Similar Expression Values Research Articles

Merging and imputation of flow cytometry data: a critical assessment.

Although most modern techniques and analysis methods in multiparameter flow cytometry (MFC) allow for increased dimensionality for the characterization and quantification of cell populations, most MFC applications depend on flow cytometers measuring relatively small (<16) numbers of parameters. When more markers than the available parameters need to be acquired, these are commonly distributed over multiple independent measurements that include a backbone of common markers. Several methods have been proposed to impute values for combinations of markers that were not measured simultaneously. These imputation methods are frequently used without proper validation and knowledge of their effects on data analysis. We evaluated the performance of existing imputation software (Infinicyt, CyTOFmerge, CytoBackBone, and cyCombine) in approximating known measured expression data in terms of similarity in visual appearance, cell expression, and gating in different datasets by splitting MFC samples into separate measurements with partially overlapping markers and re-calculating missing marker expression. Out of the assessed packages, CyTOFmerge showed the most accurate approximation of the known expression in terms of similar expression values and concordance with manual gating, with a mean F-score between 0.53-0.87 when retrieving cell populations in different datasets. Performance remained inadequate for all methods, with only limited similarity at the cell level. In conclusion, the use of imputed MFC data should take such limitations into account and include independent validation of results to justify conclusions. This article is protected by copyright. All rights reserved.

Genetic assimilation of ancestral plasticity during parallel adaptation to zinc contamination in Silene uniflora

Phenotypic plasticity in ancestral populations is hypothesized to facilitate adaptation, but evidence is piecemeal and often contradictory. Further, whether ancestral plasticity increases the probability of parallel adaptive changes has not been explored. The most general finding is that ancestral responses to a new environment are reversed following adaptation (known as reversion). We investigated the contribution of ancestral plasticity to adaptive evolution of gene expression in two independently evolved lineages of zinc-tolerant Silene uniflora. We found that the general pattern of reversion is driven by the absence of a widespread stress response in zinc-adapted plants compared with zinc-sensitive plants. We show that ancestral plasticity that moves expression closer to the optimum value in the new environment influences the evolution of gene expression among genes that are likely to be involved in adaptation and increases the chance that genes are recruited repeatedly during adaptation. However, despite convergence in gene expression levels between independently adapted lineages, ancestral plasticity does not influence how similar expression values of adaptive genes become. Surprisingly, we also observed that ancestral plasticity that increases fitness often becomes genetically determined and fixed, that is, genetically assimilated. These results emphasize the important role of ancestral plasticity in parallel adaptation.

Mapping genetic changes in the cAMP-signaling cascade in human atria

AimTo obtain a quantitative expression profile of the main genes involved in the cAMP-signaling cascade in human control atria and in different cardiac pathologies. Methods and resultsExpression of 48 target genes playing a relevant role in the cAMP-signaling cascade was assessed by RT-qPCR. 113 samples were obtained from right atrial appendages (RAA) of patients in sinus rhythm (SR) with or without atrium dilation, paroxysmal atrial fibrillation (AF), persistent AF or heart failure (HF); and left atrial appendages (LAA) from patients in SR or with AF. Our results show that right and left atrial appendages in donor hearts or from SR patients have similar expression values except for AC7 and PDE2A. Despite the enormous chamber-dependent variability in the gene-expression changes between pathologies, several distinguishable patterns could be identified. PDE8A, PI3Kγ and EPAC2 were upregulated in AF. Different phosphodiesterase (PDE) families showed specific pathology-dependent changes. ConclusionBy comparing mRNA-expression patterns of the cAMP-signaling cascade related genes in right and left atrial appendages of human hearts and across different pathologies, we show that 1) gene expression is not significantly affected by cardioplegic solution content, 2) it is appropriate to use SR atrial samples as controls, and 3) many genes in the cAMP-signaling cascade are affected in AF and HF but only few of them appear to be chamber (right or left) specific. TopicGenetic changes in human diseased atria. Translational perspectiveThe cyclic AMP signaling pathway is important for atrial function. However, expression patterns of the genes involved in the atria of healthy and diseased hearts are still unclear. We give here a general overview of how different pathologies affect the expression of key genes in the cAMP signaling pathway in human right and left atria appendages. Our study may help identifying new genes of interest as potential therapeutic targets or clinical biomarkers for these pathologies and could serve as a guide in future gene therapy studies.

Small nucleolar RNA U91 is a new internal control for accurate microRNAs quantification in pancreatic cancer.

BackgroundRT-qPCR quantification of miRNAs expression may play an essential role in pancreatic ductal adenocarcinoma (PDAC) diagnostics. RT-qPCR-based experiments require endogenous controls for the result normalization and reliability. However, expression instability of reference genes in tumors may introduce bias when determining miRNA levels.MethodsWe investigated expression of 6 miRNAs, isolated from FFPE samples of pancreatic adenocarcinomas. Four internal controls were utilized for RT-qPCR result normalization: artificial miR-39 from C. elegans, U6 snRNA, miR-16 and snoRNA U91.ResultsWe found miR-21, miR-155 or miR-217 expression values in tumors may differ up to several times, depending on selected internal controls. Moreover, different internal controls can produce controversial results for miR-96, miR-148a or miR-196a quantification. Also, expression of our endogenous controls varied significantly in tumors. U6 demonstrated variation from −1.03 to 8.12-fold, miR-16 from −2.94 up to 7.38-fold and the U91 from −3.05 to 4.36-fold respectively. On the other hand, the most stable gene, determined by NormFinder algorithm, was U91. Each miRNA normalized relatively to the spike or U91, demonstrated similar expression values. Thus, statistically significant and insignificant differences between tumors and normal tissues for miRNAs were equal for the spike and the U91. Also, the differences between the spike and U91 were statistically insignificant for all of miRs except miR-217. Among three endogenous controls, U91 had the lowest average expression values and standard deviation in cancer tissues.ConclusionsWe recommend U91 as a new normalizer for miRNA quantification in PDACs.

