Prunus mume Sieb. et Zucc., P. armeniaca L., and P. salicina L. are economically important fruit trees in temperate regions. These species are taxonomically perplexing because of shared interspecific morphological traits and variation, which are mainly attributed to hybridization. The chloroplast is cytoplasmically inherited and often used for evolutionary studies. We sequenced the complete chloroplast genomes of P. mume, P. armeniaca, and P. salicina using Illumina sequencing followed by de novo assembly. The three chloroplast genomes exhibit a typical quadripartite structure with conserved genome arrangement, structure, and moderate divergence. The lengths of the genomes are 157,815, 157,797, and 157,916 bp, respectively. The length of the large single-copy region (LSC) region is 86,113, 86,283, and 86,122 bp, and the length of the SSC region is 18,916, 18,734, and 19,028 bp; the IR region is 26,393, 26,390, and 26,383 bp, respectively. Each of the three chloroplast genomes encodes 133 genes, including 94 protein-coding, 31 tRNA, and eight rRNA genes. Differential gene analysis for the three species revealed that trnY-ATA is a unique gene in P. armeniaca; in contrast, the gene trnI-TAT is only present in P. mume and P. salicina, though the position of the gene in these chloroplast genomes differs. Further comparative analysis of the complete chloroplast genome sequences revealed that the ORF genes and the sequences of linked regions rps16 and atpA, atpH and atpI, trnc-GCA and psbD, ycf3 and atpB, and rpL32 and ndhD are significantly different and may be used as molecular markers in taxonomic studies. Phylogenetic evolution analysis of the three species suggests that P. mume has a closer genetic relationship to P. armeniaca than to P. salicina.

Thymoquinone (TQ), a bioactive constituent of Nigella sativa Linn (N. sativa) has demonstrated several neuropharmacological attributes. In the present study, the neuroprotective properties of TQ were investigated by studying its anti-apoptotic potential to diminish β-amyloid peptide 1-40 sequence (Aβ1-40)-induced neuronal cell death in primary cultured cerebellar granule neurons (CGNs). The effects of TQ against Aβ1-40-induced neurotoxicity, morphological damages, DNA condensation, the generation of reactive oxygen species, and caspase-3, -8, and -9 activation were investigated. Pretreatment of CGNs with TQ (0.1 and 1 μM) and subsequent exposure to 10 μM Aβ1-40 protected the CGNs against the neurotoxic effects of the latter. In addition, the CGNs were better preserved with intact cell bodies, extensive neurite networks, a loss of condensed chromatin and less free radical generation than those exposed to Aβ1-40 alone. TQ pretreatment inhibited Aβ1-40-induced apoptosis of CGNs via both extrinsic and intrinsic caspase pathways. Thus, the findings of this study suggest that TQ may prevent neurotoxicity and Aβ1-40-induced apoptosis. TQ is, therefore, worth studying further for its potential to reduce the risks of developing Alzheimer's disease.

Understanding how a mutation might affect protein stability is of significant importance to protein engineering and for understanding protein evolution genetic diseases. While a number of computational tools have been developed to predict the effect of missense mutations on protein stability protein stability upon mutations, they are known to exhibit large biases imparted in part by the data used to train and evaluate them. Here, we provide a comprehensive overview of predictive tools, which has provided an evolving insight into the importance and relevance of features that can discern the effects of mutations on protein stability. A diverse selection of these freely available tools was benchmarked using a large mutation-level blind dataset of 1342 experimentally characterised mutations across 130 proteins from ThermoMutDB, a second test dataset encompassing 630 experimentally characterised mutations across 39 proteins from iStable2.0 and a third blind test dataset consisting of 268 mutations in 27 proteins from the newly published ProThermDB. The performance of the methods was further evaluated with respect to the site of mutation, type of mutant residue and by ranging the pH and temperature. Additionally, the classification performance was also evaluated by classifying the mutations as stabilizing (∆∆G ≥ 0) or destabilizing (∆∆G < 0). The results reveal that the performance of the predictors is affected by the site of mutation and the type of mutant residue. Further, the results show very low performance for pH values 6–8 and temperature higher than 65 for all predictors except iStable2.0 on the S630 dataset. To illustrate how stability and structure change upon single point mutation, we considered four stabilizing, two destabilizing and two stabilizing mutations from two proteins, namely the toxin protein and bovine liver cytochrome. Overall, the results on S268, S630 and S1342 datasets show that the performance of the integrated predictors is better than the mechanistic or individual machine learning predictors. We expect that this paper will provide useful guidance for the design and development of next-generation bioinformatic tools for predicting protein stability changes upon mutations.

BackgroundLong non-coding RNAs (lncRNAs) are transcripts more than 200 bp in length do not encode proteins. Up to the present, it has been reported that lncRNAs play an essential role in developmental processes through their regulatory functions. However, their characteristics, expression inheritance patterns, and functions in Prunus mume are quite unidentified.ResultsIn this present study, we exposed the specific characters of pistil development process between single pistil cv ‘Qingjia No.2’ (QJN2) and multiple pistils cv ‘Da Yu’ (DY). We found that early October is the key stage for pistil differentiation. The similarity epidermis was observed between two types of pistil. We also further investigated a complete pistil development lncRNA profiles through RNA-seq in Prunus mume. 2572 unique lncRNAs and 24,648 genes mapped to Prunus mume genome, furthermore, 591 novel lncRNAs were predicted. Both unique lncRNAs and novel lncRNAs are shorter in length than the mRNAs, and the overall expression level of lncRNAs was lower than mRNAs in Prunus mume. 186 known lncRNAs, 1638 genes and 89 novel lncRNAs were identified as significant differential expressed in QJN2 compared with DY. We predicted 421 target genes of differentially expressed known lncRNAs (DEKLs) and 254 target genes of differentially expressed novel lncRNAs (DENLs). 153 miRNAs were predicted interacted with 100 DEKLs while 112 miRNAs were predicted interacted with 55 DENLs. Further analysis of the DEKLs showed that the lncRNA of XR_514690.2 down-regulated its target ppe-miR172d, and up-regulated AP2, respectively. Meanwhile, the other lncRNA of TCONS_00032517 induced cytokinin negative regulator gene A-ARR expression via repressing its target miRNA ppe-miR160a/b in DY. At the same time we found that the AP2 expression was significantly up-regulated by zeatin (ZT) treatment in flower buds. Our experiments suggest that the two lncRNAs of XR_514690.2 and TCONS_00032517 might contribute the formation of multiple pistils in Prunus mume.ConclusionThis study shows the first characterization of lncRNAs involved in pistil development and provides new indications to elucidate how lncRNAs and their targets play role in pistil differentiation and flower development in Prunus mume.Electronic supplementary materialThe online version of this article (10.1186/s12870-019-1672-7) contains supplementary material, which is available to authorized users.