Sustained proliferation in cancer: mechanisms and novel therapeutic targets

Proliferation is an important part of cancer development and progression. This is manifest by altered expression and/or activity of cell cycle related proteins. Constitutive activation of many signal transduction pathways also stimulates cell growth. Early steps in tumor development are associated with a fibrogenic response and the development of a hypoxic environment which favors the survival and proliferation of cancer stem cells. Part of the survival strategy of cancer stem cells may manifested by alterations in cell metabolism. Once tumors appear, growth and metastasis may be supported by overproduction of appropriate hormones (in hormonally dependent cancers), by promoting angiogenesis, by undergoing epithelial to mesenchymal transition, by triggering autophagy, and by taking cues from surrounding stromal cells. A number of natural compounds (e.g., curcumin, resveratrol, indole-3-carbinol, brassinin, sulforaphane, epigallocatechin-3-gallate, genistein, ellagitannins, lycopene and quercetin) have been found to inhibit one or more pathways that contribute to proliferation (e.g., hypoxia inducible factor 1, nuclear factor kappa B, phosphoinositide 3 kinase/Akt, insulin-like growth factor receptor 1, Wnt, cell cycle associated proteins, as well as androgen and estrogen receptor signaling). These data, in combination with bioinformatics analyses, will be very important for identifying signaling pathways and molecular targets that may provide early diagnostic markers and/or critical targets for the development of new drugs or drug combinations that block tumor formation and progression

From Disease Association to Risk Assessment: An Optimistic View from Genome-Wide Association Studies on Type 1 Diabetes

Genome-wide association studies (GWAS) have been fruitful in identifying disease susceptibility loci for common and complex diseases. A remaining question is whether we can quantify individual disease risk based on genotype data, in order to facilitate personalized prevention and treatment for complex diseases. Previous studies have typically failed to achieve satisfactory performance, primarily due to the use of only a limited number of confirmed susceptibility loci. Here we propose that sophisticated machine-learning approaches with a large ensemble of markers may improve the performance of disease risk assessment. We applied a Support Vector Machine (SVM) algorithm on a GWAS dataset generated on the Affymetrix genotyping platform for type 1 diabetes (T1D) and optimized a risk assessment model with hundreds of markers. We subsequently tested this model on an independent Illumina-genotyped dataset with imputed genotypes (1,008 cases and 1,000 controls), as well as a separate Affymetrix-genotyped dataset (1,529 cases and 1,458 controls), resulting in area under ROC curve (AUC) of ∼0.84 in both datasets. In contrast, poor performance was achieved when limited to dozens of known susceptibility loci in the SVM model or logistic regression model. Our study suggests that improved disease risk assessment can be achieved by using algorithms that take into account interactions between a large ensemble of markers. We are optimistic that genotype-based disease risk assessment may be feasible for diseases where a notable proportion of the risk has already been captured by SNP arrays

An Incomplete TCA Cycle Increases Survival of Salmonella Typhimurium during Infection of Resting and Activated Murine Macrophages

In comparison to the comprehensive analyses performed on virulence gene expression, regulation and action, the intracellular metabolism of Salmonella during infection is a relatively under-studied area. We investigated the role of the tricarboxylic acid (TCA) cycle in the intracellular replication of Salmonella Typhimurium in resting and activated macrophages, epithelial cells, and during infection of mice.We constructed deletion mutations of 5 TCA cycle genes in S. Typhimurium including gltA, mdh, sdhCDAB, sucAB, and sucCD. We found that the mutants exhibited increased net intracellular replication in resting and activated murine macrophages compared to the wild-type. In contrast, an epithelial cell infection model showed that the S. Typhimurium ΔsucCD and ΔgltA strains had reduced net intracellular replication compared to the wild-type. The glyoxylate shunt was not responsible for the net increased replication of the TCA cycle mutants within resting macrophages. We also confirmed that, in a murine infection model, the S. Typhimurium ΔsucAB and ΔsucCD strains are attenuated for virulence.Our results suggest that disruption of the TCA cycle increases the ability of S. Typhimurium to survive within resting and activated murine macrophages. In contrast, epithelial cells are non-phagocytic cells and unlike macrophages cannot mount an oxidative and nitrosative defence response against pathogens; our results show that in HeLa cells the S. Typhimurium TCA cycle mutant strains show reduced or no change in intracellular levels compared to the wild-type. The attenuation of the S. Typhimurium ΔsucAB and ΔsucCD mutants in mice, compared to their increased net intracellular replication in resting and activated macrophages suggest that Salmonella may encounter environments within the host where a complete TCA cycle is advantageous

