3rd COLLOQUIUM OF GENETICS...COLLOQUIUM of Genetics (3 ; 2013 ; Piran) Proceedings / 3th Colloquium of Genetics, Piran 13th September 2013 ; [organizers] Genetic Society of Slovenia

GENETIC SOCIETY OF

SLOVENIAin collaboration with

THE SLOVENIAN SOCIETY OF HUMAN GENETICS

3rd COLLOQUIUM OF GENETICS

Proceedings

PIRAN

SEPTEMBER 13th 2013


GENETIC SOCIETY OF SLOVENIA IN COLLABORATION WITH THE

SLOVENIAN SOCIETY OF HUMAN GENETICS


Proceedings

Marine Biology Station Piran National Institute of Biology

Piran

September 13th 2013

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Organizers

Genetic Society Slovenia in collaboration with The Slovenian Society of Human Genetics National Institute of Biology Marine Biology Station Piran University of Ljubljana Biotehnical Faculty

Faculty of Medicine University of Maribor Faculty of Medicine

Faculty of Chemistry and Chemical Technology

Editors Andreja Ramšak Uroš Potočnik

Reviewers Peter Dovč Branka Javornik Tanja Kunej Vladimir Meglič Alberto Pallavicini Uroš Potočnik Darja Žgur Bertok

Design: Emanuela Boštjančič Publisher: Genetic Society Slovenia, Ljubljana, September 2013 Number of issues: 60 USB keys Contributing authors are responsible for proof‐reading corrections.

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CIP - Kataložni zapis o publikaciji Narodna in univerzitetna knjižnica, Ljubljana 575(082)(0.034.2) COLLOQUIUM of Genetics (3 ; 2013 ; Piran) Proceedings / 3th Colloquium of Genetics, Piran 13th September 2013 ; [organizers] Genetic Society of Slovenia in collaboration with Slovenian Society of Human Genetics ; [editors Andreja Ramšak, Uroš Potočnik]. - Ljubljana : Genetic Society of Slovenia, 2013 ISBN 978-961-90534-9-2 ISBN 978-961-93545-0-6 (pdf) 1. Ramšak, Andreja, 1970- 2. Slovensko genetsko društvo 3. Slovensko društvo za humano genetiko 268724992

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Members of boards

Scientific board Peter Dovč

Damjan Glavač Simon Horvat

Branka Javornik Alberto Pallaviccini

Uroš Potočnik Darja Žgur Bertok

Organization board Andreja Ramšak

Emanuela Boštjančič Tanja Kunej Petra Perin Uroš Potočnik Katja Repnik Larisa Zemljič

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CONTENT p.

MEETING PROGRAMME 8

LECTURES 11

SPONSOR LECTURE 12

Erzsebet Csibi: PERSONALIZED TEACHING AND LEARNING WITH PEARSON 13

POPULATION GENETICS 15

Victoria Bertucci: POPULATION GENETICS OF THE CRAYFISH Austropotamobius pallipes COMPLEX IN FRIULI VENEZIA GIULIA (ITALY)

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Barbara Pipan: ANALYSIS OF GENETIC STRUCTURE OF Brassica napus L. AND ITS SEXUALLY COMPATIBILE RELATIVES USING POPULATION GENETIC PARAMETERS

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GENOMICS 24

Chiara Manfrin: CRUSTACEAN TRANSCRIPTOMIC: NEW INSIGHTS DRIVEN BY THE NEXT GENERATION SEQUENCING

25

Sabina Ott: DEVELOPMENT OF A CENTRAL WEBSITE FOR RESEARCH OF FRACTAL GEOMETRY IN MEDICINE AND MOLECULAR BIOLOGY

26

Žiga Strmšek: ATLAS OF miR‐34 GENE FAMILY REGULATORY NETWORK AND ITS THERAPEUTICAL POTENTIAL

27

BIOTECHNOLOGY 28

Jernej Pavšič: COMPARISON OF DNA EXTRACTION METHODS FOR QUANTIFICATION OF HUMAN CYTOMEGALOVIRUS BY qPCR

29

Tine Pokorn: IDENTIFICATION OF TARGETS FOR VIROID DERIVED SMALL RNAS (vd‐sRNA) IN HOPS

30

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MOLECULAR BASIS OF DISEASES 31

Marko Flajšman: DEVELOPMENT OF A TRANSFORMATION SYSTEM FOR GENE KNOCK‐OUT IN VERTICILLIUM ALBO‐ATRUM AND TESTING KNOCK‐OUT VIRULENCE ON NICOTIANA BENTHAMIANA

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Marco Gerdol: DE NOVO DISCOVERY OF ANTIMICROBIAL PEPTIDES FROM INVERTEBRATE TRANSCRIPTOMES

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Danijela Krgović: GENOMIC STRUCTURAL VARIATION IN SLOVENIAN CHILDREN WITH NEURODEVELOPMENTAL DISORDERS

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Petra Perin: GENETICS AND PHARMACOGENOMICS OF CHILDHOOD ASTMA 35

Minja Zorc: DEVELOPMENT OF BIOMARKERS FOR FAT DEPOSITION USING INTEGRATION OF GENOMIC DATA AND BIOINFORMATICS ANALYSIS

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Larisa Zemljič: ASSOCIATION AND GENE EXPRESSION ANALYSIS OF ORMDL3 AND TNF IN MULTIPLE SCLEROSIS, ASTHMA AND RHEUMATOID ARTHRITIS

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POSTERS 58

POPULATION GENETICS 59

Daša Perko: MEFV GENE MUTATIONS IN CENTRAL AND SOUTH‐EASTERN EUROPEAN COUNTRIES

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Martina Planinc: GENOTYPE BY ENVIRONMENT INTERACTION FOR GROWTH IN ON‐FARM TESTED GILTS

61

GENOMICS 68

Jernej Bravničar: CATALOG OF GENETIC VARIABILITY RELATED TO microRNA NETWORK IN TWO FISH SPECIES: Danio rerio AND Tetraodon nigroviridis

69

Jana Obšteter: CATALOG OF POLYMORPHISMS ASSOCIATED WITH MICRORNA SILENCING MACHINERY

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Aleksandra Šakanović: ISOLATION AND ACTIVITY OF GENOTOXIN Usp OF BACTERIA Escherichia coli

77

GENOM INTERACTION 78

Jasmina Beltram: A MOUSE ATLAS OF TST GENE REGULATORY NETWORK 79

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BIOTECHNOLOGY 80

Matevž Rumpret: EFFICIENCY OF DIFFERENT DONOR STRAINS IN DELIVERING THE ColE7 BASED TOXICITY VIA CONJUGAL TRANSFER

81

Mateja Zupin: CHARACTERIZATION OF THE COMMON BEAN (Phaseolus vulgaris L.) PARENT CULTIVARS FOR FURTHER GENOMIC AND TRANSCRIPTOMIC ANALYSES

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AUTHOR INDEX 83

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PROGRAM OF MEETING

Registration 8.30 – 9.00

Opening of the 3rd Colloquium of Genetics

Welcome: Andreja Ramšak and Uroš Potočnik

9.00 – 9.10

Sponsor lecture

Chairman: Darja Žgur Bertok, Tanja Kunej

Erzsebet Csibi

PERSONALIZED TEACHING AND LEARNING WITH PEARSON

9.10 ‐ 9.50

Population Genetics and Genomics

Chairmen: Alberto Pallavicini, Branka Javornik

9.50 ‐ 10.50

Victoria Bertucci

POPULATION GENETICS OF THE CRAYFISH Austropotamobius pallipes COMPLEX IN FRIULI VENEZIA GIULIA (ITALY)

9.50 ‐ 10.05

Barbara Pipan

ANALYSIS OF GENETIC STRUCTURE OF Brassica napus L. AND ITS SEXUALLY COMPATIBILE RELATIVES USING POPULATION GENETIC PARAMETERS

10.05 – 10:20

Chiara Manfrin

CRUSTACEAN TRANSCRIPTOMIC: NEW INSIGHTS DRIVEN BY THE NEXT GENERATION SEQUENCING

10.20 – 10.35

Žiga Strmšek

ATLAS OF miR‐34 GENE FAMILY REGULATORY NETWORK AND ITS THERAPEUTICAL POTENTIAL

10.35 – 10.50

Coffe breake and posters viewing 10.50 – 11.30

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Molecular basis of diseases and Biotechnology

Chairmen: Uroš Potočnik, Peter Dovč

11:30 ‐ 13:30

Jernej Pavšič

COMPARISON OF DNA EXTRACTION METHODS FOR QUANTIFICATION OF HUMAN CYTOMEGALOVIRUS BY qPCR

11.30 – 11.45

Tine Pokorn

IDENTIFICATION OF TARGETS FOR VIROID DERIVED SMALL RNAS (vd‐sRNA) IN HOPS

11.45 – 12.00

Marko Flajšman

DEVELOPMENT OF A TRANSFORMATION SYSTEM FOR GENE KNOCK‐OUT IN VERTICILLIUM ALBO‐ATRUM AND TESTING KNOCK‐OUT VIRULENCE ON NICOTIANA BENTHAMIANA

12.00 – 12.15

Marco Gerdol

DE NOVO DISCOVERY OF ANTIMICROBIAL PEPTIDES FROM INVERTEBRATE TRANSCRIPTOMES

12.15 – 12.30

Danijela Krgović: GENOMIC STRUCTURAL VARIATION IN SLOVENIAN CHILDREN WITH NEURODEVELOPMENTAL DISORDERS

12.30 – 12.45

Petra Perin

GENETICS AND PHARMACOGENOMICS OF CHILDHOOD ASTMA

12.45 – 13.00

Minja Zorc

DEVELOPMENT OF BIOMARKERS FOR FAT DEPOSITION USING INTEGRATION OF GENOMIC DATA AND BIOINFORMATICS ANALYSIS

13.00 – 13.15

Larisa Zemljič

ASSOCIATION AND GENE EXPRESSION ANALYSIS OF ORMDL3 AND TNF IN MULTIPLE SCLEROSIS, ASTHMA AND RHEUMATOID ARTHRITIS

13.15 ‐ 13.30

Snack, self‐service bar, poster viewing 13.30 – 15.00

Meeting of the Genetic Society Slovenia and announcement of the best lecture and the best poster awards

15. 00 ‐ 16.00

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Poster section 10.50 – 11.30 13.30 – 16.00

Daša Perko

MEFV GENE MUTATIONS IN CENTRAL AND SOUTH‐EASTERN EUROPEAN COUNTRIES

Martina Planinc

GENOTYPE BY ENVIRONMENT INTERACTION FOR GROWTH IN ON‐FARM TESTED GILTS

Jernej Bravničar

CATALOG OF GENETIC VARIABILITY RELATED TO microRNA NETWORK IN TWO FISH SPECIES: Danio rerio AND Tetraodon nigroviridis

Jana Obšteter

CATALOG OF POLYMORPHISMS ASSOCIATED WITH MICRORNA SILENCING MACHINERY

Aleksandra Šakanović

ISOLATION AND ACTIVITY OF GENOTOXIN Usp OF BACTERIA Escherichia coli

Jasmina Beltram

A MOUSE ATLAS OF TST GENE REGULATORY NETWORK

Matevž Rumpret

EFFICIENCY OF DIFFERENT DONOR STRAINS IN DELIVERING THE ColE7 BASED TOXICITY VIA CONJUGAL TRANSFER

Mateja Zupin

CHARACTERIZATION OF THE COMMON BEAN (Phaseolus vulgaris L.) PARENT CULTIVARS FOR FURTHER GENOMIC AND TRANSCRIPTOMIC ANALYSES

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LECTURES

11


SPONSOR LECTURE

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Corresponding author: Erzsebet Csibi, [email protected]

Sponsor lecture

PERSONALIZED TEACHING AND LEARNING WITH PEARSON

Erzsebet Csibi

Pearson Central Europe, Hungary

With a range of Higher Education learning solutions, everything we publish — from textbooks to eLearning programmes — is designed to help students learn and achieve success. We offer products in a variety of formats because we recognise that all learners do not learn in the same way. Pearson has the most widely trusted and respected programmes in educational and professional publishing. Its imprints, including Prentice Hall, Financial Times Publishing, Benjamin Cummings, Addison Wesley, Allyn&Bacon, Cisco Press, to name a few, all stand for quality, consistency and innovation in education and life‐long learning. Pearson academic textbooks are written by award‐winning (e.g. Nobel Prize) authors, professionals widely recognised around the world. Our eLearning and Assessment solutions, MyLab and Mastering, offer personalised study paths, customised teaching resources and powerful results reporting. Our goal is to save educators time and improve students’ learning, understanding and grades. Pearson Custom Publishing allows you to pick and choose content from one or more texts and combining it into a bespoke book, unique to your course. To give you an idea of the flexibility and scope of our Custom Textbook Solutions, here are some examples of how fellow academics across Europe have successfully utilised our custom services in their courses:

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specific e‐books they need throughout their studies. In Hungary we have already worked on Custom Publishing projects with Corvinus and University of Pannonia. In Slovenia, University of Ljubljana has just completed their new customized book.

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POPULATION

GENETICS

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Corresponding author: Alberto Pallavicini, [email protected]

Abstract

POPULATION GENETICS OF THE CRAYFISH Austropotamobius pallipes COMPLEX IN FRIULI VENEZIA GIULIA (ITALY)

Victoria Bertucci, Chiara Manfrin, Paolo Edomi, Piero Giulio Giulianini, Alberto Pallavicini

University of Trieste, Department of Life Sciences, Italy

The white‐clawed crayfish A. pallipes has suffered in recent decades a strong decrease in the number of individuals throughout its entire distributional range, so much to be included in the red list of the IUCN (International Union for Conservation of Nature) as a species at risk of extinction. Several causes led to drastic reduction of the populations of A. pallipes in Friuli Venezia Giulia (FVG) and still represent potential threats in terms of deterioration and fragmentation of the habitat where the species lives, the spread of crayfish plague caused by the fungus Aphanomyces astaci, and the competition with non‐native crayfish species such as the Louisiana red crayfish. In the frame of RARITY (http://www.life‐rarity.eu ) a LIFE+ project for the eradication of the invasive Louisiana red swamp and for the preservation of the native white clawed crayfish in Friuli Venezia Giulia, the University of Trieste is responsible for the genetic characterization of A. pallipes in the Region FVG with the aim of identify any evolutionarily significant unit (ESU), that is represented by a population which shows a significant differentiation and is characterized by a local ecotype and that is important to treat as a separate management unit and to select the best breeders for the selection of juveniles to be used in restocking practices in order to increase the genetic variability and thus the “genetic health” at the sites of introduction or reintroduction. The analysis of about 370 individuals from 45 monitored sites showed that the FVG population of crayfish belonging to A. pallipes complex is rather homogeneous, certainly ascribable to the sub‐species A. italicus meridionalis, with the only exception of the specimens from Val Rosandra (TS), which are genetically different and are identified as a population of A. italicus carsicus.

REFERENCES 1. De Luise G. I crostacei decapodi d’acqua dolce in Friuli Venezia Giulia. Recenti acquisizioni sul comportamento e sulla distribuzione nelle acque dolci della Regione. Venti anni di studi e ricerche. Ente Tutela Pesca ‐Regione Autonoma Friuli Venezia Giulia 2006, 91. 2. Cataudella R, Paolucci M, Delaunay C, Ropiquet A, Hassanin A, Balsamo M et al. Genetic variability of austropotamobius italicus in the marches region: Implications for conservation. Aquatic Conservation: Marine and Freshwater Ecosystems 2010, 20, 261‐268. 3. Fratini S, Zaccara S, Barbaresi S, Grandjean F, Souty‐Grosset C, Crosa G, e tal. Phylogeography of the threatened crayfish (genus Austropotamobius) in Italy: Implications for its taxonomy and conservation. Heredity 2005, 94, 108‐118. 4. Stefani F, Zaccara S, Delmastro GB, Buscarino M. The endangered white‐clawed crayfish Austropotamobius pallipes (Decapoda, Astacidae) east and west of the Maritime Alps: A result of human translocation? Conservation Genetics 2011, 12, 51‐60.

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http://www.life-rarity.eu/


Presenting author: Barbara Pipan, [email protected]

Article

ANALYSIS OF GENETIC STRUCTURE OF Brassica napus L. AND ITS SEXUALLY COMPATIBILE RELATIVES USING POPULATION GENETIC PARAMETERS

Barbara Pipan, Jelka Šuštar‐Vozlič, Vladimir Meglič

Agricultural institute of Slovenia, Crop Science Department, Slovenia

The parameters of population genetics allow the display of the results of genetic analysis of different organisms. The paper describes the display of important parameters of population genetics in the analysis of the genetic structure of economically important cruciferous species and its sexually compatible relatives according to their biological characteristics through spontaneous gene flow and uncontrolled appearance inside production area. Analysis of genetic structure and genetic diversity of the Brassica napus L. and its sexually compatible relatives (SKR) was performed on the basis of temporal (four years) and spatial (the entire Slovenian production space) level using microsatellite markers. Reference varieties and forms of B. napus and SKR, which appear in Slovenia, were included in the analysis. Most volunteer and feral populations of B. napus were collected within the regions with the highest oilseed rape production. Feral populations of B. napus are exhibiting characteristics of self‐recruited natural populations. We have also found that the selected set of microsatellite markers is suitable for genetic differentiation of genotypes at the genus, species, subspecies and variety level within the Brassicaceae family and that the origin of microsatellites or its structure does not affect the polymorphic information content. Volunteer and feral populations are genetically more diverse than the reference varieties of B. napus, which are less diverse than gene pool of the reference varieties of SKR. Temporal monitoring of gene flow within volunteer and feral forms of B. napus, B. rapa and S. arvensis shows that the settlement and conservation of gene flow in those self‐recruited genomes, via spontaneous intra‐and inter‐specific hybridizations in nature, is possible. Genotyping of B. napus and its SKR within the family Brassicaceae, based on codominant input matrix on diploid level are appropriate, which allows the calculation of estimates of gene flow and the conservation of genes via spontaneous pollination in nature.

INTRODUCTION

Brassica napus L. is a widely cultivated plant species which belongs to the diverse cruciferous family (Brassicaceae). The species is divided into two subspecies groups. The first group includes swedes (B. napus ssp. napobrassica), the second one includes winter and spring B. napus ssp. napus forms which are used for oil production or fodder 1. B. napus originated through spontaneous inter‐specific hybridisation (followed by polyploidisation) between turnip rape (B. rapa L.; genome AA, 2n=20) and cabbage (B. oleracea L.; genome CC, 2n=18), resulting in alotetraploid genome comprising the full chromosome complements of its two progenitors 2. Due to the variable out‐crossing rate, intra‐specific gene flow between B. napus plants from different habitats in nature (crop, volunteers inside and feral populations outside the cultivated area) may occur. Pollination of B. napus is also possible with its sexually compatible relatives (SKR) from the other genera and species of the Brassicaceae family (inter‐specific hybridization). The highest pollination affinity to B. napus in Slovenia has especially B. nigra L., B. rapa (B. campestris L.), B. oleracea, Diplotaxis muralis (L.) DC., Diplotaxis tenuifolia (L.) DC., Sinapis alba L., Sinapis arvensis L. (B. kaber (D) LC. Wheeler), Raphanus raphanistrum L., Raphanus sativus L. and Rapistrum rugosum (L.) All. Their naturally present forms inside production areas can enable gene flow through spontaneous intra‐ and inter‐specific hybridization in nature due to simultaneous fulfillment of the factors that determine the pollen transfer. There is also possibility of introduction of genetically modified (GM) genotypes of B. napus

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into Slovenian production area, which would result in new sources of transgenes with a pollinating potential 3. Molecular markers, microsatellite markers or SSR's (Simple Sequence Repeats) could be used to analyse the genetic profiles and changes in the allelic structure of the genotypes on the DNA level on the basis of spatial and temporal component. The comparable studies according to its methodology and aim were published by Elling et al. (2009) 4, Hassan et al. (2006 5, 2008 6) and Pascher et al. (2006 7, 2010 8). The aim of this paper is based on the parameters of population genetics, which were calculated with different computer programs; to give a comparison of the genetic structure of different forms of B. napus and it’s SKR under Slovenian production area. The calculated parameters were used to evaluate genetic diversity, the level of gene flow rate, allele diversity, settlement of the transferred genes through spontaneous pollination in nature, out‐crossing rate, characteristics of natural populations, the suitability of marker system and processing of the results. This paper presents an integrated workflow of spatial identification of sampling locations, field survey in a four‐year period (2007‐2010), the acquisition of reference material of included species, extraction of DNA from plant material, analysis of genetic structure using microsatellite markers, processing results on the diploid (2n) and tetraploid (4n) level, statistical analysis and presentation of results.

