Human and mouse switch-like genes share common transcriptional regulatory mechanisms for bimodality

Species differential regulation of COX2 can be described by an NFκB-dependent logic AND gate

Cyclooxygenase 2 (COX2), a key regulatory enzyme of the prostaglandin/eicosanoid pathway, is an important target for anti-inflammatory therapy. It is highly induced by pro-inflammatory cytokines in a Nuclear factor kappa B (NFκB)-dependent manner. However, the mechanisms determining the amplitude and dynamics of this important pro-inflammatory event are poorly understood. Furthermore, there is significant difference between human and mouse COX2 expression in response to the inflammatory stimulus tumor necrosis factor alpha (TNFα). Here, we report the presence of a molecular logic AND gate composed of two NFκB response elements (NREs) which controls the expression of human COX2 in a switch-like manner. Combining quantitative kinetic modeling and thermostatistical analysis followed by experimental validation in iterative cycles, we show that the human COX2 expression machinery regulated by NFκB displays features of a logic AND gate. We propose that this provides a digital, noise-filtering mechanism for a tighter control of expression in response to TNFα, such that a threshold level of NFκB activation is required before the promoter becomes active and initiates transcription. This NFκB-regulated AND gate is absent in the mouse COX2 promoter, most likely contributing to its differential graded response in promoter activity and protein expression to TNFα. Our data suggest that the NFκB-regulated AND gate acts as a novel mechanism for controlling the expression of human COX2 to TNFα, and its absence in the mouse COX2 provides the foundation for further studies on understanding species-specific differential gene regulation.

Long-acting beneficial effect of percutaneously intramyocardially delivered secretome of apoptotic peripheral blood cells on porcine chronic ischemic left ventricular dysfunction

The quantity of cells with paracrine effects for use in myocardial regeneration therapy is limited. This study investigated the effects of catheter-based endomyocardial delivery of secretome of 2.5 × 10(9) apoptotic peripheral blood mononuclear cells (APOSEC) on porcine chronic post-myocardial infarction (MI) left ventricular (LV) dysfunction and on gene expression. Closed-chest reperfused MI was induced in pigs by 90-min occlusion followed by reperfusion of the mid-LAD (day 0). At day 30, animals were randomized to receive porcine APOSEC (n = 8) or medium solution (control; n = 8) injected intramyocardially into the MI border zone using 3D NOGA guidance. At day 60, cardiac MRI with late enhancement and diagnostic NOGA (myocardial viability) were performed. Gene expression profiling of the infarct core, border zone, and normal myocardium was performed using microarray analysis and confirmed by quantitative real-time PCR. Injection of APOSEC significantly decreased infarct size (p < 0.05) and improved cardiac index and myocardial viability compared to controls. A trend towards higher LV ejection fraction was observed in APOSEC vs. controls (45.4 ± 5.9% vs. 37.4 ± 8.9%, p = 0.052). Transcriptome analysis revealed significant downregulation of caspase-1, tumor necrosis factor and other inflammatory genes in APOSEC-affected areas. rtPCR showed higher expression of myogenic factor Mefc2 (p < 0.05) and downregulated caspase genes (p < 0.05) in APOSEC-treated pigs. In conclusion, overexpression of MEF2c and repression of caspase was related to decreased infarct size and improved cardiac function in secretome-treated animals. Altered gene expression 1-month post-APOSEC treatment proved the long-acting effects of cell-free therapy with paracrine factors.

Genetic aspects of cell line development from a synthetic biology perspective

Animal cells can be regarded as factories for the production of relevant proteins. The advances described in this chapter towards the development of cell lines with higher productivity capacities, certain metabolic and proliferation properties, reduced apoptosis and other features must be regarded in an integrative perspective. The systematic application of systems biology approaches in combination with a synthetic arsenal for targeted modification of endogenous networks are proposed to lead towards the achievement of a predictable and technologically advanced cell system with high biotechnological impact.

Bimodal distribution of RNA expression levels in human skeletal muscle tissue

Background

Many human diseases and phenotypes are related to RNA expression, levels of which are influenced by a wide spectrum of genetic and exposure-related factors. In a large genome-wide study of muscle tissue expression, we found that some genes exhibited a bimodal distribution of RNA expression, in contrast to what is usually assumed in studies of a single healthy tissue. As bimodality has classically been considered a hallmark of genetic control, we assessed the genome-wide prevalence, cause, and association of this phenomenon with diabetes-related phenotypes in skeletal muscle tissue from 225 healthy Pima Indians using exon array expression chips.

