Zhong-Hui Duan

Fold change and p-value cutoffs significantly alter microarray interpretations

BackgroundAs context is important to gene expression, so is the preprocessing of microarray to transcriptomics. Microarray data suffers from several normalization and significance problems. Arbitrary fold change (FC) cut-offs of >2 and significance p-values of <0.02 lead data collection to look only at genes which vary wildly amongst other genes. Therefore, questions arise as to whether the biology or the statistical cutoff are more important within the interpretation. In this paper, we reanalyzed a zebrafish (D. rerio) microarray data set using GeneSpring and different differential gene expression cut-offs and found the data interpretation was drastically different. Furthermore, despite the advances in microarray technology, the array captures a large portion of genes known but yet still leaving large voids in the number of genes assayed, such as leptin a pleiotropic hormone directly related to hypoxia-induced angiogenesis.ResultsThe data strongly suggests that the number of differentially expressed genes is more up-regulated than down-regulated, with many genes indicating conserved signalling to previously known functions. Recapitulated data from Marques et al. (2008) was similar but surprisingly different with some genes showing unexpected signalling which may be a product of tissue (heart) or that the intended response was transient.ConclusionsOur analyses suggest that based on the chosen statistical or fold change cut-off; microarray analysis can provide essentially more than one answer, implying data interpretation as more of an art than a science, with follow up gene expression studies a must. Furthermore, gene chip annotation and development needs to maintain pace with not only new genomes being sequenced but also novel genes that are crucial to the overall gene chips interpretation.

Differential protein profiling in renal-cell carcinoma

Characterizing the alterations of protein expression in cancer cells can be very useful in providing insight into the changes in the functional pathways and thus the fundamental mechanisms of cancer development at the molecular level. In this study, we profiled protein expressions in eleven pairs of primary cell cultures derived from renal‐cell carcinoma (RCC) tissues and patient‐matched normal kidney tissues utilizing two‐dimensional polyacrylamide gel electrophoresis (2‐D PAGE). Together with the immunoblot analysis of proteins from the RCC tissues, the study also demonstrated that the alterations of protein expression observed in RCC primary cell cultures reflected those observed in the original RCC tissues. We analyzed the expression profiles and identified proteins differentially expressed in RCC primary cell cultures by 2‐D PAGE and mass spectrometry (MS). We found sixteen proteins were overexpressed and seven proteins underexpressed in RCC. The deregulated expressions of proteins include those involved in metabolism, cellular morphology, heat shock response, cell growth, etc. Overexpression of three proteins, αβ‐crystallin, manganese superoxide dismutase (MnSOD), and annexin IV, most commonly observed in primary RCC cell cultures, were also observed by immunoblot analysis of proteins from the RCC tissues from which the primary cell cultures were derived. Semi‐quantitative reverse transcription (RT)‐polymerase chain reaction (PCR) analysis revealed the direct correlation between deregulated gene expression and the corresponding protein abundance in two of the three most commonly upregulated proteins we found in RCC.

Microarray gene expression profiling and analysis in renal cell carcinoma.

BackgroundRenal cell carcinoma (RCC) is the most common cancer in adult kidney. The accuracy of current diagnosis and prognosis of the disease and the effectiveness of the treatment for the disease are limited by the poor understanding of the disease at the molecular level. To better understand the genetics and biology of RCC, we profiled the expression of 7,129 genes in both clear cell RCC tissue and cell lines using oligonucleotide arrays.MethodsTotal RNAs isolated from renal cell tumors, adjacent normal tissue and metastatic RCC cell lines were hybridized to affymatrix HuFL oligonucleotide arrays. Genes were categorized into different functional groups based on the description of the Gene Ontology Consortium and analyzed based on the gene expression levels. Gene expression profiles of the tissue and cell line samples were visualized and classified by singular value decomposition. Reverse transcription polymerase chain reaction was performed to confirm the expression alterations of selected genes in RCC.ResultsSelected genes were annotated based on biological processes and clustered into functional groups. The expression levels of genes in each group were also analyzed. Seventy-four commonly differentially expressed genes with more than five-fold changes in RCC tissues were identified. The expression alterations of selected genes from these seventy-four genes were further verified using reverse transcription polymerase chain reaction (RT-PCR). Detailed comparison of gene expression patterns in RCC tissue and RCC cell lines shows significant differences between the two types of samples, but many important expression patterns were preserved.ConclusionsThis is one of the initial studies that examine the functional ontology of a large number of genes in RCC. Extensive annotation, clustering and analysis of a large number of genes based on the gene functional ontology revealed many interesting gene expression patterns in RCC. Most notably, genes involved in cell adhesion were dominantly up-regulated whereas genes involved in transport were dominantly down-regulated. This study reveals significant gene expression alterations in key biological pathways and provides potential insights into understanding the molecular mechanism of renal cell carcinogenesis.

