Identification of genes differentially expressed in hemocytes of Scylla paramamosain in response to lipopolysaccharide

Although the crab Scylla paramamosain has been cultured in China for a long time, little knowledge is available on how crabs respond to infection by bacteria. A forward suppression subtractive hybridization (SSH) cDNA library was constructed from their hemocytes and the up-regulated genes were identified in order to isolate differentially expressed genes in S. paramamosain in response to bacterial lipopolysaccharide (LPS). A total of 721 clones on the middle scale in the SSH library were sequenced. Among these genes, 271 potentially functional genes were recognized based on the BLAST searches in NCBI and were categorized into seven groups in association with different biological processes using AmiGO against the Gene Ontology database. Of the 271 genes, 269 translatable DNA sequences were predicted to be proteins, and the putative amino acid sequences were searched for conserved domains and proteins using the CD-Search service and BLASTp. Among 271 genes, 179 (66.1%) were annotated to be involved in different biological processes, while 92 genes (33.9%) were classified as an unknown-function gene group. It was noted that only 18 of the 271 genes (6.6%) had previously been reported in other crustaceans and most of the screened genes showed less similarity to known sequences based on BLASTn results, suggesting that 253 genes were found for the first time in S. paramamosain. Furthermore, two up-regulated genes screened from the SSH library were selected for full-length cDNA sequence cloning and in vivo expression study, including Sp-superoxide dismutase (Sp-Cu-ZnSOD) gene and Sp-serpin gene. The differential expression pattern of the two genes during the time course of LPS challenge was analyzed using real-time PCR. We found that both genes were significantly expressed in LPS-challenged crabs in comparison with control. Taken together, the study primarily provides the data of the up-regulated genes associated with different biological processes in S. paramamosain in response to LPS, by which the interesting genes or proteins potentially involved in the innate immune defense of S. paramamosain will be investigated in future.     

Identification and comparative analysis of the ovary and testis microRNAome of mud crab Scylla paramamosain

The role of microRNA (miRNA) in reproductive regulation is attracting increasingly more attention. In this study, we obtained 9,643,114 and 15,498,999 raw reads from the ovary and testis library of important farmed mud crab Scylla paramamosain, respectively. After data mining, a total of 4,096,464 and 11,737,973 mappable small RNA sequences remained for analysis. By mapping to the reference genome and expressed sequence tag (EST) of Daphnia pulex and other crabs, a total of 1,417 miRNAs were identified. On the basis of 1,417 miRNAs, 514 (36.3%) unique miRNAs coexpressed in the gonad of female and male libraries, and 336 (23.7%) and 567 (40%) expressed preferentially in female and male libraries, respectively. Analysis of library sequencing data resulted in the identi?cation of 108 miRNAs (out of 1,417; 7.6%) that showed signi?cant differential expression between the two samples. Of these, 13 miRNAs were expressed only in the testis, two miRNAs were expressed only in the ovary, and 93 miRNAs were coexpressed: 57 (61.3%) were upregulated (ovary/testis) and 36 (38.7%) were downregulated (ovary/testis). To confirm the expression patterns of the predicted miRNAs, we randomly selected 14 candidate miRNAs from 108 differentially expressed miRNAs and performed stem–loop real time quantitative PCR (RT‐qPCR) assays in five ovary developing stages. Five miRNAs showed similar expression patterns in almost every stage as those revealed by identification of differentially expressed genes (IDEG6) analysis. The above five miRNAs were predicted to match the 3′‐untranslated region of the published S. paramamosain gene. Four out of five miRNA had a regulation effect on many genes, especially the genes related to gonadal development.     

