NUMERICAL TAXONOMY OF ENTEROBACTER SPP. ISOLATED FROM DIFFERENT AREAS IN SOUTH IRAQ

A numerical classification study was carried out on 153 strains of Enterobacter using 64 unit characters including: cell characteristic, growth features, tolerance, biochemical and antibiotic susceptibility. The data were examined using the simple matching (SSM), and clustering was achieved using unweighted pair group average linkage method (UPGMA). The results of the analyses were arranged in the form of dendrogram according to the similarity matrix. At similarity level (83%), all isolates were divided into five groups, the major group was comprised of 80 isolates belong to species Enterobacter cloacae. Group 3 was composed of 67 isolates belong to species E. aerogenes. Group 5 was composed of 4 isolates representing the species: E. sakazakii, and two group (2,4) contain only one isolate representing the species Enterobacter hormaechei and E. asburiae respectively. KEYWARDS: Numerical taxonomy, Enterobacter, UPGMA. INTRODUCTION Numerical taxonomy (also termed adansonian analysis, taxonometrics, taxometrics, phonetics, and computer taxonomy) method has been employed for bacterial classification and identification for many decades.Numerical taxonomy analysis was first suggested by Michel Adanson in 19 th century (Loman, 2012). Sneath in 1957 published a first account of the use of computer methods for the classification of bacteria and principles of numerical taxonomy were published in 1963(Sneath, 1995). wjert, 2017, Vol. 3, Issue 3, 85 -93. World Journal of Engineering Research and Technology WJERT www.wjert. org ISSN 2454-695X Original Article SJIF Impact Factor: 4.326 *Corresponding Author Ghosoon Al-Kanaany Department of biology, College of Science, University of Basrah, Iraq. Ghosoon et al. World Journal of Engineering Research and Technology www.wjert. org 86 This is essentially statistical method that use groups of traits that taken together, point to specific taxa (Staley and colwell, 1973; Sneath, 1984). Application of statistical approaches provides a mechanism for using a wide range of morphological, physiological, biochemical, serological features (frequently 100 or more) each given equal weight (Sneath,1984). This analysis is referred to the "unweighted-pair group method with arithmetic mean" (UWPGA) technique. From this perspective, the computer clusters different strains at selected levels over all similarity (usually that isolate must have at least 80-85% similarity to belong to given species based on unweighted-pair group method analysis (Janda and Abbott, 2002). Numerical classification provides percentage frequencies of positive character states for all strains within each cluster. such data provide a basis for the construction of a frequency matrix for identification of unknown strains against the defined taxa (Krieg and Holt, 1984). Cluster analysis is the name given to various procedures whereby a set of individuals or units (termed as OTUs "Operational taxonomic units" a useful expression proposed by Sokal and Sneath, 1963, to designate the entities whose classification is in question-individuals, strains, or low-rank taxa already recognized is divided into two or more subgroups (clusters) on the basis of a set of attributes which they share (Meerman, 1993). Computerized databases have been used to develop diagnostic tests that identify clinically relevant isolates through numerical codes (Krieg and Holt, 1984). This method became the dominant one for classification of bacteria in the latter third of the twentieth century. In this method, researchers chose characteristics that strongly differentiated among taxa when strains were directly compared (Janda and Abbott, 2002). Much of research in numerical taxonomy of bacteria has consisted of detailed analysis of individual taxa. Numerical taxonomy was used to analyze phenotypic data obtained from 126 isolates of Aeromonas strains isolated from different sources and the strains clustered into 10 aggregate groups by using the SSM coefficients and UPGMA clustering algorithm (Erdem etal., 2011). Greipsson and Priest (1983) found that all Hafnia alvei strains formed a single phenon with a simple matching coefficient by performing a numerical analysis of 101 features because the similarity among all of the strains was very high. Thirteen phena were found at 78% similarity level when tested 89 phenotypic were numerically analyzed against 17 reference strains,using the simple matching coefficient (SSM) where five of these phena were assigned to the family Enterobacteriaceae (Prado etal.,2001) The aim of this study was to classification of Enterobacter by numerical taxonomy. Ghosoon et al. World Journal of Engineering Research and Technology www.wjert. org 87 MATERIALS AND METHODS Samples collection The samples were collected from different areas of Basrah hospitals (Al-Fayhaa General hospital, Al-Mawanee General hospital, Al-Sadder teaching hospital, Al-Basrah hospital for gynecology and obstetrics, Al-Basrah childrenʼs specialty hospital, Al-Basrah General hospital). Identification of Enterobacter spp. Enterobacter spp. were identified by conventional methods, they were examined according to the appearance, color and morphology of the colonies and Positive cultures were subjected to biochemical tests (sugar fermentation, IMVC, TSI, Oxidase, Catalase) for identification of bacteria and confirmatory identification of Enterobacter spp by Vitek® 2 compact. Numerical taxonomy All isolates (OTU) were submitted to numerical taxonomy by using (64) characters (t) Table(1). Clusters analysis was done according to unweighted between group mathematic average linkage (UPGMA) using Simple matching coefecient(SSM). Numerical taxonomy program used within Spss IBM 20 package.Tests results were input to program as (0,1). The program showed the species groups according to similarity percentages, the results appeared as diagram (dendrogram). Table 1: Phenotypic characteristics were used in numerical taxonomy of Enterobacter