ISSN 15804003 THE SCIENTIFIC JOURNAL OF THE VETERINARY FACULTY UNIVERSITY OF LJUBLJANA SLOVENIAN VETERINARY RESEARCH SLOVENSKI VETERINARSKI ZBORNIK Volume /N 50 2 Slov Vet Res • Ljubljana • 2013 • Volume 50 • Number 2 • 41-88 THE SCIENTIFIC JOURNAL OF THE VETERINARY FACULTY UNIVERSITY OF LJUBLJANA SLOVENIAN VETERINARY RESEARCH SLOVENSKI VETERINARSKI ZBORNIK Volume 50 Slov Vet Res • Ljubljana • 2013 • Volume 50 • Number 2 • 41-88 The Scientific Journal of the Veterinary Faculty University of Ljubljana SLOVENIAN VETERINARY RESEARCH SLOVENSKI VETERINARSKI ZBORNIK Previously: RESEARCH REPORTS OF THE VETERINARY FACULTY UNIVERSITY OF LJUBLJANA Prej: ZBORNIK VETERINARSKE FAKULTETE UNIVERZA V LJUBLJANI 4 issues per year / izhaja štirikrat letno Editor in Chief / glavni in odgovorni urednik: Gregor Majdič Technical Editor / tehnični urednik: Matjaž Uršič Assistants to Editor / pomočnici urednika: Valentina Kubale Dvojmoč, Klementina Fon Tacer Editorial Board / uredniški odbor: Frangež Robert, Polona Juntes, Matjaž Ocepek, Seliškar Alenka, Modest Vengušt, Milka Vrecl, Veterinary Faculty University of Ljubljana / Veterinarska fakulteta Univerze v Ljubljani; Vesna Cerkvenik, Reziduum s.p. Editorial Advisers / svetovalca uredniškega odbora: Gita Grecs-Smole for Bibliography (bibliotekarka), Leon Ščuka for Statistics (za statistiko) Reviewing Editorial Board / ocenjevalni uredniški odbor: Ivor D. Bowen, Cardiff School of Biosciences, Cardiff, Wales, UK; Antonio Cruz, Paton and Martin Veterinary Services, Adegrove, British Columbia; Gerry M. Dorrestein, Dutch Research Institute for Birds and Exotic Animals, Veldhoven, The Netherlands; Sara Galac, Utrecht University, The Netherlands; Wolfgang Henninger, Veterinärmedizinische Universität Wien, Austria; Simon Horvat, Biotehniška fakulteta, Univerza v Ljubljani, Slovenia; Nevenka Kožuh Eržen, Krka, d.d., Novo mesto, Slovenia; Louis Lefaucheur, INRA, Rennes, France; Bela Nagy, Veterinary Medical Research Institute Budapest, Hungary; Peter O'Shaughnessy, Institute of Comparative Medicine, Faculty of Veterinary Medicine, University of Glasgow, Scotland, UK; Milan Pogačnik, Veterinarska fakulteta, Univerza v Ljubljani, Slovenia; Peter Popelka, University of Veterinary Medicine, Košice, Slovakia; Detlef Rath, Institut für Tierzucht, Forschungsbericht Biotechnologie, Bundesforschungsanstalt für Landwirtschaft (FAL), Neustadt, Germany; Henry Stämpfli, Large Animal Medicine, Department of Clinical Studies, Ontario Veterinary College, Guelph, Ontario, Canada; Frank J. M. Verstraete, University of California Davis, Davis, California, US; Thomas Wittek, Veterinärmedizinische Universität, Wien, Austria Slovenian Language Revision / lektor za slovenski jezik: Viktor Majdič Address: Veterinary Faculty, Gerbičeva 60, 1000 Ljubljana, Slovenia Naslov: Veterinarska fakulteta, Gerbičeva 60, 1000 Ljubljana, Slovenija Tel.: +386 (0)1 47 79 100, 47 79 129, Fax: +386 (0)1 28 32 243 E-mail: slovetres@vf.uni-lj.si Sponsored by the Slovenian Book Agency Sofinancira: Javna agencija za knjigo Republike Slovenije ISSN 1580-4003 Printed by / tisk: DZS, d.d., Ljubljana Indexed in / indeksirano v: Agris, Biomedicina Slovenica, CAB Abstracts, IVSI Urlich's International Periodicals Directory, Science Citation Index Expanded, Journal Citation Reports/Science Edition http://www.slovetres.si/ SLOVENIAN VETERINARY RESEARCH SLOVENSKI VETERINARSKI ZBORNIK Slov Vet Res 2013; 50 (2) Original Scientific Articles Hadžiabdic S, Rešidbegovic E, Gruntar I, Kušar D, Pate M, Zahirovic L, Kustura A, Gagic A, Goletic T, Ocepek M. Campylobacters in broiler flocks in Bosnia and Herzegovina: Prevalence and genetic diversity..................45 Podpečan O, Mrkun J, Zrimšek P. Associations between the fat to protein ratio in milk, health status and reproductive performance in dairy cattle..........................................................57 Fazarinc G, Uršič M , Gjurčevic Kantura V, Trbojevic Vukičevic T, Škrlep M, Čandek - Potokar M. Expression of myosin heavy chain isoforms in longissimus muscle of domestic and wild pig.....................67 Gombač M, Švara T, Paller T, Vergles Rataj A, Pogačnik M. Post-mortem findings in bottlenose dolphins (Tursiops truncatus) in the Slovene sea...............................................................75 Case Report Cociancich V, Gombač M, Švara T, Pogačnik M. Malignant mesenchymoma of the aortic valve in a dog ................83 Slov Vet Res 2013; 50 (2): 45-55 UDC 636.5.09:579.84:579.25:577.21 Original Scientific Article CAMPYLOBACTERS IN BROILER FLOCKS IN BOSNIA AND HERZEGOVINA: prevalence and GENETIC diversity Sead Hadžiabdič1, Emina Rešidbegovič1, Igor Gruntar2, Darja Kušar2, Mateja Pate2, Lejla Zahirovič3, Aida Kustura1, Abdulah Gagič1, Teufik Goletič1, Matjaž Ocepek2 1Centre for Poultry and Rabbits, Veterinary Faculty, University of Sarajevo, Zmaja od Bosne 90, 71000 Sarajevo, Bosnia and Herzegovina; 2Institute of Microbiology and Parasitology, Veterinary Faculty, University of Ljubljana, Gerbičeva 60, 1115 Ljubljana, Slovenia; 3Cantonal Veterinary Station Sarajevo, Nikole Šopa 41, 71210 Ilidža, Bosnia and Herzegovina Corresponding author, E-mail: sead.hadziabdic@vfs.unsa.ba Summary: Campylobacters are the most commonly reported bacterial gastrointestinal pathogens in humans. In the EU, the number of reported and confirmed human campylobacteriosis cases was 48.6 per 100,000 population in 2010. Poultry is considered to be the main reservoir of Campylobacter because they persist in the gastrointestinal tract of birds in industrial poultry flocks; Campylobacter-contaminated poultry meat and meat products are an important risk factor for campylobacteriosis in humans. The aim of this study was to establish the prevalence, genetic diversity and geographical relationships of Campylobacter isolates from an assortment of broiler flocks in Bosnia and Herzegovina. The calculated Campylobacter prevalence in faecal samples, based on isolation of Campylobacter spp. from selected broiler farms in the period from October 2009 to June 2010, was 62.0 %. At slaughter line, skin/carcass samples were positive in 18 out of 31 Campylobacter-positive farms (58.1 %). A total of 44 isolates (35 Campylobacter jejuni and nine Campylobacter coli) from caecal contents (n=31) and skin/carcasses (n=13) of chicken were genotyped by pulsed-field gel electrophoresis (PFGE) using Sma I. In general, the obtained C. jejuni and C. coli isolates exhibited limited genetic diversity. Only isolates with identical or very similar profiles were found on individual Campylobacter-positive farms. In addition, skin/carcass isolates showed the same or very similar profiles to campylobacters isolated from pooled caecal content originating from the same broiler batch. Accordingly, carcass cross contamination could not be observed in slaughter line samples. Key words: Campylobacter; poultry; Bosnia and Herzegovina; PFGE; caecal contents; skin; carcass Introduction Bacteria from the genus Campylobacter have long been known as a causative agent of diarrhoea in cattle and septic abortion in cattle and sheep, but were only recognized as an important cause of human illness in the mid-1970s when Campylobacter jejuni was found to be responsible for infectious diarrhoea in man for the first time (1). Campylobacters preferentially inhabit the intestines of birds, including chickens, turkeys, Received: 7 May 2012 Accepted for publication: 26 November 2012 quails, ducks, wild birds and even ostriches (2). Epidemiological studies have revealed a firm association between Campylobacter infections in humans and the handling and consumption of raw or undercooked poultry meat; this has been confirmed in many cases (3-8). It is commonly assumed that contamination of poultry meat with campylobacters occurs during slaughterhouse processing and that campylobacters survive throughout the food chain, posing a major risk to public health (4,9). In addition to poultry products, outbreaks of campylobacteriosis have been associated with the consumption of some other animal products, e.g., raw milk (10). In 2010, Campylobacters continued to be the most commonly reported gastrointestinal pathogens in humans with a notification rate increasing from 45.6 per 100,000 population in 2009 to 48.6 per 100,000 population in 2010 (11). A typical seasonal pattern is often exhibited, especially in northern countries, with peaks during the warm summer months (11,12). The most commonly reported Campylobacter species in the EU is