苯硼酸修饰的聚乙烯亚胺系统在 T 细胞小 RNA 转染的应用

小 RNA 是 T 细胞发育、分化和功能的重要调控分子，但目前仍缺乏安全有效的 T 细胞 RNA 转 染系统。基于 T 细胞表面唾液酸化的特性，该文利用苯硼酸和唾液酸的相互作用，带动苯硼酸修饰的聚乙烯亚胺系统(Polyethylenimine-Phenylboronic Acid，PEI-PBA)介导小 RNA 的 T 细胞转染。通过细胞计数试剂盒(CCK-8)和羟基荧光素二醋酸盐琥珀酰亚胺脂(CFSE)实验显示，PEI-PBA 转染系统对 T 细胞无显著细胞毒性和异常增殖。同时，流式检测人源抗 CD3/28 磁珠激活下的分化抗原 3(CD3)阳性 T 细胞在 PEI-PBA 转染系统下，小 RNA 平均摄取率增加到18.43%，而在鼠源 T 细胞中 PEI-PBA 介导的小 RNA 转染并无明显效果。结果显示，PEI-PBA 纳米转染系统介导小 RNA 的递送具有无毒高效的特点。     

Neuroproteomics and its applications in research on nicotine and other drugs of abuse

The rapidly growing field of neuroproteomics is able to track changes in protein expression and protein modifications underlying various physiological conditions, including the neural diseases related to drug addiction. Thus, it presents great promise in characterizing protein function, biochemical pathways, and networks to understand the mechanisms underlying drug dependence. In this article, we first provide an overview of proteomics technologies and bioinformatics tools available to analyze proteomics data. Then we summarize the recent applications of proteomics to profile the protein expression pattern in animal or human brain tissues after the administration of nicotine, alcohol, amphetamine, butorphanol, cocaine, and morphine. By comparing the protein expression profiles in response to chronic nicotine exposure with those appearing in response to treatment with other drugs of abuse, we identified three biological processes that appears to be regulated by multiple drugs of abuse: energy metabolism, oxidative stress response, and protein degradation and modification. Such similarity indicates that despite the obvious differences among their chemical properties and the receptors with which they interact, different substances of abuse may cause some similar changes in cellular activities and biological processes in neurons.     

A proteomic approach to immune-mediated epithelial-mesenchymal transition

Increasing evidence suggests epithelial-mesenchymal transition (EMT) plays an important role in renal fibrosis. Initial renal injury enables the infiltration of mononuclear cells into the interstitium, and the resulting generation of inflammatory mediators that favour EMT may have a direct role in the development of renal fibrosis. The aim of this study was to investigate the proteome of renal tubular epithelial cells undergoing EMT in vitro. The human proximal tubular cell line (HK-2), exposed to conditioned medium from activated peripheral blood mononuclear cells (PBMC-CM), undergo phenotypic change, from an epithelial towards a fibroblastic phenotype, as evidenced by decreased E-cadherin and increased fibronectin protein expression. Further proteomic analysis, using 2-DE and Progenesis software, revealed the down-regulation of 4 proteins and up-regulation of 23 proteins. MS analysis allowed the positive identification of 15 differentially expressed proteins, including annexin A2, adipocyte plasma membrane-associated protein, T-complex protein 1, reticulocalbin-1 precursor and moesin among others. Western blotting and quantitative real-time PCR confirmed the increase in annexin A2 at the protein and gene level, respectively. Since annexin A2 and S100A6 exist as complexes in B-1 cells, we investigated the S100A6 gene expression further and show an increased expression in HK-2 cells following exposure to activated PBMC-CM. Therefore, we have identified several potential proteins that could play key roles in immune-mediated EMT.     

CoBi: Pattern Based Co-Regulated Biclustering of Gene Expression Data

Co-regulation is a common phenomenon in gene expression. Finding positively and negatively co-regulated gene clusters from gene expression data is a real need. Existing techniques based on global similarity are unable to detect true up- and down-regulated gene clusters. This paper presents an expression pattern based biclustering technique, CoBi, for grouping both positively and negatively regulated genes from microarray expression data. Regulation pattern and similarity in degree of fluctuation are accounted for while computing similarity between two genes. Unlike traditional biclustering techniques, which use greedy iterative approaches, it uses a BiClust tree that needs single pass over the entire dataset to find a set of biologically relevant biclusters. Biclusters determined from different gene expression datasets by the technique show highly enriched functional categories.     

