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Methods S1 RNA-seq and qRT-PCR
Transcriptome Analysis
RNA Isolation and Sequencing
Total RNA was isolated from individual spleen samples (n = 6/treatment group) by
TRIzol extraction according to the manufacturer’s protocol (Ambion, Inc., Austin, TX). RNA
samples were DNase-treated with the Turbo DNA-freeTM Kit (Ambion, Inc.) and stored at -80°C.
RNA quality and concentration were initially assessed by spectrophotometry (Nanodrop 1000,
Nanodrop Technologies, Wilmington, DE) and denaturing gel electrophoresis. Individual RNA
samples (n = 3/treatment group) at 10 μg of total RNA/sample were submitted to the University
of Minnesota Genomics Center (UMGC) for library preparation and sequencing. RNA integrity
was verified on the 2100 Bioanalyzer (Aligent Technologies, Santa Clara, CA) and all samples
had an RNA Integrity Number (RIN) between 6.4 and 7.3. Indexed libraries were constructed
for each individual sample (n = 12), and were pooled for multiplexed sequencing on the Illumina
Genome Analyzer IIx (Illumina, Inc., San Diego, CA). Samples were run in 7 flow cell lanes to
produce approximately 113 M 76 bp single-end sequencing reads.
Read Filtering, Trimming, and Dataset QC Analysis
RNA-seq datasets were de-multiplexed according to the unique index tag for each library
(Table S1). Raw reads were filtered and trimmed according to the protocols utilized in Monson
et al. (2014), then passed through a final length filter discarding any reads less than 40 bp.
Quality of each dataset before and after processing was measured with FastQC (Andrews
2010).
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Transcript Assembly, Annotation and Filtering
Corrected reads were de novo assembled into predicted transcripts using Velvet
(Zerbino and Birney 2008) and Oases (Schulz et al. 2012) pipelines as previously described in
Monson et al. (2014). Corrected reads were mapped back to the assembled transcripts by
BWA (Li and Durbin 2009) and read coverage was determined for each transcript in each
individual dataset with HTSeq (intersect-nonempty mode; Anders 2010). Only reads that
uniquely mapped to a single predicted transcript were incorporated into these counts.
Annotation of predicted transcripts was performed using BLAST alignment to NCBI Transcript
Reference Sequence (RefSeq) mRNAs from the turkey genome (UMD 2.01), to RefSeq mRNAs
from the chicken genome (Galgal 4.0), and to proteins in the UniProtKB Swiss-Prot database as
described in Monson et al. (2014). To identify any non-protein coding RNAs or known but unannotated cDNAs, transcripts with significant differential expression (DE) were also BLAST
aligned to the NCBI non-redundant (NR) database. Predicted transcripts identified as potential
LINE-1 elements (28 transcripts) were disregarded in downstream analyses, as these repetitive
sequences are found throughout the genome and cannot be properly assembled, annotated or
mapped. Transcripts were also filtered for sufficient read depth using a minimum coverage
threshold of 0.1 read/million mapped. Thus in each treatment group (n = 3 datasets), mapping
of at least 2 reads to a transcript was required it to be considered expressed. Minimum read
depth was considered for each treatment group, rather than each individual dataset, to allow
identification of final transcriptome content at the treatment level (Monson et al. 2014).
Differential Expression Analysis
Pair-wise comparisons of expression between treatment groups were made with the R
package DESeq (Anders and Huber 2010). Read counts for all predicted transcripts were
analyzed in DESeq to prevent skewing of dispersion estimates, rather than limiting the
expression data to the filtered transcripts. For each transcript in each pair-wise comparison,
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read counts were size-scaled to normalize for differences in sequencing depth between
libraries, normalized mean expression (n = 3 replicates/treatment group) was calculated, and
within-group variance was estimated. Transcripts were evaluated in DESeq for statistically
significant DE using the relationship between their mean and dispersion estimates in pair-wise
inference tests based on a negative binomial distribution. A transcript was considered to have
significant DE if q-values (FDR adjusted p-values based on the Benjamin-Hochberg procedure)
were ≤ 0.05. Principle component analysis (PCA) was performed in DESeq to examine the
within-treatment and between-treatment variation between biological replicates. Scatter plots
and Venn diagrams were generated in R to visualize the DE results as previously described
(Monson et al. 2014).
Genome and Functional Analysis
Filtered transcripts were aligned to the domestic turkey genome build UMD 2.01 (Dalloul
et al. 2010) with GMAP (Wu and Watanabe 2005). Functional characterization of transcripts
with significant DE was performed using Blast2GO V.2.6.6 (Conesa et al. 2005; Götz et al.
