Investigating the mechanisms of glyphosate resistance in goosegrass(Eleusine indica) population from South China

Glyphosate has been used worldwide for nearly 40 years,and 30 types of resistant weeds have been reported.Glyphosate is mass-produced and widely used in China,but few studies and reports on glyphosate-resistant weeds and resistance mechanisms exist.Previous studies found a goosegrass species with high glyphosate resistance from orchards in South China and its glyphosate resistant mechanism was described in this study.The cDNAof 5-enolpyruvylshikimate-3-phosphate synthase（EPSPS,EC 2.5.1.19）,the target enzyme of glyphosate,was cloned from the glyphosate-resistant and-susceptible goosegrass,respectively,and referred as EPSPS-R and EPSPS-S.The Pro106 residue was known to be involved in the glyphosate resistance in most goosegrass populations.However,sequence analysis did not find the mutation at the Pro106 residue in the R biotype EPSPS amino acid sequence.The residue 133 and 382 was mutated in the R biotype EPSPS amino acid sequence instead,but it did not affect the EPSPS-S and EPSPS-R genes sensitivities to glyphosate.RT-PCR and Western blot analyses suggested that EPSPS mRNA and protein are mainly present in the shoot tissues both in the R and S goosegrass biotypes.The EPSPS-R rapidly responds to the glyphosate in R-biotype goosegrass and the induced expression was detected at 12 h post glyphosate treatment.The mRNA and protein expression of EPSPS-R increased constantly as the increasing concentration of glyphosate.However,the expression of the EPSPS-S was not induced significantly by glyphosate in the S goosegrass biotype.Quantification of real-time PCR results showed that the copy number of the EPSPS in R-biotype goosegrass was 4.7 times higher than that in the S goosegrass biotype.All the results implied that EPSPS gene amplification might mainly caused the glyphosate resistance of a goosegrass population collected from orchards in South China.

A computational model of the human glucose-insulin regulatory system

Objective：A computational model of insulin secretion and glucose metabolism for assisting the diagnosis of diabetes mellitus in clinical research is introduced.The proposed method for the estimation of parameters for a system of ordinary differential equations（ODEs）that represent the time course of plasma glucose and insulin concentrations during glucose tolerance test（GTT）in physiological studies is presented.The aim of this study was to explore how to interpret those laboratory glucose and insulin data as well as enhance the Ackerman mathematical model.Methods：Parameters estimation for a system of ODEs was performed by minimizing the sum of squared residuals（SSR）function,which quantifies the difference between theoretical model predictions and GTT＇s experimental observations.Our proposed perturbation search and multiple-shooting methods were applied during the estimating process.Results：Based on the Ackerman＇s published data,we estimated the key parameters by applying R-based iterative computer programs.As a result,the theoretically simulated curves perfectly matched the experimental data points.Our model showed that the estimated parameters,computed frequency and period values,were proven a good indicator of diabetes.Conclusion：The present paper introduces a computational algorithm to biomedical problems,particularly to endocrinology and metabolism fields,which involves two coupled differential equations with four parameters describing the glucose-insulin regulatory system that Ackerman proposed earlier.The enhanced approach may provide clinicians in endocrinology and metabolism field insight into the transition nature of human metabolic mechanism from normal to impaired glucose tolerance.