Searching for potential biomarkers of cisplatin resistance in human ovarian cancer using a label-free LC/MS-based protein quantification method

Platinum-based chemotherapy, such as cisplatin, is the primary treatment for ovarian cancer. However, drug resistance has become a major impediment to the successful treatment of ovarian cancer. To date, the molecular mechanisms of resistance to platinum-based chemotherapy remain unclear. In this study, we applied an LC/MS-based protein quantification method to examine the global protein expression of two pairs of ovarian cancer cell lines, A2780/A2780-CP (cisplatin-sensitive/cisplatin-resistant) and 2008/2008-C13*5.25 (cisplatin-sensitive/cisplatin-resistant). We identified and quantified over 2000 proteins from these cell lines and 760 proteins showed significant expression changes with a false discovery rate of less than 5% between paired groups. Based on the results we obtained, we suggest several potential pathways that may be involved in cisplatin resistance in human ovarian cancer. This study provides not only a new proteomic platform for large-scale quantitative protein analysis, but also important information for discovery of potential biomarkers of cisplatin resistance in ovarian cancer. Furthermore, these results may be clinically relevant for diagnostics, prognostics, and therapeutic improvement for ovarian cancer treatment.     

Gene expression and protein–protein interaction data for identification of colon cancer related genes using <Emphasis Type="Italic">f</Emphasis>-information measures

One of the most important and challenging problems in functional genomics is how to select the disease genes. In this regard, the paper presents a new computational method to identify disease genes. It judiciously integrates the information of gene expression profiles and shortest path analysis of protein–protein interaction networks. While the \(f\)-information based maximum relevance-maximum significance framework is used to select differentially expressed genes as disease genes using gene expression profiles, the functional protein association network is used to study the mechanism of diseases. An important finding is that some \(f\)-information measures are shown to be effective for selecting relevant and significant genes from microarray data. Extensive experimental study on colorectal cancer establishes the fact that the genes identified by the integrated method have more colorectal cancer genes than the genes identified from the gene expression profiles alone, irrespective of any gene selection algorithm. Also, these genes have greater functional similarity with the reported colorectal cancer genes than the genes identified from the gene expression profiles alone. The enrichment analysis of the obtained genes reveals to be associated with some of the important KEGG pathways. All these results indicate that the integrated method is quite promising and may become a useful tool for identifying disease genes.     

基因表达谱数据分析技术

人类基因组计划的研究已进入后基因组时代,后基因组时代研究的焦点已经从测序转向功能研究,主要采用无监督和有监督技术来分析基因表达谱和识别基因功能,通过基因转录调控网络分析细胞内基因之间的相互作用关系的整体表示,说明生命功能在基因表达层面的展现,对目前基因表达谱数据分析技术及它们的发展,进行了综述性的研究,分析了它们的优缺点,提出了解决问题的思路和方法,为基因表达谱的进一步研究提供了新的途径。     

Understanding dendritic cell biology and its role in immunological disorders through proteomic profiling

Dendritic cells (DC) have always been present on the bright spot of immune research. They have been extensively studied for the last 35 years, and much is known about their different phenotypes, stimulatory capacity, and role in the immune system. During the last 15 years, great attention has been given to studies on global gene and protein expression profiles during the differentiation and maturation processes of these cells. It is well understood that studying the proteome, together with information on the role of protein post-translational modifications (PTM), will reveal the real dynamics of a living cell. The rapid increase of proteomic studies during the last decade describing the differentiation and maturation process in DCs, as well as modifications brought by the use of different compounds that either increase or decrease their immunogenicity, reflects the importance of understanding the molecular processes behind the functional properties of these cells. In the present review, we will give an overview of proteomic studies focusing on DCs. Thereby we will concentrate on the importance of these studies in understanding DC behavior from a molecular point of view and how these findings have aided in understanding the differences in functional properties of these cells.     

Reconstruction of the Dynamics of Drosophila Genes Expression from Sets of Images Sharing a Common Pattern

