key: cord-1042172-87eie5nx authors: Mehrbod, Parvaneh; Harun, Mohammad Syamsul Reza; Shuid, Ahmad Naqib; Omar, Abdul Rahman title: Transcriptome Analysis of Feline Infectious Peritonitis Virus Infection date: 2014-12-18 journal: Coronaviruses DOI: 10.1007/978-1-4939-2438-7_20 sha: cde51fb9e3c69afff2f2c85dbe3a3ca165e7bab4 doc_id: 1042172 cord_uid: 87eie5nx Feline infectious peritonitis (FIP) is a lethal systemic disease caused by FIP virus (FIPV). There are no effective vaccines or treatment available, and the virus virulence determinants and pathogenesis are not fully understood. Here, we describe the sequencing of RNA extracted from Crandell Rees Feline Kidney (CRFK) cells infected with FIPV using the Illumina next-generation sequencing approach. Bioinformatics analysis, based on Felis catus 2X annotated shotgun reference genome, using CLC bio Genome Workbench is used to map both control and infected cells. Kal’s Z test statistical analysis is used to analyze the differentially expressed genes from the infected CRFK cells. In addition, RT-qPCR analysis is used for further transcriptional profiling of selected genes in infected CRFK cells and Peripheral Blood Mononuclear Cells (PBMCs) from healthy and FIP-diagnosed cats. The use of a next-generation sequencing approaches in RNA sequencing has facilitated understanding and defi ning the expression profi les of cellular responses during pathogen infection. This method has been proven to be helpful in explaining the pathogenesis of various viruses [ 1 , 2 ] , including Feline Immunodefi ciency Virus (FIV) [ 3 , 4 ] . Furthermore, the increasing availability of complete genome sequences for a number of model organisms makes host transcriptome analysis a valuable tool for elucidating mechanisms of virus pathogenesis and host responses to virus infection. Feline infectious peritonitis virus (FIPV) is thought to be the causative agent of feline infectious peritonitis (FIP). Understanding the molecular basis of FIPV pathogenesis will provide valuable information to devise effective treatments and formulate vaccines with higher effi cacy. Once established, focus can be directed at disrupting the virulent determinants or formulating new vaccine or even designing gene therapy treatment. Facilitating this, the complete 1.9X cat genome, using the Whole Genome Shotgun (WGS) approach, provides valuable information for bioinformatics analysis of feline host responses following pathogen infection. Moreover, the cat genome contigs were aligned, mapped, and annotated to NCBI annotated genome sequences of six index mammalian genomes (human, chimpanzee, mouse, rat, dog and cow) using MegaBLAST [ 5 ] . In this chapter we describe a pipeline for transcriptome analysis using FIPV infection of feline cells in culture as an example. Specifi cally, mRNA harvested from CRFK cells 3 h post infection with FIPV strain 79-1146 were sequenced using Illumina next-generation sequencing technology. The generated data was then analyzed using CLC bio Genomic Workbench, where the genes were compared to Felis catus 1.9X annotated shotgun reference genome. Kal's Z-test on expression proportions [ 6 ] was used to determine signifi cantly expressed genes. Genes expressed with a False Discovery Rate (FDR) <0.05 and >1.99and <-1.99-fold change were considered for further analysis. 6. NanoDrop Nanophotometer or spectrophotometer. Step kit (Bioline). 8. Primers specifi c for genes of interest and reference genes. 9 . BD Vacutainer (BD) EDTA-K2 tubes. 10. Ficoll-Paque Plus. To take advantages of this technology, simultaneous analysis of virus-host interactions is investigated in one single experiment where both the transcription of viral genomes and host cell responses are scrutinized. Figure 1 illustrates the work fl ow for transcriptome analysis of this study. 1. Seed CRFK cells into 75 cm 2 fl asks and incubate at 37 °C with 5 % CO 2 until cells reach 60-70 % confl uency. 3. Infect the fl asks with virus at multiplicity of infection (MOI) 2 in 2 ml, or 2 ml D -PBS as a mock control, and incubate at 37 °C with 5 % CO 2 for 1 h to allow attachment. Perform inoculations in duplicates, one for RNA extraction and the other for CPE visualization. 4. Add 10 ml of maintenance medium with 10 % FBS and incubate the fl asks for 3 h. 6. Add 2 ml TrypLE and incubate for 1-2 min until cells detach. 7. Transfer cells to a centrifuge tube and pellet the cells by centrifugation at 120 × g for 5 min and discard supernatant. 8. Add 10 ml D -PBS and repeat centrifugation in order to remove every trace of medium and TrypLE, which could reduce RNA yield. 9. Discard the supernatants and store the cell pellets at −80 °C until RNA purifi cation. The RNeasy kit was used to extract and purify RNA samples in this study ( see Note 1 ) but other RNA extraction protocols may also be suitable. 1. Spray all micropipettes, gloves, working area, and other things with RNase AWAY to remove any RNase and DNA contamination. 2. Extract RNA using RNeasy kit according to the manufacturer's instruction. 3. Aliquot the eluted RNAs (500 µl) into three different tubes to avoid repeated thawing and freezing of the sample which could affect the quality of the RNA. 4. Use two tubes for quality control analysis with spectrophotometer and Illumina Agilent 2100 bio-analyzer and store the third one at −80 °C for sequencing. Nano kit together with Agilent 2100 Bio-analyzer to conduct quality and quantity analysis to the extracted total RNA samples ( see Note 2 ). 4. Load and prime gel-dye mixtures, then load RNA 6000 Nano marker, ladder, and samples in the specifi ed manner. 5. Vortex the chip and insert in the Agilent 2100 Bio-analyzer machine. Analyze the chips based on the method recommended by the manufacturer. Verify whether the run is successful and whether the sample is properly prepared and handled by means of properly pipetted into the wells ( see Note 3 ). Perform the following steps using the reagents provided in the paired end sample preparation kit, according to manufacturer's instructions. 