Normalization and quantification of differential expression in gene expression microarrays

Array-based gene expression studies frequently serve to identify genes that are expressed differently under two or more conditions. The actual analysis of the data, however, may be hampered by a number of technical and statistical problems. Possible remedies on the level of computational analysis lie in appropriate preprocessing steps, proper normalization of the data and application of statistical testing procedures in the derivation of differentially expressed genes. This review summarizes methods that are available for these purposes and provides a brief overview of the available software tools.     

Fast quantification of immunohistochemistry tissue microarrays in lung carcinoma

Tissue microarrays (TMAs) are an effective tool for high-throughput molecular analysis of tissues to help identify new diagnostic and prognostic markers and targets in human cancers. We have developed a fully automated method for rapid, continuous and quantitative analysis of TMAs based on immunohistochemistry. The method deals with complex and varying tissue architectures, segments tumour cells from normal cells, conducts cell compartmentalisation, identifies nuclei and cytoplasm and produces three different continuous measurements of marker expression levels within tumour cell nuclei, tumour cell cytoplasm and total tumour cell protein expression. We have demonstrated this method using three independent protein markers (BAK, BAX and a novel biomarker, named KS) over 7 TMAs, involving 2 BAK stained TMAs with 229 tumour tissue cores, 2 BAX stained TMAs with 229 tumour tissue cores and 3 KS stained TMAs with 373 tumour cores of lung carcinomas. We validated the automated method, showing that the automated scoring is significantly correlated with the pathologist-based scoring.     

Refining protein subcellular localization

The study of protein subcellular localization is important to elucidate protein function. Even in well-studied organisms such as yeast, experimental methods have not been able to provide a full coverage of localization. The development of bioinformatic predictors of localization can bridge this gap. We have created a Bayesian network predictor called PSLT2 that considers diverse protein characteristics, including the combinatorial presence of InterPro motifs and protein interaction data. We compared the localization predictions of PSLT2 to high-throughput experimental localization datasets. Disagreements between these methods generally involve proteins that transit through or reside in the secretory pathway. We used our multi-compartmental predictions to refine the localization annotations of yeast proteins primarily by distinguishing between soluble lumenal proteins and soluble proteins peripherally associated with organelles. To our knowledge, this is the first tool to provide this functionality. We used these sub-compartmental predictions to characterize cellular processes on an organellar scale. The integration of diverse protein characteristics and protein interaction data in an appropriate setting can lead to high-quality detailed localization annotations for whole proteomes. This type of resource is instrumental in developing models of whole organelles that provide insight into the extent of interaction and communication between organelles and help define organellar functionality.     

Prediction of protein subcellular localization

Because the protein's function is usually related to its subcellular localization, the ability to predict subcellular localization directly from protein sequences will be useful for inferring protein functions. Recent years have seen a surging interest in the development of novel computational tools to predict subcellular localization. At present, these approaches, based on a wide range of algorithms, have achieved varying degrees of success for specific organisms and for certain localization categories. A number of authors have noticed that sequence similarity is useful in predicting subcellular localization. For example, Nair and Rost (Protein Sci 2002;11:2836-2847) have carried out extensive analysis of the relation between sequence similarity and identity in subcellular localization, and have found a close relationship between them above a certain similarity threshold. However, many existing benchmark data sets used for the prediction accuracy assessment contain highly homologous sequences-some data sets comprising sequences up to 80-90% sequence identity. Using these benchmark test data will surely lead to overestimation of the performance of the methods considered. Here, we develop an approach based on a two-level support vector machine (SVM) system: the first level comprises a number of SVM classifiers, each based on a specific type of feature vectors derived from sequences; the second level SVM classifier functions as the jury machine to generate the probability distribution of decisions for possible localizations. We compare our approach with a global sequence alignment approach and other existing approaches for two benchmark data sets-one comprising prokaryotic sequences and the other eukaryotic sequences. Furthermore, we carried out all-against-all sequence alignment for several data sets to investigate the relationship between sequence homology and subcellular localization. Our results, which are consistent with previous studies, indicate that the homology search approach performs well down to 30% sequence identity, although its performance deteriorates considerably for sequences sharing lower sequence identity. A data set of high homology levels will undoubtedly lead to biased assessment of the performances of the predictive approaches-especially those relying on homology search or sequence annotations. Our two-level classification system based on SVM does not rely on homology search; therefore, its performance remains relatively unaffected by sequence homology. When compared with other approaches, our approach performed significantly better. Furthermore, we also develop a practical hybrid method, which combines the two-level SVM classifier and the homology search method, as a general tool for the sequence annotation of subcellular localization.     

