Kinome-Wide Functional Genomics Screen Reveals a Novel Mechanism of TNFα-Induced Nuclear Accumulation of the HIF-1α Transcription Factor in Cancer Cells

Hypoxia-inducible factor-1 (HIF-1) and its most important subunit, HIF-1α, plays a central role in tumor progression by regulating genes involved in cancer cell survival, proliferation and metastasis. HIF-1α activity is associated with nuclear accumulation of the transcription factor and regulated by several mechanisms including modulation of protein stability and degradation. Among recent advances are the discoveries that inflammation-induced cytokines and growth factors affect protein accumulation of HIF-1α under normoxia conditions. TNFα, a major pro-inflammatory cytokine that promotes tumorigenesis is known as a stimulator of HIF-1α activity. To improve our understanding of TNFα-mediated regulation of HIF-1α nuclear accumulation we screened a kinase-specific siRNA library using a cell imaging–based HIF-1α-eGFP chimera reporter assay. Interestingly, this systematic analysis determined that depletion of kinases involved in conventional TNFα signaling (IKK/NFκB and JNK pathways) has no detrimental effect on HIF-1α accumulation. On the other hand, depletion of PRKAR2B, ADCK2, TRPM7, and TRIB2 significantly decreases the effect of TNFα on HIF-1α stability in osteosarcoma and prostate cancer cell lines. These newly discovered regulators conveyed their activity through a non-conventional RELB-depended NFκB signaling pathway and regulation of superoxide activity. Taken together our data allow us to conclude that TNFα uses a distinct and complex signaling mechanism to induce accumulation of HIF-1α in cancer cells. In summary, our results illuminate a novel mechanism through which cancer initiation and progression may be promoted by inflammatory cytokines, highlighting new potential avenues for fighting this disease

A Detailed History of Intron-rich Eukaryotic Ancestors Inferred from a Global Survey of 100 Complete Genomes

Protein-coding genes in eukaryotes are interrupted by introns, but intron densities widely differ between eukaryotic lineages. Vertebrates, some invertebrates and green plants have intron-rich genes, with 6–7 introns per kilobase of coding sequence, whereas most of the other eukaryotes have intron-poor genes. We reconstructed the history of intron gain and loss using a probabilistic Markov model (Markov Chain Monte Carlo, MCMC) on 245 orthologous genes from 99 genomes representing the three of the five supergroups of eukaryotes for which multiple genome sequences are available. Intron-rich ancestors are confidently reconstructed for each major group, with 53 to 74% of the human intron density inferred with 95% confidence for the Last Eukaryotic Common Ancestor (LECA). The results of the MCMC reconstruction are compared with the reconstructions obtained using Maximum Likelihood (ML) and Dollo parsimony methods. An excellent agreement between the MCMC and ML inferences is demonstrated whereas Dollo parsimony introduces a noticeable bias in the estimations, typically yielding lower ancestral intron densities than MCMC and ML. Evolution of eukaryotic genes was dominated by intron loss, with substantial gain only at the bases of several major branches including plants and animals. The highest intron density, 120 to 130% of the human value, is inferred for the last common ancestor of animals. The reconstruction shows that the entire line of descent from LECA to mammals was intron-rich, a state conducive to the evolution of alternative splicing

Magnetic effects in sulfur-decorated graphene

The interaction between two different materials can present novel phenomena that are quite different from the physical properties observed when each material stands alone. Strong electronic correlations, such as magnetism and superconductivity, can be produced as the result of enhanced Coulomb interactions between electrons. Two-dimensional materials are powerful candidates to search for the novel phenomena because of the easiness of arranging them and modifying their properties accordingly. In this work, we report magnetic effects in graphene, a prototypical non-magnetic two-dimensional semi-metal, in the proximity with sulfur, a diamagnetic insulator. In contrast to the well-defined metallic behaviour of clean graphene, an energy gap develops at the Fermi energy for the graphene/sulfur compound with decreasing temperature. This is accompanied by a steep increase of the resistance, a sign change of the slope in the magneto-resistance between high and low fields, and magnetic hysteresis. A possible origin of the observed electronic and magnetic responses is discussed in terms of the onset of low-temperature magnetic ordering. These results provide intriguing insights on the search for novel quantum phases in graphene-based compounds.open1165sciescopu

