The Somatostatin Analogue Octreotide Inhibits Growth of Small Intestine Neuroendocrine Tumour Cells

Octreotide is a widely used synthetic somatostatin analogue that significantly improves the management of neuroendocrine tumours (NETs). Octreotide acts through somatostatin receptors (SSTRs). However, the molecular mechanisms leading to successful disease control or symptom management, especially when SSTRs levels are low, are largely unknown. We provide novel insights into how octreotide controls NET cells. CNDT2.5 cells were treated from 1 day up to 16 months with octreotide and then were profiled using Affymetrix microarray analysis. Quantitative real-time PCR and western blot analyses were used to validate microarray profiling in silico data. WST-1 cell proliferation assay was applied to evaluate cell growth of CNDT2.5 cells in the presence or absence of 1 mM octreotide at different time points. Moreover, laser capture microdissected tumour cells and paraffin embedded tissue slides from SI-NETs at different stages of disease were used to identify transcriptional and translational expression. Microarrays analyses did not reveal relevant changes in SSTR expression levels. Unexpectedly, six novel genes were found to be upregulated by octreotide: annexin A1 (ANXA1), rho GTPase-activating protein 18 (ARHGAP18), epithelial membrane protein 1 (EMP1), growth/differentiation factor 15 (GDF15), TGF-beta type II receptor (TGFBR2) and tumour necrosis factor (ligand) superfamily member 15 (TNFSF15). Furthermore, these novel genes were expressed in tumour tissues at transcript and protein levels. We suggest that octreotide may use a potential novel framework to exert its beneficial effect as a drug and to convey its action on neuroendocrine cells. Thus, six novel genes may regulate cell growth and differentiation in normal and tumour neuroendocrine cells and have a role in a novel octreotide mechanism system

Paraneoplastic Antigen Ma2 Autoantibodies as Specific Blood Biomarkers for Detection of Early Recurrence of Small Intestine Neuroendocrine Tumors

Small intestine neuroendocrine tumors (SI-NETs) belong to a rare group of cancers. Most patients have developed metastatic disease at the time of diagnosis, for which there is currently no cure. The delay in diagnosis is a major issue in the clinical management of the patients and new markers are urgently needed. We have previously identified paraneoplastic antigen Ma2 (PNMA2) as a novel SI-NET tissue biomarker. Therefore, we evaluated whether Ma2 autoantibodies detection in the blood stream is useful for the clinical diagnosis and recurrence of SI-NETs

Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data

Deciphering transcription factor networks from microarray data remains difficult. This study presents a simple method to infer the regulation of transcription factors from microarray data based on well-characterized target genes. We generated a catalog containing transcription factors associated with 2720 target genes and 6401 experimentally validated regulations. When it was available, a distinction between transcriptional activation and inhibition was included for each regulation. Next, we built a tool (www.tfacts.org) that compares submitted gene lists with target genes in the catalog to detect regulated transcription factors. TFactS was validated with published lists of regulated genes in various models and compared to tools based on in silico promoter analysis. We next analyzed the NCI60 cancer microarray data set and showed the regulation of SOX10, MITF and JUN in melanomas. We then performed microarray experiments comparing gene expression response of human fibroblasts stimulated by different growth factors. TFactS predicted the specific activation of Signal transducer and activator of transcription factors by PDGF-BB, which was confirmed experimentally. Our results show that the expression levels of transcription factor target genes constitute a robust signature for transcription factor regulation, and can be efficiently used for microarray data mining

Hepatocyte MyD88 affects bile acids, gut microbiota and metabolome contributing to regulate glucose and lipid metabolism

