Relationship between the population of viable sclerotia ofSclerotium rolfsii in soil to cropping sequence in the Nigerian savanna

Populations of viable sclerotia ofSclerotium rolfsii were highest in soil in a field in which tomato was planted for three successive years before sampling and in one in which tomato followed groundnut in the 2 years prior to this study. The lowest sclerotial numbers were recorded in fields in which groundnut followed maize or in which maize or sorghum was the last crop before sampling.     

Characterization and Identification of Caprine,Genital Mycoplasmas

A total of 55 mycoplasma strains, isolated from the vagina of goats, were examined. Three strains, being arginine positive and glucose negative, could not be finally classified. Five isolates were identified as Acholeplasma laidlawii. Fourty-seven strains were phosphatase positive, glucose and arginine negative. Nine of these formed “film and spots” on standard growth medium, and reduced tetrazolium aerobically. Serological examination identified 7 as M. agalactiae, while 2 were M. bovis. The remaining 38 isolates did not reduce tetrazolium aerobically, and did not produce “film and spots.” on standard growth medium. All these except one were identified as M. bovigenitalium by immunofluorescence. Their relationship to group 11 otf Al-Aubaidi is discussed.     

BiPPred: Combined sequence‐ and structure‐based prediction of peptide binding to the Hsp70 chaperone BiP

Substrate binding to Hsp70 chaperones is involved in many biological processes, and the identification of potential substrates is important for a comprehensive understanding of these events. We present a multi‐scale pipeline for an accurate, yet efficient prediction of peptides binding to the Hsp70 chaperone BiP by combining sequence‐based prediction with molecular docking and MMPBSA calculations. First, we measured the binding of 15mer peptides from known substrate proteins of BiP by peptide array (PA) experiments and performed an accuracy assessment of the PA data by fluorescence anisotropy studies. Several sequence‐based prediction models were fitted using this and other peptide binding data. A structure‐based position‐specific scoring matrix (SB‐PSSM) derived solely from structural modeling data forms the core of all models. The matrix elements are based on a combination of binding energy estimations, molecular dynamics simulations, and analysis of the BiP binding site, which led to new insights into the peptide binding specificities of the chaperone. Using this SB‐PSSM, peptide binders could be predicted with high selectivity even without training of the model on experimental data. Additional training further increased the prediction accuracies. Subsequent molecular docking (DynaDock) and MMGBSA/MMPBSA‐based binding affinity estimations for predicted binders allowed the identification of the correct binding mode of the peptides as well as the calculation of nearly quantitative binding affinities. The general concept behind the developed multi‐scale pipeline can readily be applied to other protein‐peptide complexes with linearly bound peptides, for which sufficient experimental binding data for the training of classical sequence‐based prediction models is not available. Proteins 2016; 84:1390–1407. © 2016 Wiley Periodicals, Inc.     

Factors Contributing to the Delay in Diagnosis and Continued Transmission of Leprosy in Brazil – An Explorative,Quantitative, Questionnaire Based Study

BackgroundLeprosy is a leading cause of preventable disability worldwide. Delay in diagnosis of patients augments the transmission of infection, and allows progression of disease and more severe disability. Delays in diagnosis greater than ten years have been reported in Brazil. To reduce this delay, it is important to identify factors that hinder patients from presenting to doctors, and those that delay doctors from diagnosing patients once they have presented. This study aimed to explore factors associated with the delayed diagnosis of leprosy in Brazil.

Methodology/ Principal Findings

This is an exploratory study using a self-constructed questionnaire delivered to patients attending three leprosy referral clinics across three states in Brazil. Data were analysed to determine associations between variables and the time taken for participants to present to the health-service, and between variables and the time taken for doctors to diagnose participants once they had presented. Participants who suspected they had leprosy but feared community isolation were 10 times more likely to wait longer before consulting a doctor for their symptoms (OR 10.37, 95% CI 2.18–49.45, p = 0.003). Participants who thought their symptoms were not serious had a threefold greater chance of waiting longer before consulting than those who did (OR 3.114, 95% CI 1.235–7.856, p = 0.016). Forty-two point six per cent of participants reported initially receiving a diagnosis besides leprosy. These had a three times greater chance of receiving a later diagnosis of leprosy compared to those not misdiagnosed or not given a diagnosis (OR 2.867, 95% CI 1.288–6.384, p = 0.010).

Conclusions/ Significance

This study implies a need for patient education regarding leprosy symptoms and the reduction of stigma to encourage patients to present. The high rate of misdiagnosis reported suggests a need to increase clinician suspicion of leprosy. Further education regarding disease symptoms in medical school curriculums may be advisable.     