2008) and gene functions and networks were explored by Ingenuity Pathway Analysis (IPA)
(Ingenuity Systems, Redwood City, CA).
Quantitative Analysis of IL2 and GZMA Expression
Reverse Transcription and qRT Primer Design
Expression of interleukin-2 (IL2) and granzyme A (GZMA) was investigated using
quantitative real-time PCR (qRT-PCR) on all spleen RNA samples (n = 6/treatment group).
DNase-treated total RNA (1 µg/sample) was reverse transcribed with random and poly-DT
primers according to the manufacturer’s protocol (USB First-Stand cDNA Synthesis kit for Real-
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Time PCR (Affymetrix/USB, Cleveland, OH)). A 1:10 dilution of each resulting cDNA sample
was made for use in qRT-PCR.
Primers for qRT-PCR were designed for IL2 (F = GAGATCAAGGAGTGCAGTCAG, R =
ACAGATCTTGCATTCACCTCC, 183 bp amplicon), GZMA (F = TTCCTGGAGATTTGTGCGTG,
R = TTTGTCTGTGAATGGGCTCC, 194 bp amplicon), and hypoxanthine
phosphoribosyltransferase 1 (HPRT1) (F = CACAAGAAGCAGCCAGTTACAGTATC, R =
TTATTGCTGTAAAAAGGAATCTGGG, 96 bp amplicon) using Primer3 v 4.0.0 software
(Koressaar and Remm 2007; Untergrasser et al. 2012). Each pair was tested on cDNA with
HotStarTaq (QIAgen Inc., Valencia, CA) using the following parameters: 94°C for 10 min, 35
cycles of 95°C for 30 sec, 58°C for 30 sec, 72°C for 30 sec, and a final extension step of 72°C
for 10 min. PCR products were Sanger sequenced to verify specificity.
Normalization and Standard Curves
HPRT-1 has previously been utilized as a reference gene for qRT-PCR on spleen RNA
samples from this challenge trial (Rawal et al. 2014). Primer sets were initially tested on each
cDNA sample and Normfinder (Andersen et al. 2004) was used to confirm that HPRT-1 had the
most stable expression (lowest stability value). To generate plasmids for standard curves, PCR
products amplified from cDNA were purified with the IBI PCR DNA Fragment Extraction kit (IBI
Scientific, Peosta, IA) and ligated into p-GEM-T Easy cloning vector (Promega Corp., Madison,
WI). Ligations were transformed into DH5α sub-cloning efficiency competent cells (Invitrogen
Corp., Carlsbad, CA) and cultures propagated as previously described in Rawal et al. 2014.
Plasmid DNA from single clones was purified using the QIAprep Spin Miniprep kit (QIAgen Inc.)
and sequence verified. Standard curves for each gene were generated by serial dilutions of the
gene-specific plasmid clones and made to encompass the concentrations of all cDNA samples.
Primer efficiency (E) and the coefficient of determination (R2) were used to access the quality of
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the standard curves (IL2 E = 79.9%, R2 = 0.986, GZMA E = 72.1%, R2 = 0.982, and HPRT-1 E =
80.0%, R2 = 0.992).
Quantitative Real-Time PCR
qRT-PCR was performed with PerfeCta SYBR Green Fastmix Low ROX (Quanta
Biosciences Inc, Gaithersburg, MD), which uses SYBR to measure expression and ROX as the
reference dye. For each gene, expression was assayed in 24 cDNA samples (n = 6/treatment
group) in duplicate, the standard curve in duplicate, a gDNA control, and a no-template control
(NTC). Reactions received 10 µL of SYBR Fastmix, 4 µL of 0.5 µM primer mix (F and R), and 5
µL of RNase-free H2O. 1 µL of cDNA dilution (1:10), gDNA or H2O was added to each reaction
as appropriate. qRT-PCR was run on an Aligent MX3000p (Stratagene Corp., La Jolla, CA)
using the SYBR green with dissociation curve assay. Reactions consisted of 1 cycle at 95°C for
3 min, 40 cycles of 95°C for 30 sec, 58°C for 30 sec, 72°C for 30 sec, and a final cycle of 95°C
for 1 min, 55°C for 30 sec ramping to 95°C for 30 sec to create the dissociation curve.
