ANALYSIS OF OVARIAN EXPRESSED GENES AND miRNAS IN SHEEP (Ovis aries) USING QPCR
Ismail Zhaboyev MSc Thesis Green Biotechnology and Food Safety University of Eastern Finland Faculty of Science and Forestry Department of Environmental and Biological Science 22nd November 2016
UNIVERSITY OF EASTERN FINLAND, Faculty of Science and Forestry Green Biotechnology and Food Security, Animal biotechnology
Ismail Zhaboyev: Analysis of ovarian expressed genes and miRNAs in sheep (Ovis aries) using qPCR.
MSc thesis 50 pages
Supervisors: Professor Juha Kantanen, MSc Kisun Pokharel, MSc Roseanna Avento 22nd November, 2016
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Keywords: mRNA, Ovary, miRNA, Finnsheep, Texel ABSTRACT
From the times when people started to breed animals, they understood that the traits of the offspring are dependent on the traits of the parents. By selecting and crossbreeding the best individuals, people for decades were creating new and better animal breeds of the higher grade, which also led to the higher interest of heredity phenomenon. In sheep breeding, one of the main objectives is to increase the productivity of the animals. One of the most important organs in fertility is the ovary. By investigating genes that are expressed in ovaries, one may be able to find genes influencing fertility traits such as ovulation rate and litter size. It has also been shown that environmental factors such as diet play important role in sheep reproduction.
In this study, ovarian transcriptome mRNA profiling of two pure breeds of sheep namely:
Finnsheep and Texel (in total 12 ewes) was studied. The 12 ewes were kept in flushing diet (additional nutrition) to study the effect of diet in gene expression. Finnsheep, a native breed of Finland is characterized as a high fecundity breed,while the Texel breed whose origins lie in the Texel island of Netherlands, is characterized as a low fecundity breed. Ovarian expressed mRNAs have been detected using RNA sequencing technology and bioinformatics toolsRNA sequencing revealed that altogether 179 genes were significantly differentially expressed between Finnsheep and Texel, kept in flushing diet. This study aimed at validating the RNA sequencing data using qPCR and focused on five randomly selected genes (SLCO2A1, GJA2, ABLM3, BMX and CNTN4). The qPCR results were analyzed using the comparative CT Method and compared with the results obtained from NGS and computational methods.
Validation using qPCR approach was inconclusive in determining that the gene expression pattern of four out of the five candidate genes (ABLIM3, SLCO2A1, GJA5, BMX) was similar to that of computational analysis, as indicated by the high standard deviation in qPCR results. Errors may have occurred due to ovarian sampling where the whole ovary was utilized, rather than focusing on specific cell types, or due to technical errors in either or both RNA sequencing and qPCR.
УНИВЕРСИТЕТ ВОСТОЧНОЙ ФИНЛЯНДИИ, Факультет естественных наук и лесного хозяйства
Зеленая Биотехнология и Пищевая безопасность, Биотехнология животных
Исмаил Жабоев: Анализ экспрессии генов и микро РНК в яичниках овец (Ovis aries) используя ПЦР в реальном времени
Магистерская работа, 50 страниц
Руководители: Профессор Юха Кантанен, Магистр Кисун Покхарель, Магистр Розанна Авенто
22 Ноябрь, 2016
Ключевые слова: иРНК, микроРНК, яичники, ПЦР в реальном времени, Финский ландрес, Тексель
АБСТРАКТ
С момента, когда люди начали разводить растения и живoтных oни начали понимать, что признаки потомства зависят oт свoйств их рoдителей. Путем отбора и скрещивания лучших особей,человек на протяжении десятилетий создавали новые породы живoтных и сорта растений с улучшенными свoйствами, что привело к повышенному интересу феномена наследственности. В наше время одной из важнейших задач овцеводства является высокая плодовитость овцематок. Яичники являются одними из важнейших органов для репродукции и изучая экспрессию генов, становится возможным найти гены, влияющие на рождаемость и количество приплода у овец.
Это исследование было сфокусировано на двух породах овец: Финский Ландрес и Тексель в общей сложности 12 животных. Финский Ландрес характеризуется как высоко плодовитая порода овец, тогда как Тексель является породой мясного направления и характеризуется как низко плодовитая порода овец. Экспрессия информационного РНКв яичниках была исследована используя технологию секвенирования нового поколения и биоинформатики, всего было найдено 179 генов с дифференциальной экспрессией между Финской породой овец и Тексель. Задача этой магистерской работы заключается в экспериментальном подтверждении экспрессии 5 случайно выбранных генов используя ПЦР в реальном времени. Экспрессия генов была проанализирована с помощью сравнительного метода CT, различия между данными ПЦР в реальном времени затем были сравнены с данными секвенирования нового поколения.
Четыре из пяти генов-кандидатов (ABLIM3, SLCO2A1, GJA5, ВМХ) соответствуют результатам компьютерного анализа, что указывает высокое стандартное отклонение результатов количественной ПЦР. Ошибки в исследовании могли возникнуть из-за выборки проб, где весь яичник был использован, а не конкретный тип ткани, или из-за технических ошибок в РНК-секвенировании или количественной ПЦР.
ШЫҒЫС ФИНЛЯНДИЯ УНИВЕРСИТЕТІ, Жаратылыстану ғылымдары және орман шаруашылығы факультеті
Жасыл Биотехнология және Тағам қауіпсіздігі, Жануар биотехнологиясы
Исмаил Жабоев: Биоинформатиканы және секвенирлеудің жаңа жетістіктерін қолдану арқылы алынған мРНҚ және микро РНҚ нәтижелерін эксперименттік растау
Магистрлік жұмыс, 50 бет
Жетекшілер: Профессор Юха Кантанен, Магистр Кисун Покхарель,Магистр Розанна Авенто
22Қараша, 2016
Кілт сөздер: аРНҚ, микроРНҚ,аналық бездері, тура уақыттағы ПТР, Финдық ландрес, Тексель
АБСТРАКТ
Адамдар өсімдіктер мен жануарларды көбейтуді қолдана бастағанда тұқымданудың тікелей олардың ата тегінің әсерінен болатынын түсінген. Таңдап алу мен шағылыстыру жолдары арқылы адамдар ондаған жылдар ішінде өсімдіктер мен жануарлардың жақсартылған қасиеттерге ие жаңа сорттарын ала білген және осындай тұқымданушылық құбылысы үлкен қызығушылыққа ие болды. Қазіргі уақытта қой шаруашылығындағы ең маңызды міндеттердің бірі саулықтардың өсімталдығын арттыру болып табылады. Аналық бездер көшірме жасауда маңызды мүше болып табылады және де гендердің экспрессиялануын зерттеу арқылы қойдың төл мөлшері мен төлдеу көрсеткішіне әсер ететін гендерді табуға мүмкін болып отыр.
Бұл зерттеу жұмысы барлығы 12 Финдық Ландрес пен Тексель қой тұқымдастарына негізделген. Финдық Ландрес көп төлдейтін, ал етті Тексель аз төлдейтін қой тұқымдасына жатады. Ақпараттық РНҚ аналық бездерінің экспрессиялануы биоинформатиканы және Жаңа Ұрпақты Секвенирлеу технологиясын қолдану арқылы зерттелген. Финдық қой тұқымдасы мен Тексель арасында дифференциалды экспрессиямен барлығы 179 ген.
