Transcriptional Response of Selenopolypeptide Genes and Selenocysteine Biosynthesis Machinery Genes in Escherichia coli during Selenite Reduction

This work was supported by a United States Department of Agriculture-Cooperative State Research, Education, and Extension Service grant (no. 2009-35318-05032), a Biotechnology Research grant (no. 2007-BRG-1223) from the North Carolina Biotechnology Center, and a startup fund from the Golden LEAF Foundation to the Biomanufacturing Research Institute and Technology Enterprise (BRITE).Bacteria can reduce toxic selenite into less toxic, elemental selenium (Se0), but the mechanism on how bacterial cells reduce selenite at molecular level is still not clear. We used Escherichia coli strain K12, a common bacterial strain, as a model to study its growth response to sodium selenite (Na2SeO3) treatment and then used quantitative real-time PCR (qRT-PCR) to quantify transcript levels of three E. coli selenopolypeptide genes and a set of machinery genes for selenocysteine (SeCys) biosynthesis and incorporation into polypeptides, whose involvements in the selenite reduction are largely unknown. We determined that 5 mM Na2SeO3 treatment inhibited growth by ∼50% while 0.001 to 0.01 mM treatments stimulated cell growth by ∼30%. Under 50% inhibitory or 30% stimulatory Na2SeO3 concentration, selenopolypeptide genes (fdnG, fdoG, and fdhF) whose products require SeCys but not SeCys biosynthesis machinery genes were found to be induced ≥2-fold. In addition, one sulfur (S) metabolic gene iscS and two previously reported selenite-responsive genes sodA and gutS were also induced ≥2-fold under 50% inhibitory concentration. Our findings provide insight about the detoxification of selenite in E. coli via induction of these genes involved in the selenite reduction process.Publisher PDFPeer reviewe

Morphological diversity and relationships among the IPGRI maize (Zea mays L) landraces held in IITA

Genetic variability estimates in maize (Zea mays L) landraces is an important information for trait improvement for food and nutrition security. Genetic diversity information in the Sub-Sahara African maize landraces is lack- ing. Agromorphological trait evaluation is a practical approach for genetic diversity estimation. Our objective was to assess genetic diversity among 60 IPGRI maize landraces held in IITA, Ibadan, against a check, «Obatanpa GH». Twenty-one quantitative traits and five qualitative traits were field-evaluated in Ghana in 2011 and 2012 wet seasons in a three-replicated randomized complete block design experiment. Large phenotypic variation was identified in all traits except cob colour, principal grain colour, and number of ears per plant. A moderate within population variation based on pooled Shannon Diversity Index was 0.68 ± 0.28. Between population variation was largest in earliness, anthesis-silking interval, and grain yield. Genetic similarity of 0.11 ± 0.00 based on squared correlation coefficient confirmed a large variability among accessions. Two major clusters, I and II, were separated on the basis of maturity characteristics, anthesis-silking interval, plant and ear heights, and grain yield. The first two principal components explained 67.89 % of the total variance. Four genotypes, TZm-1125 and TZm-1117 (5.0 Mg ha-1), TZm-1119 (5.4 Mg ha-1), and TZm-1139 (6.2 Mg ha-1) competed with the check (5.8 Mg ha-1) in grain yield. The IPGRI genotypes represent a large genetic reserve awaiting exploitation for trait improvement

Chitosan and chitosan derivatives: Recent advancements in production and applications in environmental remediation

