Journal of the science of food and agriculture, tập 90, số 11, 2010

Review Received: 26 October 2009 Revised: 12 May 2010 Accepted: 17 May 2010 Published online in Wiley Interscience: 16 June 2010 (www.interscience.wiley.com) DOI 10.1002/jsfa.4041 Genetic evaluation of dairy cattle using a simple heritable genetic ground Josef Pribyl,a Vaclav Rehout,b Jindrich Citekb and Jana Pribylovaa Abstract The evaluation of an animal is based on production records, adjusted for environmental effects, which gives a reliable estimation of its breeding value Highly reliable daughter yield deviations are used as inputs for genetic marker evaluation Genetic variability is explained by particular loci and background polygenes, both of which are described by the genomic breeding value selection index Automated genotyping enables the determination of many single-nucleotide polymorphisms (SNPs) and can increase the reliability of evaluation of young animals (from 0.30 if only the pedigree value is used to 0.60 when the genomic breeding value is applied) However, the introduction of SNPs requires a mixed model with a large number of regressors, in turn requiring new algorithms for the best linear unbiased prediction and BayesB Here, we discuss a method that uses a genomic relationship matrix to estimate the genomic breeding value of animals directly, without regressors A one-step procedure evaluates both genotyped and ungenotyped animals at the same time, and produces one common ranking of all animals in a whole population An augmented pedigreegenomic relationship matrix and the removal of prerequisites produce more accurate evaluations of all connected animals c 2010 Society of Chemical Industry Keywords: genomic breeding value; methods; QTL; SNP; linear model; genomic relationship INTRODUCTION J Sci Food Agric 2010; 90: 17651773 advanced reproductive methods are routinely applied and the BV of sires is therefore highly reliable; there is a huge global market for sperm and breeding animals encompassing many companies and breeder associations; and worldwide workshops on animal evaluation are frequently organised through publications such as the Interbull Bulletin.9 The aim of this review is to provide a survey of the procedures used to evaluate animal production traits using simple heritable genetic markers Some basic methodological approaches will be emphasised, particularly those that connect genomic breeding value (GEBV) to traditional methodologies ANIMAL EVALUATION An overview of the standard procedures used worldwide in the genetic evaluation (BV prediction) of production traits in farm animals, as well as new developments, are continuously published by ICAR, Interbeef, Interbull, Interstallion and other international organisations A number of countries cooperate in the international evaluation of dairy cattle, which invokes international inspection of the methods used to estimate BV in domestic country.9 Here, special attention is paid to the continuous updating of national and international evaluation procedures Correspondence to: Jindrich Citek, Department of Genetics, Faculty of e Agriculture, South Bohemia University, Studentska 13, CZ 370 05 Cesk Budejovice, Czech Republic E-mail: citek@zf.jcu.cz a Institute of Animal Science, CZ 104 01 Praha 10-Uhrineves, Czech Republic b Department of Genetics, Faculty of Agriculture, South Bohemia University, Studentska 13, CZ 370 05 Ceske Budejovice, Czech Republic www.soci.org c 2010 Society of Chemical Industry 1765 Laboratory techniques and mathematical and statistical methods for the evaluation of animal breeding values (BV) are undergoing continuous improvement Molecular genetic data can be analysed for associations with production traits.1 However, the relationships between farm animal production traits and molecular-genetic information are often measured imprecisely Many studies of the relationships between genetic markers and quantitative traits are methodologically flawed and not reflect contemporary breeding practices; sometimes even the basic context of breeding and farming conditions are not taken into consideration This type of research requires very careful experimental design that considers pedigree structure and generates an adequate quantity of data using sophisticated mathematical and statistical methods The general objective of each evaluation is to explain the variability of the characteristics studied and determine why animals or groups of animals differ from one another Farm animal productivity is simultaneously influenced by many genetic and non-genetic factors, and it is practically impossible to plan a completely balanced experiment Therefore, sophisticated statistical procedures must be used Recently, the evaluation of animal performance based on molecular-genetic information has become more widespread Dairy cattle populations evaluated for several groups of traits of moderate and low heritability (production, conformation, reproduction) using genetic markers have been presented by several authors2 as well as in Interbull Bulletin No 39.9 In pig and poultry populations, whole-genome scanning and genetic diversity analysis are quite extensive.10,11 The methodologies used may be generalised across species, but several facts influence methodological advancements in relation to dairy cattle: the cost of genotyping is favourable relative to the price of each animal; www.soci.org Generally, mixed linear models of best linear unbiased prediction (BLUP) in an animal model (AM) are used, and a pedigree of three or more generations of ancestors is taken into account according to the model equation: Y = Xb + Zu + e (1) 1766 where Y is the vector of measured performances, X and Z are known matrices that relate performance to the systematic effects of the breeding environment and the animals, b and u are the estimated vectors of fixed systematic environmental effects and the random effects of an animal (BV) with the additive numerator relationship matrix (A), and e is the vector of random error Using this model equation, a system of normal equations is constructed in which the unknown constants (b) and (u) are estimated These systems of equations are vast, and special algorithms are required for their solution.12 Most of the variability in any measured production trait is caused by systematic environmental effects The influence of the herdyearseason, or herdtest-day, which identifies a contemporary group of animals kept under the same conditions, is usually the most important factor Evaluations are generally oriented to the MT-AM (multi-trait animal model), RR-TDAM (random regression test-day animal model), AM-maternal, and nonlinear methodologies for survival (kit) analysis.13 23 It is important to find a method of evaluation that minimises residual error and simultaneously considers all of the effects that may influence the performance variable being measured From a genetic perspective, it is important to ask: What proportion of variability is explained by the statistical model used? Is this proportion different in a model that does not account for genetic effects? Is this model the best (optimal) of all the possibilities tested? The proportion of variability explained by the statistical model used (R2 ) and other information criteria for testing the suitability of the model, such as Akaikes information criterion (AIC), Bayes information criterion (BIC), Bayes factor (BF) and the likelihood ratio test (LRT), are very important in answering these questions.24 28 Molecular-genetic information can be used to improve selection programmes.29 Animals are evaluated more accurately when their entire genetic value is partitioned into causal factors and withinfamily genetic components are exploited The use of moleculargenetic markers in breeding is the inclusion of additional criteria in the selection indices These markers increase selection differences relative to existing traditional breeding programmes by decreasing the correlation among sib individuals, increasing the accuracy of animal selection, allowing the utilisation of genetic variability that is usually included in non-utilisable residuum, and allowing a shortening of the generation interval (because they may be analysed in young animals) The use of selection markers is conditional upon the timely laboratory analysis of the entire subpopulation subjected to pre-selection (e.g., young bulls) and rapid application before the determined gene linkages change This requires frequent updates of selection indices, as shown below (Eqn (2)) The consistent application of genomic selection markedly reduces the cost of a selection programme.3,30 However, data analysis becomes more complicated, the number of estimated parameters becomes higher, and a modified information criterion (mBIC) is necessary for the selection of a suitable model of evaluation.31,32 In order to select individuals for breeding, marker-assisted selection (MAS) may be applied if several genetic markers are to www.interscience.wiley.com/jsfa J Pribyl et al be used Alternatively, genomic selection utilises high numbers of markers that densely cover the whole genome.3,33 BV is usually calculated in two steps In the first step, the regression coefficients (v) (substitution effects of the alleles of a considered locus) are determined in a reference population with known performance and highly reliable BVs This reference population usually includes only a part of the population under selection From the first step, quantitative trait loci (QTL) effects are estimated Subsequently, BV is determined for all of the young animals in the evaluated (sub)population by means of a selection index, as described in Eqn (2).30 The reference population and the evaluated population are separated by at least one generation Therefore, the relationships between markers and QTLs determined in the older generation may not be fully applicable to the younger evaluated population, as the QTLs are not fully covered by study markers Furthermore, the influences of selection, mutation, immigration of sires used intensively in artificial insemination, changes in environment, and the development of the commercial population under selection can also affect the applicability of QTL data across generations Therefore, it is necessary to periodically redetermine u in Eqn (1), allele frequencies (q) in Eqn (5), their inherence in the genotypes of individual animals (T), and regression coefficients (v) in (3) so that the gap between the reference and the evaluated population will be as small as possible.3,30,34 The GEBV of a given trait is calculated based on known loci and remaining polygenes according to the selection index: GEBVj = k1 DGVj + k2 u j (2) where GEBVj is the genomic (total) BV for an individual (j) determined based on the genomic information at the locus (i) and remaining polygenic effect DGVj is the direct genetic value, calculated as the sum of BVs for a particular loci: DGVj = j Tij vij (3) where Tij (with regard to Eqn (9)) is the ith element in the jth row of the known incidence matrix correlating the genetic effects of particular alleles to the observed individual, vij is the vector of genetic marker effects, u j represents the BV calculated based on the remaining polygenes, and k1 and k2 are the weights of the information sources in the index.35 If the GEBV is calculated for young animals without their own production records, u j represents only information about their parents In cases where a high density of genetic markers is available, the u j in Eqn (2) is frequently omitted GENETICALLY CONDITIONED VARIABILITY OF PERFORMANCE Reliably determined population-genetic parameters are a precondition for genetic evaluation We are usually interested in phenotype variability ( P ), which can be separated into genetic additive ( A ), genetic dominant ( D ), genetic epistatic ( I ), and unpredictable residual ( E ) components, plus covariance caused by genotype/environment interaction (2GE ).36 It is generally assumed that the genotype/environment interaction is negligible Therefore: 2P = 2G + 2E = 2A + 2D + 2I + 2E (4) The additive effects that are accumulated over successive generations of selection are used in breeding Nevertheless, other c 2010 Society of Chemical Industry J Sci Food Agric 2010; 90: 17651773 Evaluation of animals by simple heritable markers www.soci.org genetic effects are also reflected in performance, and if they are omitted the results of the evaluation may be distorted One locus Some genes may have a direct impact on quantitative production traits, and therefore efforts are made to utilise them directly in breeding These candidate genes (or quantitative trait loci (QTL), if describing markers) explain a portion of the genetic variability in the trait being considered At two alleles in a locus, the portion of the additive genetic variance conditioned by one gene (i) can be approximated by a binomial distribution:36 Ai = 2qi (1 qi )v i (5) where qi is the frequency of the studied allele at locus i and vi is the additive substitution effect of alleles at locus i The portion of the variance caused by a dominant allele at a given locus is (6) Di = (2qi (1 qi )di )2 where di is the dominance effect in locus i Often, it is assumed that di = In the case of genes with major effects on the trait being studied, the analysis is slightly easier because animals carrying a desirable allele frequently exceed the normal variable range for the measured production trait This is obvious from the distribution function of the estimated BV of the evaluated trait (additional peaks, outliers), which indicates that a special genetic effect is occurring and should be included as a separate factor in the model.37 Several loci Correlations may exist between the genes in question Therefore, the variability of an observed trait that is explained by several genes depends on the variability caused by each gene and combinations thereof ().38 The additive covariance between two loci can be expressed as cov(Ai , Ai ) = (1 2)2 (Ai Ai ) (7) J Sci Food Agric 2010; 90: 17651773 Polygenic effects: the infinitesimal model (pol) A large number of unknown genes are assumed to affect the majority of production traits, and their overall influence on performance and its variability is the object of