Publicaciones 2015

Vilo, C., Galetovic, A., Araya, JE., Gómez-Silva, B., Dong, Q. (2015) Draft Genome Sequence of a Bacillus Bacterium from the Atacama Desert Wetlands Metagenome. Genome Announcements. 3;4;e00955-15.


We report here the draft genome sequence of a Bacillus bacterium isolated from the microflora of Nostoccolonies grown at the Andean wetlands in northern Chile. We consider this genome sequence to be a molecular tool for exploring microbial relationships and adaptation strategies to the prevailing extreme conditions at the Atacama Desert.

Gajardo, H.,  Wittkop, B.,  Soto-Cerda, B.,  Higgins, E., Parkin, I., Snowdon, J.,  Federico, M.,  Iniguez-Luy, F.(2015) Association mapping of seed quality traits in Brassica napus L. using GWAS and candidate QTL approaches. Molecular Breeding. 35;6;1-19.


Single nucleotide polymorphisms (SNPs) have rapidly become the molecular marker of choice in plant and animal association mapping (AM) studies. In this work, a genome-wide association study (GWAS) and candidate quantitative trait loci (cQTL) approaches were used to identify SNP markers associated with seed quality traits, in a Brassica napus L. association panel composed of 89 adapted winter oilseed rape accessions. Six seed quality traits (oil and protein content, linolenic acid, total glucosinolates, hemicellulose and cellulose content) were evaluated in two different locations for two seasons. For GWAS, 4025 SNP markers evenly distributed along the B. napusgenome were genotyped using a 6K Illumina array platform. For cQTL, 100 SNP markers previously discovered in genomic regions underlying seed quality QTL were genotyped using a competitive allele-specific PCR (KASPar). Analysis of the population structure revealed the presence of two weakly differentiated subpopulations (F ST  = 0.037), with 82 % of the pairwise kinship comparisons ranging from 0 to 0.1. The GWAS approach resulted in the identification of 17 and 5 significant associations for seed glucosinolate content and seed hemicellulose content, respectively. The cQTL approach identified 4 significant associations for seed glucosinolate content and 6 significant associations for seed hemicellulose content. The associated SNPs were consistently identified across environments and were mapped to previously reported QTL. These results illustrate the suitability of AM to identify SNP markers associated with seed quality traits in B. napus.

Burgos-Díaz, C., Rubilar, M., Morales, E., Medina, C., Acevedo, F., Marqués, A.M., Shene, C. (2015). Naturally occurring protein–polysaccharide complexes from linseed (Linum usitatissimum) as bioemulsifiers. European Journal of Lipid Science. 118; 2; 165-174.


The use of proteins, polysaccharides, and their mixtures as bioemulsifiers is becoming increasingly important due to their high versatility and environmental acceptability. In this study, three different fractions mainly composed of protein and polysaccharides, extracted from linseed were evaluated as bioemulsifiers. The three fractions showed the same functional groups, and the amino acid profile revealed the presence of apolar amino acids which are important for forming emulsions. All negatively charged fractions were affected at pH values below 6 and above 100 mM NaCl, confirming their ionic character. Fraction 3 formed oil-in-water emulsion (O/W) and its estimated hydrophilic–lipophilic balance (HLB) value was 10–13. A phase diagram was used to produce a long-term stable O/W emulsion using Fraction 3 as a bioemulsifier. The emulsion containing linseed oil Fraction 3 and water of 5:5:90% w/w exhibited 100% stability under a wide pH range (5–11), ionic strengths (0–500 mM NaCl), and temperatures (4–70°C). Based on these results, Fraction 3, composed of 47.20% w/w protein and 37.88% w/w polysaccharide from linseed, can be considered a potential natural emulsifier for improving stability of O/W emulsions in the face of environmental stresses.

Piornos, J.A., Burgos-Díaz, C., Ogura, T., Morales, E., Rubilar, M., Maureira-Butler, I., Salvo-Garrido, H. (2015).Functional and physicochemical properties of a protein isolate from AluProt-CGNA: A novel protein-rich lupin variety (Lupinus luteus). Food Reserach International. 76;3; 719-724.


This study describes the isolation of proteins from the novel lupin variety AluProt-CGNA (Lupinus luteus) and the influence of pH and NaCl on their functional properties. AluProt-CGNA variety showed to have a great protein content in dehulled seeds (60.60 g protein/100 g, dry matter), which is higher than soybean and other lupin varieties. A lupin protein isolate (97.54 g protein/100 g) from AluProt-CGNA, LPIA, was prepared from lupin flour by alkali solubilization and isoelectric precipitation. The solubility profile of the LPIA was affected by pH, where the minimal values were observed at pH values close to its isoelectric point range (pH 4–5). The highest values of water absorption capacity (1.71 cm3 H2O/g protein), oil absorption capacity (1.43 g trapped oil/g protein), emulsifying capacity (61.94%), emulsion stability (96.43%), foaming capacity (114.29%), foam stability (65.69%) and least gelation concentration (20 g/100 cm3) were observed at pH values lower and higher than its isoelectric point. In the presence of 100 mM of NaCl, their functional properties were improved. SDS-PAGE showed that LPIA mainly contained high molecular weight proteins (α and β-conglutin). These results are useful for increasing the utilization of this protein isolate as a potential functional ingredient in food industry.

M. Olivos-Trujillo, H. A. Gajardo, S. Salvo, A. González and C. Muñoz, (2015): Assessing the stability of parameters estimation and prediction accuracy in regression methods for estimating seed oil content in Brassica napus L. using NIR spectroscopyCHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), Santiago.  p.p 25-30.


Brassica napus L., is an oilseed species of great economic importance due to its high oil content in the seed, representing the second worldwide source of edible oil after soybean. To measure the seed oil content a destructive chemical analysis (Soxhlet) is typically used. In addition, Soxhlet is an expensive, time consuming and labor intensive methodology. In order to overcome these drawbacks the use of near infrared spectroscopy (NIR) has been a low cost alternative to determine oil content and other seed quality traits. However, in order to implement accurate NIR based measurements, stable prediction models need to be developed. In the present work, we assess parameters stability using bootstrap and prediction error through Predicted Residual Error Sum of Squares (PRESS) for three methods: Multiple Linear Regression (MLR), Support Vector Regression (SVR) and Artificial Neural Networks (ANN). The results showed that the best behavior of the three methods analized was ANN, where the variance for stability of parameters was 0.027 and the values of PRESS index were 75.65, 226.07 and 314.91 for ANN, MLR and SVR, respectively. These results will contribute to improve the development of regression models for more accurate seed oil content measurements using NIR technology.