A Staining Combination for Phloem and Contiguous Tissues

A technic is described for producing critically stained preparations of phloem tissue. The preparations promise to be relatively stable. Sections of fixed unembedded or of embedded (paraffin or celloidin) phloem, cambium, and xylem are (1) stained in Foster's tannic acid-ferric chloride combination; (2) treated with 1% NaHCOg in 25% or 50% ethyl alcohol for 30 minutes; (3) stained in a saturated solution of lacmoid (made alkaline by adding a few ml. of 1% NaHCO₃ in 25% alcohol) for 12 to 18 hours; (4) dehydrated and cleared in a series composed of 1% solution of NaHCO_s in 50% ethyl alcohol, 80%, 95%, and absolute alcohol, equal proportions of absolute alcohol, clove oil, and xylene, and finally pure xylene; and (5) mounted in a neutral resin. Callose and lignified secondary walls are blue or blue-green in color, cellulose walls and stainable protoplasmic contents are generally light brown. The technic has been successful with sections from 5 to 40μ in thickness, and the staining has been satisfactory for both color and black and white photomicrography.     

Calculation of pK(a) in proteins with the microenvironment modulated-screened coulomb potential

The MM-SCP has been applied to predict pK(a) values of titratable residues in wild type and mutants of staphylococcal nuclease (SNase). The calculations were based on crystal structures made available by the Garcia-Moreno Laboratory. In the mutants, mostly deeply buried hydrophobic residues were replaced with ionizable residues, and thus their pK(a) values could be measured and calculated using various methods. The data set used here consisted of a set of WT SNase for which His pK(a) at several ionic strengths had been measured, a set of mutants for which measured pK(a) were available and a set of 11 mutants for which the measured pK(a) were not known at the time of calculation. For this latter set, blind predictions were submitted to the protein pK(a) cooperative, 2009 workshop at Telluride, where the results of the blind predictions were discussed (the RMSD of the submitted set was 1.10 pH units). The calculations on the structures with known pK(a) indicated that in addition to weaknesses of the method, structural issues were observed that led to larger errors (>1) in pK(a) predictions. For example, different crystallographic conditions or steric clashes can lead to differences in the local environment around the titratable residue, which can produce large differences in the calculated pK(a) . To gain further insight into the reliability of the MM-SCP, pK(a) of an extended set of 54 proteins belonging to several structural classes were carried out. Here some initial results from this study are reported to help place the SNase results in the appropriate context.     

Routing misfolded proteins through the multivesicular body (MVB) pathway protects against proteotoxicity

The secretory pathway maintains multiple quality control checkpoints. Initially, endoplasmic reticulum-associated degradation pathways monitor protein folding to retain and eliminate aberrant products. Despite its broad client range, some molecules escape detection and traffic to Golgi membranes. There, a poorly understood mechanism termed Golgi quality control routes aberrant proteins for lysosomal/vacuolar degradation. To better understand Golgi quality control, we examined the processing of the obligate substrate Wsc1p. Misfolded Wsc1p does not use routes of typical vacuolar membrane proteins. Instead, it partitions into intralumenal vesicles of the multivesicular body (MVB) pathway, mediated by the E3 ubiquitin ligase Rsp5p. Its subsequent transport to the vacuolar lumen is essential for complete molecule breakdown. Surprisingly, the transport mode plays a second crucial function in neutralizing potential substrate toxicity. Eliminating the MVB sorting signal diverted molecules to the vacuolar limiting membrane, resulting in the generation of toxic by-products. These data demonstrate a new role of the MVB pathway in protein quality control.     

Molecular pattern discovery based on penalized matrix decomposition

A reliable and precise identification of the type of tumors is crucial to the effective treatment of cancer. With the rapid development of microarray technologies, tumor clustering based on gene expression data is becoming a powerful approach to cancer class discovery. In this paper, we apply the penalized matrix decomposition (PMD) to gene expression data to extract metasamples for clustering. The extracted metasamples capture the inherent structures of samples belong to the same class. At the same time, the PMD factors of a sample over the metasamples can be used as its class indicator in return. Compared with the conventional methods such as hierarchical clustering (HC), self-organizing maps (SOM), affinity propagation (AP) and nonnegative matrix factorization (NMF), the proposed method can identify the samples with complex classes. Moreover, the factor of PMD can be used as an index to determine the cluster number. The proposed method provides a reasonable explanation of the inconsistent classifications made by the conventional methods. In addition, it is able to discover the modules in gene expression data of conterminous developmental stages. Experiments on two representative problems show that the proposed PMD-based method is very promising to discover biological phenotypes.     

Prediction of dengue incidence using search query surveillance

Background

The use of internet search data has been demonstrated to be effective at predicting influenza incidence. This approach may be more successful for dengue which has large variation in annual incidence and a more distinctive clinical presentation and mode of transmission.

Methods

We gathered freely-available dengue incidence data from Singapore (weekly incidence, 2004–2011) and Bangkok (monthly incidence, 2004–2011). Internet search data for the same period were downloaded from Google Insights for Search. Search terms were chosen to reflect three categories of dengue-related search: nomenclature, signs/symptoms, and treatment. We compared three models to predict incidence: a step-down linear regression, generalized boosted regression, and negative binomial regression. Logistic regression and Support Vector Machine (SVM) models were used to predict a binary outcome defined by whether dengue incidence exceeded a chosen threshold. Incidence prediction models were assessed using and Pearson correlation between predicted and observed dengue incidence. Logistic and SVM model performance were assessed by the area under the receiver operating characteristic curve. Models were validated using multiple cross-validation techniques.