Және бұл магистрлік жұмыстың мақсаты кездейсоқ таңдалынып алынған 5 ген және 8 жаңа микро РНҚ-ны тура уақыттағы ПТР қолдану арқылы эксперименттік тұрғыдан растау болып табылады.Генді экспрессиялау СТ салыстырмалы әдісін қолдану арқылы жүрізілді, тура уақыттағы ПТР деректері арасындағы айырмашылық Жаңа Ұрпақты Секвенирлеу деректерімен салыстырылды. (ABLIM3, SLCO2A1, GJA5, ВМХ) 5 үміткер гендердің 4-еуі компьютерлік анализдеу нәтижелерімен сәйкес келді. Микро РНҚ-ға арналған тура уақыттағы ПТР зерттеулері барлық 8 жаңа микро РНҚ экспрессиясы мен компьютер анализдерімен сәйкес келді, бұл Жаңа Ұрпақты Секвенирлеу нәтижелерінің шынайы екенін көрсетті.
Бес ген кандидаттардың арасында төртеуі ғана компьютерлік зерттеуге байланысты өтті (ABLIM3 SLCO2A1 GJA5 ВМХ) бүкіл сынамаларын іріктеу ПТР жоғары стандартты ауытқу көрсетеді. Онда нақты қатесі сынамаларды таңдау процессінде мүмкін пайда болған, ол бүкіл аналық жыныс безі емес оның тіндердің нақты түрін алынғаннан мүмкін, РНҚ секвенинаторда немесе сандық ПТРда.
ACKNOWLEDGEMENTS
I would like to express my great gratitude to all the staff in the Natural Resources Institute Finland in Jokioinen, and to my mentor Nasser Ghanem and all my supervisors Kisun Pokharel, Professor Juha Kantanen and my teacher Professor Jaakko Mononen for all their support, help, patience and the time they have spent during the course of this study.
I am so grateful for the opportunity to be a part of this project and to have a chance to work in an environment of wonderful scientists, who are always going to be a great example for me to follow.
I am also grateful to the administration of the University of Eastern Finland and Kazakh National Agrarian University for the creating this program and for the opportunity to study at one of the best universities in the world.
I also wish to express special gratitude to my coordinator Roseanna Avento for her support, patience, help and care. Thank you for the discipline that you taught me.
My deepest thanks go to my family for their constant support and love. I would like to express special thanks to my cousin Amina Tsoraeva, for the tremendous support that I received since enrolling at the University of Eastern Finland and until the completion of this thesis. I am so grateful to you for everything you have done for me. I love you and miss you and I would like to dedicate this thesis to you.
ABBREVATIONS
B.P. Before present
cDNA Complementary DNA DNA Deoxyribonucleic acid
FSH Follicle-stimulating hormone GnRH Gonadotropin-releasing hormone LH Luteinizing hormone
miRNA microRNA mRNA Messenger RNA
NGS Next-Generation Sequencing PCR Polymerase chain reaction
qPCR Quantitative polymerase chain reaction RIN RNA Integrity Number
RNA Ribonucleic acid
CONTENTS
1. INTRODUCTION ... 8
2. LITERATURE REVIEW ... 10
2.1 GENES AND THEIR EXPRESSION ... 10
2.2. MOLECULAR TECHNIQUES FOR STUDYING GENE EXPRESSION ... 11
2.2.1. Low complexity methods ... 12
2.2.2. High complexity methods ... 14
2.3. SHEEP AND SHEEP BREEDING ... 16
2.3.1 Domestication of sheep ... 16
2.3.2 Sheep Breeds ... 19
2.3.3 Sheep breeding and reproduction ... 21
3. OBJECTIVES ... 26
4. MATERIALS AND METHODS ... 27
4.1. Detection of ovarian expressed mRNAs by NGS and bioinformatics tools ... 27
4.1.1. RNA extraction and concentration measurements ... 29
4.1.2. cDNA synthesis ... 30
4.1.3. QPCR analyses ... 30
4.1.4. Gene Annotation ... 32
5. RESULTS ... 33
5.1. GENE EXPRESSION IN PROLIFIC AND NON-PROLIFIC SHEEP BREEDS ... 33
5.1.1. Quantitative and qualitative parameters of RNA samples ... 33
5.1.2. Relative gene expression between Finnsheep and Texel sheep by qPCR analysis 34 5.1.3. Gene Annotation ... 35
6. DISCUSSION AND CONCLUSION ... 38
REFERENCES ... 41
1. INTRODUCTION
From the times when people started to breed animals, they understood that the traits of offspring are dependent on the traits of the parents (Conner, 2003). By selecting and crossbreeding the best individuals, people for decades produced new and better animal breeds of higher grade, that also lead to the higher interest of heredity phenomenon (Cobb, 2006). Modern animal breeding pays a lot of attention to genetics. Molecular science is well developed now, new discoveries are made weekly (Pareek et al., 2011), and there is a huge difference in what scientists knew when they first learned about genetics and what they actually know now.
In sheep breeding, a main goal is to increase the fertility of the animals. Important fertility traits like ovulation rate and litter sizes are influenced by both genetic and environmental factors.
Short-term nutritional supplements, also known as flushing diet, has been found to increase the ovulation rate in sheep (Somchit-Assavacheep et al., 2013), whereas poor nutrition lead to a number of physiological imbalances such as infertility and delayed onset of puberty (Hernandez-Medrano et al., 2012; Robinson, 1990).
Scientists pay great attention to study genes that are responsible for increased ovulation rate and thereby the high litter size that an ewe can have. Generally, the litter size (the number of offspring at one birth of animals) in the majority of sheep breeds is one lamb or sometimes two lambs a year, but there are also sheep breeds which are highly prolific, like Finnsheep and the Russian Romanov sheep (SanCristobal-Gaudy et al., 2001). The ovary is an important organ in fertility and by investigating genes that are expressed in ovaries, one may be able to find genes influencing on fertility and litter size in sheep.
Ovarian transcriptome profiling, using qPCR, of two pure breeds of sheep namely: Finnsheep and Texel was investigated in this study, which was conducted at the Jokioinen research station of the Natural Research Institute Finland (Luke) in July 2015.
Finnsheep is a native breed of Finland and is known for high litter size and thus imported to more than 40 countries for crossbreeding with local and commerical breeds. The fertility of this breed is very high, and puberty begins at 7-8 months old (Maijala and Osterberg, 1977). In addition, they can breed out of season (not only in autumn) and even twice a year (Oltenacu and Boylan, 1981). The number of lamb varies from two to four; however, there are cases when a sheep had five lambs (Hamilton et al., 2002).
The Texel breed originates from Texel island in the Netherlands and is one of the best meat sheep breeds in the world (Freking and Leymaster, 2004). However, the fertility of the Texel ewe is typically low: an ewe gives birth to one, seldom two or three lambs a year (Clop et al., 2006).
In gene expression studies, messenger ribonucleic acid (mRNA) molecules are examined and compared, for example, between different individuals and animal breeds. The development of molecular technology makes it possible to study gene expression, where the sequence of DNA nucleotides converts into RNA and finally protein. The gene expression levels and differences between breeds can be studied using RNA sequencing technology and bioinformatics analysis.