Data Analysis
MxPro QPCR software (Aligent Tecnologies, Santa Clara, CA) was used to calculate the
threshold cycles (Ct) for each qRT-PCR reaction, the E for each primer pair and the R2 for each
standard curve. After normalization using HPRT-1 (delta CT (ΔCt)), relative expression levels
for IL2 and GZMA in each treatment group compared to the CNTL group were calculated using
the delta delta Ct (ΔΔCt) method (Livak and Schmittgen 2001). Expression (as copy number)
was directly quantified based on the standard curves. Analysis of Variance (ANOVA) and
Tukey multiple comparison tests were run on the ΔCt values using the stats and multcomp
packages in R v 3.1.2 (Hothorn et al. 2008; R Core Team 2014); the difference between
treatments must have a p-value ≤ 0.05 to be considered statistically significant.
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REFERENCES
Anders S (2010) HTSeq: Analysing high-throughput sequencing data with Python. http://wwwhuber.embl.de/users/anders/HTSeq/. Accessed September 2011
Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome
Biol 11:R106
Andersen CL, Jensen JL, Ørntoft TF (2004) Normalization of real-time quantitative reverse
transcription-PCR data: a model-based variance estimation approach to identify genes
suited for normalization, applied to bladder and colon cancer data sets. Cancer Res
64:5245-5250
Andrews S (2010) FastQC: A quality control tool for high throughput sequence data.
http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Accessed 6 October 2011
Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M (2005) Blast2GO: a universal
tool for annotation, visualization and analysis in functional genomics research.
Bioinformatics 21:3674-3676
Dalloul RA, Long JA, Zimin AV, Aslam L, Beal K, Blomberg LA, Bouffard P, Burt DW, Crasta O,
Crooijmans RP, Cooper K, Coulombe RA, De S, Delany ME, Dodgson JB, Dong JJ, Evans
C, Frederickson KM, Flicek P, Florea L, Folkerts O, Groenen MA, Harkins TT, Herrero J,
Hoffmann S, Megens HJ, Jiang A, de Jong P, Kaiser P, Kim H, Kim KW, Kim S,
Langenberger D, Lee MK, Lee T, Mane S, Marcais G, Marz M, McElroy AP, Modise T,
Nefedov M, Notredame C, Paton IR, Payne WS, Pertea G, Prickett D, Puiu D, Qioa D,
Raineri E, Ruffier M, Salzberg SL, Schatz MC, Scheuring C, Schmidt CJ, Schroeder S,
Searle SM, Smith EJ, Smith J, Sonstegard TS, Stadler PF, Tafer H, Tu ZJ, Van Tassell CP,
Vilella AJ, Williams KP, Yorke JA, Zhang L, Zhang HB, Zhang X, Zhang Y, Reed KM (2010)
Multi-platform next-generation sequencing of the domestic turkey (Meleagris gallopavo):
genome assembly and analysis. PLoS Biol 8:e1000475
Götz S, García-Gómez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ, Robles M, Talón M,
Dopazo J, Conesa A (2008) High-throughput functional annotation and data mining with the
Blast2GO suite. Nucleic Acids Res 36:3420-3435
Hothorn T, Bretz F, Westfall P (2008) Simultaneous Inference in General Parametric Models.
Biom J 50:346-363
Koressaar T, Remm M (2007) Enhancements and modifications of primer design program
Primer3. Bioinformatics 23:1289-1291
Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler Transform.
Bioinformatics 25:1754-1760
Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time
quantitative PCR and the 2-ΔΔCT Method. Methods 25:402-408
Monson MS, Settlage RE, McMahon KW, Mendoza KM, Rawal S, El-Nemazi HS, Coulombe
RA, Reed KM (2014) Response of the hepatic transcriptome to aflatoxin B1 in domestic
turkey (Meleagris gallopavo). PLoS One 9:e100930
R Core Team (2014) R: A language and environment for statistical computing. http://www.Rproject.org/. Update accessed December 2014
7
Rawal S, Bauer MM, Mendoza KM, El-Nezami H, Hall JR, Kim JE, Stevens JR, Reed KM,
Coulombe RA (2014) Aflatoxicosis chemoprevention by probiotic Lactobacillius and lack of
effect on the Major Histocompatibility Complex. Res Vet Sci 97:274-281
Schulz MH, Zerbino DR, Vingron M, Birney E (2012) Oases: robust de novo RNA-seq assembly
across the dynamic range of expression levels. Bioinformatics 28:1086-1092
Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, Rozen SG (2012)
Primer3--new capabilities and interfaces. Nucleic Acids Res 40:e115
Wu TD, Watanabe CK (2005) GMAP: a genomic mapping and alignment program for mRNA
and EST sequences. Bioinformatics 21:1859-1875
Zerbino DR, Birney E (2008) Velvet: algorithms for de novo short read assembly using de Bruijn
graphs. Genome Res 18:821-829