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Abstract Background Treatment of non-small cell lung cancer with novel targeted therapies is a major unmet clinical need. Alternative splicing is a mechanism which generates diverse protein products and is of functional relevance in cancer. Results In this study, a genome-wide analysis of the alteration of splicing patterns between lung cancer and normal lung tissue was performed. We generated an exon array data set derived from matched pairs of lung cancer and normal lung tissue including both the adenocarcinoma and the squamous cell carcinoma subtypes. An enhanced workflow was developed to reliably detect differential splicing in an exon array data set. In total, 330 genes were found to be differentially spliced in non-small cell lung cancer compared to normal lung tissue. Microarray findings were validated with independent laboratory methods for CLSTN1, FN1, KIAA1217, MYO18A, NCOR2, NUMB, SLK, SYNE2, TPM1, (in total, 10 events) and ADD3, which was analysed in depth. We achieved a high validation rate of 69%. Evidence was found that the activity of FOX2, the splicing factor shown to cause cancer-specific splicing patterns in breast and ovarian cancer, is not altered at the transcript level in several cancer types including lung cancer. Conclusions This study demonstrates how alternatively spliced genes can reliably be identified in a cancer data set. Our findings underline that key processes of cancer progression in NSCLC are affected by alternative splicing, which can be exploited in the search for novel targeted therapies.

Pancreatic ductal adenocarcinoma (PDAC) represents the most common form of pancreatic cancer with rising incidence in developing countries and overall 5-year survival rates of less than 5%. The most frequent mutations in PDAC are gain-of-function mutations in KRAS as well as loss-of-function mutations in p53. Both mutations have severe impacts on the metabolism of tumor cells. Many of these metabolic changes are mediated by transporters or channels that regulate the exchange of metabolites and ions between the intracellular compartment and the tumor microenvironment. In the study presented here, our goal was to identify novel transporters or channels that regulate oxidative phosphorylation (OxPhos) in PDAC in order to characterize novel potential drug targets for the treatment of these cancers. We set up a Seahorse Analyzer XF based siRNA screen and identified previously described as well as novel regulators of OxPhos. The siRNA that resulted in the greatest change in cellular oxygen consumption was targeting the KCNN4 gene, which encodes for the Ca2+-sensitive K+ channel KCa3.1. This channel has not previously been reported to regulate OxPhos. Knock-down experiments as well as the use of a small molecule inhibitor confirmed its role in regulating oxygen consumption, ATP production and cellular proliferation. Furthermore, PDAC cell lines sensitive to KCa3.1 inhibition were shown to express the channel protein in the plasma membrane as well as in the mitochondria. These differences in the localization of KCa3.1 channels as well as differences in the regulation of cellular metabolism might offer opportunities for targeted therapy in subsets of PDAC. [ABSTRACT FROM AUTHOR]

Functional RNAi based screening is affected by large numbers of false positive and negative hits due to prevalent sequence based off-target effects. We performed a druggable genome targeting siRNA screen intended to identify novel regulators of E-cadherin (CDH1) expression, a known key player in epithelial mesenchymal transition (EMT). Analysis of primary screening results indicated a large number of false-positive hits. To address these crucial difficulties we developed an analysis method, SENSORS, which, similar to published methods, is a seed enrichment strategy for analyzing siRNA off-targets in RNAi screens. Using our approach, we were able to demonstrate that accounting for seed based off-target effects stratifies primary screening results and enables the discovery of additional screening hits. While traditional hit detection methods are prone to false positive results which are undetected, we were able to identify false positive hits robustly. Transcription factor MYBL1 was identified as a putative novel target required for CDH1 expression and verified experimentally. No siRNA pool targeting MYBL1 was present in the used siRNA library. Instead, MYBL1 was identified as a putative CDH1 regulating target solely based on the SENSORS off-target score, i.e. as a gene that is a cause for off-target effects down regulating E-cadherin expression. [ABSTRACT FROM AUTHOR]

Background: Treatment of non-small cell lung cancer with novel targeted therapies is a major unmet clinical need. Alternative splicing is a mechanism which generates diverse protein products and is of functional relevance in cancer. Results: In this study, a genome-wide analysis of the alteration of splicing patterns between lung cancer and normal lung tissue was performed. We generated an exon array data set derived from matched pairs of lung cancer and normal lung tissue including both the adenocarcinoma and the squamous cell carcinoma subtypes. An enhanced workflow was developed to reliably detect differential splicing in an exon array data set. In total, 330 genes were found to be differentially spliced in non-small cell lung cancer compared to normal lung tissue. Microarray findings were validated with independent laboratory methods for CLSTN1, FN1, KIAA1217, MYO18A, NCOR2, NUMB, SLK, SYNE2, TPM1, (in total, 10 events) and ADD3, which was analysed in depth. We achieved a high validation rate of 69%. Evidence was found that the activity of FOX2, the splicing factor shown to cause cancerspecific splicing patterns in breast and ovarian cancer, is not altered at the transcript level in several cancer types including lung cancer. Conclusions: This study demonstrates how alternatively spliced genes can reliably be identified in a cancer data set. Our findings underline that key processes of cancer progression in NSCLC are affected by alternative splicing, which can be exploited in the search for novel targeted therapies. [ABSTRACT FROM AUTHOR]