New technology and researches in genetics can be very useful and bring many advantages to agriculture, for example, scientists can genetically compare animals with different economically important characteristics through the sequencing of different points of scientific interest and can distinguish important genes such as high fertility genes or genes that allow reproducing in any season of the year (Dunn and Ryan, 2015).
Real time polymerase chain reaction or quantitative PCR (qPCR) can also be used for similar analysis. QPCR is used to create additional copies of fragments of DNA and to quantifying the number of copies and quantifying mRNA and miRNA levels in cells and tissues (Novikov et al., 2012). QPCR can be used to validate the results of RNA sequencing and bioinformatics analysis. Experimental validation plays an important role in RNA sequencing studies, because high throughput sequencing approaches like RNA sequencing sometime suffer from technical biases and can be error prone (Robasky et al., 2014). Thus, it is recommended to validate a subset of results to confirm that both the experimental and computational methods have been correctly applied.
2. LITERATURE REVIEW
2.1 GENES AND THEIR EXPRESSION
Gene expression is the process where DNA is transcribed into RNA, and then translated to the protein and is a strictly regulated mechanism that controls the adaptability of all living organisms, including prokaryotes and eukaryotes, whereby cells can respond to external signals and adapt to changes in the environment (Eugene, 2015; Joanne, 2009).
DNA is the basis of all living organisms: it contains genetic information and instructions for protein synthesis and regulations (Travers and Muskhelishvili, 2015). Instructions for the synthesis of a particular protein are encoded in the DNA fragment, which is called a gene (Gerstein et al., 2007). The specific location of the gene on the chromosome is called the locus.
Suitable loci in paired chromosomes may contain the same or slightly different DNA segments, which are called alleles (Simm, 1998). The central dogma of molecular biology states that genetic information from DNA converts to RNA and then to protein (Gerstein et al., 2007;
Francis, 1970).
Protein is synthesized from DNA in two ways: transcription and translation. Transcription is the process by which RNA polymerase copies the sequence of nucleotides from gene to mRNA (messenger RNA) using DNA as a template, whereas translation is the process by which mRNA is converted into a protein and takes place in the ribosomes (Eugene, 2015). The frequency of functional proteins in the cell production is regulated at different stages of gene expression, mainly at the transcription level
The study of mRNA consists of several key steps. The first step is the extraction of RNA from a sample of biological material (Qiagen, 2016). The next step is the measurement of the amount, purity, concentration and integrity of the extracted RNA, and the preparation of complimentary DNA or cDNA through the reverse transcription (Conesa et al., 2016). The level of the absorbance values at 260 nm and 280 nm (A260/A280) provides an estimate of RNA purity in relation to pollutants which are absorbed in the UV spectrum, such as proteins (Porterfield and Zlotnick, 2010).
The RNA integrity number (RIN) is an algorithm for assigning integrity values to RNA measurements, which is critical for gene expression studies (Mueller et al., 2004) whereby the degree of degradation of RNA into shorter fragments, due to rapid digestion in the presence of RNase enzymes, is evaluated (Schroeder et al., 2006). Ideally, the RNA purity has a ratio from 1.8 to 2.1 (William et al., 1997), and RNA integrity number (RIN) varies from 10 (intact) to 1 (completely degraded) (Fleige and Pfaffl, 2006).
2.2. MOLECULAR TECHNIQUES FOR STUDYING GENE EXPRESSION
There are several methods to study and quantify gene expression and regulation. These methods can be classified into two groups (Figure 1): low to mid-complexity methods and high complexity methods.
Figure 1.Methods of Analysing Gene Expression
2.2.1. Low complexity methods
Western blotting
Western blotting analysis can detect one protein in a solution that contains any number of proteins and gives the protein information (Dechend et al., 2006). The solution may contain proteins, which are related to a particular cell type or tissue. This method may help define the size of a protein and how it is expressed (Mahmood and Ping-Chang, 2012).
Northern blotting
Northern blotting is used to identify particular molecules of RNA presented in the RNA mixture (Brown, 2001). Northern blotting involves the isolation of RNA, size fractionation of the denatured RNA through gel electrophoresis, transfer of the separated RNA to a membrane, hybridization with a specific probe, and detection and is commonly used to evaluate gene expression both qualitatively and quantitatively (Zimmers-Koniaris, 2001)
Reporter gene assay
Gene reporter systems play a key role in gene expression and regulation studies. A reporter gene is a one whose product can be readily detected, be fused to a gene of interest or can replace it (Atar et al., 2015) and its phenotypic expression is easy to monitor and used to study promoter activity in various tissues or developmental stages (Livet et al., 2007).
Genes have two working sections: the first section encodes a DNA sequence that include guidance for protein production, while the second section, the promoter,is connected to the coding zone and controls the transcription of a gene, by activating or suppressing its expression (Schenborn and Groskreutz, 1999).
A reporter gene assay is used to identify the unknown regulatory potential of the DNA sequence. Normally, the reporter gene is linked to a promoter sequence through an expression vector that is further transferred into cells. After transfer, the cells are assayed for the presence of the reporter by directly testing the amount of either the reporter mRNA, the reporter proteins, or the enzymatic activities of the reporter proteins (Mango, 2001).
Fluorescent in situ hybridization (FISH)
This is a cytogenetic method used to study the structure and function of the cell, especially the chromosomes (Williams, 2006), and is a powerful technique used in the detection of chromosomal abnormalities (Kearney, 2001). Fluorescent in situ hybridization (FISH) is used to display the amount of aberration copies such as deletion, movement or amplification of
chromosomes and is based on DNA probes annealing to a specific target sequence of a sample DNA. Fluorescent reporter molecules are attached to probes and confirm the presence or absence of a particular genetic aberration when viewed under fluorescence microscopy (Bishop, 2010). This method has become well established in its potential as a diagnostic and discovery tool in the fight against cancer (Linping et al., 2014).
Reverse transcription polymerase chain reaction (RT-PCR)
Polymerase chain reaction (PCR) is a scientific method in molecular biology used to amplify DNA pieces to generate large number of copies (Mohini and Deshpande, 2010). PCR platforms in real time were first developed and implemented more than 20 years ago and nowadays they play an important role in science (Pabingera et al., 2014), providing the base for many applications in the field of research and identification of pathogens and biomedical diagnostics, and is now known as “the golden standard” for the gene expression analysis (Dina et al., 2014).
cDNA enables the study of RNA using the same methods used to study DNA, by creating a new DNA strand (cDNA) that compliments RNA (Cooper, 2007). The cDNA that is created through the reverse transcription can be amplified with PCR (Rahman et al., 2013). During the PCR, cDNA is first denatured under high temperatures (92°C), because high temperatures destroy hydrogen bonds (Rahman et al., 2013). The temperature is then lowered, allowing the primers to join their complimentary sequences, and after this process DNA polymerase initiates the synthesis of DNA (Rahman et al., 2013). This process can be repeated many times, depending on the assigned program, increasing the number of DNA molecules twice with each cycle.
Quantitative real time PCR (qPCR) is one of the most widely used approaches to perform gene expression analyses (Vieira et al., 2016) where it allows for the simultaneous amplification and measurement of a number of target DNA molecules in real time (Higuchi et al., 1992) allowing for the quantification of the initial amount of the template molecules, by comparing the number of the amplification cycles required to the response curves to achieve a quantitative threshold of the fluorescence signal (Kubista et al., 2006).
