Cytoplasmic PML promotes TGF-β-associated epithelial–mesenchymal transition and invasion in prostate cancer

Epithelial–mesenchymal transition (EMT) is a key event that is involved in the invasion and dissemination of cancer cells. Although typically considered as having tumour-suppressive properties, transforming growth factor (TGF)-β signalling is altered during cancer and has been associated with the invasion of cancer cells and metastasis. In this study, we report a previously unknown role for the cytoplasmic promyelocytic leukaemia (cPML) tumour suppressor in TGF-β signalling-induced regulation of prostate cancer-associated EMT and invasion. We demonstrate that cPML promotes a mesenchymal phenotype and increases the invasiveness of prostate cancer cells. This event is associated with activation of TGF-β canonical signalling pathway through the induction of Sma and Mad related family 2 and 3 (SMAD2 and SMAD3) phosphorylation. Furthermore, the cytoplasmic localization of promyelocytic leukaemia (PML) is mediated by its nuclear export in a chromosomal maintenance 1 (CRM1)-dependent manner. This was clinically tested in prostate cancer tissue and shown that cytoplasmic PML and CRM1 co-expression correlates with reduced disease-specific survival. In summary, we provide evidence of dysfunctional TGF-β signalling occurring at an early stage in prostate cancer. We show that this disease pathway is mediated by cPML and CRM1 and results in a more aggressive cancer cell phenotype. We propose that the targeting of this pathway could be therapeutically exploited for clinical benefit

A new inhibitor of glucose-6-phospate dehydrogenase blocks pentose phosphate pathway and suppresses malignant proliferation and metastasis in vivo

Pentose Phosphate Pathway (PPP) is a major glucose metabolism pathway which has a fundamental role in cancer growth and metastasis. Even though PPP blockade has been pointed out as a very promising strategy against cancer, effective anti-PPP agents are not still available in the clinical setting. Here, we demonstrate that the natural molecule polydatin inhibits glucose-6-phosphate dehydrogenase (G6PD), the key enzyme of PPP. Polydatin blocks G6PD causing accumulation of reactive oxygen species and strong increase of endoplasmic reticulum stress. These effects are followed by cell cycle block in S phase, an about 50% of apoptosis, and 60% inhibition of invasion in vitro. Accordingly, in an orthotopic metastatic model of tongue cancer, 100 mg/kg polydatin induced an about 30% tumor size reduction with an about 80% inhibition of lymph node metastases and 50% reduction of lymph node size (p< 0.005). Polydatin is not toxic in animals up to a dose of 200 mg/kg and a phase II clinical trial shows that a is also well tolerated in humans (40 mg twice a day for 90 days). Thus, polydatin may be used as a reliable tool to limit human cancer growth and metastatic spread

Twisted fallopian tube in pregnancy: a case report

BACKGROUND: Isolated twisted fallopian tube is an uncommon event, isolated twisted fallopian tube in pregnancy is also very rare. The diagnosis is often difficult and established during the operation. The right fallopian tube is most common affected. CASE PRESENTATION: We report an uncommon twisted left fallopian tube in pregnancy. A 34-year-old G(3)P(2) 28 weeks pregnant woman presented with acute left lower abdominal pain. The clinical and ultrasonographic findings led to diagnosis of twisted left ovarian cyst. Emergency exploratory laparotomy was performed. A twisted left fallopian tube and paratubal cyst was noted and left salpingectomy was performed. The postoperative course was uneventful and the pregnancy continued until term without complication. CONCLUSIONS: Although isolated twisted fallopian tube during pregnancy is very rare, it should be included in the differential diagnosis of acute abdomen in pregnancy. Early surgical intervention will decrease obstetric morbidity and may allow preservation of the fallopian tube

Glucose-6-phosphate dehydrogenase blockade potentiates tyrosine kinase inhibitor effect on breast cancer cells through autophagy perturbation

Background: Glucose-6-phospate dehydrogenase (G6PD) is the limiting enzyme of the pentose phosphate pathway (PPP) correlated to cancer progression and drug resistance. We previously showed that G6PD inhibition leads to Endoplasmic Reticulum (ER) stress often associated to autophagy deregulation. The latter can be induced by target-based agents such as Lapatinib, an anti-HER2 tyrosine kinase inhibitor (TKI) largely used in breast cancer treatment. Methods: Here we investigate whether G6PD inhibition causes autophagy alteration, which can potentiate Lapatinib effect on cancer cells. Immunofluorescence and flow cytometry for LC3B and lysosomes tracker were used to study autophagy in cells treated with lapatinib and/or G6PD inhibitors (polydatin). Immunoblots for LC3B and p62 were performed to confirm autophagy flux analyses together with puncta and colocalization studies. We generated a cell line overexpressing G6PD and performed synergism studies on cell growth inhibition induced by Lapatinib and Polydatin using the median effect by Chou-Talay. Synergism studies were additionally validated with apoptosis analysis by annexin V/PI staining in the presence or absence of autophagy blockers. Results: We found that the inhibition of G6PD induced endoplasmic reticulum stress, which was responsible for the deregulation of autophagy flux. Indeed, G6PD blockade caused a consistent increase of autophagosomes formation independently from mTOR status. Cells engineered to overexpress G6PD became resilient to autophagy and resistant to lapatinib. On the other hand, G6PD inhibition synergistically increased lapatinib-induced cytotoxic effect on cancer cells, while autophagy blockade abolished this effect. Finally, in silico studies showed a significant correlation between G6PD expression and tumour relapse/resistance in patients. Conclusions: These results point out that autophagy and PPP are crucial players in TKI resistance, and highlight a peculiar vulnerability of breast cancer cells, where impairment of metabolic pathways and autophagy could be used to reinforce TKI efficacy in cancer treatment

