SLC25A32 sustains cancer cell proliferation by regulating flavin adenine nucleotide (FAD) metabolism.

SLC25A32 is a member of the solute carrier 25 family of mitochondrial transporters. SLC25A32 transports tetrahydrofolate (THF) as well as FAD into mitochondria and regulates mitochondrial one-carbon metabolism and redox balance. While it is known that cancer cells require one-carbon and FAD-dependent mitochondrial metabolism to sustain cell proliferation, the role of SLC25A32 in cancer cell growth remains unexplored. Our results indicate that the SLC25A32 gene is highly amplified in different tumors and that amplification correlates with increased mRNA expression and reduced patients´ survival. siRNA-mediated knock-down and CRISPR-mediated knock-out of SLC25A32 in cancer cells of different origins, resulted in the identification of cell lines sensitive and resistant to SLC25A32 inhibition. Mechanistically, tracing of deuterated serine revealed that SLC25A32 knock-down does not affect the mitochondrial/cytosolic folate flux as measured by Liquid Chromatography coupled Mass Spectrometry (LC-MS). Instead, SLC25A32 inhibition results in a respiratory chain dysfunction at the FAD-dependent complex II enzyme, induction of Reactive Oxygen Species (ROS) and depletion of reduced glutathione (GSH), which impairs cancer cell proliferation. Moreover, buthionine sulfoximine (BSO) treatment further sensitizes cells to ROS-mediated inhibition of cell proliferation upon SLC25A32 knock-down. Treatment of cells with the FAD precursor riboflavin and with GSH rescues cancer cell proliferation upon SLC25A32 down-regulation. Our results indicate that the reduction of mitochondrial FAD concentrations by targeting SLC25A32 has potential clinical applications as a single agent or in combination with approved cancer drugs that lead to increased oxidative stress and reduced tumor growth.

Hypoxia response and VEGF-A expression in human proximal tubular epithelial cells in stable and progressive renal disease.

Proteinuria, inflammation, chronic hypoxia, and rarefaction of peritubular capillaries contribute to the progression of renal disease by affecting proximal tubular epithelial cells (PTECs). To study the transcriptional response that separates patients with a stable course from those with a progressive course of disease, we isolated PTECs by laser capture microdissection from cryocut tissue sections of patients with proteinuric glomerulopathies (stable n=20, progressive n=11) with a median clinical follow-up of 26 months. Gene-expression profiling and a systems biology analysis identified activation of intracellular vascular endothelial growth factor (VEGF) signaling and hypoxia response pathways in progressive patients, which was associated with upregulation of hypoxia-inducible-factor (HIF)-1alpha and several HIF target genes, such as transferrin, transferrin-receptor, p21, and VEGF-receptor 1, but downregulation of VEGF-A. The inverse expression levels of HIF-1alpha and VEGF-A were significantly superior in predicting clinical outcome as compared with proteinuria, renal function, and degree of tubular atrophy and interstitial fibrosis at the time of biopsy. Interactome analysis showed the association of attenuated VEGF-A expression with the downregulation of genes that usually stimulate VEGF-A expression, such as epidermal growth factor (EGF), insulin-like growth factor-1 (IGF-1), and HIF-2alpha. In vitro experiments confirmed the positive regulatory effect of EGF and IGF-1 on VEGF-A transcription in human proximal tubular cells. Thus, in progressive but not in stable proteinuric kidney disease, human PTECs show an attenuated VEGF-A expression despite an activation of intracellular hypoxia response and VEGF signaling pathways, which might be due to a reduced expression of positive coregulators, such as EGF and IGF-1.

High EMT Signature Score of Invasive Non-Small Cell Lung Cancer (NSCLC) Cells Correlates with NFκB Driven Colony-Stimulating Factor 2 (CSF2/GM-CSF) Secretion by Neighboring Stromal Fibroblasts.

We established co-cultures of invasive or non-invasive NSCLC cell lines and various types of fibroblasts (FBs) to more precisely characterize the molecular mechanism of tumor-stroma crosstalk in lung cancer. The HGF-MET-ERK1/2-CREB-axis was shown to contribute to the onset of the invasive phenotype of Calu-1 with HGF being secreted by FBs. Differential expression analysis of the respective mono- and co-cultures revealed an upregulation of NFκB-related genes exclusively in co-cultures with Calu-1. Cytokine Array- and ELISA-based characterization of the "cytokine fingerprints" identified CSF2 (GM-CSF), CXCL1, CXCL6, VEGF, IL6, RANTES and IL8 as being specifically upregulated in various co-cultures. Whilst CXCL6 exhibited a strictly FB-type-specific induction profile regardless of the invasiveness of the tumor cell line, CSF2 was only induced in co-cultures of invasive cell lines regardless of the partnered FB type. These cultures revealed a clear link between the induction of CSF2 and the EMT signature of the cancer cell line. The canonical NFκB signaling in FBs, but not in tumor cells, was shown to be responsible for the induced and constitutive CSF2 expression. In addition to CSF2, cytokine IL6, IL8 and IL1B, and chemokine CXCL1 and CXCL6 transcripts were also shown to be increased in co-cultured FBs. In contrast, their induction was not strictly dependent on the invasiveness of the co-cultured tumor cell. In a multi-reporter assay, additional signaling pathways (AP-1, HIF1-α, KLF4, SP-1 and ELK-1) were found to be induced in FBs co-cultured with Calu-1. Most importantly, no difference was observed in the level of inducibility of these six signaling pathways with regard to the type of FBs used. Finally, upon tumor fibroblast interaction the massive induction of chemokines such as CXCL1 and CXCL6 in FBs might be responsible for increased recruitment of a monocytic cell line (THP-1) in a transwell assay.