The quantification of the target DNA in each cycle of qPCR is based on the measurement of the radiation from a fluorescent dye for example SYBR Green, which is one of the most widely used types of dye. SYBR Green is tied to the DNA chain and emits light (Wittwer et al., 1997).
The more the DNA template present at the beginning of the experiment, the less cycles of PCR are needed to generate enough material for the detection (Mohini and Deshpande, 2010).
Using the measured value quantification cycle (Cq) as also known as the threshold cycle (Ct) (i.e. the number of cycles required to reach a threshold level of the fluorescent signal) a quantification of nucleic acids can be determined by the absolute quantification using a standard curve or by the relative quantification delta Cq (Wong and Medrano, 2005).
QPCR serves as a validation tool for confirming and verifying gene expression results obtained from microarray and RNA-sequence analysis (Allison et al., 2006; Morey et al., 2006).
2.2.2. High complexity methods
Serial Analysis of Gene Expression (SAGE)
SAGE is an experimental technique used to measure gene expression quantitatively, where unique sequence tags (9–10 bp in length) are isolated from individual mRNAs to provide information on how specific genes are transcribed at a given point in time in a given cell (Tarasov et al., 2007; Yamamoto et al., 2001). The SAGE method can be applied to studies exploring virtually any kinds of biological phenomena caused by changes in cellular transcription (Yamamoto et al., 2001). The level of the transcript expression can be defined by evaluating the number of times each tag is found. This method allows for the complex analysis of the expression in the genome (Tuteja and Tuteja, 2004).
DNA microarray
DNA microarray consists of a small chip, with a firm covering surface, having 96 tiny wells.
Each of these wells contains thousands of DNA probes or oligonucleotides arranged in a grid pattern on the chip (Sundberg et al., 2001). Microarrays are available in two different formats:
oligonucleotide arrays and cDNA microarrays (Druker et al., 2001). The study of gene expression using microarrays investigates competitive hybridization of differently labeled populations of cDNAs, where fluorescent dyes like Cy3 and Cy5 are used to distinguish cDNA pools that are reverse transcribed from different mRNA samples isolated from cells or tissues (Gray and Collins, 2001).
Labeled cDNAs are applied to a microarray and allowed to hybridize, afterwhich the slide is washed to remove nonspecific hybridization, is read in a laser scanner that can differentiate between Cy3- and Cy5-signals, collecting fluorescence intensities for each channel (Venter et al., 2001). The relative intensities obtained for each channel are then normalized to adjust for differences in labeling and detection efficiencies, so that the two data sets can be compared and the ratios of intensity for each spot can be calculated (Khan et al., 2010).
RNA Sequencing
This method is used to determine a sequence of RNA molecules, whereby a RNA is converted to cDNA fragments and each molecule is sequenced in a high-throughput manner to obtain short sequences from one end (single-end sequencing) or both ends (pair-end sequencing), after which bioinformatics analysis is conducted where reads are either aligned to a reference genome or reference transcripts of assembled genes to produce a genomic-scale transcription map including the transcriptional structure and the level of expression (Zhong et al, 2009;
Morozova and Marco, 2008).
New technologies such as next generation sequencing (NGS) method allows for the accurate determination of the exact sequence in the molecule of DNA or RNA (Ayman and Weinbrecht, 2013). The development of the first generation sequencing began in 1975 by Frederick Sanger, for which he was awarded a Nobel Prize for Chemistry in 1980 (Barba et al., 2014) and his research was used for another 2.5 decades after that (Sanger et al., 1977).
After the completion the sequencing of the human genome, faster and cheaper sequencing methods became more significantleading to the development of NGS, in the beginning of the 21st century (Morozova and Marco, 2008). NGS was considered significantly cheaper and provided higher efficiency and speed of DNA and RNA sequencing compared to the previous Sanger sequencing method (Barba et al., 2014).
NGS platforms provide sequencing on a larger scale, during which the millions of DNA fragments from one sample can be sequenced simultaneously (Ayman and Weinbrecht, 2013) with lengths ranging from 25 to 400 bp. These reads (sequence of DNA or RNA) are shorter than the traditional reads by Sanger sequencing of 300 to 750 bp. However, the latest NGS technology is constantly improving and can generate DNA reads of more than 750 bp (Barba et al., 2014).
These new methods have been applied to sequence many genomes starting from the smaller prokaryotic genomes to eukaryotic viral genomes of animals and plants and are included in the public databases. These new technologies and databases are used in many branches of the science, such as comparative genomic studies, the sequencing of the genomes of various organisms, provides scientists with better understanding of the various organisms' origin.
NGS is applied in the medicine and epidemiology through the sequencing of bacteria and viruses, which helps to determine the new virulence factors. In addition, scientists now have an opportunity to study gene expression with the help of RNA-sequencing, with the ability to show the RNA expression in the sequenced form (Ayman and Weinbrecht, 2013).
The development of the molecular study of NGS and bioinformatics has triggered extensive studies of the genomes of agricultural animals. Currently partially or completely sequenced genomes of economically important agricultural animals such as cattle, pigs, sheep and chickens are available (Yongsheng et al., 2012). NGS technologies require the preparation of a library in which fragments of RNA molecules of the particular size are combined with adapters of a subsequent amplification and sequencing (Dijk et al., 2014).
Sequencing technology allows for the study of reactions and changes on the molecular level in order to successfully breed animals and prevent infectious diseases, depending, for example on the climate or feed, which in the end, can significantly lower prices for livestock products (Yongsheng et al., 2012).
2.3. SHEEP AND SHEEP BREEDING 2.3.1 Domestication of sheep
One of the most important steps in the history of humanity was the transition from hunting and gathering to the period of farming and agriculture in general (Gross, 2013). The main event during this transition was a controlled reproduction of animals for the benefit of humankind, which is now known as domestication (Zeder, 2008). The British zoologist Clutton-Brock gave a universal definition of the term domesticated animal: "A domesticated animal is an animal that was born in captivity for the purpose of subsistence or income for the person who controls the reproduction, and provides land and food" (Clutton-Brock, 1994). During the domestication process animals became phenotypically and genetically different from their ancestral wild species (Zeder, 2008). Sheep (Ovis aries) were the first animals that were used during the
transition from hunting to farming in South-East Anatolia (modern-day Turkey) about 11,000 years ago (Figure 2) (Zeder, 2008). The domestication of goats (Capra hircus) occured at about the same time frame as the sheep domestication, supposedly between 11,000 and 10,500 years ago (Zeder, 2008).
Figure 2. Domestication of animals (Zeder, 2008)
Shaded areas show the general region and approximate dates in calibrated years B.P. in which domestication is thought to take place. Dates outside of the shaded areas show the approximate date when domestication first appears in the region
The result of the excavations in Cyprus in the beginning of 1990s provided scientists with new information about the neolithic expansion in the Mediterranean region (Guilaine and Briois, 2007). Prior to this discovery, historians considered that Cyprus was colonized about 8500 years (LeBrun et al., 1987). It is an interesting fact that the excavations in Cyprus are about 11500- 12000 BC old, suggesting this is when people arrived in the region. It is considered that people seafaring to Cyprus brought major types of livestock (sheep, goats, pigs and cattle) with them, because none of these species were present in Cyprus earlier (Guilaine and Briois, 2007;
Peltenburg, 2004).
