Diclofenac Inhibits Tumor Growth in a Murine Model of Pancreatic Cancer by Modulation of VEGF Levels and Arginase Activity

BACKGROUND: Diclofenac is one of the oldest anti-inflammatory drugs in use. In addition to its inhibition of cyclooxygenases (COX), diclofenac potently inhibits phospholipase A(2) (PLA(2)), thus yielding a broad anti-inflammatory effect. Since inflammation is an important factor in the development of pancreatic tumors we explored the potential of diclofenac to inhibit tumor growth in mice inoculated with PANCO2 cells orthotopically. METHODOLOGY/PRINCIPAL FINDINGS: We found that diclofenac treatment (30 mg/kg/bw for 11 days) of mice inoculated with PANC02 cells, reduced the tumor weight by 60%, correlating with increased apoptosis of tumor cells. Since this effect was not observed in vitro on cultured PANCO2 cells, we theorized that diclofenac beneficial treatment involved other mediators present in vivo. Indeed, diclofenac drastically decreased tumor vascularization by downregulating VEGF in the tumor and in abdominal cavity fluid. Furthermore, diclofenac directly inhibited vascular sprouting ex vivo. Surprisingly, in contrast to other COX-2 inhibitors, diclofenac increased arginase activity/arginase 1 protein content in tumor stroma cells, peritoneal macrophages and white blood cells by 2.4, 4.8 and 2 fold, respectively. We propose that the subsequent arginine depletion and decrease in NO levels, both in serum and peritoneal cavity, adds to tumor growth inhibition by malnourishment and poor vasculature development. CONCLUSION/SIGNIFICANCE: In conclusion, diclofenac shows pronounced antitumoral properties in pancreatic cancer model that can contribute to further treatment development. The ability of diclofenac to induce arginase activity in tumor stroma, peritoneal macrophages and white blood cells provides a tool to study a controversial issue of pro-and antitumoral effects of arginine depletion

Non-Linear Elasticity of Extracellular Matrices Enables Contractile Cells to Communicate Local Position and Orientation

Most tissue cells grown in sparse cultures on linearly elastic substrates typically display a small, round phenotype on soft substrates and become increasingly spread as the modulus of the substrate increases until their spread area reaches a maximum value. As cell density increases, individual cells retain the same stiffness-dependent differences unless they are very close or in molecular contact. On nonlinear strain-stiffening fibrin gels, the same cell types become maximally spread even when the low strain elastic modulus would predict a round morphology, and cells are influenced by the presence of neighbors hundreds of microns away. Time lapse microscopy reveals that fibroblasts and human mesenchymal stem cells on fibrin deform the substrate by several microns up to five cell lengths away from their plasma membrane through a force limited mechanism. Atomic force microscopy and rheology confirm that these strains locally and globally stiffen the gel, depending on cell density, and this effect leads to long distance cell-cell communication and alignment. Thus cells are acutely responsive to the nonlinear elasticity of their substrates and can manipulate this rheological property to induce patterning

Nano Random Forests to mine protein complexes and their relationships in quantitative proteomics data

Ever-increasing numbers of quantitative proteomics data sets constitute an underexploited resource for investigating protein function. Multiprotein complexes often follow consistent trends in these experiments, which could provide insights about their biology. Yet, as more experiments are considered, a complex’s signature may become conditional and less identifiable. Previously we successfully distinguished the general proteomic signature of genuine chromosomal proteins from hitchhikers using the Random Forests (RF) machine learning algorithm. Here we test whether small protein complexes can define distinguishable signatures of their own, despite the assumption that machine learning needs large training sets. We show, with simulated and real proteomics data, that RF can detect small protein complexes and relationships between them. We identify several complexes in quantitative proteomics results of wild-type and knockout mitotic chromosomes. Other proteins covary strongly with these complexes, suggesting novel functional links for later study. Integrating the RF analysis for several complexes reveals known interdependences among kinetochore subunits and a novel dependence between the inner kinetochore and condensin. Ribosomal proteins, although identified, remained independent of kinetochore subcomplexes. Together these results show that this complex-oriented RF (NanoRF) approach can integrate proteomics data to uncover subtle protein relationships. Our NanoRF pipeline is available online