Effects of Resveratrol on Gene Expression in Renal Cell Carcinoma

Studies have shown that Resveratrol (RE) can inhibit cancer initiation, promotion, and progression. However the gene expression profile in renal cell carcinoma (RCC) in response to RE treatment has never been reported. To understand the potential anticancer effect of RE on RCC at molecular level, we profiled and analyzed the expression of 2059 cancer-related genes in a RCC cell line RCC54 treated with RE. Biological functions of 633 genes were annotated based on biological process ontology and clustered into functional categories. Twenty-nine highly differentially expressed genes in RE treated RCC54 were identified and the potential implications of some gene expression alterations in RCC carcinogenesis were identified. RE was also shown to inhibit cell growth and induce cell death of RCC cells. The expression alterations of selected genes were validated using reverse transcription polymerase chain reaction. In addition, the gene expression profiles under different RE treatments were analyzed and visualized using singular value decomposition. The findings from this study support the hypothesis that RE induces differential expression of genes that are directly or indirectly related to the inhibition of RCC cell growth and induction of RCC cell death. In addition, it is apparent that the gene expression alterations due to RE treatment depend strongly on RE concentration. This study provides a general understanding of the overall genetic response of RCC54 to RE treatment and yields insights into the understanding of the cancer preventive mechanism of RE in RCC.

The relationship between protein sequences and their gene ontology functions

BackgroundOne main research challenge in the post-genomic era is to understand the relationship between protein sequences and their biological functions. In recent years, several automated annotation systems have been developed for the functional assignment of uncharacterized proteins. The underlying assumption of these systems is that similar sequences imply similar biological functions. However, it has been noted that matching sequences do not always infer similar functions.ResultsIn this paper, we present the correlation between protein sequences and protein functions for the yeast proteome in the context of gene ontology. A novel measure is introduced to define the overall similarity between two protein sequences. The effects of the level as well as the size of a gene ontology group on the degree of similarity were studied. The similarity distributions at different levels of gene ontology trees are presented. To evaluate the theoretical prediction power of similar sequences, we computed the posterior probability of correct predictions.ConclusionThe results indicate that protein pairs of similar biological functions tend to have higher sequence similarity, although the similarity distribution in each functional group is heterogeneous and varies from group to group. We conclude that sequence similarity can serve as a key measure in protein function prediction. However, the resulting annotations must be verified through other means. A method that combines a broader range of measures is more likely to provide more accurate prediction. Our study indicates that the posterior probability of a correct prediction could serve as one of the key measures.

Novel α1-Adrenergic Receptor Signaling Pathways: Secreted Factors and Interactions with the Extracellular Matrix

α1-Adrenergic receptor (α1-ARs) subtypes (α1A, α1B, and α1D) regulate multiple signal pathways, such as phospholipase C, protein kinase C (PKC), and mitogen-activated protein kinases. We employed oligonucleotide microarray technology to explore the effects of both short- (1 h) and long-term (18 h) activation of the α1A-AR to enable RNA changes to occur downstream of earlier well characterized signaling pathways, promoting novel couplings. Polymerase chain reaction (PCR) studies confirmed that PKC was a critical regulator of α1A-AR-mediated gene expression, and secreted interleukin (IL)-6 also contributed to gene expression alterations. We next focused on two novel signaling pathways that might be mediated through α1A-AR stimulation because of the clustering of gene expression changes for cell adhesion/motility (syndecan-4 and tenascin-C) and hyaluronan (HA) signaling. We confirmed that α1-ARs induced adhesion in three cell types to vitronectin, an interaction that was also integrin-, FGF7-, and PKC-dependent. α1-AR activation also inhibited cell migration, which was integrin- and PKC-independent but still required secretion of FGF7. α1-AR activation also increased the expression and deposition of HA, a glycosaminoglycan, which displayed two distinct structures: pericellular coats and long cable structures, as well as increasing expression of the HA receptor, CD44. Long cable structures of HA can bind leukocytes, which this suggests that α1-ARs may be involved in proinflammatory responses. Our results indicate α1-ARs induce the secretion of factors that interact with the extracellular matrix to regulate cell adhesion, motility and proinflammatory responses through novel signaling pathways.

