BackgroundSiberian apricot (Prunus sibirica L.) has emerged as a novel potential source of biodiesel in China, but the molecular regulatory mechanism of oil accumulation in Siberian apricot seed kernels (SASK) is still unknown at present. To better develop SASK oil as woody biodiesel, it is essential to profile transcriptome and to identify the full repertoire of potential unigenes involved in the formation and accumulation of oil SASK during the different developing stages.ResultsWe firstly detected the temporal patterns for oil content and fatty acid (FA) compositions of SASK in 7 different developing stages. The best time for obtaining the high quality and quantity of SASK oil was characterized at 60 days after flowering (DAF), and the representative periods (10, 30, 50, 60, and 70 DAF) were selected for transcriptomic analysis. By Illumina/Solexa sequencings, approximately 65 million short reads (average length = 96 bp) were obtained, and then assembled into 124,070 unigenes by Trinity strategy (mean size = 829.62 bp). A total of 3,000, 2,781, 2,620, and 2,675 differentially expressed unigenes were identified at 30, 50, 60, and 70 DAF (10 DAF as the control) by DESeq method, respectively. The relationship between the unigene transcriptional profiles and the oil dynamic patterns in developing SASK was comparatively analyzed, and the specific unigenes encoding some known enzymes and transcription factors involved in acetyl-coenzyme A (acetyl-CoA) formation and oil accumulation were determined. Additionally, 5 key metabolic genes implicated in SASK oil accumulation were experimentally validated by quantitative real-time PCR (qRT-PCR). Our findings could help to construction of oil accumulated pathway and to elucidate the molecular regulatory mechanism of increased oil production in developing SASK.ConclusionsThis is the first study of oil temporal patterns, transcriptome sequencings, and differential profiles in developing SASK. All our results will serve as the important foundation to further deeply explore the regulatory mechanism of SASK high-quality oil accumulation, and may also provide some reference for researching the woody biodiesel plants.Electronic supplementary materialThe online version of this article (doi:10.1186/s13068-015-0213-3) contains supplementary material, which is available to authorized users.

Background Lindera glauca fruit with high quality and quantity of oil has emerged as a novel potential source of biodiesel in China, but the molecular regulatory mechanism of carbon flux and energy source for oil biosynthesis in developing fruits is still unknown. To better develop fruit oils of L. glauca as woody biodiesel, a combination of two different sequencing platforms (454 and Illumina) and qRT-PCR analysis was used to define a minimal reference transcriptome of developing L. glauca fruits, and to construct carbon and energy metabolic model for regulation of carbon partitioning and energy supply for FA biosynthesis and oil accumulation.ResultsWe first analyzed the dynamic patterns of growth tendency, oil content, FA compositions, biodiesel properties, and the contents of ATP and pyridine nucleotide of L. glauca fruits from seven different developing stages. Comprehensive characterization of transcriptome of the developing L. glauca fruit was performed using a combination of two different next-generation sequencing platforms, of which three representative fruit samples (50, 125, and 150 DAF) and one mixed sample from seven developing stages were selected for Illumina and 454 sequencing, respectively. The unigenes separately obtained from long and short reads (201, and 259, respectively, in total) were reconciled using TGICL software, resulting in a total of 60,031 unigenes (mean length = 1061.95 bp) to describe a transcriptome for developing L. glauca fruits. Notably, 198 genes were annotated for photosynthesis, sucrose cleavage, carbon allocation, metabolite transport, acetyl-CoA formation, oil synthesis, and energy metabolism, among which some specific transporters, transcription factors, and enzymes were identified to be implicated in carbon partitioning and energy source for oil synthesis by an integrated analysis of transcriptomic sequencing and qRT-PCR. Importantly, the carbon and energy metabolic model was well established for oil biosynthesis of developing L. glauca fruits, which could help to reveal the molecular regulatory mechanism of the increased oil production in developing fruits.ConclusionsThis study presents for the first time the application of an integrated two different sequencing analyses (Illumina and 454) and qRT-PCR detection to define a minimal reference transcriptome for developing L. glauca fruits, and to elucidate the molecular regulatory mechanism of carbon flux control and energy provision for oil synthesis. Our results will provide a valuable resource for future fundamental and applied research on the woody biodiesel plants.Electronic supplementary materialThe online version of this article (doi:10.1186/s13068-017-0820-2) contains supplementary material, which is available to authorized users.