Results

Two independent batches of microarrays were used for bimodal assessment and comparison. Of the 17,881 genes analyzed, eight (GSTM1, HLA-DRB1, ERAP2, HLA-DRB5, MAOA, ACTN3, NR4A2, and THNSL2) were found to have bimodal expression replicated in the separate batch groups, while 24 other genes had evidence of bimodality in only one group. Some bimodally expressed genes had modest associations with pre-diabetic phenotypes, of note ACTN3 with insulin resistance. Most of the other bimodal genes have been reported to be involved with various other diseases and characteristics. Association of expression with cis genetic variation in a subset of 149 individuals found all but one of the confirmed bimodal genes and nearly half of all potential ones to be highly significant expression quantitative trait loci (eQTL). The rare prevalence of these bimodally expressed genes found after controlling for batch effects was much lower than the prevalence reported in other studies. Additional validation in data from separate muscle expression studies confirmed the low prevalence of bimodality we observed.

Conclusions

We conclude that the prevalence of bimodal gene expression is quite rare in healthy muscle tissue (<0.2%), and is much lower than limited reports from other studies. The major cause of these clearly bimodal expression patterns in homogeneous tissue appears to be cis-polymorphisms, indicating that such bimodal genes are, for the most part, eQTL. The high frequency of disease associations reported with these genes gives hope that this unique feature may identify or actually be an underlying factor responsible for disease development.

MMP1 bimodal expression and differential response to inflammatory mediators is linked to promoter polymorphisms

Background

Identifying the functional importance of the millions of single nucleotide polymorphisms (SNPs) in the human genome is a difficult challenge. Therefore, a reverse strategy, which identifies functionally important SNPs by virtue of the bimodal abundance across the human population of the SNP-related mRNAs will be useful. Those mRNA transcripts that are expressed at two distinct abundances in proportion to SNP allele frequency may warrant further study. Matrix metalloproteinase 1 (MMP1) is important in both normal development and in numerous pathologies. Although much research has been conducted to investigate the expression of MMP1 in many different cell types and conditions, the regulation of its expression is still not fully understood.

Results

In this study, we used a novel but straightforward method based on agglomerative hierarchical clustering to identify bimodally expressed transcripts in human umbilical vein endothelial cell (HUVEC) microarray data from 15 individuals. We found that MMP1 mRNA abundance was bimodally distributed in un-treated HUVECs and showed a bimodal response to inflammatory mediator treatment. RT-PCR and MMP1 activity assays confirmed the bimodal regulation and DNA sequencing of 69 individuals identified an MMP1 gene promoter polymorphism that segregated precisely with the MMP1 bimodal expression. Chromatin immunoprecipation (ChIP) experiments indicated that the transcription factors (TFs) ETS1, ETS2 and GATA3, bind to the MMP1 promoter in the region of this polymorphism and may contribute to the bimodal expression.

Conclusions

We describe a simple method to identify putative bimodally expressed RNAs from transcriptome data that is effective yet easy for non-statisticans to understand and use. This method identified bimodal endothelial cell expression of MMP1, which appears to be biologically significant with implications for inflammatory disease. (271 Words)

RB-pathway disruption in breast cancer: differential association with disease subtypes, disease-specific prognosis and therapeutic response

In breast cancer, inactivation of the RB tumor suppressor gene is believed to occur via multiple mechanisms to facilitate tumorigenesis. However, the prognostic and predictive value of RB status in disease-specific clinical outcomes has remained uncertain. We investigated RB pathway deregulation in the context of both ER-positive and ER-negative disease using combined microarray datasets encompassing over 900 breast cancer patient samples. Disease-specific characteristics of RB pathway deregulation were investigated in this dataset by evaluating correlation among pathway genes as well as differential expression across patient tumor populations defined by ER status. Survival analysis among these breast cancer samples demonstrates that the RB-loss signature is associated with poor disease outcome within several independent cohorts. Within the ER-negative subpopulation, the RB-loss signature is associated with improved response to chemotherapy and longer relapse-free survival. Additionally, while individual genes in the RB target signature closely reproduce its prognostic value, they also serve to predict and monitor response to therapeutic compounds, such as the cytostatic agent PD-0332991. These results indicate that the RB-loss signature expression is associated with poor outcome in breast cancer, but predicts improved response to chemotherapy based on data in ER-negative populations. While the RB-loss signature, as a whole, demonstrates prognostic and predictive utility, a small subset of markers could be sufficient to stratify patients based on RB function and inform the selection of appropriate therapeutic regimens.

Asymmetric microarray data produces gene lists highly predictive of research literature on multiple cancer types

Background

Much of the public access cancer microarray data is asymmetric, belonging to datasets containing no samples from normal tissue. Asymmetric data cannot be used in standard meta-analysis approaches (such as the inverse variance method) to obtain large sample sizes for statistical power enrichment. Noting that plenty of normal tissue microarray samples exist in studies not involving cancer, we investigated the viability and accuracy of an integrated microarray analysis approach based on significance analysis of microarrays (merged SAM) using a collection of data from separate diseased and normal samples.

Results

We focused on five solid cancer types (colon, kidney, liver, lung, and pancreas), where available microarray data allowed us to compare meta-analysis and integrated approaches. Our results from the merged SAM significantly overlapped gene lists from the validated inverse-variance method. Both meta-analysis and merged SAM approaches successfully captured the aberrances in the cell cycle that commonly occur in the different cancer types. However, the integrated SAM analysis replicated the known cancer literature (excluding microarray studies) with much more accuracy than the meta-analysis.

