Use of two fungal species to induce agarwood resin formation in Gyrinops walla

Gyrinops walla Gaertn. is the only species growing in Sri Lanka that belongs to the agarwood family,Thymelaeaceae. Although agarwood resin induction and extraction from Aquilaria species of the same family have been practised for many decades in Southeast Asian region,the ability of producing agarwood resins in G. walla was discovered recently. Since previous studies were on agarwood resins formed due to natural causes, the present study was conducted to identify the potential fungal species that are capable of artificially inducing agarwood resin formation in G. walla. Since this is the first ever study conducted on artificial inducement of agarwood resin formation in G.walla, Aspergillus niger and Fusarium solani were selected owing to their high abundance in the naturally formed agarwood resinous tissues collected from 25 G. walla trees.Both fungal species were separately grown in yeast extract glucose agar and used to inoculate healthy G. walla trees under aseptic conditions. Three holes were made for each tree and 2 g of fungal culture including the medium were placed in each hole. Tissue discoloration, characteristic aroma, resin content and resin constituents were checked at10 cm intervals above and below the inoculation points for a period of 1 year. Results revealed that tissue discoloration and resin content were higher in the trees inoculated with A. niger. Other than at 10 cm above and below the inoculation points, samples collected at all locations had significantly higher resin contents when inoculated with A. niger compared to F. solani. Sixteen agarwood resin constituents, which were also recorded in Aquilaria species, were identified from the discolored tissues using GC–MS analysis. Jinkohol, agarospirol and 2(2-phenyl)chromone derivatives were found in all discolored tissues collected at 10-cm intervals of the trees inoculated with both fungi. b-Seline, c-eudesmol and valerenal were found in nine of 10 sample points on the stem. c-Elemene was recorded only in one sample. The characteristic aroma during burning was stronger for dark-colored tissues than the light-colored ones. The present study confirmed the potential use of certain fungal species to induce agarwood resin in G. walla and that A. niger is more effective than F.solani.

Identification of chemical compounds in agarwood-producing species Aquilaria malaccensis and Gyrinops versteegii

Agarwood is a non-timber forest product found in tropical rain forests.It is a black and fragrant resin valued for the perfume industry and demand continues to increase.However,the Indonesian agarwood-producing species,Aquilaria malaccensis and Gyrinops versteegii do not automatically produce such quality resin.Bio-induction technology or inoculation using Fusarium solani is usually applied to these species to trigger resin production.This research aims to identify agarwood compounds formed in seedlings and trees of A.malaccensis and G.versteegii after these species were inoculated with the fungus F.solani.The chemical compounds were identified by comparing the patterns of mass spectra fragmentation in the sample and in previous studies.Five groups of agarwood compounds were identified:(1)sesquiterpen group-cisjasmone and aromadendrenepoxide;(2)chromones group-8-methoxy-2-(2-phenylethyl)chromen-4-one and newly-discovered chromone derivative,7-(benzyloxy)-5-hydroxy-2-methylchromone found only in G.versteegii;(3)aromatic group-benzylacetone,guaiacol,p-ethylguaiacol,phenol,syringaldehyde,vanilin,furfuryl alcohol,and furfural;(4)fatty acid group-palmitic acid,oleic acid,and lauric acid;and,(5)triterpen group-squalene.

Prediction of chemical composition for callus production in Gyrinops walla Gaetner through machine learning

Developing new tissue culture protocols and optimization of plant media composition for particular plant species are precluded by the higher labor,time and cost.The usage of machine learning becomes rewarding tools for efficient,cost-effective method of optimization of growth media composition for desires growth performance.Difficulties are often encountered in determination of media composition for raising calli from explants of woody Gyrinops walla.This paper provides the finding of the study performed on modeling and optimization of chemical composition of culture media for raising callus from explants of G.walla.Number of experiments were conducted to raise calli from sterilized leaf and node explants with varying concentrations of 1-Naphthaleneacetic acid(NAA),6-Benzylaminopurine(BAP)and coconut water in Murashige and Skoog medium(MSM)and Woody plant medium(WPM)were conducted.Artificial Neural Networks(ANNs)were developed to predict the best media composition on callus weight(CW),callus induction percentage(CIP)and days taken to initiate callus(DC).Callus formation was observed in all the experiments except for controls.Three ANN models were developed to predict the best media composition for CW,CIP and DC.The results of ANN models predict optimum media composition during the training,validation and testing with satisfactory levels of performance.The R2 values of the best ANN models;CWnet,CIPnet and DCnet were 0.95,0.95 and 0.88 respectively.The mean sums of square errors(RMSRs)of CWnet,CIPnet and DCnet were 0.1410,0.1391 and 0.1961 and mean residual(MRs)were 0.0199,0.0194 and 0.0385.The corrected Akaike information criterion(AICc)of the models was331.431,254.926 and210.777,and Schwarz Bayesian information criterion(SBC)were490.495,454.229 and382.123.The predicted best media consisting MSM+3.0 mg/l NAA+0.5 mg/l BAP and MSM+1.5 mg/l NAA+0.5 mg/l BAP were the optimum combination for leaves and nodal explants respectively.The AICc and SBC of CWnet indicated better performance over the rest of the models in predicting media composition.The developed ANN models indicated a novel approach using ANN models in predicting media composition for raising callus from woody G.walla by hypothetical chemical composition saving cost,time and labor.Findings suggest that application of artificial intelligence in predicting media composition for micro-propagation of woody plants in future studies.