C. jejuni, accounting for 93.4 % of the confirmed human cases characterized at the species level in 2010 (11). Among the Member States, the prevalence of both the Campylobacter colonization in broiler batches (>72 %) and of the Campylobacter contamination of fresh poultry meat sampled at slaughter, processing or at retail (>70 %) can be extremely high; however, the prevalence greatly varies at the community level (11). Data demonstrate that the percentage of contaminated carcasses roughly reflects the Campylobacter prevalence in broiler batches and that the prevalence is much lower in northern than in central and southern European countries, probably due to different climatic conditions over the year (7,11,12). A geographical relationship of some Campylobacter genotypes has also been noticed (13,14). In Bosnia and Herzegovina (BIH), detailed research on Campylobacter prevalence in primary poultry production had not been conducted until the present study. However, the prevalence in broiler flocks was partly studied, giving the main information on the extent of Campylobacter carcass contamination during the slaughtering process, since research was performed on poultry retail meat samples (6). Encouraged by the 2008 EU Baseline Study (12), the present research was performed as an initial investigation on the topic. Additionally, pulsed-field gel electrophoresis (PFGE) was employed to discover the genetic diversity of campylobacters on broiler farms and perhaps to demonstrate some geographical relationships of broiler farms, since PFGE has been proven to be appropriate for epidemiological studies (15,16) and a useful tool for identification of potential campylobacteriosis outbreaks (17). To date, PFGE has been used to evaluate the genetic diversity of Campylobacter isolates originating from poultry retail meat, human isolates and some isolates of live farm chickens (6) but not for Campylobacter isolates originating from different stages of broiler breeding. The aim of our study was to determine the Campylobacter prevalence at different stages of the broiler production cycle, to analyze the genetic diversity of isolates from individual broiler flocks and to compare it among different broiler flocks in BIH. Materials and methods Samples From October 2009 to June 2010, 50 broiler flocks originating from 29 municipalities were randomly selected for the isolation and identification of Campylobacter species within the scope of a pilot Campylobacter surveillance program conducted in BIH (Figure 1). With the highest density of poultry population, central and northern BIH were selected for sampling. Sampling (10 caeca per sample) of farms (Table 1) started with one-day-old chickens on their arrival at the farm (day 1) and was subsequently performed every seventh day until the end of breeding, when the animals were sent to the slaughterhouse (days 7, 14, 21, 28, 35 and 42). After collection, samples were transported to the laboratory within six hours in a cooling box (4-8 °C) and analysed according to recommended and standardized methods (12,18,19). In total, 3500 caeca (350 samples) were investigated. In addition, five skin/carcass samples were collected at the slaughter line from every Campylobacter-positive flock (155 samples in total). The first broiler flock that was confirmed as Campylobacter-positive was subjected to more intensive sampling, i.e., every Table 1: Timetable of sampling for all 50 farms Period of sampling Farm numbers (1-50) October 2009 1-4; 41-43 November 15, 16, 38, 45, 46 December 5-8, 17-19 January 2010 39, 40 February 9, 10, 20, 32-34 March 23, 24, 35-37, 48-50 April 11, 21, 22, 44 May 25, 26 June 12-14, 27-31, 47 Note: For farm numbers, see Table 2. For geographical distribution of farms, see Figure 1. Farms selected for PFGE typing are underlined (farms 1-14). day after confirmation of Campylobacter infection until slaughtering (eight samples of 10 caeca each, in addition to the seven regular samples) and at 12 different positions of the slaughter line (seven skin/carcass samples, in addition to the five regular samples). Campylobacter isolates Isolation and identification of Campylobacter spp. from faecal material was performed according to the EU guidelines prepared for the 2008 Baseline Study on the prevalence of Campylobacter in broiler flocks and Campylobacter/ Salmonella in broiler carcasses (18). Isolation and identification from broiler skin/carcasses was performed according to ISO 10272-1:2006 (19). Briefly, one inoculation loop of 10 pooled caecum contents was streaked onto the selective media mCCDA (modified Charcoal Cefoperazone Deoxycholate Agar) and Skirrow agar. Skin/carcass samples were enriched by the use of modified Bolton broth (1:9), incubated at 41.5 °C in a micro-aerobic atmosphere for 24-48 hrs, then streaked onto the mCCDA and Skirrow media and incubated at 41.5 °C in a micro-aerobic atmosphere for 24-48 hrs. Bacteria from suspected Campylobacter colonies were examined for morphology and motility by dark-field microscopy. After sub-culturing on blood agar plates and antibiotic susceptibility disc-diffusion testing in nalidixic acid (30 ^g) and cephalotin (30 ^g), they were subjected to determination by selected biochemical tests (catalase, oxidase, indoxyl acetate and hippurate hydrolysis) and aerobic growth at 41.5 °C. Isolates identified as C. jejuni or C. coli were stored at -76 °C in a cryo-protective medium for PFGE genotyping. PFGE typing PFGE was conducted for selected C. jejuni and C. coli isolates, based on their geographical origin and its importance if occurring in major poultry production regions (Figure 1). From frozen beads, isolates were recovered on blood agar medium and subjected to PFGE genotyping employing SmaI restriction endonuclease according to the PulseNet standardised one-day protocol (20). The obtained fragments were electrophoretically separated under the following conditions: 18 h at 6 V/cm and 14 °C, with pulse-times from 6.7 s to 35.4 s employing the CHEF-DR II System (BioRad, USA). PFGE profiles (i.e., pulsotypes) were subjected to computer-assisted analysis with BioNumerics software (version 6.6; Applied Maths, Belgium). In brief, normalization was done according to molecular size standard (three lanes per gel), i.e., Salmonella serotype Braenderup H9812 (ATCC BAA-664). Similarity matrices were constructed using the band-based Dice coefficient with optimization and band-matching tolerance set to 1.5 %. Cluster analysis was based on the UPGMA algorithm and the cut-off value defining clusters of isolates was 90 % of similarity according to the dendrogram (21). Nomenclature of isolates that were subjected to PFGE typing was based on the scheme CJ (for C. jejuni) or CC (for C. coli) followed by the farm name (abbreviation) and age of chicken at sampling of their caeca (in days; usually 35 or 42). Where chicken skin/carcass samples were Campylobacter-positive at the slaughter line, designation S was added to the isolate name (i.e., 42-S). Results Distribution of C. jejuni and C. coli C. jejuni and/or C. coli were isolated from 31 (62.0 %) out of 50 investigated farms. From three of the Campylobacter-positive farms, both C. jejuni and C. coli were isolated (9.7 %), from 23 only C. jejuni (74.2 %) and from five only C. coli (16.1 %). Skin/carcass samples were Campylobacter-positive in 18 out of 31 positive farms (58.1 %). Skin/carcasses originating from 15 out of 26 C. jejuni-positive farms were positive for C. jejuni at slaughtering (57.7 %) and from three out of eight C. coli-positive farms positive for C. coli (37.5 %). Detailed results are shown in Table 2. PFGE typing of C. jejuni A total of 35 C. jejuni (CJ) isolates were subjected to PFGE typing: 22 faecal isolates originating from five municipalities (denoted 1-4, 5-8, 9-10, 11 and 13-14 in Figure 1, corresponding to locations Visoko, Gračanica, Srbac, Gradiška and Sarajevo, respectively) and 13 skin/carcass isolates from two farms (S2 and Sr1) (Table 2). Table 2: Campylobacter jejuni and Campylobacter coli distribution and origin in Campylobacter-positive farms Farm Isolates PFGE No.1 Name2 Location3 Ceaca4 Day 35 Day 42 S5 Isolate name 1 V1 nd CC CC CC V1-42 2 V2 nd CJ and CC nd CJ V2-42 3 V4 Visoko nd CJ nd CJ V4-42 4 V5 CC CC nd CC V5-35 CC V5-42 5 G1 nd CC CC CC G1-42 6 G2 CJ CJ nd CJ G2-35 CJ G2-42 7 G3 Gračanica CJ CJ nd CJ G3-35 CJ G3-42 8 G4 CJ CJ nd CJ G4-35 CJ G4-42 CJ Sr1-35 9 Sr1 Srbac CJ CJ CJ CJ Sr1-42 CJ Sr1-42-S 10 Sr2 nd CJ nd CJ Sr2-42 11 Gr1 