Computational analysis workflows for Omics data interpretation.

Progress in experimental procedures has led to rapid availability of Omics profiles. Various open-access as well as commercial tools have been developed for storage, analysis, and interpretation of transcriptomics, proteomics, and metabolomics data. Generally, major analysis steps include data storage, retrieval, preprocessing, and normalization, followed by identification of differentially expressed features, functional annotation on the level of biological processes and molecular pathways, as well as interpretation of gene lists in the context of protein-protein interaction networks. In this chapter, we discuss a sequential transcriptomics data analysis workflow utilizing open-source tools, specifically exemplified on a gene expression dataset on familial hypercholesterolemia.

Linking molecular feature space and disease terms for the immunosuppressive drug rapamycin.

Next to development of novel drugs also drug repositioning appears promising for tackling unmet clinical needs. Here Omics provided the ground for novel analysis strategies for linking drug and disease by integrating profiles on the molecular as well as the clinical data level. We developed a workflow for linking drugs and diseases for identifying repositioning options, and exemplify the procedure for the immunosuppressive drug rapamycin. Our strategy rests on delineating a drug-specific molecular profile by combining Omics data reflecting the drug's impact on the cellular status as well as drug-associated molecular features extracted from the scientific literature. For rapamycin the respective profile held 905 unique molecular features reflecting defined molecular processes as identified by molecular pathway and process enrichment analysis. Literature mining identified 419 diseases significantly associated with this rapamycin molecular feature list, and transforming the significance of gene-disease associations into a continuous score allowed us to compute ROC and precision-recall for comparing this disease list with diseases already undergoing clinical trials utilizing rapamycin. The AUC of this assignment was computed as 0.84, indicating excellent recovery of relevant disease terms solely based on the drug molecular feature profile. We verified relevant indications by comparing molecular feature sets characteristic for the identified diseases to the drug molecular feature profile, demonstrating highly significant overlaps. The presented workflow allowed positive identification of diseases associated with rapamycin utilizing the drug-specific molecular feature profile, and may be well applicable to other drugs of interest.

An interaction network predicted from public data as a discovery tool: application to the Hsp90 molecular chaperone machine.

Understanding the functions of proteins requires information about their protein-protein interactions (PPI). The collective effort of the scientific community generates far more data on any given protein than individual experimental approaches. The latter are often too limited to reveal an interactome comprehensively. We developed a workflow for parallel mining of all major PPI databases, containing data from several model organisms, and to integrate data from the literature for a protein of interest. We applied this novel approach to build the PPI network of the human Hsp90 molecular chaperone machine (Hsp90Int) for which previous efforts have yielded limited and poorly overlapping sets of interactors. We demonstrate the power of the Hsp90Int database as a discovery tool by validating the prediction that the Hsp90 co-chaperone Aha1 is involved in nucleocytoplasmic transport. Thus, we both describe how to build a custom database and introduce a powerful new resource for the scientific community.

Synthetic lethal hubs associated with vincristine resistant neuroblastoma.

Chemotherapy of cancer experiences a number of shortcomings including development of drug resistance. This fact also holds true for neuroblastoma utilizing chemotherapeutics as vincristine. We performed a comparative analysis of molecular and cellular mechanisms associated with vincristine resistance utilizing cell line as well as human tissue data. Differential gene expression analysis revealed molecular features, processes and pathways afflicted with drug resistance mechanisms in general, and specifically with vincristine significantly involving actin associated features. However, specific mode of resistance as well as underlying genotype of parental, vincristine sensitive cells apparently exhibited significant heterogeneity. No consensus profile for vincristine resistance could be derived, but resistance-associated changes on the level of individual neuroblastoma cell lines as well as individual patient profiles became clearly evident. Based on these prerequisites we utilized the concept of synthetic lethality aimed at identifying hub proteins which when inhibited promise to induce cell death due to a synthetic lethal interaction with down-regulated, chemoresistance associated features. Our screening procedure identified synthetic lethal hub proteins afflicted with actin associated processes holding synthetic lethal interactions to down-regulated features individually found in all chemoresistant cell lines tested, therefore promising an improved therapeutic window. Verification of such synthetic lethal hub candidates in human neuroblastoma tissue expression profiles indicated the feasibility of this screening approach for addressing vincristine resistance in neuroblastoma.