This review, which presents recent advancements in production and applications in environmental remediation of chitosan and chitosan derivatives, covers relevant literature in the last 7–8 years, which is nearly half-term of the 15-year period where the global agenda focuses on achieving the United Nations Sustainable Development Goals (SDGs) by 2030. Chitosan is a natural, non-toxic, polycationic polymer synthesized by deacetylating chitin, using several methods for producing its derivatives. The polycationic nature of chitosan and its derivatives offers them huge prospects as adsorbents due to their extraordinary and diverse properties of biocompatibility, biodegradability, non-hazardousness and chemical versatility. As such, they are very useful in environmental remediation. Chitosan and its derivatives have stood the test of time. They effectively remove fine suspended particles and heavy metals from polluted water and wastewater. Also, the marine environment is usually heavily polluted by hydrocarbons through oil spills with an effectual removal by these adsorbents. Soil, water, and air remediation techniques using chemical methods have the propensity to generate secondary pollution by modifying their chemical structures, which significantly impact the environment, ecosystem integrity, and health. Hence, the need for less costly, efficacious, and environmentally benign methods, such as using chitosan and its derivatives, is warranted. The current review first identifies relevant literature sources and knowledge gaps, such as a rare exposition of issues in the order of the first two stages of the traditional and industrial chitosan production process. It presents an overview of its sources and then examines recent advancements in chitosan chemistry. Chitosan transformation and applications in environmental remediation are also highlighted. Finally, the review identifies research gaps and future dimensions to clear issues about the chitosan production process and promote sustainable production and utilization as the world has embraced a paradigm shift toward circular and green economies, particularly during the period of the SDGs

Transcriptional Response of Selenopolypeptide Genes and Selenocysteine Biosynthesis Machinery Genes in Escherichia coli during Selenite Reduction

This work was supported by a United States Department of Agriculture-Cooperative State Research, Education, and Extension Service grant (no. 2009-35318-05032), a Biotechnology Research grant (no. 2007-BRG-1223) from the North Carolina Biotechnology Center, and a startup fund from the Golden LEAF Foundation to the Biomanufacturing Research Institute and Technology Enterprise (BRITE).Bacteria can reduce toxic selenite into less toxic, elemental selenium (Se0), but the mechanism on how bacterial cells reduce selenite at molecular level is still not clear. We used Escherichia coli strain K12, a common bacterial strain, as a model to study its growth response to sodium selenite (Na2SeO3) treatment and then used quantitative real-time PCR (qRT-PCR) to quantify transcript levels of three E. coli selenopolypeptide genes and a set of machinery genes for selenocysteine (SeCys) biosynthesis and incorporation into polypeptides, whose involvements in the selenite reduction are largely unknown. We determined that 5 mM Na2SeO3 treatment inhibited growth by ∼50% while 0.001 to 0.01 mM treatments stimulated cell growth by ∼30%. Under 50% inhibitory or 30% stimulatory Na2SeO3 concentration, selenopolypeptide genes (fdnG, fdoG, and fdhF) whose products require SeCys but not SeCys biosynthesis machinery genes were found to be induced ≥2-fold. In addition, one sulfur (S) metabolic gene iscS and two previously reported selenite-responsive genes sodA and gutS were also induced ≥2-fold under 50% inhibitory concentration. Our findings provide insight about the detoxification of selenite in E. coli via induction of these genes involved in the selenite reduction process.Publisher PDFPeer reviewe

Tensile strength and elongation of selected Kenaf fibres of Ghana

AbstractThere are a number of genotypes/varieties of kenaf (Hibiscus cannabinus L) fibre in Ghana. The kenaf fibre is used in both engineering and social products and its properties improve its choice. This paper reports the tensile strength and elongation of five genotypes of the kenaf fibre in Ghana. The fibres are cultivated and harvested at maturity and prepared with water retting. A gauge length of 20 mm was used and cardboard as the initial support. The fibre diameters are found for each genotype to be 34.2 µm, 40.4 µm, 38.2 µm, 43.2 µm and 39.2 µm for EB31, TN11, EN31, PN11 and HN11 respectively. The densities are 0.53 g/cm3 for EB31; 0.60 g/cm3 for TN11; 0.63 g/cm3 for EN31; 0.94 g/cm3 for PN11 and 1.00 g/cm3 for HN11. The tensile strengths are 734.53 MPa for EB31, 1292.37 MPa for TN11, 1241.53 MPa for EN31, 979.35 MPa for PN11 and 1365.14 MPa for HN11. The corresponding percentage elongations are found to be 10.88 for EB31; 12.23 for TN11; 14.66 for EN31; 15.81 for PN11 and 13.13 for HN11. These results will make the application of fibre in various engineering applications, especially in composites, more predictive in properties and performance