METHODS We have predicted occurrence of different B. napus and its SKR forms from different habitats within the region, where oilseed rape is widely cultivated, at the transport infrastructure and on locations with uncontrolled movements, loading and unloading and distribution of rape seed. During the flowering time of oilseed rape, a field survey within Slovenian production area was performed from 2007 to 2010 and young leaves of B. napus and its SKR were sampled. In addition, we collected in 2008 the seed samples of harvested oilseed rape from various producers. Information and reference seeds from B. napus varieties, which were grown in the period of 1984 to 2010 in Slovenia and reference SKS varieties that occur in our country, were acquired through various sources and national gene banks. From acquired seeds of reference material, we raised young plants. Bulks were prepared from plant material and subsequently DNA was isolated. For genetic identification, we used microsatellite markers, with different structures and repeat motifs isolated from related species 9, 10, 11, 12, 13. To analyze the genetic diversity of SKS reference genotypes using PCR (polymerase chain reaction) procedure, 15 loci from the set of 45 were included. PCR amplification was performed in a 11.5 µl total volume after 4 different touchdown PCR protocols with optimal temperature settings for each pair of primers, which were in the process of synthesis modified with 18‐bp M13 (‐21) sequence. Using bulked post‐PCR products (three fluorescent labels FAM, HEX and NED) we have determined the exact allele lengths on the basis of fragment analysis using length standard, ROX‐350 (ABI) and the ABI3130 genetic analyzer (ABI). Reading of the results on the diploid and tetraploid level was performed by GenScan4.0 (ABI). Processing and presentation of the genetic analysis results on diploid and tetraploid level was carried out in various statistical software packages and programs for the evaluation of genetic diversity and population structure (Arlequin 3.5.1.2 (Excoffier and Lischer, 2010) 14, FreeTree (Pavlicek et al., 1999) 15, Fstat 2.9.3.2 (Goudet, 2002) 16, GenAlEx 6.4.1 (Peakall and Smouse, 2006) 17 , GenePop 4.1.0 (Rousset, 2008) 18 , Genetix 4.02 (Belkhir et al., 1999) 19, Identity 1.0 (Wagner and Sefc, 1999) 20, Microsatellite Toolkit (Park, 2001) 21, Populations 1.2.28 (Langella, 2002) 22, Structure 2.3.3 (Pitchard e tal., 2009) 23 and TreeView (Page, 1996) 24 ).

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RESULTS Field survey of B. napus forms around Slovenia in the period 2007‐2010 resulted in 19 cultivars, 66 samples of volunteers, and 195 samples of feral populations, which includes a total of 280 samples (sites). Acquired reference samples of B. napus included 56 varieties of B. napus subsp. napus in three repetitions, two varieties of the B. napus subsp. napobrassica; in total 170 reference genotypes of B. napus. During the field survey, we have collected 22 samples of S. arvensis and 4 samples of B. rapa, representing 26 samples of B. napus SKR. Acquired reference samples of B. napus SKR included 10 different species and genera of the Brassicaceae family, which appear in Slovenia as cultivated, weedy or wild; a total of 22 genotypes. Since the area of Slovenia is divided into 12 statistical regions, we have collected samples during field survey within gorenjska, notranjsko‐kraška, obalno‐kraška, osrednjeslovenska, podravska, pomurska, savinjska, spodnjeposavska, zasavska, and jugovzhodna Slovenia region, while goriška and koroška region were excluded due to the lack of appropriate samples. The highest number of samples of volunteer and feral populations during the four‐year period was found inside regions where oilseed rape is grown in its highest extent. On the basis of the collected and acquired samples (reference from the period 1984‐2010 and from the field survey during the 2007‐2010) inside Slovenian production area, we preformed the analysis of the genetic structure of 498 different genotypes in total. From these, 468 were analyzed using 45 different markers and 30 using 15 different markers that were proved to be the most informative and optimal for the evaluation of genetic diversity and relatedness within the Brassicaceae in the preliminary analysis. Statistical analysis was performed using 11 computer programs, producing different population genetics parameters which were used to obtain useful results and informative estimations reflecting the actual pollinating relations of B. napus and its SKR within Brassicaceae. (Table 1).

Program/Program package Parameter/estimation /result

Microsatellite Toolkit, Identity Evaluation of parameters of genetic variability. Fstat Level of genetic diversity. GenAlEx Population statistics, estimation and settlement of gene flow, principal

coordinate analysis (PCoA), analysis of genetic and geographic distance and comparison between parameters of the genetic diversity on 2n and 4n level with Mantel test.

GenePop Estimation of gene flow frequency. Genetix Factorial correspondence analysis (FCA). Arlequin Analysis of molecular variance (AMOVA). Populations, FreeTree Hierarchic cluster analysis based on genetic distance and bootstrapping. TreeView Visualization the phylogenetic trees. Structure Determination of the genetic structure and real number of genetic clusters

according to Bayesian approach on 2n and 4n level.

Table 1: Computer programs and program packages used in the analysis of the genetic structure through parameters of population genetic.

The summary of significant points included in population genetics is presented in Table 2 due to the complex results of the analysis. Each parameter is described using levels 1 to 5, based on the calculated values obtained by each group of B. napus and its SKR genotypes.

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Parameter/ appeared form

Reference varieties of B. napus

Volunteers of B. napus

Feral populations of B. napus

Reference varieties of SKR

Genotypes of SKR from field survey (B. rapa in S. arvensis)

Origin and structure of microsatellite marker

We found that either the origin of microsatellites (from which species is isolated) or its structure (repeat motif), could not affect its informational content.

Genetic diversity 2 3 4 1 5 Gene flow (frequency Na>5 %) 2 4 3 1 5 Allelic diversity 2 5 4 1 3 Estimation of gene flow settlement (Np)

3 2 5 1 4

Estimation level of spontaneous gene flow (F)

2 3 4 1 5

Out‐crossing rate / 3 5 / 4 Natural populations characteristics (HWE)

1 3 4 1 5

Analysis based on codominant matrix on 2n level

4 4 4 4 4

Analysis based on binary matrix on 4n level

3 3 3 3 3

Analysis based on codominant matrix on 4n level

5 5 5 5 5

Analysis based on binary matrix on 2n level

2 2 2 2 2

Table 2: Evaluation of genetic diversity on different levels of B. napus and its SKR. Classification of each group of genotypes according to the calculated value of results is labelled with levels, where 1‐ is the lowest and 5‐ is the highest value for each parameter. Label / is used in cases where calculated value is not present or relevant.

We have found that the selected set of microsatellite markers is suitable for distinguishing genotypes at the level of the same genus, species, subspecies and varieties within Brassicaceae, and that the origin of microsatellites or its structure does not affect its informational content. Volunteer and feral populations are genetically more diverse than the reference varieties of B. napus, which are less genetically diverse than the reference varieties of SKR. Temporal monitoring of gene flow within the volunteers and ferals of B. napus and its SKR species from field survey (B. rapa and S. arvensis), shows that the settlement and preservation of the transferred genes in self‐recruited plant genomes through spontaneous intra‐and interspecies pollination in nature, is possible. The Mantel coefficient of genetic and geographic distances between genotypes from the field survey shows that within the same region in Slovenia appear genetically more similar genotypes, implying that they can pollinate each other and are self‐recruited through generations. Settlement of gene flow through spontaneous pollination of B. napus over time explains the parameter Np (number of private alleles). The highest Np value was calculated within feral populations, followed by volunteers while the lowest Np value was calculated within cultivated B. napus. The highest level of spontaneous hybridization in nature occurs within feral populations that exposed intra‐ and inter‐specific pollination for a longer temporal period, reflected in highest positive value of the fixation index (F). Lower F value was calculated within volunteers, the lowest, while a negative value has been calculated within cultivated B. napus, suggesting a limited breeding selection, since these genotypes are included in reference gene pool of varieties grown in Slovenia. Was also calculated that volunteer and feral populations do have not meet Hardy‐Weinberg equilibrium (HWE) conditions (p < 0.05), corresponding to definition

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of natural populations for any of the loci. Genotyping of B. napus and its SKR within Brassicaceae, based on codominant input matrices on the 2n level is appropriate, since it allows the calculation of the important and useful parameters of population genetics (estimates of the gene flow, settlement) and potential for out‐crossing (out‐crossing rate) through spontaneous hybridizations in nature.

DISCUSSION Analysis of genetic diversity and gene flow of B. napus and its SKRs was performed at the national level, where the samples from the whole production area of Slovenia during four‐year period, were included. Comparable studies from other countries covered only a specified region (Pascher et al., 2006, 2010) 7,8. From all the SKR's of B. napus occurring in Slovenia during the flowering season (when the plants could be easily identified and determined), only species B. rapa and S. arvensis were found. It is well known that these two types of weedy species are widely present within the production areas originated also from the soil seed bank and are flowering at the same time as B. napus. Since this study covers the reference as well as naturally present forms of B. napus and its SKRs during the four‐year period, the results reflect the actual situation and persistence of these plants within Slovenian production area. Spatial and temporal component during four‐year period and the involvement of 80 reference varieties of B. napus and its SKRs actually reflects the genetic diversity at the national level which has not been published yet in any of the studies. The results of this analysis are also a reflection of the actual situation, where the possibility of spontaneous pollination occurring in nature exists. Moreover, in other studies, the genetic and geographic distances between genotypes from filed surveys were evaluated using a small number of microsatellite markers (five and nine), that originated only from B. napus, while markers from SKR's were not included in the analysis. Results are presented only on the basis of annual genetic identification, where reference varieties of B. napus, grown from the selected region in last four‐years, were included. Their aim was to identify the origin of B. napus feral populations without inter‐specific gene flow possibility (Pascher et al. 2006, 2010) 7,8. Our analysis includes reference varieties of B. napus which were cultivated in Slovenia over the last 36 years, except for varieties 'Petrol', 'Danica' and 'Zora' (seeds are not available any more) and reference varieties of SKR's that were found during this period or occur as weedy or wild species in Slovenia. None of these studies, however, did not include the vegetable forms of B. napus (Elling et al. 2009, Hasan et al. 2006) 4,5. The results of two other studies (Pascher et al. 2006, 2010) 7,8 were presented on the basis of codominant input matrices on 2n level. The study published by Elling et al. (2009), was also carried out in a four‐year period (2004‐2007) using four microsatellite markers (three of these are also included in our analysis), and isolated only from B. napus structured by GA repeat motif. Those results were presented on the basis of binary input matrix. Our study includes the microsatellite markers which originated from different species of SKRs and were used for genetic identification through binary and codominant input matrices on 2n and 4n level. Moreover, our analysis also covers the SKRs occurring in nature as weedy or wild species. Their presence within the cultivation area consequently enables the introduction of new alleles in genomes of feral B. napus through spontaneous pollination (Pipan et al. 2013) 3, which has been proved by the highest Np and F values within feral B. napus. Higher genetic diversity within feral populations compared to the commercial varieties was found as well by Pascher et al. (2010) 8. Until now, there was no objective comparison of informativity and suitability of different data processing ways within Brassicaceae, according to

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binary or codominant matrices and different ploidy levels. Moreover, main part of our results were obtained on the basis of codominant input data on 2n level because this approach represents the optimal and most efficient output between ploidy level of Brassicaceae species and bioinformatics computer programs support for genetic structure analysis using parameters of population genetics. In addition, we performed the Mantel comparison of the Bayesian cluster analysis using codominant input matrix at 4n level with the binary input matrix at 4n level and with codominant input matrix at 2n level. Such a comparison gave an objective assessment of the two approaches most commonly used in the genetic diversity studies of B. napus with the parameters of population genetics for Brassicaceae. There we have observed difference between binary and codominant data processing, where codominant approach allows the calculation of the agronomically and ecologically important population genetics parameters (estimation level of spontaneous gene flow, out‐crossing rate, estimation of gene flow settlement), which is not possible using binary data processing. As a result we have obtained informative parameters as an outcome from appropriate data reading and statistical processing (genetic origin of included species form Brassicaceae, four‐year period field survey, wide range of reference genotypes included, spatial assessment on the national level, codominant input matrix and suitable usage of available bioinformatics computer programs) which enables to assess and analyze the actual situation in the Slovenian production area.

REFERENCES 1. Snowdon R, Lühs W, Friedt W. Genome Mapping and Molecular Breeding in Plants. In: Oilseeds, Volume 2. Kole C (ed.). The Pennsylvania State University, Springer‐Verlag Berlin Heidelberg 2007, 55‐114. 2. Friedt W, Snowdon R. Oil crops, Handbook of Plant Breeding 4. In: Oilseed rape. Vollman J, Rajcan I (eds.). Giessen, Springer Science+Business Media 2009, 91‐126. 3. Pipan B, Šuštar‐Vozlič J, Meglič V. Genetic differentiation among sexually compatible relatives of Brassica napus L. Genetika 2013, 45, in press. 4. Elling B, Neuffer B, Bleeker W. Sources of genetic diversity in feral oilseed rape (Brassica napus) populations. Basic and Applied Ecology 2009, 10, 544–553. 5. Hasan M, Seyis F, Badani AG, Pons‐Kühnemann J, Friedt W, Lühs W et al. Analysis of genetic diversity in the Brassica napus L. gene pool using SSR markers. Genetic Resources and Crop Evolution 2006, 53, 793–802. 6. Hasan M, Friedt W, Pons‐Kühnemann J, Freitag NM, Link K, Snowdon RJ. Association of gene‐linked SSR markers to seed glucosinolate content in oilseed rape (Brassica napus ssp. napus). Theoretical Applied Genetics 2008, 116, 1035–1049. 7. Pascher K, Narendja F, Rau D. Feral Oilseed Rape‐Investigations on its Potential Hybridisation. Viena, Federal Ministry of Health and Women 2006, 56 pp. 8. Pascher K, Macalka S, Rau D, Gollman G, Reiner H, Glössl J et al. Molecular differentiation of commercial varieties and feral populations of oilseed rape (Brassica napus L.). BMC Evolutinary Biology 2010, 10, 63. 9. Lowe AJ, Moule C, Trick M, Edwards KJ. Efficient large‐scale development of microsatellites for marker and mapping applications in Brassica crop species. Theoretical Applied Genetics 2004, 108, 1103–1112. 10. Szewc‐McFadden AK, Kresovich S, Bliek SM, Mitchell SE, McFerson JR. Identification of polymorphic, conserved simple sequence repeats (SSRs) in cultivated Brassica species. Teheoretical Applied Genetics 1996, 93, 534–538. 11. Uzanova M I, Ecke W. Abundance, polymorphism and genetic mapping of microsatellites in oilseed rape (B. napus L.). Plant Breeding 199, 118, 323–236. 12. Suwabe K, Iketani H, Nunome T, Kage T. Isolation and characterization of microsatellites in B. rapa. Theoretical Applied Genetics 2002, 104, 1092–1098. 13. Wang N, Hu J, Ohsawa R, Ohta M, Fujimura T. Identification and characterization of microsatellite markers derived from expressed sequence tags (ESTs) of raTdish (Raphanus satvius L.). Molecular Ecology Notes 2007, 7, 503–506. 14. Excoffier L, Lischer H. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Molecular Ecology Resources 2010, 10, 564–567. 15. Pavlicek A, Hrda S, Flegr J. FreeTree ‐ Freeware program for construction of phylogenetic trees on the basis of distance data and bootstrap/jackknife analysis of the tree robustness. Application in the RAPD analysis of the genus Frenkelia. Folia Biologica (Praha) 1999, 45, 97–99.

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16. Goudet J. FSTAT: a program to estimate and test gene diversities and fixation indices. Version 2.9.3.2. Lausanne, Institute of Ecology and Evolution, 2 pp. 2002, http://www.unil.ch/izea/softwares/fstat.html (February 2012). 17. Peakall R, Smouse PE. GenAlEx 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes 2006, 6, 288–295. 18. Rousset F. Genepop 4.1.0: a complete reimplementation of the Genepop software for Windows and Linux. Molecular Ecology Resources 2008, 8, 103–106. 19. Belkhir K, Borsa P, Goudet J, Bonhomme F. Genetix: logicel sousWindows pour la génétique des populations, Version 4.02. Université de Montpellier II, Laboratorie Genome, Populations, Interactions, 1 p. 1999, http://kimura.univ‐montp2.fr/genetix/ (January 2012). 20. Wagner HW, Sefc KM. IDENTITY4.0. Centre for Applied Genetics, University of Agricultural Sciences Vienna, 1999. 21. Park S. Microsatellite Toolkit. Ireland, Genetics Department TCD, 1 pp. 2001, http://oscar.gen.tcd.ie/sdepark/ms‐toolkit (January 2012). 22. Langella O. Population 1.2.28. Logiciel de ge´ne´tique des populations. Boston, Laboratoire Populations, 6 pp. 2002, http://bioinformatics.org/~tryphon/populations/ (January 2012). 23. Pritchard JK, Wen X, Falush D. STRUCTURE ver. 2.3 Chicago, University of Chicago, 38 pp. 2009, http://pritch.bsd.uchicago.edu/ (January 2012). 24. Page R D M. TREEVIEW: an application to display phylogenetic trees on personal computers. Computer Applications in the Biosciences 1996, 12, 357–358.

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http://www.unil.ch/izea/softwares/fstat.html

http://kimura.univ-montp2.fr/genetix/

http://kimura.univ-montp2.fr/genetix/

http://oscar.gen.tcd.ie/sdepark/ms-toolkit

http://pritch.bsd.uchicago.edu/


GENOMICS

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Corresponding author: Alberto Pallavicini, [email protected] Abstract

CRUSTACEAN TRANSCRIPTOMIC: NEW INSIGHTS DRIVEN BY THE NEXT GENERATION SEQUENCING

Chiara Manfrin1, Moshe Tom2, Gianluca De Moro1, Marco Gerdol1, Alberto Pallavicini1, Piero Giulio

Giulianini1

1University of Trieste, Department of Life Sciences, Italy 2Israel Oceanographic and Limnological Research Center, Israel

The use of emerging techniques, such as the Next Generation Sequencing (NGS), permits the

detailed study of transcriptomes, genomes and the analysis of gene expression, thus obtaining an amount of data not comparable with any of the methods applied before and allowing to go deeper in the collection of information of previously unknown processes1.

An important family of neurohormones typical of crustaceans, the cHH superfamily (crustacean Hyperglycemic Hormone), that presides important vital functions as osmoregulation, control of glucose level, molt and reproduction, has been studied in two decapod species, Pontastacus leptodactylus and Procambarus clarkii. The first one is a species native of eastern Europe and western Asia, important under the commercial point of view, while the second one, originating from the Louisiana Country, is nowadays considered as an invasive species around the world, very difficult to control through the available trapping techniques. By an administration experiment of the same cHH hormone, in two different isomeric variants the L‐ and D‐ configurations, we evaluated the effects at the transcriptomic level in the hepatopancreas of P. leptodactylus by RNA seq2. We observed a significant suppression of glycolysis in specimens injected with D‐cHH that could be directly linked to the increase of glucose level obtained after the cHH‐injection. In P.clarkii, instead, we created the first reference database of the eyestalk transcriptome, site of production of several neurohormones, light receptors and many other important molecules, significantly increasing the nucleotide information for the genus Procambarus through Illumina sequencing. This study was undertaken in order to obtain the full length of some hormones, including the GIH (Gonad Inhibiting Hormone) that could be used in experimental studies for negatively influence the development of further generations, so as to reduce the number of progeny of this species that every year threatens the population of autochthon crayfish. Work in part supported by the European Community programme Life 10NAT/IT/000239 RARITY.