Gradiška CC CJ and CC nd CJ Gr1-42 CC Gr1-35 CC Gr1-42 12 T1 Tarčin CC CC nd CC T1-35 CC T1-42 13 S1 nd CJ nd CJ S1-42 Days 28, 33-42 CJ S2-28 14 S2 Sarajevo CJ and CC6 CJ7 CJ S2-33...35 CJ S2-38...42 CJ S2-42-S1...S12 CC S2-37 15 BH1 Begov Han CC nd CC 16 O1 Orašje CJ nd CJ 17 Z1 Zenica CJ nd CJ 18 19 Te1 Te2 Tešanj nd CJ CJ nd nd CJ 20 P1 Pale CJ nd CJ 21 N1 Nemila nd CJ CJ 22 Kl1 Kladanj nd CJ nd 23 K1 Kakanj CJ nd CJ 24 Va1 Vareš CJ nd CJ 25 Gra1 Gradačac nd CJ nd 26 Tr1 Travnik nd CJ CJ 27 DG1 D. Golubinja CJ nd CJ 28 Z1 Zepče nd CJ CJ 29 M1 Maglaj nd CJ CJ 30 Br1 Breza CJ nd CJ 31 Po1 Posušje CJ nd CJ Note: S2 was the earliest Campylobacter-positive farm and was therefore subjected to more intensive sampling: in addition to days 1, 7, 14, 21, 28, 35 and 42 (see text), also at intermediate days 33, 34 and 36-41 and more intensively at slaughtering (12 skin/carcass samples from different positions on the slaughter line). From farm S2, 22 isolates were subjected to PFGE typing (21 C. jejuni and one C. coli). From all the Campylobacter-positive farms, 44 isolates (abbreviations CJ and CC that are underlined in Isolates column) were subjected to PFGE typing, namely 35 C. jejuni isolates from 10 farms and nine C. coli isolates from six farms. Legend: CJ, C. jejuni; CC, C. coli; nd, not detected Farm numbers (1-31, Campylobacter-positive farms shown in Table 2; 32-50, Campylobacter-negative farms not shown in Table 2); 2, Abbreviated farm names; 3, Location of farms (for their geographical distribution according to municipalities, see Figure 1); 4, Caecal samples (age of chicken in days); 5, Skin/carcass samples from the slaughter line; 6, From farm S2, C. jejuni was isolated at days 28, 33-36 and 38-42, and C. coli at day 37; 7, From farm S2, 12 skin/carcass isolates of C. jejuni were obtained from 12 positions on the slaughter line Figure 1: A map of BIH with depicted municipalities showing the geographical distribution of Campylobacter-positive and -negative broiler farms denoted with numbers 1-50. Green, municipalities with Campylobacter-positive farms 1-14 that were subjected to PFGE typing; Orange, municipalities with the remaining Campylobacter-positive farms 15-31; White (numbered), municipalities containing farms that were Campylobacter-negative during the sampling period. For timetable of sampling, see Table 1. For details on Campylobacter-positive farms 1-31, see Table 2 Pulsotypes revealed five clusters (A1, A2, B, C and D+) containing 3-4 isolates (farm S2 was subjected to different sampling because of having the earliest Campylobacter-positive samples) (Figure 2). Isolates from the farm S2 were assigned to clusters A (A1 and A2), since they showed an 88.9 % similarity due to the difference in position of only one fragment. According to the 90 % cut-off value, cluster D+ contained three isolates from farms G3 and G4; however, the second isolate from G4 was assigned to the same cluster as it showed a marked similarity of 80 % and did not have other neighbours by similarity in the dendrogram. Four CJ isolates (V4-42, Sr2-42, Gr1-42 and V2-42) with more distinct profiles were not assigned to any cluster. In clusters A1 and A2 (isolates from S2) and C (isolates from Sr1), containing all CJ isolates from the slaughter line (noted as S), identical C. jejuni pulsotypes were revealed when caecal and S isolates were compared. Clusters A1 and A2 comprised 21 isolates with identical (cluster A1 or A2) or very similar (cluster A1 vs. A2) profiles, belonging to C. jejuni isolates obtained from animals of different age or from different positions on the slaughter line. Cluster D+ contained four CJ isolates (G4-42, G3-35, G3-42 and G4-35) showing high genetic similarity, from two farms (G3 and G4) located in the same municipality (denoted 5-8 in Figure 1). Similarly, cluster B also contained isolates, namely three CJ isolates (G2-35, S1-42 and G2-42), from two different Campylobacter-positive farms (G2 and S1). However, these originated from geographically distant municipalities (denoted 5-8 and 13-14 in Figure 1), were sampled in two different time periods (G2 in December 2009 and S1 in June 2010; Table 1) and the pulsotypes in cluster B differed in one band in terms of number or position. In general, pulsotypes of C. jejuni isolates originating from different farms were heterogeneous in comparison with homogeneous pulsotypes of isolates belonging to the same broiler flock, with the exception of farms G2/S1 and G3/G4; however, the latter two shared the geographical area. H 94.1 91.5 C CJ S2-42-S3 CJ S2-28 CJ S2-39 CJ S2-42-S8 CJ S2-34 CJ S2-35 CJ S2-38 CJ S2-33 CJ S2-40 CJ S2-41 CJ S2-42 CJ S2-42-S1 CJ S2-42-S2 CJ S2-42-S4 CJ S2-42-S5 CJ S2-42-S6 CJ S2-42-S9 CJ S2-42-S10 CJ S2-42-S12 CJ S2-42-S7 CJ S2-42-S1 CJ V4-42 CJ G2-35 CJ S1-42 CJ G2-42 CJ Sr2-42 CJ Gr1 -42 CJ V2-42 CJ Sr1-35 CJ Sr1-42 CJ Sr1 -42-S CJ G4-42 CJ G3-35 CJ G3-42 CJ G4-35 > Cluster A1 J Cluster A2 > Cluster B - Cluster C 1 Cluster D+ 9 73.8 70.2 5 51.3 5 39.5 80.0 Figure 2: Dendrogram of 35 Campylobacter jejuni pulsotypes showing the genetic relatedness of isolates obtained in October 2009 - June 2010 from 10 out of 26 C. jejuni-positive broiler farms in BIH. Isolate name consisted of CJ (for C. jejuni) followed by the abbreviated farm name (G2, G3, G4, Gr1, S1, S2, Sr1, Sr2, V2 and V4), age of chicken at sampling (35 and 42; for farm S2, also 28, 33, 34 and 38-41) and, when needed for the skin/carcass samples, designation S (42-S; for farm S2, S1-S12 note different positions on the slaughter line). For details, see Table 2 Figure 3: Dendrogram of nine Campylobacter coli pulsotypes showing genetic relatedness of isolates obtained in October 2009 - June 2010 from six out of eight C. coli-positive broiler farms in BIH. Isolate name consisted of CC (for C. coli) followed by the abbreviated farm name (G1, Gr1, S2, T1, V1 and V5) and age of chicken at sampling (35, 37 and 42). For details, see Table 2 54.1 CC V5-35 CC V5-42 CC Gr1 -35 CC Gr1-42 CC G1-42 CC T1-35 CC T1-42 CC V1-42 CC S2-37 Cluster A Cluster B Cluster C 85.7 76.1 51.3 PFGE typing of C. coli A total of nine C. coli isolates obtained from the caecal contents of chickens were PFGE typed. Pulsotypes revealed three clusters (A-C; Figure 3) with 2-3 isolates exhibiting identical profiles and belonging to the same farm (V5 or Gr1; cluster A or B) or two separate farms (G1 and T1; cluster C) from two geographically distant municipalities (denoted 5-8 and 12, respectively, in Figure 1, that were, as shown in Table 1, sampled in two different time periods). In addition, two isolates from two different farms (V1 and S2) originating from two neighbouring municipalities (denoted 1-4 and 13-14, respectively, in Figure 1) exhibited distinct profiles. In general, five pulsotypes (representing clusters A-C and two separate isolates) were observed, belonging to six locations from five municipalities (denoted 1-4, 5-8, 11, 12 and 1314 in Figure 1) from three different geographical areas. However, clusters A and B (farm V5 and farm Gr1) contained isolates with similar profiles and cluster C (farms G1 and T1) isolates with identical profiles, although obtained over an extended time period and originating from poultry flocks in markedly different geographical areas. Discussion Bacteria of the genus Campylobacter remain the most frequently reported cause of human gastrointestinal disease in the EU (11,22). Poultry has often been associated with campylobacteriosis (23-28). To date, there have not been sufficient studies estimating the prevalence of Campylobacter spp. in primary poultry production in BIH. Bearing in mind the high prevalence of campylobacters in most European countries (11,22), the aim of our study was to carry out a more detailed research on Campylobacter prevalence at farm level. The obtained results can confirm the presence of Campylobacter spp. in BIH and also reveal their genetic diversity. Our research showed that broilers in BIH are frequently colonised with Campylobacter spp. at farms and at slaughtering; contamination of carcasses, poultry meat and meat products consequently occurs, as has been confirmed by previous studies (6-8). During October 2009 and June 2010, the prevalence of campylobacters in the investigated farms was 62.0 %, which is in accordance with data from other countries, e.g., Germany 48.9 %, UK 75.0 %, France 76.0 %, Slovenia 78.2 % (12,22) and in some previously released publications (23,24,29,30). Given that the sampling period