OBJECTIVE: To examine the role of hepatocyte myeloid differentiation primary-response gene 88 (MyD88) on glucose and lipid metabolism. DESIGN: To study the impact of the innate immune system at the level of the hepatocyte and metabolism, we generated mice harbouring hepatocyte-specific deletion of MyD88. We investigated the impact of the deletion on metabolism by feeding mice with a normal control diet or a high-fat diet for 8 weeks. We evaluated body weight, fat mass gain (using time-domain nuclear magnetic resonance), glucose metabolism and energy homeostasis (using metabolic chambers). We performed microarrays and quantitative PCRs in the liver. In addition, we investigated the gut microbiota composition, bile acid profile and both liver and plasma metabolome. We analysed the expression pattern of genes in the liver of obese humans developing non-alcoholic steatohepatitis (NASH). RESULTS: Hepatocyte-specific deletion of MyD88 predisposes to glucose intolerance, inflammation and hepatic insulin resistance independently of body weight and adiposity. These phenotypic differences were partially attributed to differences in gene expression, transcriptional factor activity (ie, peroxisome proliferator activator receptor-α, farnesoid X receptor (FXR), liver X receptors and STAT3) and bile acid profiles involved in glucose, lipid metabolism and inflammation. In addition to these alterations, the genetic deletion of MyD88 in hepatocytes changes the gut microbiota composition and their metabolomes, resembling those observed during diet-induced obesity. Finally, obese humans with NASH displayed a decreased expression of different cytochromes P450 involved in bioactive lipid synthesis. CONCLUSIONS: Our study identifies a new link between innate immunity and hepatic synthesis of bile acids and bioactive lipids. This dialogue appears to be involved in the susceptibility to alterations associated with obesity such as type 2 diabetes and NASH, both in mice and humans

miR-15a-5p and miR-21-5p contribute to chemoresistance in cytogenetically normal acute myeloid leukaemia by targeting PDCD4, ARL2 and BTG2

Cytarabine and daunorubicin are old drugs commonly used in the treatment of acute myeloid leukaemia (AML). Refractory or relapsed disease because of chemotherapy resistance is a major issue. microRNAs (miRNAs) were incriminated in resistance. This study aimed to identify miRNAs involved in chemoresistance in AML patients and to define their target genes. We focused on cytogenetically normal AML patients with wild-type NPM1 without FLT3-ITD as the treatment of this subset of patients with intermediate-risk cytogenetics is not well established. We analysed baseline AML samples by small RNA sequencing and compared the profile of chemoresistant to chemosensitive AML patients. Among the miRNAs significantly overexpressed in chemoresistant patients, we revealed miR-15a-5p and miR-21-5p as miRNAs with a major role in chemoresistance in AML. We showed that miR-15a-5p and miR-21-5p overexpression decreased apoptosis induced by cytarabine and/or daunorubicin. PDCD4, ARL2 and BTG2 genes were found to be targeted by miR-15a-5p, as well as PDCD4 and BTG2 by miR-21-5p. Inhibition experiments of the three target genes reproduced the functional effect of both miRNAs on chemosensitivity. Our study demonstrates that miR-15a-5p and miR-21-5p are overexpressed in a subgroup of chemoresistant AML patients. Both miRNAs induce chemoresistance by targeting three pro-apoptotic genes PDCD4, ARL2 and BTG2

A combined biological and bioinformatics approach for the analysis of transcription factors regulated by PDGF