BackgroundBased on our previous studies of 17 Prunus sibirica germplasms, one plus tree with high quality and quantity of seed oils has emerged as novel potential source of biodiesel. To better develop P. sibirica seed oils as woody biodiesel, a concurrent exploration of oil content, FA composition, biodiesel yield and fuel properties as well as prediction model construction for fuel properties was conducted on developing seeds to determine the optimal seed harvest time for producing high-quality biodiesel. Oil synthesis required supply of carbon source, energy and FA, but their transport mechanisms still remains enigmatic. Our recent 454 sequencing of P. sibirica could provide long-read sequences to identify membrane transporters for a better understanding of regulatory mechanism for high oil production in developing seeds.ResultsTo better develop the seed oils of P. sibirica as woody biodiesel, we firstly focused on a temporal and comparative evaluation of growth tendency, oil content, FA composition, biodiesel yield and fuel properties as well as model construction for biodiesel property prediction in different developing seeds from P. sibirica plus tree (accession AS-80), revealing that the oils from developing seeds harvested after 60 days after flowering (DAF) could be as novel potential feedstock for producing biodiesel with ideal fuel property. To gain new insight into membrane transport mechanism for high oil yield in developing seeds of P. sibirica, we presented a global analysis of transporter based on our recent 454 sequencing data of P. sibirica. We annotated a total of 116 genes for membrane-localized transporters at different organelles (plastid, endoplasmatic reticulum, tonoplast, mitochondria and peroxisome), of which some specific transporters were identified to be involved in carbon allocation, metabolite transport and energy supply for oil synthesis by both RT-PCR and qRT-PCR. Importantly, the transporter-mediated model was well established for high oil synthesis in developing P. sibirica seeds. Our findings could help to reveal molecular mechanism of increased oil production and may also present strategies for engineering oil accumulation in oilseed plants.ConclusionsThis study presents a temporal and comparative evaluation of developing P. sibirica seed oils as a potential feedstock for producing high-quality biodiesel and a global identification for membrane transporters was to gain better insights into regulatory mechanism of high oil production in developing seeds of P. sibirica. Our findings may present strategies for developing woody biodiesel resources and engineering oil accumulation.Electronic supplementary materialThe online version of this article (10.1186/s13068-018-1347-x) contains supplementary material, which is available to authorized users.

Knowledge of drug–target interaction (DTI) plays an important role in discovering new drug candidates. Unfortunately, there are unavoidable shortcomings; including the time-consuming and expensive nature of the experimental method to predict DTI. Therefore, it motivates us to develop an effective computational method to predict DTI based on protein sequence. In the paper, we proposed a novel computational approach based on protein sequence, namely PDTPS (Predicting Drug Targets with Protein Sequence) to predict DTI. The PDTPS method combines Bi-gram probabilities (BIGP), Position Specific Scoring Matrix (PSSM), and Principal Component Analysis (PCA) with Relevance Vector Machine (RVM). In order to evaluate the prediction capacity of the PDTPS, the experiment was carried out on enzyme, ion channel, GPCR, and nuclear receptor datasets by using five-fold cross-validation tests. The proposed PDTPS method achieved average accuracy of 97.73%, 93.12%, 86.78%, and 87.78% on enzyme, ion channel, GPCR and nuclear receptor datasets, respectively. The experimental results showed that our method has good prediction performance. Furthermore, in order to further evaluate the prediction performance of the proposed PDTPS method, we compared it with the state-of-the-art support vector machine (SVM) classifier on enzyme and ion channel datasets, and other exiting methods on four datasets. The promising comparison results further demonstrate that the efficiency and robust of the proposed PDTPS method. This makes it a useful tool and suitable for predicting DTI, as well as other bioinformatics tasks.

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