interest In general, the components of variance are currently determined as in Eqn (1), by REML methods or by applying the Bayesian approach using the Gibbs sampling method.12 These methods require the analysis and adjustment of input datasets so that specific components of variance (for example, within families, between families, caused by different effects of genes) can be estimated.19,21 Joint effects of particular loci with remaining polygenes The overall influence of genetic effects on the observed production trait is expressed by the coefficient of heritability (h2 = A / P ) The specific roles of the genes which exert these effects generally remain unknown The total additive genetic variability is the sum of the known loci according to Eqn (5), adjusted for mutual linkages (7) and residual additive genetic variability caused by the remaining polygene Apol : 2A = j Ai + j j; cov(Ai , Ai ) + Apol (8) Hence both single-locus effects and the remaining polygenic effects of the genetic background should be considered simultaneously.7,42 45 EXPERIMENTAL DESIGN FOR THE EVALUATION OF GENETIC MARKERS The objective is to estimate the genetic contribution to specific production traits However, the experimental design should ensure the reliable estimation of all factors that influence performance The power of the evaluation of data depends on the structure and the size of the experiment, and the minimum number of observations required to achieve adequate predictive power can be calculated.46 Large datasets spanning progeny from many sires are usually necessary.2,3,30,43 Generally, thousands of animals are included in any one experiment Laboratory analyses are expensive, and therefore the decision of which animals from which generation should be genotyped should be made carefully, to achieve the highest possible reliability with the lowest possible cost Several methods based on the relationship matrices between animals have been developed for this purpose.49 Both the screening of allele frequencies and the evaluation of their relationship to production traits require a pedigree analysis Sires, especially those imported from other populations for artificial insemination, can dramatically change the frequencies of alleles in a herd or an entire population in a short period of time There are differences in the methodologies used to evaluate F1/F2 generation-designed experiments in which extremely c 2010 Society of Chemical Industry www.interscience.wiley.com/jsfa 1767 The theory of selection indices is used to determine the shares of several loci in the total genotype.39,40 Genes interact with one another, and any gene may have pleiotropic effects These interactions are mostly unknown and may be quite extensive This implies genetic epistatic variability ( I ) based on two or more interactions among all loci studied It is expected that multi-generational, similarly oriented ongoing selection in commercial breeds will lead to the stabilisation of favourable genetic combinations The fixation of desirable alleles could also occur at a number of loci in an improved breed However, breeding conditions change constantly, and combinations of genes are disturbed by selection, mutation and by the immigration of sires from other populations Therefore, inter-gene interactions within some families may be expressed differently for a certain period before the gene linkages are again stabilised This can be exploited in selection When studying the influence of a selected gene on performance, the effects of nearby (linked) genes will also be included; thus the result does not correspond only to the studied gene or to the studied markerQTL relationship Therefore, when a low number of sparsely located markers is analysed, the effects of any given marker are frequently overestimated.33 The effects calculated for each genetic parameter strongly depend on the number and density of genetic parameters included in a simultaneous analysis One locus can also have an epistatic effect on several traits, which may be either positively or negatively correlated It is therefore necessary to distinguish between pleiotropic and closely linked QTL effects.41 www.soci.org different breeds are crossed38,50 and studies involving stably selected commercial populations, where alleles are expected to be in favourable interactions The second case, which is connected with the continuous improvement of already productive breeds, is generally of greater interest to breeders DD + GDD Sib animals, belonging to the same families, generally have similar performance capabilities They share both the observed genes and background polygenes It is crucial to determine whether performance is influenced by the studied locus or by other polygenes Study designs that incorporate data from multiple generations have been developed for the analysis of small numbers of markers These daughter design (DD) and granddaughter design (GDD) analyses allow estimation of the effects of the studied loci within families, i.e., within groups of sib animals with a similar genetic background.38,51 In this type of analysis, the initial generations of sires (i.e., parents or grandparents) must be heterozygous at the studied locus In this way, each initial animal gives rise to two genetically different groups of progeny with respect to the alleles studied In the proposed GDD experiment, only generations of ancestors without their own performance measurements can be genotyped Their performance scores are assigned by means of progeny testing from a large set of non-genotyped progeny This considerably decreases the number of individuals that must be genotyped despite achieving a high reliability of evaluation The total number of animals required for the experiment is relative to the proportion of genetic variability influenced by the locus, allele frequencies, and the level of recombination between QTL and the marker However, only the additive effects of genes can be estimated in this design The GDD and general pedigree design analysis of QTL in dairy cattle have been compared in simulation studies.52 Design with a large number of markers (SNP) An increased number of markers introduces more complexity The size of the reference population of sires with highly reliable BV estimates is particularly important.2,3,43 Larger numbers are better, and several thousands of sires are desirable A MODEL FOR ESTIMATING GENETIC EFFECTS The principle of evaluation consists in the separation of the effects of purely heritable loci from the effects of other genetic background.43,47,48,53 The BLUP method and similar approaches are the best procedures that can be used to adjust measured BV values Consistent with Eqns (1) and (2), the evaluation can be formally expressed by a modified mixed linear model: Y = Xb + Z[u + Tv] + e (9) 1768 where T is the known matrix of the experiment design that links an animal to the genetic effects of particular alleles Each row may include columns according to particular loci and several genetic effects of each locus with values for additive effects (tAi Ô ), dominance effects (tDi Ô ), twolocus epistatic interactions between loci (tIii = tAi tAi );32 u is the estimated vector of the random residual polygenic effects for each animal (i.e., the partial BV after the effects of the studied loci www.interscience.wiley.com/jsfa J Pribyl et al are excluded) with the additive relationship matrix; and v is the estimated vector of the effects of genetic markers This may also comprise several loci and encompass additive, dominance and epistatic effects Genetic markers (v) can be considered to have fixed32,53 or random effects In the latter case, either the diagonal genetic matrix alone, I Ai , is considered54 for each random effect (i), or the complete covariance structure and its relationship with the identity by descent matrix (IBD), IBD Ai , is taken into account IBD describes the probable positional relationships between each marker/QTL pair and the probability of inheriting the paternal or maternal QTL allele The construction of IBD depends on whether linkage analysis (LA), linkage disequilibrium (LD) or a combination of both methods (LDLA) was used to determine linkage status.8 In this context, several teams have developed algorithms for the construction of an IBD matrix.41,44,55 They have also derived genotype values for non-genotyped sib animals whose performance data may be then used to identify candidate genes DATA FOR EVALUATION Several types of data describing performance can be used for these evaluations Either direct performance records or adjusted values may be used For the second approach, data are adjusted for non-genetic noise as precisely as possible, when BV with high reliability is estimated in large populations This yields adjusted (pseudo) values for BV, yield deviation (YD) or daughter yield deviation (DYD) that may be used in further analyses Direct individual performance If genetic parameters (markers) are determined directly in animals from their performance records, the evaluated trait (Y) according to Eqn (9) is their recorded performance Given the pedigree structure and design of the experiment, it is possible to estimate additive, dominance and epistatic genetic effects, all of which could be included in v However, in practice relatively few performance values are known for each animal Therefore the values of vectors u , v and other effects b in Eqn (9) can be estimated only with considerable error.56,57 Breeding value Animals with highly reliable BV are used for evaluation (usually sires whose value has been proven by progeny testing) In BV analysis, the effects of selected loci on major traits associated with milk performance are determined58 60 according to the following model: (10) u = Tv + u where u is the vector of BV determined by a routine method BLUP-AM according to Eqn (1) based on all polygenes The BV of an animal summarises the data on performance deviations of the contemporaries of all sib animals The expected BV of the progeny (uO ) is related to the BV of sires (uS ) and mothers (uM ) and to the random Mendelian sampling of parental gametes (MS) (11) uO = 0.5 [uS + uM ] + MS One half of the additive genetic variability of (uO ) is caused by MS Therefore the result of Eqn (10) significantly depends on the volume and sources of information that contributed to the BV A reliable input BV, which can be achieved only for animals with a c 2010 Society of Chemical Industry J Sci Food Agric 2010; 90: 17651773 Evaluation of animals by simple heritable markers www.soci.org large set of progeny, is a condition for a correct evaluation This, however, implies that it is possible to evaluate only the additive genetic component We must take into account the fact that BV represents a random effect (regressed value) and its value directly depends on the reliability of estimation (r2 ) The variability of BV ( u ) is therefore higher at higher reliabilities, as shown by the following relationship: (12) r2 = u / A This demonstrates that for BV estimates with low, unbalanced reliabilities the animal rank may change and the results of markers analysis are not very reliable Daughter yield deviations DYD computed from Eqn (1) are used in most routine evaluations of markers.45 Initially, yield deviations (YD) adjusted for all non-genetic effects are determined according to the following equation:61 YD = Y Xb (13) where YD is the vector of yield deviations The average deviations of sires daughters (DYD) is then determined and adjusted for 0.5 BV of their mothers: DYD = Z S [YD 0.5Z M uM ]N1 (14) METHODS FOR EVALUATING GENETIC MARKERS When only a small number of genes is studied, it is not possible to evaluate the experiment correctly without splitting the genotype influence into the part played by singular observed genes and the part played by the other (residual) polygenic genetic background.7,33,64,65 On the other hand, single observed genes also contribute to the additive effects of all genes, and the polygenic effect (u) is the sum of these additive effects Therefore, it is not easy to distinguish between the influence of the polygenic genetic background and the effects of individual genes; in these cases the effect of the individual observed genes is frequently reduced.66 Therefore careful experimental design, particularly with respect to the size of the experiment, is necessary to estimate the effects of genetic markers Several connected questions must be asked in any evaluation of genetic markers: (A) What proportion of the genetic variability of the evaluated trait is explained by the studied genetic factors? (B) What is the genetic correlation between the influence of the factors studied and the influence of the remaining genetic background on the evaluated trait? (C) Do the results from a model that considers only polygenic effects and a model that includes both QTL and remaining polygenic effects differ from each other? (D) What is the influence of each allele? (Note that it does not make sense to answer (D) without first answering (A).) (E) Do the studied genetic factors have similar effects in all groups of related animals? A small number of QTLs When a small number of loci are evaluated, QTLs are often used to represent fixed effects of the genotype in a linear model, for example in GLM/SAS.67 69 The evaluation model also reflects systematic effects of the breeding environment or of groups of animals according to their relationships With this method, it is not possible to wholly avoid the influence of correlated loci, and the effects of individual loci are therefore usually significantly overestimated.33 The model can be improved by including a parameter for the random effects of the parents of genotyped animals.56,57 The effect of the studied locus depends on the genetic background of the animal and could differ between populations.43,65 The BLUP, REML and Bayesian analysis methods incorporate common fixed effects for particular loci and residual random effects of remaining polygenes to provide more exact results.7,43,45 Another approach for obtaining more exact results is also to use particular loci as random effects with IBD to account for their variability.8,55 The weight (w) for weighted analysis, which is the inverse of DYD variance, corresponds to the value of