating rate in 1 h decreased by 38.89 and 61.11%, respectively. In addition, six mature peptides of NlMIP were synthesized and they were able to regulate AV production and mating behavior in females, with NlMIP2 having the strongest effect. The A-family neuropeptide GPCR 10 (NlA10) was found to be a potential receptor for NlMIP based on a phylogenetic tree analysis and the fact that NlMIP mature peptides effectively activated NlA10. After NlA10 was knocked down, the probability of females producing AV and the mating rate in 1 h had reductions of 28.89 and 43.33%, respectively. When activated by NlMIP2, NlA10 coupled the Gαi/q signalling pathways, thereby inhibiting the downstream AC/cAMP/PKA, activating the PLC/Ca2+/PKC signalling pathways and then activating MEK1/2 in a cascade to mediate the phosphorylation of ERK1/2, and finally regulating the AV of females. These results provide a basis for the prevention and control of the brown planthopper pest by disrupting female AV.
Characterization of subunits encoded by SnRK1 and dissection of combinations among these subunits in sorghum (Sorghum bicolor L.)
Sucrose nonfermenting-related protein kinase 1 (SnRK1) is one of the critical serine/threonine protein kinases. It commonly mediates plant growth and development, cross-talks with metabolism processes and physiological responses to biotic or abiotic stresses. It plays a key role in distributing carbohydrates and sugar signal transporting. In the present study, eight SnRK1 coding genes were identified in sorghum (Sorghum bicolor L.) via sequences alignment, with three for α subunits (SnRK1α1 to SnRK1α3), three for β (SnRK1β1 to SnRK1β3), and one for both γ (SnRK1γ) and βγ (SnRK1βγ). These eight corresponding genes located on five chromosomes (Chr) of Chr1–3, Chr7, and Chr9 and presented collinearities to SnRK1s from maize and rice, exhibiting highly conserved domains within the same subunits from the three kinds of cereals. Expression results via qRT-PCR showed that different coding genes of SnRK1s in sorghum possessed similar expression patterns except for SnRK1α3 with a low expression level in grains and SnRK1β2 with a relatively high expression level in inflorescences. Results of subcellular localization in sorghum leaf protoplast showed that SnRK1α1/α2/α3/γ mainly located on organelles, while the rest four of SnRK1β1/β2/β3/βγ located on both membranes and some organelles. Besides, three combinations were discovered among eight SnRK1 subunits in sorghum through yeast two hybrid, including α1-β2-βγ, α2-β3-γ, and α3-β3-γ. These results provide informative references for the following functional dissection of SnRK1 subunits in sorghum.
Genotype imputation is essential for increasing marker density and maximizing the utility of existing SNP array data in animal breeding. Although a wide range of software is available for genotype imputation, a comprehensive benchmark in pigs is still lacking. In this study, we benchmarked 24 combinations of genotype imputation software for SNP arrays in pigs, comprising six independent pre-phasing software (fastPHASE, MaCH, BIMBAM, Eagle, SHAPEIT, Beagle) and four distinct imputation software (pbwt, Minimac, IMPUTE, Beagle), using 1,602 whole-genome sequencing (WGS) pigs from a multibreed pig genomics reference panel (PGRP) in PigGTEx. Our results indicated that the combination of Beagle for pre-phasing and Minimac for imputation achieves the highest imputation accuracy with a concordance of 0.983, especially for low-frequency SNPs (MAF<0.05). Finally, we proposed three recommended strategies: i) the combination of Beagle and Minimac is optimal for achieving the highest accuracy; ii) the combination of Beagle and Beagle is recognized for its convenience and relatively high accuracy despite it being memory-intensive; iii) the combination of Eagle and pbwt is feasible for its minimal computational cost with relatively high accuracy. This study provides valuable insights for implementing genotype imputation for pig SNP arrays toward sequence data and offers a basis for applications in livestock and poultry breeding.