Different statins produce highly divergent changes in gene expression profiles of human hepatoma cells: a pilot study

Statins are inhibitors of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR), the key enzyme of the sterol biosynthesis pathway. Statin therapy is commonly regarded as well tolerated. However, serious adverse effects have also been reported, especially during high-dose statin therapy. The aim of our study was to investigate the effect of statins on gene expression profiles in human hepatoma HepG2 cells using Affymetrix Human Genome U133 Plus 2.0 arrays. Expression of 102, 857 and 1091 genes was changed substantially in HepG2 cells treated with simvastatin, fluvastatin and atorvastatin, respectively. Pathway and gene ontology analysis showed that many of the genes with changed expression levels were involved in a broad range of metabolic processes. The presented data clearly indicate substantial differences between the tested statins.     

A comparative study of discriminating human heart failure etiology using gene expression profiles

Background  

Human heart failure is a complex disease that manifests from multiple genetic and environmental factors. Although ischemic and non-ischemic heart disease present clinically with many similar decreases in ventricular function, emerging work suggests that they are distinct diseases with different responses to therapy. The ability to distinguish between ischemic and non-ischemic heart failure may be essential to guide appropriate therapy and determine prognosis for successful treatment. In this paper we consider discriminating the etiologies of heart failure using gene expression libraries from two separate institutions.     

Circular RNA expression profiles and features in human tissues: a study using RNA-seq data

Background

Circular RNA (circRNA) is one type of noncoding RNA that forms a covalently closed continuous loop. Similar to long noncoding RNA (lncRNA), circRNA can act as microRNA (miRNA) ‘sponges’ to regulate gene expression, and its abnormal expression is related to diseases such as atherosclerosis, nervous system disorders and cancer. So far, there have been no systematic studies on circRNA abundance and expression profiles in human adult and fetal tissues.

Results

We explored circRNA expression profiles using RNA-seq data for six adult and fetal normal tissues (colon, heart, kidney, liver, lung, and stomach) and four gland normal tissues (adrenal gland, mammary gland, pancreas, and thyroid gland). A total of 8120, 25,933 and 14,433 circRNAs were detected by at least two supporting junction reads in adult, fetal and gland tissues, respectively. Among them, 3092, 14,241 and 6879 circRNAs were novel when compared to the published results. In each adult tissue type, we found at least 1000 circRNAs, among which 36.97–50.04% were tissue-specific. We reported 33 circRNAs that were ubiquitously expressed in all the adult tissues we examined. To further explore the potential “housekeeping” function of these circRNAs, we constructed a circRNA-miRNA-mRNA regulatory network containing 17 circRNAs, 22 miRNAs and 90 mRNAs. Furthermore, we found that both the abundance and the relative expression level of circRNAs were higher in fetal tissue than adult tissue. The number of circRNAs in gland tissues, especially in mammary gland (9665 circRNA candidates), was higher than that of other adult tissues (1160–3777).

Conclusions

We systematically investigated circRNA expression in a variety of human adult and fetal tissues. Our observation of different expression level of circRNAs in adult and fetal tissues suggested that circRNAs might play their role in a tissue-specific and development-specific fashion. Analysis of circRNA-miRNA-mRNA network provided potential targets of circRNAs. High expression level of circRNAs in mammary gland might be attributed to the rich innervation.

     

PathAct: a novel method for pathway analysis using gene expression profiles

We developed PathAct, a novel method for pathway analysis to investigate the biological and clinical implications of the gene expression profiles. The advantage of PathAct in comparison with the conventional pathway analysis methods is that it can estimate pathway activity levels for individual patient quantitatively in the form of a pathway-by-sample matrix. This matrix can be used for further analysis such as hierarchical clustering and other analysis methods. To evaluate the feasibility of PathAct, comparison with frequently used gene-enrichment analysis methods was conducted using two public microarray datasets. The dataset #1 was that of breast cancer patients, and we investigated pathways associated with triple-negative breast cancer by PathAct, compared with those obtained by gene set enrichment analysis (GSEA). The dataset #2 was another breast cancer dataset with disease-free survival (DFS) of each patient. Contribution by each pathway to prognosis was investigated by our method as well as the Database for Annotation, Visualization and Integrated Discovery (DAVID) analysis. In the dataset #1, four out of the six pathways that satisfied p < 0.05 and FDR < 0.30 by GSEA were also included in those obtained by the PathAct method. For the dataset #2, two pathways (“Cell Cycle” and “DNA replication”) out of four pathways by PathAct were commonly identified by DAVID analysis. Thus, we confirmed a good degree of agreement among PathAct and conventional methods. Moreover, several applications of further statistical analyses such as hierarchical cluster analysis by pathway activity, correlation analysis and survival analysis between pathways were conducted.     