EDC The exact derivation of the weight factor for special situations has been described previously.61,62 DYD has been used in several GDD studies; one evaluated 39 markers in a set of 4993 sires and another evaluated 263 markers in a set of 872 sires.7,63 As in the evaluation of BV, only the additive genetic component can be determined by DYD If the number of progeny per sire is large then they prevail in his BV, r2 is high and balanced, and the sires MS is almost completely contained both in BV and in DYD The correlation between BV and DYD is in this case high, and the results of evaluation for genetic markers on the basis of BV and DYD are similar.32 A large number of SNPs Production traits depend on a large number of mutually linked, interacting genes that may be distributed across the entire genome Currently, it is possible to sequence tens to hundreds of thousands of single-nucleotide polymorphisms (SNPs) for many individual animals, densely covering the entire genome A multiple regression analysis of all SNP markers describes their relationships to the production trait in question Thus this analysis can be used to find the DGV and GEBV according to Eqns (3) and (2) Because a large number of SNPs is considered, there is less emphasis on the quantitative relationships between individual markers and the relevant QTL; instead, the overall relationship to the production trait in question is important J Sci Food Agric 2010; 90: 17651773 c 2010 Society of Chemical Industry www.interscience.wiley.com/jsfa 1769 where ZS is the known matrix that relates daughter performance to the sire; ZM is the known matrix that relates daughter performance to the mother; uM is the vector of mothers BV; and N is the diagonal matrix that describes the number of daughters per sire The values of DYD are independent of the reliability of sires BV estimates, and therefore are more comparable between sires with different reliabilities of BV estimation In agreement with Eqn (11), DYD comprise 0.5 BV of a sire, including MS and random error The alternative of DYD is de-regressed BV.3 Performances adjusted in this way are evaluated by weighted analysis according to (9), where the vector Y is substituted by the vector DYD, and vector b may encompass additional fixed effects DYD values are the means for n daughters of sires Taking into account the number of contemporaries connected to each daughter in DYD, and the structure of the entire dataset, we can generate the effective daughter contribution (EDC), which is determined based on the reliabilities of the estimation of sires BVs (r2 ): EDC = (r2 /(1 r2 ))((4 h2 )/h2 ) (15) www.soci.org The high density of markers also allows the generalisation of effects Relationships no longer need to be calculated individually within particular families and the effects of alleles are assumed to be consistent across all families for simplification.33 While the breeding values of young dairy cattle can be predicted with a reliability of about 30% by pedigree value on the basis of polygenes, an increase in reliability (to 5070%) can be expected when large number of SNPs are evaluated.2,3,33 Computational strategies used to evaluate SNP data Generally, techniques based on the BLUP and BayesB methods are used to evaluate large numbers of SNPs.33 Depending on the total number of SNPs sequenced, it is usually necessary to calculate many genetic regression relationships between a given production trait and the studied alleles.54,70 These relationships may be formally solved according to Eqn (9) Compared to a general AM calculated on the basis of polygenes only, the size of the vector DYD is relatively smaller (thousands of sires) but the size of the vector v is large (tens of thousands of regression coefficients) When there is a high density of SNPs across the entire genome, the term u is often omitted from the solution and only SNPs are used (vector v).43 In practice, however, it is expected that even when a high density of markers is obtained some QTLs will not be covered and the polygene effect is therefore still considered.3 Only additive effects are evaluated due to the large number of SNPs; the inclusion of non-additive effects would increase the number of effects in the model enormously After simplification, the computation model can be expressed as DYD = Xb + Tv + e (16) where Xb describes the total mean and fixed effects included in this step Often, the majority of SNPs not have any information content If the relationships between the markers and QTLs are already known, it is possible to reduce the number of regressors in the model, which will simplify the solution and also reduce the cost of laboratory analyses.71 Because of the large number of independent variables, these systems of equations (16) are poorly conditioned and cannot always be solved Therefore, the systems of equations and algorithms of solutions must be rearranged For example, ridge regression may be applied, which means that SNPs are treated as random effects At the same time, numerical values are added to the diagonal of the matrix of the system to ensure the solubility of the equations.34 The added values are the inverse of the genetic variabilities of each SNP These values are not usually known for many genetic parameters, so other simplifications must be used when constant components of variance are required for all SNPs.33 The sum of components across all loci yields the total additivegenetic variability of the studied trait A Matrix T in Eqn (16) has f rows corresponding to the number of evaluated sires with known daughter performance If only additive gene effects are considered, matrix T has m columns corresponding to the number of SNP markers considered (m > f ) Therefore, a system of the matrix size m ì m at least is solved, and tens of thousands of regression coefficients are estimated.54 J Pribyl et al which describes deviations of allelic frequencies in the basic nonselected population The ith column of Q contains the deviation of the frequency of the second allele in locus i from the expected value (0.5) multiplied by two Qi = 2(qi 0.5).72 The dimensions of matrix Q correspond to those of matrix T The matrix G has the form (17) G = ([T Q][T Q] )/(2 qi (1 qi )) which is analogous to the generally used numerator relationship matrix (A) in Eqns (1) and (9) Its dimensions are f ì f , where the diagonal indicates the number of homozygous loci in the evaluated animal and the elements off the diagonal indicate the numbers of alleles shared by sib animals The diagonal residual covariance matrix R E of dimensions f ì f is then constructed This matrix corresponds to the residual effect (e) in Eqn (16) Relative to Eqn (15), the elements on diagonal R are connected with the reliabilities of BV estimates for particular sires, but only on the basis of their progeny from which DYD were computed (excluding other sources of information): Rjj = (1 r2 jP )/r2 jP where r2 jP is the partial reliability of the sires BV based on his progeny Based on the theory of selection indices, it is then possible to determine the direct genetic value of sires with known daughter performance (DGVS )72 by adapting 2DYD for the vector of observations:42 DGVS = G[G + Rk]1 2DYD 1770 www.interscience.wiley.com/jsfa (19) where k = E / A The genomic covariance matrix between proven sires (S) and young unevaluated animals (O) is C = ([TO QO ][TS QS ] )/(2 qi (1 qi )) (20) where TO and TS are the known matrices assigning particular loci to the young animals and proven sires and QO and QS are Q matrices that have been modified according to the number of young animals and proven sires included The predicted direct genetic value for young animals (DGVO ) is a genomic regression based on proven animals with already known BV: (21) DGVO = CG1 DGVS The solution by means of the selection index according to Eqns (17)(21) is identical to the preceding solutions in Eqns (16) and (3) but the dimensions of the matrices are substantially smaller, corresponding to the numbers of genotyped animals (f ).72,73 The estimation of genetic regression coefficients according to the particular loci (v) may also be omitted Hence the solution is simplified and does not require iterative methods.72 Therefore, a direct determination of DGV estimate reliability is feasible For sires with known daughter performance (S), reliability estimates correspond to the diagonal elements of the term: G[G + Rk]1 G (22) For young animals without known performance (O), reliability estimates correspond to the diagonal elements of the term C[G + Rk]1 C DIRECT ESTIMATION OF GEBV Based on T, a genomic relationships matrix (G) can be determined.72,73 This requires the introduction of the matrix Q, (18) (23) A similar solution can also be obtained by the weighted analysis of a linear model as in Eqn (1) with DYD substituted for input data c 2010 Society of Chemical Industry J Sci Food Agric 2010; 90: 17651773 Evaluation of animals by simple heritable markers www.soci.org and weighted according to Eqn (18) In this case, A is substituted by G and Xb covers only the general mean.72,73 A one-step approach The process of evaluation described above has several disadvantages, namely that it is influenced by the input parameters used in a multi-step procedure Inaccuracies in these parameters may bias the evaluation It is also difficult to compare genotyped and ungenotyped animals evaluated by different procedures This may be overcome by incorporating all parts of the evaluation into a one-step procedure From Eqn (19), it follows that molecular-genetic information is collected in G The additive numerator relationship matrix (A) is probability based and deviates from expected values due to random Mendelian sampling.74 The realised genomic relationship matrix (G) should therefore be more precise and lead to more precise selection.73 A single-step evaluation using original measured performances (Y) as input has been proposed, in which the pedigree-based numerator relationship matrix (A) covering all evaluated animals is augmented by a contribution from (G) with genotyped animals.75 A matrix H has been derived, which is substituted for the usual matrix (A) in Eqn (1).76 Further, a computational procedure has been developed for the solution of animal models directly from the accumulated measured data of all genotyped and non-genotyped animals in large commercial populations.6,75 The essential component of the system of equations constructed according to Eqn (1) is the inverse of the relationship matrix, in this case: 0 (24) H1 = A1 + (G1 A1 22 ) where H is the pedigreegenomic relationship matrix, is a scaling factor and A22 is a block of A that corresponds to the genotyped animals This one-step procedure eliminates several assumptions that must be made for multi-step procedures It is less biased and allows the evaluation of large commercial populations even when only some individuals in the population are genotyped This improves evaluation accuracy both for genotyped and ungenotyped animals and generates a single common rank for all animals This model further enables the use of multi-trait AM and models with different complexities, which are now common in animal evaluations.6 CONCLUSIONS J Sci Food Agric 2010; 90: 17651773 ACKNOWLEDGEMENTS This work was supported by the Ministry of Agriculture of the Czech Republic (MZe 0002701404) and by the Ministry of Education of the Czech Republic (MSM6007665806) We gratefully acknowledge the helpful comments of anonymous reviewers REFERENCES Czarnik U, Galinski M, Pareek ChS, Zabolewicz T and Wielgosz-Groth Z, Study of an association between SNP 775C>T within the bovine ITBG2 gene and milk performance traits in Black and White cows Czech J Anim Sci 52:16 (2007) VanRaden PM, 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www.interscience.wiley.com/jsfa 1771 The majority of traits are conditioned in a complex way; it can be assumed that a production trait will only rarely be genetically conditioned in a simple way by a small number of independent genes In practice, a large number of markers and a large number of animals with accurate performance estimates are necessary for the reliable evaluation of animals.2,3 Simplifications are often used in evaluations, but at the cost of lower reliability of results A description of the variability of the evaluated data and the validity of the model are therefore necessary Considerable attention should be paid to the development of traditional methods of BV estimation on the basis of polygenes, which enables the correct adjustment of performance for environmental effects Typically, a two-step evaluation of genotyped animals is performed The first step is the estimation of traditional BV for recorded traits according to BLUP-AM or a similar method Based on these results, DYD are computed for particular sires with a highly reliable BV In the second step, GEBV is determined for young animals without performance records Marker-assisted selection is gradually being replaced by genomic selection, in which a huge number of genetic markers (SNPs) that densely cover the entire genome are analysed In this case, polygenic effects can be eliminated from the solution without strongly affecting the results Molecular-genetic information can be gathered in the genomic relationship matrix G in order to estimate DGV and GEBV with computationally simpler procedures A one-step procedure that combines the animal model with a pedigreegenomic relationship matrix can be used to evaluate all animals in a population This protocol is useful as it produces more accurate evaluations than other methods and also generates a common rank for all genotyped and ungenotyped animals in the population The methods described here have significant practical importance in animal 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effects of the ESR gene on litter size and production traits in Czech Large White sows Czech J Anim Sci 49:373382 (2004) 54 Legarra A and Misztal I, Technical note: Computing strategies in genome-wide selection J Dairy Sci 91:360366 (2008) 55 Meuwissen THE and Goddard ME, Prediction of identity by descent probabilities from marker-haplotypes Genet Sel Evol 33:605634 (2001) 56 Humpolc ek P, Urban T, Matousek V and Tvrdon Z, Effect of estrogen receptor, follicle