Conclusion

The merged SAM test is a powerful, robust approach for combining data from similar platforms and for analyzing asymmetric datasets, including those with only normal or only cancer samples that cannot be utilized by meta-analysis methods. The integrated SAM approach can also be used in comparing global gene expression between various subtypes of cancer arising from the same tissue.

Analysis of the CYP1A1 mRNA dose-response in human keratinocytes indicates that relative potencies of dioxins, furans, and PCBs are species and congener specific

Reports indicate that toxic equivalency factors (TEFs) based primarily on rodent data do not accurately predict in vitro human responsiveness to certain dioxin-like chemicals (DLCs). To investigate this in cells responsive to dioxins and relevant to chloracne, normal human epidermal keratinocytes were treated with 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and several DLCs, each with a TEF value of 0.1, representing three classes of congeners. We estimated half maximal effective concentration (EC50)-based donor-specific relative potency (REP) values for cytochrome P450 1A1 (CYP1A1) messenger RNA (mRNA) induction for TCDD, 1,2,3,6,7,8-hexachlorodibenzo-p-dioxin (HxCDD), 2,3,7,8-tetrachlorodibenzofuran (TCDF), 1,2,3,6,7,8-hexachlorodibenzofuran (HxCDF), and 3,3',4,4',5-pentachlorobiphenyl (PCB 126). We also determined EC50-based population-level REP values (n = 4) for CYP1A1 mRNA induction for TCDD, HxCDF, and PCB 126. Furthermore, an alternative factor, the relative threshold factor (RTF) based on the low end (threshold) of the dose-response curve, was calculated. Our results demonstrated that HxCDF had a population-based REP value of 0.98, 9.8-fold higher than its assigned TEF value of 0.1. Conversely, PCB 126 had an REP value of 0.0027 and an RTF of 0.0022, 37-fold and 45-fold less than its assigned TEF of 0.1, respectively. The REP values for HxCDD and TCDF were 0.24 and 0.10, respectively, similar to their assigned value of 0.1. Therefore, although the DLCs tested in the current study all possessed the same assigned TEF value of 0.1, congener-specific differences in REPs and RTFs were observed for human keratinocytes. These congener-specific discrepancies are likely because of differences in interspecies factors that have yet to be defined.

Discovering Disease-specific Biomarker Genes for Cancer Diagnosis and Prognosis

The large amounts of microarray data provide us a great opportunity to identify gene expression profiles (GEPs) in different tissues or disease states. Disease-specific biomarker genes likely share GEPs that are distinct in disease samples as compared with normal samples. The similarity of the GEPs may be evaluated by Pearson Correlation Coefficient (PCC) and the distinctness of GEPs may be assessed by Kolmogorov-Smirnov distance (KSD). In this study, we used the PCC and KSD metrics for GEPs to identify disease-specific (cancer-specific) biomarkers. We first analyzed and compared GEPs using microarray datasets for smoking and lung cancer. We found that the number of genes with highly different GEPs between comparing groups in smoking dataset was much larger than that in lung cancer dataset; this observation was further verified when we compared GEPs in smoking dataset with prostate cancer datasets. Moreover, our Gene Ontology analysis revealed that the top ranked biomarker candidate genes for prostate cancer were highly enriched in molecular function categories such as ‘cytoskeletal protein binding’ and biological process categories such as ‘muscle contraction’. Finally, we used two genes, ACTC1 (encoding an actin subunit) and HPN (encoding hepsin), to demonstrate the feasibility of diagnosing and monitoring prostate cancer using the expression intensity histograms of marker genes. In summary, our results suggested that this approach might prove promising and powerful for diagnosing and monitoring the patients who come to the clinic for screening or evaluation of a disease state including cancer.

Classification of genes and putative biomarker identification using distribution metrics on expression profiles

Background

Identification of genes with switch-like properties will facilitate discovery of regulatory mechanisms that underlie these properties, and will provide knowledge for the appropriate application of Boolean networks in gene regulatory models. As switch-like behavior is likely associated with tissue-specific expression, these gene products are expected to be plausible candidates as tissue-specific biomarkers.

Methodology/Principal Findings

In a systematic classification of genes and search for biomarkers, gene expression profiles (GEPs) of more than 16,000 genes from 2,145 mouse array samples were analyzed. Four distribution metrics (mean, standard deviation, kurtosis and skewness) were used to classify GEPs into four categories: predominantly-off, predominantly-on, graded (rheostatic), and switch-like genes. The arrays under study were also grouped and examined by tissue type. For example, arrays were categorized as ‘brain group’ and ‘non-brain group’; the Kolmogorov-Smirnov distance and Pearson correlation coefficient were then used to compare GEPs between brain and non-brain for each gene. We were thus able to identify tissue-specific biomarker candidate genes.

Conclusions/Significance

The methodology employed here may be used to facilitate disease-specific biomarker discovery.