During this work, we developed TFactS (www.tfacts.org), a bioinformatics tool that predicts the activation (or inhibition) of transcription factors based on a list of regulated genes generated by expression microarrays. Using Fisher's statistics, TFactS compares a list of regulated genes with a database of transcription factors associated with well-established target genes. This approach is an alternative to the systematic analysis of gene promoters using consensus transcription factor binding sites. We showed that it can be used to analyze published cancer microarray data. TFactS was validated experimentally by analyzing gene regulation by platelet-derived growth factor (PDGF) receptors. PDGF stimulates cell migration and proliferation in wound healing, angiogenesis and embryogenesis, as well as in different types of cancers. We performed microarrays to identify genes regulated by PDGF receptors in fibroblasts and leukemic cells. TFactS suggested that PDGF activates transcription factors of the STAT and SREBP families, in line with our experimental data. TFactS has also predicted an inhibition of Forkhead box O (FOXO) transcription factors. It was already known that PDGF inhibits these factors by stimulating their phosphorylation by AKT. Our data demonstrate that PDGF also represses the expression of FOXO genes at the mRNA level. We showed that FOXO3 and FOXO1 can bind to FOXO1 gene promoter and stimulate FOXO1 mRNA transcription in a positive feed-back loop that is disrupted by growth factors. This process contributes to the growth promoting activity of PDGF. The data also suggest a mechanism by which growth factors may down-regulate FOXO genes in tumors. In conclusion, this work describes a new approach for transcription factor mining in microarray expression data. This allowed us to decipher the transcriptional regulations triggered by PDGF receptor signaling. This tool may contribute to the global analysis of transcriptional networks in cancer cells.Nous avons développé TFactS (www.tfacts.org), un outil qui prédit l'état de régulation (activation ou inhibition) des facteurs de transcription à partir des données des microarrays. TFactS utilise le test de Fisher pour comparer les listes de gènes régulés à une base de données liant les facteurs de transcription à leur gènes cibles. Cette approche représente une alternative aux méthodes dites in silico, qui se basent sur la recherche de motifs de fixation de facteurs de transcription dans les promoteurs de gènes régulés. TFactS a été validé expérimentalement sur les gènes régulés par le PDGF. Le PDGF est un facteur de croissance qui induit la migration et la prolifération des cellules lors de la cicatrisation, l'angiogenèse et l'embryogenèse, ainsi que dans différents types de cancers. Nous avons réalisé des expériences de microarrays pour identifier les gènes régulés par le récepteur du PDGF dans les fibroblastes normaux et dans une lignée cancéreuse de leucémie. TFactS, appliqué sur ces données, a suggéré une régulation de plusieurs facteurs de transcription dont STAT et SREBP, validés expérimentalement. TFactS a aussi prédit l'inhibition par le PDGF d'un groupe de facteurs de transcription FOXO, suppresseurs de tumeurs, impliqués dans l'apoptose et l'arrêt du cycle cellulaire. Le récepteur du PDGF induit la phosphorylation de FOXO par AKT provoquant son inactivation et son exclusion du noyau. Nous avons montré que ce récepteur, en plus, inhibe l'expression des gènes FOXO. En effet, nos résultats montrent que FOXO3 et FOXO1 activent l'expression du gène FOXO1 en se fixant directement sur son promoteur. Ceci suggère un maintien de l'activité de FOXO, en absence d'un signal de croissance, par une boucle d'activation positive dans laquelle l'activation de FOXO induit sa propre expression. Lors d'une stimulation par le PDGF, cette boucle est rompue et la cellule retrouve les conditions nécessaires pour la prolifération. Ce mécanisme pourrait être impliqué dans différents cancers. Ce travail présente une nouvelle approche pour la détection des facteurs de transcription régulés à partir de données d'expression. Son application au PDGF a contribué à la compréhension des régulations induites par ce facteur. TFactS pourrait permettre l'élaboration et l'analyse des réseaux de transcription dans les tumeurs.(SBIM 3) -- UCL, 201

A combined biological and bioinformatics approach for the analysis of transcription factors regulated by PDGF / Ahmed Essaghir ; promoteur : Jean-Baptiste Demoulin