stimulating hormone and myogenin genes on the performance of Large White sows Czech J Anim Sci 52:334340 (2007) 57 Vykoukalova Z, Knoll A and Cepica S, Porcine perilipin (PLIN) gene: structure, polymorphism and association study in Large White pigs Czech J Anim Sci 54:359364 (2009) tek J, Panicke L, Rehout 58 Hradecka E, C V and Hanusova L, The relation of GH1, GHR and DGAT1 polymorphisms with estimated breeding values for milk production traits of German Holstein sires Czech J Anim Sci 53:238246 (2008) pkova M, 59 Matejc ek A, 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A, Aguilar I and Misztal I, A relationship matrix including full pedigree and genomic information JDairySci 92:46564663 (2009) 1773 J Sci Food Agric 2010; 90: 17651773 c 2010 Society of Chemical Industry www.interscience.wiley.com/jsfa Research Article Received: March 2010 Revised: 11 May 2010 Accepted: 17 May 2010 Published online in Wiley Interscience: 16 June 2010 (www.interscience.wiley.com) DOI 10.1002/jsfa.4039 Improvement of L(+)-lactic acid production from cassava wastewater by Lactobacillus rhamnosus B 103 Luciana Fontes Coelho,a Cristian J Bolner de Lima,a Marcela Piassi Bernardo,a Georgina Michelena Alvarezb and Jonas Contieroa Abstract BACKGROUND: L(+)-Lactic acid is used in the pharmaceutical, textile and food industries as well as in the synthesis of biodegradable plastics The aim of this study was to investigate the effects of different medium components added in cassava wastewater for the production of L(+)-lactic acid by Lactobacillus rhamnosus B 103 RESULTS: The use of cassava wastewater (50 g L1 of reducing sugar) with Tween 80 and corn steep liquor, at concentrations (v/v) of 1.27 mL L1 and 65.4 mL L1 respectively led to a lactic acid concentration of 41.65 g L1 after 48 h of fermentation The maximum lactic acid concentration produced in the reactor after 36 h of fermentation was 39.00 g L1 using the same medium, but the pH was controlled by addition of 10 mol L1 NaOH CONCLUSION: The use of cassava wastewater for cultivation of L rhamnosus is feasible, with a considerable production of lactic acid Furthermore, it is an innovative proposal, as no references were found in the scientific literature on the use of this substrate for lactic acid production c 2010 Society of Chemical Industry Keywords: cassava wastewater; corn steep liquor; response surface methodology INTRODUCTION 1944 Lactic acid has a wide range of applications in the pharmaceutical, food, leather, textile and cosmetic industries.1 One of the most important applications of lactic acid is in biodegradable markets, such as polylactic acid, which can be used to improve physical properties in the production of garbage bags, agricultural plastic sheeting and computer parts.2 It can also be applied in sutures and surgical implants owing to its biocompatible and bioabsorbable characteristics.3 L(+)-Lactic acid with high optical purity provides polylactic acid with a high melting point and high crystallinity.4,5 Lactic acid is industrially produced either through chemical synthesis or microbial fermentation The advantage of the fermentation method resides in the fact that an optically pure lactic acid can be obtained by choosing a strain of lactic acid bacteria, whereas chemical synthesis always results in a mixture of L(+)- and D()-lactic acid.6 Refined sugars such as glucose or sucrose have been more frequently used as the carbon source and yeast extract as a nitrogen source for lactic acid production, but this is economically unfavorable In order to lower the cost of the production process, a number of agro-industrial by-products or wastes have been evaluated as substrates for the production of lactic acid, such as sugarcane,7 molasses8 and whey9 as carbon sources and corn steep liquor (CSL),10 a by-product of the corn wet milling industry, as a nitrogen source J Sci Food Agric 2010; 90: 19441950 Lactic acid bacteria have complex nutrient requirements due to their limited ability to biosynthesize B vitamins and amino acids.11 Therefore CSL is an excellent source of nitrogen for lactic acid bacteria because it has a high concentration of amino acids and polypeptides, with considerable amounts of B-complex vitamins.12 Cassava wastewater (CW) is a residue, generated in large amounts during the production of cassava flour, composed of carbohydrates, nitrogen, minerals, and trace elements, and therefore has potential as a substrate for biotechnological processes.13 Cassava wastewater has been used to produce a surfactin by Bacillus subtilis,14 citric acid by Aspergillusniger,15 Polyhydroxyalkanoates (PHAs) and rhamnolipids by Pseudomonas aeruginosa.16 Some studies in the literature report the use of cassava bagasse in the production of lactic acid,17 21 but no papers were found on lactic acid production using cassava wastewater as a substrate Correspondence to: Jonas Contiero, Department of Biochemistry and Microbiology, Universidade Estadual Paulista UNESP, 13506-900 Rio Claro, SP, Brazil E-mail: jconti@rc.unesp.br a UNESP Department of Biochemistry and Microbiology, Biological Sciences Institute, Universidade Estadual Paulista, Rio Claro, SP, Brazil b Instituto Cubano de Investigaciones de los Derivados de la Cana de Azucar, (ICIDCA), 488243 Havana, Cuba www.soci.org c 2010 Society of Chemical Industry Improvement of L(+)-lactic acid production from cassava wastewater www.soci.org Therefore, the use of CW for lactic acid production is an innovative proposal Lactobacillus rhamnosus B 103 has an advantage over other lactic acid bacteria such as homofermentative metabolism, highest productivity and production of L(+)-lactic acid that is optically pure Therefore the aim of the present study was to investigate the effects of different medium components on cassava wastewater for the production of L(+)-lactic acid by Lactobacillus rhamnosus B 103 (0.1) and MnSO4 4H2 O (0.05) Initial pH of the medium was adjusted to 6.2 The inocula were incubated at 37 C, 200 rpm for 18 h 10% (v/v) of inoculum was added in all experiments Calcium carbonate (50 g L1 ) was added to experimental medium (PlackettBurman experimental design and central composite design), the pH was adjusted to 6.2 with mol L1 NaOH After that, 45 mL of experimental medium was transferred to a 250 mL Erlenmeyer flask and incubated in orbital shakers at 37 C, 200 rpm for 48 h In all experiments, the concentration of initial reducing sugar was 50 g L1 MATERIALS AND METHODS PlackettBurman experimental design The purpose of this first step of the optimization was to identify the medium components with a significant effect on lactic acid production Twelve experiments were generated from seven factors: CSL, sodium acetate, magnesium sulfate, manganese sulfate, ammonium citrate, potassium phosphate and Tween 80 Variables with a confidence level greater than 95% were considered to have a significant influence on lactic acid production The PlackettBurman experimental design was based on the first-order model, with no interaction among the factors The concentrations used for each variable are displayed in Table A central composite design (CCD) was performed with the variables that significantly increased the production of lactic acid Microorganism Lactobacillus rhamnosus B 103 was obtained from the Instituto de Azugar Cubano de Investigaciones de los Derivados de la Cana (ICIDCA) The strain was stored in Man, Rogosa and Sharpe (MRS) medium with 20% (v/v) glycerol at 20 C Substrates CSL was obtained from Corn Products Co (Sao Paulo state, Brazil) and CW was collected from cassava flour manufacturer Plaza SA (Sao Paulo state, Brazil) and stored at 20 C until needed Solids as well as cyanide were removed from the CW by boiling for min, followed by cooling and centrifugation at 5000 ì g for 10 min.14,22 The supernatant was used as a production medium The same CW was used for all experiments CW composition: fructose (24.5 g L1 ), glucose (30.1 g L1 ), maltose (1.8 g L1 ), nitrate (0.7 g L1 ), phosphorus (0.9 g L1 ), potassium (3.9 g L1 ), magnesium (0.5 g L1 ), nitrite (0.05 mg L1 ), sodium (23.1 mg L1 ), iron (6.1 mg L1 ), zinc (11.1 mg L1 ), manganese (4.1 mg L1 ), copper (14.1 mg L1 ) and protein (9 g L1 ) The composition of the CW was determined using the methodology described by Nitschke and Pastore.14 Cultivation The inocula were prepared through the transference of mL of stock culture to Erlenmeyer flasks containing 100 mL of growth medium (MRS) MRS growth medium composition (g L1 ): peptone (10.0), yeast extract (5.0), meat extract (10.0), glucose (20.0), sodium acetate (5.0), ammonium citrate (2.0), K2 HPO4 (5.0), MgSO4 7H2 O CCD and optimization by response surface methodology A CCD for two independent variables each at five levels with four star points ( = 1.41) and four replicates at the center points was used to develop a second-order polynomial model, which determined the optimal values of variables for lactic acid production Screened through previous work, CSL and Tween 80 were taken as the variables for investigation The variables of the experiments were coded according to the following equation: xi = (Xi Xcp )/Xi i = 1, 2, , K (1) in which xi is the coded value of an independent variable; Xi is the real value of an independent variable; Xcp is the real value of Table PlackettBurman design (real and coded values) with respective resulting lactic acid production Independent variablesa Run 10 11 12 a X1 X2 X3 X4 X5 X6 X7 Lactic acid (g L1 ) (1)b (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) 0.2 (1) (1) 0.2 (1) 0.2 (1) (1) 0.2 (1) 0.2 (1) 0.2 (1) (1) (1) (1) (1) (1) 0.05 (1) (1) 0.05 (1) 0.05 (1) (1) 0.05 (1) 0.05 (1) 0.05 (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) 30 (1) (1) (1) (1) 30 (1) (1) 30 (1) 30 (1) (1) 30 (1) 30 (1) (1) 29.53 19.685 16.215 22.795 34.05 19.07 30.66 29.945 28.1 39.54 34.38 16.585 X1 , citrate; X2 , acetate; X3 , K2 HPO4 ; X4 , MgSO4 ; X5 , MnSO4 ; X6 , Tween 80; X7 , CSL (1) and (1) are coded levels J Sci Food Agric 2010; 90: 19441950 c 2010 Society of Chemical Industry 1945 b Result www.interscience.wiley.com/jsfa www.soci.org Table Central composite design for optimization of two variables (each at five levels) and production of lactic acid Independent variables (mL L1 ) Run 10 11 12 a Results (g L1 ) CSL (X1 ) Tween 80 (X2 ) Lactic acid 20 (1)a 20 (1) 60 (1) 60 (1) 11.8 (1.41) 68.2 (1.41) 40 (0) 40 (0) 40 (0) 40 (0) 40 (0) 40 (0) 0.4 (1) 1.6 (1) 0.4 (1) 1.6 (1) (0) (0) 0.154 (1.41) 1.846 (1.41) (0) (0) 1(0) (0) 33.42 36.21 36.41 41.82 31.71 41.61 35.61 38.52 38.48 38.90 38.79 38.60 an independent variable at the center point; and Xi is the step change value The behavior of the system is explained by the following quadratic equation: b i xi + bii x i + bij xi xj (2) in which Y is the predicted response, i.e lactic acid concentration; b0 is the offset term; bi is the linear effect; bii is the squared effect; bij is the interaction effect; and xi is the independent variable The Statistica 7.0 (StatSoft, Tulsa, OK, USA) software package was used for the experimental design and regression analysis of the experimental data The response surface was generated to understand the interactions among the variables The optimal points for the variables were obtained from Maple 9.5 (Waterloo Maple Inc., Waterloo, Ontario, Canada) Using the CCD method, a total of 12 experiments with various combinations of CSL and Tween 80 were conducted Table displays the range and levels of the variables investigated In order to validate the optimization of the medium composition, tests were carried out using the optimized condition in order to confirm the results of the response surface analysis Scale-up fermentation of lactic acid with the optimized medium A scale-up fermentation of lactic acid with the optimized medium was then carried out in a 1.5 L glass vase bioreactor, with an initial culture volume of 500 mL Agitation speed and culture temperature were controlled at 200 rpm and 37 C, respectively The pH was controlled at 6.2 by the automatic addition of 10 mol L1 NaOH Samples of mL were withdrawn from the fermentation broth every h for 48 h and centrifuged at 7800 ì g for 10 1946 Analysis Lactic acid concentrations were determined using a highperformance liquid chromatography system equipped with a UV detector at 210 nm A Rezex ROA (300 ì 7.8 mm, Phenomenex, Torrance, CA, USA) column was eluted with mmol L1 H2 SO4 www.interscience.wiley.com/jsfa as a mobile phase at a flow rate of 0.4 mL min1 and the column temperature was maintained at 60 C Reducing sugars were measured using the 3.5-dinitrosalicylic acid method.23 Cell growth was determined using a spectrophotometer at 650 nm (OD650) after centrifugation and washing of cells The dry mass was determined through a standard curve of optical density versus dry mass RESULTS AND DISCUSSION (1.41), (1), (0), (1) and (1.41) are coded levels Y = b0 + LF Coelho et al PlackettBurman experimental design Table displays the PlackettBurman design matrix (real and coded values) in 12 experiments with seven variables added to CW (X1 = citrate, X2 = acetate, X3 = K2 HPO4 , X4 = MgSO4 , X5 = MnSO4 , X6 = Tween 80, X7 = CSL) and respective lactic acid production Figure (Pareto chart) illustrates the effects of the different variables CSL was the most influential variable in the production of lactic acid, followed by Tween 80 and K2 HPO4 Only CSL and Tween 80 had a significant positive effect on lactic acid production, with a 95% confidence level, and were therefore used in the optimization of lactic acid production Although Buy and Harsa24 report that MnSO4 is essential ă ukkileci ă for L casei to produce lactic acid because of Mn2+ , which stimulates lactate dehydrogenase activity,25,26 MnSO4 did not significantly increase the production of lactic acid in the present study Using the PlackettBurman design to study solid-state fermentation by Lactobacillus amylophilus GV6, Naveena et al.27 report that MnSO4 H2 O had negative coefficients; MgSO4 , sodium acetate and CSL were found to be insignificant; and ammonium citrate and Tween 80 improved the production of lactic acid According to Honorato et al.,28 the addition of phosphate to the culture medium increases microorganism growth and enhances lactic acid production; in this case the component