Peptide-Based Technologies to Alter Adenoviral Vector Tropism: Ways and Means for Systemic Treatment of Cancer

Due to the fundamental progress in elucidating the molecular mechanisms of human diseases and the arrival of the post-genomic era, increasing numbers of therapeutic genes and cellular targets are available for gene therapy. Meanwhile, the most important challenge is to develop gene delivery vectors with high efficiency through target cell selectivity, in particular under in situ conditions. The most widely used vector system to transduce cells is based on adenovirus (Ad). Recent endeavors in the development of selective Ad vectors that target cells or tissues of interest and spare the alteration of all others have focused on the modification of the virus broad natural tropism. A popular way of Ad targeting is achieved by directing the vector towards distinct cellular receptors. Redirecting can be accomplished by linking custom-made peptides with specific affinity to cellular surface proteins via genetic integration, chemical coupling or bridging with dual-specific adapter molecules. Ideally, targeted vectors are incapable of entering cells via their native receptors. Such altered vectors offer new opportunities to delineate functional genomics in a natural environment and may enable efficient systemic therapeutic approaches. This review provides a summary of current state-of-the-art techniques to specifically target adenovirus-based gene delivery vectors.     

快速流程模式下肝切除患者围手术期不同营养支持的比较

目的：比较快速流程（FT）模式下肝切除患者围手术期不同营养支持方法的疗效。
　　方法：将104例拟行肝切除患者随机均分为2组,分别接受肠内营养（EN）与肠外营养（PN组）,所有患者均采用FT围手术期处理。比较两组患者手术前后体质量（WT）、血红蛋白（HB）、总蛋白（TP）、白蛋白（ALB）及总淋巴细胞计数（TLC）变化,以及术后肛门排气排便时间、住院天数、消化道不良反应和并发症发生率。
　　结果：PN组术后7d各项营养指标较术前3d均明显降低,而EN组内只有TP及ALB较术前降低,但降幅均明显小于PN组（均P<0.05）;EN组术后肛门排气排便时间较PN组明显提前,术后不良反应发生率低于PN组（均P<0.05）;两组术后住院时间及并发症发生率差异无统计学意义（均P>0.05）。
　　结论：FT模式下肝切除患者围手术期应用EN支持有利于改善营养状况及免疫功能,促进术后康复。     

From the Cover: Searching molecular structure databases with tandem mass spectra using CSI:FingerID

Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually rely on tandem MS to identify the thousands of compounds in a biological sample. Today, the vast majority of metabolites remain unknown. We present a method for searching molecular structure databases using tandem MS data of small molecules. Our method computes a fragmentation tree that best explains the fragmentation spectrum of an unknown molecule. We use the fragmentation tree to predict the molecular structure fingerprint of the unknown compound using machine learning. This fingerprint is then used to search a molecular structure database such as PubChem. Our method is shown to improve on the competing methods for computational metabolite identification by a considerable margin.Metabolites, small molecules that are involved in cellular reactions, can provide detailed information about cellular state. Untargeted metabolomic studies may use NMR or MS technologies, but liquid chromatography followed by MS (LC/MS) can detect the highest number of metabolites from minimal amounts of sample (1, 2). Untargeted metabolomics comprehensively compares the mass spectral intensities of metabolite signals (peaks) between two or more samples (3, 4). Advances in MS instrumentation allow us to simultaneously detect thousands of metabolites in a biological sample. Identification of these compounds relies on tandem MS (MS/MS) data, produced by fragmenting the compound and recording the masses of the fragments. Structural elucidation remains a challenging problem, in particular for compounds that cannot be found in any spectral library (1): In total, all available spectral MS/MS libraries of pure chemical standards cover fewer than 20,000 compounds (5). Growth of spectral libraries is limited by the unavailability of pure reference standards for many compounds.In contrast, molecular structure databases such as PubChem (6) and ChemSpider (7) contain millions of compounds, with PubChem alone having surpassed 50 million entries. Searching in molecular structure databases using MS/MS data is therefore considered a powerful tool for assisting an expert in the elucidation of a compound. This problem is considerably harder than the fundamental analysis step in the shotgun proteomics workflow, namely, searching peptide MS/MS data in a peptide sequence database (8): Unlike proteins and peptides, metabolites show a large structural variability and, consequently, also large variations in MS/MS fragmentation. Computational approaches for interpreting and predicting MS/MS data of small molecules date back to the 1960s (9): Due to the unavailability of molecular structure databases at that time, structure libraries were combinatorially generated and then “searched” using the experimental MS/MS data. “Modern” methods for this question have been developed since mid-2000. Particular progress has been made for restricted metabolite classes such as lipids (5), but as with peptides, results cannot be generalized to other metabolite classes. For the general case, several strategies have been proposed during recent years, including simulation of mass spectra from molecular structure (10, 11), combinatorial fragmentation (12–17), and prediction of molecular fingerprints (18, 19).Searching in a molecular structure database is clearly limited to those compounds present in the database. Fragmentation trees have been introduced as a means of analyzing MS/MS data without the need of any (structural or spectral) database (20–22). In this paper, the term “fragmentation tree” is exclusively used to refer to the graph-theoretical concept introduced in ref. 20, not “spectral trees” that describe the dependencies of multiple MS measurements; see Vaniya and Fiehn (23) for a review. In more detail, our fragmentation trees are predicted from MS/MS data by an automated computational method such that peaks in the MS/MS spectrum are annotated with molecular formulas of the corresponding fragments, and fragments are connected via assumed losses. Clearly, there exist other approaches with the broad aim of identifying metabolites, such as network-based methods (24–26) and combined approaches (27); see Hufsky et al. (28) for a review of computational methods in MS-based metabolite identification.It is undisputed that MS/MS data alone are insufficient for full structural elucidation of metabolites. We argue that elucidation of stereochemistry is currently beyond the power of automated search engines, so we try to recover the correct constitution (bond structure) of the query molecule, that is, the identity and connectivity (with bond multiplicities) of the atoms, but no stereochemistry information. Throughout this paper, we refer to the constitution of the molecule as its structure. In practice, orthogonal information is usually available, both analytical (retention time, ion mobility drift time, infrared and UV spectroscopy, and NMR data) and on the experimental setup (extraction procedure and organism) (29, 30). We assume that this information is not presented to the search engines but rather used in a postprocessing step to manually select the best solution from the output list of the engine. This is comparable to the everyday use of search engines for the internet.Here, we present CSI (Compound Structure Identification):FingerID for searching a molecular structure database using MS/MS data. Our method combines computation and comparison of fragmentation trees with machine learning techniques for the prediction of molecular properties of the unknown compound (19). Our method shows significantly increased identification rates compared with all existing state-of-the-art methods for the problem. CSI:FingerID is available at www.csi-fingerid.org/. Our method can expedite the identification of metabolites in an untargeted workflow for the numerous cases where no reference measurements are available in spectral libraries.     

