Constitutive cytoplasmic localization of p21Waf1/Cip1 affects the apoptotic process in monocytic leukaemia

In the present study, we analysed the expression and localization of p21Waf1/Cip1 in normal and malignant haematopoietic cells. We demonstrate that in normal monocytic cells, protein kinase C (PKC)-induced p21 gene activation, which is nuclear factor-κB (NF-κB) independent, results in predominantly cytoplasmic localized p21 protein. In acute monocytic leukaemia (M4, M5), monocytic blasts (N=12) show constitutive cytoplasmic p21 expression in 75% of the cases, while in myeloid leukaemic blasts (N=10), low nuclear and cytoplasmic localization of p21 could be detected, which is also PKC dependent. Constitutive p21 expression in monocytic leukaemia might have important antiapoptotic functions. This is supported by the finding that in U937 cells overexpressing p21, VP16-induced apoptosis is significantly reduced (20.0±0.9 vs 55.8±3.8%, P<0.01, N=5), reflected by a reduced phosphorylation of p38 and JNK. Similarly, AML blasts with high cytoplasmic p21 were less sensitive to VP16-induced apoptosis as compared to AML cases with low or undetectable p21 expression (42.25 vs 12.3%, P<0.01). Moreover, complex formation between p21 and ASK1 could be demonstrated in AML cells, by means of coimmunoprecipitation. In summary, these results indicate that p21 has an antiapoptotic role in monocytic leukaemia, and that p21 expression is regulated in a PKC-dependent and NF-κB independent manner.

Rare coding SNP in DZIP1 gene associated with late-onset sporadic Parkinson's disease

We present the first application of the hypothesis-rich mathematical theory to genome-wide association data. The Hamza et al. late-onset sporadic Parkinson's disease genome-wide association study dataset was analyzed. We found a rare, coding, non-synonymous SNP variant in the gene DZIP1 that confers increased susceptibility to Parkinson's disease. The association of DZIP1 with Parkinson's disease is consistent with a Parkinson's disease stem-cell ageing theory.Comment: 14 page

Serotonylation of Vascular Proteins Important to Contraction

BACKGROUND:Serotonin (5-hydroxytryptamine, 5-HT) was named for its source (sero-) and ability to modify smooth muscle tone (tonin). The biological effects of 5-HT are believed to be carried out by stimulation of serotonin receptors at the plasma membrane. Serotonin has recently been shown to be synthesized in vascular smooth muscle and taken up from external sources, placing 5-HT inside the cell. The enzyme transglutaminase uses primary amines such as 5-HT to covalently modify proteins on glutamine residues. We tested the hypothesis that 5-HT is a substrate for transglutaminase in arterial vascular smooth muscle, with protein serotonylation having physiological function. METHODOLOGY/PRINCIPAL FINDINGS:The model was the rat aorta and cultured aortic smooth muscle cells. Western analysis demonstrated that transglutaminase II was present in vascular tissue, and transglutaminase activity was observed as a cystamine-inhibitable incorporation of the free amine pentylamine-biotin into arterial proteins. Serotonin-biotin was incorporated into alpha-actin, beta-actin, gamma-actin, myosin heavy chain and filamin A as shown through tandem mass spectrometry. Using antibodies directed against biotin or 5-HT, immunoprecipitation and immunocytochemistry confirmed serotonylation of smooth muscle alpha-actin. Importantly, the alpha-actin-dependent process of arterial isometric contraction to 5-HT was reduced by cystamine. CONCLUSIONS:5-HT covalently modifies proteins integral to contractility and the cytoskeleton. These findings suggest new mechanisms of action for 5-HT in vascular smooth muscle and consideration for intracellular effects of primary amines

Top scoring pairs for feature selection in machine learning and applications to cancer outcome prediction