was predominantly during the colder period of the year and that campylobacters show a seasonal pattern (11,12,31-33), the actual prevalence could probably be expected to be even higher. Our results suggest that colonisation of caecum with campylobacters begins around the 28th day during poultry breeding, although it has been suggested that colonisation could occur much earlier (2,34). In our study, C. jejuni was more frequently isolated than C. coli, namely C. jejuni from 74.2 % and C. coli from 16.1 % of the Campylobacter-positive farms, which is consistent with other publications (11,12,22,31,35). In three cases, both C. jejuni and C. coli were isolated from the same farm in our study (9.7 %), also consistent with some previously released publications on the presence of both Campylobacter species in a broiler flock (36,37). The obtained PFGE results indicate a limited variability of pulsotypes belonging to Campylobacter isolates at farm level. Other publications suggest a greater genetic diversity of Campylobacter isolates, both within a farm and within geographical areas (37,38). Despite difficulties in the epidemiological research of Campylobacter bacteria caused by their diversity, our results suggest that a persistent and dominant type of Campylobacter strain could occur within a flock and, consequently, at the slaughter line. On the other hand, identical or very similar C. jejuni genotypes were obtained from two neighbouring farms (G3 and G4), although that could be a result of many circumstances, such as the presence of house flies (39), rodents, wild birds, flies or humans (e.g., transmission by protective clothing) as vectors (40). It was also revealed that certain C. jejuni and C. coli isolates obtained from farms in different geographical areas, and over extended time periods, showed marked genetic similarity. Vertical transmission of campylobacters could be suspected, especially if it was proven that both farms obtain animals from the same parent flock. Since evidence of vertical transmission of Campylobacter strains in chickens is lacking from publications (41,42), a more detailed sampling program must be performed in parent flocks and hatcheries. In addition, it can be concluded that certain genotypes can persist over time, revealing C. jejuni or C. coli isolates obtained in different time periods but showing very similar or identical genetic fingerprints. Pulsotypes of C. coli showed somewhat higher homogeneity than those of C. jejuni; when a strain of C. coli was isolated more than once from a broiler flock, it showed an identical genotype profile (e.g., farms Gr1, T1 and V5). In addition, PFGE results revealed that cross-contamination of carcasses at the slaughter line is probably not present; although C. jejuni pulsotypes belonging to farm S2 were not identical (cluster A1 vs. A2 in Figure 2), the two pulsotypes that differed in the position of only one band (cluster A2) were very similar to others belonging to skin/carcass isolates from the same farm (cluster A1) and no similar pulsotypes could be observed belonging to samples from other poultry flocks. Our results revealed and confirmed that different strains of C. jejuni and C. coli are present in different farms and geographical areas. In view of the considerable number of isolates, the results also indicated that a dominant Campylobacter strain may be present in a broiler flock and, consequently, at the slaughter line, consistent with other studies (43). If this hypothesis proves to be correct, it would enable epidemiological research and prevention of campylobacteriosis by linking a particular strain to its source and checking sources and transmission routs in a flock and poultry retail products. Prevention of Campylobacter contamination at the farm level would therefore be much more efficient if the critical points were highlighted and strict bio-security measures taken. For better understanding of the epidemiology of Campylobacter bacteria in a flock, it is necessary to design successful prevention programs at the farm level. With this in mind, an extensive surveillance program in BIH will be conducted during 2012 in order to gain more knowledge on the genetic diversity of campylobacters. We believe that the obtained results have scientific value, especially since previous research of this kind in primary poultry production has not given enough data on the prevalence and diversity of specific Campylobacter strains. The obtained knowledge brings new possibilities to the epidemiological research of campylobacters and indicates the importance of cooperation between veterinary and public health laboratories. Acknowledgements This work was supported by the Federal Ministry of Education and Science and the Ministry of Agriculture, Water and Forestry of Bosnia and Herzegovina, the Ministry of Higher Education, Science and Technology of Slovenia (grant no. V4-0529-0406 and V4-1110-0406) and by Bilateral Project no. BI-BA/10-11-001. Alenka Magdalena Usenik is gratefully acknowledged for skilful technical support. References 1. Skirrow MB. Campylobacter enteritis: a 'new' disease. Brit Med J 1977; 2: 9-11. 2. Newell DG, Fearnley C. Sources of Campylobacter colonization in broiler chickens. Appl Environ Microbiol 2003; 69: 4343-51. 3. Oosterom J, den Uyl CH, Bänffer JRJ, Huisman J. Epidemiological investigations on Campylobacter jejuni in households with a primary infection. J Hyg Camb 1984; 93: 325-32. 4. 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Occurrence and genotypes of Campylobacter in broiler flocks, other farm animals, and the environment during several rearing periods on selected poultry farms. Int J Food Microbiol 2008; 125: 182-7. 39. Rosef O, Kapperud G. House flies (Musca domestica) as possible vectors of Campylobacter fetus subsp. jejuni. Appl Environ Microbiol 1983; 45: 381-3. 40. Meerburg BG, Kijlstra A. Role of rodents in transmission of Salmonella and Campylobacter. J Sci Food Agr 2007; 87: 2774-81. 41. Callicott KA, Friöriksdöttir V, Reiersen J, et al. Lack of evidence for vertical transmission of Campylobacter spp. in chickens. Appl Environ Microbiol 2006; 72: 5794-8. 42. Petersen L, Nielsen EM, On SL. Serotype and genotype diversity and hatchery transmission of Campylobacter jejuni in commercial poultry flocks. Vet Microbiol 2001; 82: 141-54. 43. Petersen L, Wedderkopp A. Evidence that certain clones of Campylobacter jejuni persist during successive broiler flock rotations. Appl Environ Microbiol 2001; 67: 2739-45. KAMPILOBAKTRI V REJAH PITOVNIH PIŠČANCEV V BOSNI IN HERCEGOVINI: PREVALENCA IN GENETSKA RAZNOLIKOST S. Hadžiabdic, E. Rešidbegovic, I. Gruntar, D. Kušar, M. Pate, L. Zahirovic, A. Kustura, A. Gagic, T. Goletic, M. Ocepek Povzetek: Bakterije iz rodu Campylobacterso najpogosteje prijavljeni bakterijski povzročitelji prebavnih obolenjih ljudi. V letu 2010 je bilo v Evropski uniji na 100.000 ljudi prijavljenih in potrjenih 48,6 primerov kampilobakterioz. Ker kampilobaktri naseljujejo prebavni trakt živali v industrijskih perutninskih rejah, je perutnina njihov glavni rezervoar; s kampilobaktri okuženo perutninsko meso in mesni izdelki predstavljajo pomemben dejavnik tveganja za kampilobakteriozo pri ljudeh. Namen našega dela je bil ugotoviti prevalenco, genetsko raznolikost in geografsko povezanost izolatov Campylobacteriz nabora rej pitovnih piščancev v Bosni in Hercegovini. Na podlagi izolacije bakterij iz rodu Campylobacteriz izbranih rej pitovnih piščancev v obdobju od oktobra 2009 do junija 2010 je bila izračunana prevalenca v vzorcih fecesa 62,0 %. Na klavni liniji so bili vzorci kože ali trupov pozitivni v 18 od 31 primerov rej, ki so bile pozitivne na kampilobaktre (58,1 %). Z metodo pulzne gelske elektroforeze (PFGE) smo z encimom Smal genotipizirali 44 izolatov (35 Campylobacterjejuni in 9 Campylobacter coli iz vsebine slepega črevesa (n=31) in kože ali trupov (n=13) piščancev. Pridobljeni sevi C. jejuni in C. coli so v splošnem izražali omejeno genetsko pestrost. V posameznih rejah, ki so bile pozitivne na kampilobaktre, smo našli samo seve z enakimi ali zelo podobnimi profili. Izolati iz kože ali trupov so imeli enake ali zelo podobne profile kot kampilobaktri, ki smo jih izolirali iz združene vsebine cekuma iz iste reje pitovnih piščancev, torej navzkrižnega okuževanja med vzorci na klavni liniji nismo opazili. Ključne besede: Campylobacter; perutnina; Bosna in Hercegovina; PFGE; vsebina slepega črevesa; koža; trup Slov Vet Res 2013; 50 (2): 57-66 UDC 