六个转录因子将人脐带间充质干细胞高效重编程为诱导性多能干细胞的实验研究

目的 建立人脐带间充质干细胞(HuMSCs)来源的诱导性多能干细胞(iPSC)系.方法 以慢病毒载体将OCT4、SOX2、KLF4、c-Myc、NANOG、LIN-28 6个转录因子转入HuMSCs使其重编程为iPSC.通过形态学观察、多能细胞特异性标志检测、碱性磷酸酶(AKP)染色、核型分析、拟胚体形成、体内成瘤实验鉴定获得的iPSC.结果 慢病毒感染后第4天,HuMSCs逐渐变成类圆形,第10天开始出现不规则细胞团,第14天出现较大的细胞团,约1.25％的细胞重编程为iPSC.建系后iPSC呈典型的克隆状生长,边缘清晰,内部细胞细小,排列紧密;AKP染色阳性;表达多能性相关基因及胚胎干细胞特异性蛋白;细胞核型正常(46,XY);体内外均能向3个胚层方向分化.结论 慢病毒携带OCT4、SOX2、KLF4、c-Myc、NANOG、LIN-28 6个因子能将HuMSCs高效诱导为完全重编程的iPSC.     

基于中医传承辅助平台挖掘姜良铎教授治疗慢性乙型肝炎方药规律

目的:探讨姜良铎教授治疗慢性乙型肝炎方剂用药规律,挖掘治疗慢性乙型肝炎的新组方,为慢性乙型肝炎的防治提供新的思路。方法:通过回顾性研究方法,收集姜良铎教授治疗慢性乙型肝炎门诊处方并建立相关数据库,通过中医传承辅助系统挖掘姜良铎教授治疗慢性乙型肝炎用药规律。结果:筛选治疗慢性乙型肝炎的方剂160首,分析得出姜良铎教授治疗慢性乙型肝炎的常用药物有赤芍、白芍、枳壳、麦芽等61种,核心组合模式116组,其中对药43组,角药46组,4味及以上药物组合27组,并演化得到12首治疗慢性乙型肝炎的新处方。结论:以中医传承辅助系统为平台,利用文本挖掘、关联规则等数据挖掘方法较好的体现了姜良铎教授治疗慢性乙型肝炎的用药规律,即擅用角药,多以柔肝疏肝立法,通补结合,用药轻灵,主张从状态辨治,注重扶正驱邪。     

