相似性度量在基因表达聚类分析中的应用研究

聚类分析是基因表达数据分析研究的主要技术之一,其算法的基本出发点在于根据对象间相似度将对象划分为不同的类,选择适当的相似性度量准则是获得有效聚类结果的关键。采用预处理过的基因数据集在不同相似性度量准则下进行的不同聚类算法的聚类分析,并得到聚类结果评价。其中算法本身的缺陷及距离相似性度量的局限性都是影响结果评价的因素,为了获得更有效的聚类结果,改进相关聚类算法并提出了一种比例相似性度量准则。     

Well‐Defined Protein‐Based Supramolecular Nanoparticles with Excellent MRI Abilities for Multifunctional Delivery Systems

Protein‐based nanoparticles are widely used for effective biomedical applications. The objective of this work is to design series of magnetic resonance imaging (MRI)‐visible cationic supramolecular nanoparticles (PGEA@BSA‐Ad/Gd³⁺) based on bovine serum albumin (BSA) and β‐cyclodextrin‐cored star ethanolamine‐functionalized poly(glycidyl methacrylate) (CD‐PGEA) in the presence of Gd³⁺ ions for multifunctional delivery systems. CD‐PGEA is prepared via atom transfer radical polymerization and ring‐opening reaction. It is found that in the absence of Gd³⁺ ions, CD‐PGEA does not well interact with adamantine‐modified BSA (BSA‐Ad). The well‐defined PGEA@BSA‐Ad/Gd³⁺ supramolecular nanoparticles could be produced through the synergistic actions of the host–guest and electrostatic self‐assemblies by mixing aqueous solutions of CD‐PGEA, BSA‐Ad, and Gd³⁺. In comparison with CD‐PGEA assembly units, such kinds of uniform PGEA@BSA‐Ad/Gd³⁺ supramolecular nanoparticles exhibit better pDNA condensation ability, lower cytotoxicity, higher gene transfection, and easier cellular uptake. In addition, PGEA@BSA‐Ad/Gd³⁺ also produces outstanding MRI abilities, much better than Magnevist (Gd‐diethylenetriaminepentacetate acid). The present design of protein–polymer supramolecular nanoparticles with MRI contrast agents would provide a new way for multifunctional gene/drug delivery systems.     

Yeast on the milky way: genetics,physiology and biotechnology of Kluyveromyces lactis

The milk yeast Kluyveromyces lactis has a life cycle similar to that of Saccharomyces cerevisiae and can be employed as a model eukaryote using classical genetics, such as the combination of desired traits, by crossing and tetrad analysis. Likewise, a growing set of vectors, marker cassettes and tags for fluorescence microscopy are available for manipulation by genetic engineering and investigating its basic cell biology. We here summarize these applications, as well as the current knowledge regarding its central metabolism, glucose and extracellular stress signalling pathways. A short overview on the biotechnological potential of K. lactis concludes this review. Copyright © 2013 John Wiley & Sons, Ltd.     

采用基因库构建的季节性服装需求预测

季节性新款服装的历史销售数据少、生命周期短,利用数据序列的趋势信息进行预测方法不合适。鉴于服装企业的POS数据中包含许多特征信息,本文利用层次聚类与动态时间弯曲距离从POS数据中进行特征信息抽取,以构建服装销售基因库。并提出基于基因库进行季节性服装需求预测的方法,根据新款服装信息、销售信息计算其与基因库中基因的相似度,根据相似度查找相似基因,并利用相似基因的信息来预测新款服装的需求。该方法为服装企业如何利用POS数据进行定量预测以构建快速响应系统提供解决方案。最后,以某知名服装2011年POS数据抽取了基因库,并对2012年3月1日到2012年7月31日的新款SKU进行预测,结果表明该预测方法的有效性。     

应用DPO-PCR技术检测肠出血性大肠杆菌O157∶H7

利用双启动寡核苷酸引物（dual-priming oligonucleotide,DPO）聚合酶链式反应（polymerase chain reaction,PCR）技术检测肠出血性大肠杆菌O157∶H7。根据DPO引物设计原则,以肠出血性大肠杆菌O157∶H7 rfbE 基因为靶基因设计一对DPO引物,经过反应体系的优化,建立了肠出血性大肠杆菌O157∶H7 DPO-PCR检测方法, 其检测灵敏度约为94 CFU/mL。与常规PCR方法相比,所建立的DPO-PCR方法对退火温度不敏感,在引物设计和实 验过程中不需要对引物及其退火温度反复优化,同时基于DPO引物的特殊结构又增强了其特异性。DPO-PCR方法 设计简易、特异性强,为致病性微生物的快速准确检测提供了新途径。     

Newly identified mutations at the CSN1S1 gene in Ethiopian goats affect casein content and coagulation properties of their milk

