期刊论文详细信息
BMC Genomics
Increasing gene discovery and coverage using RNA-seq of globin RNA reduced porcine blood samples
Le Luo Guan1  Joan K Lunney4  Samuel M Abrams4  Eric Fritz-Waters3  James M Reecy3  Christopher K Tuggle3  Graham S Plastow1  Paul Stothard1  Yan Meng1  Xu Sun1  Afshin Hosseini2  Arun Kommadath1  Hua Bao1  Igseo Choi4 
[1] Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada;Present address: Institute for Animal Science, Physiology and Hygiene Unit, University of Bonn, Katzenburgweg 7-9, 53115 Bonn, Germany;Department of Animal Science, Iowa State University, Ames, IA, USA;Animal Parasitic Diseases Laboratory, ARS, USDA, Beltsville, MD, USA
关键词: Transcriptome;    RNA-seq;    Globin reduction;    Blood;    Pig;   
Others  :  1127736
DOI  :  10.1186/1471-2164-15-954
 received in 2014-10-01, accepted in 2014-10-16,  发布年份 2014
PDF
【 摘 要 】

Background

Transcriptome analysis of porcine whole blood has several applications, which include deciphering genetic mechanisms for host responses to viral infection and vaccination. The abundance of alpha- and beta-globin transcripts in blood, however, impedes the ability to cost-effectively detect transcripts of low abundance. Although protocols exist for reduction of globin transcripts from human and mouse/rat blood, preliminary work demonstrated these are not useful for porcine blood Globin Reduction (GR). Our objectives were to develop a porcine specific GR protocol and to evaluate the GR effects on gene discovery and sequence read coverage in RNA-sequencing (RNA-seq) experiments.

Results

A GR protocol for porcine blood samples was developed using RNase H with antisense oligonucleotides specifically targeting porcine hemoglobin alpha (HBA) and beta (HBB) mRNAs. Whole blood samples (n = 12) collected in Tempus tubes were used for evaluating the efficacy and effects of GR on RNA-seq. The HBA and HBB mRNA transcripts comprised an average of 46.1% of the mapped reads in pre-GR samples, but those reads reduced to an average of 8.9% in post-GR samples. Differential gene expression analysis showed that the expression level of 11,046 genes were increased, whereas 34 genes, excluding HBA and HBB, showed decreased expression after GR (FDR <0.05). An additional 815 genes were detected only in post-GR samples.

Conclusions

Our porcine specific GR primers and protocol minimize the number of reads of globin transcripts in whole blood samples and provides increased coverage as well as accuracy and reproducibility of transcriptome analysis. Increased detection of low abundance mRNAs will ensure that studies relying on transcriptome analyses do not miss information that may be vital to the success of the study.

【 授权许可】

   
2014 Choi et al.; licensee BioMed Central Ltd.

【 预 览 】
附件列表
Files Size Format View
20150221064354722.pdf 2024KB PDF download
Figure 5. 53KB Image download
Figure 4. 68KB Image download
Figure 3. 80KB Image download
Figure 2. 83KB Image download
Figure 1. 46KB Image download
【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