A Type-2 Fuzzy Method for Identification of Disease-related Genes on Microarrays

Fuz zy set theory has been widely used in the analysis of gene microarray data. However, due to noise and uncertainty inherent in microarray data, traditional fuzzy methods sometimes do not perform well. In this paper, we propose a type-2 fuzzy membership test (Type-2 FM test) for disease-associated gene identification on microarrays to improve traditional fuzzy methods. We apply this method on diabetes and lung cancer microarrays and make a comparison with traditional fuzzy methods. For diabetes data, we can identify 7 genes which have been confirmed to be related to diabetes treatment in the published literature and one more gene can be identified than original approaches. For lung cancer data, we can also identify 7 genes which have been confirmed to be associated with lung cancer treatment in published literature and the type-2 d-values are significantly different. The results show that our type-2 FM test performs better than traditional fuzzy methods when analyzing microarray data with similar expression values and noise.

Average correlation clustering algorithm (ACCA) for grouping of co-regulated genes with similar pattern of variation in their expression values

Distance based clustering algorithms can group genes that show similar expression values under multiple experimental conditions. They are unable to identify a group of genes that have similar pattern of variation in their expression values. Previously we developed an algorithm called divisive correlation clustering algorithm (DCCA) to tackle this situation, which is based on the concept of correlation clustering. But this algorithm may also fail for certain cases. In order to overcome these situations, we propose a new clustering algorithm, called average correlation clustering algorithm (ACCA), which is able to produce better clustering solution than that produced by some others. ACCA is able to find groups of genes having more common transcription factors and similar pattern of variation in their expression values. Moreover, ACCA is more efficient than DCCA with respect to the time of execution. Like DCCA, we use the concept of correlation clustering concept introduced by Bansal et al. ACCA uses the correlation matrix in such a way that all genes in a cluster have the highest average correlation values with the genes in that cluster. We have applied ACCA and some well-known conventional methods including DCCA to two artificial and nine gene expression datasets, and compared the performance of the algorithms. The clustering results of ACCA are found to be more significantly relevant to the biological annotations than those of the other methods. Analysis of the results show the superiority of ACCA over some others in determining a group of genes having more common transcription factors and with similar pattern of variation in their expression profiles. Availability of the software: The software has been developed using C and Visual Basic languages, and can be executed on the Microsoft Windows platforms. The software may be downloaded as a zip file from http://www.isical.ac.in/~rajat. Then it needs to be installed. Two word files (included in the zip file) need to be consulted before installation and execution of the software.

Using Gene Ontology annotations in exploratory microarray clustering to understand cancer etiology

Gene expression profiling provides insight into the functions of genes at a molecular level. Clustering of gene expression profiles can facilitate the identification of the underlying driving biological program causing genes’ co-expression. Standard clustering methods, grouping genes based on similar expression values, fail to capture weak expression correlations potentially causing genes in the same biological process to be grouped separately. We have developed a novel clustering algorithm, which incorporates functional gene information from the Gene Ontology into the clustering process, resulting in more biologically meaningful clusters. We have validated our method using two multi-cancer microarray datasets. In addition, we show the potential of such methods for the exploration of cancer etiology.

Endometrial-Peritoneal Interactions during Endometriotic Lesion Establishment

The pathophysiology of endometriosis remains unclear but involves a complex interaction between ectopic endometrium and host peritoneal tissues. We hypothesized that disruption of this interaction would suppress endometriotic lesion formation. We hoped to delineate the molecular and cellular dialogue between ectopic human endometrium and peritoneal tissues in nude mice as a first step toward testing this hypothesis. Human endometrium was xenografted into nude mice, and the resulting lesions were analyzed using microarrays. A novel technique was developed that unambiguously determined whether RNA transcripts identified via microarray analyses originated from human cells (endometrium) or mouse cells (mesothelium). Four key pathways (ubiquitin/proteasome, inflammation, tissue remodeling/repair, and ras-mediated oncogenesis) were revealed, demonstrating communication between host mesothelial cells and ectopic endometrium. Morphometric analysis of nude mouse lesions confirmed that necrosis, inflammation, healing and repair, and cell proliferation occurred during xenograft development. These processes were entirely consistent with the molecular networks revealed by the microarray data. The transcripts detected in the xenografts overlapped with differentially expressed transcripts in a comparison between paired eutopic and ectopic endometria from human endometriotic patients. For the first time, components of the interaction between ectopic endometrium and peritoneal stromal tissues are revealed. Targeted disruption of this dialogue is likely to inhibit endometriotic tissue formation and may prove to be an effective therapeutic strategy for endometriosis.