REFERENCES 1. Morozova O, Marra MA. Applications of next‐generation sequencing technologies in functional genomics. Genomics 2008, 92, 255‐264. 2. Manfrin C, Tom M, De Moro G, Gerdol M, Guarnaccia C, Mosco A et al. Application of D‐Crustacean Hyperglycemic Hormone Induces Peptidases Transcription and Suppresses Glycolysis‐Related Transcripts in the Hepatopancreas of the Crayfish Pontastacus leptodactylus ‐ Results of a Transcriptomic Study. PLoS ONE 2013, 8, e65176.

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Presenting author: Sabina Ott, [email protected]

Abstract

DEVELOPMENT OF A CENTRAL WEBSITE FOR RESEARCH OF FRACTAL GEOMETRY IN MEDICINE AND MOLECULAR BIOLOGY

Sabina Ott1, Minja Zorc2, Tanja Kunej2

1University of Ljubljana, Biotechnical Faculty, Study of Biotechnology, Slovenia

2University of Ljubljana, Biotechnical faculty, Department of Animal Science, Slovenia

Up to now, Euclidian geometry, which is based on whole dimensions, has conventionally been used to explain the structure of selected shapes1. However, nature is based on fractions of dimensions and shows a different level of complexity. Therefore a new geometry based on fractals has been proposed, termed fractal geometry 2. Fractals have typical characteristics such as self‐similarity and symmetry. Numerous researches have been conducted to prove the statistic or symmetric self ‐ similarity of every hierarchic level of a human being2. The researches indirectly form a hypothesis that all hierarchic levels of the human being, even hierarchic levels, greater than the human being, are connected into a self‐organizing system through the same mathematical algorithm. However, the indirect hypothesis has not yet been proven as the literature regarding fractal geometry of biological systems is unorganized and dispersed

The aim of our study was therefore to develop a central website with relevant information to aid the research of fractal geometry in medicine and molecular biology. The web page consists of three types of information: 1) collected and systematically reviewed literature regarding fractal geometry in medicine and molecular biology and sorted to categories from molecules to organ systems, 2) a list of journals, which published papers related to this topics and 3) a list of software for studying fractals.

By providing a critical overview of the literature regarding fractal geometry, we aimed to provide a basis for the conformation of the hypothesis. Also, by constructing a central website for global research of fractal geometry our goal was to provide a unified information center for scientists to further investigate the field. The website will open a new perspective in studying fractal geometry of biological systems and introduce a new, systems approach of studying the field, a “fractalomics” approach3.

REFERENCES 1. Lipton B. The biology of Belief: Unleashing the Power of Consciousness, Matter and Miracles, 1 ed. Hay House UK 2008. 2. Mandelbrot BB. Fractal geometry of nature, 1 ed. W.H. Freeman and company 1977. 3. Losa GA. The fractal geometry of life. Rev biol. 2009, 102, 29‐59.

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mailto:[email protected]


Presenting author: Žiga Strmšek, [email protected]

Abstract

ATLAS OF miR‐34 GENE FAMILY REGULATORY NETWORK AND ITS THERAPEUTICAL POTENTIAL

Žiga Strmšek, Daša Jevšinek Skok, Tanja Kunej

University of Ljubljana, Biotechnical Faculty, Department of Animal Science, Slovenia

MicroRNAs (miRNAs) are estimated to regulate approximately two thirds of human genes, and are involved in regulation of physiological processes and pathophysiology of several diseases, including cancer. This gives miRNAs the biomarker potential in diagnosis, prognosis, and treatment. Aberrant miRNA gene expression signatures (either up‐ or down‐regulated) are characteristic in cancer cells and can also be explained by epigenetic mechanisms which has been the topic of an increasing number of publications. Therefore, the aim of this study was to integrate data from publications and databases regarding miRNA silencing by DNA methylation and thus facilitate biomarker and therapeutic development. The study revealed that among 2578 currently known human mature miRNAs and 158 known to be regulated by DNA methylation, miR‐34 gene family (miR‐34a, ‐34b, and ‐34c) is silenced by DNA methylation in the highest number of cancer types (24). Consequently we focused the research on miR‐34 gene family and developed the Atlas of miR‐34 gene family regulatory network, consisting of: 1.) its genetic variability, and overlapping QTL, 2.) upstream regulators (transcription factor binding sites (TFBSs)) and CpG islands and 3.) downstream targets. The results indicate that miR‐34 gene family is a good candidate for the experimental internal control in epigenetic studies, has a potential as a general cancer biomarker and target for epigenetic drugs.

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BIOTECHNOLOGY

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Presenting author: Jernej Pavšič, [email protected]

Abstract

__________________________________________________________________________________

COMPARISON OF DNA EXTRACTION METHODS FOR QUANTIFICATION OF HUMAN CYTOMEGALOVIRUS BY qPCR

Jernej Pavšič, Mojca Milavec, Ion Gutierrez Aguirre, Jana Žel

National Institute of Biology, Ljubljana, Slovenia

Infectious diseases are still one of the mayor health problems, causing 20 % of human deaths globally. Methods for rapid and accurate monitoring of microbial kinetics are important for public health protection. In hospitals and clinics, the conventional microbiological methods are being complemented by molecular approaches, which are fast, accurate and offer higher level of sensitivity. However, measurement support for molecular approaches is still not provided, as quality, comparability and traceability of measurements haven’t been assessed yet. The international project INFECT MET is focused on metrology for monitoring infectious diseases, harmful organisms and antimicrobial resistance. NIB is strongly involved in providing the measurement support for quantification of DNA viruses, which is one of the main project’s goals. Attention is put on different DNA extraction methods, as they are supposed to significantly contribute to variabilities of quantification assays. Repeatability and efficiency assessments were done on two open methods and three commercial kits. The extraction methods were evaluated using several concentrations of two different standard materials, each being extracted from two different matrices. The use of different matrices was shown to influence the extraction of DNA, when some commercial kits are used. Commercial kits proved to be better than open methods in terms of efficiency and repeatability, as they extracted at least 5x more DNA and showed lower inter‐ and intra‐assay variability than open methods. Despite their dominant performance over open methods, their relatively low repeatability must be always taken into account, when quantification with molecular methods is performed.

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Presenting author: Tine Pokorn, [email protected]‐lj.si

Abstract

IDENTIFICATION OF TARGETS FOR VIROID DERIVED SMALL RNAS (vd‐sRNA) IN HOPS

Tine Pokorn1, Sebastjan Radišek2, Branka Javornik1, Jernej Jakše1

1University of Ljubljana, Biotechnical Faculty, Department for Agronomy, Slovenia 2Slovenian Institute for Hop Growing and Brewing, Slovenia

Major plant pathogens belong to fungi, bacteria, phytoplasmas, viruses and viroids. Most studies are focused on interactions between plants and fungi, bacteria or viruses, while interactions between viroids and plants are still poorly understood. Recent studies have shown that a viroid‐plant interaction also involves viroid derived small RNAs (vd‐sRNA). Four viroids are known to infect hop plants: hop latent viroid (HLVd), hop stunt viroid (HSVd), citrus bark cracking viroid (CBCVd) and apple fruit crinkle viroid (AFCVd). In order to investigate the role of vd‐sRNA in the interaction with hop plants, de novo reconstruction of hop transcriptome and a search for possible targets for vd‐sRNAs was undertaken. Various hop tissues of healthy and infected plants were sampled throughout the growing season in the Slovenian Institute for Hop Growing and Brewing hop fields, RNAs were isolated and their quantity and quality determined by spectrophotometry, formaldehyde gel electrophoresis and Agilent's run. An RNA bulk sample was made from healthy plants and sent to Illumina sequencing using a paired–end module. We received a total of 348 M sequences in forward and reverse directions. FastQC analysis confirmed the suitability of the received data. Transcriptome assembly with bubble size 50 and word size 24 was performed using the CLC Genomics Server. The assembled transcriptome length was 74 Mb, represented by 140.443 scaffolds with N50 length of 984 bp. RNA molecules for three different viroids (HLVd, CBCVd and HSVd) were cut to all possible 21 bp, 22 bp and 24 bp sequences, which were searched against the transcriptome using two different tools: UEA Small RNA Workbench and psRNATarget. Both tools revealed 11.241 possible targets for vd‐sRNA of which 898 targets were in common. The targets were annotated using the BLAST2GO tool. Further work will include reverse transcriptase real time PCR (RT‐qPCR) analysis to confirm the silencing of the target transcription by vd‐sRNAs.

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MOLECULAR BASIS OF DISEASES

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Presenting author: Marko Flajšman, [email protected]‐lj.si

Abstract

DEVELOPMENT OF A TRANSFORMATION SYSTEM FOR GENE KNOCK‐OUT IN VERTICILLIUM ALBO‐ATRUM AND TESTING KNOCK‐OUT VIRULENCE ON NICOTIANA BENTHAMIANA

Marko Flajšman, Stanislav Mandelc, Branka Javornik

University of Ljubljana, Biotechnical Faculty, Agronomy Department, Jamnikarjeva 101, Ljubljana SI‐1000, Slovenia

The knock‐out (KO) technique is a reverse genetic tool for functional analysis of various phytopathogens. The genome of Verticillium albo‐atrum, which is a destructive soil‐borne fungal pathogen that causes vascular wilt diseases, has already been sequenced. Translation of genome sequence information into biological functions is therefore possible. We successfully established a protocol for generating knock‐outs of V. albo‐atrum, which comprises two methods. The first is the creation of knock‐out plasmids by the USER Friendly cloning technique. Two knock‐out plasmids for two genes (EEY18971 V. albo‐atrum predicted protein, found to be highly expressed at the protein level in the xylem of infected hop plants, and the g9697gene, found to be highly expressed in xylem simulating medium), were constructed. The second method is transformation of the fungal pathogen by Agrobacterium tumefaciens‐mediated transformation (ATMT). V. albo‐atrum knock‐out transformants were verified by PCR testing, which confirmed that deletion of the target gene had been successful. Three‐week‐old Nicotiana benthamiana plants were inoculated through root‐dipping in a conidial suspension of KO trasformants and preliminary results show that knock‐out of gene EEY18971 even increase virulence of KO transformants. On the other hand, knock‐out of the g9697gene has no effect on the virulence of the KO transformants. A major advantage of gene knock‐out is its capacity to target a specific genetic region. Using targeted gene disruption, many genes implicated in the virulence and pathogenicity of the phytopathogen V. albo‐atrum can be characterized. REFERENCES 1. Bhadauria V, Banniza S, Wei Y, Peng YL. Reverse genetics for functional genomics of phytopathogenic fungi and Ooomycetes. Comparative and Functional Genomics 2009, e380719. 2. Fradin EF, Thomma BPHJ. Physiology and molecular aspects of Verticillium wilt diseases caused by V. dahliae and V. albo‐atrum. Molecular Plant Pathology 2006, 7, 71–86. 3. Frandsen RJN, Andersson JA, Kristensen MB, Giese H. Efficient four fragment cloning for the construction of vectors for targeted gene replacement in filamentous fungi. BMC Molecular Biology 2008, 9, 70‐81. 4. Knight CJ, Bailey AM, Foster GD. Agrobacterium‐mediated transformation of the plant pathogenic fungus, Verticillium albo‐atrum. Journal of Plant Pathology 2009, 91, 745–750.

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Corresponding author: Alberto Pallavicini, [email protected]

Abstract

DE NOVO DISCOVERY OF ANTIMICROBIAL PEPTIDES FROM INVERTEBRATE TRANSCRIPTOMES

Marco Gerdol1, Gianluca De Moro1, Gabriele Leoni1, Valentina Torboli1, Chiara Manfrin1, Paola

Venier2, Alberto Pallavicini1

1University of Trieste, Department of Life Sciences, Italy 2University of Padova, Department of Biology, Italy

The innate defense systems of aquatic invertebrates include antimicrobial peptides (AMPs), usually small, positively charged molecules effective against a broad range of pathogens. Several AMP families have been widely studied and described in a many different phyla, while others represent genus‐ or even species‐specific acquisitions. The methodological advances achieved in the last decade now allow a large scale analysis of entire genomes and transcriptomes of non‐model animals. Bioinformatics can be used for the development of in silico tools aimed at the mining of large sequence databases and identification of AMPs belonging to known families and novel candidates satisfying specific user‐defined requirements. Here we present a bioinformatic pipeline for the whole‐transcriptome scale mining of sequences encoding peptides with a potential antimicrobial activity. Based on the known chemical‐physical properties of these bioactive peptides, we developed a Perl script which permits to filter a target sequence file based on user‐defined parameters, including sequence length, isoelectric point, amino acid composition or the presence of specific amino acid patterns. All these searches can be performed on windows of variable length to deal with the peptide precursors. We have initially applied this software to mine the Mytilus galloprovincialis transcriptome1, an organism with a well know reputation to counteract a numbers of potential pathogens2. We were able to identify the known AMPs3‐4 and to highlight several novel families of peptides with intriguing sequence motifs. This ab initio approach may be successfully applied to de novo transcriptome assemblies of non‐model marine and freshwater invertebrates. In this respect we are approaching with this pipeline to the wealth of public data available for non‐model invertebrate organisms. Work supported by BIVALIFE (FP7‐KBBE‐2010‐4). REFERENCES 1. Venier P, De Pitta C, Bernante F, Varotto L, De Nardi B, Bovo G et al. MytiBase: a knowledgebase of mussel (M. galloprovincialis) transcribed sequences. BMC Genomics 2009, 10: 72. 2. Venier P, Varotto L, Rosani U, Millino C, Celegato B, Bernante F et al. Insights into the innate immunity of the Mediterranean mussel Mytilus galloprovincialis. BMC Genomics 2011 12: 69. 3. Gerdol M, De Moro G, Manfrin C, Venier P, Pallavicini A. Big defensins and mytimacins, new AMP families of the Mediterranean mussel Mytilus galloprovincialis. Developmental & Comparative Immunology 2012, 36: 390‐399. 4. Pallavicini A, del Mar Costa M, Gestal C, Dreos R, Figueras A, Venier P et al. High sequence variability of myticin transcripts in hemocytes of immune‐stimulated mussels suggests ancient host–pathogen interactions. Developmental & Comparative Immunology 2008 32: 213‐226.

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Presenting author: Danijela Krgović, danijela.krgovic@ukc‐mb.si

Abstract

GENOMIC STRUCTURAL VARIATION IN SLOVENIAN CHILDREN WITH NEURODEVELOPMENTAL DISORDERS

Danijela Krgović1, Marta Macedoni‐Lukšič2, Anamarija Brezigar3, Nataša Marčun Varda4, Peter Gradišnik4, Jernej Dolinšek4, Andreja Zagorac1, Boris Zagradišnik1, Alenka Erjavec Škerget1, Špela

Stangler Herodež1, Nadja Kokalj‐Vokač1,5

1University Medical Clinical Centre Maribor, Laboratory of Medical Genetics, Slovenia 2University Medical Centre Ljubljana, University Children's Hospital, Slovenia

3Medgen d.o.o., Ljubljana, Slovenia 4University Medical Clinical Centre Maribor, Department of Paediatrics, Slovenia

5University of Maribor, Faculty of Medicine, Slovenia

Genomic structural variations are an important cause of neurodevelopmental disorders. It is believed that in 15‐25% of patients their phenotype can be explained by copy number variations (CNVs), based on the gene content, size, and origin. Over time several techniques have been developed to study these variations, including array CGH, which has proven to be very effective. We performed an array CGH study on two‐hundred and fifty children with diagnosed Autism Spectrum Disorders (ASD) and/or intellectual disabilities/developmental delay (ID/DD). In the group of 145 ASD patients, 10% of pathogenic submicroscopic aberration which could explain patients' phenotype was detected. In 105 examinees diagnosed with ID/DD, pathogenic structural variation was detected in 18%. Partial analysis of the obtained CNVs showed, that larger CNVs are more frequently observed in ID/DD than in ASD group of patients. Also, in the ASD group, pathogenic CNVs usually segregate in known chromosomal regions linked to ASD, while in the ID/DD group of patients, pathogenic CNVs were detected in both, already known ID/DD linked regions as well as in other chromosomal regions. Rare chromosomal abnormalities were also identified in the latter: already published previously unreported de novo deletion of region 11q22.3 and a rare terminal deletion with inverted duplication of chromosome 5p. In both groups of patients structural variants with unknown clinical significance were detected. Hopefully, further analysis of these variants will lead to characterization of potentially new clinically relevant CNVs.

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Prsenting author: Petra Perin ([email protected])

Article

GENETICS AND PHARMACOGENOMICS OF CHILDHOOD ASTMA

Petra Perin1, 2, 3, Uroš Potočnik1, 2

1University of Maribor, Faculty of chemistry and chemical technology, Laboratory for Biochemistry, Molecular biology and

Genomics, Slovenia 2University of Maribor, Faculty of Medicine, Center for human molecular genetics and pharmacogenomics, Slovenia

3University of Maribor, Faculty of Health science, Department of Bioinformatics, Molecular biology and Genomics, Slovenia Asthma is the most common serious chronic respiratory disease in children with two major sub‐types; atopic and non‐atopic form. Genome wide association studies (GWAs) have identified more than 250 single nucleotide polymorphisms (SNPs) as genetic risk factors for asthma; however their influence on disease behavior and treatment response is still unclear. There is also a lack of studies which would determinate function of non‐coding candidate SNPs. The aim of our study was the association analysis of the SNPs recently reported by asthma GWA studies, SNPs previously associated with other immune diseases and asthma candidate SNPs which association results differ among independent studies, in different sub‐types of childhood asthma in Slovenian population and also analysis of correlation between these SNPs, clinical parameters, response to different therapies and their influence on gene expression. In our study we included 359 children with asthma and 276 healthy individuals for control group. Genotyping was performed by polymerase chain reaction (PCR) followed by the restriction fragment length polymorphism (RFLP) analysis or by high resolution melting (HRM) analysis using real time (rt)‐PCR. Gene expression was measured using Syber green and rt‐PCR. Out of 40 SNPs we identified association of rs967676, rs3087243, rs37972 and rs2631372 with asthma in general and association of rs333, rs1295696, rs2139142, rs1440095 and rs1800629 with non‐atopic form of disease. We found correlation between 25 candidate SNPs and asthma behavior and between 10 SNPs and glucocorticoids treatment outcome, but their influence depend on disease sub‐type. We were first to found the higher expression of IL12B in asthma patients compared with the control group (p<0.001), which is reduced after treatment with antileukotriens (p=0.015). We also found that expression of IL12B is regulated by rs6887695. We provide several new data which contribute to understanding of asthma genetics. Our results also represent additional genetic evidence suggesting different role of genes in atopic and non‐atopic asthma phenotypes. In addition, we identified new pharmacogenomics biomarkers, which could in future lead to a more appropriate choice of therapy, which will be tailored to each individual with asthma, and thus much more efficient, faster and with fewer side effects.

INTRODUCTION

Asthma is the most common serious chronic respiratory disease of childhood and affects about 10% of children younger than 18 1. It is a complex, polygenetic disease and its pathogenesis cannot be explained by a single mechanism or gene 2.

Several candidate genes for asthma were suggested by candidate gene association studies focused primarily on genes that encode proteins of immune response or allergic inflammation 3. The most replicated asthma associated genes so far include TNFα, IL4, IL13, ADAM33, GSTP1 and CD14 gene 4,5. Association results for replicated genes differ among different studies in different populations. There are also strong evidences that asthma genetics and pathogenesis are different and depend on its phenotype 6 therefore it is important to analyzing candidate genes separately in different sub‐types of asthma, such as atopic / non‐atopic and childhood / adult form of disease. Recent genome‐wide association studies (GWAs) have identified several new genetic risk factors and

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suggested new biological pathways that were previously not associated with asthma 7‐11, but many of the highly significant asthma candidate genes still lack replication in independent populations.