Very high casein content and good coagulation properties previously observed in some Ethiopian goat breeds led to investigating the α_s1-casein (CSN1S1) gene in these breeds. Selected regions of the CSN1S1 gene were sequenced in 115 goats from 5 breeds (2 indigenous: Arsi-Bale and Somali, 1 exotic: Boer, and 2 crossbreeds: Boer × Arsi-Bale and Boer × Somali). The DNA analysis resulted in 35 new mutations: 3 in exons, 3 in the 5′ untranslated region (UTR), and 29 in the introns. The mutations in exons that resulted in an amino acid shift were then picked to evaluate their influence on individual casein content (α_s1-, α_s2-, β-, and κ-CN), micellar size, and coagulation properties in the milk from the 5 goat breeds. A mutation at nucleotide 10657 (exon 10) involved a transversion: CAG→CCG, resulting in an amino acid exchange Gln₇₇→Pro_77. This mutation was associated with the indigenous breeds only. Two new mutations, at nucleotide 6072 (exon 4) and 12165 (exon 12), revealed synonymous transitions: GTC→GTT in Val₁₅ and AGA→AGG in Arg₁₀₀ of the mature protein. Transitions G→A and C→T at nucleotides 1374 and 1866, respectively, occurred in the 5′ UTR, whereas the third mutation involved a transversion T→G at nucleotide location 1592. The goats were grouped into homozygote new (CC), homozygote reference (AA), and heterozygote (CA) based on the nucleotide that involved the transversion. The content of α_s1-CN (15.32 g/kg) in milk samples of goats homozygous (CC) for this newly identified mutation, Gln₇₇→Pro₇₇ was significantly higher than in milks of heterozygous (CA; 9.05 g/kg) and reference (AA; 7.61 g/kg) genotype animals. The α_s2-, β-, and κ-CN contents showed a similar pattern. Milk from goats with a homozygous new mutation had significantly lower micellar size. Milk from both homozygote and heterozygote new-mutation goats had significantly shorter coagulation rate and stronger gel than the reference genotype. Except the transversion, the sequence corresponded to allele A and presumably derived from it. Therefore, this allele is denoted by A₃. All goats from the reference genotype (AA) were homozygous for the allele at nucleotide position 1374 and 1866, whereas all mutations in the 5′ UTR existed in a heterozygous form in both heterozygous (CA) and the new mutation (CC) genotype. The newly identified mutation (CC) detected in some of the goat breeds is, therefore, important in selection for genetic improvement and high-quality milk for the emerging goat cheese-producing industries. The finding will also benefit farmers raising these goat breeds due to the increased selling price of goats. Further studies should investigate the effect of this amino acid exchange on the secondary and tertiary structure of the α_s1-CN molecule and on the susceptibility of peptide hydrolysis by digestive enzymes.     

Genomic analyses and biological validation of candidate genes for rectal temperature as an indicator of heat stress in Holstein cattle

Heat stress is a major cause of welfare issues and economic losses to the worldwide dairy cattle industry. Genetic selection for heat tolerance has a great potential to positively affect the dairy industry, as the gains are permanent and cumulative over generations. Rectal temperature (RT) is hypothesized to be a good indicator trait of heat tolerance. Therefore, this study investigated the genetic architecture of RT by estimating genetic parameters, performing genome-wide association studies, and biologically validating potential candidate genes identified to be related to RT in Holstein cattle. A total of 33,013 RT records from 7,598 cows were used in this study. In addition, 1,114 cows were genotyped using the Illumina 150K Bovine BeadChip (Illumina, San Diego, CA). Rectal temperature measurements taken in the morning (AMRT) and in the afternoon (PMRT) are moderately heritable traits, with estimates of 0.09 ± 0.02 and 0.04 ± 0.01, respectively. These 2 traits are also highly genetically correlated (r = 0.90 ± 0.08). A total of 10 SNPs (located on BTA3, BTA4, BTA8, BTA13, BTA14, and BTA29) were found to be significantly associated with AMRT and PMRT. Subsequently, gene expression analyses were performed to validate the key functional genes identified (SPAG17, FAM107B, TSNARE1, RALYL, and PHRF1). This was done through in vitro exposure of peripheral blood mononuclear cells (PBMC) to different temperatures (37°C, 39°C, and 42°C). The relative mRNA expression of 2 genes, FAM107B and PHRF1, significantly changed between the control and heat stressed PBMC. In summary, RT is heritable, and enough genetic variability exists to enable genetic improvement of heat tolerance in Holstein cattle. Important genomic regions were identified and biologically validated; FAM107B and PHRF1 are the main candidate genes identified to influence heat stress response in dairy cattle.     

Assessment of rumen bacteria in dairy cows with varied milk protein yield

The present study was conducted to assess rumen bacteria in lactating cows with different milk protein yield, aiming to understand the role of rumen bacteria in this trait. Cows with high milk protein yield (high milk yield and high milk protein content, HH; n = 20) and low milk protein yield (low milk yield and low milk protein content, LL; n = 20) were selected from 374 mid-lactation Holstein dairy cows fed a high-grain diet. Measurement of the rumen fermentation products showed that the concentrations of ruminal total volatile fatty acids, propionate, butyrate, and valerate and the proportion of isobutyrate were higher in the HH cows than in the LL cows. Amplicon sequencing analysis of the rumen bacterial community revealed that the richness (Chao 1 index) of rumen microbiota was higher in the LL cows than in the HH cows. Among the 10 predominant bacterial phyla (relative abundance being >0.10%, present in >60% of animals within each group), the relative abundance of Proteobacteria was 1.36-fold higher in the HH cows than in the LL cows. At the genus level, the relative abundance of Succinivibrio was significantly higher and that of Clostridium tended to be higher in the LL cows than in the HH cows. Sharpea was 2.28-fold enriched in the HH cows compared with the LL cows. Different relationships between the relative abundances of rumen microbial taxa and volatile fatty acid concentrations were observed in the HH and the LL animals, respectively. Succinivibrio and Prevotella were positively correlated with acetate, propionate, and valerate in the LL cows, whereas Sharpea was positively correlated with propionate and valerate concentrations in the HH cows. Collectively, our results revealed that rumen bacterial richness and the relative abundances of several bacterial taxa significantly differed between dairy cows with high and low milk protein yields, suggesting the potential roles of rumen microbiota contributing to milk protein yield in dairy cows.