Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.)

Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Little is known regarding the genetic controllers of variation for these compounds in oat seed. We characterized natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leveraged this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for 8 of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional “omics” data to a set of biologically meaningful variables and translate inferences on these data into improved breeding decisions.     

基于Web服务的流感病毒基因组自动化翻译注释系统

随着流感病毒基因组测序数据的急剧增加,深入挖掘流感病毒基因组大数据蕴含的生物学信息成为研究热点。基于中国流感病毒流行特征数据,建设一个集自动化、一体化和信息化的序列库系统,对于实现流感病毒基因组批量快速翻译、注释、存储、查询、分析具有重要的应用价值。本课题组通过集成一系列软件和工具包,并结合自主研发的其他功能,在底层维护的2个关键的参考数据集基础上另外追加了翻译注释信息最佳匹配的精细化筛选规则,构建具有流感病毒基因组信息存储、自动化翻译、蛋白序列精准注释、同源序列比对和进化树分析等功能的自动化系统。结果显示,通过Web端输入fasta格式的流感病毒基因序列,本系统可针对参考序列片段数据集(blastdb.fasta)进行Blast同源性检索,可以鉴定流感病毒的型别(A、B或C)、亚型和基因片段(1~8片段);在此基础上,通过查询数据库底层用于翻译、注释的基因片段参考数据集,可以获得一组肽段数据集,然后通过循环调用ProSplign软件对其进行预测。结合精细化的筛选准入规则,选出与输入序列匹配最好的翻译后产物,作为该输入序列的预测蛋白,输出为gbk,asn和fasta等通用格式的文件,给出序列长度、是否全长、病毒型别、亚型、片段等信息。基于以上工作,另外自主研发了系统其他的附加功能如进化树分析展示、基因组数据存储等功能,构建成基于Web服务的流感病毒基因组自动化翻译注释系统。本研究提示,系统高度集成系列软件以及自有的注释翻译数据库文件,实现从序列存储、翻译、注释到序列分析和展示的功能,可全面满足我国高通量基因检测数据共享化、本土化、一体化、自动化的需求。     

Phytochemical Constituents and Biological Activities of Plants from the Genus Cissampelos

Cissampelos is a significant genus comprising of approximately 21 species of the medicinal plants (Menispermaceae). The plants of this genus are used in traditional medicine for the treatment of various ailments such as asthma, arthritis, dysentery, hyperglycemia, cardiopathy, hypertension and other related problems. These plants are rich in bioactive dibenzylisoquinoline and aborphine as well as small amounts of other ingredients. In recent years, the chemical constituents and pharmacological activities of Cissampelos genus have been paid more and more attention due to their diversity. Herein, we compile the chemical constituents and biological activities on this genus, and summarize the ¹³C-NMR data of the main bioactive ingredients. All information comes from scientific databases such as Google Scholar, PubMed, Sci-Finder, ScienceDirect, Web of Science and CNKI. It provides valuable data for the future research and development of Cissampelos genus.     

Construction of a robust prognostic model for adult adrenocortical carcinoma: Results from bioinformatics and real-world data

This study aims to construct a robust prognostic model for adult adrenocortical carcinoma (ACC) by large-scale multiomics analysis and real-world data. The RPPA data, gene expression profiles and clinical information of adult ACC patients were obtained from The Cancer Proteome Atlas (TCPA), Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Integrated prognosis-related proteins (IPRPs) model was constructed. Immunohistochemistry was used to validate the prognostic value of the IPRPs model in Fudan University Shanghai Cancer Center (FUSCC) cohort. 76 ACC cases from TCGA and 22 ACC cases from GSE10927 in NCBI’s GEO database with full data for clinical information and gene expression were utilized to validate the effectiveness of the IPRPs model. Higher FASN (P = .039), FIBRONECTIN (P < .001), TFRC (P < .001), TSC1 (P < .001) expression indicated significantly worse overall survival for adult ACC patients. Risk assessment suggested significantly a strong predictive capacity of IPRPs model for poor overall survival (P < .05). IPRPs model showed a little stronger ability for predicting prognosis than Ki-67 protein in FUSCC cohort (P = .003, HR = 3.947; P = .005, HR = 3.787). In external validation of IPRPs model using gene expression data, IPRPs model showed strong ability for predicting prognosis in TCGA cohort (P = .005, HR = 3.061) and it exhibited best ability for predicting prognosis in GSE10927 cohort (P = .0898, HR = 2.318). This research constructed IPRPs model for predicting adult ACC patients’ prognosis using proteomic data, gene expression data and real-world data and this prognostic model showed stronger predictive value than other biomarkers (Ki-67, Beta-catenin, etc) in multi-cohorts.     

Large vertical movements by a goosefish,Lophius americanus,suggests the potential of data storage tags for behavioral studies of benthic fishes

The extensive periodic vertical movements of up to 14 h and 209 m observed in this study for an individual goosefish, Lophius americanus, challenges previous assumptions about the benthic and highly sedentary behavior of the species as well as of other lophiids. Researchers should consider conducting similar data storage tagging studies with other benthic fishes to test assumptions of sedentary behavior.     

Comparative analysis of bone remodelling models with respect to computerised tomography-based finite element models of bone

Subject-specific finite element models are an extensively used tool for the numerical analysis of the biomechanical behaviour of human bones. However, bone modelling is not an easy task due to the complex behaviour of bone tissue, involving non-homogeneous and anisotropic mechanical properties. Moreover, bone is a living tissue and therefore its microstructure and mechanical properties evolve with time in a known process called bone remodelling. This phenomenon has been widely studied, many being the numerical models that have been formulated to predict density distribution and its evolution in several bones. The aim of the present study is to assess the capability of a bone remodelling model to predict the bone density distribution of different types of human bone (femur, tibia and mandible) comparing the obtained results with the bone density estimated by means of computerised tomography. Good accuracy was observed for the bone remodelling predictions including the thickness of the cortical layer.     

Electrocardiogram data mining based on frame classification by dynamic time warping matching

This paper presents an electrocardiogram (ECG) data mining scheme based on the ECG frame classification realised by a dynamic time warping (DTW) matching technique, which has been used successfully in speech recognition. We use the DTW to classify ECG frames because ECG and speech signals have similar non-stationary characteristics. The DTW mapping function is obtained by searching the frame from its end to start. A threshold is setup for DTW matching residual either to classify an ECG frame or to add a new class. Classification and establishment of a template set are carried out simultaneously. A frame is classified into a category with a minimal residual and satisfying a threshold requirement. A classification residual of 1.33% is achieved by the DTW for a 10-min ECG recording.     

Development of tree-ring maximum latewood density chronologies for the western Tien Shan Mountains,China: Influence of detrending method and climate response

Three tree-ring maximum latewood density chronologies were developed from high elevation Picea schrenkiana sites in the western Tien Shan Mountains using different detrending methods. The new chronologies extend back to the early 16th and late 17th centuries, and contain significant late spring and summer temperature signals, respectively. An assessment of varying detrending methods and band-pass filtering the chronologies revealed only slightly differing low frequency trends retained in the maximum latewood densities. The distance between sampling sites and the varying seasonality of limiting climatic factors are identified as key drivers affecting the correlation among the maximum latewood density records in the study region. The new chronologies represent reliable proxies of high elevation late spring and summer temperature variability in an area underrepresented by such data, and are ready-to-use for network analyses addressing longer-term climate variations in eastern central Asia.