SVM Approach to Breast Cancer Classification

Inference of evolutionary trees using the maximum likelihood principle is NP-hard. Therefore, all practical methods rely on heuristics. The topological transformations often used in heuristics are nearest neighbor interchange (NNI), sub-tree prune and regraft (SPR) and tree bisection and reconnection (TBR). However, these topological transformations often fall easily into local optima, since there are not many trees accessible in one step from any given tree. Another more exhaustive topological transformation is p-Edge Contraction and Refinement (p-ECR). However, due to its high computation complexity, p-ECR has rarely been used in practice. This paper proposes a method p-ECRNJ with a O(p3) time complexity to make the p-ECR move efficient by using neighbor joining (NJ) to refine the unresolved nodes produced in p-ECR. Moreover, the demonstrated topological accuracy for small datasets of NJ can guarantee the accuracy of the p-ECRNJ move. Experiments with simulated and real datasets show that p-ECRNJ can find better trees than the best-known maximum likelihood methods so far and can efficiently improve local topological transforms in reasonable time.The purpose of the proposed study was to provide a solution to the Wisconsin diagnostic breast cancer (WDBC) classification problem. The WDBC dataset, provided by the University of Wisconsin Hospital, was derived from a group of images using fine needle aspiration (biopsies) of the breast. An ensemble of support vector machines (SVMs) was employed in this study. Support vectors with linear, polynomial and RBF kernel functions were trained using a fraction of the WDBC dataset as a training set. The five top performing models were recruited into the ensemble. The classification was then carried out using the majority opinion of the ensemble. The SVM ensemble successfully classified more than 99 percent of the testing data and in the process yielded 100 percent benign tumor prediction accuracy.

Gene Expression Based Leukemia Sub‑Classification Using Committee Neural Networks

Analysis of gene expression data provides an objective and efficient technique for sub-classification of leukemia. The purpose of the present study was to design a committee neural networks based classification systems to subcategorize leukemia gene expression data. In the study, a binary classification system was considered to differentiate acute lymphoblastic leukemia from acute myeloid leukemia. A ternary classification system which classifies leukemia expression data into three subclasses including B-cell acute lymphoblastic leukemia, T-cell acute lymphoblastic leukemia and acute myeloid leukemia was also developed. In each classification system gene expression profiles of leukemia patients were first subjected to a sequence of simple preprocessing steps. This resulted in filtering out approximately 95 percent of the non-informative genes. The remaining 5 percent of the informative genes were used to train a set of artificial neural networks with different parameters and architectures. The networks that gave the best results during initial testing were recruited into a committee. The committee decision was by majority voting. The committee neural network system was later evaluated using data not used in training. The binary classification system classified microarray gene expression profiles into two categories with 100 percent accuracy and the ternary system correctly predicted the three subclasses of leukemia in over 97 percent of the cases.

Amino Acid Function and Docking Site Prediction Through Combining Disease Variants, Structure Alignments, Sequence Alignments, and Molecular Dynamics: A Study of the HMG Domain

BackgroundThe DNA binding domain of HMG proteins is known to be important in many diseases, with the Sox sub-family of HMG proteins of particular significance. Numerous natural variants in HMG proteins are associated with disease phenotypes. Integrating these natural variants, molecular dynamic simulations of DNA interaction and sequence and structure alignments give detailed molecular knowledge of potential amino acid function such as DNA or protein interaction.ResultsA total of 33 amino acids in HMG proteins are known to have natural variants in diseases. Eight of these amino acids are normally conserved in human HMG proteins and 27 are conserved in the human Sox sub-family. Among the six non-Sox conserved amino acids, amino acids 16 and 45 are likely targets for interaction with other proteins. Docking studies between the androgen receptor and Sry/Sox9 reveals a stable amino acid specific interaction involving several Sox conserved residues.ConclusionThe HMG box has structural conservation between the first two of the three helixes in the domain as well as some DNA contact points. Individual sub-groups of the HMG family have specificity in the location of the third helix, DNA specific contact points (such as amino acids 4 and 29), and conserved amino acids interacting with other proteins such as androgen receptor. Studies such as this help to distinguish individual members of a much larger family of proteins and can be applied to any protein family of interest.

A weighted k-nearest neighbor method for gene ontology based protein function prediction

Numerous genome projects have produced a large and ever increasing amount of genomic sequence data. However, the biological functions of many proteins encoded by the sequences remain unknown. Protein function annotation and prediction become an essential and challenging task of post-genomic research. In this paper, we present an automated protein function prediction system based on a set of proteins of known biological functions. The functions of the proteins are characterized with gene ontology (GO) annotations. The prediction system uses a novel measure to calculate the pair-wise overall similarity between protein sequences. The protein function prediction is performed based on the GO annotations of similar sequences using a weighted k-nearest neighbor method. We show the prediction accuracies obtained using the model organism yeast (Sacchyromyces cerevisiae). The results indicate that the weighted k-nearest neighbor method significantly outperforms the regular k-nearest neighbor method for protein molecular function prediction.In tennis competition, there are some reasons leading to the competition results inaccurately. So I put forward a real-time competition simulation system to solve the tennis problem. It can overcome the limitations and the blind spots that occur in human observation. The system includes four parts: image collection, moving object detection and tracking, static scene simulation, competition simulation. This paper presents an algorithm for the detection and tracking of moving objects in sequence images that can be applied in simulations of tennis competition. The approach differs from most other methods that solve the problems of object occlusion and object deformation. The experimental results proved that our method is feasibility and usefulness.