Comparing methods to estimate treatment effects on a continuous outcome in multicentre randomized controlled trials: A simulation study

<p>Abstract</p> <p>Background</p> <p>Multicentre randomized controlled trials (RCTs) routinely use randomization and analysis stratified by centre to control for differences between centres and to improve precision. No consensus has been reached on how to best analyze correlated continuous outcomes in such settings. Our objective was to investigate the properties of commonly used statistical models at various levels of clustering in the context of multicentre RCTs.</p> <p>Methods</p> <p>Assuming no treatment by centre interaction, we compared six methods (ignoring centre effects, including centres as fixed effects, including centres as random effects, generalized estimating equation (GEE), and fixed- and random-effects centre-level analysis) to analyze continuous outcomes in multicentre RCTs using simulations over a wide spectrum of intraclass correlation (ICC) values, and varying numbers of centres and centre size. The performance of models was evaluated in terms of bias, precision, mean squared error of the point estimator of treatment effect, empirical coverage of the 95% confidence interval, and statistical power of the procedure.</p> <p>Results</p> <p>While all methods yielded unbiased estimates of treatment effect, ignoring centres led to inflation of standard error and loss of statistical power when within centre correlation was present. Mixed-effects model was most efficient and attained nominal coverage of 95% and 90% power in almost all scenarios. Fixed-effects model was less precise when the number of centres was large and treatment allocation was subject to chance imbalance within centre. GEE approach underestimated standard error of the treatment effect when the number of centres was small. The two centre-level models led to more variable point estimates and relatively low interval coverage or statistical power depending on whether or not heterogeneity of treatment contrasts was considered in the analysis.</p> <p>Conclusions</p> <p>All six models produced unbiased estimates of treatment effect in the context of multicentre trials. Adjusting for centre as a random intercept led to the most efficient treatment effect estimation across all simulations under the normality assumption, when there was no treatment by centre interaction.</p

A Critical Analysis of Atoh7 (Math5) mRNA Splicing in the Developing Mouse Retina

The Math5 (Atoh7) gene is transiently expressed during retinogenesis by progenitors exiting mitosis, and is essential for ganglion cell (RGC) development. Math5 contains a single exon, and its 1.7 kb mRNA encodes a 149-aa polypeptide. Mouse Math5 mutants have essentially no RGCs or optic nerves. Given the importance of this gene in retinal development, we thoroughly investigated the possibility of Math5 mRNA splicing by Northern blot, 3′RACE, RNase protection assays, and RT-PCR, using RNAs extracted from embryonic eyes and adult cerebellum, or transcribed in vitro from cDNA clones. Because Math5 mRNA contains an elevated G+C content, we used graded concentrations of betaine, an isostabilizing agent that disrupts secondary structure. Although ∼10% of cerebellar Math5 RNAs are spliced, truncating the polypeptide, our results show few, if any, spliced Math5 transcripts exist in the developing retina (<1%). Rare deleted cDNAs do arise via RT-mediated RNA template switching in vitro, and are selectively amplified during PCR. These data differ starkly from a recent study (Kanadia and Cepko 2010), which concluded that the vast majority of Math5 and other bHLH transcripts are spliced to generate noncoding RNAs. Our findings clarify the architecture of the Math5 gene and its mechanism of action. These results have implications for all members of the bHLH gene family, for any gene that is alternatively spliced, and for the interpretation of all RT-PCR experiments

siRNA Off-Target Effects Can Be Reduced at Concentrations That Match Their Individual Potency

