ATF2 knockdown reinforces oxidative stress-induced apoptosis in TE7 cancer cells

Abstract Cancer cells showing low apoptotic effects following oxidative stress-induced DNA damage are mainly affected by growth arrest. Thus, recent studies focus on improving anti-cancer therapies by increasing apoptosis sensitivity. We aimed at identifying a universal molecule as potential target to enhance oxidative stress-based anti-cancer therapy through a switch from cell cycle arrest to apoptosis. A cDNA microarray was performed with hydrogen peroxide-treated oesophageal squamous epithelial cancer cells TE7. This cell line showed checkpoint activation via p21 WAF1 , but low apoptotic response following DNA damage. The potential target molecule was chosen depended on the following demands: it should regulate DNA damage response, cell cycle and apoptosis. As the transcription factor ATF2 is implicated in all these processes, we focused on this protein. We investigated checkpoint activation via ATF2. Indeed, ATF2 knockdown revealed ATF2-triggered p21 WAF1 protein expression, suggesting p21 WAF1 transactivation through ATF2. Using chromatin immunoprecipitation (ChIP), we identified a hitherto unknown ATF2-binding sequence in the p21 WAF1 promoter. p-ATF2 was found to interact with p-c-Jun, creating the AP-1 complex. Moreover, ATF2 knockdown led to c-Jun downregulation. This suggests ATF2-driven induction of c-Jun expression, thereby enhancing ATF2 transcriptional activity via c-Jun-ATF2 heterodimerization. Notably, downregulation of ATF2 caused a switch from cell cycle arrest to reinforced apoptosis, presumably via p21 WAF1 downregulation, confirming the importance of ATF2 in the establishment of cell cycle arrest. 1-Chloro-2,4-dinitrobenzene also led to ATF2-dependent G2/M arrest, suggesting that this is a general feature induced by oxidative stress. As ATF2 knockdown also increased apoptosis, we propose ATF2 as a target for combined oxidative stress-based anti-cancer therapies. Keywords: oxidative stress-induced DNA damage cell cycle arrest ATF2 knockdown increase in apoptosis sensitivities combined treatment p21 WAF

Leaf metabolic traits reveal hidden dimensions of plant form and function

International audienceThe metabolome is the biochemical basis of plant form and function, but we know little about its macroecological variation across the plant kingdom. Here, we used the plant functional trait concept to interpret leaf metabolome variation among 457 tropical and 339 temperate plant species. Distilling metabolite chemistry into five metabolic functional traits reveals that plants vary on two major axes of leaf metabolic specialization—a leaf chemical defense spectrum and an expression of leaf longevity. Axes are similar for tropical and temperate species, with many trait combinations being viable. However, metabolic traits vary orthogonally to life-history strategies described by widely used functional traits. The metabolome thus expands the functional trait concept by providing additional axes of metabolic specialization for examining plant form and function

Leaf metabolic traits reveal hidden dimensions of plant form and function

The metabolome is the biochemical basis of plant form and function, but we know little about its macroecological variation across the plant kingdom. Here, we used the plant functional trait concept to interpret leaf metabolome variation among 457 tropical and 339 temperate plant species. Distilling metabolite chemistry into five metabolic functional traits reveals that plants vary on two major axes of leaf metabolic specialization—a leaf chemical defense spectrum and an expression of leaf longevity. Axes are similar for tropical and temperate species, with many trait combinations being viable. However, metabolic traits vary orthogonally to life-history strategies described by widely used functional traits. The metabolome thus expands the functional trait concept by providing additional axes of metabolic specialization for examining plant form and function

Combining Genomics, Metabolome Analysis, and Biochemical Modelling to Understand Metabolic Networks

Now that complete genome sequences are available for a variety of organisms, the elucidation of gene functions involved in metabolism necessarily includes a better understanding of cellular responses upon mutations on all levels of gene products, mRNA, proteins, and metabolites. Such progress is essential since the observable properties of organisms – the phenotypes – are produced by the genotype in juxtaposition with the environment. Whereas much has been done to make mRNA and protein profiling possible, considerably less effort has been put into profiling the end products of gene expression, metabolites. To date, analytical approaches have been aimed primarily at the accurate quantification of a number of pre-defined target metabolites, or at producing fingerprints of metabolic changes without individually determining metabolite identities. Neither of these approaches allows the formation of an in-depth understanding of the biochemical behaviour within metabolic networks. Yet, by carefully choosing protocols for sample preparation and analytical techniques, a number of chemically different classes of compounds can be quantified simultaneously to enable such understanding. In this review, the terms describing various metabolite-oriented approaches are given, and the differences among these approaches are outlined. Metabolite target analysis, metabolite profiling, metabolomics, and metabolic fingerprinting are considered. For each approach, a number of examples are given, and potential applications are discussed

Promoter methylation of CDKN2A and lack of p16 expression characterize patients with hepatocellular carcinoma