&lt;b&gt;Background&lt;/b&gt; The widely used k top scoring pair (k-TSP) algorithm is a simple yet powerful parameter-free classifier. It owes its success in many cancer microarray datasets to an effective feature selection algorithm that is based on relative expression ordering of gene pairs. However, its general robustness does not extend to some difficult datasets, such as those involving cancer outcome prediction, which may be due to the relatively simple voting scheme used by the classifier. We believe that the performance can be enhanced by separating its effective feature selection component and combining it with a powerful classifier such as the support vector machine (SVM). More generally the top scoring pairs generated by the k-TSP ranking algorithm can be used as a dimensionally reduced subspace for other machine learning classifiers.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Results&lt;/b&gt; We developed an approach integrating the k-TSP ranking algorithm (TSP) with other machine learning methods, allowing combination of the computationally efficient, multivariate feature ranking of k-TSP with multivariate classifiers such as SVM. We evaluated this hybrid scheme (k-TSP+SVM) in a range of simulated datasets with known data structures. As compared with other feature selection methods, such as a univariate method similar to Fisher's discriminant criterion (Fisher), or a recursive feature elimination embedded in SVM (RFE), TSP is increasingly more effective than the other two methods as the informative genes become progressively more correlated, which is demonstrated both in terms of the classification performance and the ability to recover true informative genes. We also applied this hybrid scheme to four cancer prognosis datasets, in which k-TSP+SVM outperforms k-TSP classifier in all datasets, and achieves either comparable or superior performance to that using SVM alone. In concurrence with what is observed in simulation, TSP appears to be a better feature selector than Fisher and RFE in some of the cancer datasets.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Conclusions&lt;/b&gt; The k-TSP ranking algorithm can be used as a computationally efficient, multivariate filter method for feature selection in machine learning. SVM in combination with k-TSP ranking algorithm outperforms k-TSP and SVM alone in simulated datasets and in some cancer prognosis datasets. Simulation studies suggest that as a feature selector, it is better tuned to certain data characteristics, i.e. correlations among informative genes, which is potentially interesting as an alternative feature ranking method in pathway analysis

External Stimuli Mediate Collective Rhythms: Artificial Control Strategies

The artificial intervention of biological rhythms remains an exciting challenge. Here, we proposed artificial control strategies that were developed to mediate the collective rhythms emerging in multicellular structures. Based on noisy repressilators and by injecting a periodic control amount to the extracellular medium, we introduced two typical kinds of control models. In one, there are information exchanges among cells, where signaling molecules receive the injected stimulus that freely diffuses toward/from the intercellular medium. In the other, there is no information exchange among cells, but signaling molecules also receive the stimulus that directionally diffuses into each cell from the common environment. We uncovered physical mechanisms for how the stimulus induces, enhances or ruins collective rhythms. We found that only when the extrinsic period is close to an integer multiplicity of the averaged intrinsic period can the collective behaviors be induced/enhanced; otherwise, the stimulus possibly ruins the achieved collective behaviors. Such entrainment properties of these oscillators to external signals would be exploited by realistic living cells to sense external signals. Our results not only provide a new perspective to the understanding of the interplays between extrinsic stimuli and intrinsic physiological rhythms, but also would lead to the development of medical therapies or devices

A forward-backward fragment assembling algorithm for the identification of genomic amplification and deletion breakpoints using high-density single nucleotide polymorphism (SNP) array

<p>Abstract</p> <p>Background</p> <p>DNA copy number aberration (CNA) is one of the key characteristics of cancer cells. Recent studies demonstrated the feasibility of utilizing high density single nucleotide polymorphism (SNP) genotyping arrays to detect CNA. Compared with the two-color array-based comparative genomic hybridization (array-CGH), the SNP arrays offer much higher probe density and lower signal-to-noise ratio at the single SNP level. To accurately identify small segments of CNA from SNP array data, segmentation methods that are sensitive to CNA while resistant to noise are required.</p> <p>Results</p> <p>We have developed a highly sensitive algorithm for the edge detection of copy number data which is especially suitable for the SNP array-based copy number data. The method consists of an over-sensitive edge-detection step and a test-based forward-backward edge selection step.</p> <p>Conclusion</p> <p>Using simulations constructed from real experimental data, the method shows high sensitivity and specificity in detecting small copy number changes in focused regions. The method is implemented in an R package FASeg, which includes data processing and visualization utilities, as well as libraries for processing Affymetrix SNP array data.</p

Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study

BACKGROUND: Quantitative molecular methods (QMMs) such as quantitative real-time polymerase chain reaction (q-PCR), reverse-transcriptase PCR (qRT-PCR) and quantitative nucleic acid sequence-based amplification (QT-NASBA) are increasingly used to estimate pathogen density in a variety of clinical and epidemiological contexts. These methods are often classified as semi-quantitative, yet estimates of reliability or sensitivity are seldom reported. Here, a statistical framework is developed for assessing the reliability (uncertainty) of pathogen densities estimated using QMMs and the associated diagnostic sensitivity. The method is illustrated with quantification of Plasmodium falciparum gametocytaemia by QT-NASBA. RESULTS: The reliability of pathogen (e.g. gametocyte) densities, and the accompanying diagnostic sensitivity, estimated by two contrasting statistical calibration techniques, are compared; a traditional method and a mixed model Bayesian approach. The latter accounts for statistical dependence of QMM assays run under identical laboratory protocols and permits structural modelling of experimental measurements, allowing precision to vary with pathogen density. Traditional calibration cannot account for inter-assay variability arising from imperfect QMMs and generates estimates of pathogen density that have poor reliability, are variable among assays and inaccurately reflect diagnostic sensitivity. The Bayesian mixed model approach assimilates information from replica QMM assays, improving reliability and inter-assay homogeneity, providing an accurate appraisal of quantitative and diagnostic performance. CONCLUSIONS: Bayesian mixed model statistical calibration supersedes traditional techniques in the context of QMM-derived estimates of pathogen density, offering the potential to improve substantially the depth and quality of clinical and epidemiological inference for a wide variety of pathogens

Nonclassical Fields and the Nonlinear Interferometer

We demonstrate several new results for the nonlinear interferometer, which emerge from a formalism which describes in an elegant way the output field of the nonlinear interferometer as two-mode entangled coherent states. We clarify the relationship between squeezing and entangled coherent states, since a weak nonlinear evolution produces a squeezed output, while a strong nonlinear evolution produces a two-mode, two-state entangled coherent state. In between these two extremes exist superpositions of two-mode coherent states manifesting varying degrees of entanglement for arbitrary values of the nonlinearity. The cardinality of the basis set of the entangled coherent states is finite when the ratio $\chi / \pi$ is rational, where $\chi$ is the nonlinear strength. We also show that entangled coherent states can be produced from product coherent states via a nonlinear medium without the need for the interferometric configuration. This provides an important experimental simplification in the process of creating entangled coherent states.Comment: 21 pages, 2 figure

Chromosomal-level assembly of the Asian Seabass genome using long sequence reads and multi-layered scaffolding

We report here the ~670 Mb genome assembly of the Asian seabass (Lates calcarifer), a tropical marine teleost. We used long-read sequencing augmented by transcriptomics, optical and genetic mapping along with shared synteny from closely related fish species to derive a chromosome-level assembly with a contig N50 size over 1 Mb and scaffold N50 size over 25 Mb that span ~90% of the genome. The population structure of L. calcarifer species complex was analyzed by re-sequencing 61 individuals representing various regions across the species' native range. SNP analyses identified high levels of genetic diversity and confirmed earlier indications of a population stratification comprising three clades with signs of admixture apparent in the South-East Asian population. The quality of the Asian seabass genome assembly far exceeds that of any other fish species, and will serve as a new standard for fish genomics

Improved Measurement of the Pseudoscalar Decay Constant fDsf_{D_{s}}

We present a new determination of the Ds decay constant, f_{Ds} using 5 million continuum charm events obtained with the CLEO II detector. Our value is derived from our new measured ratio of widths for Ds -> mu nu/Ds -> phi pi of 0.173+/- 0.021 +/- 0.031. Taking the branching ratio for Ds -> phi pi as (3.6 +/- 0.9)% from the PDG, we extract f_{Ds} = (280 +/- 17 +/- 25 +/- 34){MeV}. We compare this result with various model calculations.Comment: 23 page postscript file, postscript file also available through http://w4.lns.cornell.edu/public/CLN