In addition to risk factors, association studies also provide information about correlation between genes and clinical characteristics of patients including disease severity and progression. There are a lot of differences in the response to anti‐asthmatic therapy between individuals and there is evidence that genetic polymorphisms could explain the great proportion of different response in asthma therapy. Genes involved in asthma pathogenesis often play important role in response to therapy as well as in disease behavior 12.

Most of identified SNPs associated with asthma, asthma behavior or treatment outcome are located in non‐coding genomic regions. They can influence the promoter activity, conformation of messenger RNA (mRNA) or sub‐cell localization of mRNA and indirectly cause the disease. Gene expression level is associated with DNA variants in cis‐regulatory elements or variants in introns which can change transcript stability and alternative splicing. Fife eQTL studies identified 9000 SNPs distributed all over the genome, which regulate gene expression13‐17, but function of many highly significant asthma candidate SNPs still need to be explored.

The aim of our study was to replicate the most important asthma candidate SNPs in different phenotype of asthma in Slovenian children and analyze the correlations between those SNPs, clinical parameters and response to different therapies. We were calculating risk and chances for good response to glucocorticoids (ICS) and anti‐leukotriene (AL) for different combination of genotypes. For selected loci we performed eQTL analysis, which aim is identified genes, which expression they effect on.

METHODS

Between January 1, 2007 and July 5, 2012, 359 children with asthma with median age 11 years were enrolled in this study. All the children had mild or moderate persistent asthma. 50.7% of them were treated in Pulmonary and Allergic Outpatients, Department of Pediatric Medicine, General Hospital Murska Sobota. Other 49.3 % were treated in University Clinical Center Maribor. Asthma was diagnosed according to National Asthma Education and Prevention Program (NAEPP) and American Thoracic Society (ATS) criteria 18, 19. Parents signed informed consent for children younger than 15 years, while older children gave informed consent themselves. Genotype data from 276 non‐atopic non‐asthmatic age and sex matched healthy served as a control group.

Allergic status of asthmatics was determined with the skin prick tests to most common aeroallergens and with the specific immunoglobulin E (IgE) measurement. Eosinophil count and total IgE were measured in the blood. Bronchial hyper‐reactivity was assessed with a methacholine bronchoprovocation test before the institution of the anti‐asthmatic treatment in all patients. We calculated provocative concentration of methacholine (PC20) causing a fall of FEV1 of 20% from the initial value. We measured fraction of exhaled nitric oxide (FENO) and used it as a measure of airway inflammation. For the purpose of our study the values of forced vital capacity (FVC) and forced expiratory volume in the first second of expiration (FEV1) were recorded. We calculated the FEV1/FVC ratio and used it as a measure of bronchial obstruction. FEV1 and FVC were expressed as a percentage of the predicted normal value for sex, height and age. Patients underwent spirometry before the treatment and repeated the test 4 weeks later. Change of FEV1 was used as a measure of response to anti‐asthmatic treatment. 226 patients were treated with ICS and 99 with AL.

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Whole blood from patients and controls was used for isolation of the total genomic DNA. First we isolated lymphocytes using Ficoll‐Paque Plus (GE Healtcare, Uppsala, Sweden) according to the manufacturer’s instruction. With TRI reagent (Sigma, Steinheim, Germany) we isolated DNA and dissolved it in water at final concentration of 25 ng/µl.

Using web database Huge‐Navigator we performed an overview of previous association and GWA studies. We chose 40 candidate SNPs, located in different loci, which were reported as most important asthma candidate genes, highly significant in recent GWAs and have not been replicated yet or have been strongly associated with other immune disease and not yet analyzed in asthma.

Using bioinformatics tools we dimensioned primers (internet application Primer3 and IDT Oligo‐analyzer). Restriction enzymes were chosen using program Gene‐Runner. Genotyping of SNPs was performed by polymerase chain reaction (PCR) followed by restriction fragment length polymorphism (RFLP). SNPs which have no restriction enzyme, which would be specific for one allele, were analyzed using high resolution melting analysis. Primers for measuring gene expression were designed using internet application Ace View, Primer3 and IDT Oligo analyzer.

The PCR reaction which was followed by RFLP, was carried out in a 10‐µl reaction volume containing 50 ng of genomic DNA, 250 nM of each oligonucleotide primer, 1.5 mM MgCl2, 0.2 mM dNTP mix, 10 mM Tris‐HCl and 0.5 U Taq polymerase. PCR conditions were as follows: preincubation at 95°C for 10 min followed by 35 cycles of 30s denaturation at 95°C, 30s annealing at 58 to 64 °C (depend on primers), 30 s extensions at 72°C and extensions at 72°C for 5 min. PCR products were digested using restriction enzymes at 37°C overnight. The PCR products were electrophoresed on 2% agarose gel and visualized using ethidium bromide under UV fluorescence.

High resolution melting analysis was carried out with quantitative PCR reaction (qPCR) on LC480 (Roche). 6‐µl reaction volume contained 50 ng of genomic DNA, HRM Mastermix (Fermentas), 250 nM of each oligonucleotide primer and 1.5 mM MgCl2. PCR conditions were as follows: preincubation at 95°C for 10 min followed by 45 cycles of 10 s denaturation at 95°C, 15s annealing at 60 °C, 10 s extensions at 72°C and high resolution melting step with a ramp‐rate 0.02 °C/s.

For gene expression measurement we at first performed reverse transcription of RNA using High Capacity Reverse Transcription kit (Applied Biosystems). Complementary DNA (cDNA) was analyzed with qPCR reaction on LC480 (Roche). 5‐µl reaction volume contained 50 ng of cDNA, Sybergreen Master Mix (Fermentas) and 250 nM of each oligonucleotide primer. PCR conditions were as follows: preincubation at 95°C for 10 min followed by 40 cycles of 15 s denaturation at 95°C, 30s annealing at 60 °C, 30 s extensions at 72°C high resolution melting step with a ramp‐rate 0.11 °C/s. We measured expression of reference genes ACTB, B2M and GADPH.

Data analysis was carried out using SPSS statistic 20.0. Genotype and allele frequencies were

calculated for the patients and control group. The χ2 test and two‐sided Fisher's exact test were used to calculate the significance of the difference in allele and genotype frequencies between asthmatic and controls. We calculated the odds ratio (OR) for asthma with 95% confidence intervals (CI). With the t‐test for two independent samples we analyzed the influence of genotype on clinical parameters which are normally distributed quantitative traits: FEV1, and change of FEV1 after inhaled corticosteroid treatment (dFEV1). With Mann‐Whitney test we analyzed the influence of genotype on quantitative traits which are not normally distributed: FEV1/FVC ratio, blood eosinophil count, total serum IgE, PC20 and FENO. Distribution was determinate by Kolmogorov‐Smirnov test for groups larger than 50 individuals or Shapiro‐Wilk test for smaller groups. We calculated expression level with 2‐∆∆Cp method. As calibrator we took average Cp value of whole samples. We compare gene expression between phenotype groups and between different genotype using Mann‐Whitney test.

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For determinate the influence of different therapies on gene expression changing with Wilcoxon test for 2 paired samples. We considered statistical significant association p value p < 0.05.

RESULTS

We found that SNPs located in or near genes CA10, CTLA4, SLC22A5 and TNF‐alfa are associated with asthma. In addition, SNPs in or near genes CCR5, MAP3K2, SGK493 and IL13 are associated with non‐atopic asthma phenotype. The most interesting results are summarized in Table 1.

SNP GENE MAF

controls MAF

all patients MAF

atopics MAF

non‐atopics Model

P all patients

P atopics

P non‐atopics

rs967676 CA10 0.472 0.383 0.408 0.347 Dominant

AA vs. AG+GG 0.001 0.009 0.007

rs3087243 CTLA4 0.468 0.435 0.444 0.431 Recessive

AA vs. AG+GG 0.023 0.041 0.128

rs37972 GLCCI1 0.380 0.441 0.427 0.516 Recessive

CC+CT vs. TT 0.011 0.128 0.007

rs2631372 SLC22A5 0.400 0.308 0.319 0.256 Dominant

CC vs. CG+GG 0.017 0.034 0.006

rs333 CCR5 0.094 0.090 0.111 0.049 Dominant del/del +N/del vs. N/N

1.000 0.310 0.042

rs1295696 IL13 0.240 0.279 0.258 0.323 Dominant

AA+AG vs. GG 0.088 0.468 0.011

rs2139142 MAP3K2 0.164 0.209 0.197 0.247 Dominant

AA+AG vs. GG 0.080 0.251 0.022

rs1440095 SGK493 0.318 0.372 0.351 0.417 Dominant

CC+CT vs. TT 0.110 0.130 0.011

rs1800629 TNF‐alfa 0.162 0.354 0.409 0.378 Dominant

AA+AG vs. GG 0.356 1.000 0.003

Table 1: SNPs locations and p values obtained after comparison of genotypes between patients and controls using different models (dominant, recessive, co‐dominant); the data (p value) for the model with the best p value is included in the table

Out of 40 candidate SNPs we found that 17 influence asthma behavior. In addition, 8 genes

have influence only in one of both sub‐type of disease, atopic or non‐atopic form. The associations with highest significance are represented in Table 2.

SNP GENE Phenotype Model Parameter p‐value

rs1800629 TNF‐alfa Asthma AA+AG vs. GG PC20 0.002

rs967676 CA10 Asthma AA vs. AG+GG Eozinophils 0.004

rs10512734 PTEGR4 Asthma AA vs. AG+GG FeNO 0.006

rs5744477 CD14 Non‐atopic asthma CC vs. CT+TT PC20 0.010

rs6887695 IL12B Non‐atopic asthma CC+CG vs. GG FEV1/FVC 0.013

rs1295686 IL13 Asthma AA+AG vs. GG IgE 0.013

rs967676 CA10 Asthma AA vs. AG+GG FEV1/FVC 0.014

rs1440095 SGK493 Non‐atopic asthma CC+CT vs. TT PC20 0.015

Table 2: Correlation among chosen SNPs clinical parameters; FEV1 = forced expiratory flow in first second; FVC = forced vital capacity; FEV1/FVC = measure of bronchial obstruction; PC20 = concentration of methacholine which causes 20 % fall of forced expiratory volume in the first; Total IgE = concentration of Total IgE In blood serum; Eozinofils = concentration of eosinophil in blood. The values in the table = p‐valuse calculated by Student t‐test for FEV1 and with non‐parametric Mann‐Whitney test for all other parameters

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We found response to ICS depends on SNPs in or near genes CA10, CCL5, CTNNA3, IL13, IL4, ORMDL3, SLC22A4, SLC22A5 and TNF‐alfa, but their influence is different among sub‐phenotype of asthma. The results are summarized in Table 3.

dFEV1 after ICS treatment (p‐value) SNP GENE Model

Genotype associated with good response

Asthma Atopic asthma

Non‐atopic asthma

rs967676 CA10 AA vs. AG+GG AG,GG 0.293 0.034 0.207

rs2107538 CCL5 CC+CT vs. TT TT 0.108 0.019 0.874

rs1786929 CTNNA3 CC vs CT+TT CT,TT 0.022 0.027 0.161

rs3087243 CTLA4 AA+AG vs. GG AA;AG 0.617 0.039 0.571

rs20451 IL13 CC vs CT+TT CC 0.717 0.059 0.026

rs2070874 IL4 CC vs. CT+TT CT,TT 0.642 0.630 0.008

rs2872507 ORMDL3 AA vs. AG+GG AA 0.017 0.021 0.542

rs1050152 SLC22A4 CC vs. CT+TT CT,TT 0.410 0.034 0.754

rs2631372 SLC22A5 CC+CG vs GG CC,CG 0.008 0.003 0.683

rs1800629 TNF‐alfa AA+AG vs. GG AA,AG 0.008 0.067 0.133

Table 3: Correlation among chosen SNPs and response to ICS therapy; dFEV1 = change of forsed expiratory flow in first second after 4 weaks of therapy

Genetic profiles show, that patients with good response to ICS therapy are carriers of bigger number of alleles associated with higher increase of pulmonary function. Individuals with atopic asthma, who have, in six analyzed SNPs, four or more genotypes associated with higher increase of pulmonary function, have 59 % chances for over‐average treatment response. Patients with only 2 or less genotype associated with good response to ICS have 67 % chances for below‐average response.

Figure 1: Genetic profile of ICS‐response associated SNP in children with atopic asthma

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In addition, we found SNPs rs10512734 (PTGER4) and rs2244012 (RAD50) influence on response to S therapy. FEV1 in asthmatics with AA genotype for SNP rs10512734 FEV1 after treatment even decrease (dFEV1 = ‐3.35 %), but in those with AG or GG genotype pulmonary function increase (dFEV1 = 3.57 %, p = 0.027). The similar role in response to S therapy we also found for SNP rs2244012; homozygotes for minor allele C had significantly greater improvement of lung function (dFEV1 = 10.33 %) than heterozygotes and TT homozygotes (dFEV1 = ‐0.05 %, p = 0.012).

Gene expression analysis shows that genes IL12B (p<0.001) are more expressed in asthmatics compared to healthy individuals but in asthmatics who were treated with AL expression of IL12B is reduced (p=0.035).

Figure 2: Expression level of IL12B in control group and asthmatics before and after treatment with AL therapy

In addition, we found that non‐coding asthma candidates SNPs are associated with IL12B, and TNF‐alfa gene expression. Expression of IL12B gene is higher in carriers of G allele in SNP rs6887695 (p=0.015). Allele A in SNP rs1800629 with higher expression of TNF‐alfa (p = 0.015).

DISCUSSION

In this study we analyzed the selected asthma candidate single nucleotide polymorphisms (SNPs) and expression of candidate genes in different phenotypes of childhood asthma and provide analysis of correlation between those SNPs, gene expression and clinical parameters, including disease severity and treatment outcome. We report here new correlation between candidate SNPs with asthma development, behavior and glucocorticoid (ICS) or anti‐leukotriene (AL) treatment response.

We have identified two novel asthma candidate genes, GLCCI1 and SLC22A5. SLC22A5, which encode the plasma membrane carnitine transporters, named organic cation transporters 2, has been previously reported as risk factor for other immune diseases, such as inflammatory bowel diseases are 20. GLCCI1 was identified as pharmacogenomics biomarker for glucocorticoids treatment in the first asthma glucocorticoid pharmacogenomics GWA study 21. Our results also provide first independent replication of GWA identified asthma associated SNPs in genes CA10 10 and SGK493 7. The gene CA10 encodes a protein that belongs to the carbonic anhydrase family of zinc metalloenzymes, which catalyze the reversible hydration of carbon dioxide in various biological processes 22. Function of the gene SGK493 is unknown. It could be involved in pathological states as protein kinases mediate most of the signal transduction in eukaryotic cells 23. According to our

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results, SNPs in CA10, GLCCI1 and SLC22A5 influence asthma development independently of disease sub‐type but SNP rs1440095 in SGK493 represents risk only for non‐atopic asthma development.

Asthma characteristics that describe the severity of disease phenotype are intensity of inflammation, pulmonary function, intermittent bronchial obstruction, bronchial hyper‐reactivity and allergy 24. In our study we found correlations between asthma candidate SNPs and asthma severity, but influence of many of those SNPs is various in both of basic forms of disease. SNPs rs5744477 in CD14 gene 25 and rs1440095 in SGK493 gene influence on bronchial hyper‐reactivity only in non‐atopic asthmatics. Our results suggest genetic heterogeneity between atopic and non‐atopic asthma. Previously meta‐analysis showed that gene IL4 represents a risk factor mainly for atopic asthma 26. A mutation in gene CCR5 is protective factor only for non‐atopic asthma 27. Genetic studies in atopic and non‐atopic asthmatics are in the line with functional studies which reported many differences in molecular pathogenesis between atopic and non‐atopic asthma 28, 29.

Asthmatics who do not respond to glucocorticoid treatments represent up to 10% of all patients affected with asthma 30. Many studies, confirmed that mechanisms of glucocorticoids is complex and depends on many genes 21, 31‐34. SNPs in genes CRHR, TBX21 and FCER2 have been confirmed to have impact on response to ICS 31, 32. Recent pharmacogenomics GWA study identified several new candidates SNPs 21. We have already reported that genes ORMDL3 and CTLA4 seem to be important factors in response to anti‐asthmatic treatment with ICS 33, 34. In this study we have identified four new associations between SNPs in or near CTNNA3, CA10, SLC22A4 and SLC22A5 genes and response to ICS treatment. CTNNA3 is a key protein of the adherence junction complex in epithelial cells and plays an important role in cellular adherence 35. Although the putative role of this protein in the lungs or airways is unknown, antibodies to “self‐antigens” including alpha‐catenin have been identified in serum of asthmatic patients 35, 36. In addition to identified pharmacogenomics association with CTNNA3, we found that increase of pulmonary function in asthma patients and thus better respond to ICS depend also on SNP rs2631372in SLC22A5. Similar influence we found for SNP rs967676 in CA10 gene and rs1050152 in SLC22A4 gene, but only in the group of patients with atopic sub‐type. Although CA10, according to our result, represents a genetic risk for asthma independent of disease phenotype, its role in treatment with glucocorticoids seems to be important only in atopic patients.

Our results show, that IL12B gene, which encode sub‐unit of IL‐12 cytokine 37, has an important role in asthma pathogenesis, as reflected in significantly increased expression in asthma patients compared to control group. We are the first to found, that expression of IL12B can be reduced by using AL drugs. AL act as 5‐lipooxigenase enzyme inhibitors or leukotriene receptor agonist 38, but mechanism of their action is not yet fully elucidated, as their impact on other components of leukotriene signaling pathway. Interestingly, the use of anti‐asthmatic drugs, which reduced the IL12B gene expression, at the same time increase expression of gene encoded cytokine receptor for IL‐23, although that the IL23R gene expression in asthmatic patients does not differ from the IL23R gene expression in the control group. Whereas IL‐12B and IL‐23R are involved in the same signaling pathway 39 it is expected that change in the quantity of one component is also reflected in the remaining components of the pathway. Previous studies found an impact of IL12B promoter polymorphism CTCTAA>GC on higher expression of this gene 40, 41. But in our study we as first found, that IL12B expression also depends on SNP rs6887695. According to our results, this SNP, this was identified as important risk factor in development of immune‐mediated diseases in previous studies, influence asthma severity. In addition, we confirmed, that SNP rs1800629 regulate expression of TNF, as it was reported before 42.

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In our study we provide several new data which contribute to understanding of asthma genetics. Our results also represent additional genetic evidence suggesting different role of genes in atopic and non‐atopic asthma phenotypes. In addition, we identified new potential pharmacogenomics biomarkers, which could in future lead to a more appropriate choice of therapy, which will be tailored to each individual with asthma, and thus much more efficient, faster and with fewer side effects.

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27. Berce V, Repnik K, Potocnik U. Association of CCR5‐delta32 mutation with reduced risk of nonatopic asthma in Slovenian children. J. Asthma. 2008, 45, 780‐784. 28. Walker C, Bauer W, Braun RK, Menz G, Braun P, Schwarz F, et al. Activated T cells and cytokines in brnochoalveolar lavages from patients with various lung diseases associated with eosinophilia. Am. J. Respir. Crit. Care. Med. 1994, 150, 1038‐48 29. Bottini N, Ronchetti F, Gloria‐Bottini F, Stefanini L, Bottini E, Lucarini N. Atopic and nonatopic asthma in children, J. Asthma. 2006, 42, 25‐8 30. Ito K, Chung KF, Adcock IM. Update on glucocorticoid action and resistance. J. Allergy. Clin. Immunol. 2006, 117, 522‐543 31. Tantisira KG, Hwang ES, Raby BA, Silverman ES, Lake SL, Richter BG et al. TBX21: a functional variant predicts improvement in asthma with the use of inhaled corticosteroids. Proc. Natl. Acad. Sci. USA. 2004, 101, 18099‐18104 32. Poon AH, Tantisira KG, Litonjua AA, Lazarus R, Xu J, Lasky‐Su J et al. Association of corticotropin‐releasing hormone receptor‐2 genetic variants with acute bronchodilator response in asthma. Pharmacogenet. Genomics. 2008, 18, 373‐382 33. Berce V, Potocnik U. Functional polymorphism in CTLA4 gene influences the response to therapy with inhaled corticosteroids in Slovenian children with atopic asthma. Biomarkers. 2010, 15, 158‐166. 34. Berce V, Kozmus CE, Potocnik U. Association among ORMDL3 gene expression, 17q21 polymorphism and response to treatment with inhaled corticosteroids in children with asthma. Pharmacogen. J. 2012, doi. 10.1038/tpj.2012.36. 35. Liu M, Subramanian V, Christie C, Castro M, Mohanakumar T. Immune responses to self‐antigens in asthma patients: Clinical and immunopathological implications. Hum. Immunol. 2012, 73, 511–516.