This data provides important information about the neolithic expansion to the west and it is considered that the spread of livestock in Europe took place in two main routes: the first one started from Cyprus and continued along the whole coast of the Mediterranean Sea. The routes of early livestock migration did not go from the center of domestication to Europe only. There
were also routes to Northern Africa and to Eurasia (Price, 2000). The neolithic expansion began from the center of animal domestication and spread in all directions, later dubbed "the first migration of sheep".
It is considered that the sheep were brought to Western Europe by this route, and that sheep were first spotted in the Iberian peninsula about 7,700-7,400 BC (Zilhao, 2001). The second route of livestock spread to Western Europe is considered to be migration through the Danube Valley (Zeder, 2008; Dobney and Larson, 2006). The "second migration of sheep" is the second mass migration of sheep that took place about 7,000 years after their domestication (Chessa et al., 2009). This migration was based on secondary products.
For many centuries, people raised sheep to meet their basic needs for meat and wool thus initially sheep were primarily reared for meat purposes, while the use of secondary products such as wool became valuable approximately 5,000 years BC in South-West Asia. In the process of evolution, such factors as mutation, migration, natural and artificial selection, resulted in a diversity of species in different environments, starting from steppe to the highlands, tropics and deserts (Maijala and Terrill, 1991). It is assumed that selection and specialization for the production of the desired secondary products such as wool, originally occurred in South- West Asia and then spread to Europe and Africa (Chessa et al., 2009).
After the Second World War, economic pressure to produce enough goods for the growing population made it necessary to improve the efficiency of manufacturing (Rasali et al., 2005).
However, in the last two decades, lamb consumption has drastically declined in Europe (Figure 3) and all over the world.
Figure 3. Consumption of lamb 1992-2013 in Europe (FAOSTAT, 2016)
Prices for the lamb and wool did not increase relative to the costs of production, thus sheep breeders were forced to increase the productivity of the animals in order to raise profits (Rasali et al., 2005). The development of highly productive animals and improved production systems, which include mechanization, sheep keeping, balanced feeding by the nutrients requirement and prevention of infectious and parasitic diseases contributed to more than doubling in productivity (Rasali et al., 2005).
2.3.2 Sheep Breeds
There are different definitions for the term "breed", but in this context it implies a population of domestic animals, which have well-defined phenotypic traits that make it easy to distinguish from other groups of animals of the same species (Clutton-Brock, 1994). One of the main characteristics of sheep is their ability to adapt to different extreme agro-ecological zones with different food, climate and living conditions (Clutton-Brock, 1994). Most probably, this fact is responsible for the development of a large number of breeds of sheep compared to other domestic animals (Rasali et al., 2005). There are more than 850 breeds of sheep recognized worldwide (Rege and Gibson, 2003).
There are other important factors such as geographical location that can also determine criterion for classification into breeds. Another important factor that determines the breed is the purpose for which the animals are raised, for example, some breeds of sheep can provide high-quality meat, while others can give better quality milk or wool (Hentati et al., 2014).
Finnsheep (Figure 4) is a native breed of Finland. This type of sheep belongs to the so-called
"short-tailed northern European sheep", the main characteristic of which is short ears, straight nose, narrow head, a short tail and typically white colour (Goot, 1973). However, Finnsheep also have black, brown and grey colour types. Adult average weight is 62 kg for ewes and 88 kg for rams (Goot, 1973). Finnsheep is well known because of it is high fertility. The ewe hason average 2.5 lambs at a time and can breed in any season (Maijala and Osterberg 1977;
Goot, 1973). This is the main reason for the popularity of this breed and from the beginning of 1962, these sheep have been exported to many countries worldwide (Maijala, 1988).
Figure 4. Finnsheep (Photo: Kantanen J.)
Texel (Figure 5) is a synthetic breed, native to the Netherlands and is known for high quality, lean meat. The sheep is characterized by a short and wide head with wide set ears, but the main characteristic of this breed is well-developed muscles (Cockett et al., 2005), that makes it easier to distinguish it from other breeds. Unfortunately, the fertility of this breed is not very high,with only about 1-2 lambs a year and ewes very seldom have 3 lambs a year (Clop et al., 2006).
Figure 5. Texel (Photo: Kantanen J.)
2.3.3 Sheep breeding and reproduction
The economic profitability of sheep is dependent on its total productivity, which is increasingly dependent on fertility and prolificacy of the ewes (Zhang, 2009). Thus scientists pin their hope on molecular assisted breeding technology (Hu et al., 2008). RNA-sequencing technology is deemed an effective method for the identification of the expression levels of numerous genes in various tissues including ovaries (Miao and Luo, 2013). The differences in fecundity can be associated with the different regulation of gene expression, which is involved in the ovulation or follicular development (Bai, 2007).
The intensification of production systems allowed for the import of exotic sheep breeds without compromising their health and creating of new breeds for different purposes with desired traits.
These purposes have been achieved through the crossbreeding of sheep breeds in the country, with exotic breeds that demonstrate a genetic potential for economic value and industrialized production, for instance Finnsheep and Romanov breeds are valued for increased litter sizes, while the Texel breed is valued for increased muscle growth (Shrestha and Heaney, 2003;
Shrestha and Heaney, 1992; Maijala, 1988; Ricordeau et al., 1978).
The reproductive system in female mammals is very complicated and crucial in different processes, such as fertilization, gametogenesis (creation of the haploid germ cell, the ovum, which carries genetic information from the maternal parent), pregnancy, lambing and nursing.
Since the ovaries are the main female reproductive organ, the research of their gene expression potentially allows for the determination of genes responsible for high fertility.
The ovary (Figure 6) is one of the most important organs in the female reproduction system and is a very functional organ involved in a number of complex processes such as follicle formation, the oocyte maturation and apoptosis occurring in oestrus/menstrual cycle. The ovarian follicle is a primary element of the ovaries, which contains eggs that ovulate in the future, undergo fertilization, and is the place where oocyte is formed (Findlay et al. 2009).All these processes take place in several stages, locally in the ovaries and on the pituitary and hypothalamus level (Ferin, 2008).
Figure 6. Sheep ovary (Patten, 1964)
The reproductive cycle of females is referred to as the oestrus cycle and is characterized as proestrus, oestrus, metestrus and diestrus (Freeman, 1988). The oestrus cycle consists of repeat physiological changes that are controlled by the endocrine system and regulated by the brain, ovaries and uterus (Figure 7). The ovary of an adult sheep contains 12,000 to 86,000 primordial follicles and between 100 and 400 growing follicles of which 10 to 40 can be seen on the surface of the ovary (McNatty et al., 1982; Cahill, et al., 1979).
Figure 7. Hormonal activity during the oestrus cycle (Homburg, 2014)
The hypothalamus sends the gonadotropin-releasing hormone (GnRH) to the pituitary gland, then the pituitary gland sends the luteinizing hormone (LH) and follicle-stimulating hormone (FSH) to the ovaries, and stimulates the maturation of the follicle, this phases of the oestrus cycle called proestrus (Hermite et al., 1972).
