INGEBI   02650
INSTITUTO DE INVESTIGACIONES EN INGENIERIA GENETICA Y BIOLOGIA MOLECULAR "DR. HECTOR N TORRES"
Unidad Ejecutora - UE
congresos y reuniones científicas
Título:
Bioprospecting of lignocellulolytic enzymes in enriched consortia of pine and eucalyptus forest soils by metagenomic sequencing
Autor/es:
MARINA REINERT; SANTIAGO REVALE; ESTEFANIA MANCINI; BELEN CARBONETTO; MARTIN P VAZQUEZ
Lugar:
Bariloche
Reunión:
Conferencia; V Argentinian Conference on Bioinformatics and Computational Biology; 2014
Institución organizadora:
A2B2C
Resumen:
Background Second generation biofuels are produced by fermentation of sugars extracted from agronomic residues to ethanol (x). Lignocellulose breakdown is a crucial step needed to obtain sugar free molecules (x). Nowadays the bottleneck for second generation biofuel production is in the cost of lignocellulolitic enzymes (X). Our aim is to use metagenomic based bioprospection to find novel lignocellulose degrading proteins and to produce them in a low cost system based on plants as biofactories. Methods We took soils samples in a Pine elliotis and in a Eucalyptus grandis forest soils in Concordia, Entre Ríos, in February 2012. Both soils contained wood decaying material. Samples were then used as inoculum for minimum media(x) with only carboximetil-celulose (CMC) or sawdust as organic matter. Additionaly, we used antibiotics or antifungals to prevent each type of organism grow in each case. They were cultured for 30 days, and an aliquot of each culture was taken every 10 days. Genomic DNA was extracted from each sample. Amplicon sequencing of the V4 region of 16s rRNA gene was then performed in order to evaluate the enrichment of lignocellulose degrading microorganisms. Whole genome metagenomic sequencing was then performed to the most enriched sample (i.e. the one with most OTUs described as lignocellulose degraders and minus commensals). Bioprospection analysis using bioinformatics tools was then performed. First, we did de novo assembly using the CAMERA assembler workflow (x). Then we used the MG-RAST platform for taxonomic and functional annotation (X). We extracted coding sequences (CDS) using Fraggene scan open reading frame (ORF) algorithm. We finally ran Blast against CAZy database (x) to find lignocellulosic enzymedomains in our CDS dataset. A customized Perl script was used to get only those glycosyl hydrolase and cellulose binding domains linked with degrading activities (x). Finally, we selected only those sequences who had shown consistence with Pfam(x), UniProt(x) and Priam(x) annotations, reasonable ORF length and not high homology with database enzymes (below 80%).