A promyelocytic leukemia protein-thrombospondin 2 axis and the risk of relapse in neuroblastoma

Purpose. Neuroblastoma is a childhood malignancy originating from the sympathetic nervous system with a complex biology, prone to metastasize and relapse. High-risk, metastatic cases are explained in part by amplification or mutation of oncogenes such as MYCN and ALK and loss of tumour suppressor genes in chromosome band 1p. However, it is fundamental to identify other pathways responsible for the large portion of neuroblastomas with no obvious molecular alterations. Experimental design. Neuroblastoma cell lines were used for assessment of tumour growth in vivo and in vitro. Protein expression in tissues and cells was assessed using immunofluorescence and immunohistochemistry. The association of PML expression with neuroblastoma outcome and relapse was calculated using log-rank and Mann-Whitney tests, respectively. Gene expression was assessed using chip microarrays. Results: PML is detected in the developing and adult sympathetic nervous system, whereas it is not expressed or low in metastatic neuroblastoma tumours. Reduced PML expression in patients with low-risk cancers - i.e. localized and negative for the MYCN protooncogene - is strongly associated with tumour recurrence. PML-I, but not PML-IV, isoform suppresses angiogenesis via upregulation of thrombospondin-2 (TSP-2), a key inhibitor of angiogenesis. Finally, PML-I and TSP-2 expression inversely correlates with tumour angiogenesis and recurrence in localized neuroblastomas. Dvorkina et al. A promyelocytic leukaemia protein-thrombospondin 2 axis and the risk of relapse in neuroblastoma 3 Conclusions: Our work reveals a novel PML-I-TSP2 axis for regulation of angiogenesis and cancer relapse, which could be used to identify patients with low-risk, localized tumours that might benefit from chemotherapy

Exact distribution of a pattern in a set of random sequences generated by a Markov source: applications to biological data

<p>Abstract</p> <p>Background</p> <p>In bioinformatics it is common to search for a pattern of interest in a potentially large set of rather short sequences (upstream gene regions, proteins, exons, etc.). Although many methodological approaches allow practitioners to compute the distribution of a pattern count in a random sequence generated by a Markov source, no specific developments have taken into account the counting of occurrences in a set of independent sequences. We aim to address this problem by deriving efficient approaches and algorithms to perform these computations both for low and high complexity patterns in the framework of homogeneous or heterogeneous Markov models.</p> <p>Results</p> <p>The latest advances in the field allowed us to use a technique of optimal Markov chain embedding based on deterministic finite automata to introduce three innovative algorithms. Algorithm 1 is the only one able to deal with heterogeneous models. It also permits to avoid any product of convolution of the pattern distribution in individual sequences. When working with homogeneous models, Algorithm 2 yields a dramatic reduction in the complexity by taking advantage of previous computations to obtain moment generating functions efficiently. In the particular case of low or moderate complexity patterns, Algorithm 3 exploits power computation and binary decomposition to further reduce the time complexity to a logarithmic scale. All these algorithms and their relative interest in comparison with existing ones were then tested and discussed on a toy-example and three biological data sets: structural patterns in protein loop structures, PROSITE signatures in a bacterial proteome, and transcription factors in upstream gene regions. On these data sets, we also compared our exact approaches to the tempting approximation that consists in concatenating the sequences in the data set into a single sequence.</p> <p>Conclusions</p> <p>Our algorithms prove to be effective and able to handle real data sets with multiple sequences, as well as biological patterns of interest, even when the latter display a high complexity (PROSITE signatures for example). In addition, these exact algorithms allow us to avoid the edge effect observed under the single sequence approximation, which leads to erroneous results, especially when the marginal distribution of the model displays a slow convergence toward the stationary distribution. We end up with a discussion on our method and on its potential improvements.</p