Epigenetic regulation of autophagy: A key modification in cancer cells and cancer stem cells

Aberrant epigenetic alterations play a decisive role in cancer initiation and propagation via the regulation of key tumor suppressor genes and oncogenes or by modulation of essential signaling pathways. Autophagy is a highly regulated mechanism required for the recycling and degradation of surplus and damaged cytoplasmic constituents in a lysosome dependent manner. In cancer, autophagy has a divergent role. For instance, autophagy elicits tumor promoting functions by facilitating metabolic adaption and plasticity in cancer stem cells (CSCs) and cancer cells. Moreover, autophagy exerts pro-survival mechanisms to these cancerous cells by influencing survival, dormancy, immunosurveillance, invasion, metastasis, and resistance to anti-cancer therapies. In addition, recent studies have demonstrated that various tumor suppressor genes and oncogenes involved in autophagy, are tightly regulated via different epigenetic modifications, such as DNA methylation, histone modifications and non-coding RNAs. The impact of epigenetic regulation of autophagy in cancer cells and CSCs is not well-understood. Therefore, uncovering the complex mechanism of epigenetic regulation of autophagy provides an opportunity to improve and discover novel cancer therapeutics. Subsequently, this would aid in improving clinical outcome for cancer patients. In this review, we provide a comprehensive overview of the existing knowledge available on epigenetic regulation of autophagy and its importance in the maintenance and homeostasis of CSCs and cancer cells.     

Albumin induces CD44 expression in glomerular parietal epithelial cells by activating extracellular signal-regulated kinase 1/2 pathway

De novo expression of CD44 in glomerular parietal epithelial cells (PECs) leads to a prosclerotic and migratory PEC phenotype in glomerulosclerosis. However, the regulatory mechanisms underlying CD44 expression by activated PECs remain largely unknown. This study was performed to examine the mediators responsible for CD44 induction in glomerular PECs in association with diabetes. CD44 expression and localization were evaluated in the glomeruli of Zucker diabetic rat kidneys and primary cultured PECs upon albumin stimulation. Real-time polymerase chain reaction confirmed an albuminuria-associated upregulation of the CD44 gene in the glomeruli of diabetic rats. Immunostaining analysis of diabetic kidneys further revealed an increase in CD44 in hypertrophic PECs, which often contain albumin-positive vesicles. Losartan treatment significantly attenuated albuminuria and lowered CD44 protein levels in the diabetic kidneys. In primary cultured rat PECs, rat serum albumin (0.25–1 mg/ml) caused a dose-dependent upregulation of CD44, claudin-1, and megalin protein expression, which was accompanied by an activation of extracellular signal-regulated kinase1/2 (ERK1/2) signaling. Albumin-induced CD44 and claudin-1 expression were greatly suppressed in the presence of the ERK1/2 inhibitor, U0126. In addition, knockdown of megalin by small interfering RNA interference in PECs resulted in a significant reduction of albumin-induced CD44 and claudin-1 proteins. Taken together, our results demonstrate that albumin induces CD44 expression by PECs via the activation of the ERK signaling pathway, which is partially mediated by endocytic receptor megalin.     

Prediction of alpha-turns in proteins using PSI-BLAST profiles and secondary structure information

In this paper a systematic attempt has been made to develop a better method for predicting alpha-turns in proteins. Most of the commonly used approaches in the field of protein structure prediction have been tried in this study, which includes statistical approach "Sequence Coupled Model" and machine learning approaches; i) artificial neural network (ANN); ii) Weka (Waikato Environment for Knowledge Analysis) Classifiers and iii) Parallel Exemplar Based Learning (PEBLS). We have also used multiple sequence alignment obtained from PSIBLAST and secondary structure information predicted by PSIPRED. The training and testing of all methods has been performed on a data set of 193 non-homologous protein X-ray structures using five-fold cross-validation. It has been observed that ANN with multiple sequence alignment and predicted secondary structure information outperforms other methods. Based on our observations we have developed an ANN-based method for predicting alpha-turns in proteins. The main components of the method are two feed-forward back-propagation networks with a single hidden layer. The first sequence-structure network is trained with the multiple sequence alignment in the form of PSI-BLAST-generated position specific scoring matrices. The initial predictions obtained from the first network and PSIPRED predicted secondary structure are used as input to the second structure-structure network to refine the predictions obtained from the first net. The final network yields an overall prediction accuracy of 78.0% and MCC of 0.16. A web server AlphaPred (http://www.imtech.res.in/raghava/alphapred/) has been developed based on this approach.     

An evaluation of beta-turn prediction methods

MOTIVATION: beta-turn is an important element of protein structure. In the past three decades, numerous beta-turn prediction methods have been developed based on various strategies. For a detailed discussion about the importance of beta-turns and a systematic introduction of the existing prediction algorithms for beta-turns and their types, please see a recent review (Chou, Analytical Biochemistry, 286, 1-16, 2000). However at present, it is still difficult to say which method is better than the other. This is because of the fact that these methods were developed on different sets of data. Thus, it is important to evaluate the performance of beta-turn prediction methods. RESULTS: We have evaluated the performance of six methods of beta-turn prediction. All the methods have been tested on a set of 426 non-homologous protein chains. It has been observed that the performance of the neural network based method, BTPRED, is significantly better than the statistical methods. One of the reasons for its better performance is that it utilizes the predicted secondary structure information. We have also trained, tested and evaluated the performance of all methods except BTPRED and GORBTURN, on new data set using a 7-fold cross-validation technique. There is a significant improvement in performance of all the methods when secondary structure information is incorporated. Moreover, after incorporating secondary structure information, the Sequence Coupled Model has yielded better results in predicting beta-turns as compared with other methods. In this study, both threshold dependent and independent (ROC) measures have been used for evaluation.