DNA microarray analysis of genes differentially expressed in adipocyte differentiation

In the present study, the human liposarcoma cell line SW872 was used to identify global changes in gene expression profiles occurring during adipogenesis. We further explored some of the genes expressed during the late phase of adipocyte differentiation. These genes may play a major role in promoting excessive proliferation and accumulation of lipid droplets, which contribute to the development of obesity. By using microarray-based technology, we examined differential gene expression in early differentiated adipocytes and late differentiated adipocytes. Validated genes exhibited a ≥10-fold increase in the late phase of adipocyte differentiation by polymerase chain reaction (RT-PCR). Compared with undifferentiated preadipocytes, we found that 763 genes were increased in early differentiated adipocytes, and 667 genes were increased in later differentiated adipocytes. Furthermore, 21 genes were found being expressed 10-fold higher in the late phase of adipocyte differentiation. The results were in accordance with the RT-PCR test, which validated 11 genes, namely, CIDEC, PID1, LYRM1, ADD1, PPARγ2, ANGPTL4, ADIPOQ, ACOX1, FIP1L1, MAP3K2 and PEX14. Most of these genes were found being expressed in the later phase of adipocyte differentiation involved in obesity-related diseases. The findings may help to better understand the mechanism of obesity and related diseases.     

Large-scale screen for genes involved in gonad development

The vertebrate gonad develops from the intermediate mesoderm as an initially bipotential organ anlage, the genital ridge. In mammals, Sry acts as a genetic switch towards testis development. Sox9 has been shown to act downstream of Sry in testis development, while Dax1 appears to counteract Sry. Few more genes have been implicated in early gonad development. However, the genetic networks controlling early differentiation events in testis and ovary are still far from being understood. In order to provide a broader basis for the molecular analysis of gonad development, high-throughput gene expression analysis was utilized to identify genes specifically expressed in the gonad. In total, among 138 genes isolated which showed tissue specific expression in the embryo, 79 were detected in the developing gonad or sex ducts. Twenty-seven have not been functionally described before, while 40 represent known genes and 12 are putative mouse orthologues. Forty-five of the latter two groups (86%) have not been described previously in the fetal gonad. In addition, 21 of the gonad specific genes showed sex-dimorphic expression suggesting a role in sex determination and/or gonad differentiation. Eighteen of the latter (86%) have not been described previously in the fetal gonad. In total we provide new data on 72 genes which may play a role in gonad or sex duct development and/or sex determination. Thus we have generated a large gene resource for the investigation of these processes, and demonstrate the suitability of high-throughput gene expression screening for the genetic analysis of organogenesis.     

A spline function approach for detecting differentially expressed genes in microarray data analysis

MOTIVATION: A primary objective of microarray studies is to determine genes which are differentially expressed under various conditions. Parametric tests, such as two-sample t-tests, may be used to identify differentially expressed genes, but they require some assumptions that are not realistic for many practical problems. Non-parametric tests, such as empirical Bayes methods and mixture normal approaches, have been proposed, but the inferences are complicated and the tests may not have as much power as parametric models. RESULTS: We propose a weakly parametric method to model the distributions of summary statistics that are used to detect differentially expressed genes. Standard maximum likelihood methods can be employed to make inferences. For illustration purposes the proposed method is applied to the leukemia data (training part) discussed elsewhere. A simulation study is conducted to evaluate the performance of the proposed method.     

Nonparametric methods for identifying differentially expressed genes in microarray data

MOTIVATION: Gene expression experiments provide a fast and systematic way to identify disease markers relevant to clinical care. In this study, we address the problem of robust identification of differentially expressed genes from microarray data. Differentially expressed genes, or discriminator genes, are genes with significantly different expression in two user-defined groups of microarray experiments. We compare three model-free approaches: (1). nonparametric t-test, (2). Wilcoxon (or Mann-Whitney) rank sum test, and (3). a heuristic method based on high Pearson correlation to a perfectly differentiating gene ('ideal discriminator method'). We systematically assess the performance of each method based on simulated and biological data under varying noise levels and p-value cutoffs. RESULTS: All methods exhibit very low false positive rates and identify a large fraction of the differentially expressed genes in simulated data sets with noise level similar to that of actual data. Overall, the rank sum test appears most conservative, which may be advantageous when the computationally identified genes need to be tested biologically. However, if a more inclusive list of markers is desired, a higher p-value cutoff or the nonparametric t-test may be appropriate. When applied to data from lung tumor and lymphoma data sets, the methods identify biologically relevant differentially expressed genes that allow clear separation of groups in question. Thus the methods described and evaluated here provide a convenient and robust way to identify differentially expressed genes for further biological and clinical analysis.     