Nous avons développé TFactS (www.tfacts.org), un outil qui prédit l’état de régulation (activation ou inhibition) des facteurs de transcription à partir des données des microarrays. TFactS utilise le test de Fisher pour comparer les listes de gènes régulés à une base de données liant les facteurs de transcription à leur gènes cibles. Cette approche représente une alternative aux méthodes dites in silico, qui se basent sur la recherche de motifs de fixation de facteurs de transcription dans les promoteurs de gènes régulés. TFactS a été validé expérimentalement sur les gènes régulés par le PDGF. Le PDGF est un facteur de croissance qui induit la migration et la prolifération des cellules lors de la cicatrisation, l’angiogenèse et l’embryogenèse, ainsi que dans différents types de cancers. Nous avons réalisé des expériences de microarrays pour identifier les gènes régulés par le récepteur du PDGF dans les fibroblastes normaux et dans une lignée cancéreuse de leucémie. TFactS, appliqué sur ces données, a suggéré une régulation de plusieurs facteurs de transcription dont STAT et SREBP, validés expérimentalement. TFactS a aussi prédit l’inhibition par le PDGF d’un groupe de facteurs de transcription FOXO, suppresseurs de tumeurs, impliqués dans l’apoptose et l’arrêt du cycle cellulaire. Le récepteur du PDGF induit la phosphorylation de FOXO par AKT provoquant son inactivation et son exclusion du noyau. Nous avons montré que ce récepteur, en plus, inhibe l’expression des gènes FOXO. En effet, nos résultats montrent que FOXO3 et FOXO1 activent l’expression du gène FOXO1 en se fixant directement sur son promoteur. Ceci suggère un maintien de l’activité de FOXO, en absence d’un signal de croissance, par une boucle d’activation positive dans laquelle l’activation de FOXO induit sa propre expression. Lors d’une stimulation par le PDGF, cette boucle est rompue et la cellule retrouve les conditions nécessaires pour la prolifération. Ce mécanisme pourrait être impliqué dans différents cancers. Ce travail présente une nouvelle approche pour la détection des facteurs de transcription régulés à partir de données d’expression. Son application au PDGF a contribué à la compréhension des régulations induites par ce facteur. TFactS pourrait permettre l’élaboration et l’analyse des réseaux de transcription dans les tumeurs.uring this work, we developed TFactS (www.tfacts.org), a bioinformatics tool that predicts the activation (or inhibition) of transcription factors based on a list of regulated genes generated by expression microarrays. Using Fisher’s statistics, TFactS compares a list of regulated genes with a database of transcription factors associated with well-established target genes. This approach is an alternative to the systematic analysis of gene promoters using consensus transcription factor binding sites. We showed that it can be used to analyze published cancer microarray data. TFactS was validated experimentally by analyzing gene regulation by platelet-derived growth factor (PDGF) receptors. PDGF stimulates cell migration and proliferation in wound healing, angiogenesis and embryogenesis, as well as in different types of cancers. We performed microarrays to identify genes regulated by PDGF receptors in fibroblasts and leukemic cells. TFactS suggested that PDGF activates transcription factors of the STAT and SREBP families, in line with our experimental data. TFactS has also predicted an inhibition of Forkhead box O (FOXO) transcription factors. It was already known that PDGF inhibits these factors by stimulating their phosphorylation by AKT. Our data demonstrate that PDGF also represses the expression of FOXO genes at the mRNA level. We showed that FOXO3 and FOXO1 can bind to FOXO1 gene promoter and stimulate FOXO1 mRNA transcription in a positive feed-back loop that is disrupted by growth factors. This process contributes to the growth promoting activity of PDGF. The data also suggest a mechanism by which growth factors may down-regulate FOXO genes in tumors. In conclusion, this work describes a new approach for transcription factor mining in microarray expression data. This allowed us to decipher the transcriptional regulations triggered by PDGF receptor signaling. This tool may contribute to the global analysis of transcriptional networks in cancer cells.Thèse de doctorat en sciences biomédicales et pharmaceutiques (SBIM3) -- UCL, 201

A Minimal Connected Network of Transcription Factors Regulated in Human Tumors and Its Application to the Quest for Universal Cancer Biomarkers

<div><p>A universal cancer biomarker candidate for diagnosis is supposed to distinguish, within a broad range of tumors, between healthy and diseased patients. Recently published studies have explored the universal usefulness of some biomarkers in human tumors. In this study, we present an integrative approach to search for potential common cancer biomarkers. Using the TFactS web-tool with a catalogue of experimentally established gene regulations, we could predict transcription factors (TFs) regulated in 305 different human cancer cell lines covering a large panel of tumor types. We also identified chromosomal regions having significant copy number variation (CNV) in these cell lines. Within the scope of TFactS catalogue, 88 TFs whose activity status were explained by their gene expressions and CNVs were identified. Their minimal connected network (MCN) of protein-protein interactions forms a significant module within the human curated TF proteome. Functional analysis of the proteins included in this MCN revealed enrichment in cancer pathways as well as inflammation. The ten most central proteins in MCN are TFs that trans-regulate 157 known genes encoding secreted and transmembrane proteins. In publicly available collections of gene expression data from 8,525 patient tissues, 86 genes were differentially regulated in cancer compared to inflammatory diseases and controls. From TCGA cancer gene expression data sets, 50 genes were significantly associated to patient survival in at least one tumor type. Enrichment analysis shows that these genes mechanistically interact in common cancer pathways. Among these cancer biomarker candidates, TFRC, MET and VEGFA are commonly amplified genes in tumors and their encoded proteins stained positive in more than 80% of malignancies from public databases. They are linked to angiogenesis and hypoxia, which are common in cancer. They could be interesting for further investigations in cancer diagnostic strategies.</p> </div

A workflow summarizing the strategy to identify accessible common cancer biomarkers.

<p>See text for details. Reg: regulation; Exp: expression; CNV: copy number variation; MCN: minimal connected network; PPI: protein-protein interactions; TF: Transcription factor.</p