Identification of fungal pathogens to control postharvest passion fruit (Passiflora edulis) decays and multi-omics comparative pathway analysis reveals purple is more resistant to pathogens than a yellow cultivar

Production of passion fruit (Passiflora edulis) is restricted by postharvest decay, which limits the storage period. We isolated, identified, and characterized fungal pathogens causing decay in two passion fruit cultivars during two fruit seasons in China. Morphological characteristics and nucleotide sequences of ITS-rDNA regions identified eighteen isolates, which were pathogenic on yellow and purple fruit. Fusarium kyushuense, Fusarium concentricum, Colletotrichum truncatum, and Alternaria alternata were the most aggressive species. Visible inspections and comparative analysis of the disease incidences demonstrated that wounded and non-wounded yellow fruit were more susceptible to the pathogens than the purple fruit. Purple cultivar showed higher expression levels of defense-related genes through expression and metabolic profiling, as well as significantly higher levels of their biosynthesis pathways. We also found fungi with potential beneficial features for the quality of fruits. Our transcriptomic and metabolomics data provide a basis to identify potential targets to improve the pathogen resistance of the susceptible yellow cultivar. The identified fungi and affected features of the fruit of both cultivars provide important information for the control of pathogens in passion fruit industry and postharvest storage

Fluctuation dynamo and turbulent induction at low magnetic Prandtl numbers

This paper is a detailed report on a programme of simulations used to settle a long-standing issue in the dynamo theory and demonstrate that the fluctuation dynamo exists in the limit of large magnetic Reynolds number Rm>>1 and small magnetic Prandtl number Pm<<1. The dependence of the critical Rm_c vs. the hydrodynamic Reynolds number Re is obtained for 1<Re<6700. In the limit Pm<<1, Rm_c is ~3 times larger than for Pm>1. The stability curve Rm_c(Re) (and, it is argued, the nature of the dynamo) is substantially different from the case of the simulations and liquid-metal experiments with a mean flow. It is not as yet possible to determine numerically whether the growth rate is ~Rm^{1/2} in the limit Re>>Rm>>1, as should be the case if the dynamo is driven by the inertial-range motions. The magnetic-energy spectrum in the low-Pm regime is qualitatively different from the Pm>1 case and appears to develop a negative spectral slope, although current resolutions are insufficient to determine its asymptotic form. At 1<Rm<Rm_c, the magnetic fluctuations induced via the tangling by turbulence of a weak mean field are investigated and the possibility of a k^{-1} spectrum above the resistive scale is examined. At low Rm<1, the induced fluctuations are well described by the quasistatic approximation; the k^{-11/3} spectrum is confirmed for the first time in direct numerical simulations.Comment: IoP latex, 27 pages, 25 figures, 3 tables. Accepted by New J. Physic

Transcriptional upregulation of human tissue kallikrein 6 in ovarian cancer: clinical and mechanistic aspects

The human tissue kallikrein family (KLK for protein; KLK for gene) includes 15 members. Twelve kallikreins, including KLK6, are concurrently upregulated in ovarian cancer. However, the mechanism of this phenomenon remains unclear. In this study, we measured KLK6 expression in a large series of ovarian tissue cytosols and examined possible mechanisms of KLK6 up-regulation in ovarian cancer. Using a newly developed enzyme-linked immunosorbent assay (ELISA) with two monoclonal antibodies, we quantified KLK6 expression in ovarian tissue cytosols, and confirmed the upregulation of KLK6 in ovarian cancer and its unfavourable prognostic value. We then examined KLK6 mRNA expression using reverse transcription–polymerase chain reaction and established its good concordance with KLK6 protein expression. This finding suggested that the KLK6 gene is under transcriptional regulation. We then scrutinised a few mechanisms that could explain KLK6 upregulation. The relative abundance of two KLK6 mRNA transcripts was studied; we found the same differential expression pattern in all samples, regardless of KLK6 levels. Genomic mutation screening of all exons and the 5′-flanking region of the KLK6 gene identified two linked single-nucleotide polymorphisms in the 5′-untranslated region, but neither correlated with KLK6 expression. Ovarian cell lines were separately treated with five steroid hormones. None of the treatments produced significant effects on KLK6 expression. We conclude that KLK6 is transcriptionally upregulated in ovarian cancer, but probably not through alternative mRNA transcript expression, genomic mutation, or steroid hormone induction

Dynamics & Predictions in the Co-Event Interpretation

Sorkin has introduced a new, observer independent, interpretation of quantum mechanics that can give a successful realist account of the 'quantum microworld' as well as explaining how classicality emerges at the level of observable events for a range of systems including single time 'Copenhagen measurements'. This 'co-event interpretation' presents us with a new ontology, in which a single 'co-event' is real. A new ontology necessitates a review of the dynamical & predictive mechanism of a theory, and in this paper we begin the process by exploring means of expressing the dynamical and predictive content of histories theories in terms of co-events.Comment: 35 pages. Revised after refereein