Sample size for detecting differentially expressed genes in microarray experiments

Background  

Microarray experiments are often performed with a small number of biological replicates, resulting in low statistical power for detecting differentially expressed genes and concomitant high false positive rates. While increasing sample size can increase statistical power and decrease error rates, with too many samples, valuable resources are not used efficiently. The issue of how many replicates are required in a typical experimental system needs to be addressed. Of particular interest is the difference in required sample sizes for similar experiments in inbred vs. outbred populations (e.g. mouse and rat vs. human).     

Identification of differentially expressed genes in mouse kidney after irradiation using microarray analysis

Irradiation of the kidney induces dose-dependent, progressive renal functional impairment, which is partly mediated by vascular damage. The molecular mechanisms underlying the development of radiation-induced nephropathy are unclear. Given the complexity of radiation-induced responses, microarrays may offer new opportunities to identify a wider range of genes involved in the development of radiation injury. The aim of the present study was to determine whether microarrays are a useful tool for identifying time-related changes in gene expression and potential mechanisms of radiation-induced nephropathy. Microarray experiments were performed using amplified RNA from irradiated mouse kidneys (1 x 16 Gy) and from sham-irradiated control tissue at different intervals (1-30 weeks) after irradiation. After normalization procedures (using information from straight-color, color-reverse and self-self experiments), the differentially expressed genes were identified. Control and repeat experiments were done to confirm that the observations were not artifacts of the array procedure (RNA amplification, probe synthesis, hybridizations and data analysis). To provide independent confirmation of microarray data, semi-quantitative PCR was performed on a selection of genes. At 1 week after irradiation (before the onset of vascular and functional damage), 16 genes were significantly up-regulated and 9 genes were down-regulated. During the period of developing nephropathy (10 to 20 weeks), 31 and 42 genes were up-regulated and 9 and 4 genes were down-regulated. At the later time of 30 weeks, the vast majority of differentially expressed genes (191 out of 203) were down-regulated. Potential genes of interest included TSA-1 (also known as Ly6e) and Jagged 1 (Jag1). Increased expression of TSA-1, a member of the Ly-6 family, has previously been reported in response to proteinuria. Jagged 1, a ligand for the Notch receptor, is known to play a role in angiogenesis, and is particularly interesting in the context of radiation-induced vascular injury. The present study demonstrates the potential of microarrays to identify changing patterns of gene expression in irradiated kidney. Further studies will be required to evaluate functional involvement of these genes in vascular-mediated normal tissue injury.     

Sample size for identifying differentially expressed genes in microarray experiments.

Microarray technology allows simultaneous comparison of expression levels of thousands of genes under each condition. This paper concerns sample size calculation in the identification of differentially expressed genes between a control and a treated sample. In a typical experiment, only a fraction of genes (altered genes) is expected to be differentially expressed between two samples. Sample size determination depends on a number of factors including the specified significance level (alpha), the desired statistical power (1-beta), the fraction (eta) of truly altered genes out of the total g genes studied, and the effect sizes (Delta) for the altered genes. This paper proposes a method to calculate the number of arrays required to detect at least 100lambda % (where 0 < lambda < or = 1) of the truly altered genes under the model of an equal effect size for all altered genes. The required numbers of arrays are tabulated for various values of alpha, beta, Delta, eta, and lambda for the one-sample and two-sample t-tests for g = 10,000. Based on the proposed approach, to identify up to 90% of truly altered genes among the unknown number of truly altered genes, the estimated numbers of arrays needed appear to be manageable. For instance, when the standardized effect size is at least 2.0, the number of arrays needed is less than or equal to 14 for the two-sample t-test and is less than or equal to 10 for the one-sample t-test. As the cost per array declines, such array numbers become practical. The proposed method offers a simple, intuitive, and practical way to determine the number of arrays needed in microarray experiments in which the true correlation structure among the genes under investigation cannot be reasonably assumed. An example dataset is used to illustrate the use of the proposed approach to plan microarray experiments.     