A paradigm for class prediction using gene expression profiles.

We propose a general framework for prediction of predefined tumor classes using gene expression profiles from microarray experiments. The framework consists of 1) evaluating the appropriateness of class prediction for the given data set, 2) selecting the prediction method, 3) performing cross-validated class prediction, and 4) assessing the significance of prediction results by permutation testing. We describe an application of the prediction paradigm to gene expression profiles from human breast cancers, with specimens classified as positive or negative for BRCA1 mutations and also for BRCA2 mutations. In both cases, the accuracy of class prediction was statistically significant when compared to the accuracy of prediction expected by chance. The framework proposed here for the application of class prediction is designed to reduce the occurrence of spurious findings, a legitimate concern for high-dimensional microarray data. The prediction paradigm will serve as a good framework for comparing different prediction methods and may accelerate the development of molecular classifiers that are clinically useful.     

Quantification of cytochrome P450 reductase gene expression in human tissues.

We have isolated and sequenced cDNA clones that code for a variant of human cytochrome P450 reductase. An RNase protection assay was used to quantify the corresponding mRNA in adult and fetal tissues. The results demonstrate that, in the samples analyzed, the cytochrome P450 reductase gene displays very little inter-individual variation in its expression in adult liver and is subject to little developmental or tissue-specific regulation.     

Prediction of treatment response using gene expression profiles

This paper concerns prediction of clinical outcome from gene expression profiles using work in a different area, nonlinear system identification. In particular, the approach can predict long-term treatment response from data of a landmark article by Golub et al. (Golub, T. R.; Slonim, D. K.; Tamayo, P.; Huard, C.; Gaasenbeek, M.; Mesirov, J. P. et al. Science 1999, 286, 531-537) that has not previously been achieved with these data. The present paper shows that, for these data, gene expression profiles taken at time of diagnosis of acute myeloid leukemia contain information predictive of eventual response to chemotherapy. This was not evident in previous work; indeed, the Golub et al. article did not find a set of genes strongly correlated with clinical outcome. However, the present approach can accurately predict outcome class of gene expression profiles even when the genes do not have large differences in expression levels between the classes.     

Human intestinal circadian clock: expression of clock genes in colonocytes lining the crypt

Biological clock components have been detected in many epithelial tissues of the digestive tract of mammals (oral mucosa, pancreas, and liver), suggesting the existence of peripheral circadian clocks that may be entrainable by food. Our aim was to investigate the expression of main peripheral clock genes in colonocytes of healthy humans and in human colon carcinoma cell lines. The presence of clock components was investigated in single intact colonic crypts isolated by chelation from the biopsies of 25 patients (free of any sign of colonic lesions) undergoing routine colonoscopy and in cell lines of human colon carcinoma (Caco2 and HT29 clone 19A). Per-1, per-2, and clock mRNA were detected by real-time RT-PCR. The three-dimensional distributions of PER-1, PER-2, CLOCK, and BMAL1 proteins were recorded along colonic crypts by immunofluorescent confocal imaging. We demonstrate the presence of per-1, per-2, and clock mRNA in samples prepared from colonic crypts of 5 patients and in all cell lines. We also demonstrate the presence of two circadian clock proteins, PER-1 and CLOCK, in human colonocytes on crypts isolated from 20 patients (15 patients for PER-1 and 6 for CLOCK) and in colon carcinoma cells. Establishing the presence of clock proteins in human colonic crypts is the first step toward the study of the regulation of the intestinal circadian clock by nutrients and feeding rhythms.     