Like all other insects, the body of the fruit fly Drosophila melanogaster is made up of repeated units called segments. At the early developmental stages prior to morphological differentiation, the segments are marked out by a chemical blueprint at cellular resolution. This blueprint is formed by the early patterns of segmentation gene expression, which become more spatially resolved over time. The precise characterization of this pattern and its temporal changes is of considerable biological significance. Such characterization faces a twofold technical barrier. First, although we are interested in the time course of expression, segmentation gene expression can only be visualized in fixed tissue and so the time course must be reconstructed from many embryos, each at a slightly different point in development. Second, available confocal microscopes can image only three gene products at once. We overcome this barrier by using data retrieved from a large number of scanned embryos which have been placed in temporal equivalence classes. Each embryo was scanned for the expression patterns of three genes. These three patterns vary from embryo to embryo because of individual differences and cannot be directly superimposed. However, if each embryo is stained for one common gene product and for two others, which vary among the dataset, it is possible to make some coordinate transformations of every embryo image so that the expression domains of the common gene will maximally coincide. To find these coordinate transformations is to solve the registration problem.We present a set of methods to reconstruct the dynamics of gene expression patterns from sets of images sharing a common pattern. For this purpose, we applied modern heuristic methods of optimization to find the elastic deformation necessary for image registration. We used the standard Genetic Algorithms technique by itself and in combination with the simplex method. By this approach, it is possible to retrace the detailed dynamics of developmental gene activity at the resolution on the level of individual nuclei for each of 4–6 thousand cells composing early fly embryo. The final result of this analysis will be the quantitative atlas of Drosophila genes expression (http://www.iephb.nw.ru/~spirov/atlas).     

An integrated microfluidic system for counting of CD4+/CD8+ T lymphocytes

This study reports on an integrated microfluidic system capable of counting CD4⁺/CD8⁺ T lymphocytes from a whole blood sample, which may be further applied for the rapid screening of the human immunodeficiency virus (HIV) infection. This system is composed of a sample incubation module for fluorescence-labeling of the target cells and a micro-fabricated flow cytometry module for cell counting. First, a pneumatically driven, vortex-type micro-mixer has been adopted for the fluorescence-labeling of CD4⁺/CD8⁺ T lymphocytes from whole blood. After the labeling process, different laser-excited fluorescent signals are detected and are used for counting of CD4⁺/CD8⁺ T lymphocytes as they pass through the detection region of the microflow cytometer. A concentration of 963 cells/μl is counted for cultured CD4⁺ T lymphocytes with a reference concentration of 1000 cells/μl. The ratio of CD4⁺/CD8⁺ T lymphocytes is then calculated. Experimental results show that the results from the microsystem are in agreement with the ones from large-scale flow cytometers. In addition, the entire diagnostic procedure, including the sample incubation and the cell counting, can be automatically performed within 35 min. Therefore, this may become a powerful tool for further biomedical applications, especially for fast screening of HIV infection.     

H9N2亚型禽流感病毒HA基因氨基酸位点分析

通过系统进化分析,分离自我国的56条全长序列H9N2亚型禽流感病毒株HA基因的氨基酸残基序列,结果表明我国病毒株在早期具有散在发生性,同属于欧亚种系,进化方向与香港人源病毒株明显不同。采用codeml程序检测到HA基因,共有5个阳性选择位点(2T,3T,197T,233Q,380R),除380R外,均位于HA1肽链上,阳性选择位点位于主要的受体结合位点附近,对此需密切监测H9N2亚型禽流感,以防其关键位点的突变。分析蛋白质基序,也发现H9N2禽流感突破种间屏障感染人,可能与病毒株糖基化位点的突变有关。     

Gene expression changes in a transgenic mouse model overexpressing human wildtype and mutant torsinA

Primary torsion dystonia is an autosomal-dominantly inherited, neurodevelopmental movement disorder caused by a GAG deletion (ΔGAG) in the DYT1 gene, encoding torsinA. This mutation is responsible for approximately 70% of cases of early-onset primary torsion dystonia. The function of wildtype torsinA is still unknown, and it is unsolved how the deletion in the DYT1 gene contributes to the development of the disease. To better understand the molecular processes involved in torsinA pathology, we used genome-wide oligonucleotide microarrays to characterize gene expression patterns in the striatum of mouse models overexpressing the human wildtype and mutant torsinA. By this approach we were able to detect gene expression changes that seem to be specific for torsinA pathology. We found an impact of torsinA, independent from genotype, on vesicle trafficking, exocytosis, and neurotransmitter release in our mouse model. In addition, we were able to identify several new pathways and processes involved in the development of the nervous system that are affected by wildtype and mutant torsinA. Furthermore, we have striking evidence from our gene expression data that glutamate receptor mediated synaptic plasticity in the striatum is the affected underlying cellular process for impaired motor learning in human ΔGAG torsinA transgenic mice.     

Promoter analysis of co-regulated genes in the yeast genome.