636.2.03:616-076:637.1:618.7-07 Original Scientific Article ASSOCIATIONS BETWEEN THE FAT TO PROTEIN RATION IN MILK, HEALTH STATuS AND REpRODuCTIVE pERFORMANCE in dairy CATTLE Ožbalt Podpečan1*, Janko Mrkun2, Petra Zrimšek2 1Savinian Veterinary Policlinic, Celjska c. 3/a, 3310 Žalec, 2Clinic for Reproduction and Horses, Veterinary Faculty, Gerbičeva 60, 1000 Ljubljana, Slovenia Corresponding author, E-mail: ozbalt.podpecan@gmail.com Summary: The aim of the present study was to evaluate pregnancy rates of 232 dairy cows in relation to fat to protein ratio (FPR) in milk, using survival analysis. Pregnancy rates of cows inseminated within 90 and 120 days postpartum in a group of clinically healthy cows were 38 and 68 %, respectively. Lower pregnancy rates are observed in groups of cows with ketosis and reproductive disorders, 44 and 28 % for pregnancy rate within 120 days. The highest correlation between FPR and calving to conception interval (CC) was observed between 30 and 60 days postpartum (r = 0.411, P < 0.001). Diagnostic evaluation of FPR using ROC (receiver operating characteristics) analysis showed that FPR at 1.37 discriminates cows with CC below and above 120 days with an accuracy of 71 %. Survival curves for the subgroups of animals with FPR below or above 1.37 differed significantlyinthe case ofclinicallyhealthy cows,whereCCinsubgroupswere87 ± 28and122 ± 42days, respectively. Although survival curves for subgroups for cows with diseases did not differ significantly we observed longer CC in all subgroups with FPR > 1.37 than in subgroups with FPR < 1.37. In all groups pregnancy rates within 90 and 120 days were lower in subgroups with FPR > 1.37 than in subgroups with FPR < 1.37. Therefore, FPR can be used by bovine practitioners to predict fertility problems in dairy herds. Key words: dairy cows; fat to protein ratio in milk; calving to conception interval; ROC analysis; survival analysis Introduction Increased fat mobilization in a period of negative energy balance (NEB) coupled with decrease in dry matter and energy intake is shown in higher milk fat concentration and lower milk protein concentration in the postpartum period of dairy cows (1). In the last fifteen years a correlation between energy balance in the early Received: 1. July 2012 Accepted for publication: 27 November 2012 postpartum period and milk composition has been reported, using different parameters such as fat to protein ratio (FPR), protein to fat ratio, fat/lactose quotient, milk yield and milk protein concentration (2, 3, 4, 5). Buttchereit et al. (6) showed FPR to be a suitable indicator of the energy status in postpartum dairy cows. FPR indicates low energy balance more reliably than body condition score (BCS) (7). A FPR threshold of 1.4 during the first month of lactation is commonly used by veterinary practitioners as a marker of NEB (8). FPR is also used as a diagnostic tool to estimate some metabolic disorders such as subclinical and clinical ketosis (1, 7). Geishauser et al. (9) found that FPR in the first test milk could be useful as a predictor for subsequent abomasal displacement. Clinical mastitis appears most commonly in the first 30 days postpartum (10). Increased incidence of mastitis during early lactation or at peak production could result from a severe NEB (11, 12) severity of induced E coli mastitis has been related to some blood metabolite concentrations characteristic of NEB (13). Animals exposed to severe NEB suffer from impaired reproductive performance (14, 15, 16) which can be shown as absence of oestrus signs, delayed onset of cyclicity, failure to conceive at 1st artificial insemination (AI) and finally in prolonged calving to conception interval. Moreover, FPR was recently assessed as a predictor of calving to conception interval (CC) of individual cows, using fixed thresholds (17). The aim of the present study was to evaluate practical use of milk and medical data records in association with reproductive performance in high yielding dairy cows. We evaluated pregnancy rates of dairy cows in relation to FPR in milk, using survival analysis. First, a complete receiver operating characteristics (ROC) analysis, was performed to provide an index of accuracy by demonstrating the limits of the test's ability to discriminate between healthy cows pregnant at day 90 (or day 120) after calving or not (18). Groups of clinically healthy cows and cows with ketosis, clinical mastitis or fertility problems were compared using Kaplan-Meier survival curves. Material and methods Animals and data Records of 232 high yielding dairy cows (Holstein-Friesian), BVD and IBR-IPV free, in a period from April 2009 to June 2010 formed a basis for the study. Cows were kept in a freestall barn system. The basic ration was composed of hay, grass and maize silage. According to the milk yield, protein concentrate (19 % digestible raw protein), roughly crushed maize grains and vitamin-mineral mixture were supplemented by a computerised feeding system. All cows could access basic ration and water ad libitum during the whole year in the stall. The voluntary waiting period of the herd was 80 days. They were inseminated by a well trained inseminator. Reproduction parameters, calving to 1st service (CFS), 1st service to conception (FSC) and calving to conception (CC) intervals were derived from farm records. All animals showing clinical signs of disease were examined and treated by bovine practitioners using standardized protocols. Treatment data were recorded for every animal individually. Animals were divided into 5 groups according to occurrence of different diseases in the first 90 days post partum (p.p.) (Table 1): Group 1: clinically healthy cows; Group 2: cows with clinical ketosis (diagnose based on clinical signs and milk tests with sodium nitoprosside); Group 3: cows with clinical mastitis; Group 4: cows with fertility problems (included gynaecological disorders such as retained placenta, puerperal metritis, cystic ovarian disease and endometritis); Group 5: other cows. Milk sampling and analysis Daily milk yield was measured and milk samples were collected at regular test days performed in a 30 day intervals in the post partum period at three stages: stage 1, 0 - 30 days post partum; stage 2, 30 - 60 days post partum; stage 3, 60 - 90 days post partum. Samples were conserved with sodium azide and sent, at outdoor temperature, to a dairy research laboratory. Protein and fat were analysed in milk samples using Fourier Transform Infrared Spectroscopy (CombiFoss 6000). Statistical analysis Groups of cows were compared with respect to CFS, FSC, CC, milk yield and FPR at stages 1, 2 and 3, using One way analysis of variance in the case of normal distribution of the data or Kruskal-Wallis one way analysis of variance on ranks in the case of non-normal distribution of data. When a significant difference among the groups was found, further pair-wise multiple comparisons were performed using the Holm-Sidak method (normal data distribution) or Dunn's method (non-normal data distribution). FPRs in stages 1, 2 and 3 were compared within each group using One way repeated measures analysis of variance or Friedman repeated measures analysis of variance on ranks, according to the normal or non-normal distribution of the data, followed by pair-wise multiple comparison using the Holm-Sidak method and Tukey's test, respectively. ROC analysis First, a correlation between milk data records and reproductive parameters of clinically healthy cows was determined using Spearman rank correlation coefficient. 