A Plasmodium Phospholipase Is Involved in Disruption of the Liver Stage Parasitophorous Vacuole Membrane

The coordinated exit of intracellular pathogens from host cells is a process critical to the success and spread of an infection. While phospholipases have been shown to play important roles in bacteria host cell egress and virulence, their role in the release of intracellular eukaryotic parasites is largely unknown. We examined a malaria parasite protein with phospholipase activity and found it to be involved in hepatocyte egress. In hepatocytes, Plasmodium parasites are surrounded by a parasitophorous vacuole membrane (PVM), which must be disrupted before parasites are released into the blood. However, on a molecular basis, little is known about how the PVM is ruptured. We show that Plasmodium berghei phospholipase, PbPL, localizes to the PVM in infected hepatocytes. We provide evidence that parasites lacking PbPL undergo completely normal liver stage development until merozoites are produced but have a defect in egress from host hepatocytes. To investigate this further, we established a live-cell imaging-based assay, which enabled us to study the temporal dynamics of PVM rupture on a quantitative basis. Using this assay we could show that PbPL-deficient parasites exhibit impaired PVM rupture, resulting in delayed parasite egress. A wild-type phenotype could be re-established by gene complementation, demonstrating the specificity of the PbPL deletion phenotype. In conclusion, we have identified for the first time a Plasmodium phospholipase that is important for PVM rupture and in turn for parasite exit from the infected hepatocyte and therefore established a key role of a parasite phospholipase in egress.     

Discovery of genomic regions and candidate genes controlling shelling percentage using QTL‐seq approach in cultivated peanut (Arachis hypogaea L.)

Cultivated peanut (Arachis hypogaea L.) is an important grain legume providing high‐quality cooking oil, rich proteins and other nutrients. Shelling percentage (SP) is the 2nd most important agronomic trait after pod yield and this trait significantly affects the economic value of peanut in the market. Deployment of diagnostic markers through genomics‐assisted breeding (GAB) can accelerate the process of developing improved varieties with enhanced SP. In this context, we deployed the QTL‐seq approach to identify genomic regions and candidate genes controlling SP in a recombinant inbred line population (Yuanza 9102 × Xuzhou 68‐4). Four libraries (two parents and two extreme bulks) were constructed and sequenced, generating 456.89–790.32 million reads and achieving 91.85%–93.18% genome coverage and 14.04–21.37 mean read depth. Comprehensive analysis of two sets of data (Yuanza 9102/two bulks and Xuzhou 68‐4/two bulks) using the QTL‐seq pipeline resulted in discovery of two overlapped genomic regions (2.75 Mb on A09 and 1.1 Mb on B02). Nine candidate genes affected by 10 SNPs with non‐synonymous effects or in UTRs were identified in these regions for SP. Cost‐effective KASP (Kompetitive Allele‐Specific PCR) markers were developed for one SNP from A09 and three SNPs from B02 chromosome. Genotyping of the mapping population with these newly developed KASP markers confirmed the major control and stable expressions of these genomic regions across five environments. The identified candidate genomic regions and genes for SP further provide opportunity for gene cloning and deployment of diagnostic markers in molecular breeding for achieving high SP in improved varieties.     

Association of Household and Community Socioeconomic Position and Urbanicity with Underweight and Overweight among Women in Pakistan

Background

Similar to other developing countries, Pakistan is going through a rapid nutrition transition where shift from underweight to overweight and obesity is occurring. In this paper, we report on the relationship of household socioeconomic position (SEP), community SEP and urbanicity with under- and over-weight categories of BMI among Pakistani women.

Methods

We analyzed data on 4,767 women ages 15-49 years enrolled in a nationally representative Pakistan Demographic Health Survey (PDHS) conducted in 2012-13 that employed a multistage, stratified cluster sampling design. We assessed the association of urbanicity, household and community SEP derived from household assets and utilities, with categories of body mass index (BMI) using multinomial regression analysis where normal weight (BMI 18.6-22.5) was the reference category.

Results

Thirteen percent of women were underweight (BMI <18.5), 15% pre-overweight (BMI: 22.6-24.9), 25% overweight (BMI: 25.0–29.9) and 14% were obese (BMI≥30). Pre-overweight, overweight and obesity among women increased across household wealth quintiles (HWQs) in a graded fashion whereas there was no significant difference in underweight by household wealth. Women in urban areas were more likely to be obese. There was a pronounced increase in adjusted odds ratios (aORs) for overweight/obesity across HWQs within urban areas compared to rural areas. There was a steeper gradient in aORs for obesity from 1st to 5th HWQs in high income communities compared to the middle- and low income communities. In community-level analyses, communities in urban areas were more likely to have higher levels of obesity while in rural areas, especially in Sindh, more communities were more likely to have a higher level of underweight.

Conclusion

A shift to higher overweight and obesity than underweight in Pakistan is associated with high household and community wealth as well as living in urban areas. Clustering of obesity and underweight in distinct communities afford opportunity for tailored intervention programs.     