maintains the pH near the optimal growth value, thereby allowing the conduction of fermentation for a longer time In the present study, however, K2 HPO4 had a negative effect on lactic acid production This may be explained by the excessive amount of this component in the medium CW is rich in manganese (4.1 mg L1 ), potassium (3.9 g L1 ) and phosphorus (0.9 g L1 ), which fulfills the requirements of the organism In shake flask fermentation the pH was maintained by CaCO3 added to the medium Thus the addition of K2 HPO4 and MnSO4 in CW is not necessary and should be avoided Response surface method CSL and Tween 80 were further optimized by using the response surface method Table displays the design matrix of the variables in coded units and real values with the respective results The application of multiple regression analysis methods yielded the following regression equation for the experimental data: Y = 38.69 + 2.82X1 + 1.54X2 0.99X1 X1 + 0.65X1 X2 0.788X2 X2 (3) in which Y is the predicted response (lactic acid concentration) and X1 and X2 are the coded values of the test variables CSL and Tween 80, respectively The highest production of lactic acid was 41.82 g L1 , obtained from 60 mL L1 of CSL and 1.6 mL L1 of Tween 80 (Table 2) The response surface quadratic model was performed in the form of analysis of variance (ANOVA) and the results are c 2010 Society of Chemical Industry J Sci Food Agric 2010; 90: 19441950 Improvement of L(+)-lactic acid production from cassava wastewater www.soci.org Figure Pareto chart for lactic acid production Table Analysis of variance for the quadratic model Source Model Error Lack of fit Pure error Total Sum of squares Degrees of freedom Mean square 93.1299 5.84974 5.74347 0.10627 98.97964 3 11 18.6259 0.9749 Table Least-squares fit and parameter estimates F-value P>F 19.1045 0.001266 Term Intercept X1 X2 X12 X1 X2 X22 Estimate Standard error t P>|t| 38.69300 2.82559 1.53892 0.99063 0.65400 0.78813 0.493698 0.349097 0.349097 0.390303 0.493698 0.390303 78.37381 8.09399 4.40828 2.53809 1.32470 2.01927 0.000000 0.000191 0.004528 0.044194 0.233489 0.089992 Adjusted R2 = 0.891 J Sci Food Agric 2010; 90: 19441950 A strong interaction between CSL and Tween 80 in lactic acid production was found (Fig 2) The area of greatest lactic acid production is located between 56 and 66 g L1 of CSL with 1.01.5 mL L1 of Tween 80 Once lactic acid bacteria are nutritionally fastidious and require various amino acids and vitamins for growth, it is very important to choose the right nitrogen source Nitrogen is necessary for the synthesis of amino acids, lipids, enzyme cofactors, some carbohydrates and other substances The nitrogen source is a major factor of influence on the growth of Lactobacillus.29 As the synthesis of lactic acid by fermentation is associated with cell growth, there is no product formation if the medium does not have an adequate concentration of nitrogen for promoting growth.30 On the other hand, high concentrations of nitrogen can lead to cell death.31 The use of a cheap nitrogen source for the complete replacement of yeast extract has been widely discussed.32 Yu et al.33 found that CSL not only replaces yeast extract as the sole nitrogen source in an optimized medium, but also helps to enhance lactic acid production when associated with other beneficial medium components In the present study, the addition of Tween 80 to the fermentation medium significantly increased the production of lactic acid (Fig 2) Authors have reported that Tween 80 is responsible for increasing the growth of Lactobacillus and c 2010 Society of Chemical Industry www.interscience.wiley.com/jsfa 1947 summarized in Table Fishers test was used to check the statistical significance of Eqn (3) ANOVA of the quadratic regression model demonstrates that the model is highly significant, as is evident from Fishers test (Fcalc (5, 6) = 19.1045 > Ft (5, 6) = 4.387), and has a very low probability value ((Pmodel > F) = 0.001266) The value of the adjusted coefficient of determination (adjusted R2 = 0.891) is high, which indicates the high significance of the model The high R value (0.969) demonstrates strong agreement between the experimental observations and predicted values This correlation was also demonstrated by the plot of predicted versus observed lactic acid values, since all points were clustered around the diagonal line, which means that no significant violations of the model were found A plot of residuals versus predicted response displays no pattern or trend, suggesting that the variance of the original observation is constant The independent variables (X1 , X2 and X ) had a significant effect (observed from the P-values) and the variables X1 , X2 had a positive effect (Table 4) Thus an increase in the concentration of these variables led to an increase in response (lactic acid production) The 3D response surface is the graphic representation of the regression equation and is plotted to determine the interaction of the variables and locate the optimal level of each variable for maximal response www.soci.org LF Coelho et al Figure Response surface of lactic acid production by L rhamnosus B 103 showing the interaction between CSL and Tween 80 Table Stationary point for lactic acid production and coded values of the variables X1 and X2 on the optimization point P0 Lactic acid Coordinates Lactic acid 1.229 0.548 X1 X2 1.276 0.450 1948 lactic acid production, unsaturated fatty acids such as Tween 80 being essential growth factors.34,35 On the other hand, higher concentrations of Tween 80 (1.6%, w/v) decreased lactic acid production An explanation for this may be that Tween 80 as a surfactant could dissolve the lipid in the cell membrane, destroy the membrane structure and then cause the death of the cell.36,37 The point of maximal lactic acid production was determined through canonical analysis of the adjusted model A study was carried out to identify the nature of the stationary point (maximal point, low response or saddle point) An algorithm carried out on the Maple 9.5 program (Waterloo Maple, Inc.) was used to calculate the stationary point (P0 ) for the synthesis of lactic acid These values are displayed in Table values referring to CSL and Tween 80 indicate that these responses have a maximal point, as they have equal, negative signs (Table 5) The analysis determined that the maximal predicted lactic acid concentration was 41.58 g L1 with the corresponding optimal values of the test variables in uncoded units at 65.4 mL L1 CSL and 1.27 mL L1 Tween 80 All optimal points were located within the experimental range and varied around their center points to different extents To confirm the adequacy of the model for predicting maximal lactic acid production, three additional experiments in a shaker were performed with this optimal medium composition The mean value of lactic acid concentration was www.interscience.wiley.com/jsfa 41.65 g L1 , which is in excellent agreement with the predicted value of 41.58 g L1 Thus the model proved adequate The scale-up fermentation of lactic acid in the optimal medium was carried out in the bioreactor The time courses are displayed in Fig During fermentation, the concentration of reducing sugar decreased from 50 to g L1 at the end of cultivation and the growth of L rhamnosus B 103 kept increasing quickly and appeared to reach stationary phase at 36 h The highest productivity (1.59 g L1 h1 ) was obtained after 12 h of fermentation and the greatest production (39 g L1 ) occurred after 36 h Moreover, the yield and the average volumetric productivity of lactic acid were as high as 96% and 4.58 g L1 h1 , respectively The high yield of lactic acid from CW can be attributed to the high nutritional value found in the production medium CW is a nutritious product containing natural sugars, proteins, amino acids and vitamins that are suitable for the growth of lactic acid bacteria Furthermore, the fermentation medium was carried out after hydrolysis with heating, which removed the inhibitory and toxic compounds (hydrogen cyanide) and favored the production of lactic acid.22 CONCLUSIONS The use of CW for cultivation of L rhamnosus is an innovative proposal, as no references were found in the scientific literature on the use of this substrate for lactic acid production Optimization of the responses revealed that the best result for lactic acid production (41.58 g L1 ) was obtained with 65.4 mL L1 CSL and 1.27 mL L1 Tween 80 Thus the results of the present study demonstrate that the use of CW for the production of lactic acid from fermentation by L rhamnosus B 103 is feasible, with a considerable production of biomass and lactic acid, requiring only supplementation with a cheap nitrogen source (CSL) and Tween 80 The PlackettBurman design, central composite c 2010 Society of Chemical Industry J Sci Food Agric 2010; 90: 19441950 Improvement of L(+)-lactic acid production from cassava wastewater www.soci.org Figure Concentrations of substrate, product and biomass as a function of fermentation time under optimal conditions in a bioreactor (g L1 ); ( ) reducing sugar; ( ) lactic acid, () biomass design, response surface method, regression analysis and model generation were effective methods for the medium optimization of lactic acid production ACKNOWLEDGEMENTS The authors thank Plaza SA and Corn Products for kindly supplying the cassava wastewater and corn steep liquor, respectively, and the Brazilian fostering agency Fundacáa o de Amparo a Pesquisa Estado de Sao Paulo (FAPESP) for the fellowships and financial support REFERENCES J Sci Food Agric 2010; 90: 19441950 c 2010 Society of Chemical Industry www.interscience.wiley.com/jsfa 1949 Datta R, Tsai SP, Bonsignor P, Moon S and Frank J, Technological and economical potential of polylactic acid and lactic acid derivatives FEMS Microbiol Rev 16:221231 (1995) Ohara H, Biorefinery Appl Microbiol Biotechnol 62:474477 (2003) Di Lorenzo ML, Crystallization behavior of poly(L-lactic acid) Eur Polym J 41:569575 (2005) Ryu HW, Yun JS and Wee YJ, Lactic acid, in Concise Encyclopedia of Bioresource Technology, ed by Pandey A Haworth Press, New York (2003) Yun JS and Ryu HW, Lactic acid production and carbon catabolite repression from single and mixed sugars using Enterococcus faecalis RKY1 Proc Biochem 37:235240 (2001) Lunt J, Large-scale production, properties and commercial applications of polylactic acid polymers Polym Degrad Stabil 59:145152 (1998) Calabia BP and Tokiwa Y, Production of D-lactic acid from sugarcane molasses, sugarcane juice and sugar beet juice by Lactobacillus delbrueckii Biotechnol Lett 29:13291332 (2007) Dumbrepatil A, Adsul M, Chaudhari S, Khire J and Gokhale D, Utilization of molasses sugar for lactic acid production by Lactobacillus delbrueckii subsp delbrueckii mutant Uc-3 in batch fermentation Appl Environ Microbiol 74:333335 (2008) Buyukkileci AO and Harsa S, Batch production of L(+) lactic acid from whey by Lactobacillus casei (NRRL B-441) J Chem Technol Biotechnol 79:10361040 (2004) 10 Yu L, Lei T, Ren X, Pei X and Feng X, Response surface optimization of L-(+)-lactic acid production using corn steep liquor as an alternative nitrogen source by Lactobacillus rhamnosus CGMCC 1466 Biochem Eng J 39:496502 (2008) 11 Fitzpatrick JJ and Keeffe UO, Influence of whey protein hydrolyzate addition to whey permeate batch fermentations for producing lactic acid Proc Biochem 37:183186 (2001) 12 Cardinal EV and Hedrick LR, Microbiological assay of corn steep liquor for amino acid content J Biol Chem 172:609612 (1948) 13 Nitschke M and Pastore GM, Cassava flour wastewater as a substrate for biosurfactant production Appl Biochem Biotechnol 106:295301 (2003) 14 Nitschke M and Pastore GM, Production and properties of a surfactant obtained from Bacillus subtilis grown on cassava wastewater Bioresour Technol 97:336341 (2006) 15 Leonel M and Cereda MP, Citric acid production by Aspergillus niger from Manipueira, a manioc liquid residue Sci Agric 52:299304 (1995) 16 Costa SGVAO, Le pine F, Milot S, Deziel E, Nitschke M and Contiero J, Cassava wastewater as a substrate for the simultaneous production of rhamnolipids and polyhydroxyalkanoates by Pseudomonas aeruginosa J Ind Microbiol Biotechnol 36:10631072 (2009) 17 Xiaodong W, Xuan G and Rakshit SK, Direct fermentative production of lactic acid on cassava and other starch substrates Biotechnol Lett 19:841843 (1997) 18 John RP, Nampoothiri KM and Pandey A, L(+) Lactic acid recovery from cassava bagasse based fermented medium using anion exchange resins Braz Arch Biol Technol 51:12411248 (2008) 19 John RP, Nampoothiri KM and Pandey A, Simultaneous saccharification and fermentation of cassava bagasse for L(+) lactic acid production using Lactobacilli Appl Biochem Biotechnol 134:263272 (2006) 20 John RP, Sukumaran RK, Nampoothiri KM and Pandey A, Statistical optimization of simultaneous saccharification and L(+) lactic acid fermentation from cassava bagasse using mixed culture of Lactobacilli by response surface methodology Biochem Eng J 36:262267 (2007) 21 Roble ND, Ogbonna JC and Tanaka H, l-Lactic acid production from raw cassava starch in a circulating loop bioreactor with cells immobilized in loofa (Luffa cylindrica) Biotechnol Lett 25:10931098 (2003) 22 Westby A, Cassava utilization, storage and small-scale processing, in Cassava: Biology, Production and Utilization, ed by Hillocks RJ, Tresh JM and Bellotti AC CAB International, Wallingford, UK, pp 281300 (2002) 23 Miller GL, Use of dinitrosalicylic acid reagent for determination of reducing sugar Anal Chem 31:426428 (1959) 24 Buy AO and Harsa S, Batch production of L(+) lactic acid from ă ukkileci ă whey by Lactobacillus casei (NRRL B-441) J Chem Technol Biotechnol 79:10361040 (2004) www.soci.org 25 Fitzpatrick JJ, Ahrens M and Smith S, Effect of manganese on Lactobacillus casei fermentation to produce lactic acid from whey permeate Proc Biochem 36:671675 (2001) 26 Krischke W, Schroder M and Trosch W, Continuous production of L-lactic acid from whey permeate by immobilized Lactobacillus casei subsp casei Appl Microbiol Biotechnol 34:573578 (1991) 27 Naveena BJ, Altaf MD, Bhadriah K and Reddy G, Selection of medium components by PlackettBurman design for production of L (+) lactic acid by Lactobacillus amylophilus GV6 in SSF using wheat bran