115 healthy cows were included in diagnostic evaluation of FPR. Receiver operating characteristics (ROC) analysis was used to evaluate FPR in stage 2 (30-60 days postpartum) to discriminate between cows with CCs above and below 120 days. Stage 2 was selected on the basis of the highest correlation between FPR and CC in stage 2, and the criterion value of 120 days was based on reproductive characteristics found in Slovenian dairy herds. Sensitivity (proportion of cows with FPR below the cut-off value in cows with CC below 120 days) and specificity (proportion of cows with FPR above the cut-off value in cows with CC above 120 days) were calculated for all possible cut-off values (Analyse-it, General + Clinical Laboratory statistics, version 1.71). ROC curves, displaying true positive rate (sensitivity) against false positive rate (1-specificity) for the complete range of cut-off points were used to determine the cut off value that minimizes the sum of false negative and false positive results (19). This optimal cut off value is found closest to the upper left hand corner of the ROC curve. The selection was supported with the plot of sensitivity and specificity as a function of cut-off value, which provides a useful visualisation in selecting optimal cut-off values on the basis of the best balance of sensitivity and specificity (20). Area under the curve (AUC) provides an index of accuracy by demonstrating the limits of an FPR's ability to discriminate between cows with different CC (18). Survival analysis Differences in proportion of non-pregnant cows among groups of healthy cows, cows with ketosis, cows with reproductive disorders and cows with clinical mastitis were measured by Kaplan-Meier survival analysis (21). Kaplan-Meier survival curves were constructed and compared using Log Rank test following pair-wise multiple comparison using the Holm-Sidak method in the case of significant difference among the curves. Criteria of censored animals included cows that did not conceive until day 300 post partum and animals culled during the study. For each cow's group, survival curves for subgroups, divided by FPR at 1.37, were constructed and compared using Log rank test. Pregnancy rates were calculated as numbers of cows conceived within 90 and 120 days, divided by the total number of cows in a group (22). Results Milk data in lactating cows Distribution of diseases and groups of cows are presented in Table 1. Reproductive performance and milk data in lactating cows are summarized in Table 2. Cows that were culled for various reasons during the study are recorded in Table 3. Mean milk yield over all cows in the first 100 days was 3069 ± 716 kg (average ± SD) and did not differ between the groups of clinically healthy cows and cows with diseases (P>0.05). No significant difference in FPR between the groups is observed in stage 1 (P>0.05), whereas FPR in stages 2 and 3 differed among the groups (P < 0.001 and P = 0.003, respectively). FPR within the group of cows suffering from ketosis did not differ significantly between stage 1 and stage 3 FPR (P > 0.05), whereas in clinically healthy cows and cows with reproductive disorders or clinical mastitis FPR decreased from stage 1 to stage 3 with a difference close to statistical significance (P=0.067, P=0.046 and P=0.048 respectively). Reproductive performance and comparison of CC among the groups The CFS interval was calculated as 87 ± 32 days; a significant difference was observed among the two groups (P=0.021), corresponding only to the difference between cows with clinical mastitis and cows with fertility problems (P<0.05) (Table 2). The average CC in the lactating cows was 113 ± 49. A significant difference was observed among the groups according to the calving to conception (CC) interval (P < 0.001). Table 1: Distribution of diseases and structure of groups of cows Groups (N) Subgroups (N) disease ketosis EM RP Clinical mastitis DA Group 1: Clinically healthy cows (n=130) / _ _ _ _ _ Group 2: Cows with clinical ketosis (n=32) 15 + _ _ _ _ 7 + _ _ + 8 + _ _ _ + 1 + + + _ 1 + + + + _ Group 3: Cows with clinical mastitis (n=32) / _ _ _ + _ Group 4: Cows with fertility problems (n=36) 15 _ + _ _ _ 11 _ _ + _ _ 5 _ + + _ _ 2 _ + + _ 1 _ _ + + _ 1 _ + + + _ 1 _ + + Group 5: Other cows (n=2) / _ _ _ _ + Legend: EM: endometritis; RP: retained placenta; DA: abomasal displacement Table 2: Reproductive performance and milk data records of dairy cows in different groups according to disease Group 1 2 3 4 Total Healthy cows Cows with clinical ketosis Cows with clinical mastitis Cows with fertility problems N 130 32 32 36 230 CC (days) 103 ± 39 132 ± 57 98 ± 46 153 ± 51 113 ± 49 FSC (days) 18 ± 29 50 ± 55 17 ± 33 53 ± 46 28 ± 40 CFS (days) 85 ± 31 85 ± 38 80 ± 27 100 ± 32 87 ± 32 Milk yield (kg) 3069 ± 716 3267 ± 693 3211± 797 2948 ± 714 3096 ± 726 FPR - stage 1 1.39 ± 0.31 1.48 ± 0.34 1.41 ± 0.27 1.45 ± 0.27 1.42 ±0.30 FPR - stage 2 1.35 ± 0.25 1.59 ± 0.30 1.37 ± 0.23 1.37 ± 0.25 1.39 ± 0.27 FPR - stage 3 1.30 ± 0.25 1.46 ± 0.19 1.29 ± 0.17 1.33 ± 0.18 1.32 ± 0.23 Legend: CC: calving to conception interval; FSC: 1st service to conception interval; CFS: calving to 1st service interval; FPR - stage 1: fat to protein ratio in milk between 0 and 30 days post partum; FPR - stage 2: fat to protein ratio in milk between 30 and 60 days post partum; FPR - stage 3: fat to protein ratio in milk between 60 and 90 days post partum. Values are expressed as mean ± SD. CC interval of clinically healthy cows and cows with clinical mastitis was comparable. Conversely, cows with ketosis and fertility problems required longer times to conceive (Table 2). Cows with clinical ketosis and fertility problems had a significant longer CC interval than those in the group of clinically healthy cows (P < 0.05). Significantly lower CC is observed in clinically healthy cows and cows with clinical mastitis than in cows with fertility problems (P<0.05) (Table 2). The proportion of non-pregnant cows in different groups was evaluated using survival analysis (Fig. 1). Significant differences were observed among Kaplan-Meier survival curves for all groups (P=0.003). Pair-wise multiple comparisons showed significant differences between survival curves of clinically healthy cows and cows with fertility problems and between cows with clinical mastitis and cows with fertility problems (P<0.05). Survival curves for clinically healthy cows and cows with ketosis differ, but not significantly (P=0.0794). Pregnancy rates within 90 and 120 days postpartum in a group of clinically healthy cows were 38 and 68 %, respectively. Lower pregnancy rates within 120 days were observed in the group of cows suffering from ketosis and in the group of cows with reproductive disorders (44% and 28% respectively), whereas 69 % of cows with clinical mastitis conceived by day 120 postpartum. Diagnostic evaluation of FPR The diagnostic ability of FPR was evaluated in the group of clinically healthy cows. First, Spearman rank correlation coefficients were calculated between reproduction parameters, milk yield and FPR in all stages. Milk yield did not correlate with any of reproductive parameters, but significant correlations were observed with FPR in stage 1 (r=0.190, P=0.0325), stage 2 (r=0.279, P=0.0015) and stage 3 (r=0.200, P=0.0243). CFS correlated with FPR in stage 1 (r=0.233, P=0.0108) and stage 2 (r=0.246, P=0.0072). No correlation was observed between CFS and FPR in stage 3 or between SP and FPR in stage 1 (P>0.05). Low correlations between FSC and FPR in stage 2 and stage 3 were observed (r=0.290 P=0.0017; r=0.254, P=0.0062); similar correlations were found between CC and FPR in stages 1 and 3 (r=0.274, P=0.0031; r=0.2565, P=0.0042). The strongest correlation between CC and FPR was observed in stage 2 (r=0.411, P<0.001). FPR in stage 2 was therefore further evaluated diagnostically. Selection of optimal cut-off values of FPR Area under the ROC curve (AUC) for the criterion value of 120 days post partum (AUC = 0.726; P<0.0001) indicates that FPR is valuable in distinguishing cows with different CC (Fig. 2). The best balance between sensitivity and specificity is observed, with 71 % accuracy, at an optimal cut-off point of FPR = 1.37, corresponding to a sensitivity of 74 % and specificity of 68 %. 