Prospecção de bactérias celulolíticas em conteúdo ruminal de bovinos

Abstract

The search for new sources of raw materials for the production of biofuels has been widely studied in the academic world to consolidate the energy matrix of countries with the reduction of the use of fossil sources. The present work aimed to prospect cellulolytic bacteria from residual lignocellulosic biomass (ruminal content of cattle) with potential application in the production of second generation ethanol. Cellulolytic bacteria were prospected for ruminal bovine contents, after which the total extracellular cellulase production (Fpase) produced by these bacteria was quantified in submerged fermentation in mineral medium supplemented with sugarcane bagasse without hydrolytic treatment, as well as characterization and identification of isolates by molecular biology. In order to reach the proposed objectives, the isolation of the bacteria in BHM medium with Carboxymethylcellulose (CMC) was performed, being developed with Congo red. After an isolate was selected and employed strategies to increase cellulase production, to select the nutritional factors of the culture medium with positive effects in the cellulase production process a Plackett-Burman and Multivariable Regression (Stepwise) were used. From the pre-selection of the best parameters for the production of cellulase, a process optimization study was carried out using a Rotational Central Compound Design (DCCR) and Artificial Neural Network (RNA) modeling to identify the best nutritional conditions that maximize the production of the enzyme. Sixteen bacteria capable of degrading cellulose were isolated, 15 of which were amplified in 16S rDNA and identified using the NCBI Genbank database, resulting in five different genera (Bacillus, Ochrobactrum, Microbacterium, Stenotrophomonas and Klebsiella). With cellulase production ranging from 0.34 to 0.63 FPU / mL. Isolate V13 (BR 13961) was selected for the optimization process because it is classified as medium efficiency. The pre-selected nutritional factors (Urea, KH2PO4 and yeast extract) had significant positive effects on the cellulase production process for this isolate. The optimization by Neural Networks presented a mathematical model more adjusted to the experimental data, being the feed-forward architecture with three neurons in the hidden layer, transfer function "trainlm" and training function "radbas" presenting increase in the production of cellulase in 2.13 times.