Annals of Microbiology, 52, 95-101 (2002)The statistical treatment of data and the Analysisof MOlecular V Ariance (AMOV A) in molecularmicrobial ecologyA. MENGONI*, M. BAZZICALUPODipartimento di Biologia Animale e Genetica ‘Leo Pardi’Via Romana 17, 50125 Firenze, ItalyAbstract - Molecular ecological studies are often characterised by an extremely high num-ber of samples and by an equal high number of molecular analyses to whom the samples have been subjected. The analysis of molecular variance (AMOV A) is a powerful statisti-cal method for the description of factors influencing the structure of populations. AMOV A has been applied to different biological systems, trying to answer as many different ques-tions. To date, AMOV A has helped the elucidation of the factors influencing the shaping of the population genetic structure in different bacterial species, showing the effects of differ-ent variables such as time, soil, drug-sensitivity, plant genotype, location in the plant root system and culture media on the genetic diversity of the bacterial populations.Key words:microbial ecology, molecular fingerprinting, analysis of molecular variance (AMOV A), bacterial populations.INTRODUCTIONThe knowledge of the genetic diversity of the bacterial populations has increased considerably over the last fifteen years, following the application of molecular techniques to ecological studies. Quantitative resolution has improved as a large number of haplotypic markers are found within each sample and as a large num-ber of samples can be simultaneously investigated. The study of molecular ecol-ogy can therefore be characterised by an extremely high number of samples and by a high number of molecular analyses to whom the samples are subjected aim-ing to provide the most complete description of the population under investiga-tion. As a consequence, an immediate interpretation of the results can be difficult, unless powerful statistical techniques are used in order to describe the structure of the populations and to highlight the contribution of its components. These tech-niques are in general methodologies to uncover the effects of categorical inde-95pendent variables on an interval dependent variable. Examples of such techniques are the Analysis of Variance (ANOV A) (Miller, 1997) and the Principal Compo-nent Analysis (PCA) (Chatfield and Collins, 1997), which can handle different data, but which are not specific for the analysis of molecular data. Moreover the significance in ANOV A is tested assuming a normal distribution of data, which is not the case of the molecular polymorphisms. The Analysis of Molecular Vari-ance (AMOV A, Excoffier et al., 1992) is a methodology for the analysis of vari-ance that makes use of molecular data derived from the analysis of DNA. Yet AMOV A is adaptable to different kind of assumption on the evolution of a genetic system. Starting from data of various origin (sequences, isoenzymatic patterns, microsatellites, RFLP, RAPD, REP, PCR-RFLP, RFLP, AFLP), AMOV A makes use of the molecular information gathered in the population study to investigate the genetic differentiation of the sampled populations. Moreover the significance in AMOV A procedure is tested via a permutational approach, eliminating the need of normal distribution that is required for the analysis of variance but inap-propriate for molecular data.The effectiveness and the usefulness of AMOV A for the analysis of molecu-lar data in microbial ecology have been widely demonstrated (Carelli et al., 2000; Chiarini et al., 2000; Dalmastri et al., 1999; Di Cello et al., 1997; Paffetti et al., 1998; Paffetti et al., 1996; Picard et al., 2000; Richner et al., 1999; Tabacchioni et al., 2000).HOW DOES AMOV A WORK?Population genetic structure has traditionally been studied using departures of allele frequencies from panmittic expectations using estimation procedures related to Wright’s F-statistics (Wright, 1951, 1965). F-statistics have been pro-posed for the treatment of several polymorphisms (for detailed references see Excoffier et al., 1992), but few studies only have tried to translate the molecular information, specific of each technique, into estimate of magnitude of intraspe-cific subdivision (Lynch and Crease, 1990; Takhata and Palumbi, 1985). AMOV A was proposed by Excoffier, Smouse and Quattro (1992) to design an alternative methodology that makes use of the available molecular information provided in population studies. AMOVA differs from analysis of variance in that it can accommodate different evolutionary assumptions without modifying the basic structure of the analysis, and in that hypotheses are tested using permutational methods so that normal distribution assumption is not required. Considering a large set of samples, they can be sorted among different groups any of which con-tains in turn different populations each formed by several