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http://www.ncbi.nlm.nih.gov/pubmed?term=Li%20T%5BAuthor%5D&cauthor=true&cauthor_uid=22652416

http://www.ncbi.nlm.nih.gov/pubmed?term=Liu%20J%5BAuthor%5D&cauthor=true&cauthor_uid=22652416

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http://www.ncbi.nlm.nih.gov/pubmed/23557460


Presenting author: Minja Zorc, [email protected]

Abstract

DEVELOPMENT OF BIOMARKERS FOR FAT DEPOSITION USING INTEGRATION OF GENOMIC DATA AND BIOINFORMATICS ANALYSIS

Minja Zorc, Daša Jevšinek Skok, Tanja Kunej

University of Ljubljana, Biotechnical faculty, Department of Animal Science, Slovenia

Obesity is polygenic disease which presents a major health issue. It affects people of all ages as well as domestic animals. The unraveling of genetic bases of fat deposition might help to develop therapeutics and understand the process of fat deposition. The amount of available genomic data and the need for genomic data analysis methods grow. Systemic approaches are becoming important in complex phenotypes research. We created the genomic atlas, which presents the central web resource of genetic causes for fat deposition. The comparative and integrative approach to collect the loci associated with fat deposition in human, mouse, rat and cattle was used. By visualization of the integrated data the insight into known fat deposition loci was enabled. We created genomic views of loci, identified candidate biological pathways and determined genetic networks for fat deposition, which were basis for candidate genes prioritization. Two bioinformatics tools for analysis of noncoding candidate genes were developed (miRNA SNiPer and miRNA Viewer). From the set of candidate loci we selected potential biomarkers (Akt1, Ubc, Grb2, Mir599) and tested their effect on fat deposition traits in mice using analysis of association between genotype and phenotype. We developed a strategy for research of genetic causes for fat deposition. The same approach can be used for analysis of other complex phenotypes.

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Corresponding author: Uroš Potočnik, [email protected]

Article

ASSOCIATION AND GENE EXPRESSION ANALYSIS OF ORMDL3 AND TNF IN MULTIPLE SCLEROSIS, ASTHMA AND RHEUMATOID ARTHRITIS

Larisa Zemljič1, Uroš Potočnik1,2

1University of Maribor, Faculty of Medicine, Center for Human Molecular Genetics and Pharmacogenomics, Slovenia

2University of Maribor, Faculty of Chemistry and Chemical technology, Laboratory for Biochemistry Molecular Biology and Genomics, Slovenia

The results of several clinical and epidemiological studies have shown that different chronic immune diseases can simultaneously occur either in the same individual or in closely related family members. To help discover shared genes and biological pathways among close related chronic immune diseases, asthma (AST), multiple sclerosis (MS) and rheumatoid arthritis (RA), we genotyped two single nucleotide polymorphisms (SNPs), rs2872507 and rs1800629, and correlated genotypes to gene expression of the neighboring genes, ORMDL3 and TNF. Genotyping was performed by two different approaches by restriction fragment length polymorphism and high resolution melting method. To evaluate gene expression levels, quantitative real‐time PCR was performed. Expression QTL analysis (eQTL) was performed using statistical calculations of data obtained by genotyping and expression analysis. The aim of our study was to evaluate the importance of the two studied genes and to rank the importance according to the disease. Our results suggest that ORMDL3 is most important in RA group, because we found association with rs2872507 genotype and with eQTL. RA patients had higher A allele (P= 0.045) and AA genotype frequency (P=0.012) compared to controls. eQTL analysis showed higher ORMDL3 expression in patients carrying G allele (P=0.0081). In the study we confirmed the association of ORMDL3 gene expression and eQTL analysis with AST group. Expression of ORMDL3 in asthmatics was higher compared to controls (P=0.0005) and G allele carriers had higher expression compared to A allele carriers (P=0.0023). In MS ORMDL3 gene plays a minor role, since only association with expression was found. MS patients had higher expression levels compared to controls (P<0.0001). Our results suggest TNF plays marginal role in RA and MS groups but has no enrolment in AST group. RA patients had higher AA genotype frequency compared to controls (P=0.033) and both groups, RA and MS, displayed higher expression levels compared to controls (P=0.0057 and P=0.0005 respectively).

INTRODUCTION

The results of several clinical and epidemiological studies have shown that different chronic immune diseases can simultaneously occur either in the same individual or in closely related family members. Simultaneously occurring of multiple diseases appears more frequently than expected if disease processes were independent. Given that each of the immune‐mediated and autoimmune disease has strong genetic effect on disease risk1‐3, the observed concurrent occurring of multiple diseases could arise because of overlapping in the causal genes and pathways.4,5 Recent GWA studies in immune‐mediated and autoimmune diseases have identified genome regions with statistically significant evidence for existence of disease susceptibility loci. A subgroup of these loci has been shown to modulate risk of multiple diseases 6,7. There is evidence, indicating that the human MHC (HLA complex) is associated with human autoimmune diseases, suggesting a common immune pathway 8,9. Nevertheless it is less definite, what is the role of non‐HLA genetic variants, associated with individual diseases, in common mechanisms for autoimmunity.



SNP rs1800629 is located at position −308 from Tumor necrosis factor (TNF) gene. Tumor necrosis factor (TNF) gene encodes a multifunctional proinflammatory cytokine that belongs to the tumor necrosis factor (TNF) superfamily. This cytokine is involved in the regulation of a wide spectrum of biological processes including cell proliferation, differentiation and apoptosis 10. Excess TNF, however, causes various autoimmune diseases, such as RA, Crohn's disease, and ulcerative colitis 11‐13. SNP rs1800629 has already been associated with the development of different diseases, including asthma (AST)14‐16, multiple sclerosis (MS)17,18 and rheumatoid arthritis (RA)19‐21, but the results of association studies are inconsistent 22‐28. In order to bring light to the problem of contradictory results of association analyses of rs1800629, we decided to perform an association study of rs1800629 in Slovene population of AST, MS and RA patients.

SNP rs2872507 is located in the noncoding part near the ORM1‐like protein 3 (ORMDL3) gene at locus 17q21. ORMDL3 gene is ubiquitously expressed and encodes a protein integrated in the endoplasmic reticulum membrane, acting as important downregulator of the sphingolipid synthesis 29,30. SNP rs2872507, which influences ORMDL3 gene expression31, has been significantly associated with AST32,33, RA2 and with other chronic immune diseases like Crohn’s disease, ankylosing spondylitis or type I diabetes34‐36. Since there are yet no association studies of rs2872507 in MS published, we decided to investigate whether there could be a significant association of the distribution of rs2872507 genotype in MS group, compared to control group. At the same time we decided to test for an association of rs2872507 in RA, since results in recent genome‐wide association study indicated rs2872507 as highly suggestive, but did not reach the genome‐wide significance2.

In order to gain a deeper insight in the association between selected SNPs genotypes, previously already associated with chronic immune diseases, and expression of neighboring genes, we carried out an eQTL analysis in three chronic immune diseases, AST, MS and RA. Our study will confirm whether the results of eQTL study in three different immune diseases are consistent. Because chronic immune diseases share common susceptibility genome loci, our study will support the search of casual genes and biological pathways, common to studied chronic immune diseases.

METHODS Participants and study design We analyzed a case–control cohort composed of 92 patients with asthma, 92 patients with multiple sclerosis, 92 patients with rheumatoid arthritis and 276 healthy unrelated but age‐ and sex‐matched controls. Patients and controls with DNA samples not passing high‐quality control standards were excluded from genotyping. Patients were diagnosed with the disease either in General Hospital Murska Sobota (Murska Sobota, Slovenia) or in the University Medical Centre Maribor (Maribor, Slovenia). All patients and controls were Caucasians of Slovenian origin. Patients with other chronic inflammatory diseases except asthma, multiple sclerosis or rheumatoid arthritis were excluded from the study. All patients signed informed consent. The study was carried out in accordance with the Helsinki declaration of the World Medical Association (1975) and approved by the Slovenian National Medical Ethics Committee. DNA and RNA extraction Total blood leukocytes were isolated using Ficoll‐Paque Plus (GE Healthcare, Sweden) gradient centrifugation, according to the manufacturer’s instructions. Total RNA and genomic DNA were

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isolated using QIAzol Lysis Reagent (QIAgen, USA). DNA was dissolved in the water at a final concentration of 50 ng µl‐1. RNA concentrations ranged from 0.1 to 1.17 µg µl‐1 as determined by an ND1000 spectrophotometer and NanoDrop 3.0.1 software (NanoDrop Technologies, Germany); 260/280 ratios ranged from 1.7 to 2.0. The integrity of RNA samples was analyzed by electrophoresis on a 2% agarose gel. All samples were immediately frozen and stored at 80°C. Genotyping of DNA polymorphisms rs2872507 and rs1800629 Genotyping was performed by two different approaches. Genotyping of SNP rs2872507 was achieved by PCR followed by restriction fragment length polymorphism. Primers used for PCR amplification were: forward 5’‐GGGATACTCAAACTGTATCTTTCC‐3’ and reverse 5’‐GTAGCATCAACATGTCATTAGAAG‐3’. PCR reaction was carried out in a 10 µl reaction volume containing 50 ng of genomic DNA, 250 nM of each of oligonucleotide primer, 1.5mM MgCl2, 0.2mM dNTP mix, 10mM TRIS‐HCl and 0.25 U of Taq polymerase (Fermentas, Vilnius, Lithuania). PCR conditions were as follows: preincubation at 95°C for 10 min followed by 35 cycles of 1 min denaturation at 95°C, 30 s annealing at 55°C, 30 s extension at 72°C and final extension at 72°C for 5 min. PCR product was 200‐bp long. PCR products were digested using 1 unit of NcoI restriction enzyme (Fermentas) at 37°C overnight. PCR products were electrophoresed on 2% agarose gel and visualized using ethidium bromide under ultraviolet fluorescence. Samples showing a 200‐bp fragment were assigned genotype GG, samples with 105‐bp and 95‐bp fragments (visible as 1 band on 2% agarose gel) were typed as AA, whereas samples showing 3 fragments (visible as 2 bands on 2% agarose gel) of 200, 105 and 95 bp were typed as AG (Figure 1). Genotyping of SNP rs1800629 was performed by High Resolution Melting (HRM) curve analysis following touchdown PCR amplification. Primers used for touchdown PCR amplification were designed using Primer3 (http://simgene.com/Primer3), manufactured by Sigma (Steinheim, Germany) and the sequence was: forward 5´‐ACCTGGTCCCCAAAAGAAAT‐3’ and reverse 5´‐ TTTGTGTGTAGGACCCTGGAG‐3’. The touchdown PCR amplification was performed using a 96 multiwell white‐plate (Cat.#04729692001, Roche Applied Science, Mannheim, Germany) on a Roche LightCycler® 480 detection system (Roche Applied Science, Mannheim, Germany). Samples were amplified in reactions containing 2 µL of genomic DNA (2.5 ng/µL), 3 µL of 2x LightCycler® 480 High Resolution Melting Master mix (Roche Applied Science, Mannheim, Germany), 0.061 µL of each primer (200 nM final concentration), 0.60 µL of MgCl2 (3.5 mM final concentration), and RNase‐free water in a final reaction volume of 6 µL. The touchdown PCR program was initiated at 95°C for 10 min, followed by 45 thermal cycles of 10 sec at 95°C, 15 sec at 63°C (secondary target temperature 53°C, with 0.5°C steps) and 10 seconds at 72°C. The HRM curve analysis was performed with a temperature range used for the melting curve generation from 65°C to 95°C with 25 signal acquisitions per °C. DNA samples not passing high‐quality control standards were excluded from genotyping which, resulted in the exclusion of 8 DNA samples from asthmatic patients, 7 DNA samples from MS patients, 9 DNA samples from RA patients and 25 DNA samples from control subjects.

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Figure 1: Representative image of genotyping results of DNA polymorphism rs2872507 by restriction fragment length

polymorphism (RFLP) method; AG = fragments of 200, 105 and 95 bp (fragments 105 and 95 bp visible on a gel as s single band); GG = single band of 200 bp; AA = fragments of 105 and 95 bp visible on a gel as a single band.

Gene expression of ORMDL3 and TNF First‐strand complementary DNA (cDNA) was generated by reverse transcription of 1 µg total RNA per sample with random primers and MultiScribe Reverse Transcriptase (50 U per reaction) using High‐Capacity cDNA Reverse Transcription kit (Cat. no. 4368813, Applied Biosystems, USA) in a final reaction volume of 20 µl. The first step of reverse transcription reaction was performed at 25°C for 10 min followed by a 2‐h incubation period at 37°C and finalized with 5 min of incubation at 85°C, according to the manufacturer’s instructions. The cDNA was diluted 1:20 with RNasefree water and stored at 80°C. The intron spanning primers used for ORMDL3, TNF and reference genes ACTB, B2M and GAPDH amplification were designed using the Universal ProbeLibrary Assay Design Center from Roche Applied Science (https://www.roche‐applied‐science.com/sis/rtpcr/upl) and manufactured by Sigma (Germany). The expression study was performed using a 96‐multiwell white plate (Cat. no. 04729692001, Roche Applied Science, Germany) on a Roche LightCycler 480 detection system (Roche Applied Science) with Maxima SYBR Green qPCR Master Mix (Fermentas). Samples were amplified in reactions containing 2 µl of cDNA, 5 µl of 2 x SYBR Green master mix, primers (concentration according to optimized standard curve of each target gene) and RNase‐free water in a final reaction volume of 10 µl. The PCR program was initiated at 95°C for 10 min to activate Taq DNA polymerase, followed by 40 thermal cycles of 15 s at 95°C, 30 s at 60°C and 30 s at 72°C. The specificity analysis of the PCR products (melting curve analysis) was performed after the real‐time PCR. The temperature range used for the melting curve generation was from 65 to 95°C. Samples were analyzed in duplicate wells and each PCR run included a no‐template control using water instead of cDNA. ORDML3 and TNF Cq values were normalized using the geometrical mean of reference genes ACTB, B2M and GAPDH Cq values37. Relative expression was calculated using the equation 2‐ΔΔCq, where ‐ΔΔCq = ‐ (Cqtarget ‐ Cqgeometrical mean reference) sample ‐ average (Cqtarget ‐ Cqgeometrical mean reference) control. Statistical analysis Data analysis was carried out using SPSS version 19.0 (SPSS, Chicago, IL, USA). The χ2 test and two‐sided Fisher’s exact test were used to calculate the significance of differences in allele and genotype frequencies between patients and controls. We calculated odds ratio for disease with 95% confidence interval. The χ2 test was also used for assessment of Hardy–Weinberg equilibrium. Data are presented as mean±s.d. when parametric tests and descriptive statistics were used. Genotype influence on ORMDL3 or TNF gene expression was assessed with Kruskal–Wallis test followed by

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Dunn’s multiple comparison post hoc test. Data are presented as median±interquartile range when nonparametric tests were used. Values of P<0.05 were considered significant.

RESULTS Participants and descriptive data In the whole group of asthmatics, there were 54.3% (n=50) males and 45.7% (n=42) females. The mean age was 11.0±3.5 years. In group of multiple sclerosis patients, 24.2% (n=22) were males and 75.8% (n=70) were females. The mean age was 43.8±11.3 years. In group of rheumatoid arthritis patients, 25.0% (n=23) were males and 75.0% (n=69) were females. The mean age was 59.2±9.9 years. Genotype and allelic frequencies of SNP rs2872507 Genotype frequencies in the patients groups and in the control group were in Hardy–Weinberg equilibrium. According to the dominant model of genetic association, the frequency of AA genotype in RA patients was 33.0%, which was significantly higher than 18.0% in controls (P=0.012). RA patients also had higher A allele frequency compared with controls (P= 0.045). We found no differences in the allele and genotype frequencies between AST and MS patients compared with the control group (Table 1). Genotype and allelic frequencies of SNP rs1800629 Genotype frequencies in the patients groups and in the control group were in Hardy–Weinberg equilibrium. According to the dominant model of genetic association, the frequency of AA genotype in RA patients was 7.0%, which was significantly higher than 3.0% in controls (P=0.033). There were no differences in the allele frequencies between RA patients and the control group. We found no differences in the allele and genotype frequencies between AST and MS patients compared with the control group (Table 1). ORMDL3 gene expression and rs287250 eQTL analysis ORMDL3 median relative expression has been statistically different in AST and MS disease groups when compared to controls. ORMDL3 median relative expression in asthmatics was 1.963±0.948 (median±interquartile range) and in controls 1.503±2.026 (P=0.0005) (Graph 1). ORMDL3 median relative expression in MS patients was 6.981±4.551 and in controls 1.503±2.026 (P<0.0001) (Graph 1). Statistically significant difference in ORMDL3 median relative expression according to SNP rs2872507 genotype has been observed in RA, AST and control group. ORMDL3 median relative expression in AST patients according to rs2872507 three genotypes was statistically significant (P=0.044), median relative expression in patients with AA genotype was 0.765±0.385, in those with AG genotype was 1.072±0.368 and in group with GG genotype was 1.157±0.492, compared with average median relative expression of AST group. The statistical significance was confirmed with both genetic models, in dominant model for G allele median relative expression was 0.765±0.385 for patients with AA genotype and 1.102±0.437 for patients with AG or GG genotype, compared with average median relative expression of AST group (P=0.0019). In dominant model for A allele median relative expression was 1.157±0.492 for patients with GG genotype and 1.015±0.430 for patients with AG or

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AA genotype, compared with average median relative expression of AST group (P=0.043). The significance was confirmed also with alleles, where median relative expression in patients with A allele was 0.935±0.432 and median relative expression in patients with G allele was 1.115±0.438, compared with average median relative expression of AST group (P=0.0023) (Graph 2). ORMDL3 median relative expression in RA patients according to rs2872507 three genotypes was statistically significant (P=0.039), median relative expression in patients with AA genotype was 0.812±0.593, in those with AG genotype was 1.049±0.934 and in group with GG genotype was 1.167±1.965, compared with average median relative expression of RA group. The statistical significance was confirmed with both genetic models, in dominant model for G allele median relative expression was 0.812±0.593 for patients with AA genotype and 1.049±0.969 for patients with AG or GG genotype, compared with average median relative expression of RA group (P=0.027). In dominant model for A allele median relative expression was 1.167±1.965 for patients with GG genotype and 0.885±0.748 for patients with AG or AA genotype, compared with average median relative expression of RA group (P=0.049). The significance was confirmed also with alleles, where median relative expression in patients with A allele was 0.875±0.579 and median relative expression in patients with G allele was 1.049±1.043, compared with average median relative expression of RA group (P=0.0081) (Graph 3). TNF gene expression and rs1800629 eQTL analysis We found statistically significant difference in TNF median relative expression in two disease groups compared to controls. TNF median relative expression in MS patients was 1.400±2.320 and in controls 0.805±1.358 (P=0.0005) (Graph 4). TNF median relative expression was different also in RA patients, where it was 1.220±1.175 and in controls 0.810±1.268 (P=0.0057) (Graph 4). TNF median relative expression according to SNP rs2872507 genotype has not been significantly different in any of the studied groups.


patients controls

AST MS RA AST vs. controls MS vs. controls RA vs. controls

Genotype or allele frequency (n = 82) (n = 92) (n = 80) (n = 251)

P‐value (OR, 95% CI) P‐value (OR, 95% CI) P‐value (OR, 95% CI)

rs2872507

AA 26% 16% 33% 18% AA vs. AG+GG

0.157 (0.652; 0.361 ‐ 1.176)

0.751 (1.182; 0.624 ‐ 2.237)

0.0122 (0.466; 0.264 ‐ 0.822)

AG 51% 49% 43% 52%

GG 23% 35% 24% 30% GG vs. AG+AA 0.322 (1.386; 0.776 ‐ 2.477)

0.360 (0.773; 0.467 ‐ 1.278)

0.479 (1.254; 0.706 ‐ 2.227)

A 51% 40% 54% 44% A vs. G 0.149 (0.761; 0.535 ‐ 1.084)

0.388 (1.178; 0.830 ‐ 1.656)

0.045 (0.688; 0.481 ‐ 0.983)

G 49% 60% 46% 56%

rs1800629

AA 4% 5% 7% 3% AA vs.. AG+GG 0.707 (0.721; 0.182 ‐ 2.855)

0.191 (0.465; 0.144 ‐ 1.503)

0.033 (0.359; 0.138 ‐ 0.929)

AG 33% 18% 25% 27%

GG 63% 77% 68% 70% GG vs. AG+AA 0.283 (1.358; 0.812 ‐ 2.270)

0.286 (0.729; 0.423 ‐ 1.259)

0.674 (1.111; 0.736 ‐ 1.677)

A 20% 15% 20% 16% A vs. G 0.245 (0.771; 0.496 ‐ 1.197)

0.131 (0.763; 0.538 ‐ 1.081)

0.239 (0.799; 0.563 ‐ 1.135)

G 80% 85% 80% 84%

Table 1: Genotype and allele frequencies of SNP rs2872507 and SNP rs1800629 in AST, MS, RA and control group; CI = confidence interval; OR = odds ratio; SNP = single‐nucleotidepolymorphism.

