Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

Knockdown of STAT3 expression by RNAi induces apoptosis in astrocytoma cells

BACKGROUND: Astrocytomas are the most common type of primary central nervous system tumors. They are frequently associated with genetic mutations that deregulate cell cycle and render these tumors resistant to apoptosis. STAT3, signal transducer and activator of transcription 3, participates in several human cancers by inducing cell proliferation and inhibiting apoptosis and is frequently activated in astrocytomas. METHODS: RNA interference was used to knockdown STAT3 expression in human astrocytes and astrocytoma cell lines. The effect of STAT3 knockdown on apoptosis, cell proliferation, and gene expression was then assessed by standard methods. RESULTS: We have found that STAT3 is constitutively activated in several human astrocytoma cell lines. Knockdown of STAT3 expression by siRNA induces morphologic and biochemical changes consistent with apoptosis in several astrocytoma cell lines, but not in primary human astrocytes. Moreover, STAT3 is required for the expression of the antiapoptotic genes survivin and Bcl-xL in the A172 glioblastoma cell line. CONCLUSION: These results show that STAT3 is required for the survival of some astrocytomas. These studies suggest STAT3 siRNA could be a useful therapeutic agent for the treatment of astrocytomas

Two <em>Dictyostelium</em> Tyrosine Kinase-Like kinases function in parallel, stress-induced STAT activation pathways

When Dictyostelium cells are hyperosmotically stressed, STATc is activated by tyrosine phosphorylation. Unusually, activation is regulated by serine phosphorylation and consequent inhibition of a tyrosine phosphatase: PTP3. The identity of the cognate tyrosine kinase is unknown, and we show that two tyrosine kinase–like (TKL) enzymes, Pyk2 and Pyk3, share this function; thus, for stress-induced STATc activation, single null mutants are only marginally impaired, but the double mutant is nonactivatable. When cells are stressed, Pyk2 and Pyk3 undergo increased autocatalytic tyrosine phosphorylation. The site(s) that are generated bind the SH2 domain of STATc, and then STATc becomes the target of further kinase action. The signaling pathways that activate Pyk2 and Pyk3 are only partially overlapping, and there may be a structural basis for this difference because Pyk3 contains both a TKL domain and a pseudokinase domain. The latter functions, like the JH2 domain of metazoan JAKs, as a negative regulator of the kinase domain. The fact that two differently regulated kinases catalyze the same phosphorylation event may facilitate specific targeting because under stress, Pyk3 and Pyk2 accumulate in different parts of the cell; Pyk3 moves from the cytosol to the cortex, whereas Pyk2 accumulates in cytosolic granules that colocalize with PTP3

Predicting disease-associated substitution of a single amino acid by analyzing residue interactions

<p>Abstract</p> <p>Background</p> <p>The rapid accumulation of data on non-synonymous single nucleotide polymorphisms (nsSNPs, also called SAPs) should allow us to further our understanding of the underlying disease-associated mechanisms. Here, we use complex networks to study the role of an amino acid in both local and global structures and determine the extent to which disease-associated and polymorphic SAPs differ in terms of their interactions to other residues.</p> <p>Results</p> <p>We found that SAPs can be well characterized by network topological features. Mutations are probably disease-associated when they occur at a site with a high centrality value and/or high degree value in a protein structure network. We also discovered that study of the neighboring residues around a mutation site can help to determine whether the mutation is disease-related or not. We compiled a dataset from the Swiss-Prot variant pages and constructed a model to predict disease-associated SAPs based on the random forest algorithm. The values of total accuracy and MCC were 83.0% and 0.64, respectively, as determined by 5-fold cross-validation. With an independent dataset, our model achieved a total accuracy of 80.8% and MCC of 0.59, respectively.</p> <p>Conclusions</p> <p>The satisfactory performance suggests that network topological features can be used as quantification measures to determine the importance of a site on a protein, and this approach can complement existing methods for prediction of disease-associated SAPs. Moreover, the use of this method in SAP studies would help to determine the underlying linkage between SAPs and diseases through extensive investigation of mutual interactions between residues.</p

STAT3 Is Activated by JAK2 Independent of Key Oncogenic Driver Mutations in Non-Small Cell Lung Carcinoma

Constitutive activation of STAT3 is a common feature in many solid tumors including non-small cell lung carcinoma (NSCLC). While activation of STAT3 is commonly achieved by somatic mutations to JAK2 in hematologic malignancies, similar mutations are not often found in solid tumors. Previous work has instead suggested that STAT3 activation in solid tumors is more commonly induced by hyperactive growth factor receptors or autocrine cytokine signaling. The interplay between STAT3 activation and other well-characterized oncogenic “driver” mutations in NSCLC has not been fully characterized, though constitutive STAT3 activation has been proposed to play an important role in resistance to various small-molecule therapies that target these oncogenes. In this study we demonstrate that STAT3 is constitutively activated in human NSCLC samples and in a variety of NSCLC lines independent of activating KRAS or tyrosine kinase mutations. We further show that genetic or pharmacologic inhibition of the gp130/JAK2 signaling pathway disrupts activation of STAT3. Interestingly, treatment of NSCLC cells with the JAK1/2 inhibitor ruxolitinib has no effect on cell proliferation and viability in two-dimensional culture, but inhibits growth in soft agar and xenograft assays. These data demonstrate that JAK2/STAT3 signaling operates independent of known driver mutations in NSCLC and plays critical roles in tumor cell behavior that may not be effectively inhibited by drugs that selectively target these driver mutations

