Gloverins of the silkworm Bombyx mori: Structural and binding properties and activities

Gloverins are basic, glycine-rich and heat-stable antibacterial proteins (～14- kDa) in lepidopteran insects with activity against Escherichia coli, Gram-positive bacteria, fungi and a virus. Hyalophora gloveri gloverin adopts a random coil structure in aqueous solution but has α-helical structure in membrane-like environment, and it may interact with the lipid A moiety of lipopolysaccharide (LPS). Manduca sexta gloverin binds to the O-specific antigen and outer core carbohydrate of LPS. In the silkworm Bombyx mori, there are four gloverins with slightly acidic to neutral isoelectric points. In this study, we investigate structural and binding properties and activities of B. mori gloverins (BmGlvs), as well as correlations between structure, binding property and activity. Recombinant BmGlv1-4 were expressed in bacteria and purified. Circular dichroism (CD) spectra showed that all four BmGlvs mainly adopted random coli structure (>50%) in aqueous solution in regardless of pH, but contained α-helical structure in the presence of 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP), smooth and rough mutants (Ra, Rc and Re) of LPS and lipid A. Plate ELISA assay showed that BmGlvs at pH 5.0 bound to rough mutants of LPS and lipid A but not to smooth LPS. Antibacterial activity assay showed that positively charged BmGlvs (at pH 5.0) were active against E. coli mutant strains containing rough LPS but inactive against E. coli with smooth LPS. Our results suggest that binding to rough LPS is the prerequisite for the activity of BmGlvs against E. coli.     

Identification of Penicillin-binding proteins employing support vector machines and random forest

Penicillin-Binding Proteins are peptidases that play an important role in cell-wall biogenesis in bacteria and thus maintaining bacterial infections. A wide class of β-lactam drugs are known to act on these proteins and inhibit bacterial infections by disrupting the cell-wall biogenesis pathway. Penicillin-Binding proteins have recently gained importance with the increase in the number of multi-drug resistant bacteria. In this work, we have collected a dataset of over 700 Penicillin-Binding and non-Penicillin Binding Proteins and extracted various sequence-related features. We then created models to classify the proteins into Penicillin-Binding and non-binding using supervised machine learning algorithms such as Support Vector Machines and Random Forest. We obtain a good classification performance for both the models using both the methods.     

Unveiling the Pathways of Dioxygen Through the C2 Component of the Environmentally Relevant Monooxygenase p‐Hydroxyphenylacetate Hydroxylase from Acinetobacter baumannii: A Molecular Dynamics Investigation

In this work, models of the homotetrameric C2 component of the monooxygenase p‐hydroxyphenylacetate hydroxylase from Acinetobacter baumannii, in complex with dioxygen (O₂) and, or not, the substrate p‐hydroxyphenylacetate (HPA) were built. Both models proved to be amenable to random‐acceleration molecular dynamics (RAMD) simulations, whereby a tiny randomly oriented external force, acting on O₂ at the active site in front of flavin mononucleotide (FMNH^?), accelerated displacement of O₂ toward the bulk solvent. This allowed us to carry out a sufficiently large number of RAMD simulations to be of statistical significance. The two systems behaved very similarly under RAMD, except for O₂ leaving the active site more easily in the absence of HPA, but then finding similar obstacles in getting to the gate as when the active site was sheltered by HPA. This challenges previous conclusions that HPA can only reach the active center after that the C4aOOH derivative of FMNH^? is formed, requiring uptake of O₂ at the active site before HPA. According to these RAMD simulations, O₂ could well get to FMNH^? also in the presence of the substrate at the active site.     

Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem

Soil organic carbon (SOC) plays an important role in soil fertility and carbon sequestration, and a better understanding of the spatial patterns of SOC is essential for soil resource management. In this study, we used boosted regression tree (BRT) and random forest (RF) models to map the distribution of topsoil organic carbon content at the northeastern edge of the Tibetan Plateau in China. A set of 105 soil samples and 12 environmental variables (including topography, climate and vegetation) were analyzed. The performance of the models was evaluated using a 10-fold cross-validation procedure. Maps of the mean values and standard deviations of SOC were generated to illustrate model variability and uncertainty. The results indicate that the BRT and RF models exhibited very similar performance and yielded similar predicted distributions of SOC. The two models explained approximately 70% of the total SOC variability. The BRT and RF models robustly predicted the SOC at low observed SOC values, whereas they underestimated high observed SOC values. This underestimation may have been caused by biased distributions of soil samples in the SOC space. Vegetation-related variables were assigned the highest importance in both models, followed by climate and topography. Both models produced spatial distribution maps of SOC that were closely related to vegetation cover. The SOC content predicted by the BRT model was clearly higher than that of the RF model in areas with greater vegetation cover because the contributions of vegetation-related variables in the two models (65% and 43%, respectively) differed significantly. The predicted SOC content increased from the northwestern to the southeastern part of the study area, average values produced by the BRT and RF models were 27.3 g kg⁻¹ and 26.6 g kg⁻¹, respectively. We conclude that the BRT and RF methods should be calibrated and compared to obtain the best prediction of SOC spatial distribution in similar regions. In addition, vegetation variables, including those obtained from remote sensing imagery, should be taken as the main environmental indicators and explicitly included when generating SOC maps in Alpine environments.     