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Colloquium of Genetics 2013

Figure 1: ORMDL3 gene expression levels according to group. Boxes represent interquartile range with medians; whiskers

illustrate the 5‐95 percentiles of samples (control, n = 220; AST, n = 84; MS, n = 78).

Figure 2: ORMDL3 gene expression levels according to rs2872507 genotype in asthmatics. Boxes represent interquartile

range with medians; whiskers illustrate the 5–95 percentiles of samples.

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Figure 3: ORMDL3 gene expression levels according to rs2872507 alleles in RA patients. Boxes represent interquartile range

with medians; whiskers illustrate the 5–95 percentiles of samples.

Figure 4: TNF gene expression levels according to group. Boxes represent interquartile range with medians; whiskers illustrate the 5‐95 percentiles of samples (control, n = 216; MS, n = 80; RA, n = 77).

DISCUSSION

In our study we performed association, expression and eQTL analysis of two SNPs and two nearby genes in three chronic immune diseases; AST, MS and RA.

The aim of our study was to evaluate the importance of the two studied genes in selected diseases and rank the importance according to the disease. Our results suggest that ORMDL3 is most

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important in RA group, due to association with rs2872507 genotype and with eQTL. RA patients had higher A allele and AA genotype frequency compared to controls, suggesting A allele acts as a risk factor for the development of the disease. Higher A allele frequency was already confirmed in previous studies, a multiethnic study and a meta‐analysis, where they identified A allele as a risk factor for RA 2,38. In our study we observed significant difference in rs2872507 allele frequencies, but not at level of genome‐wide significance, therefore the results of our study are consistent with GWA study meta‐analysis conducted in RA patients by Stahl and colleagues2, where rs2872507 failed to reach genome‐wide significance, but was observed as highly suggestive2 eQTL analysis in RA patients showed higher ORMDL3 expression in carriers of G allele. To the best of our knowledge, this is the first study on ORMDL3 expression in RA group.

The results of our study confirmed the association of ORMDL3 gene expression and eQTL analysis with AST group. Expression of ORMDL3 in asthmatics was higher compared to controls and G allele carriers had higher expression compared to A allele carriers. The latter has already been confirmed in our previous study, where also corticosteroid treatment response has been associated with rs2872507 genotype39. The eQTL association of rs2872507 and ORMDL3 expression in asthmatics has also been confirmed in allele‐specific chromatin remodeling study, conducted on immortalized lymphoblastoid cell lines31.

In MS group however ORMDL3 gene plays a minor role, since only association with expression was found. MS patients had higher expression levels compared to controls. To the best of our knowledge, there are yet no other studies of the MS ORMDL3 expression. ORMDL3 gene is ubiquitously expressed and encodes a protein integrated in the endoplasmic reticulum membrane, acting as important downregulator of the sphingolipid synthesis 29,30. Recent study on influence of ORMDL3 overexpression showed strong association of gene overexpression with increased transcriptional activation of immediate‐early genes and other genes directly related with the onset of inflammation 40. The latter is in concordance with the results of our study since all of the studied diseases had either changed gene expression levels according to control group or positive eQTL association.

The results of TNF gene suggest it plays a marginal role in RA and MS groups but has no enrolment in AST group. RA patients had higher AA genotype frequency compared to controls (P=0.033), suggesting A allele is a risk factor for the development of the disease. The latter was already confirmed in an Egyptian study, where AA genotype was more prevalent among patients 19. Both groups, RA and MS, displayed higher expression levels compared to controls. TNF is a potent cytokine that exerts diverse effects by stimulating a variety of cells. It is an autocrine stimulator as well as a potent paracrine inducer of other inflammatory cytokines41. Excess TNF was shown to cause various autoimmune diseases, including RA, Crohn's disease, and ulcerative colitis 11‐13. Increased TNF levels can be found in serum of RA patients and anti‐TNF therapy is well established method for management of inflammation of arthrtitic joints 41. High TNF levels were also found in cerebrospinal fluid of MS patients and levels of TNF correlate with severity and progression of the disease42.

There is limitation of our study that has to be taken into account when interpreting our data. The study was conducted on a somewhat smaller sample size, and therefore a replication study to confirm our findings would be helpful.

The aim of our study was to evaluate the importance of the two studied genes and to rank the importance according to the disease. Our results suggest that ORMDL3 is most important in RA group, because we found association with rs2872507 genotype and with eQTL. Similar importance

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was shown also for AST group, since an association of ORMDL3 gene expression and eQTL analysis with AST group was found. In MS however ORMDL3 gene plays a minor role, as only association with expression was found. The study of TNF revealed it plays marginal role in RA and MS groups but has no enrolment in AST group. In RA group association with rs1800629 genotype was found. In both groups, RA and MS, association with TNF expression levels was found.

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POSTERS

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POPULATION

GENETICS

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Presenting author: Daša Perko, [email protected]

Abstract

MEFV GENE MUTATIONS IN CENTRAL AND SOUTH‐EASTERN EUROPEAN COUNTRIES Daša Perko1, Maruša Debeljak2, Nataša Toplak1, Anna Šedivá3, Tomáš Dallos4, Miroslav Harjaček5, Marija Jelušič6, Goran Ristić7, Beata Derfavli8, Kristina Mironska9, Skirmante Rusoniene10, Dafina

Kuzmanovska11, Natalja Kurjane12, Mihaela Bataneant13, Tadej Avčin1

1University Medical Center Ljubljana, University Children's Hospital, Department of Allergology, Rheumatology and Clinical

immunology, Slovenia 2University Medical Center Ljubljana, University Children's Hospital, Center for Medical Genetics, Slovenia

3Universit lty, Department of Immunology, Czech Republic y Hospital Motol and 2nd Medical facu4Comenius University in Bratislava, 2nd Department of Paediatrics, Slovakia

5Children's Hospital Srebrnjak, Rheumatology clinic, Croatia 6University Hospital Centre Zagreb, Department of Pediatrics, Division of Pediatric Rheumatology and Immunology, Croatia

7Institute for Health Protection of Mother and Child 'Dr Vukan Cupic', Serbia 8Sammelweis University, 2ndDepartment of Paediatrics, Hungary

9University clinic for children diseases, Department of Pediatrics Immunology, Division for Primary Immunodeficiences, Republic of Macedonia

10Children‘s Hospital, Affiliate of Vilnius University Hospital, Santariskiu Klinikos, Lithuania 11Ss. Cyriland Methodius University, Medical Faculty, University Pediatric Clinic, Republic of Macedonia

12Stradina Clinical University Hospital, Centre of Clinical Immunology, Latvia 13University of Medicine and Pharmacy 'Victor Babes', 3rd Pediatric Clinic, Romania

Familial Mediterranean fever (FMF) is rarely reported in patients from CCSEE countries. The

reason for this might be that the prevalence of FMF in CSEE is exceedingly low or that the disease is under‐recognized among local physicians. The aim was to assess the frequency of MEFV gene mutations in periodic fever (PF) patients from CSEE countries.

We analyzed all the data of PF patients who were followed at the University Children's Hospital Ljubljana from 2006 to 2013. In addition, free genetic testing was provided for suspected FMF patients from the CSEE region. In total, 156 PF patients (53% female, 47% male) were tested; 118/Slovenia, 14/Czech Republic, 6/Slovakia, 4/Croatia, 4/Romania, 3/Macedonia, 2/Serbia, 2/Hungary, 2/Latvia and 1/Lithuania. 73% of population were children (mean age 6.6 years) and 27% were adult (mean age 46.4 years).

31 patients (20%) were found to have at least one mutation. 22 patients have had one only; Slovenia 9/15, Czech Republic 7/8, Slovakia 1/3, Macedonia 2/2, Latvia 1/1, Hungary 1/1 and Croatia 1/1. 8 patients have had two mutations; Slovenia 6/15, Slovakia 1/3, Czech Republic 1/8. 1 patient has had 3 mutations (Slovakia). One homozygous mutation was found (Czech Republic) and one novel mutation was identified (S730F, Slovenia). 12 different mutations were found: M694V(27%), K695R(22%), P369S(12%), R408Q(12%), I591T(7%), E148Q(5%), E167D(2%), A289V(2%), F479L(2%), V726A(2%), S730F(2%) and A744S(2%).

We suspect that clinical manifestations of FMF could be influenced by the regional environment. We are planning to evaluate genotype‐phenotype correlation in MEFV mutation positive patients in CSEE countries in the future.

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http://www.researchgate.net/institution/Comenius_University_in_Bratislava


Presenting author: Martina Planinc, [email protected]‐lj.si Article

GENOTYPE BY ENVIRONMENT INTERACTION FOR GROWTH IN ON‐FARM TESTED GILTS

Martina Planinc, Špela Malovrh, Milena Kovač

University of Ljubljana, Biotechnical Faculty, Animal Science Department, Chair for Pig Production, Slovenia

Differences in environment sensitivity between individuals results in genotype by environment interaction (GEI). If GEI occurs, the trait is partly influenced by different genes in different environment. The objective of this study was to evaluate genotype by environment interaction for growth traits in gilts of maternal genotypes in Slovenia. Animals were classified into two environments. The variable used to characterize the environment was average daily gain in herd and year. The analysis included 10479 gilts with known pedigree. Gilts were tested on 44 family farms since year 2000. Animals belonged to four maternal genotypes: Slovenian Landrace – line 11 (11), Slovenian Large White (22) and Hybrids 11x22 and 22x11. To study GEI, animal model methodology was used to estimate genetic correlations between the traits. Genetic correlation for backfat thickness was 0.85 and for days in test 0.49. If estimated genetic correlation is bellow 0.8 the GEI is present.

INTRODUCTION Living organisms respond to changes in their environment. This is called phenotypic plasticity or environment sensitivity 1. Genotypes that show highly variable phenotypes across different environments are ‘plastic’. Differences in environment sensitivity among individuals result in genotype by environment interaction (GEI). If GEI occurs, the trait is partly influenced by expression different of genes in different environment. GEI may lead to re‐ranking of animals among different environments. This interaction could be studied by estimating the genetic correlation of the same trait in two separated environments 2. The existence of GEI may render accurate quantitative estimates of the effects of matting system, breed choice and also selection schemes. In pig breeding, GEI may give problems if breeding values for growth are predicted based on specially test environment, but production animals are raised in total different environment. Therefore it makes sense, to test animal on family farms in production conditions. Genetic evaluation requires consistency of sire ranking across environments. The existence of GEI might lead to a re‐ranking of boars. To quantify GEI, Falconer and Mckay 3 proposed the estimation of genetic correlation between traits in different environments. Robertson 4 suggested value 0.8 for correlation coefficient if GEI is present. At present, no information is available on the existence of a GEI in Slovenian pig populations. The aim of this study was to evaluate genotype by environment interaction for growth traits in gilts of maternal genotypes in Slovenia using multiple trait animal model approach.

METHODS Growth records and pedigree information from 10479 are routinely collected for gilts of maternal genotype in Slovenia. Data were obtained from central data base of SloHibrid, Slovenian breeding program. Animals are four genotypes: Slovenian Landrace ‐ line 11 (11), Slovenian Large

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White (22) and Hybrides 11x22 (12) and 22x11 (21). Gilts in study were raised on 44 family farms. The variables used to describe the environment for each animal was average daily gain in common herd‐year environment. The observations were dived into three environments. In analyses only low and high environment were included, while records of animal from intermediate environment were excluded. For the low environment the limit for daily gain was 530 g/day or lower. In high environment the gilts gained 580 g/day at least. There were 6074 measured animals in the low environment and 4405 measured animals in the high environment. Average daily gain in the low environment was 501 g/day and it was 106 g/day smaller than in the high environment (Table 1). Traits measurement in the lowin and the high production environments were treated as different traits in analysis. Days on test were precorrected at 110 kg. Average for days on test was 223 days for gilts in the low environment (Table 1), while animals in the high environment were 40 days younger at the end of the test. In the high environment, the youngest animal was 131 days on test. Days on test were limited up to 300 days. Backfat thickness in animals in the high environment was for 0.6 mm thinner than animals in the low environment, where it was 10.7 mm on average. The thickest backfat (22.7 mm) was measured in the low environment.

Trait Environment Mean SD Min. Max.

Daily gain (g/day) Low 500.6 60.0 367.0 730.0 Daily gain (g/day) High 607.5 64.7 376.0 839.0

Days on test (days) Low 223.0 27.0 150.7 299.8

Days on test (days) High 183.2 20.2 131.1 293.5

Backfat thickness (mm) Low 10.7 2.3 4.3 22.7 Backfat thickness (mm) High 10.1 2.0 5.0 20.7

Table 1: Descriptive statistics for traits. SAS software 5 was used for data preparation. The genetic correlation between pairs of traits was used to study the presence of GEI. Data structure and available pedigree allowed animal model. Pedigree of animal with measurements was traced back for four generations. Dispersion parameters were estimated using residual maximum likelihood methodology as applied in the VCE‐6 package 6. Breeding values were predicted using PEST software 7, 8. The following statistical models were used:

( )[ ] tijklmntntmtltijklmnttktjtittijklmn eahgxxbRSGy ++++−++++= μ (1)

where is trait where t depicts trait and environment (t = 1, 2, 3, 4). Odd numbers depict days

on test and even numbers depict backfat thickness. Traits 1 and 2 were measured in low

environment, while traits 3 and 4 are measured in high environment. The

tijklmny

tμ is overall mean for trait

in environment. Fixed part of model included breed ( , i=1, 2, 3, 4), season ( , j=1, 2, … 157) and

herd ( , k=1, 2, … 44). Animal body weight ( ) was included as covariate only in model for

backfat thickness where is linear regression coefficient. Random part of model included common

litter environment ( ), common herd‐year environment ( ) and direct additive genetic effect ( ).

tiG tjS

tkR tijklmnx

tb

tll tsh tnaRandom residual is written as . In matrix notation, model (Equation 1) can be written as: tijklmne

ehZlZaZXy ++++= hlaβ (2)

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where is data vecy tor, β is vector of unknown parameters for fixed effects, , , are vectors

of unknown parameters for random effects of direct additive genetic effect, common litter

environment and common herd‐year environment, respectively. , , and are

corresponding incidence matrices. Direct additive genetic effect was assumed to be normally distributed with zero mean and covariances structure:

a l

lZ

h

X aZ hZ

( ) A

aaaa

a ⊗

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

=

2

2

2

2

4

3

2

1

4342414

3433213

2423212

1413121

varvar

aaaa

aaaa

aaaa

aaaa

t

σσσσσσσσσσσσσσσσ

(3)

where are direct additive genetic variance for traits, 2aσ aσ are covariances among traits in different

environment and is the numerater relationship matrix. Covariances among traits among environments were assumed to be zero, because each animal was measured in one environment only. For trivial random, like litter and herd‐year effects it was assumed to be normally distributed with zero mean, and covariance structure below:

A

( ) l

ll

ll

ll

ll

t I

llll

l ⊗

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

=

2

2

2

2

4

3

2

1

434

343

212

121

0000

0000

varvar

σσσσ

σσσσ

(4)

( ) h

hh

hh

hh

hh

t I

hhhh

h ⊗

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

=

2

2

2

2

4

3

2

1

434

343

212

121

0000

0000

varvar

σσσσ

σσσσ

(5)

In Equations 4 and 5, is common litter environment, is variance for common herd‐year

environment. The and are corresponding identity matrices. The residuals were assumed to be

normally independent and identically distributed within environment with zero mean and covariance structure in equation 6.

2lσ

I

2hσ

lI h

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

=

000000000000

2

2

212

121

ee

ee

LRσσσσ

, , (6)

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

=

2

2

434

343

0000

00000000

ee

eeHR

σσσσ sR

eeee

⊕=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

4

3

2

1

var

63


RESULTS In the low environment, heritability for days on test was estimated to 0.21 and in high environment to 0.18 (Table 2). Heritability for backfat thickness was estimated to 0.23 and 0.28 in the low and in the high environment, respectively. Genetic correlation within environment between days on test and backfat thickness was 0.49 and 0.85, respectively. Genetic correlation for days in test was estimated bellow 0.8 and this value indicated the presence of GEI. Therefore, we present breeding values just for days on test. Common litter environment accounted between 15 and 18% of the variation associated with days on test in both environments. For backfat thickness, common litter environment accounted for about 10% of the variation. Approximately 20% of the total variation in days on test and backfat thickness in low environment was explained by the herd‐year effects. Slightly less variation (15 and 11%) herd‐year effect represented for traits in high environment. Ratio of unexplained variance accounted about 50% of the variation for all traits.

Days on test L Backfat thickness L Days on test H Backfat thickness H

Direct additive genetic effect

Days on test L 0.21 ± 0.03 0.22 ± 0.07 0.49 ± 0.17 ‐0.12 ± 0.15 Backfat thickness L 0.23 ± 0.03 0.44 ± 0.12 0.85 ± 0.08 Days on test H 0.18 ± 0.02 0.42 ± 0.10 Backfat thickness H 0.28 ± 0.04

Common litter environment

Days on test L 0.18 ± 0.01 ‐0.09 ± 0.05 / / Backfat thickness L 0.08 ± 0.01 / / Days on test H 0.15 ± 0.01 ‐0.24 ± 0.08 Backfat thickness H 0.11 ± 0.02

Herd‐year effect

Days on test L 0.20 ± 0.02 0.18 ± 0.05 / / Backfat thickness L 0.24 ± 0.02 / / Days on test H 0.14 ± 0.02 ‐0.22 ± 0.09 Backfat thickness H 0.11 ± 0.02

Residual

Days on test L 0.42 ± 0,02 ‐0.18 ± 0.03 / / Backfat thickness L 0.55 ± 0.02 / / Days on test H 0.53 ± 0.02 ‐0.18 ± 0.03 Backfat thickness H 0.49 ± 0.04

Table 2: Correlations (above diagonal) and rations (on diagonal) with standard errors for random effects and residual for days on test and backfat thickness in low and high environment.