Lysosomal protease deficiency or substrate overload induces an oxidative-stress mediated STAT3-dependent pathway of lysosomal homeostasis

How cells regulate their lysosomal proteolytic capacity is only partly understood. Here, the authors show that lysosomal protease deficiency or substrate overload induces lysosomal stress leading to activation of a STAT3-dependent, TFEB-independent pathway of lysosomal hydrolase expression

Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set

We report a measurement of the bottom-strange meson mixing phase \beta_s using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays in which the quark-flavor content of the bottom-strange meson is identified at production. This measurement uses the full data set of proton-antiproton collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity. We report confidence regions in the two-dimensional space of \beta_s and the B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2, -1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in agreement with the standard model expectation. Assuming the standard model value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +- 0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +- 0.009 (syst) ps, which are consistent and competitive with determinations by other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012

Search for Kaluza-Klein Graviton Emission in ppˉp\bar{p} Collisions at s=1.8\sqrt{s}=1.8 TeV using the Missing Energy Signature

We report on a search for direct Kaluza-Klein graviton production in a data sample of 84 ${pb}^{-1}$ of \ppb collisions at $\sqrt{s}$ = 1.8 TeV, recorded by the Collider Detector at Fermilab. We investigate the final state of large missing transverse energy and one or two high energy jets. We compare the data with the predictions from a $3+1+n$-dimensional Kaluza-Klein scenario in which gravity becomes strong at the TeV scale. At 95% confidence level (C.L.) for $n$=2, 4, and 6 we exclude an effective Planck scale below 1.0, 0.77, and 0.71 TeV, respectively.Comment: Submitted to PRL, 7 pages 4 figures/Revision includes 5 figure

Observation of associated near-side and away-side long-range correlations in √sNN=5.02 TeV proton-lead collisions with the ATLAS detector

Two-particle correlations in relative azimuthal angle (Δϕ) and pseudorapidity (Δη) are measured in √sNN=5.02  TeV p+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1  μb-1 of data as a function of transverse momentum (pT) and the transverse energy (ΣETPb) summed over 3.1<η<4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2<|Δη|<5) “near-side” (Δϕ∼0) correlation that grows rapidly with increasing ΣETPb. A long-range “away-side” (Δϕ∼π) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small ΣETPb, is found to match the near-side correlation in magnitude, shape (in Δη and Δϕ) and ΣETPb dependence. The resultant Δϕ correlation is approximately symmetric about π/2, and is consistent with a dominant cos⁡2Δϕ modulation for all ΣETPb ranges and particle pT

Jet energy measurement with the ATLAS detector in proton-proton collisions at root s=7 TeV

The jet energy scale and its systematic uncertainty are determined for jets measured with the ATLAS detector at the LHC in proton-proton collision data at a centre-of-mass energy of √s = 7TeV corresponding to an integrated luminosity of 38 pb-1. Jets are reconstructed with the anti-kt algorithm with distance parameters R=0. 4 or R=0. 6. Jet energy and angle corrections are determined from Monte Carlo simulations to calibrate jets with transverse momenta pT≥20 GeV and pseudorapidities {pipe}η{pipe}<4. 5. The jet energy systematic uncertainty is estimated using the single isolated hadron response measured in situ and in test-beams, exploiting the transverse momentum balance between central and forward jets in events with dijet topologies and studying systematic variations in Monte Carlo simulations. The jet energy uncertainty is less than 2. 5 % in the central calorimeter region ({pipe}η{pipe}<0. 8) for jets with 60≤pT<800 GeV, and is maximally 14 % for pT<30 GeV in the most forward region 3. 2≤{pipe}η{pipe}<4. 5. The jet energy is validated for jet transverse momenta up to 1 TeV to the level of a few percent using several in situ techniques by comparing a well-known reference such as the recoiling photon pT, the sum of the transverse momenta of tracks associated to the jet, or a system of low-pT jets recoiling against a high-pT jet. More sophisticated jet calibration schemes are presented based on calorimeter cell energy density weighting or hadronic properties of jets, aiming for an improved jet energy resolution and a reduced flavour dependence of the jet response. The systematic uncertainty of the jet energy determined from a combination of in situ techniques is consistent with the one derived from single hadron response measurements over a wide kinematic range. The nominal corrections and uncertainties are derived for isolated jets in an inclusive sample of high-pT jets. Special cases such as event topologies with close-by jets, or selections of samples with an enhanced content of jets originating from light quarks, heavy quarks or gluons are also discussed and the corresponding uncertainties are determined. © 2013 CERN for the benefit of the ATLAS collaboration