individuals. The aim of the analysis is to find out and describe that hierarchical structure splitting the total variance into covariance components due to intra-populations, inter-populations and inter-groups differences (Excoffier, 2000). AMOV A computes the differences between groups, among populations within group and among strains (or individ-uals) within the population (Fig. 1).Molecular biology techniques (i.e. RFLP, RAPD, REP, PCR-RFLP, AFLP) produces bands after electrophoresis that can be interpreted in a binary form, i.e. in the form of 0s and 1s, where 1 denotes the presence of a band and zero its absence. On these data a distance matrix can be computed. For binary data, dis-96 A.MENGONI and M. BAZZICALUPOtances can be defined as Euclidean distance (the number of allelic differences between two haplotypes, see Nei and Tajima, 1981). The formalisation for the Euclidean distance is:E=n(1-2n xy /2n),where n is the total number of bands and n xy the number of shared bands.AMOV A can also handle sequence data computing the distance using several parameters (i.e. pairwise differences, Jukes-Cantor, Kimura 2-parameter, Tamura,Tajima-Nei, Tamura-Nei, for a review see Li, 1997), and can analyse allozyme and microsatellite data.To understand the working principle of AMOV A the equation for the i-th haplotype frequency vector should be considered. This equation is in the form:X ijk =x+a k +b jk +c ijkfrom the j-th population in the k-th group. The vector x is the unknown expecta-tion of X ijk , averaged over the whole study. To generate the real haplotype (X ijk ),the deviations from the expected haplotype (x ) are taken into account. These could be subdivided into three component: a , b and c ; a is the effect due to the group, b for being into a population, and c for being different from the other hap-lotypes into the same population. These effects are assumed to be additive, ran-dom, independent, and to have associated covariance components. The total mol-ecular variance is the sum of covariance components due to differences among haplotypes within a population, those due to differences among haplotypes in different populations within the same group, and those due to differences among Ann. Microbiol., 52, 95-101 (2002)97FIG. 1 –Representation of the AMOV A working. The circles indicate the groups and the populations (filled with diagonal rows). The black dots indicate the strains (individuals). The double arrows show the comparisons made: among strains within the population, among populations, among groups.groups. AMOV A computes the percentage that each set of differences (covariance components) has on the total genetic variance found. The covariance components are used to compute fixation (Φ) indices (Wright, 1951, 1965): ΦCT is the corre-lation of random pairs of haplotypes drawn from a group relative to the correla-tion pairs of random haplotypes drawn from the whole population. ΦSC is the cor-relation of random pairs of haplotypes drawn from a population relative to the correlation pairs of random haplotypes drawn from the whole group, averaged over all populations. ΦST is the correlation of random pairs of haplotypes drawn from within populations relative to the correlation pairs of random haplotypes drawn from the whole population. Statistical significance of the distribution of variance components is tested via a test of permutation. In practice, the signifi-cance of fixation indices is tested using a non-parametric permutation approach (Excoffier et al ., 1992), which is based in a permutation of haplotypes, individu-als, or populations, among individuals, populations, or groups of populations.After each round of permutation, all statistics are recomputed to obtain their null distribution.In Table 1 an example of AMOV A result is reported. The hypothesis tested is the existence of an uneven distribution of the genetic variation of nodulating bac-teria (Sinorhizobium meliloti ) linked to the difference among plant cultivars (Medicago sativa ). Groups were formed joining together isolates from different plants belonging to the same cultivar. The total variance derived from the molec-ular data has been divided into the three hierarchical partitions aiming to test the hypothesis of a genetic differentiation based on the different plant cultivar.Columns show the percent of total variance attributed to the partition, the Φsta-tistics and its significance (P-value). In general, the differences within popula-tions (plants) (3rd row) are the main component in the variance partition (highest percentage and ΦST value). P-value is highly significant in the first partition indi-cating that the plant cultivar is a factor influencing the shaping of populations.Moreover, the highly significant values in the second and third partition indicates that the different plants belonging to the same cultivar represent different bacter-ial populations