基于NIRS技术和PCA-SVM算法6种树脂及其他类中药的快速鉴别

目的:利用近红外漫反射光谱(NIRS)法,结合主成分分析(PCA)和支持向量机(SVM)联用算法,建立6种树脂及其他类中药安息香(Benzoinum),琥珀(Succinum),没药(Myrrha),乳香(Olibanum),松香(Colophonium),天竺黄(Bambusaen Concretio Silicea)的NIR模式识别模型,用于该6味中药的快速鉴别。方法:收集上述6种中药样品,经性状鉴别和理化分析确定正品药材55批,粉碎成均匀粉末,在4 000~12 000 cm~(-1)光谱区,采集各样品粉末的NIR光谱,选取特征谱段9 000~5 400,5 000~4 000 cm~(-1)为建模谱段,分别采用矢量归一化法(vector normalization,VN),一阶导数法(first derivative,FD),二阶导数法(second derivative,SD)3种不同光谱预处理方法进行预处理并分别进行PCA降维。根据主成分空间散点图,优选最佳预处理方法。利用最佳预处理方法处理后光谱的PCA降维数据,建立SVM模式识别模型,SVM模型参数c和g采用网格搜索法结合五折交叉验证进行寻优。对比不同主成分数所建PCA-SVM模型的预测准确率,确定最佳的主成分数,最终建立6种中药NIR快速鉴别模型。结果:在9 000~5 400,5 000~4 000 cm~(-1)建模谱段,确定最佳光谱预处理方法为SD,SD预处理光谱PCA降维后,确定最佳主成分数为3个,累计贡献率达93.57%。经网格搜索法确定最佳SVM建模参数组为c=65 536,g=512。所建PCA-SVM模型对训练集和验证集样品预测正确率均达100%,模型五折交叉验证准确率亦达100%。结论:所建的6种中药NIR光谱PCA-SVM鉴别模型,预测准确率高,模型预测能力强,结合NIRS技术无损、快速的优点,该模型可用于上述6种中药的无损、快速鉴别。     

ADAM28基因原核表达载体的构建和在大肠杆菌中的融合表达

目的:利用消减杂交技术克隆出牙齿发育相关基因ADAM28。方法:本研究为构建ADAM28基因的原核表达载体,在大肠杆菌中诱导其表达并纯化出ADAM28蛋白;采用反转录-多聚酶链式反应(RT-PCR)扩增出ADAM28基因的蛋白编码序列,克隆到中间载体pMD18-TVector中产生pMD18-T-ADAM28,再经双酶切得到ADAM28片段定向克隆到pGEX-4T-1载体中,构建融合表达载体pGEX-4T-ADAM28;在大肠杆菌中利用IPTG进行诱导表达,得到GST-ADAM28融合蛋白,并经SDS-PAGE电泳初步纯化。结果:①成功构建融合表达载体pGEX-4T-ADAM28,并经酶切鉴定、PCR鉴定和测序验证显示插入的ADAM28序列正确,阅读框架完整,未发现突变。②得到初步纯化的GST-ADAM28融合蛋白。经IPTG诱导后出现一条约35.3×103的新蛋白带。结论:ADAM28基因编码蛋白质能够在原核细胞中表达并纯化,为进一步研究该蛋白质的功能打下基础。     

mcpr1基因的真核表达载体的构建与鉴定

目的 :构建腭裂相关基因mcpr1与pcDNA3 .1/V5 HisB融合的高效真核表达载体 ,为研究mcpr1基因的功能奠定基础。方法 :根据mcpr1基因的核苷酸序列 ,设计并合成引物 ,通过PCR方法 ,从包含有mcpr1基因全长的克隆载体T easy/mcpr1中 ,扩增出该基因外显子片段 ,将扩增产物连接到真核表达载体pcDNA3 .1/V5 HisB中。对该重组体进行PCR和酶切鉴定 ,以及测序验证。结果 :以重组体为模板扩增出40 0bp左右的特异性基因片段 ,与mcpr1基因片段大小一致 ,酶切鉴定也显示有 40 0bp左右的基因片断。测序结果显示与已知基因序列一致。结论 :成功构建mcpr1基因的真核表达载体 ,为下一步研究mcpr1基因的功能奠定了基础。     

骨保护素和碱性成纤维生长因子pIRES载体的构建及初步鉴定

目的构建pIRES-opg-fgf2真核分泌表达穿梭载体,为进一步构建双表达重组腺病毒载体,并应用基因工程技术联合组织工程技术治疗牙周病的研究打下基础。方法将具有抑制破骨细胞功能的opg基因和促进细胞增殖分化和趋化粘附的作用的fgf2基因重组到pIRES载体中。结果编码opg基因片段和fgf2基因片段重组到pIRES载体,重组质粒经酶切、PCR鉴定,电泳显示均能得到与预期大小相符的片段。结论本研究成功构建了pIRES-opg-fgf2表达穿梭载体。