Bioresour Technol 96:485490 (2005) 28 Honorato TL, Rabelo MC, Pinto GAS and Rodrigues S, Producáa o de a cido latico e dextrana utilizando suco de caju como substrato Cienc Technol Aliment 27:254258 (2007) 29 Wood BJB and Holzapfel WH, The Genera of Lactic Acid Bacteria (1st edn) Blackie Academic and Professional, London (1995) 30 Pritchard G and Coolbear T, The physiology and biochemistry of the proteolytic system in lactic acid bacteria FEMS Microbiol 12:179206 (1993) 31 De Lima CJB, Coelho LF, Blanco KC and Contiero J, Response surface optimization of D() lactic acid production by Lactobacillus SMI8 32 33 34 35 36 37 LF Coelho et al using corn steep liquor and yeast autolysate as an alternative nitrogen source Afr J Biotechnol 8:58425846 (2009) Liggett RW and Koffler H, Corn steep liquor in microbiology Microbiol Mol Biol 12:297311 (1948) Yu L, Lei T, Ren X, Pei X and Feng X, Response surface optimization of l-(+)-lactic acid production using corn steep liquor as an alternative nitrogen source by Lactobacillus rhamnosus CGMCC 1466 Biochem Eng J 39:496502 (2008) Oh S, Rheem S, Sim J, Kim S and Baek Y, Optimizing conditions for the growth of Lactobacillus casei YIT 9018 in tryptone-yeast extractglucose medium by using response surface methodology Appl Environ Microbiol 61:38093814 (1995) Belhocine D, Investigations on lactose valorization by lactic acid fermentation PhD dissertation, University of Rennes I, France (1987) Ben-Kun Q, Ri-Sheng Y, Min L and Sheng-Song D, Effect of Tween 80 on production of lactic acid by Lactobacillus casei Songklanakarin J Sci Technol 31:8589 (2009) Ben-kun Q and Ri-sheng Y, L-Lactic acid production from Lactobacillus casei by solid state fermentation using rice straw BioResources 2:419429 (2007) 1950 www.interscience.wiley.com/jsfa c 2010 Society of Chemical Industry J Sci Food Agric 2010; 90: 19441950 Research Article Received: March 2010 Revised: 25 April 2010 Accepted: 12 May 2010 Published online in Wiley Interscience: 16 June 2010 (www.interscience.wiley.com) DOI 10.1002/jsfa.4040 Antioxidant activities of aged oat vinegar in vitro and in mouse serum and liver Ju Qiu,a Changzhong Ren,b,c Junfeng Fand and Zaigui Lia Abstract BACKGROUND: The present study focused on the antioxidant activities of aged oat (Avena sativa L.) vinegar The antioxidant activities of oat and vinegar have been proved by many previous research studies It should be noted that oat vinegar, as a novel seasoning, has antioxidant activity RESULTS: Oat vinegar showed stronger radical scavenging activities, reducing power, and inhibition of lipid peroxidation than rice vinegar The concentrations of polyphenols and flavonoids in oat vinegar were higher than those in rice vinegar Ethyl acetate extract of oat vinegar possessed the most varieties of phenolic acids and showed the strongest antioxidant activity compared with ethanol and water extracts At suitable doses of oat vinegar, the malondialdehyde value was decreased, activities of superoxide dismutase and glutathione peroxidase were promoted, and hepatic damage induced by 60 Co -irradiation was ameliorated in aging mice CONCLUSION: Oat vinegar manifested antioxidant activity which was stronger than that of rice vinegar in vitro and the same as that of vitamin E in vivo c 2010 Society of Chemical Industry Keywords: oat (Avena sativa L.) vinegar; antioxidant; in vitro; in vivo INTRODUCTION J Sci Food Agric 2010; 90: 19511958 were analyzed by 2,2 -azino-bis-(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) radical scavenging activity, reducing power and inhibition of lipid peroxidation Aging model mice exposed to 60 Co -irradiation once were orally administered with different doses of oat vinegar or vitamin E (VE) (as positive control) Enzyme activities of superoxide dismutase (SOD) and glutathione peroxidase (GSHPX), and the values of malondialdehyde (MDA) in liver and blood serum of mice, as well as liver histopathological section, were detected to demonstrate the antioxidant activity in mice MATERIALS AND METHODS Materials Oat vinegar, produced in 2007, was obtained from Shanxi Ziyuan Microorganism R&D Co., Ltd (Shanxi, China) The vinegar was produced from oats (about 700 g kg1 of all raw materials) mixed Correspondence to: Zaigui Li, Box 40, China Agricultural University, No 17 Qinghua Dong Lu, Haidian District, Beijing 100083, China E-mail: lizg@cau.edu.cn a College of Food Science and Nutritional Engineering, China Agricultural University, Haidian, Beijing 100083, China b Department of Agronomy, China Agricultural University, Haidian, Beijing 100094, China c Oat Engineering and Technique Research Center of Jilin Province, Baicheng, Jilin 137000, China d College of Bioscience and Biotechnology, Beijing Forestry University, Haidian, Beijing 100083, China www.soci.org c 2010 Society of Chemical Industry 1951 Oxidative free radicals will induce oxidative stress if the unnecessary free radicals are not eradicated efficiently Overproduction of free radicals is toxic to hepatocytes and initiates reactive oxygen species (ROS).1 Previous studies indicated that oxidative stress might play a key role in the pathogenesis of liver diseases, including drug-induced hepatic damage, alcoholic hepatitis, and viral hepatitis or ischemic liver injury.2 Therefore, antioxidative treatment has been proposed as a potential means to prevent or attenuate oxidative damage in vivo Vinegar, as a widely used acidic seasoning, has medicinal uses by virtue of its physiological effects, such as antioxidant, antibacterial activity, promoting recovery from exhaustion, and regulating blood pressure and blood glucose.3 There have been many reports concerning antioxidant activities of different vinegars made from various materials, such as rice vinegar, wine vinegar, balsamic vinegar and aromatic rice vinegar.7 10 Oat vinegar is a new kind of aged vinegar, which is produced mainly from oats Oats (Avena sativa L.) contain several families of phytochemicals that display antioxidant properties, such as tocotrienols, phenolic compounds, flavonoids, sterols, phytic acids and avenanthramides.11 Oats contain 8.7 mg kg1 free phenolic acids, 20.6 mg kg1 soluble phenolic acids and 57 mg kg1 insoluble phenolic acids The total phenolic acid content (mainly avenanthramides and ferulic and vanillic acids) was significantly correlated with antioxidant activity in vitro.12 However, the antioxidant activity of oat vinegar and its antioxidant compounds still need to be researched This study focused on the antioxidant activities of oat vinegar in vitro and in vivo The indexes of antioxidant activity in vitro www.soci.org Table Compositions of oat vinegar Protein (g L1 ) Amino acid (g L1 ) Fat (g L1 ) Reducing sugar (g L1 ) Moisture (g L1 ) pH value Content Method 64.8 0.82 15.2 0.00 9.8 0.02 129.8 2.09 889.4 0.11 3.95 0.01 AACC 46-11A, 2000 AACC 07-01, 2000 AACC 30-20, 2000 AACC 80-68, 2000 AACC 44-19, 2000 with rice hull, wheat bran and Koji (oats and pea yeast) It was processed through saccharification, alcohol fermentation, static surface acetic acid fermentation, roasting, leaching and aging Rice vinegar was purchased from Beijing Liubiju Co (Beijing, China) The composition of the oat vinegar is shown in Table Polyphenolic and flavonoid content of vinegar Total polyphenolic content in oat vinegar was determined using the FolinCiocalteu method.13 Oat vinegar (200 àL) at an appropriate concentration (diluted 20, 40 or 80 times with distilled water) was added sequentially with 1.0 mL FolinCiocalteu reagent, 1.0 mL of 0.707 mol L1 Na2 CO3 solution and 2.8 mL distilled water The mixture was analyzed at 765 nm by spectrophotometer (UVmini-1240, Shimadzu, Kyoto, Japan) 30 later Results were expressed as mg gallic acid equivalents mL1 vinegar Rice vinegar was used as a control and compared with oat vinegar Total flavonoid content in oat vinegar was determined as described by Zhishen et al.14 Briefly, 1.0 mL of appropriately diluted vinegar (diluted four, six or eight times with distilled water) was added to 4.0 mL distilled water NaNO2 (0.3 mL, 0.725 mol L1 ) was added immediately, and AlCl3 (0.3 mL, 0.758 mol L1 ) was added later After min, 2.0 mL of mol L1 NaOH was added and the solution was made up to 10.0 mL with distilled water and mixed for s by vibrator Absorbance was determined at 510 nm The total flavonoid content was expressed in mg rutin equivalent mL1 vinegar 1952 Antioxidant activity of vinegar in vitro The effect of the vinegar on ABTS radical was detected using a modified ABTS decolorization assay,15 which is applicable to both hydrophilic and lipophilic compounds The ABTS radical cation was produced by reacting a 7.0 mmol L1 stock solution of ABTS with 2.45 mmol L1 potassium persulfate The mixture was stood in the dark for at least 12 h at room temperature before use The ABTS radical solution was diluted to obtain an absorbance of 0.75 0.05 at 734 nm Diluted ABTS solution, prepared as described above, was mixed with vinegar and measured at 405 nm after The parameter IC50 , reflecting the concentration of scavenging free radical to 50%, was calculated using the standard curve, plotting the percentage for inhibition of absorbance versus the concentration of sample The IC50 of the sample was compared with that of Trolox (6-hydroxy-2,5,7,8-tetramethylchroman-2carboxylic acid), a reference standard Finally, the ABTS radical scavenging activities of samples were expressed as mg Trolox equivalents mL1 The reducing power of oat vinegar was determined according to the method of Amarowicz et al.16 Oat vinegar (0.5 mL), 200 mmol L1 sodium phosphate buffer (0.5 mL, pH 6.6) and 0.5 mL potassium ferricyanide (0.03 mol L1 ) were mixed and www.interscience.wiley.com/jsfa J Qiu et al incubated in a water bath at 50 C After 20 min, 0.5 mL trichloroacetic acid (0.612 mol L1 ) was added to the mixture and centrifuged at 240 ì g for 10 The supernatant (1.0 mL) was then mixed with mL distilled water and mL ferric chloride solution (6.16 mmol L1 ) The intensity of blue-green color was measured at 700 nm using the spectrophotometer The reducing powers of rice vinegar and VE solution (4.5 mg mL1 ) were measured in the same way The efficacy of inhibiting lipid peroxidation was determined according to the method of Zin et al.17 Vinegar sample (diluted five times, 4.0 mL), 0.089 mol L1 linoleic acid in ethanol (4.1 mL), 0.05 mol L1 phosphate buffer (pH 7.0, 8.0 mL) and distilled water (3.9 mL) were mixed and then kept at 40 C in the dark Every 24 h, 0.1 mL of this reaction mixture was drawn and mixed with 9.7 mL of 75.0% (v/v) ethanol, 0.1 mL of 3.94 mol L1 ammonium thiocyanate and 0.1 mL of 0.02 mol L1 ferrous chloride in 0.96 mol L1 hydrochloric acid After min, the intensity of red color was measured at 500 nm These procedures were repeated until the blank control without sample reached maximum absorbance Rice vinegar and VE solution (4.5 mg mL1 ) were measured in the same way The antioxidant activities of all samples detected by three methods were analyzed in triplicate Phenolic compounds and antioxidant activities of oat vinegar in solvents with different polarities Ethyl acetate, ethanol and water with different polarities were used to extract phenolic compounds of oat vinegar consecutively Oat vinegar (100 mL) was concentrated to a final volume of 50 mL at 37 C in a vacuum rotary evaporator (Laborota 4000, Heidolph, Schwabach, Germany) Concentrated oat vinegar was extracted with 150 mL ethyl acetate with sufficient shaking for 30 s and standing in a water bath at 40 C for 10 The ethyl acetate layer and residue were separated absolutely These procedures were repeated three times and ethyl acetate layers were mixed The residue was further extracted successively with ethanol (150 mL) and water (150 mL) using the same steps as that of ethyl acetate extracts All three solutions of extracts were evaporated to dryness at 37 C under vacuum The dried extracts were redissolved in methanol to a final volume of 50 mL for further analysis by high-performance liquid chromatographydiode array detection (HPLC-DAD) Aliquots of dissolved extracts were diluted with Tris-HCl buffer (pH 7.4, 0.1 mmol L1 ) to measure ABTS radical scavenging activity and total polyphenols The solutions of ethyl acetate, ethanol and water extracts were analyzed in triplicate Ethyl acetate, ethanol and water extracts were analyzed by HPLC-DAD (Shimadzu, Kyoto, Japan), including a (model LC10ATvp instrument with two pumps and DGU-12A degasser) and a diode array detector (model SPD-M10Avp) Separation was performed on a Shim-Pack VP-ODS column (150 ì 4.6 mm i.d., particle size àm) with a guard column (Shim-pack G VPODS, 10 ì 4.6 mm, particle size àm) (Shimadzu) Mobile phases consisted of acetonitrile (solvent B) and purified water with 0.1% trifluoroacetic acid (solvent A) at a flow rate of 1.0 mL min1 Gradient elution was performed as described by Tian et al.18 Phenolic compounds in the samples were detected at 280 nm by an external standard method and identified by comparing their relative retention times and UV spectra with authentic compounds (Sigma-Aldrich, Steinheim, Germany) Animals and diets Sixty Kun Ming male mice (body weight (b.w.) 1822 g) were purchased from the Laboratory Animal Center of the Academy c 2010 Society of Chemical Industry J Sci Food Agric 2010; 90: 19511958 Preparation of blood serum and tissue samples All mice were cared for according to the Guiding Principles in the Care and Use of Animals The experiments were approved by Peking University Council on Animal Care Committee At the end of the trial, blood samples of mice in each group were collected by extirpating their eyes, and then the livers were immediately excised The blood samples collected into tubes were centrifuged at 3000 ì g for 10 for the separation of serum, which was stored at 80 C until analysis The livers, washed with ice-cold physiological saline solution (0.155 mol L1 ), were stored at 80 C in liquid nitrogen Part of the liver tissue, dipped in 0.1 mol L1 of ice-cold phosphatebuffered saline (PBS, pH 7.4, 0.158 mol L1 KCl), was cut into pieces and milled to prepare a 100 g L1 solution of tissue homogenate This tissue homogenate was centrifuged at 10 000 ì g for 15 and the supernatant was kept for analysis The tissue samples of livers and blood serum of all mice