Variation in styles of rifting in the Gulf of California

Author Posting. © Nature Publishing Group, 2007. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature 448 (2007): 466-469, doi:10.1038/nature06035.The rifting of continental lithosphere is a fundamental solid-earth process that leads to the formation of rifted continental margins and ocean basins. Understanding of this process comes from observations of the geometry of rifted margins and the magmatism resulting from rifting, which inform us about the strength of the lithosphere, the state of the underlying mantle, and the transition from rifting to seafloor spreading. Here we describe results from the PESCADOR seismic experiment in the southern Gulf of California and present the first crustal-scale images across conjugate margins of multiple segments within a single rift that has reached the stage of oceanic spreading. A surprisingly large variation in rifting style and magmatism is observed between these segments, from wide rifting with minor syn-rift magmatism to narrow rifting in magmatically robust segments. These differences encompass much of the variation observed across nearly all other non-end-member continental margins. The characteristics of magmatic endmember margins are typically explained in terms of mantle temperature. Our explanations for the variation in the Gulf of California, in contrast, invoke mantle depletion to account for wide, magma-poor rifting and mantle fertility and possibly the influence of sediments to account for robust rift and post-rift magmatism in the Gulf of California. These factors may vary laterally over small distances in regions that have transitioned from convergence to extension, as is the case for the Gulf of California and many other rifts.This work was funded by a grant from the U.S. NSF-MARGINS program

Alcohol and head and neck cancer risk in a prospective study

We investigated the relation between head and neck cancer risk and alcohol consumption in the NIH-AARP Diet and Health Study. During 2 203 500 person-years of follow-up, 611 men and 183 women developed head and neck cancer. With moderate drinking (up to one alcoholic drink per day) as the referent group, non-drinkers showed an increased risk of head and neck cancer (men: hazard ratio (HR) 1.68, 95% confidence interval (95% CI) 1.37–2.06; women: 1.46, 1.02–2.08). Among male and female alcohol drinkers, we observed a significant dose–response relationship between alcohol consumption and risk. The HR for consuming >3 drinks per day was significantly higher in women (2.52, 1.46–4.35) than in men (1.48, 1.15–1.90; P for interaction=0.0036). The incidence rates per 100 000 person-years for those who consumed >3 drinks per day were similar in men (77.6) and women (75.3). The higher HRs observed in women resulted from lower incidence rates in the referent group: women (14.7), men (34.4). In summary, drinking >3 alcoholic beverages per day was associated with increased risk in men and women, but consumption of up to one drink per day may be associated with reduced risk relative to non-drinking

A review and meta-analysis of prospective studies of red and processed meat intake and prostate cancer

Over the past decade, several large epidemiologic investigations of meat intake and prostate cancer have been published. Therefore, a meta-analysis of prospective studies was conducted to estimate potential associations between red or processed meat intake and prostate cancer. Fifteen studies of red meat and 11 studies of processed meat were included in the analyses. High vs. low intake and dose-response analyses were conducted using random effects models to generate summary relative risk estimates (SRRE). No association between high vs. low red meat consumption (SRRE = 1.00, 95% CI: 0.96-1.05) or each 100 g increment of red meat (SRRE = 1.00, 95% CI: 0.95-1.05) and total prostate cancer was observed. Similarly, no association with red meat was observed for advanced prostate cancer (SRRE = 1.01, 95% CI: 0.94-1.09). A weakly elevated summary association between processed meat and total prostate cancer was found (SRRE = 1.05, 95% CI: 0.99-1.12), although heterogeneity was present, the association was attenuated in a sub-group analysis of studies that adjusted for multiple potential confounding factors, and publication bias likely affected the summary effect. In conclusion, the results of this meta-analysis are not supportive of an independent positive association between red or processed meat intake and prostate cancer

Application of Multi-SNP Approaches Bayesian LASSO and AUC-RF to Detect Main Effects of Inflammatory-Gene Variants Associated with Bladder Cancer Risk

The relationship between inflammation and cancer is well established in several tumor types, including bladder cancer. We performed an association study between 886 inflammatory-gene variants and bladder cancer risk in 1,047 cases and 988 controls from the Spanish Bladder Cancer (SBC)/EPICURO Study. A preliminary exploration with the widely used univariate logistic regression approach did not identify any significant SNP after correcting for multiple testing. We further applied two more comprehensive methods to capture the complexity of bladder cancer genetic susceptibility: Bayesian Threshold LASSO (BTL), a regularized regression method, and AUC-Random Forest, a machine-learning algorithm. Both approaches explore the joint effect of markers. BTL analysis identified a signature of 37 SNPs in 34 genes showing an association with bladder cancer. AUC-RF detected an optimal predictive subset of 56 SNPs. 13 SNPs were identified by both methods in the total population. Using resources from the Texas Bladder Cancer study we were able to replicate 30% of the SNPs assessed. The associations between inflammatory SNPs and bladder cancer were reexamined among non-smokers to eliminate the effect of tobacco, one of the strongest and most prevalent environmental risk factor for this tumor. A 9 SNP-signature was detected by BTL. Here we report, for the first time, a set of SNP in inflammatory genes jointly associated with bladder cancer risk. These results highlight the importance of the complex structure of genetic susceptibility associated with cancer risk.The work was partially supported by the Fondo de Investigacion Sanitaria, Instituto de Salud Carlos III (G03/174, 00/0745, PI051436, PI061614, PI09-02102, G03/174 and Sara Borrell fellowship to ELM) and Ministry of Science and Innovation (MTM2008-06747-C02-02 and FPU fellowship award to VU), Spain; AGAUR-Generalitat de Catalunya (Grant 2009SGR-581); Fundaciola Maratode TV3; Red Tematica de Investigacion Cooperativa en Cancer (RTICC); Asociacion Espanola Contra el Cancer (AECC); EU-FP7-201663; and RO1-CA089715 and CA34627; the Spanish National Institute for Bioinformatics (www.inab.org); and by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, USA. MD Anderson support for this project included U01 CA 127615 (XW); R01 CA 74880 (XW); P50 CA 91846 (XW, CPD); Betty B. Marcus Chair fund in Cancer Prevention (XW); UT Research Trust fund (XW) and R01 CA 131335 (JG)

