水稻大粒种质资源和遗传分析

谷粒重量是构成产量的三要素之一,对提高水稻产量具有重要意义.本文概述了国内外水稻大粒种质资源的现状,同时对粒重基因遗传分析的研究进展进行了综述.粒重是一个受多基因控制的数量性状,目前定位的粒重数量性状位点至少达89个、精细定位1个粒重基因gw3.1和1个长粒基因Lk-4（t）以及克隆1个粒重基因GS3,并在此基础上讨论了粒重在育种上的应用.     

棉铃虫叉头框蛋白A类似蛋白基因HarmFoxAl的克隆及表达谱分析

【目的】本研究旨在克隆并分析一种棉铃虫Helicoverpa armigera叉头框蛋白A (forkhead box protein A, FoxA)类似蛋白基因HarmFoxAl,探讨2-十三烷酮胁迫下棉铃虫中肠中HarmFoxAl的表达情况,为进一步明确棉铃虫FoxA的功能和参与棉铃虫生长发育的调控通路提供依据。【方法】从棉铃虫幼虫中肠中扩增得到HarmFoxAl的cDNA序列,并对其氨基酸序列和蛋白结构进行分析。将HarmFoxAl的ORF序列连接至pET32a载体并转化大肠杆菌Escherichia coli Transetta菌株,IPTG诱导后检测目的蛋白的表达形式,并利用镍柱亲和层析法纯化融合蛋白。通过qPCR检测棉铃虫不同发育阶段(1-6龄幼虫和预蛹),6龄幼虫不同组织(脂肪体、中肠、体壁和头部)以及10 mg/g 2-十三烷酮处理6龄幼虫不同时间后中肠中HarmFoxAl的表达谱。【结果】HarmFoxAl(GenBank登录号:XM021331806)的开放阅读框为669 bp,编码222个氨基酸,蛋白的相对分子质量和等电点分别为25.03 kD和6.34。氨基酸序列分析表明,HarmFoxAl单体蛋白无信号肽、跨膜区和二硫键,核心区域是由4个α螺旋和3个β折叠组成的球状结构。将重组的Transetta (pET32a-HarmFoxAl)菌株用0.5 mmol/L IPTG在25℃条件下诱导5 h,约45 kD的融合蛋白His-HarmFoxAl能以可溶的形式存在于重组菌中,这与预测的分子量(42.8 kD)相一致。发育阶段特异性表达谱表明,HarmFoxAl在棉铃虫1-3龄幼虫期、6龄幼虫期和预蛹期均有表达,且预蛹期的表达量最高。组织表达谱结果表明,该基因在6龄幼虫的脂肪体、中肠和体壁中表达,且脂肪体内的表达量最高,而在头部中不表达。10 mg/g 2-十三烷酮处理棉铃虫6龄幼虫后中肠中HarmFoxAl的表达量显著降低,但随着时间延长其表达量逐渐升高,处理48 h后表达量显著高于对照。【结论】棉铃虫HarmFoxAl在预蛹期和幼虫脂肪体中表达量最高,2-十三烷酮处理幼虫后HarmFoxAl的表达量急速降低后逐渐升高,推测其在棉铃虫变态发育和解毒代谢过程中发挥重要作用。     

北京地区油菜软腐病病原菌的鉴定

摘要:【目的】确定引发北京地区油菜［Brassica campestris L．ssp．chinensis (L.) Makino var． communis Tsen et Lee］软腐病的病原。【方法】结合病原菌形态、BIOLOG 及生理生化、16S rＲNA 基因序列及亚种IGS区特征分析,对从北京大兴和通州区油菜软腐病样中病原菌进行生物学鉴定。【结果】分离的40 个菌株均能引发 油菜软腐病,但分别为胡萝卜果胶杆菌(Pectobacterium carotovorum) 的2个不同亚种,其中13株为P．carotovorum subsp．carotovorum(Pcc),另27株为P．arotovorum subsp． brasiliensis(Pcb)。接种白菜(Brassica campestris L．ssp．pekinensis)致病力测定分析表明,亚种内、来源相同与16S rRNA基因序列相同的菌株间均存在明显的致病力分化。【结论】Pectobacterium carotovorum subsp．carotovorum和Pectobacterium carotovorum subsp. brasiliensis是引发北京地区油菜软腐病的致病菌,后者为首次报道能引起白菜类蔬菜软腐病的常见致病菌。     