Heart failure with preserved ejection fraction (HFpEF) can arise from cardiac and vascular remodeling processes following long-lasting hypertension. Efficacy of common HF therapeutics is unsatisfactory in HFpEF. Evidence suggests that stimulators of the nitric oxide-sensitive soluble guanylyl cyclase (NOsGC) could be of use here. We aimed to characterize the complex cardiovascular effects of NOsGC stimulation using NO-independent stimulator BAY 41-8543 in a double-transgenic rat (dTGR) model of HFpEF. We show a drastically improved survival rate of treated dTGR. We observed less cardiac fibrosis, macrophage infiltration, and gap junction remodeling in treated dTGR. Microarray analysis revealed that treatment of dTGR corrected the dysregulateion of cardiac genes associated with fibrosis, inflammation, apoptosis, oxidative stress, and ion channel function toward an expression profile similar to healthy controls. Treatment reduced systemic blood pressure levels and improved endothelium-dependent vasorelaxation of resistance vessels. Further comprehensive in vivo phenotyping showed an improved diastolic cardiac function, improved hemodynamics, and less susceptibility to ventricular arrhythmias. Short-term BAY 41-8543 application in isolated untreated transgenic hearts with structural remodeling significantly reduced the occurrence of ventricular arrhythmias, suggesting a direct nongenomic role of NOsGC stimulation on excitation. Thus, NOsGC stimulation was highly effective in improving several HFpEF facets in this animal model, underscoring its potential value for patients.

Pancreatic ductal adenocarcinoma (PDAC) represents the most common form of pancreatic cancer with rising incidence in developing countries and overall 5-year survival rates of less than 5%. The most frequent mutations in PDAC are gain-of-function mutations in KRAS as well as loss-of-function mutations in p53. Both mutations have severe impacts on the metabolism of tumor cells. Many of these metabolic changes are mediated by transporters or channels that regulate the exchange of metabolites and ions between the intracellular compartment and the tumor microenvironment. In the study presented here, our goal was to identify novel transporters or channels that regulate oxidative phosphorylation (OxPhos) in PDAC in order to characterize novel potential drug targets for the treatment of these cancers. We set up a Seahorse Analyzer XF based siRNA screen and identified previously described as well as novel regulators of OxPhos. The siRNA that resulted in the greatest change in cellular oxygen consumption was targeting the KCNN4 gene, which encodes for the Ca2+-sensitive K+ channel KCa3.1. This channel has not previously been reported to regulate OxPhos. Knock-down experiments as well as the use of a small molecule inhibitor confirmed its role in regulating oxygen consumption, ATP production and cellular proliferation. Furthermore, PDAC cell lines sensitive to KCa3.1 inhibition were shown to express the channel protein in the plasma membrane as well as in the mitochondria. These differences in the localization of KCa3.1 channels as well as differences in the regulation of cellular metabolism might offer opportunities for targeted therapy in subsets of PDAC.

Measurement of cardiac output (CO) is of importance in the diagnostic of critically ill patients. The invasive approach of thermodilution (TD) via pulmonary artery catheter is clinically widely used. A new non-invasive technique of inert gas rebreathing (IGR) shows a good correlation with TD measurements in spontaneously breathing individuals. For the first time, we investigated whether IGR can also be applied to sedated and mechanically ventilated subjects with a clinical point of care device. CO data from IGR were compared with TD in six healthy mongrel dogs. Data sampling was repeated under baseline conditions (rest) and under stress challenge by applying 10 μg/kg/min of dobutamine intravenously. Switching from mechanical ventilation to IGR, as well as the rebreathing procedures, were carried out manually. Cardiac output data from IGR and TD correlated with a coefficient of r=0.90 (95% confidence interval [0.81; 0.95]). The Bland-Altman analysis showed a bias of 0.46 l/min for the IGR CO measurements. Ninety-five percent of all differences fall in the interval [-1.03; 1.95], being the limit of the ± 1.96 standard deviation lines. IGR is a new approach for non-invasive cardiac output measurement in mechanically ventilated individuals, but requires further investigation for clinical use.

Clustering of microarray gene expression data is performed routinely, for genes as well as for samples. Clustering of genes can exhibit functional relationships between genes; clustering of samples on the other hand is important for finding e.g. disease subtypes, relevant patient groups for stratification or related treatments. Usually this is done by first filtering the genes for high-variance under the assumption that they carry most of the information needed for separating different sample groups. If this assumption is violated, important groupings in the data might be lost. Furthermore, classical clustering methods do not facilitate the biological interpretation of the results. Therefore, we propose to methodologically integrate the clustering algorithm with prior biological information. This is different from other approaches as knowledge about classes of genes can be directly used to ease the interpretation of the results and possibly boost clustering performance. Our approach computes dendrograms that resemble decision trees with gene classes used to split the data at each node which can help to find biologically meaningful differences between the sample groups. We have tested the proposed method both on simulated and real data and conclude its usefulness as a complementary method, especially when assumptions of few differentially expressed genes along with an informative mapping of genes to different classes are met.

Functional genomics is a challenging new way to address complex diseases such as osteoarthritis on a molecular level. This complements previous research and will open up new areas of so far unrecognized molecular networks. In this respect, articular cartilage is a good target for functional genomics as it contains only one cell type to which all expression signals can be attributed to. Despite considerable limitations at present, such as a low sensitivity and insensitivity to alternative splicing, posttranscriptional regulation, and posttranslational modification, cDNA-array technology provides a powerful tool to obtain an overview on gene expression patterns hardly achievable with other techniques. This has been shown to be true for known genes as well as for the identification of new genes of interest. Therefore, gene expression analysis will help to identify single genes depending on the disease and experimental conditions investigated. However, the expression pattern of the plethora of expressed genes will paint a picture (network) of disease context, maybe even more pushing forward our understanding of complex diseases such as osteoarthritis.