90 % sensitivity was found for the cut-off at 1.22, whereas cut-off at 1.53 provides over 85 % specificity of FPR (Fig. 3). Comparison of CC according to FPR in healthy cows and cows with ketosis, fertility problems or clinical mastitis Survival curves for subgroups, divided by FPR at 1.37, differed only for clinically healthy cows (P=0.007; Fig. 4A), showing CC intervals in subgroups with FPR below and above 1.37 were 87 ± 28 and 122 ± 42 days, respectively. Pregnancy rates within 90 or 120 days were higher in a subgroup of FPR < 1.37 than in a subgroup with FPR > 1.37. Subgroups of FPR < 1.37 has a pregnancy rate of 52 % at 90 days and 79 % at 120 days, whereas a subgroup of FPR > 1.37 has a pregnancy rate of 19 % at 90 days and 53 % at 120 days. days after calving Figure 1: Kaplan-Meier survival analysis for the proportion of lactating dairy cows non pregnant, according to disease status 1 - specificity Figure 2: ROC curve of FPR for identifying healthy cows with pre-selected minimum of postpartum period at 120 days Characteristics for the ROC curve are as follows: Area underthe curve(AUC):0.726, P<0.0001 optimal cut off value FPR Figure 3: Plot of diagnostic parameters of FPR according to pregnancy statusat 120 days post partum for the selection of optimal cut-off values A days after calving days after calving cows with fertility problems; FPR < 1,37 cows with fertiliy problems; FPR > 1,37 150 200 250 days after calving cows with mastitis; FPR < 1.37 cows with mastitis; FPR > 1.37 150 200 250 days after calving Figure 4: Kaplan-Meier survival analysis for the proportion of cows clinically healthy cows (A), cows with clinical ketosis (B), cows with fertility problems (C) and cows with clinical mastitis (D) non pregnant, according to the cut off value of FPR at 1.37 C D 1,0 0,8 J,ö 0,6 0,4 0,2 0,2 0,0 0 50 300 350 Table 3: Reproductive performance of dairy cows in different groups according to the diseases Groups 1 2 3 4 Healthy cows Cows with clinical ketosis Cows with clinical mastitis Cows with fertility problems total group N 130 32 32 36 cows censored (%) 12 16 13 17 mean CC (days) 103 ± 39 132 ± 57 98 ± 46 153 ± 51 pregnancy rate at 90 days (%) 38 22 50 8 pregnancy rate at 120 days (%) 68 44 69 28 subgroup; FPR < 1.37 N 73 6 16 20 cows censored (%) 12 0 19 20 mean CC (days) 87 ± 28 121 ± 54 82 ± 24 136 ± 48 pregnancy rate at 90 days (%) 52 33 56 15 pregnancy rate at 120 days (%) 79 83 75 35 subgroup; FPR > 1.37 N 57 26 16 16 cows censored (%) 11 19 6 13 mean CC (days) 122 ± 42 135 ± 59 111 ± 56 171 ± 50 pregnancy rate at 90 days (%) 19 19 44 0 pregnancy rate at 120 days (%) 53 35 63 19 Legend: FPR: fat to protein ratio; CC: calving to conception interval Survival curves for subgroups for cows with diseases did not differ significantly (P>0.05) (Fig. 4: B, C, D), although longer CC in all subgroups with FPR > 1.37 than in subgroups with FPR < 1.37 were observed (Table 3). Discussion The aim of this study was to evaluate fat to protein ratio (FPR) in milk for the first three months of lactation and find a threshold which is most appropriate to predict reproductive performance of dairy cows. Results of many studies in the past decade have shown significant correlations between body condition score, energy balance, mobilization of body reserves, blood metabolite concentrations, milk traits and different production and reproductive disorders (3, 4, 6, 15, 23). Our results demonstrated poor reproductive performance in cows with ketosis and reproductive disorders. They had significantly longer CFS and CC intervals than healthy cows and cows with clinical mastitis. Pregnancy rates were calculated as the proportion of cows conceived within 90 or 120 days (22). A lower proportion of pregnant cows were observed in groups with ketosis and fertility problems, while the proportion of cows with clinical mastitis conceiving up to day 120 postpartum is similar to that for healthy ones. Prolonged CC intervals in cows with clinical ketosis were probably related to exposure to NEB, which can be caused by either high milk yield or displaced abomasum. Cows in NEB are subject to increased risk of clinical mastitis (12). The group of mastitic cows in the first three months post partum, contrary to our expectation, did not differ significantly from the clinically healthy one in FPR, CFS, FSC or CC intervals. Reasons may be found in rapid response to treatment, better management and care of treated animals, but also in the statistically smaller number of mastitic cows included in the study compared to healthy ones. Buttchereit et al. (6) showed FPR as a suitable indicator of the energy status of dairy cows during the most critical period for their metabolic constitution. FPR is also used as a diagnostic tool for estimating nutritional imbalance and some metabolic disorders such as subclinical or clinical ketosis (1, 7). Therefore in our study FPR was evaluated by correlation with reproduction parameters and its association with certain diseases. The strongest correlations were observed between FPR and milk yield, CFS, FSC and CC intervals in stage 2. In our previous study (17) the strongest correlations were observed in stage 3, but clinically healthy and diseased animals were not differentiated. From the diagnostic point of view, the strongest correlations in stage 2 enable us to predict animals at risk before the voluntary waiting period ends. Although FPRs were calculated for only three lactation stages, the tendency to decrease is evident in all groups and comparable to the results of Buttchereit et al. (6). ROC analysis showed that the optimal cut-off value at 1.37 in our study allowed discrimination between cows with CC above 120 days and cows with CC below 120 days with an accuracy of 71 %. High sensitivity of FPR was found at a cut-off value of 1.22, which enabled around 90 % correct identification of cows with CC lower than 120 days. On the other hand, cows with FPR more than 1.53 were over 85 % correctly identified as cows with CC above 120 days. It appears that the results of optimal cut-off values of FPR differ among studies due to the numbers of animals or herds included in the studies, their general nutrition status, lactation time frame and the statistical methods used (1, 7, 8, 17, 24). Nevertheless it is clear that cows with FPR values above 1.4 in early postpartum are at high risk of NEB-dependent disorders such as ketosis, displaced abomasum and fertility problems (1). Survival curves calculated for subgroups of animals with FPR below and above 1.37 differed significantly only in the case of clinically healthy cows. According to survival curves, pregnancy rates within 90 and 120 days were higher in the subgroup of FPR < 1.37 than in the subgroup with FPR > 1.37 (19 and 53 %). This indicates that even some clinically healthy cows (FPR>1.37) are exposed to intensive NEB. Although survival curves for subgroups for cows with diseases did not differ significantly (P>0.05), pregnancy rates within 90 and 120 days in all groups were lower in subgroups with FPR > 1.37 than in subgroups with FPR < 1.37. The reason that no significant difference is observed between subgroups of cows with ketosis could be the small number of cows with FPR < 1.37. However, longer CC intervals were observed in all subgroups with FPR > 1.37 than in subgroups with FPR < 1.37. It can thus be concluded that FPR is strongly associated with the pregnancy rate in NEB-related diseases such as clinical ketosis, whereas in cows suffering from other diseases the increased FPR contributes to prolonged CC, but the effect is not as strong as in cows with clinical ketosis. In our previous study it was shown that FPR in milk could be an indicator of the ability of a cow to adapt to the demands of milk production and reproduction in the post partum period, resulting in prolongation of the latter (17). It is also in accordance with the fact that the rate of mobilization of body reserves is directly related to the postpartum interval to first ovulation and to lower conception rate (25). The present study clearly demonstrates that milk data records (e.g. FPR) and medical data can be used by bovine practitioners to analyse and to predict some fertility problems, mainly failure to conception and address metabolic disorders such as ketosis more quickly. The results presented here offer a simple and useful tool for assessing energy balance in a dairy herd in order to predict reproductive performance. Acknowledgements This work was supported by the Slovenian Ministry of Higher Education, Science and Technology, programme group ''Endocrine, immune, nervous and enzyme responses in healthy and sick animals'' (P4-0053). Authors thank Brigita Podpečan, DVM for her help in data collection and Prof. Roger Pain for review of English language. References 1. Eicher R. Evaluation of the metabolic and nutritional situation in dairy herds diagnostic use of milk components. Med Vet Quebec 2004; 34 (1): 36-8. 2. Čejna V, Chladek G. The importance of monitoring changes in milk fat to protein ratio in Holstein cows during lactation. J Centr Eur Agric 2005; 4: 539-46. 3. Heuer C, Van Straalen CWM, Schukken YH, Dirkzwager A, Noordhuizen JPMT. Prediction of energy balance in a high yielding dairy herd in early lactation: model development and precision. Livest Prod Sci 2000; 65: 91-105. 4. Reist M, Erdin D, von Euw D, et al. Estimation of energy balance at the individual and herd level using blood and milk traits in high-yielding dairy cows. J Dairy Sci 2002; 85: 3314-7. 5. Steen A, Osteras O, Gronstol H. Evaluation of bulk milk analyses of aceton, urea and the fat-lactose-quotient as diagnostic aids in preventive veterinary medicine. J Vet Med 1996; 43: 261-9. 