1. Fragment genomic DNA into fragments of less than 800 bp. 2. Perform end repair of DNA fragments to generate 5′-phosphorylated blunt ends. 3. Add an "A" base to the 3′ ends to make 3′-dA overhang. 4. Ligate adapters to the ends of the DNA fragments. 5. Purify ligation products by removing un-ligated adapters. 6. Enrich the Adapter-Modifi ed DNA Fragments by PCR. 7. Obtain the Genomic DNA library. 1. Once data has been obtained, import the raw data (~17.3 Gigabyte) into the CLC bio GWB. Once imported, subject the raw data to sequence reads trimming by quality trimming, ambiguity trimming and adapter trimming with the settings as in Fig. 2 . The program uses the modifi ed-Mott trimming algorithm for this purpose (see manufacturers instructions) ( see Note 6 ). 2. BLAST the list of genes that were upregulated and downregulated, using the built-in BLAST program in the CLC bio GWB. 3. Based on the BLAST result, select homologous sequence with the lowest e-value, highest score and lowest percentage of gaps to the query sequence as the gene identity. 4. Briefl y, opt blastn: DNA sequence and database program and references mRNA sequences (refseq_rna) or nucleotide collection (nr) database for analysis. In silico analysis which is also a part of bioinformatics analysis is able to analyze the interactions of different genes by integrating data available on bioinformatics databases ( see Note 7 ). (Table 2 ). One cycle for the dissociation curve for all reactions is added and melting curve analysis is performed. 3. The fi rst feature of a successful total RNA run is that the electropherogram must contain three peaks where one peak represents marker peak while the others two are 18S and 28S ribosomal peaks. Absence of one or both of the ribosomal peaks indicates poor sample preparation or poor sample pipetting technique. The second feature of a successful run is a complete ladder electropherogram. A complete ladder electropherogram must feature one marker peak and six RNA peaks where all seven peaks are well resolved. 4. Adapters used by common high-throughput sequencing vendors such as Illumina and SOLiD were predefi ned and are available by the software. Removing the adapters will increase the specifi city of the raw sequence reducing false match. 5. Perform expression analysis based on the method described by Mortazavi et al. in 2008 [ 7 ] and CLC bio manual, CLC bio tutorials and recommendations from CLC bio support services. 6. In short, high quality trim value allowed low quality base or base with low Phred quality score to be included in the sequence. The ambiguity trimming trims the sequence ends based on the presence of ambiguous nucleotides usually denoted as N making the sequence more specifi c. 7. Other Bioinformatics analysis : A gene in eukaryotic organism is commonly regulated by other genes and proteins in its system. The interactions among genes expressed and between gene expressed and other genes can be elucidated by means of computer or in silico analysis. In silico analysis, a part of bioinformatics analysis, able to do this by integrating data with available data on bioinformatics databases. Such integration will allow a researcher to make accurate predictions and designing experiments to test the hypothesis. The main objective of in silico analysis is gene ontology which is defi ned as the process of elucidating associated pathways, molecular function, biological process, cellular components and protein products of a gene [ 8 ] . The bioinformatics database used to analyze gene identity and gene interaction is Protein Analysis Through Evolutionary Relationships Classifi cation System or in short known as PANTHER ( http://www.pantherdb.org/ ). It is a unique resource that classifi es genes by their functions, using published scientifi c experimental evidence and evolutionary relationships to predict function even in the absence of direct experimental evidence and is a part of the Gene Ontology Reference Genome Project ( http://www.geneontology.org/ GO.refgenome.shtml#curation ). PANTHER provides tools for gene expression analysis for data interpretation ( http://www.pantherdb.org/tools/ genexAnalysis.jsp ). Multiple gene lists will be mapped to PANTHER molecular function, biological process, and cellular Hepatic transcriptome analysis of hepatitis C virus infection in chimpanzees defi nes unique gene expression patterns associated with viral clearance Kinetic analysis of a complete poxvirus transcriptome an immediate early class of gene Geneexpression changes induced by Feline immunodefi ciency virus infection differ in epithelial cells and lymphocytes Viral transcriptome analysis of feline immunodefi ciency virus infected cells using second generation sequencing technology A greedy algorithm for aligning DNA sequences Dynamics of gene expression revealed by comparison of serial analysis of gene expression transcript profi les from yeast grown on two different carbon sources Mapping and quantifying mammalian transcriptomes by RNA-Seq Gene ontology: tool for the unifi cation of biology. The Gene Ontology Consortium Analysis of relative gene expression data using real-time quantitative PCR and the 2(−Delta Delta C(T)) method Polymorphism in human APOBEC3H affects a phenotype dominant for subcellular localization and antiviral activity 329 component categories as well as to biological pathways. The gene expression data interpretation is conducted by comparing genes in a given list and statistically compares the list to the reference list to look for under and over represented functional categories. The step-by-step method and the statistical test employed are The authors wish to thank laboratory personnel at Virology Lab, Faculty of Veterinary Medicine, UPM and Laboratory of Vaccines and Immunotherapeutics, Institute of Bioscience, UPM. This project was funded by Fundamental Research Project No: 01-11-08-6390FR, Ministry of Higher Education, Malaysia. The funding source has no role in this study.