Characterisation of human kallikrein 6/protease M expression in ovarian cancer

Kallikrein 6 (hK6, also known as protease M/zyme/neurosin) is a member of the human kallikrein gene family. We have previously cloned the cDNA for this gene by differential display and shown the overexpression of the mRNA in breast and ovarian primary tumour tissues and cell lines. To thoroughly characterise the expression of this kallikrein in ovarian cancer, we have developed a novel monoclonal antibody specific to hK6 and employed it in immunohistochemistry with a wide range of ovarian tumour samples. The expression was found elevated in 67 of 80 cases of ovarian tumour samples and there was a significant difference in the expression levels between normal and benign ovarian tissues and the borderline and invasive tumours (P&lt;0.001). There was no difference of expression level between different subtypes of tumours. More significantly, high level of kallikrein 6 expression was found in many early-stage and low-grade tumours, and elevated hK6 proteins were found in benign epithelia coexisting with borderline and invasive tissues, suggesting that overexpression of hK6 is an early phenomenon in the development of ovarian cancer. Quantitative real-time reverse transcription-polymerase chain reactions also showed elevated kallikrein 6 mRNA expression in ovarian tumours. Genomic Southern analysis of 19 ovarian tumour samples suggested that gene amplification is one mechanism for the overexpression of hK6 in ovarian cancer

Classification and biomarker identification using gene network modules and support vector machines

<p>Abstract</p> <p>Background</p> <p>Classification using microarray datasets is usually based on a small number of samples for which tens of thousands of gene expression measurements have been obtained. The selection of the genes most significant to the classification problem is a challenging issue in high dimension data analysis and interpretation. A previous study with SVM-RCE (Recursive Cluster Elimination), suggested that classification based on groups of correlated genes sometimes exhibits better performance than classification using single genes. Large databases of gene interaction networks provide an important resource for the analysis of genetic phenomena and for classification studies using interacting genes.</p> <p>We now demonstrate that an algorithm which integrates network information with recursive feature elimination based on SVM exhibits good performance and improves the biological interpretability of the results. We refer to the method as SVM with Recursive Network Elimination (SVM-RNE)</p> <p>Results</p> <p>Initially, one thousand genes selected by t-test from a training set are filtered so that only genes that map to a gene network database remain. The Gene Expression Network Analysis Tool (GXNA) is applied to the remaining genes to form <it>n </it>clusters of genes that are highly connected in the network. Linear SVM is used to classify the samples using these clusters, and a weight is assigned to each cluster based on its importance to the classification. The least informative clusters are removed while retaining the remainder for the next classification step. This process is repeated until an optimal classification is obtained.</p> <p>Conclusion</p> <p>More than 90% accuracy can be obtained in classification of selected microarray datasets by integrating the interaction network information with the gene expression information from the microarrays.</p> <p>The Matlab version of SVM-RNE can be downloaded from <url>http://web.macam.ac.il/~myousef</url></p

Gravitational Coupling and Dynamical Reduction of The Cosmological Constant

We introduce a dynamical model to reduce a large cosmological constant to a sufficiently small value. The basic ingredient in this model is a distinction which has been made between the two unit systems used in cosmology and particle physics. We have used a conformal invariant gravitational model to define a particular conformal frame in terms of large scale properties of the universe. It is then argued that the contributions of mass scales in particle physics to the vacuum energy density should be considered in a different conformal frame. In this manner, a decaying mechanism is presented in which the conformal factor appears as a dynamical field and plays a key role to relax a large effective cosmological constant. Moreover, we argue that this model also provides a possible explanation for the coincidence problem.Comment: To appear in GR

Protein-Binding Microarray Analysis of Tumor Suppressor AP2α Target Gene Specificity

Cheap and massively parallel methods to assess the DNA-binding specificity of transcription factors are actively sought, given their prominent regulatory role in cellular processes and diseases. Here we evaluated the use of protein-binding microarrays (PBM) to probe the association of the tumor suppressor AP2α with 6000 human genomic DNA regulatory sequences. We show that the PBM provides accurate relative binding affinities when compared to quantitative surface plasmon resonance assays. A PBM-based study of human healthy and breast tumor tissue extracts allowed the identification of previously unknown AP2α target genes and it revealed genes whose direct or indirect interactions with AP2α are affected in the diseased tissues. AP2α binding and regulation was confirmed experimentally in human carcinoma cells for novel target genes involved in tumor progression and resistance to chemotherapeutics, providing a molecular interpretation of AP2α role in cancer chemoresistance. Overall, we conclude that this approach provides quantitative and accurate assays of the specificity and activity of tumor suppressor and oncogenic proteins in clinical samples, interfacing genomic and proteomic assays

Delineating Electrogenic Reactions during Lactose/H+ Symport†

Electrogenic reactions accompanying downhill lactose/H+ symport catalyzed by the lactose permease of Escherichia coli (LacY) have been assessed using solid-supported membrane-based electrophysiology with improved time resolution. Rates of charge translocation generated by purified LacY reconstituted into proteoliposomes were analyzed over a pH range from 5.2 to 8.5, which allows characterization of two electrogenic steps in the transport mechanism: (i) a weak electrogenic reaction triggered by sugar binding and observed under conditions where H+ translocation is abolished either by acidic pH or by a Glu325 -> Ala mutation in the H+ binding site (this step with a rate constant of ~200 s-1 for wildtype LacY leads to an intermediate proposed to represent an “occluded” state) and (ii) a major electrogenic reaction corresponding to 94% of the total charge translocated at pH 8, which is pH-dependent with a maximum rate of ~30 s-1 and a pK of 7.5. This partial reaction is assigned to rate-limiting H+ release on the cytoplasmic side of LacY during turnover. These findings together with previous electrophysiological results and biochemical-biophysical studies are included in an overall kinetic mechanism that allows delineation of the electrogenic steps in the reaction pathway