Deciphering the binding of carbendazim (fungicide) with human serum albumin: A multi-spectroscopic and molecular modelling studies

Carbendazim is a benzimidazole fungicide used to control the fungal invasion. However, its exposure might lead to potential health problems. The present study evaluates the interaction of carbendazim (CAR) with human serum albumin (HSA) which is an important drug carrier protein and plays a very crucial role in the transportation of small molecules. A number of biophysical techniques were employed to investigate the binding of CAR with HSA. The increased UV-absorption of HSA on titrating with CAR suggests the formation of HSA–CAR complex and it could be due to the exposure of aromatic residues. The fluorescence study confirmed that CAR quenches the fluorescence of HSA and showed the static mode of quenching. CAR (50 µM) quenches around 56.14% of the HSA fluorescence. The quenching constant, binding constant, number of binding site and free energy change was calculated by fluorescence quenching experiment. Competitive displacement assay showed Sudlow’s site I as the primary binding site of CAR on HSA. The synchronous fluorescence study revealed the perturbation in the microenvironment around tyrosine and tryptophan residues upon binding of CAR to HSA. The circular dichroism results suggested that the binding of CAR to HSA altered its secondary structure. Molecular docking experiment demonstrated the binding of CAR to Sudlow’s site I of HSA. Docking studies suggested that the hydrogen bonding, van der Waals and pi-alkyl are playing role in the interaction of CAR with HSA. The study confirmed the conformational changes within HSA upon binding of CAR.     

The role of chemical fingerprinting: application to Ephedra

Ephedra sinica, known as Ma Huang, is one of the oldest medicinal herbs in Traditional Chinese Medicine (TCM). Preparations, namely teas, of E. sinica have been used for over 5000 years as a stimulant and as an antiasthmatic. In the West, extracts of E. sinica, E. intermedia or E. equisetina are most commonly used in dietary supplements as a stimulant and to promote weight loss. More than 50 species of Ephedra are native to both hemispheres, but the detection of ephedrine alkaloids has been limited to species in Eurasia. Currently, methods exist to quantitate the ephedrine alkaloids in extracts of plant material or dietary supplements, but the methods are not able to verify the extract is of an Ephedra species. Reverse phase high performance liquid chromatography with photodiode array detection was applied for the chemical fingerprinting of the Ephedra species. Two regions of comparison were determined in the chromatograms at 320 nm. The series of peaks between 52 and 64 min confirms an Ephedra species is being analyzed. The aforementioned peaks also could distinguish between Ephedra species from Eurasia, North America and South America. Peaks at ca. 57 and 59 min were isolated and determined to be two new compounds, 4-(2-eicosyloxycarbonyl-vinyl)-benzoic acid and 4-(2-docosyloxycarbonyl-vinyl)-benzoic acid respectively. Authentication of ground plant material as Ephedra can be achieved by this chemical fingerprinting method.     

The prevalence of androstenone anosmia

It has been estimated that approximately 30% of the population is unable to detect the odor of androstenone. These estimates, however, were made using tests and criteria optimized for identifying detection. Such criteria favor Type II over Type I errors--that is, they are excellent at identifying true detectors at the cost of erroneously labeling some detectors as non-detectors. Because these criteria were used to identify non-detectors, it is possible that the rate of non-detection may have been overestimated. To test this we screened 55 subjects for non-detection employing previously used methods. This screen yielded nine putative non-detectors, a 16.3% putative non-detection rate. We then retested these putative non-detectors using a forced choice (yes-no) paradigm to obtain a precise measure of their sensitivity. We found that this group of putative non-detectors was significantly above chance at detecting androstenone (P < 0.001), despite very low self-confidence in their performance. Based on the results of the signal detection analysis in this sample, we estimate the rate of actual androstenone non-detection in young healthy adults is between 1.8 and 5.96%, which is significantly lower than previously estimated. This finding is significant considering the implications of specific anosmias on the understanding of odor discrimination.     

Industrial attributes of β-glucanase produced by Bacillus sp. CSB55 and its potential application as bio-industrial catalyst

Bioprocess and Biosystems Engineering - The β-glucanase produced from Bacillus sp. CSB55 not only depicts the potent industrial characteristics but also relates as bio-industrial catalyst...     

GPCR-MPredictor: multi-level prediction of G protein-coupled receptors using genetic ensemble

G protein-coupled receptors (GPCRs) are transmembrane proteins, which transduce signals from extracellular ligands to intracellular G protein. Automatic classification of GPCRs can provide important information for the development of novel drugs in pharmaceutical industry. In this paper, we propose an evolutionary approach, GPCR-MPredictor, which combines individual classifiers for predicting GPCRs. GPCR-MPredictor is a web predictor that can efficiently predict GPCRs at five levels. The first level determines whether a protein sequence is a GPCR or a non-GPCR. If the predicted sequence is a GPCR, then it is further classified into family, subfamily, sub-subfamily, and subtype levels. In this work, our aim is to analyze the discriminative power of different feature extraction and classification strategies in case of GPCRs prediction and then to use an evolutionary ensemble approach for enhanced prediction performance. Features are extracted using amino acid composition, pseudo amino acid composition, and dipeptide composition of protein sequences. Different classification approaches, such as k-nearest neighbor (KNN), support vector machine (SVM), probabilistic neural networks (PNN), J48, Adaboost, and Naives Bayes, have been used to classify GPCRs. The proposed hierarchical GA-based ensemble classifier exploits the prediction results of SVM, KNN, PNN, and J48 at each level. The GA-based ensemble yields an accuracy of 99.75, 92.45, 87.80, 83.57, and 96.17% at the five levels, on the first dataset. We further perform predictions on a dataset consisting of 8,000 GPCRs at the family, subfamily, and sub-subfamily level, and on two other datasets of 365 and 167 GPCRs at the second and fourth levels, respectively. In comparison with the existing methods, the results demonstrate the effectiveness of our proposed GPCR-MPredictor in classifying GPCRs families. It is accessible at .