Small interfering RNAs (siRNAs) are routinely used to reduce mRNA levels for a specific gene with the goal of studying its function. Several studies have demonstrated that siRNAs are not always specific and can have many off-target effects. The 3′ UTRs of off-target mRNAs are often enriched in sequences that are complementary to the seed-region of the siRNA. We demonstrate that siRNA off-targets can be significantly reduced when cells are treated with a dose of siRNA that is relatively low (e.g. 1 nM), but sufficient to effectively silence the intended target. The reduction in off-targets was demonstrated for both modified and unmodified siRNAs that targeted either STAT3 or hexokinase II. Low concentrations reduced silencing of transcripts with complementarity to the seed region of the siRNA. Similarly, off-targets that were not complementary to the siRNA were reduced at lower doses, including up-regulated genes that are involved in immune response. Importantly, the unintended induction of caspase activity following treatment with a siRNA that targeted hexokinase II was also shown to be a concentration-dependent off-target effect. We conclude that off-targets and their related phenotypic effects can be reduced for certain siRNA that potently silence their intended target at low concentrations

Monsoons: global energetics and local physics as drivers of past, present and future monsoons

Global constraints on momentum and energy govern the structure of the zonal mean tropical circulation and rainfall. The continental-scale monsoon systems are also facets of a momentum- and energy-constrained global circulation, but their modern and paleo variability deviates substantially from that of the longitudinal mean through mechanisms neither fully understood nor well simulated. A framework grounded in global constraints yet encompassing the complexities of monsoon dynamics is needed to identify the causes of mismatch between theory, models, and observations and, ultimately, improve regional climate projection. In a first step towards this goal, disparate regional processes must be distilled into gross measures of energy flow in and out of continents and from the surface to the tropopause, so that monsoon dynamics may be coherently diagnosed across modern and paleo observations and across idealized and comprehensive simulations. Accounting for zonal asymmetries in the circulation, land/ocean differences in surface fluxes, and the character of convective systems, such a monsoon framework would integrate our understanding at all relevant scales: from the fine details of how moisture and energy are lifted in the updrafts of thunderclouds, up to the global circulations

A Computational Approach to Finding Novel Targets for Existing Drugs

Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM), suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects

High-Affinity Inhibitors of Human NAD+-Dependent 15-Hydroxyprostaglandin Dehydrogenase: Mechanisms of Inhibition and Structure-Activity Relationships

BACKGROUND: 15-Hydroxyprostaglandin dehydrogenase (15-PGDH, EC 1.1.1.141) is the key enzyme for the inactivation of prostaglandins, regulating processes such as inflammation or proliferation. The anabolic pathways of prostaglandins, especially with respect to regulation of the cyclooxygenase (COX) enzymes have been studied in detail; however, little is known about downstream events including functional interaction of prostaglandin-processing and -metabolizing enzymes. High-affinity probes for 15-PGDH will, therefore, represent important tools for further studies. PRINCIPAL FINDINGS: To identify novel high-affinity inhibitors of 15-PGDH we performed a quantitative high-throughput screen (qHTS) by testing &gt;160 thousand compounds in a concentration-response format and identified compounds that act as noncompetitive inhibitors as well as a competitive inhibitor, with nanomolar affinity. Both types of inhibitors caused strong thermal stabilization of the enzyme, with cofactor dependencies correlating with their mechanism of action. We solved the structure of human 15-PGDH and explored the binding modes of the inhibitors to the enzyme in silico. We found binding modes that are consistent with the observed mechanisms of action. CONCLUSIONS: Low cross-reactivity in screens of over 320 targets, including three other human dehydrogenases/reductases, suggest selectivity of the present inhibitors for 15-PGDH. The high potencies and different mechanisms of action of these chemotypes make them a useful set of complementary chemical probes for functional studies of prostaglandin-signaling pathways. ENHANCED VERSION: This article can also be viewed as an enhanced version in which the text of the article is integrated with interactive 3D representations and animated transitions. Please note that a web plugin is required to access this enhanced functionality. Instructions for the installation and use of the web plugin are available in Text S2