Mining protein loops using a structural alphabet and statistical exceptionality

<p>Abstract</p> <p>Background</p> <p>Protein loops encompass 50% of protein residues in available three-dimensional structures. These regions are often involved in protein functions, e.g. binding site, catalytic pocket... However, the description of protein loops with conventional tools is an uneasy task. Regular secondary structures, helices and strands, have been widely studied whereas loops, because they are highly variable in terms of sequence and structure, are difficult to analyze. Due to data sparsity, long loops have rarely been systematically studied.</p> <p>Results</p> <p>We developed a simple and accurate method that allows the description and analysis of the structures of short and long loops using structural motifs without restriction on loop length. This method is based on the structural alphabet HMM-SA. HMM-SA allows the simplification of a three-dimensional protein structure into a one-dimensional string of states, where each state is a four-residue prototype fragment, called structural letter. The difficult task of the structural grouping of huge data sets is thus easily accomplished by handling structural letter strings as in conventional protein sequence analysis. We systematically extracted all seven-residue fragments in a bank of 93000 protein loops and grouped them according to the structural-letter sequence, named structural word. This approach permits a systematic analysis of loops of all sizes since we consider the structural motifs of seven residues rather than complete loops. We focused the analysis on highly recurrent words of loops (observed more than 30 times). Our study reveals that 73% of loop-lengths are covered by only 3310 highly recurrent structural words out of 28274 observed words). These structural words have low structural variability (mean RMSd of 0.85 Å). As expected, half of these motifs display a flanking-region preference but interestingly, two thirds are shared by short (less than 12 residues) and long loops. Moreover, half of recurrent motifs exhibit a significant level of amino-acid conservation with at least four significant positions and 87% of long loops contain at least one such word. We complement our analysis with the detection of statistically over-represented patterns of structural letters as in conventional DNA sequence analysis. About 30% (930) of structural words are over-represented, and cover about 40% of loop lengths. Interestingly, these words exhibit lower structural variability and higher sequential specificity, suggesting structural or functional constraints.</p> <p>Conclusions</p> <p>We developed a method to systematically decompose and study protein loops using recurrent structural motifs. This method is based on the structural alphabet HMM-SA and not on structural alignment and geometrical parameters. We extracted meaningful structural motifs that are found in both short and long loops. To our knowledge, it is the first time that pattern mining helps to increase the signal-to-noise ratio in protein loops. This finding helps to better describe protein loops and might permit to decrease the complexity of long-loop analysis. Detailed results are available at <url>http://www.mti.univ-paris-diderot.fr/publication/supplementary/2009/ACCLoop/</url>.</p

Transcriptional Regulation of Ribosome Components Are Determined by Stress According to Cellular Compartments in Arabidopsis thaliana

Plants have to coordinate eukaryotic ribosomes (cytoribosomes) and prokaryotic ribosomes (plastoribosomes and mitoribosomes) production to balance cellular protein synthesis in response to environmental variations. We identified 429 genes encoding potential ribosomal proteins (RP) in Arabidopsis thaliana. Because cytoribosome proteins are encoded by small nuclear gene families, plastid RP by nuclear and plastid genes and mitochondrial RP by nuclear and mitochondrial genes, several transcriptional pathways were attempted to control ribosome amounts. Examining two independent genomic expression datasets, we found two groups of RP genes showing very different and specific expression patterns in response to environmental stress. The first group represents the nuclear genes coding for plastid RP whereas the second group is composed of a subset of cytoribosome genes coding for RP isoforms. By contrast, the other cytoribosome genes and mitochondrial RP genes show less constraint in their response to stress conditions. The two subsets of cytoribosome genes code for different RP isoforms. During stress, the response of the intensively regulated subset leads to dramatic variation in ribosome diversity. Most of RP genes have same promoter structure with two motifs at conserved positions. The stress-response of the nuclear genes coding plastid RP is related with the absence of an interstitial telomere motif known as telo box in their promoters. We proposed a model for the “ribosome code” that influences the ribosome biogenesis by three main transcriptional pathways. The first pathway controls the basal program of cytoribosome and mitoribosome biogenesis. The second pathway involves a subset of cytoRP genes that are co-regulated under stress condition. The third independent pathway is devoted to the control of plastoribosome biosynthesis by regulating both nuclear and plastid genes

Activation of NF-kB Pathway by Virus Infection Requires Rb Expression

The retinoblastoma protein Rb is a tumor suppressor involved in cell cycle control, differentiation, and inhibition of oncogenic transformation. Besides these roles, additional functions in the control of immune response have been suggested. In the present study we investigated the consequences of loss of Rb in viral infection. Here we show that virus replication is increased by the absence of Rb, and that Rb is required for the activation of the NF-kB pathway in response to virus infection. These results reveal a novel role for tumor suppressor Rb in viral infection surveillance and further extend the concept of a link between tumor suppressors and antiviral activity

Networks of Neuronal Genes Affected by Common and Rare Variants in Autism Spectrum Disorders

Autism spectrum disorders (ASD) are neurodevelopmental disorders with phenotypic and genetic heterogeneity. Recent studies have reported rare and de novo mutations in ASD, but the allelic architecture of ASD remains unclear. To assess the role of common and rare variations in ASD, we constructed a gene co-expression network based on a widespread survey of gene expression in the human brain. We identified modules associated with specific cell types and processes. By integrating known rare mutations and the results of an ASD genome-wide association study (GWAS), we identified two neuronal modules that are perturbed by both rare and common variations. These modules contain highly connected genes that are involved in synaptic and neuronal plasticity and that are expressed in areas associated with learning and memory and sensory perception. The enrichment of common risk variants was replicated in two additional samples which include both simplex and multiplex families. An analysis of the combined contribution of common variants in the neuronal modules revealed a polygenic component to the risk of ASD. The results of this study point toward contribution of minor and major perturbations in the two sub-networks of neuronal genes to ASD risk