and that in the same plant the different isolates can be distin-guished.A flow-chart of the steps to be performed in an AMOV A approach can be summarised as follows: i) binary scoring of the molecular fingerprint of each iso-98 A.MENGONI and M. BAZZICALUPO TABLE 1 –An example of Analysis of Molecular Variance (AMOV A) from populations of nodulating Sinorhizobium meliloti analysed with RAPD markers Source of variation% of total ΦStatistics P-value Among cultivars12.8ΦCT =0.128<0.0001Plants within cultivars25.7ΦSC =0.295<0.0001Isolates within plant 61.5ΦST =0.385<0.0001The nodulating bacterial populations have been isolated from different plant cultivars (groups). Rows indicate the three hierarchical partitions: among groups, among popula-tions (plants) within groups and within populations (isolates from different nodules within the same plant).late; ii) computing of the distance matrix; iii) assuming an hypothesis of variance partition; iv) testing the hypothetical partition. A software suite (Arlequin, Schneider et al., 2000) has been developed which allows for the computation of AMOV A(Arlequin can be freely downloaded from http://lgb.unige.ch/arlequin/).SOME APPLICATIONS OF AMOV A TO QUESTIONSOF MOLECULAR MICROBIAL ECOLOGYAMOV A was first used to analyse RFLP profiles of human mitochondrial DNA (Excoffier et al., 1992). Later on, AMOV A was utilised for the study of plant and animal ecological problems using RAPD, microsatellites and sequences (Huff et al., 1993; Haig et al., 1994; Roewer et al., 1996; Barbujani et al., 1996; Mengoni et al., 2000a, 2000b).The first report on the use of AMOV A to address questions of microbial ecol-ogy was by Paffetti and co-workers (1996). In that study the genetic polymor-phism of a population of Sinorhizobium meliloti strains was analysed using RAPD markers, aiming at factors that could play a role in shaping the genetic dif-ferentiation of the population. Strains were recovered from nodules of four alfalfa (Medicago sativa) varieties grown in two different soils. AMOV A was utilised to show the correlation of the detected genetic variation with the different environ-mental conditions. In two other studies (Paffetti et al., 1998; Carelli et al., 2000) the question of the different weights of plant variety, soil and season in shaping the population genetic structure of nodulating S. meliloti strains was addressed. AMOV A results indicated that plant genotype was one of the main factor in struc-turing the population and that its contribute, together with that of the soil type, changed over the time allowing the evolution of the bacterial population to be described.AMOVA has also been applied to RAPD data of Burkholderia cepacia colonising maize rhizosphere with the aim to show the influence of variables such as plant growth stage, soil type, cultivar, and position along the plant root system on the genetic structure of bacterial populations (Di Cello et al., 1997; Dalmastri et al., 1999; Chiarini et al., 2000). Here AMOV A successfully allowed to describe the influence of each variable on the genetic differentiation of the population. In particular, a high percentage of the genetic variability was attrib-uted to differences among populations isolated at various soil depths. Moreover, soil was shown to be a major component in affecting the genetic diversity of maize-root associated B. cepacia populations. AMOV A has recently been applied to similar questions with RAPD profiles of Pseudomonas strains (Picard et al., 2000) isolated from roots and rhizosphere of maize. Here AMOV A showed that plant age plays an important role in the genetic variability of the rhizosphere bac-teria, but not of the root bacteria.A third example of application of AMOV A to the ecology of microbial popu-lations came from the analysis of clinical isolates of Mycobacterium tuberculosis (Richner et al., 1999). In this case the aim was to relate the genetic differentiation to the sensitivity to drugs and to the site of isolation (rural or urban). AMOV A indicated the existence of a degree of population subdivision between drug sensi-tive and drug-resistant isolates higher than between isolates from urban and rural areas.Ann. Microbiol., 52, 95-101 (2002)99Finally, AMOV A was recently used in to address the question of possible biases caused by the use of different culture media for the isolation of a bacterial population (Tabacchioni et al., 2000). Strains of B. cepacia from maize rhizos-phere were isolated in two different media and a RAPD fingerprint of each strain was performed. AMOV A was able to recognise two different groups, accordingly with the two media used for the isolation.From these case-studies stand out that AMOV A is a powerful technique to cope with the ecological problems of microbial populations when those are stud-ied at the molecular level. 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