Identifying gene locus associations with promyelocytic leukemia nuclear bodies using immuno-TRAP.

Important insights into nuclear function would arise if gene loci physically interacting with particular subnuclear domains could be readily identified. Immunofluorescence microscopy combined with fluorescence in situ hybridization (immuno-FISH), the method that would typically be used in such a study, is limited by spatial resolution and requires prior assumptions for selecting genes to probe. Our new technique, immuno-TRAP, overcomes these limitations. Using promyelocytic leukemia nuclear bodies (PML NBs) as a model, we used immuno-TRAP to determine if specific genes localize within molecular dimensions with these bodies. Although we confirmed a TP53 gene-PML NB association, immuno-TRAP allowed us to uncover novel locus-PML NB associations, including the ABCA7 and TFF1 loci and, most surprisingly, the PML locus itself. These associations were cell type specific and reflected the cell's physiological state. Combined with microarrays or deep sequencing, immuno-TRAP provides powerful opportunities for identifying gene locus associations with potentially any nuclear subcompartment

Soft X-ray seeding studies for the SLAC Linac Coherent Light Source II

We present the results from studies of soft X-ray seeding options for the LCLS-II X-ray free electron laser (FEL) at SLAC. The LCLS-II will use superconducting accelerator technology to produce X-ray pulses at up to 1 MHz repetition rate using 4 GeV electron beams. If properly seeded, these pulses will be nearly fully coherent, and highly stable in photon energy, bandwidth, and intensity, thus enabling unique experiments with intense high-resolution soft X-rays. Given the expected electron beam parameters from start to end simulations and predicted FEL performance, our studies reveal echo enabled harmonic generation (EEHG) and soft X-ray self-seeding (SXRSS) as promising and complementary seeding methods. We find that SXRSS has the advantage of simplicity and will deliver 5-35 times higher spectral brightness than EEHG in the 1-2 nm range, but lacks some of the potential for phase-stable multipulse and multicolor FEL operations enabled by external laser seeding with EEHG

Lovastatin induces apoptosis of ovarian cancer cells and synergizes with doxorubicin: potential therapeutic relevance

<p>Abstract</p> <p>Background</p> <p>Ovarian carcinoma is a rarely curable disease, for which new treatment options are required. As agents that block HMG-CoA reductase and the mevalonate pathway, the statin family of drugs are used in the treatment of hypercholesterolemia and have been shown to trigger apoptosis in a tumor-specific manner. Recent clinical trials show that the addition of statins to traditional chemotherapeutic strategies can increase efficacy of targeting statin-sensitive tumors. Our goal was to assess statin-induced apoptosis of ovarian cancer cells, either alone or in combination with chemotherapeutics, and then determine these mechanisms of action.</p> <p>Methods</p> <p>The effect of lovastatin on ovarian cancer cell lines was evaluated alone and in combination with cisplatin and doxorubicin using several assays (MTT, TUNEL, fixed PI, PARP cleavage) and synergy determined by evaluating the combination index. The mechanisms of action were evaluated using functional, molecular, and pharmacologic approaches.</p> <p>Results</p> <p>We demonstrate that lovastatin induces apoptosis of ovarian cancer cells in a p53-independent manner and synergizes with doxorubicin, a chemotherapeutic agent used to treat recurrent cases of ovarian cancer. Lovastatin drives ovarian tumor cell death by two mechanisms: first, by blocking HMG-CoA reductase activity, and second, by sensitizing multi-drug resistant cells to doxorubicin by a novel mevalonate-independent mechanism. This inhibition of drug transport, likely through inhibition of P-glycoprotein, potentiates both DNA damage and tumor cell apoptosis.</p> <p>Conclusions</p> <p>The results of this research provide pre-clinical data to warrant further evaluation of statins as potential anti-cancer agents to treat ovarian carcinoma. Many statins are inexpensive, off-patent generic drugs that are immediately available for use as anti-cancer agents. We provide evidence that lovastatin triggers apoptosis of ovarian cancer cells as a single agent by a mevalonate-dependent mechanism. Moreover, we also show lovastatin synergizes with doxorubicin, an agent administered for recurrent disease. This synergy occurs by a novel mevalonate-independent mechanism that antagonizes drug resistance, likely by inhibiting P-glycoprotein. These data raise important issues that may impact how statins can best be included in chemotherapy regimens.</p