Breeding values for days on test predicted by multiple trait animal model for measured gilts in both environments are graphically presented in Figure 1. Regression line (red) deviates from diagonal (black) which represents unity of breeding values. Around 60 sires had at least 10 daughters with observations in each environment. Re‐ranking of this boars based on breeding value prediction in the two environments are presented in Figure 2.

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Figure 1: Prediction of breeding value of gilts for days on test in low and high environment

Figure 2: Differences in ranking of boars’ breeding values for days on test, based on offspring performance in low and high

environment

DISCUSSION The estimated correlation between low and high environment breeding values for days on test was below unity. If genetic correlation is close to one, it is expected that prediction of breeding values as well as ranking of animals is the same in both environments. In the low environment

65


breeding values are more dispersed than in the high. Magnitude of genetic correlation coefficient indicates that GEI exists for days on test between environments. Merks 9 and Wallenbeck et al. 10 reported the existence of GEI for carcass leanness and growth rate, respectively. Werner et al. 11 reported the GEI for both traits, grow rate and carcass leanness in different breeds. GEI is more often assessed for milk yield traits in cattle due to more variable rearing environments. GEI for milk yield traits was confirmed for Nordic dairy cattle by Kolmodin et al. 12. For Holstein caws GEI was confirmed by Kearney et al. 13 and Shariati et al. 14. On contrary, GEI has not been confirmed by Boettcher et al. 15 in dairy cattle in Canada, Fikse et al. 16 for Guernesy cattle populations worldwide and Logar et al. 17 for Holstein, Simmental and Brown cattle in Slovenia. Based on genetic correlations of 0.49 we expect re‐ranking of sires between environments. Re‐ranking of some sires for days on test actually occurred (Figure 2). Some sires have higher breeding value in the low environment than in the high environment while some of the sires rank higher in the high environment and lower in the low environment. Re‐ranking of sires could be important when we select boars for family farms, especially if rearing environments have wide range. If there is weak GEI, it is possible to use common breeding program. Strong GEI is more evident between organic and conventional traits 16. Wallenbeck et al. 10 suggested that in such cases, separate breeding programs should be used. Existence of GEI for gilts in Slovenia was studied using animal model. Our results indicate GEI for days on test but not for backfat thickness. However, the method used in this study is simplified approach to estimate GEI. In the future, reaction norms methodology as more sophisticated method will be applied.

REFERENCES

1. de Jong G, Bijma P. Selection and phenotypic plasticity in evolutionary biology and animal breeding. Livest Prod Sci 2002, 78, 195‐214. 2. Falconer DS. The problem of environment and selection. Am Nat 1952, 86, 293‐289. 3. Falconer DS, Mackay TF. Introduction to quantitative genetics. London, Longmann & Co, 1996, p. 464. 4. Robertson A. The sampling variance of the genetic correlation coefficient. Biometrics 1959, 15, 469‐485. 5. SAS Inst. Inc. The SAS System for Linux. Release 9.2, Cary, NC, 2008. 6. Groeneveld E, Kovač M, Mielenz N. VSE6 User’s Guide and Reference Manual. Mariensee, Institute of Farm Animal Genetics, FLI 2010, p. 125. 7. Groeneveld E, Kovač M, Wang T. PEST, a general purpose BLUP package for multivariate prediction and estimation. Proceedings of the 4th World congress on genetics applied to livestock production. Edinburgh 1990, 488‐491. 8. Groeneveld E, Kovač M, Wang T, Fernando RL. Computing algorithms in a general purpose BLUP package for multivariate prediction and estimation. Arch Tierz 1992, 35, 399‐412. 9. Merks JWM. Genotype x Environment Interaction in Pig Breeding Programmes. VI. Genetic relations between performances in central test, on‐farm test and commercial fattening. Livest Prod Sci 1989, 22, 325‐339. 10. Wallenbeck A, Rydhmer L, Lundeheim N. GxE interaction for growth and carcass leanness: Re‐ranking of boars in organic and conventional production. Livest. Sci. 2009, 123, 154‐160. 11. Werner D, Brade W, Weismann F, Brandt H. Performance and carcass quality of genetically pigs under conventional and organic conditions. In: van der Honing Y. (Ed.), Annual Meeting of the European Association for Animal Production. Wageningen Academic Publisher, Dublin, Ireland 2007, p. 277. 12. Kolmodin R, Strandberg E, Madsen P, Jensen J, Jorjani H. Genotype by environment interaction in Nordic dairy cattle studied using reaction norms. Acta Agric. Scand. Sect. A Anim. Sci. 2002, 52, 11‐24. 13. Kearney JF, Schutz MM, Boettcher PJ, Weigel KA. Genotype x Environment Interaction for in Grazing versus Confinement. I. Production Traits. J. Dairy Sci. 2004, 87, 501‐509. 14. Shariati MM, Su G, Madsen P, Sorensen D. Analysis of Milk Production in Early Lactation Using Reaction Norm Model with Unknown Covariates. J. Dairy Sci. 2007, 90, 5759‐5766.

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15. Boettcher PJ, Fatehi J, Schutz MM. Genotype x Environment Interaction for in Conventional versus Pasture‐Based Dairies in Canada. J. Dairy Sci. 2003, 86, 383‐389. 16. Fikse WF, Rekaya R, Weigel A. Genotype x Environment Interaction for Milk Production in Guernsey Cattle. J. Dairy Sci. 2003, 86, 1821‐1827. 17. Logar B, Malovrh Š, Kovač M. Multiple trait analysis of genotype by environment interaction for milk yield traits in Slovenian cattle. In: Recent advances and future prioritetes of animal product quality in EU: papers of 15th International Symposium Animal Science Days. Jurković D. (Ed.). Agriculture 2007, 13, 1, 83‐88. 18. Boelling D, Groen AF, Sorensen P, Madsen P, Jensen J. Genetic improvement of livestock for organic farming system. Livest. Prod. Sci. 2003, 80, 79‐88.

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GENOMICS

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Presenting author: Jernej Bravničar, [email protected]

Article

CATALOG OF GENETIC VARIABILITY RELATED TO microRNA NETWORK IN TWO FISH SPECIES: Danio rerio AND Tetraodon nigroviridis

Jernej Bravničar, Daša Jevšinek Skok, Tanja Kunej

Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Groblje 3, 1230 Domžale, Slovenia

Genetic variability of microRNA (miRNA) network can have effects on phenotypes, diseases or cell function. Most of the miRNA research has been performed in human and mouse, however little is known about genetic variability of miRNA genes in fish species. Due to food‐wise importance this field will probably raise quite considerably in next few years as more genomic data on commercial fishes becomes available. As of now most of the genomic resources are available on two non‐commercial fish species Danio rerio and Tetraodon nigroviridis. The aim of this study was to perform a genome wide in silico screening (GWISS) of genomic sources and determine the genetic variability of miRNA network in these two species. Online tool miRNA SNiPer 3.0 was used to search for miRNA polymorphisms. Genetic variability of genes encoding for miRNA processing machinery was extracted from Ensemble e71 database. Validated miRNA‐target interactions were obtained from mirTarBase. Out of 344 and 144 known miRNA genes in D. rerio and T. nigroviridis we found 8 and 5 miRNA genes respectively with SNPs in their pre‐mature miRNA sequences. Out of 31 genes involved in miRNA processing machinery in D.rerio 16 genes had 32 non‐synonymous SNPs and 24 genes had 33 non‐synonymous SNPs in T. nigroviridis. Currently there are 30 miRNAs with validated targets in D. rerio. The developed catalogue provides an important source of information for development of potential biomarkers. In the coming years as other genomes will become available miRNA SNiPer tool will enable prioritization of biomarkers associated with improving aquacultural yield. As miRNA genes and targets are conserved a catalog of miRNA gene SNPs could further facilitate the research on closely related species like Cyprinus carpio, which is an important food source in aquaculture.

INTRODUCTION

MicroRNAs exist across viruses, plants and animals and play important roles in almost every biological process through the posttranscriptional regulation of genes by targeting mRNAs for cleavage or translational repression. Approximately 22 (nt) nucleotides in length these RNAs comprise one of the more abundant classes of gene regulatory molecules in multicellular organisms and likely influence the output of many protein coding genes1. MicroRNAs regulate gene expression through translational repression and/or messenger RNA (mRNA) deadenylation and decay. In addition, more recent observations report that miRNA also upregulate translation and target other genic regions thus have a far more complex role at regulating expression as was first thought of [reviewed in 2] 2.

Biogenesis of miRNA begins in the nucleus with the primary transcript (pri‐miRNA) several hundred nt in length being transcribed. Pri‐miRNA transcript is cleaved by RNase III Drosha endonuclease and later in cytoplasm by RNase III endonuclease Dicer. This cleaved product is a duplex of mature miRNA and its complementary sequence miRNA. Duplex is loaded into a ribonucleoprotein complex called RISC (RNA‐induced silencing complex). These proteins are only a few in complex miRNA processing machinery3. Mature miRNA serves as key binding location for translational suppression. Key binding location called seed region resides within mature miRNA

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sequence. It has been shown that nearly perfect complementarity between first nine miRNA nucleotides is needed in order for the protein coding genes to be repressed4.

MicroRNAs bind to their target genes based on sequence complementarity. More than 80% of miRNAs lower mRNA levels, demonstrating that mRNA destabilization is the primary mode of action of miRNAs5. Single nucleotide polymorphisms (SNPs) have been reported to impair or enhance miRNA regulation as well as to alter miRNA biogenesis. Single nucleotide polymorphisms are often associated with diseases or traits6. Polymorphisms in miRNA targets and machinery genes have also substantial role in miRNA regulated gene expression profiles7. Many studies have reported the effect of SNPs in the 3’untranslated regions (3’UTRs) of the target genes. For example, in Texel sheep, a SNP in the 3’UTR of myostatin gene (MSTN; previous GDF8) created a binding site targeted by two miRNAs, miR‐1 and miR‐206, resulting in MSTN inhibition and increased muscular hypertrophy8. It has been demonstrated that SNPs may modulate regulatory mechanisms in a tissue specific manner9. As of SNPs within a miRNA gene, especially in the seed region can alter its secondary structure and affect miRNA processing. Some of these are potentially interesting in aquaculture. Namely, it has been shown that miRNA SNPs alter fat deposition in mouse10 and meat quality in farm animals11, 12.

Because of the recently recognized significance of SNPs in miRNA biogenesis and regulation, many reports have concentrated on collecting miRNA‐related SNPs and investigating their influence on miRNA function8, 13. However, all these studies have focused mainly on mouse and human, because there is a substantial amount of information on miRNAs and SNPs available for these two species. Discovery and role of miRNA SNPs will probably be as important in all commercial fish species as their genomes get fully sequenced. The miRNA SNiPer 3.0 tool enables analysis of polymorphic miRNA genes and prioritization of potential regulatory polymorphisms14, and therefore can importantly contribute to the development of miRNA‐based biomarkers for production traits in fish species as more commercial fish genomes become available. As non‐synonymous SNPs in miRNA biogenesis pathway have also been proven to modify expression profiles3, this type of genetic variability also represents potential biomarkers.

Therefore, the aim of this study was to compile genetic variability residing within miRNA network: 1.) miRNA genes, 2.) miRNA processing machinery, and 3.) miRNA targets. The developed catalog will be updated with upcoming discoveries and will serve the researchers for development of novel biomarkers in fish species.

METHODS

Online tool miRNA SNiPer 3.014, 15 (http:// http://www.integratomics‐time.com/miRNA‐SNiPer/index.php) was used to search for miRNA polymorphisms in two fish species Danio rerio and Tetraodon nigroviridis. The source databases for the tool are Ensembl Variation database release e71, to retrieve data for polymorphisms (http://www.ensembl.org/index.html) and miRBase database release 19 (http://www.mirbase.org/)16 to retrieve the location of miRNA genes. Seed region was defined according to the 7mer used in TargetScan (release 6.2; http://www.targetscan.org/)17, the area of 2‐8 nucleotides from the 5’ end in the mature miRNA region. The tool searches for miRNA polymorphisms by retrieving data from matching releases of genomic databases. Using the miRNA SNiPer tool we performed a genome wide in silico search (GWISS) of genomic resources and generated a catalog of miRNA polymorphisms. A list of genes encoding for miRNA processing machinery was obtained from Patrocles database

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71

(http://www.patrocles.org/Patrocles_machinery.htm)18. Orthologous genes in D. rerio and T. nigroviridis were searched using Ensemble genome browser and later screened for non‐synonymous polymorphisms. List of experimentally validated targets was constructed from data deposited in mirTarBase database (http://mirtarbase.mbc.nctu.edu.tw/).

RESULTS

In this study we performed a genome‐wide in silico screening (GWISS) for polymorphisms residing within all three categories of miRNA‐related genetic variability: 1.) miRNA genes, 2.) genes present in miRNA biogenesis pathway and 3.) miRNA‐ target gene interactions. 1. MicroRNA genes

To generate a catalog of miRNA polymorphisms we used miRNA SNiPer 3.0 tool which accepts a list of miRNA genes and returns a table of variations located within different regions of miRNA genes: pre‐miRNA, mature, and seed region (Figure 1). All relevant accompanying data that was available was added to the table (Table 1): genomic location, homologue in other species, miRNA host gene and location within a host gene.

From 344 known miRNA genes in D. rerio there were seven genes (dre‐let‐7a‐5, dre‐mir‐15a‐2, ‐26a‐3, ‐27d, ‐138, ‐729, ‐2192) with one SNP and one gene with two SNPs (dre‐mir‐740) in pre‐mature miRNA gene (Table 1). From 144 known sequences in T. nigroviridis there were five miRNA genes comprising SNPs: tni‐mir‐7, ‐10d, ‐21, ‐142a, ‐218a‐1. All SNPs were located within in pre‐miRNA and none in mature or seed region. Out of all SNPs in both species 10 are located in the upstream region of the pre‐miRNA and four in downstream region (example shown on the Figure 1). Most of the polymorphic miRNA genes are intergenic (9 out of 14); they do not reside in any of the known host genes. Dre‐mir‐26a‐3 transcript overlaps with exon 1 and introns 3 and 4 of ctdspla gene (carboxy‐terminal domain of RNA polymerase II polypeptide A). Dre‐mir2192 resides within intron 2 of gene Pvalb3 (pravalbumin 3). MicroRNA genes dre‐mir‐740 and tni‐mir‐218a‐1 are residing within genes predicted based on homology, therefore the function of those genes was not yet researched.

Figure 1: Print screen of the output of the miRNA sniper tool for miRNA dre‐mir‐26a‐3.

http://www.patrocles.org/Patrocles_machinery.htm


miRNA SNP Genomic location Location of homologe in other species miRNA host gene Genic location

D. rerio

dre‐let‐7a‐5 rs179685575;G > C 23:5478470‐5478593[‐] tni‐let‐7a3, 11:8659681‐8663764 [‐] Intergenic /

dre‐mir‐15a‐2 rs179633115;T > C 9:30685819‐30685920[‐] No homologue Intergenic /

dre‐mir‐26a‐3 rs179598720,T > C 24:21079406‐21079542[‐] /, 15: 2348336‐2348429 [+] CTD s/ exon 1, introns 3, 4

dre‐mir‐27d rs180130201;G > T 10:15983301‐15983389[+] No homologue Intergenic /

dre‐mir‐138 rs41022377,C > T 18:17239327‐17239413[+] tni‐mir‐138, 13: 1437200‐1437278 [+] Intergenic /

dre‐mir‐729 rs180035489;A > T 4:12649007‐12649105[+] No homologue Intergenic /

dre‐mir‐740 rs180009160;T > C, rs180009204;C > T

1:29153407‐29153519[+] No homologue homologue vps8 (S. cervisiae) s/ introns 4, 8, 22, 23

dre‐mir‐2192 rs41021655;C > G 12:18774341‐18774450[‐] No homologue Pvalb3 s/ intron 2

T. nigroviridis

tni‐mir‐7 rs82048876;T > C 8:1820450‐1820514[+] dre‐mir‐7a‐2, 7: 15327893‐15327989 [‐] Intergenic /

tni‐mir‐10d rs82339962;A > G 17:9715774‐9715893[+] dre‐mir‐10d‐1, 6: 10777971‐10778088 [+] Intergenic /

tni‐mir‐21 rs81794510;T > A 7:1044906‐1045027[‐] dre‐mir‐21, 10: 28880675‐28880822 [‐] Intergenic /

tni‐mir‐142a rs81777942;G > T 7:2557207‐2557296[+] dre‐mir‐142a‐5, 5: 3156809‐3156899 [+] Intergenic /

tni‐mir‐218a‐1 rs82289137;C > T 1:5655172‐5655254[‐] dre‐mir‐218‐a‐1, 14: 25562465‐25562547 [‐] homologue SLIT 3 (Drosophila) s/ intron 2

Table 1: MicroRNA genes with polymorphisms in two fish species D. rerio and T. nigroviridis; CTD = carboxy‐terminal domain, RNA polymerase II, polypeptide A; Pvalb3 = parvalbumin 3; s = sense orientation with host gene; / = not applicable.

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2. MicroRNA processing machinery We constructed a catalogue of non‐synonymus SNPs present within genes encoding for

miRNA biogenesis pathway in D. rerio and T. nigroviridis genes. The list of genes encoding for miRNA processing machinery was obtained from the Patrocles database and orthologues to human genes were searched in two researched fish species. Based on the current database version there are 32 non‐synonymous SNPs present within 16 genes in D. rerio and 33 non‐synonymous SNPs in 24 genes in T. nigroviridis. Example for four polymorphic genes encoding for miRNA processing machinery is shown in Table 2.

Non‐synonymous SNPs

Gene D. rerio T. nigroviridis

ddx20 rs41016410, rs179590840, rs179590793, rs179590800 rs82368904, rs82368895

dicer1 rs180092734 rs82241567

fmr1 rs41169478 /

gemin4 rs41152190, rs41120134, rs41217202, rs40756889 rs81906928, rs81906899

Table 2: Non‐synonymous SNPs within genes associated with miRNA processing machinery in D. rerio and T. nigroviridis. Table shows a shortened example list; / = no SNP present in the gene. 3. MicroRNA targets Based on the current mirTarBase database (http://mirtarbase.mbc.nctu.edu.tw/) information there are 32 miRNAs that have validated targets in D. rerio (Table 3). Currently there are no reported miRNA‐target interactions in T. nigroviridis.

miRNA Validated miRNA target gene

dre‐let‐7a hspd1, trim71, ascl1a, lin28a, pax6b, klf4b, myca, mycb, pou5f1

dre‐let‐7f hspd1, lin28a, ascl1a, pax6b, klf4b, myca, mycb, pou5f1

dre‐miR‐1 rab13, smarca5, idh1, arpc4l, copz1, spryd7b, cnn3a, actb1, ddx18, atp6v1ba, cnn2, arcn1b,

atp6v0d1, pdlim1, slc9a3r1, alg9, smarcb1b, ap2m1b, hand2, atp6v1e1b, tpm3, dpm1, smarcd1

Table 3: Examples of validated miRNA‐target interactions in D. rerio.

Currently there are no databases available that would integrate the data related to genetic variability miRNA target interaction sites in the researched fish species.

DISCUSION

Despite the fact that there was little research performed related to miRNA in fish, it was possible to develop a catalog of miRNA‐related polymorphisms in two fish species. The catalog consists of data belonging to three categories: miRNA genes, miRNA processing machinery and miRNA targets.