Results

The linear model selected by AIC step-down was found to be superior to other models considered. In Bangkok, the model has an , and a correlation of 0.869 between fitted and observed. In Singapore, the model has an , and a correlation of 0.931. In both Singapore and Bangkok, SVM models outperformed logistic regression in predicting periods of high incidence. The AUC for the SVM models using the 75th percentile cutoff is 0.906 in Singapore and 0.960 in Bangkok.

Conclusions

Internet search terms predict incidence and periods of large incidence of dengue with high accuracy and may prove useful in areas with underdeveloped surveillance systems. The methods presented here use freely available data and analysis tools and can be readily adapted to other settings.     

Novel nucleotide and amino acid covariation between the 5'UTR and the NS2/NS3 proteins of hepatitis C virus: bioinformatic and functional analyses

Molecular covariation of highly polymorphic viruses is thought to have crucial effects on viral replication and fitness. This study employs association rule data mining of hepatitis C virus (HCV) sequences to search for specific evolutionary covariation and then tests functional relevance on HCV replication. Data mining is performed between nucleotides in the untranslated regions 5' and 3'UTR, and the amino acid residues in the non-structural proteins NS2, NS3 and NS5B. Results indicate covariance of the 243(rd) nucleotide of the 5'UTR with the 14(th), 41(st), 76(th), 110(th), 211(th) and 212(th) residues of NS2 and with the 71(st), 175(th) and 621(st) residues of NS3. Real-time experiments using an HCV subgenomic system to quantify viral replication confirm replication regulation for each covariant pair between 5'UTR??? and NS2-41, -76, -110, -211, and NS3-71, -175. The HCV subgenomic system with/without the NS2 region shows that regulatory effects vanish without NS2, so replicative modulation mediated by HCV 5'UTR??? depends on NS2. Strong binding of the NS2 variants to HCV RNA correlates with reduced HCV replication whereas weak binding correlates with restoration of HCV replication efficiency, as determined by RNA-protein immunoprecipitation assay band intensity. The dominant haplotype 5'UTR???-NS2-41-76-110-211-NS3-71-175 differs according to the HCV genotype: G-Ile-Ile-Ile-Gly-Ile-Met for genotype 1b and A-Leu-Val-Leu-Ser-Val-Leu for genotypes 1a, 2a and 2b. In conclusion, 5'UTR??? co-varies with specific NS2/3 protein amino acid residues, which may have significant structural and functional consequences for HCV replication. This unreported mechanism involving HCV replication possibly can be exploited in the development of advanced anti-HCV medication.     

Passiflin,a novel dimeric antifungal protein from seeds of the passion fruit

The intent was to isolate an antifungal protein from seeds of the passion fruit (Passiflora edulis) and to compare its characteristics with other antifungal proteins and bovine β-lactoglobulin in view of its N-terminal amino acid sequence similarity to β-lactoglobulin. The isolation procedure entailed ion-exchange chromatography on Q-Sepharose, hydrophobic interaction chromatography on Phenyl-Sepharose, ion-exchange chromatography on DEAE-cellulose, and FPLC-gel filtration on Superdex 75. The isolated 67-kDa protein, designated as passiflin, exhibited an N-terminal amino acid sequence closely resembling that of bovine β-lactoglobulin. It is the first antifungal protein found to have a β-lactoglobulin-like N-terminal sequence. Its dimeric nature is rarely found in antifungal proteins. It impeded mycelial growth in Rhizotonia solani with an IC₅₀ of 16 μM and potently inhibited proliferation of MCF-7 breast cancer cells with an IC₅₀ of 15 μM. There was no cross-reactivity of passiflin with anti-β-lactoglobulin antiserum. Intact β-lactoglobulin lacks antifungal and antiproliferative activities and is much smaller in molecular size than passiflin. However, it has been reported that hydrolyzed β-lactoglobulin shows antifungal activity. The data suggest that passiflin is distinct from β-lactoglobulin.     

Concentration of plasminogen activators and inhibitor in the human endometrium at different phases of the menstrual cycle.

The concentrations of tissue plasminogen activator (t-PA), urokinase plasminogen activator (u-PA) and plasminogen activator inhibitor (PAI-1) have been determined in endometrial curettings obtained from 46 subfertile women during proliferative, early or late secretory phases of the menstrual cycle. t-PA activity and antigen concentrations was significantly higher (P < 0.001) in late secretory endometrium than in proliferative or early secretory endometrium. Higher concentrations of PAI-1 antigen (P < 0.05) were also noted in late secretory phase than in proliferative and early secretory endometrium. However, u-PA concentration was not significantly different and no PAI activity could be demonstrated in the menstrual phases studied. Zymography studies confirmed the presence of both t-PA and u-PA in the endometrium. Ovarian hormonal patterns may therefore influence the activity of plasminogen activators especially of t-PA in the endometrium during various phases of the menstrual cycle.