Leishmania (Viannia) braziliensis: epidemiology of canine cutaneous leishmaniasis in the State of Paraná (Brazil)

The present study examines the role that dogs play in the maintenance of the Leishmania cycle in the State of Paraná, Southern Brazil. Dogs were examined in three regions where cutaneous leishmaniasis is endemic or epidemic (R1-Vale da Ribeira; R2-Central region of Paraná State and R3-Northern region). To determine serum prevalence rates ELISA was used. In regions endemic for Trypanosoma cruzi (R1 and R3), serum from dogs seroreactive towards Leishmania antigen was subjected to T. cruzi adsorption in order to eliminate cross-reaction with common antigen epitopes. Concomitantly, dogs with cutaneous lesions were biopsied to isolate and identify parasites using RAPD. Leishmania were classified by the phenetic method using the Jaccard coefficient of similarity, and grouped by Unweighted Pair-Group Method using an Arithmetic Average (UPGMA). A total of 410 dogs were studied. In R1 (Vale da Ribeira) 159 dogs were evaluated of which 10 had anti-Leishmania antibody. In R2 (Central Paraná), 39 animals were examined of which 8 were seropositive. In R3 (the North) 212 dogs were evaluated of which 39 animals were seropositive. Thirteen dogs had cutaneous lesions and the parasites were isolated from a dog with mucocutaneous lesion in R1, two animals with simple skin lesions in R2 and 10 dogs with multiple lesions in R3. The identification of the parasite by molecular methods showed it to be L. (Viannia) braziliensis. Based on this information, the role of domestic dogs in Leishmania infection of cutaneous leishmaniasis in Paraná is discussed.     

Modeling multivariate survival data by a semiparametric random effects proportional odds model

In this article, the focus is on the analysis of multivariate survival time data with various types of dependence structures. Examples of multivariate survival data include clustered data and repeated measurements from the same subject, such as the interrecurrence times of cancer tumors. A random effect semiparametric proportional odds model is proposed as an alternative to the proportional hazards model. The distribution of the random effects is assumed to be multivariate normal and the random effect is assumed to act additively to the baseline log-odds function. This class of models, which includes the usual shared random effects model, the additive variance components model, and the dynamic random effects model as special cases, is highly flexible and is capable of modeling a wide range of multivariate survival data. A unified estimation procedure is proposed to estimate the regression and dependence parameters simultaneously by means of a marginal-likelihood approach. Unlike the fully parametric case, the regression parameter estimate is not sensitive to the choice of correlation structure of the random effects. The marginal likelihood is approximated by the Monte Carlo method. Simulation studies are carried out to investigate the performance of the proposed method. The proposed method is applied to two well-known data sets, including clustered data and recurrent event times data.     

A semiparametric model for the analysis of recurrent-event panel data

In many longitudinal studies, interest focuses on the occurrence rate of some phenomenon for the subjects in the study. When the phenomenon is nonterminating and possibly recurring, the result is a recurrent-event data set. Examples include epileptic seizures and recurrent cancers. When the recurring event is detectable only by an expensive or invasive examination, only the number of events occurring between follow-up times may be available. This article presents a semiparametric model for such data, based on a multiplicative intensity model paired with a fully flexible nonparametric baseline intensity function. A random subject-specific effect is included in the intensity model to account for the overdispersion frequently displayed in count data. Estimators are determined from quasi-likelihood estimating functions. Because only first- and second-moment assumptions are required for quasi-likelihood, the method is more robust than those based on the specification of a full parametric likelihood. Consistency of the estimators depends only on the assumption of the proportional intensity model. The semiparametric estimators are shown to be highly efficient compared with the usual parametric estimators. As with semiparametric methods in survival analysis, the method provides useful diagnostics for specific parametric models, including a quasi-score statistic for testing specific baseline intensity functions. The techniques are used to analyze cancer recurrences and a pheromone-based mating disruption experiment in moths. A simulation study confirms that, for many practical situations, the estimators possess appropriate small-sample characteristics.     

集胞藻的随机插入诱变和光激活异养突变株的筛选

以4－碱基限制性内切酶部分酶切集胞藻PCC6803基因文库总质粒DNA,并插入卡那霉素抗性基因标记,构建了二级随机插入诱变文库。以该诱变文库总DNA转化集胞藻PCC6803,得到大量有抗性标记基因随机插入的转化子,利用这一方法获得了不能进行光激活异养生长的突变株,并克隆了抗性标记基因插入部位DNA片段,在持续光照但加DCMU抑制光合作用的情况下,这些突变株仍然能够利用葡萄糖异养生长,推测突变基因与短时光信号的感应有关。     

Discrimination of intact mycobacteria at the strain level: a combined MALDI-TOF MS and biostatistical analysis

New methodologies for surveillance and identification of Mycobacterium tuberculosis are required to stem the spread of disease worldwide. In addition, the ability to discriminate mycobacteria at the strain level may be important to contact or source case investigations. To this end, we are developing MALDI-TOF MS methods for the identification of M. tuberculosis in culture. In this report, we describe the application of MALDI-TOF MS, as well as statistical analysis including linear discriminant and random forest analysis, to 16 medically relevant strains from four species of mycobacteria, M. tuberculosis, M. avium, M. intracellulare, and M. kansasii. Although species discrimination can be accomplished on the basis of unique m/z values observed in the MS fingerprint spectrum, discrimination at the strain level is predicted on the relative abundance of shared m/z values among strains within a species. For the 16 mycobacterial strains investigated in the present study, it is possible to unambiguously identify strains within a species on the basis of MALDI-TOF MS data. The error rate for classification of individual strains using linear discriminant analysis was 0.053 using 37 m/z variables, whereas the error rate for classification of individual strains using random forest analysis was 0.023 using only 18 m/z variables. In addition, using random forest analysis of MALDI-TOF MS data, it was possible to correctly classify bacterial strains as either M. tuberculosis or non-tuberculous with 100% accuracy.