were used to measure enzyme activities of SOD and GSH-PX, and the value of MDA Antioxidant activity of oat vinegar in vivo The protein contents of different blood serum and tissue samples were measured using a bovine serum albumin (BSA) protein assay kit using BSA as standard Malondialdehyde (MDA) level was determined using an MDA assay kit A003 based on the method of Esterbauer and Cheeseman.19 SOD activity was determined with SOD assay kit A001 according to Oyanaguis method.20 GSH-PX activity was determined using a GSH-PX assay kit A005.21 All kits were purchased from the Institute of Biological Engineering of Nanjing Jianchen, Nanjing, China Liver tissues were removed immediately after sacrifice and fixed in 10.0% (v/v) buffered formalin solution for at least 24 h, then embedded in paraffin wax and sectioned (5.0 àm thickness) for histopathological evaluation Liver sections were stained with hematoxylin and eosin using a standard protocol, and then analyzed by light microscopy J Sci Food Agric 2010; 90: 19511958 Oat vinegar Rice vinegar * * 4 3 * 2 1 Polyphenols Flavonoids Figure Concentration of polyphenolic and flavonoid and activity of ABTS radical scavenging of vinegars Values represent the mean standard deviation (n = 3) Data were analyzed by ANOVA and within each column an asterisk indicates statistically different values according to Duncans multiple range test at P < 0.01 RESULTS AND DISCUSSION Antioxidant activities of oat vinegar in vitro Polyphenols and flavonoids were found to be the most effective antioxidant constituents in oats and vinegars.9 11,22 As shown in Fig 1, the contents of polyphenols and flavonoids in oat vinegar were 5.29 and 2.04 mg mL1 , respectively They were not only significantly higher than rice vinegar (P < 0.01) but also higher than traditional balsamic vinegar (3.72 and 0.58 mg mL1 )9 and Zhenjiang aromatic vinegar (4.18 and 1.10 mg mL1 ).10 ABTS radical scavenging activity was determined as a function of concentration and calculated with the reactivity of Trolox as standard.15 The IC50 value of Trolox measured in this study was 4.65 àg mL1 , which was the same as in a previous report.23 The ABTS radical scavenging activity of oat vinegar was 4.42 mg mL1 , which was significantly higher (P < 0.05) than that of rice vinegar (0.37 mg mL1 ) (Fig 1) Reducing power was measured by the potassium ferricyanide reduction method Antioxidants reduced the ferric ion/ferricyanide complex to the ferrous form: the Perls Prussian blue complex.24 Antioxidant activity was expressed as the increase in absorbance As shown in Fig 2, reducing power was increased with the increase in concentrations of vinegars and VE Oat vinegar showed significantly higher reducing power than rice vinegar (P < 0.05), but was not significantly different from VE (P > 0.05) Peroxidation of fatty acids causes deleterious effects in foods by forming complex mixtures of secondary breakdown products of lipid peroxides Therefore, the vinegar was further characterized for antioxidant activities by assessing the ability to protect linoleic acid against oxidation The oxidation of linoleic acid without vinegar was accompanied by a rapid increase in peroxide value (Fig 3) Conversely, the peroxide value of oat or rice vinegar increased slowly, indicating that vinegars had an inhibitory activity on lipid peroxidation VE exhibited the most obvious effect and oat vinegar was the second Different systems focused on different mechanisms of the oxidant defense system were used to measure the antioxidant activity of oat vinegar in vitro ABTS assay is based on the activation of metmyoglobin with hydrogen peroxide in the presence of ABTS to produce the radical cation, in the presence or absence of antioxidants.15 Reducing power based on the c 2010 Society of Chemical Industry www.interscience.wiley.com/jsfa 1953 Statistical analysis Statistical analyzes were run using SPSS 13.0 software (SPSS Inc., Chicago, IL, USA) Data were expressed as means and standard deviations Data were subjected to one-way analysis of variance (ANOVA) Duncans multiple range test was used to determine differences among means Results were considered statistically significant at P < 0.05 Antioxidant compounds mg mL-1 of Military Medical Sciences of China, Beijing, China The mice, in stainless wire netting cages, were acclimatized under laboratory conditions (temperature 2123 C; relative humidity 5560%) for week These mice were then divided randomly into six groups of 10 animals each, referred to as blank, aging model, VE, and three oat vinegar groups (low, medium and high) All mice were fed with basic diet ad libitum Mice in the VE group were administered VE at a dose of 25.0 mg kg1 b.w by gavage every day Mice in the oat vinegar groups of low, medium and high were administered oat vinegar at doses of 1.5, 3.0 and 6.0 mL kg1 b.w., respectively, by gavage every day After 26 days of gastric perfusion, all mice except the blank group were exposed to 60 Co -irradiation at a dose of Gy min1 for After 30 days of gastric perfusion, all mice were fasted for 12 h before operation The basic diet was purchased from the Laboratory Animal Center of the Academy of Military Medical Sciences of China, Beijing, China The pH value of oat vinegar was adjusted to 6.0 0.1 by NaOH www.soci.org ABTS radical scavenging activity (mg trolox eq mL-1) Antioxidant activities of oat vinegar in vitro and in vivo www.soci.org Figure Reducing power of vinegars and vitamin E (VE) VE was used as a positive control Values represent the mean standard deviation (n = 3) Figure Inhibition of lipid peroxidation of vinegars and vitamin E (VE) VE was used as a positive control Values represent the mean standard deviation (n = 3) 1954 FeCl3 /K3 Fe(CN)6 system offers a sensitive method for detecting the electron-donating ability of antioxidants Antioxidants such as polyphenols should be able to terminate radical chain reactions by converting free radicals to more stable products.16 Besides, the inhibition of lipid peroxidation was measured by ferric thiocyanate (FTC) methods This method measures the ability of antioxidants to scavenge peroxyl radicals, which react with polyunsaturated fatty acids, through hydrogen donation.25 Therefore, reducing power and inhibition of lipid peroxidation can explain the radical scavenging ability In the present study, the antioxidant activity of oat vinegar was first evaluated by ABTS radical scavenging activity This demonstrated that the antioxidant activity of mL oat vinegar was equal to that of 4.42 mg Trolox According to this relationship, 4.5 mg mL1 VE solution was used as a positive control in the following measurements of reducing power and lipid peroxidation VE showed the same reducing power as oat vinegar www.interscience.wiley.com/jsfa J Qiu et al but higher inhibition of lipid peroxidation That is to say, the ability of oat vinegar to supply electron and eliminate ABTS radical was the same as that of VE, but the capacity of scavenging peroxyl radicals was lower than that of VE The results also showed that oat vinegar possessed stronger antioxidant activity than rice vinegar in all systems It is considered that high contents of polyphenols and flavonoids play an important role in antioxidant activity of oat vinegar It has been reported that the antioxidant activities of vinegars are result mainly from antioxidant compounds such as polyphenols and flavonoids.7 Polyphenols potentially have antioxidant properties due to the presence of an aromatic phenolic ring that can stabilize and delocalize the unpaired electron within its aromatic ring.12 Verzelloni et al.9 demonstrated that polyphenols and flavonoids were highly correlated (r = 0.975 and r = 0.914, respectively) with the ABTS radical scavenging activity of traditional balsamic vinegar, as well as red wine vinegar They also showed that the concentrations of polyphenols and flavonoids were positive correlated with reducing power Alonso et al.8 showed that the content of phenolic acids in sherry wine vinegar was highly correlated with ABTS radical scavenging activities, especially gallic acid, protocatechuic acid, vanillin, p-coumaric acid, ferulic acid and vanillic acid In this study, excellent antioxidant activities of oat vinegar with high concentrations of polyphenols and flavonoids also confirmed the results Phenolic compounds of oat vinegar extracts Liquidliquid extraction is commonly used to analyze polyphenols and simple phenolics in natural plants for its efficiency and wideranging applicability.26 Ethyl acetate, ethanol and water were used as solvents in the present study The compositions of polyphenols and antioxidant activities of their extracts from oat vinegar are shown in Table Concentrations of gallic acid and (+)-catechin in ethanol extract were the highest, but the other phenolic acids in ethyl acetate extract were higher than in ethanol or water extract Water extract had less phenolic acid, and only gallic and protocatechuic acids were detected Protocatechuic acid in extracts of oat vinegar was highest, with gallic acid second Total polyphenols in ethanol and water extracts of oat vinegar were significantly (P < 0.05) lower than that in ethyl acetate extracts, which showed the strongest ABTS radical scavenging activity However, the content of total polyphenols and ABTS radical scavenging activity of water extracts were higher than those of ethanol extract, which may be ascribed to the aqueous antioxidant compounds in vinegar In addition, the summation of Trolox equivalents of three extracts accounted for about 87% of that of oat vinegar That is, three extracts contained primary antioxidant compounds of oat vinegar, which played a central role as antioxidant Early work aiming at identifying the compounds that were responsible for the antioxidant properties of oat or vinegar showed a good correlation between antioxidant capacity and content of phenolic compounds.8,27 In oat, ferulic acid was the dominant phenolic acid, while p-coumaric, caffeic and vanillic acids could be detected in small quantities.11 In vinegar, the contents of gallic, protocatechuic, p-coumaric, ferulic, vanillic and caffeic acids were very closely correlated with antioxidant activities, according to research on sherry wine vinegar.8 Moreover, some studies showed that (+)-catechin and ()-epicatechin were present in red wine vinegar.28 The concentration of protocatechuic acid in oat vinegar extracts was higher than that of red wine vinegar,28 sherry wine vinegar,8 common white vinegar and rose vinegar.29 c 2010 Society of Chemical Industry J Sci Food Agric 2010; 90: 19511958 Antioxidant activities of oat vinegar in vitro and in vivo www.soci.org Table Compositions of polyphenols and antioxidant activities in different extracts of oat vinegar Gallic acid (mg L1 ) Protocatechuic acid (mg L1 ) (+)-Catechin (mg L1 ) Vanillic acid (mg L1 ) Caffeic acid (mg L1 ) ()-Epicatechin (mg L1 ) p-Coumaric acid (mg L1 ) Ferulic acid (mg L1 ) Total polyphenols (mg g1 ) ABTS radical scavenging activity (mg Trolox eq mL1 )a Ethyl acetate extract Ethanol extract Water extract 10.12 0.78Aa 185.26 1.48Ab 37.39 0.50c 39.99 2.52c 16.98 2.83Ad 10.82 0.79Aa 9.31 1.40a 39.94 3.50A 2.13 0.11A 50.38 2.48Ba 91.94 1.73Bb 7.26 0.32c 9.28 2.49Bc 4.17 0.31Bd 9.48 1.71B 0.70 0.10B 5.48 0.12Ca 20.65 1.02Cb 15.75 2.30C 1.04 0.08C mg L1 : mean of triplicate determinations SD expressed as milligrams of phenolic acid per liter of oat vinegar mg g1 : mean of triplicate determinations SD expressed as milligrams of gallic acid equivalents per gram of oat vinegar extracts a Mean of triplicate determinations SD expressed as milligrams of Trolox equivalents of extracts per milliliter of oat vinegar Data were analyzed by ANOVA and statistically significant differences were analyzed by Duncans multiple range test at P < 0.05 Different capital letters (A, B, C) in a row indicate significant difference among extracts Different lower-case letters (a, b, c) in a column indicate significant difference among phenolic acids Table Values of MDA, SOD and GSH-PX activities in blood serum of mice Groups MDA (nmol mL1 ) SOD (U mL1 ) GSH-PX (U mL1 ) Blank Aging model VE Oat vinegar (low) Oat vinegar (medium) Oat vinegar (high) 5.07 0.55ab 5.98 0.38a 5.10 0.53ab 4.83 0.36b 4.70 0.412b 4.95 0.32b 212.81 19.97a 174.95 37.92b 214.62 24.82a 229.30 19.96a 236.49 25.67a 176.71 29.54b 872.18 82.14a 734.83 87.63b 943.35 95.46a 863.70 83.79a 875.35 84.44a 853.82 80.23a Data were analyzed by ANOVA and the groups in the same column with different letters indicate statistically significant differences according to Duncans multiple range test at P < 0.05 Values represent the mean standard deviation of duplicate assays in 10 animals in each group Blank: fed with basic diet; Aging model: fed with basic diet; VE: fed with basic diet and administered VE at a dose of 25 mg kg1 b.w by gavage every day; Oat vinegar low, medium and high: fed with basic diet and oat vinegar administered at a dose of 1.5, 3.0 and 6.0 mL kg1 b.w respectively With the exception of Blank, other groups were exposed to 60 Co -irradiation at a dose of Gy min1 for However, the content of gallic acid was less than that of red wine vinegar28 or sherry wine vinegar.8 Ferulic acid, the most abundant phenolic acid in oat, was not so high in oat vinegar, indicating that the contents of phenolic compounds were changed during processing Cerezo et al have shown the effects of aging and wood on the phenolic profile of wine vinegar.30 The effect of processing such as fermentation, roasting and aging on the antioxidant activity of oat vinegar should be further studied J Sci Food Agric 2010; 90: 19511958 c 2010 Society of Chemical Industry www.interscience.wiley.com/jsfa 1955 Antioxidant activities of oat vinegar in vivo Many studies have shown that 60 Co -irradiation induces hepatic damage and reduces SOD and GSH-PX activities in liver and blood serum.31 This study used 60 Co -irradiation to set up the aging model It was shown that SOD and GSH-PX activities of the aging model group were decreased both in blood serum (Table 3) and in liver (Table 4) VE, as a peroxyl radical scavenger, is one of the most popular natural phenolic type antioxidants.23 In this study MDA values in liver were lower, while activities of SOD and GSH-PX in liver