Statistical tests for identifying differentially expressed genes in time-course microarray experiments

MOTIVATION: Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. In time-course experiments in which gene expression is monitored over time, we are interested in testing gene expression profiles for different experimental groups. However, no sophisticated analytic methods have yet been proposed to handle time-course experiment data. RESULTS: We propose a statistical test procedure based on the ANOVA model to identify genes that have different gene expression profiles among experimental groups in time-course experiments. Especially, we propose a permutation test which does not require the normality assumption. For this test, we use residuals from the ANOVA model only with time-effects. Using this test, we detect genes that have different gene expression profiles among experimental groups. The proposed model is illustrated using cDNA microarrays of 3840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells.     

Ranking analysis of microarray data: a powerful method for identifying differentially expressed genes

Microarray technology provides a powerful tool for the expression profile of thousands of genes simultaneously, which makes it possible to explore the molecular and metabolic etiology of the development of a complex disease under study. However, classical statistical methods and technologies fail to be applicable to microarray data. Therefore, it is necessary and motivating to develop powerful methods for large-scale statistical analyses. In this paper, we described a novel method, called Ranking Analysis of Microarray Data (RAM). RAM, which is a large-scale two-sample t-test method, is based on comparisons between a set of ranked T statistics and a set of ranked Z values (a set of ranked estimated null scores) yielded by a "randomly splitting" approach instead of a "permutation" approach and a two-simulation strategy for estimating the proportion of genes identified by chance, i.e., the false discovery rate (FDR). The results obtained from the simulated and observed microarray data show that RAM is more efficient in identification of genes differentially expressed and estimation of FDR under undesirable conditions such as a large fudge factor, small sample size, or mixture distribution of noises than Significance Analysis of Microarrays.     

An in situ hybridization screen for the rapid isolation of differentially expressed genes

To rapidly isolate genes specifically expressed during medaka development we generated a cDNA library enriched for genes expressed in the head region of the developing embryo. Clones were spotted on filters automatically and preselected for abundantly expressed genes by hybridizing them with a probe derived from RNA of undifferentiated totipotent cells. Of the nonhybridizing clones 153 were chosen randomly and further analyzed by whole-mount in situ hybridization. There were 67 selected clones differentially expressed in the developing embryos, and 48 of these were expressed in the developing head. Differentially expressed genes were either of novel type or showed homology to known genes containing DNA binding motifs or to putative housekeeping genes. Received: 1 December 1998 / Accepted: 17 May 1999     

Not proper ROC curves as new tool for the analysis of differentially expressed genes in microarray experiments

Background  

Most microarray experiments are carried out with the purpose of identifying genes whose expression varies in relation with specific conditions or in response to environmental stimuli. In such studies, genes showing similar mean expression values between two or more groups are considered as not differentially expressed, even if hidden subclasses with different expression values may exist. In this paper we propose a new method for identifying differentially expressed genes, based on the area between the ROC curve and the rising diagonal (ABCR). ABCR represents a more general approach than the standard area under the ROC curve (AUC), because it can identify both proper (i.e., concave) and not proper ROC curves (NPRC). In particular, NPRC may correspond to those genes that tend to escape standard selection methods.     

A non-parametric approach for identifying differentially expressed genes in factorial microarray experiments

We introduce a non-parametric approach using bootstrap-assisted correspondence analysis to identify and validate genes that are differentially expressed in factorial microarray experiments. Model comparison showed that although both parametric and non-parametric methods capture the different profiles in the data, our method is less inclined to false positive results due to dimension reduction in data analysis.     

Determination of the differentially expressed genes in microarray experiments using local FDR

Background  

Thousands of genes in a genomewide data set are tested against some null hypothesis, for detecting differentially expressed genes in microarray experiments. The expected proportion of false positive genes in a set of genes, called the False Discovery Rate (FDR), has been proposed to measure the statistical significance of this set. Various procedures exist for controlling the FDR. However the threshold (generally 5%) is arbitrary and a specific measure associated with each gene would be worthwhile.