Identification of potentially critical genes in the development of heart failure after ST-segment elevation myocardial infarction (STEMI)

Heart failure (HF) remains a common complication after acute ST-segment elevation myocardial infarction (STEMI). Here, we aim to identify critical genes related to the developed HF in patients with STEMI using bioinformatics analysis. The microarray data of GSE59867, including peripheral blood samples from nine patients with post-infarct HF and eight patients without post-infarct HF, were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between HF and non-HF groups were screened by LIMMA package. Functional enrichment analyses of DEGs were conducted, followed by construction of a protein-protein interaction (PPI) network. The dynamic messenger RNA (mRNA) level of the hub genes during the follow-up was analyzed to further elucidate their role in HF development. A total of 58 upregulated and 75 downregulated DEGs were screen out. They were mainly enriched in biological processes about inflammatory response, extracellular matrix organization, response to cAMP, immune response, and positive regulation of cytosolic calcium ion concentration. Pathway analysis revealed that the DEGs were also involved in hematopoietic cell lineage, pathways in cancer, and extracellular matrix-receptor interaction. In the PPI network consisting of 58 nodes and 72 interactions, CXCL8 (degree = 15), THBS1 (degree = 8), FOS (degree = 7), and ITGA2B (degree = 6) were identified as the hub genes. In the comparison of patients with and without post-infarct HF, the mRNA level of these hub genes were all higher within 30 days but reached similar at 6 months after STEMI. In conclusion, CXCL8, THBS1, FOS, and ITGA2B may play important roles in the development of HF after acute STEMI.     

Fitness costs associated with acetyl‐coenzyme A carboxylase mutations endowing herbicide resistance in American sloughgrass (Beckmannia syzigachne Steud.)

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Apple Fruit Diameter and Length Estimation by Using the Thermal and Sunshine Hours Approach and Its Application to the Digital Orchard Management Information System

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QC-Chain: Fast and Holistic Quality Control Method for Next-Generation Sequencing Data

Next-generation sequencing (NGS) technologies have been widely used in life sciences. However, several kinds of sequencing artifacts, including low-quality reads and contaminating reads, were found to be quite common in raw sequencing data, which compromise downstream analysis. Therefore, quality control (QC) is essential for raw NGS data. However, although a few NGS data quality control tools are publicly available, there are two limitations: First, the processing speed could not cope with the rapid increase of large data volume. Second, with respect to removing the contaminating reads, none of them could identify contaminating sources de novo, and they rely heavily on prior information of the contaminating species, which is usually not available in advance. Here we report QC-Chain, a fast, accurate and holistic NGS data quality-control method. The tool synergeticly comprised of user-friendly tools for (1) quality assessment and trimming of raw reads using Parallel-QC, a fast read processing tool; (2) identification, quantification and filtration of unknown contamination to get high-quality clean reads. It was optimized based on parallel computation, so the processing speed is significantly higher than other QC methods. Experiments on simulated and real NGS data have shown that reads with low sequencing quality could be identified and filtered. Possible contaminating sources could be identified and quantified de novo, accurately and quickly. Comparison between raw reads and processed reads also showed that subsequent analyses (genome assembly, gene prediction, gene annotation, etc.) results based on processed reads improved significantly in completeness and accuracy. As regard to processing speed, QC-Chain achieves 7–8 time speed-up based on parallel computation as compared to traditional methods. Therefore, QC-Chain is a fast and useful quality control tool for read quality process and de novo contamination filtration of NGS reads, which could significantly facilitate downstream analysis. QC-Chain is publicly available at: http://www.computationalbioenergy.org/qc-chain.html.