The use of high density DNA arrays to monitor gene expression at a genome-wide scale constitutes a fundamental advance in biology. In particular, the expression pattern of all genes in Saccharomyces cerevisiae can be interrogated using microarray analysis where cDNAs are hybridized to an array of more than 6000 genes in the yeast genome. In an effort to build a comprehensive Yeast Promoter Database and to develop new computational methods for mapping upstream regulatory elements, we started recently in an on going collaboration with experimental biologists on analysis of large-scale expression data. It is well known that complex gene expression patterns result from dynamic interacting networks of genes in the genetic regulatory circuitry. Hierarchical and modular organization of regulatory DNA sequence elements are important information for our understanding of combinatorial control of gene expression. As a bioinformatics attempt in this new direction, we have done some computational exploration of various initial experimental data. We will use cell-cycle regulated gene expression as a specific example to demonstrate how one may extract promoter information computationally from such genome-wide screening. Full report of the experiments and of the complete analysis will be published elsewhere when all the experiments are to be finished later in this year (Spellman, P.T., et al. 1998. Mol. Biol. Cell 9, 3273-3297).     

Systematic investigation of lycopene effects in LNCaP cells by use of novel large‐scale proteomic analysis software

Lycopene, the red pigment of tomatoes, is a carotenoid with potent antioxidant properties. Although lycopene may function as a prostate cancer chemoprevention agent, little is known about its effects at the cellular level. To define general changes induced by treatment of cells with lycopene, and to gain insights into the possible chemoprevention properties of lycopene, we investigated changes in protein expression after lycopene treatment in human LNCaP cells. The high throughput proteomics data were then visualized and analyzed by novel biological protein pathway modeling software. Differentially expressed proteins were identified, and the data were analyzed by protein pathway simulation software, without the need for specialized programming, by importing pathway models from a number of sources or by creating our own. One notable outcome was the identification of a group of upregulated proteins involved in detoxification of reactive oxygen species. This finding suggests that a possible mechanism of lycopene chemoprevention is the stimulation of detoxification enzymes associated with the antioxidant response element. Novel biological pathway modeling software enhances analysis of large proteomics data. When applied to the analysis of proteins differentially expressed in prostate cancer cells upon treatment with lycopene, the up‐regulation of detoxification enzymes was identified.     

SILAC for biomarker discovery

SILAC has been employed in MS-based proteomics for nearly a decade. This method is based on cells in culture metabolically incorporating isotope-coded essential amino acids and allows the quantification of global protein populations to identify characteristic changes. Variations of this technique developed over the years allow the application of SILAC not only to cell culture derived samples but also to tissues and human specimens, making this powerful technique amenable to clinically relevant samples. In this review, we provide an overview of different SILAC-derived methods and their use in the identification and development of biomarkers.     

Clustering of protein expression data: a benchmark of statistical and neural approaches

Clustering issues are fundamental to exploratory analysis of bioinformatics data. This process may follow algorithms that are reproducible but make assumptions about, for instance, the ability to estimate the global structure by successful local agglomeration or alternatively, they use pattern recognition methods that are sensitive to the initial conditions. This paper reviews two clustering methodologies and highlights the differences that result from the changes in data representation, applied to a protein expression data set for breast cancer (n = 1,076). The two clustering methodologies are a reproducible approach to model-free clustering and a probabilistic competitive neural network. The results from the two methods are compared with existing studies of the same data set, and the preferred clustering solutions are profiled for clinical interpretation.     

Proteomics analysis of liver pathological calcification suggests a role for the IQ motif containing GTPase activating protein 1 in myofibroblast function

To date the cellular and molecular mechanisms by which liver pathological calcifications occur and are regulated are poorly investigated. To study the mechanisms linked to their appearance, we performed a proteomics analysis of calcified liver samples. To this end, human liver biopsies collected in noncalcified (N), precalcified (P), and calcified (C) areas of the liver were subjected to weak ion exchange chromatography, SDS‐PAGE, and LC‐ESI MS/MS analyses. As we previously demonstrated that alpha‐smooth muscle actin (α‐SMA) expressing myofibroblasts were involved in liver pathological calcification, we performed a targeted analysis of actin cytoskeleton remodeling‐related proteins. This revealed dramatic changes in protein expression patterns in the periphery of the calcified areas. More particularly, we found that IQGAP1 and IQGAP2 proteins were subjected to major expression changes. We show that IQGAP1 expression within P and C areas of the liver correlates with the high abundance of myofibroblasts and that IQGAP1 is specifically expressed in these cells. In addition, we find that IQGAP1 is part of a protein complex including β‐catenin and Rac1 mainly in P and C regions of the liver. These results suggest that IQGAP1 may play a critical role in the regulation of cytoskeleton remodeling in liver myofibroblasts in response to liver injury and consequently impact on their function.