HIV-1 TAT-mediated protein transduction and subcellular localization using novel expression vectors

Several novel prokaryotic and eukaryotic expression vectors were constructed for protein transduction and subcellular localization. These vectors employed an N-terminal stretch of 11 basic amino acid residues (47-57) from the human immunodeficiency virus type 1 (HIV-1) TAT protein transduction domain (PTD) for protein translocation and cellular localization. The vectors also contained a six-histidine (His(6)) tag at the N- or C-terminus for convenient purification and detection, and a multiple cloning site for easy insertion of foreign genes. Some heterologous genes including HSV-TK, Bcl-rambo, Smac/DIABLO and GFP were fused in-frame to TAT PTD and successfully overexpressed in Escherichia coli. The purified TAT-GFP fusion protein was able to transduce into the mammalian cells and was found to locate mainly in the cytosol when exogenously added to the cell culture medium. However, using a transfection system, mammalian-expressed TAT-GFP predominantly displayed a nuclear localization and nucleolar accumulation in mammalian cell lines. This discrepancy implies that the exact subcellular localization of transduced protein may depend on cell type, the nature of imported proteins and delivery approach. Taken together, our results demonstrate that a TAT PTD length of 11 amino acids was sufficient to confer protein internalization and its subsequent cellular localization. These novel properties allow these vectors to be useful for studying protein transduction and nuclear import.     

Seabream antiquitin: molecular cloning, tissue distribution, subcellular localization and functional expression

Subsequent to our earlier report on the first purification of antiquitin protein from seabream liver and demonstration of its enzymatic activity [FEBS Letters 516 (2002) 183-186], we report herein the cloning of its full-length cDNA sequence. The open reading frame encodes a protein of 511 amino acids. Results of RT-PCR indicate that antiquitin is highly expressed in both the seabream liver and kidney. Transfection studies in cultured eukaryotic cells provided further evidence that it is a cytosolic protein. Bacterial expression of the enzyme was also performed. The purified recombinant protein was demonstrated to exhibit similar kinetic properties as the native enzyme.     

蛋白质亚细胞定位预测中的序列编码技术

蛋白质序列的编码是亚细胞定位预测问题中的关键技术之一。该文较为详细地介绍了目前已有的蛋白质序列编码算法;并指出了序列编码中存在的一些问题及可能的发展方向。     

Differential expression and subcellular localization for subunits of cAMP-dependent protein kinase during ram spermatogenesis

The expression of mRNAs for the RI alpha, RII alpha, and C alpha subunits of cAMP-dependent protein kinase has been studied in different ram germ cells. The sizes of the specific RI alpha, RII alpha, and C alpha mRNAs, observed in germ cells were 1.6, 2.0, and 2.6 kb, respectively. RI alpha and C alpha mRNAs were mainly expressed in primary spermatocytes. A postmeiotic expression predominating in early spermatids was unique to RII alpha mRNA. The location of RI, RII alpha, and C subunits in well-defined organelles of ram spermatids and epididymal sperm was assessed by immunogold electron microscopy. In spermatids, RI, RII alpha, and C were essentially present in the forming acrosome and, to a lesser extent, in the nucleus. During sperm epididymal maturation, the protein kinases disappeared from the acrosome and were detected in a variety of sperm functional areas, such as the tip of the acrosome, the motility apparatus, and the membrane network. The present study on subunits of cAMP-dependent protein kinase supports the concept that specific functions are attached to the different subunits in that it shows differential expression and differential subcellular localization in germ cells.     

Automated selection of DAB-labeled tissue for immunohistochemical quantification.