Integrative bioinformatics analysis of proteins associated with the cardiorenal syndrome.

The cardiorenal syndrome refers to the coexistence of kidney and cardiovascular disease, where cardiovascular events are the most common cause of death in patients with chronic kidney disease. Both, cardiovascular as well as kidney diseases have been extensively analyzed on a molecular level, resulting in molecular features and associated processes indicating a cross-talk of the two disease etiologies on a pathophysiological level. In order to gain a comprehensive picture of molecular factors contributing to the bidirectional interplay between kidney and cardiovascular system, we mined the scientific literature for molecular features reported as associated with the cardiorenal syndrome, resulting in 280 unique genes/proteins. These features were then analyzed on the level of molecular processes and pathways utilizing various types of protein interaction networks. Next to well established molecular features associated with the renin-angiotensin system numerous proteins involved in signal transduction and cell communication were found, involving specific molecular functions covering receptor binding with natriuretic peptide receptor and ligands as well known example. An integrated analysis of identified features pinpointed a protein interaction network involving mediators of hemodynamic change and an accumulation of features associated with the endothelin and VEGF signaling pathway. Some of these features may function as novel therapeutic targets.

A dependency graph approach for the analysis of differential gene expression profiles.

A central aim of differential gene expression profile analysis is to provide an interpretation of given data at the level of biological processes and pathways. However, traversing descriptive data into context is not straightforward. We present a gene-centric dependency graph approach supporting an interpretation of omics profiles at the level of affected networks. The core of our dependency graph comprises data objects encoding the functional categorization of a particular gene, its tissue-specific reference gene expression, as well as known interactions and subcellular location of assigned proteins. On the basis of these genome, transcriptome, and proteome data we compute pair-wise object (gene) dependencies and interpret them as weighted edges in a dependency graph. Mapping of omics profiles on this graph can be used to identify connectors linking features of the omics list, in turn providing the basis for identification of subgraphs and motifs characterizing the cellular state under analysis. We exemplify this approach by analyzing differential gene expression data characterizing B-cell lymphoma and demonstrate the identification of B-cell lymphoma associated subgraphs.

Biomarker candidates for cardiovascular disease and bone metabolism disorders in chronic kidney disease: a systems biology perspective.

Patients with chronic kidney disease (CKD) show a panel of partially de-regulated serum markers indicative for bone metabolism disorders and cardiovascular diseases (CVDs). This review provides an overview of currently reported biomarker candidates at the interface of kidney disease, bone metabolism disorders and CVDs, and gives details on their functional interplay on the level of protein-protein interaction data. We retrieved 13 publications from 1999 to 2006 reporting 31 genes associated with CVDs, and 46 genes associated with bone metabolism disorders in patients with CKD. We identified these genes to be functionally involved in signal transduction processes, cell communication, immunity and defence, as well as skeletal development. On the basis of the given set of 77 genes further 276 interacting proteins were identified using reference data on known protein interactions. Their functional interplay was estimated by linking properties reflected by gene expression data characterizing CKD, gene ontology terms as provided by the gene ontology consortium and transcription factor binding site profiles. Highly connected sub-networks of proteins associated with CKD, CVDs or bone metabolism disorders were detected involving proteins like collagens (COL1A1, COL1A2), fibronectin, transforming growth factor-?1, or components of fibrinogen (FG-alpha, FG-beta, FG-gamma). A systems biology approach provides a methodological framework for linking singular biomarker candidates towards deriving functional dependencies among clinically interlinked diseases.

Linking the ovarian cancer transcriptome and immunome.

BACKGROUND: Autoantigens have been reported in a variety of tumors, providing insight into the interplay between malignancies and the immune response, and also giving rise to novel diagnostic and therapeutic concepts. Why certain tumor-associated proteins induce an immune response remains largely elusive. RESULTS: This paper analyzes the proposed link between increased abundance of a protein in cancerous tissue and the increased potential of the protein for induction of a humoral immune response, using ovarian cancer as an example. Public domain data sources on differential gene expression and on autoantigens associated with this malignancy were extracted and compared, using bioinformatics analysis, on the levels of individual genes and proteins, transcriptional coregulation, joint functional pathways, and shared protein-protein interaction networks. Finally, a selected list of ovarian cancer-associated, differentially regulated proteins was tested experimentally for reactivity with antibodies prevalent in sera of ovarian cancer patients.Genes reported as showing differential expression in ovarian cancer exhibited only minor overlap with the public domain list of ovarian cancer autoantigens. However, experimental screening for antibodies directed against antigenic determinants from ovarian cancer-associated proteins yielded clear reactions with sera. CONCLUSION: A link between tumor protein abundance and the likelihood of induction of a humoral immune response in ovarian cancer appears evident.