6. Buttchereit N, Stamer E, Junge W, Thaller G. Evaluation of five curve models fitted for fat: protein ratio of milk and daily energy balance. J Dairy Sci 2010; 93: 1702-12. 7. Heuer C, Schukken YH, Dobbelaar P. Postpartum body condition score and results from the first test milk as predictors of disease, fertility, yield and culling in commercial dairy herds. J Dairy Sci 1999; 82: 295-304. 8. Cook NB, Oetzel GR, Nordlund KV. Modern techniques for monitoring high producing dairy cows 1. Principles of herd-level diagnoses. In Practe 2006; 28: 510-5. 9. Geishauser T, Leslie K, Duffield T, Edge V. Fat/protein ratio in first DHI test milk as test for displaced abomasums in dairy cows. J Vet Med 1997; 44: 265-70. 10. Barkema HW, Schukken YH, Lam TJ et al. Incidence of clinical mastitis in dairy herds grouped in three categories by bulk milk somatic cell counts. J Dairy Sci 1998; 8(1): 411-9. 11. Leslie KE, Duffield TF, Sandals D, Robinson E. The influence of negative energy balance on udder health. In: Proceedings of the 2nd International Symposium on Mastitis and Milk Quality. Vancouver, Canada: 2001:19-9. 12. Suriyasathaporn W, Heuer C, Noordhuizen-Stassen EN, Schukken YH. Hyperketonemia and the impairment of udder defence: a review. Vet Res 2000; 31: 397-412. 13. Van Werven T. The role of leucocytes in bovine Echerichia coli mastitis: Ph.D. thesis. Utrecht: University of Utrecht, The Netherlands, 1999: 45-51. 14. Loefler SH, de Vries MJ. Schukken YH. The effects of time of disease occurrence, milk yield and body condition on fertility of dairy cows. J Dairy Sci 1999; 82: 2589-604. 15. Opsomer G, Grohn YT, Hertl J, Coryn M, Deluyker H, de Kruif A. Risk factors for postpartum ovarian dysfunction in high producing dairy cows in Belgium: a field study. Theriogenology 2000; 53: 841-57. 16. Podpečan O, Kosec M, Cestnik V, Čebulj-Kadunc N, Mrkun J. Impact of negative energy balance on production and fertility in Slovenian brown-breed dairy cows. Acta Vet Beogr 2007; 57 (1): 69-79. 17. Podpečan O, Mrkun J, Zrimšek P. Diagnostic evaluation of fat to protein ratio in prolonged calving to conception interval using receiver operating characteristic analyses. Reprod Domest Anim 2008; 43: 249-54. 18. Zwieg MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 1993; 39/40: 561-77. 19. Greiner M, Pfeiffer D, Smith RD. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev Vet Med 2000; 54: 23-41. 20. Weiss HL, Niwas S, Grizzle WE, Piyathilake C. Receiver operating characteristic (ROC) to determine cut-off points of biomarkers in lung cancer patients. Dis Markers 2003-2004; 19(6): 273-8. 21. Petrie A, Watson P. Statistics for veterinary and animal science. Oxford: Blackwell Science, 1999: 168-81. 22. Yusuf M, Nakao T, Ranasinghe RMS, et al. Reproductive performance of repeat breeders in dairy herds. Theriogenology 2010; 73: 1220-9. 23. Vanholder T, Leroy J, Dewulf J, et al. Hormonal and metabolic profiles of high yielding dairy cows prior to ovarian cyst formation on first ovulation postpartum. Reprod Domest Anim 2005; 40: 460-9. 24. Duffield TF, Kelton DF, Leslie KE, Lissemore K, Lumsden JH. Use of test day milk fat and milk protein to predict subclinical ketosis in Ontario dairy cattle. Can Vet J 1997; 38: 713-8. 25. Butler WR, Smith RD. Interrelationships between energy balance and postpartum reproductive function in dairy cattle. J Dairy Sci 1989; 72: 767-83. POVEZAVA MED RAZMERJEM MAŠČOB IN BELJAKOVIN V MLEKU, ZDRAVSTVENIM STATUSOM IN REPRODUKCIJSKO SPOSOBNOSTJO KRAV MOLZNIC O. Podpečan, J. Mrkun, P. Zrimšek povzetek: V raziskavi smo z analizo preživetja ovrednotili deleže brejosti 232 krav mlečne pasme v povezavi z razmerjem med maščobami in proteini (koeficient M/B) v mleku. Delež brejih krav v obdobju 90 oziroma 120 dni po porodu je bil 38 oziroma 68 %. Pri kravah s ketozo in reprodukcijskimi problemi smo po 120 dneh po porodu ugotovili nižji delež brejih krav in sicer 44 oziroma 28 %. Najvišjo korelacijo med poporodnim premorom (PP) in razmerjem M/B smo ugotovili v obdobju med 30 in 60 dnevi po porodu (r = 0,411; P < 0,001). Na podlagi diagnostičnega vrednotenja razmerja M/B z uporabo krivulj ROC (receiver operating characteristics) smo ugotovili, da razmerje M/B pri 1,37 z 71 % zanesljivostjo loči krave s poporodnim premorom pod 120 dnevi in nad tem obdobjem. Krivulje preživetja za podskupine krav z razmerjem M/B nad 1,37 in pod 1,37 so se statistično značilno razlikovale pri zdravih kravah, kjer je povprečni poporodni premor znašal 87 ± 28 dni za krave z razmerjem M/B pod 1,37 in 122 ± 42 dni za krave z razmerjem nad 1,37. Krivulje preživetja za omenjene podskupine se pri kravah z različnimi boleznimi niso statistično značilno razlikovale, čeprav smo pri vseh skupinah opazili daljši poporodni premor pri kravah, ki so imele razmerje M/B višje od 1,37. Delež brejih krav v obdobju med 90 in 120 dnevi po porodu je bil pri vseh skupinah višji v podskupini z razmerjem M/B pod 1,37 v primerjavi s kravami, kjer je bilo razmerje M/B višje od 1,37. Rezultati raziskave nam dokazujejo, da je razmerje M/B lahko v pomoč veterinarjem praktikom pri predvidevanju težav s plodnostjo v čredah krav mlečnih pasem. Ključne besede: krave mlečnih pasem; razmerje med maščobami in proteini v mleku; poporodni premor; analiza ROC; analiza preživetja Slov Vet Res 2013; 50 (2): 67-74 UDC 611.018.62:612.741:612.744:57.088:636.4:599.731.1 Original Scientific Article expression of myosin heavy chain isoforms in longissimus muscle of domestic and wild pig Gregor Fazarinc1, Matjaž Uršič1, Vesna Gjurčevič Kantura2, Tajana Trbojevič Vukičevič2, Martin Škrlep3, Meta Čandek - Potokar3 1Veterinary Faculty, University of Ljubljana, Gerbičeva 60, 1000 Ljubljana, Slovenia, 2Faculty of Veterinary Medicine, University of Zagreb, Heinzelova 55, 1000 Zagreb, Croatia, ^Agricultural Institute of Slovenia, Hacquetova ulica 17, 1000 Ljubljana, Slovenia Corresponding author, E-mail: meta.candek-potokar@kis.si Summary: The expression of myosin heavy chain (MyHC) isoforms in the myofibers of domestic and wild pig was studied to characterize muscle tissue differences related to species domestication and selection. Muscle samples were obtained from longissimus muscle of five two years old wild and domestic Large White pigs. Four different MyHC isoforms (MyHC-I, MyHC-IIa, MyHC-IIb, MyHC-IIx) were determined in the myofibers of both, domestic and wild pig, and allowed the distinction of I, IIa, IIx/b and IIb myofiber types. Oxidative types I and IIa and type IIx/b myofibers were notably more abundant in longissimus muscle of wild than domestic pig. On the contrary, the number of glycolytic IIb myofibers prevailed in domestic pig. The cross sectional areas (CSA) of different MyHC myofiber types were 2 to 3 times smaller in wild than in domestic pig. In wild pig, CSA was more homogeneous between myofiber types with no difference between CSA of types I, IIx/b and IIb myofibers, whereas IIx/b and IIb myofibers exhibited greater CSA in domestic pigs. Type IIa myofibers were the smallest ones in both, domestic and wild pig. The presence of MyHC-IIb isoform was clearly established in the myfibers of wild pigs denoting that its expression is not just the result of the intensive selection for growth efficiency and muscularity. On the other hand, it is evident that domestication and breeding goals in pigs resulted in the hypertrophy of all myofiber types, in particular of those in which MyHC-IIb isoform is expressed. Key words: myosin heavy chains; myofiber; immunohistochemistry; domestic pig; wild pig Introduction Skeletal muscles of mammals are composed of heterogeneous myofibers, in which distinct sets of structural proteins and metabolic enzymes are expressed. Such heterogeneity of skeletal muscles is related to the diversity of myofibrillar proteins, in particular myosin heavy chains (MyHC). In mammalian skeletal muscles up to 9 MyHC isoforms have been identified, each encoded by a distinct MyHC gene (1). Some of them are expressed transitorily during development or only in some functionally specialized muscles (2). Received: 12 July 2012 Accepted for publication: 4 February 2013 In the adult mammalian locomotor skeletal muscles four MyHC isoforms have been described: one slow (MyHC-I) and three fast isoforms (MyHC-IIa, MyHC-IIx and MyHC-IIb). Studies on isolated myofibers in rodents showed that the maximal shortening velocity increased in the following pattern: I