Non-steroidal anti-inflammatory drugs and risk of gastric and oesophageal adenocarcinomas: results from a cohort study and a meta-analysis

Use of aspirin or other non-steroidal anti-inflammatory drugs (NSAIDs) may reduce the risk of gastric or oesophageal adenocarcinomas. We examined the association between self-reported use of aspirin or non-aspirin NSAIDs in the earlier 12 months and gastric non-cardia (N=182), gastric cardia (N=178), and oesophageal adenocarcinomas (N=228) in a prospective cohort (N=311 115) followed for 7 years. Hazard ratios (HRs) and 95% confidence intervals (CIs) come from Cox models adjusted for potential confounders. Use of any aspirin (HR, 95% CI: 0.64, 0.47–0.86) or other NSAIDs (0.68, 0.51–0.92) was associated with a significantly lower risk of gastric non-cardia adenocarcinoma. Neither aspirin (0.86, 0.61–1.20) nor other NSAIDs (0.91, 0.67–1.22) had a significant association with gastric cardia cancer. We found no significant association between using aspirin (1.00, 0.73–1.37) or other NSAIDs (0.90, 69–1.17) and oesophageal adenocarcinoma. We also performed a meta-analysis of the association between the use of NSAIDs and risk of gastric and oesophageal adenocarcinoma. In this analysis, aspirin use was inversely associated with both gastric and oesophageal adenocarcinomas, with summary odds ratios (95% CI) for non-cardia, cardia, and oesophageal adenocarcinomas of 0.64 (0.52–0.80), 0.82 (0.65–1.04), and 0.64 (0.52–0.79), respectively. The corresponding numbers for other NSAIDs were 0.68 (0.57–0.81), 0.80 (0.67–0.95), and 0.65 (0.50–0.85), respectively

Melanism in Peromyscus Is Caused by Independent Mutations in Agouti

Identifying the molecular basis of phenotypes that have evolved independently can provide insight into the ways genetic and developmental constraints influence the maintenance of phenotypic diversity. Melanic (darkly pigmented) phenotypes in mammals provide a potent system in which to study the genetic basis of naturally occurring mutant phenotypes because melanism occurs in many mammals, and the mammalian pigmentation pathway is well understood. Spontaneous alleles of a few key pigmentation loci are known to cause melanism in domestic or laboratory populations of mammals, but in natural populations, mutations at one gene, the melanocortin-1 receptor (Mc1r), have been implicated in the vast majority of cases, possibly due to its minimal pleiotropic effects. To investigate whether mutations in this or other genes cause melanism in the wild, we investigated the genetic basis of melanism in the rodent genus Peromyscus, in which melanic mice have been reported in several populations. We focused on two genes known to cause melanism in other taxa, Mc1r and its antagonist, the agouti signaling protein (Agouti). While variation in the Mc1r coding region does not correlate with melanism in any population, in a New Hampshire population, we find that a 125-kb deletion, which includes the upstream regulatory region and exons 1 and 2 of Agouti, results in a loss of Agouti expression and is perfectly associated with melanic color. In a second population from Alaska, we find that a premature stop codon in exon 3 of Agouti is associated with a similar melanic phenotype. These results show that melanism has evolved independently in these populations through mutations in the same gene, and suggest that melanism produced by mutations in genes other than Mc1r may be more common than previously thought

Comparative genetic analysis: the utility of mouse genetic systems for studying human monogenic disease

One of the long-term goals of mutagenesis programs in the mouse has been to generate mutant lines to facilitate the functional study of every mammalian gene. With a combination of complementary genetic approaches and advances in technology, this aim is slowly becoming a reality. One of the most important features of this strategy is the ability to identify and compare a number of mutations in the same gene, an allelic series. With the advent of gene-driven screening of mutant archives, the search for a specific series of interest is now a practical option. This review focuses on the analysis of multiple mutations from chemical mutagenesis projects in a wide variety of genes and the valuable functional information that has been obtained from these studies. Although gene knockouts and transgenics will continue to be an important resource to ascertain gene function, with a significant proportion of human diseases caused by point mutations, identifying an allelic series is becoming an equally efficient route to generating clinically relevant and functionally important mouse models