Jamil Shafi

Bacillus species as versatile weapons for plant pathogens: a review

ABSTRACT Plant pathogens are the main threat for profitable agricultural productivity. Currently, chemical-based pesticides are thought to be an effective and reliable agricultural management measure for controlling pests. Chemical pesticides are highly effective and convenient to use but they are a potential threat for the environment and all kinds of life on earth. Therefore, the use of biological control agents for the management of plant pathogens is considered as a safer and sustainable strategy for safe and profitable agricultural productivity. Bacillus-based biocontrol agents play a fundamental role in the field of biopesticides. Many Bacillus species have proved to be effective against a broad range of plant pathogens. They have been reported as plant growth promoter, systemic resistance inducer, and used for production of a broad range of antimicrobial compounds (lipopeptides, antibiotics and enzymes) and competitors for growth factors (space and nutrients) with other pathogenic microorganisms through colonization. The aim of this article is to present the biocontrol potential of Bacillus species in relation with their antagonizing attributes against plant pathogens. These attributes include production of lipopeptides, antibiotics and enzymes as well as plant growth promotion and systemic induced resistance.

Antimicrobial Metabolites from Streptomyces sp. SN0280

One new indole derivative, chloroindole (1), one new diketone, streptoone A (2), two new ketonic acids, streptoones B (3) and C (4), and one known macrolide antibiotic, X-14952B (5), were isolated from Streptomyces sp. SN0280. Extensive NMR, HRESIMS, and IR analysis was used to elucidate their structures. Streptoone A (2) displayed antibacterial activity (MIC value of 7.81 μg/mL) against Clavibater michiganensis, comparable with the positive control streptomycin (MIC value of 7.81 μg/mL). Streptoone B (3) showed antifungal activity (MIC value of 15.63 μg/mL) against Phytophthora capsici (positive control carbendazol MIC value of 7.81 μg/mL). These molecules provide new templates for the potential treatment and management of these phytopathogens.

Screening, identification, optimization of fermentation conditions, and extraction of secondary metabolites for the biocontrol of Rhizoctonia Solani AG-3

ABSTRACT In this study a strain of Streptomyces sp. was isolated from soil and identified by 16S rRNA gene sequencing technology. The strain was screened for antibiotics production effective against biocontrol of Rhizoctonia solani AG-3 to cure the target spot disease in tobacco. For enhance production of secondary metabolites, central composite design of response surface methodology (RSM) was applied in submerged fermentation. The maximum metabolite production was using medium volume of 55 mL in 250 mL flask, agitation speed of 165 rpm, incubation temperature 30 °C, initial medium pH of 6.8 and inoculum size of 7%. Solvent extraction method was used to extract the secondary metabolites and active compounds were purified by silica gel column chromatography. The purified fractions were further investigated by gas chromatography-mass spectrophotometer (GC-MS). GC-MS analysis showed 48 compounds, among them 12 were active against pathogen. These findings indicated that the strain Streptomyces TA 1123 was a potential antagonist against R. solani AG-3.

ANN and RSM based modelling for optimization of cell dry mass of Bacillus sp. strain B67 and its antifungal activity against Botrytis cinerea

ABSTRACT The present study was conducted to present the comparative modelling, predictive and generalization abilities of response surface methodology (RSM) and artificial neural network (ANN) for optimization of fermenting medium. Cell dry mass and inhibition zone of strain B67 against Botrytis cinerea were used as response variables. The response variables were optimized and modelled as a function of five independent variables (pH, gelatine percentage, incubation period, agitation speed, and temperature) using response surface methodology and artificial neural network. The results of both approaches were compared for their modelling abilities in terms of root-mean-squared error (RMSE), mean absolute error (MAD), chi-square, and correlation coefficient, computed from experimental and predicted data. ANN models were proved to be superior to RSM with lower RMSE, MAD, and chi-square and higher values for correlation coefficient, coefficient of determination, and predictive coefficient of determination. The optimum fermenting conditions predicted were pH 6.65, gelatine 3.30%, incubation period 35 h, agitation speed 163 rpm, and incubation temperature 33.64 °C, with 15.00 g/L and 31.64 mm cell dry mass and inhibition zone, respectively. The predictive models were validated experimentally and were found in agreement with experimentally obtained values.

Non-target-site resistance to ALS-inhibiting herbicides in a Sagittaria trifolia L. population

Sagittaria trifolia L. is one of the most competitive weeds in rice fields in northeastern China. The continuous use of acetolactate synthase (ALS)-inhibitors has led to the evolution of herbicide resistant S. trifolia. A subpopulation BC1, which was derived from the L1 population, was analyzed using DNA sequencing and ALS enzyme activity assays and levels of resistance to five ALS-inhibiting herbicides was determined. DNA sequencing and ALS enzyme assays revealed no amino acid substitutions and no significant differences in enzyme sensitivity between susceptible and resistant populations. Whole-plant dose-response experiments showed that the BC1 population exhibited different levels of resistance (resistance ratios ranging from 2.14 to 51.53) to five ALS herbicides, and the addition of malathion (P450 inhibitor) to bensulfuron-methyl, penoxsulam and bispyribac-sodium strongly reduced the dry weight accumulation of the BC1 population compared with the effects of the three herbicides alone. The results of the present study demonstrated that the BC1 population has evolved non-target-site resistance to ALS-inhibiting herbicides.

Bensulfuron-methyl resistant Sagittaria trifolia L.: Multiple resistance, cross-resistance and molecular basis of resistance to acetolactate synthase-inhibiting herbicides

Acetolactate synthase (ALS)-inhibiting herbicides play an important role in controlling broad-leaved weeds. Populations of Sagittaria trifolia L. showed resistance to ALS-inhibiting sulfonylurea herbicides (e.g. bensulfuron-methyl) in paddy fields in the northeast of China. In our study, whole-plant bioassays were performed on eight suspected resistant S. trifolia populations that showed high levels of resistance to bensulfuron-methyl, with resistance indices from 31.06 to 120.35. The results of ALS-activity assays were consistent with the observed whole-plant dose-response data. This confirmed that resistant populations displayed significantly higher ALS activity than the sensitive population due to prevention of normal enzyme-herbicide interaction. The mutations Pro-197-Ser, Pro-197-His, Pro-197-Thr and Pro-197-Leu were identified in the ALS gene of resistant populations. Pro-197-His and Pro-197-Thr mutations conferring resistance to bensulfuron-methyl are reported for the first time in S. trifolia . All resistant populations were resistant to sulfonylurea (SU) herbicides, but not to imidazolinone (IMI) herbicides. HLJ-5 and JL-3 populations were resistant to bispyribac-sodium of the pyrimidinyl-thiobenozoate (PTB) class of ALS herbicides, JL-2 to penoxsulam of triazolopyrimidine (TP) class and JL-1 to pyribenzoxim, also of PTB class. The eight S. trifolia populations were susceptible to other herbicide modes of action tested. https://doi.org/10.2298/ABS170210010F Received: February 10, 2017; Revised: March 6, 2017; Accepted: March 9, 2017; Published online: March 20, 2017 How to cite this article: Fu D, Shafi J, Zhao B, Li X, Zhu H, Wei S, Ji M. Bensulfuron-methyl resistant Sagittaria trifolia L.: Multiple resistance, cross-resistance and molecular basis of resistance to acetolactate synthase-inhibiting herbicides. Arch Biol Sci. 2017;69(4):649-58.