Exercise affects the gene expression profiles of human white blood cells.

White blood cells (WBCs) express tens of thousands of genes, whose expression levels are modified by genetic and external factors. The purpose of the present study was to investigate the effects of acute exercise on gene expression profiles (GEPs) of WBCs and to identify suitable genes that may serve as surrogate markers for monitoring exercise and training load. Five male participants performed an exhaustive treadmill test (ET) at 80% of their maximal O(2) uptake (Vo(2 max)) and a moderate treadmill test (MT) at 60% Vo(2 max) for exactly the same time approximately 2 wk later. WBCs were isolated by the erythrocyte lysis method. GEPs were measured using the Affymetrix GeneChip technology. After scaling, normalization, and filtering, groupwise comparisons of gene expression intensities were performed, and several measurements were validated by real-time PCR. We found 450 genes upregulated and 150 downregulated (>1.5-fold change; ANOVA with Benjamini-Hochberg correction, P < 0.05) after ET that were closely associated with the gene ontology lists "response to stress" and "inflammatory response". Analysis of mean expression levels after MT showed that the extent of up- and downregulation was workload dependent. The genes for the stress (heat shock) proteins HSPA1A and HSPH1 and for the matrix metalloproteinase MMP-9 showed the most prominent increases, whereas the YES1 oncogene (YES1) and CD160 (BY55) were most strongly reduced. Despite different methodological approaches used, the consistency of our results with the expression data of another study (Connolly PH, Caiozzo VJ, Zaldivar F, Nemet D, Larson J, Hung SP, Heck JD, Hatfield GW, Cooper DM. J Appl Physiol 97: 1461-1469, 2004) suggests that expression fingerprints are useful tools for monitoring exercise and training loads and thereby help to avoid training-associated health risks.     

Tissue classification with gene expression profiles.

Constantly improving gene expression profiling technologies are expected to provide understanding and insight into cancer-related cellular processes. Gene expression data is also expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. In this work we examine three sets of gene expression data measured across sets of tumor(s) and normal clinical samples: The first set consists of 2,000 genes, measured in 62 epithelial colon samples (Alon et al., 1999). The second consists of approximately equal to 100,000 clones, measured in 32 ovarian samples (unpublished extension of data set described in Schummer et al. (1999)). The third set consists of approximately equal to 7,100 genes, measured in 72 bone marrow and peripheral blood samples (Golub et al, 1999). We examine the use of scoring methods, measuring separation of tissue type (e.g., tumors from normals) using individual gene expression levels. These are then coupled with high-dimensional classification methods to assess the classification power of complete expression profiles. We present results of performing leave-one-out cross validation (LOOCV) experiments on the three data sets, employing nearest neighbor classifier, SVM (Cortes and Vapnik, 1995), AdaBoost (Freund and Schapire, 1997) and a novel clustering-based classification technique. As tumor samples can differ from normal samples in their cell-type composition, we also perform LOOCV experiments using appropriately modified sets of genes, attempting to eliminate the resulting bias. We demonstrate success rate of at least 90% in tumor versus normal classification, using sets of selected genes, with, as well as without, cellular-contamination-related members. These results are insensitive to the exact selection mechanism, over a certain range.     

Cardiac function-related gene expression profiles in human atrial myocytes

To obtain insights into the molecular pathogenesis of heart failure in humans, we have analyzed the expression profiles of>12,000 genes in a total of 17 human specimens of right atrial myocytes. From this large data set, we here tried to identify gene clusters, expression level of which is correlated precisely with clinical parameter values of cardiac function. We could reveal that cardiac myocytes with normal sinus rhythm were clearly differentiated, in the point of view of gene expression, from those with atrial fibrillation. Further, an expression profile-based prediction of arrhythmia by a newly developed "weighted-distance method" could efficiently diagnose our samples. We could even construct calculation formulae for the values of left ventricular ejection fraction based on the expression level of selected genes. To our best knowledge, this is the first report to indicate that pumping ability of heart can be predicted by any measures of atrium.