and blood serum of mice in the VE group were higher than those in the aging model group That is, VE showed obvious antioxidant activities in blood serum (Table 3) and liver (Table 4) There was no significant difference of MDA value and enzyme activities among VE and blank group in blood serum and liver, indicating that the antioxidant activity of VE was strong enough to make aging mice become normal, as the report of Wang et al.23 Compared with the aging model group, the MDA value of oat vinegar groups was obviously decreased (P < 0.05) in blood serum (Table 3) There was no significant difference in MDA values (P > 0.05) among VE and oat vinegar groups That is, oat vinegar had a strong ability to reduce MDA content, similar to VE The SOD activities of low and medium oat vinegar groups were higher (P < 0.05) than that of the aging model group, and not obviously different (P > 0.05) from that of VE Hence oat vinegar at a dose of medium or low could strengthen SOD activity The GSH-PX activity of the aging model group was evidently lower (P < 0.05) than that of other groups, suggesting that oat vinegar could promote GSHPX activity in the blood serum of mice There was no significant difference in MDA, SOD and GSH-PX values among oat vinegar (low and medium) and blank groups (P > 0.05) It was concluded that the antioxidant activity of oat vinegar was high enough to restore the blood serum of aging mice to normal Table demonstrates that the MDA content of oat vinegar groups was lower (P < 0.05) than that of the aging model in liver of mice In the case of low and high doses, there was no significant difference in MDA value among oat vinegar groups www.soci.org J Qiu et al Table Values of MDA and SOD and GSH-PX activities in liver of mice Groups MDA (nmol mg1 protein) SOD (U mg1 protein) GSH-PX (U mg1 protein) Blank Aging model VE Oat vinegar (low) Oat vinegar (medium) Oat vinegar (high) 2.21 0.29ab 2.63 0.23b 2.12 0.31a 1.80 0.20ac 1.60 0.24c 1.74 0.29ac 164.83 26.65a 121.05 28.77b 165.24 7.10a 150.63 20.85a 160.38 22.16a 127.79 14.98b 120.30 26.92a 97.61 7.17b 223.73 18.52c 120.16 21.81a 180.99 34.89ac 110.51 20.81a Data were analyzed by ANOVA and the groups in the same column with different letters indicate statistically significant differences according to Duncans multiple range test at P < 0.05 Values represent the mean standard deviation of duplicate assays in 10 animals in each group For explanation of groups see note to Table Figure Effect of oat vinegar on acute liver damage induced by 60 Co -irradiation in mice (A) Normal liver from blank group (magnification 200ì) (B) Liver from aging model (magnification 200ì) (C) Liver from aging model (magnification 400ì) (D) Liver from VE group (magnification 200ì) (E) Liver from low oat vinegar group (magnification 200ì) (F) Liver from medium oat vinegar group (magnification 200ì) 1: hepatocyte necrosis or apoptosis; 2: inflammatory cell infiltration; 3: vacuolar degeneration 1956 and VE or blank group For medium dose, however, the MDA value of oat vinegar was higher than that of VE or blank group SOD and GSH-PX activities of medium oat vinegar group were significantly higher (P < 0.05) than those of the aging model group, but not evidently different (P > 0.05) from VE and blank groups These results suggested that oat vinegar at a medium dose (3.0 mL kg1 b.w.) showed the most efficient effects on decreasing MDA content, while increasing SOD and GSH-PX activities in mouse liver The relatively weaker effect of oat vinegar at low dose (1.5 mL kg1 b.w.) may be attributed to insufficient intake of antioxidant compounds such as polyphenols and flavonoids in the diet, because of the high correlation between concentrations of polyphenols and flavonoids and the antioxidant activity.9 As for high dose (6.0 mL kg1 b.w.), the antioxidant activities were not obvious, which may be due to an excessively high concentration of acetic acid in vinegar, which can cause gastric ulcer32 or colitis,33 thus affecting the healthy growth of mice MDA content is a good indicator of lipid peroxidation In blood serum and liver of mice, the values for oat vinegar groups were decreased significantly (Tables and 4) This indicated that www.interscience.wiley.com/jsfa the protective role of oat vinegar against oxidative damage in vivo might be due to the decrease in lipid oxidation Another antioxidant mechanism of oat vinegar may be related to antioxidant enzymes Antioxidant enzymes such as SOD and GSHPX are capable of eliminating active oxygen species and inhibiting lipid peroxidation to protect tissues from oxidative damage In fact, the normal body possesses enzymatic systems to protect tissues and organs.34 However, 60 Co -irradiation can lead to the oxidation and the decrease of antioxidant enzyme in vivo.35 Compared with the aging model group, low and medium dose of oat vinegar promoted the activities of SOD and GSH-PX both in blood serum and in liver This suggests that oat vinegar could protect tissues and organs from oxidative damage by promoting the antioxidant enzyme Interestingly, a high dose of oat vinegar can promote GSH-PX activity but not SOD activity, which might be attributed to the different effects of oat vinegar on different antioxidant enzymes Moreover, the change in enzyme activities may be related to the absorption and metabolism of antioxidant components in oat vinegar Phenolic type antioxidant influences the polyethylene c 2010 Society of Chemical Industry J Sci Food Agric 2010; 90: 19511958 Antioxidant activities of oat vinegar in vitro and in vivo network formulation and radical yield during the process of irradiation.35 Phenolic acids are believed to act mainly as free radical scavengers or chelators of transition metals However, their mechanisms of action in vivo are not fully elucidated Histopathological analyses Histopathological changes of mice from blank, aging model, VE and oat vinegar (low and medium) groups are compared in Fig There was no abnormal appearance or histological change in the liver of normal mice (Fig 4(A)) Irradiation with 60 Co -rays caused damage in the mouse liver, demonstrated by severe hepatocyte necrosis or apoptosis, inflammatory cell infiltration (Fig 4(B)) and vacuolar degeneration in the aging model group (Fig 4(C)) It was reported that 60 Co -irradiation induced physical and chemical damage to tissues, leading to organelle injuries, cell death or neoplastic transformation.31 Conversely, in the VE group, much vacuolar degeneration was observed but hepatocyte necrosis, apoptosis and inflammatory cell infiltration were not obvious (Fig 4(D)) This means that irradiation damage in the VE group was mitigated somewhat compared with that of the aging model group VE acts as a free radical scavenger, more specifically within cell membranes via preventing the oxidation of polyunsaturated lipids by free radicals such as hydroxyl radicals (OH) Thus VE can improve various parameters of oxidative stress in animals.12 The same result was detected in vivo in this study Compared with the blank group, some vacuolar degeneration and hepatocyte necrosis or apoptosis could still be observed in liver from the low oat vinegar group (Fig 4(E)) However, in the medium oat vinegar group, not only were inflammatory cell infiltration and vacuolar not obvious, but also little hepatocyte necrosis or apoptosis was found (Fig 4(F)), suggesting that oat vinegar of the medium group significantly ameliorated the liver injuries induced by 60 Co -irradiation Exposure of cells to ionizing radiation could lead to an increase in ROS, including hydroxyl radicals (OH ã), superoxide anions (O2 ), singlet oxygen (1 O2 ) and hydrogen peroxide (H2 O2 ).34 Our experiment showed that oat vinegar has obvious radical scavenging activity in vitro and the ability to promote antioxidant activity of SOD or GSH-PX in vivo Compared with the result of the blank group, oat vinegar also restored antioxidant enzymes like SOD and GSH-PX to near-normal levels Thus oat vinegar can attenuate oxidative damage in liver and exhibit antioxidant activities in aging mice CONCLUSIONS J Sci Food Agric 2010; 90: 19511958 hepatocyte necrosis or apoptosis was rarely found when aging mice were administered suitable oat vinegar Further studies on the identification and purification of other components, besides phenolic acid, responsible for the antioxidant activities in oat vinegar are now in progress ACKNOWLEDGEMENTS This study was supported by nyhyzx07-009 (public industry project of the Ministry of Agriculture) and nycytx07-14 (the earmarked fund for Modern Agro-industry Technology Research System) REFERENCES Bissel DM, Gores GJ, Laskin DL and Hoorhagle JH, Drug-induced liver injury: mechanisms and test systems Hepatology 33:10091013 (2001) Albano E, 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the strongest antioxidant activity Also, histopathological assessment suggested that oat vinegar significantly ameliorated the liver injuries induced by 60 Co -irradiation because less cell infiltration and vacuolar degeneration were observed, and www.soci.org www.soci.org 20 Oyanagui Y, Reevaluation of assay methods and establishment of kit for superoxide dismutase activity Anal Biochem 142:290296 (1984) 21 Paglia DE and Valentine WNJ, Studies on the quantitative and qualitative characterization of erythrocyte glutathione peroxidase J Lab Clin Med 70:158169 (1967) 22 Peterson DM, Hahn MJ and Emmons CL, Oat avenanthramides exhibit antioxidant activities in vitro Food Chem 79:473478 (2002) 23 Wang D, Wang LJ, Zhu FX, Zhu JY, Chen XD, Zou L, et al, In vitro and in vivo studies on the antioxidant activities of the aqueous extracts of Douchi (a traditional Chinese salt-fermented soybean food) Food Chem 107:14211428 (2008) 24 Chou ST, Chao WW and Chung YC, Antioxidative activity and safety 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protein kinases Int J Biochem Cell B 39:426438 (2007) 35 Andreucetti NA, Sarmoria C, Curzio OA and Valles EM, Effect of the phenolic antioxidants on the structure of gamma-irradiated model polyethylene Radiat Phys Chem 52:177182 (1998) 1958 www.interscience.wiley.com/jsfa c 2010 Society of Chemical Industry J Sci Food Agric 2010; 90: 19511958 [...]... with the banding patterns differing by sample Subsequently, to identify the main bands, each band was recovered from the DGGE gel and sequenced The results obtained from clone sequencing are shown in Table 3 In the case of aji-narezushi, there was only one main band for sample As-1, which differed from the main bands of the other two samples Sequencing of the recovered DGGE gels indicated that the predominant... 1–5 show the results for the effects of the two factors and their interaction on the variables evaluated At the end of the experiment the extent of AMF colonisation ranged from 65 to 80% None of the treatments affected the fresh weight, diameter and length of fruits (Table 1) In terms of colour, different N concentrations resulted in statistically significant effects only on fruit lightness and absorbance... (DGGE), to determine the bacterial flora of various traditional fermented foods such as fermented milk and soybean ∗ Correspondence to: Takashi Kuda, Department of Food Science and Technology, Tokyo University of Marine Science and Technology, Minato-ku, Tokyo 108-8477, Japan E-mail: kuda@kaiyodai.ac.jp Department of Food Science and Technology, Tokyo University of Marine Science and Technology, Minato-ku,... The homogenates were kept at 4 ◦ C for 12 h and then centrifuged at 15 000 × g for 20 min The supernatants were collected, and extraction of the residue was repeated using the same conditions The two supernatants of methanol were combined and divided into two equal aliquots and then stored at −20 ◦ C until analysis The first supernatant was used for the quantitative analysis of phenolic compounds and. .. chlorpromazine The standard calibration curve with final chlorpromazine concentrations of 0.05, 0.25, 0.5, 1.0, 2.0 and 10.0 ng mL−1 was run in PBST Then the solution was evaporated to dryness at low pressure at 55 ◦ C Then, 1 mL of PBST was added to the flask and the solution was treated as the blank sample which was stored at 0–4 ◦ C for future use The pretreatment of the samples (swine liver and chicken) The. .. One gram of the chicken was weighed into a polythene tube, and then 4 mL of the extraction solution was added The mixture was shaken vigorously for 5 min and centrifuged at room temperature (20–25 ◦ C) for 10 min at 3000×g Two millilitres of the supernatant was transferred and mixed with 4 mL of 1 mol L−1 NaOH, and then 10 mL of n-hexane was added After being shaken vigorously for 5 min, the mixture... takes up nutrients with its extraradical mycelium and provides them to the host plant.1 The uptake of nitrogen (N) by the extraradical mycelium has been shown before and this N is available to the host plant,2,3 so the AMF improves the N status of the host.4,5 Nevertheless, it also has been reported that the N availability in the soil affects the dynamic of plant–AMF association.4,6 Nitrogen is an essential... that the colour changes we observed as a result of mycorrhization are possibly due to changes in the levels of these two anthocyanins The flavour of strawberry fruits is determined by the balance of sugars and acids.12 Glucose, fructose and sucrose are the most important sugars for the sensory quality of strawberry fruits, representing 99% of the total carbohydrate content.45 Moreover, citric acid and. .. not shown) The higher production of sugars in the 6 mmol L−1 N treatment could www.soci.org www.soci.org V Castellanos-Morales et al the acquisition of Cu and Zn by the roots and their translocation to the fruits Our results on the effect of N fertilisation on phenolic compounds in strawberry fruits show that from the 3 mmol L−1 N treatment to the 18 mmol L−1 N treatment the concentrations of ellagic... Mycorrhizas: Physiology and Function, ed by Kapulnick Y and Douds DDJ Kluwer Academic, Dordrecht, pp 107–129 (2000) 50 Fallahi E, Conway W, Hickey K and Sams C, The role of calcium and nitrogen in postharvest quality and disease resistance of apples HortScience 32:831–835 (1997) 51 Tolley-Henry L and Raper Jr CD, Expansion and photosynthetic rate of leaves of soybean plants during onset of and recovery from