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Using miRNA SNiPer 3.0 tool we were able to construct a catalogue of polymorphisms within miRNA genes. This catalogue provides researchers a novel resource to develop biomarkers for D. rerio and T. nigroviridis in miRNA genes. Our analysis returned 14 SNPs residing within 7 miRNA genes in D. rerio and 5 genes in T. nigrovirids. High number of miRNA gene annotations in T. nigrovoridis was actually proposed based on homologies with D. rerio miRNA genes. T. nigrovidis has the smallest known vertebrate genome; roughly 340 million bps19 and has thus been an important model organism. However, it has approximately the same number of coding genes as human genome. All found miRNA gene mutations reside within pre‐mature region. It has been shown previously that these SNPs can alter the processing of mature miRNA thus altering its binding capabilities20, 21. Validation of the SNP rs179685575 residing within dre‐let‐7a‐5 could represent a potential biomarker for developmental studies22, 23. MicroRNA gene dre‐let‐7a‐5 which belongs to the most extensively studied miRNA family let‐7 is the only miRNA gene with SNP that has validated target genes. However it’s SNP should first be validated to exclude the possibility of SNP being a sequencing error. MicroRNA gene dre‐mir‐2192 overlaps with intron 2 of its host gene Pvalb3 (pravalbumin 3), based on the assumption that miRNA silences the gene it is transcribed with, SNP in dre‐mir‐2192 could influence cells ability to store calcium ions.

There were several non‐synonymous variations found within genes encoding for miRNA machinery in two researched fish species. It has been shown previously that SNPs in GEMIN4 contribute to different expression profile in human cancer24. Therefore SNPs in miRNA processing machinery compiled in this study could have a possible phenotypic effect and present a foundation for further biomarker discovery. The third researched category in this study was analysis of polymorphisms within miRNA targets. Currently, there are no tools that would enable search for genetic variability within miRNA‐target binding region in fish species. However, we constructed a list of currently known miRNA‐targets in there two fish species, which will be supplemented with genetic variability in the future research. Development of tool for fish such as the Patrocles tool, would greatly contribute to discovery of potentially interesting biomarkers.

Despite little research performed on miRNA in fish species at the moment, the available data enabled us to build a catalog of genetic variations related to miRNA network in two fish species. These variations can have a potential effect on phenotype and can represent potential biomarkers. As the data will be freely available online the collected data can be used by the research community to prioritize SNPs for biomarker development.

The number of assembled miRNA polymorphisms is not final and will change with time as all miRNAs have not yet been systematically sequenced and screened for polymorphisms. With more genomes becoming available miRNA SNiPer tool used will ease the effort of prioritizing which biomarkers researchers should focus on to improve aquacultural yield.

REFERENCES 1. Bartel D . MicroRNAs: Genomics, Biogenesis, Mechanism, and Function. Cell 2004, 116, 281‐229. 2. Kunej T, Godnic I, Horvat S, Zorc M, Calin GA. Cross talk between microRNA and coding cancer genes. Cancer J 2012, 18, 223‐231. 3. Horikawa Y, Wood C, Yang H, Zhao H, Ye Y, Gu J et al. Single nucleotide polymorphisms of microRNA machinery genes modify the risk of renal cell carcinoma. Clin Cancer Res 2008, 14, 7956‐7962. 4. Kiriakidou M, Nelson PT, Kouranov A, Fitziev P, Bouyioukos C, Mourelatos et al. A combined computational‐experimental approach predicts human microRNA targets. Genes Dev. 2004, 18, 1165‐1187.

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5. Guo H, Ingolia NT, Weissman JS, Bartel DP. Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature 2010, 466, 835‐840. 6. Zhu Y, Xue W, Wang JT, Wan YM, Wang SL, Xu P et al. Identification of common carp (Cyprinus carpio) microRNAs and microRNA‐related SNPs. BMC genomics 2012, 13, 413. 7. Georges M. Polymorphic miRNA‐mediated gene regulation: contribution to phenotypic variation and disease. Current opinion in genetics & Development Curr Opin Genet Dev 2007, 17, 1‐11. 8. Clop A, Marcq F, Takeda H, Pirottin D, Tordoir X, Bibe B et al. A mutation creating a potential illegitimatemicroRNA target site in the myostatin gene affects muscularity in sheep. Nat. Genet. 2006, 38, 813–818. 9. Dimas AS, Deutsch S, Stranger BE, Montgomery SB, Borel C, Attar‐Cohen H et al. Common regulatory variation impacts gene expression in a cell type‐dependent manner. Science 2009, 325, 1246‐1250. 10. Kunej T, Skok D, Horvat S, Dovc P & Jiang Z. The Glypican 3‐Hosted Murine Mir717 Gene: Sequence Conservation, Seed Region Polymorphisms and Putative Targets. International Journal of Biological Sciences 2010, 6, 769‐772. 11. Li H, Sun GR, Lv SJ, Wei Y, Han RL, Tian YD et al. Association study of polymorphisms inside the miR‐1657 seed region with chickedn growth and meat traits. British Poultry Science 2012, 53:6, 770‐776. 12. Lee JS, Kim JM, Lim KS, Hong JS, Hong KC & Lee YS. Effects of polymorphisms in the porcine microRNA MIR206 / Mir133B cluster on muscle fiber and meat quality traits. Anim. Genet., 2013, 44, 101‐106. 13. Sethupathy P, Collins FS. MicroRNA target site polymorphisms and human disease. Trends Genet., 2008, 24, 489–497. 14. Jevsinek Skok D, Godnic I, Zorc M, Horvat S, Dovc P, Kovac M et al. Genome‐wide in silico search (GWISS) for microRNA genetic variability in livestock species. Plos One 2013, in Press. 15. Zorc M, Skok DJ, Godnic I, Calin GA, Horvat S, Jiang Z et al. Catalog of microRNA seed polymorphisms in vertebrates. PLoS ONE 2012, 7, e30737. 16. Kozomara A & Griffiths‐Jones S. miRBase: intergrating microRNA annotation and deep sequencing data. Nucleic Acids Research 2011, 39, D152‐D157. 17. Lewis BP, Burge CB, Bartel DP. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 2005, 120, 15‐20. 18. Hiard S, Charlier C, Coppieters W, Georges M, Baurain D. Patrocles: a database of polymorphic miRNA‐mediated gene regulation in vertebrates. Nucleic Acids Research 2010, 38 (Database issue), D640‐651. 19. Jaillon O, Aury JM, Brunet F, Petit JL, Stange‐Thomann N et al. Genome duplication in the teleost fish Tetraodon nigroviridis reveals the early vertebrate proto‐karyotype. Nature 2004, 431, 946‐957. 20. Sun G, Yan J, Noltner K, Feng J, Li H, Sarkis DA et al. SNPs in human miRNA genes affect biogenesis and function. RNA 2009, 15, 1640–1651. 21. Duan R, Pak C, Jin P. Single nucleotide polymorphism associated with mature miR‐125a alters the processing of pri‐miRNA. Hum. Mol. Genet. 2007, 16, 1124–1131. 22. Reinhart BJ, Slack F, Basson M, Pasquinelli A, Bettinger J, Rougvie A et al. The 21‐nucleotide let‐7 RNA regulates developmental timing in Caenorhabditis elegans. Nature 2000, 403, 901‐906. 23. Chen PY, Manninga H, Slanchev K, Chien M, Russo JJ, Ju J et al. The developmental miRNA profiles of zebrafish as determined by small RNA cloning. Genes Dev. 2005, 19, 1288‐1293. 24. Brid R, Robles A, Harris C. Genetic variation in mciroRNA networks: the implications for cancer research. Nature Reviews ‐ Cancer 2010, 10, 389‐402.

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Presenting author: Jana Obšteter, [email protected]

Abstract

CATALOG OF POLYMORPHISMS ASSOCIATED WITH MICRORNA SILENCING MACHINERY

Jana Obšteter1, Peter Dovč2 Tanja Kunej2

1University of Ljubljana, Biotechnical Faculty, Academic Study in Biotechnology 2University of Ljubljana, Biotechnical Faculty, Department of Animal Science

ABSTRACT MicroRNAs (miRNAs) are short non‐coding single‐stranded RNA molecules, ~22 nucleotides (nt) in length, which act as post‐transcriptional repressors1. During their biogenesis they undergo many protein interactions, including two catalytic steps performed by two ribonuclease III family enzymes, Drosha and Dicer. Drosha and its cofactor DGCR8 form a complex called Microprocessor2, which cleaves pri‐miRNA into ~70 nt long pre‐miRNA. In the next catalytic step Dicer cleaves pre‐miRNA to create mature miRNA4. It has been shown before that polymorphisms within miRNA genes and target mRNAs can influence phenotype5. On the other hand, genetic variability associated with miRNA silencing machinery has not been systematically analyzed. Therefore, the aim of this study was to create a catalog of genetic variability associated with miRNA silencing machinery (miR‐SM‐SNPs) in human consisting of: genetic variability residing within Drosha and Dicer cleavage sites and within genes encoding the components of the silencing machinery. The catalog was created using miRBase, Patrocles, Ensembl, SIFT (Sorting Intolerant From Tolerant), and miRNA SNiPer databases and tools. Based on the latest database versions there are 53 polymorphisms located within Drosha cleavage sites and 56 polymorphisms located within Dicer cleavage sites. Additionally, there are 43, 21, and 73 missense variants with predicted deleterious effect on protein function residing within DROSHA, DGCR8, and DICER1 genes, respectively. The developed catalog will be useful for further functional studies and development of biomarkers associated with diseases and phenotype traits.

REFERENCES 1. Bartel, D. P., MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004, 116 , 2, 281‐297. 2. Gregory, R. I.; Yan, K. P.; Amuthan, G.; Chendrimada, T.; Doratotaj, B.; Cooch, N.; Shiekhattar, R., The Microprocessor complex mediates the genesis of microRNAs. Nature 2004, 432, 7014, 235‐240. 3. Murchison, E. P.; Hannon, G. J., miRNAs on the move: miRNA biogenesis and the RNAi machinery. Curr Opin Cell Biol 2004, 16, 3, 223‐229. 4. Bao, L.; Zhou, M.; Wu, L.; Lu, L.; Goldowitz, D.; Williams, R. W.; Cui, Y., PolymiRTS Database: linking polymorphisms in microRNA target sites with complex traits. Nucleic Acids Res 2007, 35, D51‐4.

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Corresponding author: Darja Žgur Bertok, [email protected]‐lj.si

Abstract

ISOLATION AND ACTIVITY OF GENOTOXIN Usp OF BACTERIA Escherichia coli

Aleksandra Šakanović, Miha Črnigoj, Damjan Nipič, Zdravko Podlesek, Darja Žgur Bertok

University of Ljubljana, Biotechnical Faculty, Department of Biology, Slovenia

The bacterium Escherichia coli is a commensal of gastrointestinal tract but pathogenic strains produce virulence factors that cause intestinal and extraintestinal infections. As reported previously, strains isolated from patients with pylonephritis, prostatitis, urosepsis and ulcerative colitis often encode the usp gene (uropathogenic‐specific protein). Gene for Usp protein and three other small genes imu1‐3 are encoded on a small pathogenicity island (of 4kb). Because of the similarity of C terminal domain of the Usp to some colicins, it was assumed that Usp acts as a bacteriocin which provides a competitive advantage to the producer strain.

Our research group revealed that the targets of the Usp are eukaryotic cells, that Usp acts as genotoxin, which degradates the genome and that the isolated protein is unstable.

In the present study, we optimized the isolation of protein Usp, analysed in detail the catalytic domain of the toxin and examined its activity in a broad range of human cell lines.

In recent years genotoxins have been extensively studied because the agents that damage DNA cause genome instability and increase the risk of tumorigenesis.

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GENOMIC INTERACTIONS

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Presenting author: Jasmina Beltram, [email protected]‐lj.si

Abstract

A MOUSE ATLAS OF TST GENE REGULATORY NETWORK

Jasmina Beltram1, Simon Horvat1, 2, Tanja Kunej1

1Biotechnical faculty, Department of Animal Science, Slovenia 2National institute of Chemistry, Slovenia

Despite rapid spreading of obesity due to the modern »obesogenic« environment, a relatively large proportion of the human population still remains lean, suggesting genetic resistance to obesity development. Our positional cloning study aimed at identifying a causal gene for the Fob3b2 QTL that confers anti‐obesity effects in a polygenic mouse model. Fine mapping, gene

expression and functional analysis of the ∼ 20 lean gene candidates within the genetic interval Fob3b2, identified the thiosulfate sulfurtransferase (Tst) as the only upregulated adipose‐specific gene. The nuclear‐encoded mitochondrial protein has so far been linked to iron‐sulfur cluster formation, cyanide detoxification, 5S ribosomal RNA import into mitochondria but not yet to obesity/leanness control. Our studies in mice selected for high or low fat content, as well as transgenic models support the view that Tst is a novel gain‐of‐function gene in adipocytes that promotes healthy leanness. Since Tst roles in other biological processes such as obesity and diabetes are still not well understood, a mouse Atlas of Tst gene regulatory network was generated, integrating data derived from various databases and experiments as well as bioinformatics predictions. This Tst gene atlas combines and evaluates functional polymorphisms, association studies, transcription factors, miRNA binding sites and the enzyme involvements in different metabolic pathways to gain a more comprehensive knowledge about its function and to identify its direct or indirect target genes. The completed Tst gene atlas therefore represents a valuable tool for planning efficient further experimental validations and analyses and for applied research of this important causal leanness gene for potential therapeutic developments for obesity and diabetes in human.

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BIOTECHNOLOGY

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Presenting author: Matevž Rumpret, [email protected]

Abstract

EFFICIENCY OF DIFFERENT DONOR STRAINS IN DELIVERING THE ColE7 BASED TOXICITY VIA CONJUGAL TRANSFER

Matevž Rumpret1, Darja Žgur‐Bertok1, Jos P. M. van Putten2, Marjanca Starčič Erjavec1

1University of Ljubljana, Biotechnical Faculty, Department of Biology, Slovenia

2Utrecht University, Department of Infectious Diseases & Immunology, the Netherlands

Multi‐drug resistance among Gram‐negative bacteria has become a great risk to public health; therefore, alternative strategies for treating/preventing infections with such strains are urgently needed. In an attempt to create a probiotic Escherichia coli strain with increased antimicrobial activity, we genetically modified a selection of laboratory and commensal E. coli strains by placing the colicin E7‐coding gene on a conjugative plasmid, while the gene encoding the immunity protein to the same colicin was either integrated into the strain’s chromosome or placed on a separate non‐conjugative plasmid1. These newly created donor strains take advantage of a unique horizontal gene transfer‐mediated mechanism for the delivery of the E7 colicin gene into the target strain cells, where the colicin is expressed and its lethal activity occurs. The objective of our research was to assess and compare the efficiency of different donor strains in acting against selected laboratory and pathogenic strains of E. coli. The efficiency of the created strains against different recipient strains was assessed by observing the frequencies of conjugation of the conjugative plasmid and the numbers of the surviving recipient strain transconjugants. The highest frequency of conjugation was observed when using laboratory strains as both donor and recipient strains. No significant differences in the frequencies of conjugation were observed among different commensal donor strains. Although the frequency of conjugation into pathogenic recipient strains was much lower, the lethal activity of the colicin E7‐coding plasmid once inside the recipient strain was very high. REFERENCES 1. Petkovšek Ž. Attempt of preparation and utilisation of genetically modified colicinogenic probiotic strain Escherichia coli for prevention from pathogenic strains of the same bacterial species. Dissertation thesis, University of Ljubljana, Biotechnical Faculty, 2012.

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Presenting author: Mateja Zupin, [email protected]

Abstract

CHARACTERIZATION OF THE COMMON BEAN (Phaseolus vulgaris L.) PARENT CULTIVARS FOR FURTHER GENOMIC AND TRANSCRIPTOMIC ANALYSES

Mateja Zupin1, Marko Maras1, Jelka Šuštar Vozlič1, Marjetka Kidrič2, Dominik Vodnik3, Jaka Razinger4,

Vladimir Meglič1

1Agricultural Institute of Slovenia, Crop Science Department, Slovenia 2Jožef Stefan Institute, Department of Biochemistry and Molecular Biology, Slovenia

3University of Ljubljana, Biotechnical faculty, Department of Agronomy, Slovenia 4Agricultural Institute of Slovenia, Plant Protection Department, Slovenia

Common bean is nutritionally very important legume plant that exhibits sensitivity to drought which affects its growth and yield. Plants developed mechanisms in adapting to drought, which are expressed by changes of gene expression1 and functional protein content2, together with responses at physiological and morphological levels. Identifying changes in responses to drought in different species will provide markers essential to characterize candidate genotypes for marker assisted selection in breeding for greater drought tolerance3.

To establish differences between plants subjected to water withdrawal and normally watered plants of common bean, the parental cultivars, drought tolerant Tiber and drought susceptible variety Starozagorski čern, were grown in a growth chamber under controlled environment conditions4. Plants were watered daily to the same pot weight. After three weeks the half of plants were stressed by withholding irrigation. The measurements were taken at the different stage of drought at the third trifoliate leaves. The hydration state of leaves was defined by their relative water content (RWC), water potential (Ψw) and different photosynthetic parameters. Detached leaf samples are kept at ‐80°C for further analyses.

In water stressed plants RWC dropped to 50% compared to control plants. Due to the reduction of water potential, the duration of the leaf stomata closure was increased as confirmed by the results of photosynthetic and other physiological parameters. In addition differences in response to drought between the cultivars were confirmed, which form the basis for a further study of genetic variation with molecular markers and mapping of loci linked to quantitative traits. REFERENCES 1. Seki M, Narusaka M, Kamiya A. Functional annotation of a full‐length Arabidopsis cDNA collection. Science 2002, 296, 141‐145. 2. Hashiguchi A, Nagib A, Komatsu S. Proteomics application of crops in the context of climatic change. Food res. Intern. 2010, 43, 1803‐1813. 3. Beaver JS, Osorno JM. Achievements and limitations of contemporary common bean breeding using conventional and molecular approaches. Euphytica 2009, 168, 145‐175. 4. Hieng b, Ugrinović K, Šuštar‐Vozlič J, Kidrič M. Different classes of proteases are involved in the response to drought of Phaseolus vulgaris L. cultivars differing in sensitivity. J. Plant Physiol. 2004, 161, 519‐530.

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AUTHOR INDEX Aguirre IG 36 Marčun Varda N 41 Avčin T 67 Meglič V 17, 82 Bataneant M 67 Milavec M 36 Berucci V 16 Mironska K 67 Betram J 79 Nipič D 77 Bravničar J 69 Obšteter J 76 Brezigar A 41 Ott S 33 Črnigoj M 77 Pallavicini A 16, 32, 40 Csibi E 13 Pavšič J 36 Dallos T 67 Perin P 42 De Moro 32, 40 Perko D 67 Debeljak M 67 Pipan B 17 Derfavli B 67 Planinc M 18 Dolinšek J 41 Podlesek Z 77 Dovč P 76 Pokorn T 37 Edomi P 16 Potočnik U 42, 52 Erjavec Škerget A 41 Radišek S 37 Flajšman B 39 Razinger J 82 Gerdol M 32, 40 Ristić G 67 Giulianini PG 16, 32 Rumpret M 81 Gradišnik P 41 Rusoniene S 67 Harjaček M 67 Šakanovič A 77 Horvat S 79 Šedivá A 67 Jakše J 37 Stangler Herodež Š 41 Javornik B 37, 39 Starčič Erjavec M 81 Jelušič M 67 Strmšek Ž 34 Jevšinek‐Skok D 34, 51, 69 Šuštar‐Vozlič 17, 82 Kidrič M 82 Tom M 32 Kokalj‐Vokač 41 Toplak N 67 Kovač M 18 Torboli V 40 Krgović D 41 Van Putten JPM 81 Kunej T 33, 34, 51, 69, 76, 79 Venier P 40 Kurjane N 67 Vodnik D 82 Kuzmanovska D 67 Zagorac A 41 Leoni G 40 Zagradišnik B 41 Macedoni‐Lukšič M 41 Žel J 36 Malovrh Š 18 Zemljič L 52 Mandelc S 39 Žgur‐Bertok D 77, 81 Manfrin C 16, 32, 40 Zorc M 33, 51 Maras M 82 Zupin M 82

Documents

3rd COLLOQUIUM OF GENETICS...COLLOQUIUM of Genetics (3 ; 2013 ; Piran) Proceedings / 3th Colloquium of Genetics, Piran 13th September 2013 ; [organizers] Genetic Society of Slovenia