<p>Abstract</p> <p>Background</p> <p>The product of CDKN2A, p16 is an essential regulator of the cell cycle controlling the entry into the S-phase. Herein, we evaluated CDKN2A promoter methylation and p16 protein expression for the differentiation of hepatocellular carcinoma (HCC) from other liver tumors.</p> <p>Methods</p> <p>Tumor and corresponding non-tumor liver tissue samples were obtained from 85 patients with liver tumors. CDKN2A promoter methylation was studied using MethyLight technique and methylation-specific PCR (MSP). In the MethyLight analysis, samples with ≥ 4% of PMR (percentage of methylated reference) were regarded as hypermethylated. p16 expression was evaluated by immunohistochemistry in tissue sections (n = 148) obtained from 81 patients using an immunoreactivity score (IRS) ranging from 0 (no expression) to 6 (strong expression).</p> <p>Results</p> <p>Hypermethylation of the CDKN2A promoter was found in 23 HCCs (69.7%; mean PMR = 42.34 ± 27.8%), six (20.7%; mean PMR = 31.85 ± 18%) liver metastases and in the extralesional tissue of only one patient. Using MSP, 32% of the non-tumor (n = 85), 70% of the HCCs, 40% of the CCCs and 24% of the liver metastases were hypermethylated. Correspondingly, nuclear p16 expression was found immunohistochemically in five (10.9%, mean IRS = 0.5) HCCs, 23 (92%; mean IRS = 4.9) metastases and only occasionally in hepatocytes of non-lesional liver tissues (mean IRS = 1.2). The difference of CDKN2A-methylation and p16 protein expression between HCCs and liver metastases was statistically significant (p < 0.01, respectively).</p> <p>Conclusion</p> <p>Promoter methylation of CDKN2A gene and lack of p16 expression characterize patients with HCC.</p

Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives

Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites relevant to a specific phenotypic characteristic can be identified. However, the reliability of the analytical data is a prerequisite for correct biological interpretation in metabolomics analysis. In this review the challenges in quantitative metabolomics analysis with regards to analytical as well as data preprocessing steps are discussed. Recommendations are given on how to optimize and validate comprehensive silylation-based methods from sample extraction and derivatization up to data preprocessing and how to perform quality control during metabolomics studies. The current state of method validation and data preprocessing methods used in published literature are discussed and a perspective on the future research necessary to obtain accurate quantitative data from comprehensive GC-MS data is provided

Stability of Metabolic Correlations under Changing Environmental Conditions in Escherichia coli – A Systems Approach

Background: Biological systems adapt to changing environments by reorganizing their cellular and physiological program with metabolites representing one important response level. Different stresses lead to both conserved and specific responses on the metabolite level which should be reflected in the underlying metabolic network. Methodology/Principal Findings: Starting from experimental data obtained by a GC-MS based high-throughput metabolic profiling technology we here develop an approach that: (1) extracts network representations from metabolic condition-dependent data by using pairwise correlations, (2) determines the sets of stable and condition-dependent correlations based on a combination of statistical significance and homogeneity tests, and (3) can identify metabolites related to the stress response, which goes beyond simple observations about the changes of metabolic concentrations. The approach was tested with Escherichia coli as a model organism observed under four different environmental stress conditions (cold stress, heat stress, oxidative stress, lactose diauxie) and control unperturbed conditions. By constructing the stable network component, which displays a scale free topology and small-world characteristics, we demonstrated that: (1) metabolite hubs in this reconstructed correlation networks are significantly enriched for those contained in biochemical networks such as EcoCyc, (2) particular components of the stable network are enriched for functionally related biochemical pathways, and (3) independently of the response scale, based on their importance in the reorganization of the correlation network a set of metabolites can be identified which represent hypothetical candidates for adjusting to a stress-specific response. Conclusions/Significance: Network-based tools allowed the identification of stress-dependent and general metabolic correlation networks. This correlation-network-based approach does not rely on major changes in concentration to identify metabolites important for stress adaptation, but rather on the changes in network properties with respect to metabolites. This should represent a useful complementary technique in addition to more classical approaches

Gastric High-Risk Gist and Retroperitoneal Liposarcoma – A Challenging Combination of Two Mesenchymal Tumor Lesions with Regard to Diagnosis and Treatment

Both gastrointestinal stromal tumors (GIST) and liposarcoma originate from mesenchymal tissue. Their coincidence requires a specific expertise in the diagnostic and therapeutic management. An unusual exemplary case is described representing a 47-year old female patient with a gastric GIST and a monstrous retroperitoneal liposarcoma with infiltration of the left kidney. The gastric tumor lesion was removed with a tangential resection of the gastric wall; the retroperitoneal tumor lesion was resected including the left kidney. Both tumors were resected with no macroscopic tumor residual. The technically difficult surgical intervention did not show any postoperative complication, and the postoperative course was also uneventful. The complete tumor resection is the treatment of choice in mesenchymal tumors (aim: R0). Depending on histologic tumor classification, resection status and tumor sensitivity, a subsequent radiation and/or chemotherapy is necessary, which allowed to achieve a postoperative tumor-free survival of 6 years including a good quality of life