As the follicles grow, they develop a hormone called estradiol, which goes back to the sheep brain and signals the beginning of heat. This phase is referred to as oestrus (McNeilly et al., 1976). Oestrus and proestrus together are part of the follicular phase of the reproductive cycle When the diameter of the follicle reaches 0.5-1 cm, estradiol concentration in the blood is increased and the hypothalamus releases a large amount of luteinizing hormone (LH) that leads to ovulation (Kennedy, 2012).
After ovulation, the follicle is destroyed and the corpus luteum is formed. This phase is called metestrus and is characterized by sexual inactivity. In turn, the corpus luteum produces progesterone (Kennedy, 2012). The increase of the progesterone sends a signal to the hypothalamus to reduce the secretion of gonadotropin-releasing hormone (GnRH which leads to increase of follicular growth), causing a suppression of oestrus and ovulation until progesterone levels are high. This phase is called diestrus (Driancourt et al., 1985).
If fertilization does not occur, then the uterus secretes the hormone prostaglandin, which stimulates the destruction of the corpus luteum and lowers the level of progesterone (Kennedy, 2012). As a result, the hypothalamus begins to produce GnRHe, and the cycle begins again (Scaramzzi and Baird, 1976).
Depending on how many eggs are released at ovulation, mammals can be mono- or poly- ovulatory (Souza et al., 2001). Ovulation rate may vary between the breeds of one species, for example the ovulation rate in Texel is 1.55 and mean litter size 1.58 (Hanrahan, 1984), in Finnsheep ovulation rate is 3.8 and mean litter size 2.7 (Maijala and Oesterberg, 1977) and in the Booroola Merino breed the ovulation rate is 4.2 and mean litter size of 2.5 (Souza et al., 2001; Hanrahan, 1984).
Although the development of ovaries and the formation of follicles occur as a series of well- timed events, they are very sensitive to environmental factors (Lea et al., 2006). Ovulation is a complex process and depends on breed as well as genetic factors and environmental factors, such as nutrition.
The formation of follicles and their further development are also influenced by both internal factors (genotype) and external factors (season and feeding) (Hernandez-Medrano, et al., 2012).
Among the external factors, diet is one of the most important factors, that influence the reproductive function of animals (Lea et al., 2006).
2.3.3.1. Factors affecting reproductive performance in sheep
The high fecundity of sheep significantly impacts on the efficiency and profitability of sheep industry. The ovulation rate of sheep and the number of lambs they have is therefore of great economic value (Notter, 2008) and the study of genes that are associated with high fecundity and inclusion of genotypic information of animals is, hence, of great importance in their breeding (Pramod et al., 2013) The development of sheep fecundity is therefore of interest to farmers (Kumm, 2008).
Domesticated sheep (Ovis aries) have 54 chromosomes with genetic material in the form of DNA (Drouilhet et al., 2009). Sheep have genes that are called fecundity genes that can control and regulate reproduction process and fecundity traits such as ovulation and litter size (Drouilhet et al., 2009).
Genes such as BMPR-1B, BMP15 and GDF9 are all part of the ovary-derived transforming growth factor-β (TGFβ) superfamily (Nicol et al., 2009). These genes encode proteins, which are considered to be significant growth factors and receptors of follicular development in the ovaries, and thus these genes impact highly on the ovulation rate and litter size in sheep (Pramod et al., 2013).
Scientists noticed that mutations in these fecundity genes significantly increases the ovulation rate in sheep (Davis, 2005) for example, mutation plays a dominant role, where just one copy of an allele is enough for the a change of expression of the characteristic in phenotype (Davis, 2005).
Mutations in the genes BMP15 and the GDF9 result in similar changes in expression in phenotypes of the ovaries, but there are several factors that may play a role in this for example:
inheritance patterns differ while different mutations can affect the ovulation rate and litter size in sheep differently (Nicol et al., 2009). It has been suggested that mutations in the BMP15 and GDF9 gene can result in over-dominance, or heterozygous advantage (relative higher fitness than either the homozygote dominant or homozygote recessive genotype). Individuals
with heterozygous mutation can express an increased ovulation rate and thereby an increase in fitness, whereas in homozygous individuals, the majority of mutations in BMP15 and GDF9 genes cause infertility (Gemmell and Slate, 2006).
Over the last thirty years, several significant reviews have addressed the topic between nutrition and reproduction (Smith, 1991a). Maternal malnutrition during early ovarian differentiation (0–
50 days of pregnancy) can have a negative effect on ovarian mass (Lea et al., 2006; Rae et al., 2001). Nutritional impact occurs when there is not enough food or when the diet is not well balanced, and later leads to the violation in the ovarian functions (Hernandez-Merando et al., 2012). Poor or severe lack of nutrition is a primary cause of embryo loss (Smith, 1991b).
Today, there is also considerable evidence that nutrition can influence the selection and development of follicles (Scaramuzzi et al., 2011; Webb and Campbell, 2007) and thatfor instance, prenatal undernutrition has a more negative effect in females than in males(Rae et al., 2001), influencing for instance the development of follicles, embryo survival and twinning rates.
Flushing or supplementing ewes just prior to, and during the breeding season can improve the ovulation rate of ewes (Burritt et al., 2012). Fazel et al. (2014) reported that the utilization of flushing diet before the sexual intercourse also improves both the sexual receptivity and the reproductive performance of sheep. The flushing diet along with added nutrients, has a positive influence on the breeding rate which is due to an increase in follicle growth rate. Extra energy supply (flushing)affects fertility through mechanisms such as the development of oocytes, and fetal survival, it indirectly affects it through blood metabolites and hormones (Robinson et al., 2006).
Flushing is implemented by increasing the level of feed offered for example as in Nedelkov et al.’s study (2013) where apart from daily grazing, all sheep were supplemented with a daily amount of 300 g barley two months prior to artificial insemination to reach flushing affect.
Similarly, Burritt et al.,(2012) has reported use of a pelleted supplement with the following composition: 68% barley, 10% alfalfa meal,12% wheat, 1.30% canola meal, 5 % molasses, 1.35
% urea, 2% limestone, 0.25% monocalcium phosphate, 0.05% bovatec; which then resulted in an increase in lambing rate to 37% for ewes on the flushing diet.
3. OBJECTIVES
The main aim of the study is validate using qPCR, transcriptome analysis of the profile of genes and mRNAs in the ovaries of sheep (Ovis aries). Specifically, the study focuses on analysis of gene expression differences between high-prolific and non-prolific sheep and the effects of flushing diet.
4. MATERIALS AND METHODS
4.1. Detection of ovarian expressed mRNAs by RNA-sequencing and bioinformatics tools
The study of Pokharel et al. (unpublished results) included two pure breeds of sheep: Finnsheep (11 animals), Texel (11 animals) and F1 crosses of Finnsheep and Texel (9 animals), thus, comprising 31 animals in total. To get an overview of the role of nutrition in gene expression, around half of the ewes from each Finnsheep (n = 6), Texel (n = 6) and F1-crosses (n = 4) were kept in flushing diet (additional 300g oats and 100g rapeseed meal) and the other half were in normal (control) diet (timothy hay ad libitum and mineral feeding) during the experiment.