Transcriptional Response of Selenopolypeptide Genes and Selenocysteine Biosynthesis Machinery Genes in Escherichia coli during Selenite Reduction

Bacteria can reduce toxic selenite into less toxic, elemental selenium (Se0), but the mechanism on how bacterial cells reduce selenite at molecular level is still not clear. We used Escherichia coli strain K12, a common bacterial strain, as a model to study its growth response to sodium selenite (Na2SeO3) treatment and then used quantitative real-time PCR (qRT-PCR) to quantify transcript levels of three E. coli selenopolypeptide genes and a set of machinery genes for selenocysteine (SeCys) biosynthesis and incorporation into polypeptides, whose involvements in the selenite reduction are largely unknown. We determined that 5 mM Na2SeO3 treatment inhibited growth by ∼50% while 0.001 to 0.01 mM treatments stimulated cell growth by ∼30%. Under 50% inhibitory or 30% stimulatory Na2SeO3 concentration, selenopolypeptide genes (fdnG, fdoG, and fdhF) whose products require SeCys but not SeCys biosynthesis machinery genes were found to be induced ≥2-fold. In addition, one sulfur (S) metabolic gene iscS and two previously reported selenite-responsive genes sodA and gutS were also induced ≥2-fold under 50% inhibitory concentration. Our findings provide insight about the detoxification of selenite in E. coli via induction of these genes involved in the selenite reduction process

Predictive model and feature importance for early detection of type II diabetes mellitus

Background: Accurate prediction and early recognition of type II diabetes (T2DM) will lead to timely and meaningful interventions, while preventing T2DM associated complications. In this context, machine learning (ML) is promising, as it can transform vast amount of T2DM data into clinically relevant information. This study compares multiple ML techniques for predictive modelling based on different T2DM associated variables in an African population, Ghana. Methods: The study involved 219 T2DM patients and 219 healthy individuals who were recruited from the hospital and the local community, respectively. Anthropometric and biochemical information including glycated haemoglobin (HbA1c), body mass index (BMI), blood pressure, fasting blood sugar (FBS), serum lipids [(total cholesterol (TC), triglycerides (TG), high and low-density lipoprotein cholesterol (HDL-c and LDL-c)] were collected. From this data, four ML classification algorithms including Naïve-Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machines (SVM) and Decision Tree (DT) were used to predict T2DM. Precision, Recall, F1-Scores, Receiver Operating Characteristics (ROC) scores and the confusion matrix were computed to determine the performance of the various algorithms while the importance of the feature attributes was determined by recursive feature elimination technique. Results: All the classifiers performed beyond the acceptable threshold of 70% for Precision, Recall, F-score and Accuracy. After building the predictive model, 82% of diabetic test data was detected by the NB classifier, of which 93% were accurately predicted. The SVM classifier was the second-best performing classifier which yielded an overall accuracy of 84%. The non-T2DM test data yielded an accurate prediction score of 75% from the 98% of the proportion of the non-T2DM test data. KNN and DT yielded accuracies of 83% and 81%, respectively. NB had the best performance (AUC = 0.87) followed by SVM (AUC = 0.84), KNN (AUC = 0.85) and DT (AUC = 0.81). The best three feature attributes, in order of importance, were HbA1c, TC and BMI whereas the least three importance of the features were Age, HDL-c and LDL-c. Conclusion: Based on the predictive performance and high accuracy, the study has shown the potential of ML as a robust forecasting tool for T2DM. Our results can be a benchmark for guiding policy decisions in T2DM surveillance in resource and medical expertise limited countries such as Ghana