The increased use of immunohistochemistry (IHC) in both clinical and basic research settings has led to the development of techniques for acquiring quantitative information from immunostains. Staining correlates with absolute protein levels and has been investigated as a clinical tool for patient diagnosis and prognosis. For these reasons, automated imaging methods have been developed in an attempt to standardize IHC analysis. We propose a novel imaging technique in which brightfield images of diaminobenzidene (DAB)-labeled antigens are converted to normalized blue images, allowing automated identification of positively stained tissue. A statistical analysis compared our method with seven previously published imaging techniques by measuring each one's agreement with manual analysis by two observers. Eighteen DAB-stained images showing a range of protein levels were used. Accuracy was assessed by calculating the percentage of pixels misclassified using each technique compared with a manual standard. Bland-Altman analysis was then used to show the extent to which misclassification affected staining quantification. Many of the techniques were inconsistent in classifying DAB staining due to background interference, but our method was statistically the most accurate and consistent across all staining levels.     

Application of protein lysate microarrays to molecular marker verification and quantification

This study presents the development and application of protein lysate microarray (LMA) technology for verification of presence and quantification of human tissue samples for protein biomarkers. Sub-picogram range sensitivity has been achieved on LMA using a non-enzymatic protein detection methodology. Results from a set of quality control experiments are presented and demonstrate the high sensitivity and reproducibility of the LMA methodology. The optimized LMA methodology has been applied for verification of the presence and quantification of disease markers for atherosclerosis. LMA were used to measure lipoprotein [a] and apolipoprotein B100 in 52 carotid endarterectomy samples. The data generated by LMA were validated by ELISA using the same protein lysates. The correlations of protein amounts estimated by LMA and ELISA were highly significant, with r² ≥ 0.98 (p ≤ 0.001) for lipoprotein [a] and with r² ≥ 0.94 (p ≤ 0.001) for apolipoprotein B100. This is the first report to compare data generated using proteins microarrays with ELISA, a standard technology for the verification of the presence of protein biomarkers. The sensitivity, reproducibility, and high-throughput quality of LMA technology make it a potentially powerful technology for profiling disease specific protein markers in clinical samples.     

Comparison of ovalbumin quantification using forward-phase protein microarrays and suspension arrays

We employed ovalbumin (a simulant used for ricin and botulism toxins in biodefense applications) and its high affinity polyclonal antibody as a model system to examine the sensitivity, dynamic range, linearity, and reproducibility of forward-phase array results in comparison to suspension arrays. It was found that protein microarrays had a dynamic range of 4 orders of magnitude and a sensitivity of less than 1 pg/mL, respectively. The dynamic range and sensitivity of suspension arrays were close to 2 orders of magnitude and 0.25 ng/mL, respectively. The sensitivity we observed for the suspension arrays is comparable to that reported for enzyme-linked immunosorbent assays (ELISAs) in the literature. We used ovalbumin samples with two different purities, 38.0% and 76.0% (w/w), as determined by polyacrylamide gel electrophoresis (PAGE). These samples were used to evaluate the effect of impure samples on detection. The data obtained from the forward-phase protein arrays gave values that were consistent with the PAGE data. The data from the suspension arrays were not as consistent and may indicate that this format may not give as reliable data with impure samples. Knowledge of the advantages and disadvantages of the two proteomic methods would allow their more rational use in clinical diagnosis.     

Methods for predicting bacterial protein subcellular localization

The computational prediction of the subcellular localization of bacterial proteins is an important step in genome annotation and in the search for novel vaccine or drug targets. Since the 1991 release of PSORT I--the first comprehensive algorithm to predict bacterial protein localization--many other localization prediction tools have been developed. These methods offer significant improvements in predictive performance over PSORT I and the accuracy of some methods now rivals that of certain high-throughput laboratory methods for protein localization identification.     

植物蛋白质亚细胞定位相关研究概述

蛋白质在植物细胞内的定位是了解蛋白质功能、 基因调控和蛋白质-蛋白质相互作用的关键.近年来随着各种蛋白质亚细胞定位方法的快速发展和技术的不断提升,蛋白质亚细胞定位实现了高通量、活体动态研究.本文总结了植物蛋白质亚细胞定位的常用技术,以及常用细胞器特异性标记的研究进展,并对此领域研究的发展前景做出了展望.