The feeding experiment lasted 4 months in autumn 2012, the normal breeding season of the ewes. Animals were kept and the samples were collected in accordance with the allowed norms of Finland and European Union regarding animal experiments. Each animal, had one ovary surgically removed and divided into 4 pieces and storedin RNA later reagent (Qiagen, Valencia, CA, USA) at -20 °C. after which RNA was isolated and sequenced. Samples for RNA isolation were taken 3 replicates for one ovary, while for others there were no replicates. A differential expression analysis within and between breeds was conducted, followed by differential gene expression analyses on the controlled diet group.
Results showed that 16,402 genes were expressed in the ovarian samples representing 60.6%
of the known (27,054) ovine genes. None of the genes were differentially expressed between the two diet groups (flushing vs control) of Finnsheep, whereas 118 and 25 genes were differentially expressed between the same comparison in Texel and F1, respectively. Of these, 71 genes were upregulated in the Texel flushing group, while 4 genes were upregulated in the F1 flushing group (Table 1).
Table 1. Gene expression in Finnsheep, Texel and F1 kept in flushing diet
Finnsheep (n=11) Texel (n=11) F1 (n=9)
Flushing Diet n=6
Control n=5
Flushing Diet n=6
Control n=5
Flushing Diet n=4
Control n=5 no genes
differentially expressed
118 genes differentially expressed 71 genes upregulated in Texel
flushing diet group
25 genes differentially expressed 4 genes upregulated in F1
flushing diet group
Comparisons were also made between groups with diet as a second factor. When comparing Finnsheep and Texel, 38 genes were differentially expressed with 34 upregulated in Texel.
When comparing Texel and the F1 group, 60 genes were differentially expressed with 44 upregulated in Texel, and finally when comparing Finnsheep and the F1 group, 5 genes were differentially expressed.
Comparisons between breeds kept in flushing diet also yielded the following results:
Comparing Finnsheep and Texel, 621 genes were found to be differentially expressed with 504 upregulated in Texel. Furthermore, when comparing Texel and the F1 group, 311 genes were found to be differentially expressed with 256 upregulated in Texel.
Figures were much lower when comparing Finnsheep and F1, where only 51 genes were differentially expressed with only 20 upregulated in Finnsheep. Of the +600 differentially expressed genes between Finnsheep and Texel, 435 genes were associated with 220 GO terms, of which the majority were associated with developmental processes
Kisun et al,’s (unpublished results) study also identified 159 known miRNAs, mostly expressed on chromosome 18, and 79 novel miRNAs of which 35 were expressed in chromosome X.
These 70 novel miRNAs were selected for validation in this MSc thesis study.
4.1.1. RNA extraction and concentration measurements
Total RNA was isolated from the ovaries (n=12, 6 Finnsheep and 6 Texel) in Eppendorf test tube, using the AllPrep DNA/RNA/miRNA Universal Kit (50) (Qiagen, Hilden Germany) according to the manufacturer’s instructions at the room temperature (Figure 8).
Figure 8. Isolation of RNA (Qiagen, 2011)
The quality and quantity RNA in each sample (n=12) was determined using Spectrophotometer NanoDrop ND 1000 (Thermo Fisher Scientific, Wilmington, DE, USA) and RNA integrity was studied using Small RNA Kit and RNA 6000 Nano Kit with 2100 Bioanalyzer (Agilent, Palo Alto, CA, USA).
4.1.2. cDNA synthesis
Total RNA was reverse transcribed using the Transcriptor First Strand cDNA Synthesis Kit (Roche Applied Science, Germany). 10 μl total RNA of 12 samples was mixed with 1 μl Anchored-oligo (dT)18 primer (50 pmol/ μl)and 2 μl random hexamer primer (600 pmol/ μl) to reach the final volume 13 µl. Each sample was incubated at 65°C for 10 minutes for primer hybridisation and cooled on ice for 1 minute.
Samples were briefly centrifuged and 7µl of reaction mix, containing 4 µl 5×Transcriptor Reverse Transcriptase Reaction Buffer, 0.5 µl Protector RNase inhibitor (40 U/ µl), 2 µl Deoxynuccleotide Mix (10mM), 0.5 µl Transcriptor Reverse Transcriptase (20U/ µl), was added, to make up the final volume 20 µl. Contents of the tubes were mixed and incubated at 25°C for 10 minutes, followed by 60 minutes at 50°C. The reaction was inactivated by incubating at 85°C for 5 minutes, followed by placing all samples placed in ice. Further, the cDNA was stored at -20°C.
4.1.3. QPCR analyses
Real-time PCR primers were designed based on the mRNA sequences of selected 5 randomly candidate genes (ABLIM3, SLCO2A1, CNTN4, GJA5, BMX) (Table 2), using Primer3 Express version 4.0.0 software (http://primer3.wi.mit.edu//) developed by the Whitehead Institute for Biomedical Research at the University of Massachusetts Medical School (Massachusetts, MA, USA).
Table 2. QPCR primer sequences of five randomly selected genes.
Gene Sequences of Oligos
ABLIM3 F:TCCCGCTCACCTCATCACTA
R:GATGAGAATCTCCTGGCCCG
SLCO2A1 F:CGCCCCTGTACATCTCCATC
R:ACCACCAGGCTCCTATCCAT
CNTN4 F:GAGAGCTCAGCTATGCCTGG
R:CACCGTTGTTCCCTTTGCAG
GJA5 F:TCCACACCCTCGCTAGTGTA
R:TAGAGGAGGTACTGGCCCAC
BMX F:GCAAGTGGAAGGGGCAGTAT
R:GAGCTGGGAGGGTTCAAGTC
GADPH F:TGCCATCAATGACCCCTTCA
R:ATGACGAGCTTCCCGTTCTC
Quantitative analysis of cDNA samples was performed using the ABI PRISM® 7000 sequence detection system (Applied Biosystems, Foster City, CA, USA). The PCR reactions were performed in a 20 µl reaction volume containing 13 µl of SYBR Green PCR master mix (Life Technologies, Helsinki, Finland).
Samples from the same cDNA source were run in duplicate in each PCR reaction. Each gene was quantified at the following thermal cycling parameter (10 minutes at 95 °C followed by 40 cycles of 15 s at 95 °C and 10 s at 60 °C).The expressions of mRNA and miRNA were analyzed with the help of MS Excel, using The Comparative CT Method (ΔΔ C(t)) described in the Guide to Performing Relative Quantitation of Gene Expression Using Real-Time Quantitative PCR(Applied Biosystems,2008) and results were reported as the relative expression or n-fold difference to the calibrator (control group) after normalization of the transcript amount relative to the value of the endogenous control gene (GAPDH).
The ΔΔ C(t) is summarized thus:
1. Calculate the differences between average of the Ct values for the housekeeping gene (GAPDH, U6) and average of the Ct values of the gene being tested and calculate the standard deviation of the ΔCT value.
∆Ct=Ct target-Ct reference (GAPDH, U6)
2. Calculate the difference between ΔCT and ∆Ct calibrator sample to arrive at the Double Delta Ct Value (ΔΔCt) and calculate the standard deviation of the ΔΔCT value
∆∆Ct=∆Ct test sample-∆Ct calibrator sample
3. All calculations are in logarithm base 2, to get the expression fold change calculate the value of 2^-∆∆CT
Fold-differences calculated using the ΔΔCT method is expressed as a range, which is a result of incorporating the standard deviation of the ΔΔCT value into the fold difference calculation.