【 参考文献 】
  • [1]Klem TB, Bleken E, Morberg H, Thoresen SI, Framstad T: Hematologic and biochemical reference intervals for Norwegian crossbreed grower pigs. Veterinary clinical pathology/American Society for Veterinary Clinical Pathology 2010, 39(2):221-226.
  • [2]Takahashi J, Misawa M, Iwahashi H: Oligonucleotide microarray analysis of age-related gene expression profiles in miniature pigs. PLoS One 2011, 6(5):e19761.
  • [3]Mastrokolias A, den Dunnen JT, van Ommen GB, 't Hoen PA, van Roon-Mom WM: Increased sensitivity of next generation sequencing-based expression profiling after globin reduction in human blood RNA. BMC Genomics 2012, 13:28. BioMed Central Full Text
  • [4]Field LA, Jordan RM, Hadix JA, Dunn MA, Shriver CD, Ellsworth RE, Ellsworth DL: Functional identity of genes detectable in expression profiling assays following globin mRNA reduction of peripheral blood samples. Clin Biochem 2007, 40(7):499-502.
  • [5]Wu K, Miyada G, Martin J, Finkelstein D: Globin reduction protocol: A method for processing whole blood RNA samples for improved array results. Affymetrix Technical Note 2007. Available at: http://media.affymetrix.com:80/support/technical/technotes/blood2_technote.pdf webcite
  • [6]Tian Z, Palmer N, Schmid P, Yao H, Galdzicki M, Berger B, Wu E, Kohane IS: A practical platform for blood biomarker study by using global gene expression profiling of peripheral whole blood. PLoS One 2009, 4(4):e5157.
  • [7]Vartanian K, Slottke R, Johnstone T, Casale A, Planck S, Choi D, Smith J, Rosenbaum J, Harrington C: Gene expression profiling of whole blood: Comparison of target preparation methods for accurate and reproducible microarray analysis. BMC Genomics 2009, 10(1):2. BioMed Central Full Text
  • [8]Wright C, Bergstrom D, Dai H, Marton M, Morris M, Tokiwa G, Wang Y, Fare T: Characterization of Globin RNA Interference in Gene Expression Profiling of Whole-Blood Samples. Clin Chem 2008, 54(2):396-405.
  • [9]Debey S, Zander T, Brors B, Popov A, Eils R, Schultze JL: A highly standardized, robust, and cost-effective method for genome-wide transcriptome analysis of peripheral blood applicable to large-scale clinical trials. Genomics 2006, 87(5):653-664.
  • [10]Liu J, Walter E, Stenger D, Thach D: Effects of globin mRNA reduction methods on gene expression profiles from whole blood. JMD 2006, 8(5):551-558.
  • [11]Whitley P, Moturi S, Santiago J, Johnson C, Setterquist R: Improved microarray sensitivity using whole blood RNA samples. Ambion Tech Notes 2005, 12:20-23.
  • [12]Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, Lopez R, McWilliam H, Remmert M, Söding J, Thompson JD, Higgins DG: Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 2011, 7(1):539-544.
  • [13]Miller ER, Ullrey DE, Ackermann I, Schmidt DA, Luecke RW, Hoefer JA: Swine hematology from birth to maturity. II. Erythrocyte population, size and hemoglobin concentration. J Anim Sci 1961, 20:890-897.
  • [14]Ramirez CG, Miller ER, Ullrey DE, Hoefer JA: Swine hematology from birth to maturity. II. Erythrocyte population, size and hemoglobin concentration. J Anim Sci 1963, 22(4):1068-1074.
  • [15]Shin H, Shannon CP, Fishbane N, Ruan J, Zhou M, Balshaw R, Wilson-McManus JE, Ng RT, McManus BM, Tebbutt SJ: Variation in RNA-Seq Transcriptome Profiles of Peripheral Whole Blood from Healthy Individuals with and without Globin Depletion. PLoS One 2014, 9(3):e91041.
  • [16]Lunney J, Steibel J, Reecy J, Fritz E, Rothschild M, Kerrigan M, Trible B, Rowland R: Probing genetic control of swine responses to PRRSV infection: current progress of the PRRS host genetics consortium. BMC proceedings 2011, 5(Suppl 4):S30. BioMed Central Full Text
  • [17]Groenen MAM, Archibald AL, Uenishi H, Tuggle CK, Takeuchi Y, Rothschild MF, Rogel-Gaillard C, Park C, Milan D, Megens H-J, Li S, Larkin DM, Kim H, Frantz LAF, Caccamo M, Ahn H, Aken BL, Anselmo A, Anthon C, Auvil L, Badaoui B, Beattie CW, Bendixen C, Berman D, Blecha F, Blomberg J, Bolund L, Bosse M, Botti S, Bujie Z, et al.: Analyses of pig genomes provide insight into porcine demography and evolution. Nature 2012, 491(7424):393-398.
  • [18]Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL: TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 2013, 14(4):R36. BioMed Central Full Text
  • [19]Anders S, Pyl PT, Huber W: HTSeq – A Python framework to work with high-throughput sequencing data. Bioinformatics 2014, ᅟ:ᅟ. Sep 25, doi:10.1093/bioinformatics/btu638. [Epub ahead of print]
  • [20]Robinson MD, McCarthy DJ: Smyth GK: edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010, 26(1):139-140.
  • [21]Wang L, Wang S, Li W: RSeQC: quality control of RNA-seq experiments. Bioinformatics 2012, 28(16):2184-2185.
  • [22]Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol 1990, 215(3):403-410.
  文献评价指标  
  下载次数:13次 浏览次数:4次