Data-driven urban management: Mapping the landscape

Big data analytics and artificial intelligence, paired with blockchain technology, the Internet of Things, and other emerging technologies, are poised to revolutionise urban management. With massive amounts of data collected from citizens, devices, and traditional sources such as routine and well-established censuses, urban areas across the world have – for the first time in history – the opportunity to monitor and manage their urban infrastructure in real-time. This simultaneously provides previously unimaginable opportunities to shape the future of cities, but also gives rise to new ethical challenges. This paper provides a transdisciplinary synthesis of the developments, opportunities, and challenges for urban management and planning under this ongoing ‘digital revolution’ to provide a reference point for the largely fragmented research efforts and policy practice in this area. We consider both top-down systems engineering approaches and the bottom-up emergent approaches to coordination of different systems and functions, their implications for the existing physical and institutional constraints on the built environment and various planning practices, as well as the social and ethical considerations associated with this transformation from non-digital urban management to data-driven urban management

Development and Validation of Apolipoprotein AI-Associated Lipoprotein Proteome Panel for the Prediction of Cholesterol Efflux Capacity and Coronary Artery Disease

BACKGROUND: Cholesterol efflux capacity (CEC) is a measure of HDL function that, in cell-based studies, has demonstrated an inverse association with cardiovascular disease. The cell-based measure of CEC is complex and low-throughput. We hypothesized that assessment of the lipoprotein proteome would allow for precise, high-throughput CEC prediction. METHODS: After isolating lipoprotein particles from serum, we used LC-MS/MS to quantify 21 lipoprotein-associated proteins. A bioinformatic pipeline was used to identify proteins with univariate correlation to cell-based CEC measurements and generate a multivariate algorithm for CEC prediction (pCE). Using logistic regression, protein coefficients in the pCE model were reweighted to yield a new algorithm predicting coronary artery disease (pCAD). RESULTS: Discovery using targeted LC-MS/MS analysis of 105 training and test samples yielded a pCE model comprising 5 proteins (Spearman r = 0.86). Evaluation of pCE in a case-control study of 231 specimens from healthy individuals and patients with coronary artery disease revealed lower pCE in cases (P = 0.03). Derived within this same study, the pCAD model significantly improved classification (P < 0.0001). Following analytical validation of the multiplexed proteomic method, we conducted a case-control study of myocardial infarction in 137 postmenopausal women that confirmed significant separation of specimen cohorts in both the pCE (P = 0.015) and pCAD (P = 0.001) models. CONCLUSIONS: Development of a proteomic pCE provides a reproducible high-throughput alternative to traditional cell-based CEC assays. The pCAD model improves stratification of case and control cohorts and, with further studies to establish clinical validity, presents a new opportunity for the assessment of cardiovascular health

Developing and applying heterogeneous phylogenetic models with XRate

Modeling sequence evolution on phylogenetic trees is a useful technique in computational biology. Especially powerful are models which take account of the heterogeneous nature of sequence evolution according to the "grammar" of the encoded gene features. However, beyond a modest level of model complexity, manual coding of models becomes prohibitively labor-intensive. We demonstrate, via a set of case studies, the new built-in model-prototyping capabilities of XRate (macros and Scheme extensions). These features allow rapid implementation of phylogenetic models which would have previously been far more labor-intensive. XRate's new capabilities for lineage-specific models, ancestral sequence reconstruction, and improved annotation output are also discussed. XRate's flexible model-specification capabilities and computational efficiency make it well-suited to developing and prototyping phylogenetic grammar models. XRate is available as part of the DART software package: http://biowiki.org/DART .Comment: 34 pages, 3 figures, glossary of XRate model terminolog

The All-Data-Based Evolutionary Hypothesis of Ciliated Protists with a Revised Classification of the Phylum Ciliophora (Eukaryota, Alveolata)

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