Similarities of biological replicates and differences between samples of the qPCR data were then compared with the results generated by next-generation sequencing
4.1.4. Gene Annotation
Gene annotation was conducted using the UniProt Knowledgebase (http://www.uniprot.org/).
UniProt Knowledgebase is a central hub for collecting information about proteins with precise sequences. The main purpose of the database is to provide the scientific community with a comprehensive, high-quality and freely available source of protein sequence and functional information.
More than 95% of protein sequences offered by UniProtKB originate from coding sequences (CDS), which are presented in public nucleic acid databases, the databases of EMBL- Bank/GenBank/DDBJ. All these sequences and all relevant information that is presented by the authors are automatically merged into UniProtKB/TrEMB (UNIPROT, 2016).
The molecular functions of differentially expressed genes were identified by searching Ensemblgene identifiers (Ids) in the search field of Uniprot.
5. RESULTS
5.1. GENE EXPRESSION IN PROLIFIC AND NON-PROLIFIC SHEEP BREEDS 5.1.1. Quantitative and qualitative parameters of RNA samples
RNA samples from the ovaries of 12 ewes kept in flushing diet (Finnsheep 6 animals, Texel 6 animals) were quantified and analysed for RNA concentration, absorbance wavelength and RIN (Table 3).
The mean RNA concentration of all samples was 569.9 Ng/μl ± 444,with values ranging from 159.5 Ng/μl to 1913 Ng/μl.The Texel breed showed a higher RNA concentration (mean 695.3±605.4 Ng/μl while Finnsheep had a lower RNA concentration (mean 458.5±184.8 Ng/μl)All samples had RNA purity at average absorbance wavelength ratio (A260/A280) of 2.01±0.02, with maximum value 2.06 and minimum value 1.87. There was not much difference in the RNA purity of Texel and Finnsheep.The RNA Integrity Number (RIN) varies from 10 (intact) to 1 (completely degraded) and all samples analysed had a meanRIN value of 8.5
±0.2and withvalues ranging from 7.5 to 9.4. Both Finnsheep and Texel had very similar RIN values with means ranging from 8.4 - 8.6.
Table 3. Quantitative and qualitative parameters of RNA samples from ovaries of 12 ewes kept in flushing diet.
Sample name
Breed RNA
concentration Ng/μl
RNA purity A260/
A280
RNA Integrity Number
RIN
13B Tex 560.3 2.03 8.30
22A Tex 594.7 2.02 9.10
379 Tex 370.1 1.99 8.50
43A Tex 1913.0 2.04 9
787 Tex 364.1 1.97 8.40
9K Tex 369.6 2.01 8.5
21J Fin 203.8 2.03 8.30
23C Fin 603.8 2.04 8.50
35C Fin 276.8 1.96 8.50
39A Fin 643.8 2.02 8.70
5B Fin 434.4 1.98 8.20
801 Fin 588.5 2.02 8.60
Mean ±SD Tex 695.3±605.4 2.01±0.02 8.6±0.3 Mean±SD Fin 458.5±184.8 2.0±0.03 8.4±0.1 Mean ± SD All
samples
576.9±444.3 2.0±0.02 8.5±0.2
Fin= Finnsheep, Tex=Texel
5.1.2. Relative gene expression between Finnsheep and Texel sheep by qPCR analysis
On quick glance, QPCR analyses (Figure 9) showed that relative gene expression level between Finnsheep and Texel (in flushing diet) in the genes SLCO2A, GJA5 and ABLIM3, was higher in Finnsheep, with upregulation in Finnsheep and downregulation in Texel, whereas the gene expression in the CNTN4 gene was downregulated in Finnsheep and upregulated in Texel.
However, the standard deviations are high and thus real differences cannot be substantiated.
There was no difference in the gene expression level (12.1±2.0) in the BMX gene in Finnsheep and Texel breeds in flushing diet. QPCR amplification plots for all the genes except SLCO2A1 (technical problems during the run meant the plot was not saved) are attached in Appendix 1.
Figure 9. Expression of genes in Finnsheep and Texel sheep kept in flushing diet
A comparison is made between the results of mRNA sequencing analyses (Pokharel et al., unpublished results) and the qPCR analyses (Table 4). The mRNA sequencing results showed that ABLIM3, GJA5, BMX, SLCO2A1 are downregulated in Finnsheep and upregulated in Texel, except CNTN4 which was downregulated in Texel and upregulated in Finnsheep. Thus, QPCR analyses are inconclusive and cannot substantiate the RNA sequencing results. The greater the standard deviation in qPCR, the lower the ability to distinguish between two-fold dilutions.
Table 4. Comparison of ovarian expressed genes in Finnsheep and Texel breeds in flushing diet by qPCR and RNA sequencing
GENE
QPCR ΔΔCt + SD
RNA sequencing Log2 fold-change
(Pokharel et al, unpublished results)
SLCO2A1 0.84±1.7 -1.77
GJA5 0.71±1.5 -1.86
ABLM3 0.62±2.0 -1.78
BMX -0.04±2.0 -2.02
CNTN4 -0.85±3.8 1.92
RNA-sequencing results: Finnsheep/Texel
qPCR analyses have been done using ΔΔCt method. ΔCtis the difference in threshold cycle between the target and reference genes.ΔΔCT method is fold differences between samples.
5.1.3. Gene Annotation
A number of annotation features including molecular functions were obtained for each gene (Table 5).
All five genes used in this research play critical role in a number of biological processes and functions. For example ABLIM3 (Actin Binding LIM Protein Family Member 3) is a protein coding gene that promotes protein-protein interactions and is involved in a wide range of biological processes, including cell-to-cell interactions and developmental and metabolic control (Braun and Gingras, 2012). In addition, the gene plays an important role in embryonic development (Krupp, 2006).
SLCO2A1 gene encodes a prostaglandin transporter, is a member of the 12-membrane- spanning superfamily of transporters, which mediates the uptake and clearance of prostaglandins in numerous tissues (Qiaoliang et al., 2015). Moreover, it mediates the release of newly synthesized prostaglandins from cells, the transepithelial transport of prostaglandins, and the clearance of prostaglandins from the circulation (Umeno et al., 2015).
GJA5 encoded protein is a component of gap junctions, made up of an array of intercellular channels that provide a route for the diffusion of low molecular weight materials from one cell to another, including nutrients, metabolites (glucose), ions (K+, Ca2+) (Evans and Martin, 2002; Beyer, 1992).
BMX (Bone marrow tyrosine kinase gene) plays diverse modulatory roles in various signaling processes involved in the regulation of actin reorganization, cell migration, cell proliferation and survival, cell adhesion, and apoptosis (Semaan et al., 2008).
CNTN4 encodes a member of the contactin family of immunoglobulins. Contactins molecules function in neuronal network formation and plasticity. The encoded protein is a glycosylphosphatidylinositol-anchored neuronal membrane protein which plays a role in the formation of axon connections in the developing nervous system (Zeng et al., 2002).
Table 5. Molecular Function of Genes selected for qPCR analysis (UNI PROT, 2016)