Copy Number Variation and Transposable Elements Feature in Recent, Ongoing Adaptation at the Cyp6g1 Locus

The increased transcription of the Cyp6g1 gene of Drosophila melanogaster, and consequent resistance to insecticides such as DDT, is a widely cited example of adaptation mediated by cis-regulatory change. A fragment of an Accord transposable element inserted upstream of the Cyp6g1 gene is causally associated with resistance and has spread to high frequencies in populations around the world since the 1940s. Here we report the existence of a natural allelic series at this locus of D. melanogaster, involving copy number variation of Cyp6g1, and two additional transposable element insertions (a P and an HMS-Beagle). We provide evidence that this genetic variation underpins phenotypic variation, as the more derived the allele, the greater the level of DDT resistance. Tracking the spatial and temporal patterns of allele frequency changes indicates that the multiple steps of the allelic series are adaptive. Further, a DDT association study shows that the most resistant allele, Cyp6g1-[BP], is greatly enriched in the top 5% of the phenotypic distribution and accounts for ∼16% of the underlying phenotypic variation in resistance to DDT. In contrast, copy number variation for another candidate resistance gene, Cyp12d1, is not associated with resistance. Thus the Cyp6g1 locus is a major contributor to DDT resistance in field populations, and evolution at this locus features multiple adaptive steps occurring in rapid succession

Measurement of D⁰-D̅⁰ mixing using the ratio of lifetimes for the decays D⁰→K⁻π⁺ and K⁺K⁻

We measure the rate of D^0-D̅ ^0 mixing with the observable y_(CP)=(τ_(Kπ)/τ_(KK))-1, where τ_(KK) and τ_(Kπ) are, respectively, the mean lifetimes of CP-even D^0→K^+K^- and CP-mixed D^0→K^-π^+ decays, using a data sample of 384  fb^(-1) collected by the BABAR detector at the SLAC PEP-II asymmetric-energy B Factory. From a sample of D^0 and D̅ ^0 decays where the initial flavor of the decaying meson is not determined, we obtain y_(CP)=[1.12±0.26(stat)±0.22(syst)]%, which excludes the no-mixing hypothesis at 3.3σ, including both statistical and systematic uncertainties. This result is in good agreement with a previous BABAR measurement of y_(CP) obtained from a sample of D^(*+)→D^0π^+ events, where the D^0 decays to K^-π^+, K^+K^-, and π^+π^-, which is disjoint with the untagged D^0 events used here. Combining the two results taking into account statistical and systematic uncertainties, where the systematic uncertainties are assumed to be 100% correlated, we find y_(CP)=[1.16±0.22(stat)±0.18(syst)]%, which excludes the no-mixing hypothesis at 4.1σ

Search for B-meson decays to b_1ρ and b_1K^*

We present a search for decays of B mesons to final states with a b_1 meson and a ρ or K^*(892) meson. The search is based on a data sample consisting of 465 million BB̅ pairs collected by the BABAR detector at the SLAC National Accelerator Laboratory. We do not observe any statistically significant signal. The upper limits we set on the branching fractions range from 1.4 to 8.0×10^(-6) at the 90% confidence level, including systematic uncertainties

Observation of the baryonic B-decay B̅⁰ → Λ꜀⁺ p̅K⁻π⁺

We report the observation of the baryonic B-decay B^0 → Λ^+_cpK^- π^+, excluding contributions from the decay B^0 → Λ^+_c ΛK^-. Using a data sample of 467 X 10^6 BB pairs collected with the BABAR detector at the PEP-II storage ring at SLAC, the measured branching fraction is (4:33 ± 0:82_(stat) ± 0:33_(syst) ± 1:13Λ^+_c) X 10^(-5). In addition we find evidence for the resonant decay B^0 → ∑_c (2455)^(++) pK^- and determine its branching fraction to be (1:11 ± 0:30_(stat) ± 0:09_(syst) ± 0:29_Λ^+_c) X 10^(-5). The errors are statistical, systematic, and due to the uncertainty in the Λ^+_c branching fraction. For the resonant decay B^0 → Λ^+_c pK^(*0) we obtain an upper limit of 2:42 X 10^(-5_ at 90% confidence level