DOI: 10.5061/dryad.63xsj3tz8
title: The utility of reptile blood transcriptomes in molecular ecology

description: Reptiles and other non-mammalian vertebrates have transcriptionally active
 nucleated red blood cells. If blood transcriptomes can provide
 quantitative data to address questions relevant to molecular ecology, this
 could circumvent the need to euthanize animals to assay tissues. This
 would allow longitudinal sampling of animals’ responses to treatments, as
 well as sampling of protected taxa. We developed and annotated blood
 transcriptomes from six reptile species. We found on average 25,000
 proteins are being transcribed in the blood, and there is a CORE group of
 9,282 orthogroups that are found in at least four of six species. In
 comparison to liver transcriptomes from the same taxa, approximately
 two-thirds of the orthogroups were found in both blood and liver; and a
 similar percentage of ecologically relevant gene groups (insulin and
 insulin-like signaling, electron transport chain, oxidative stress,
 glucocorticoid receptors) were found transcribed in both blood and liver.
 As a resource, we provide a user-friendly database of gene ids identified
 in each blood transcriptome. Although, on average 37% of reads mapped to
 hemoglobin, importantly, the majority of non-hemoglobin transcripts had
 sufficient depth (e.g., 97% at &gt;10 reads) to be included in
 differential gene expression analysis. Thus, we demonstrate that RNAseq
 blood transcriptomes from a very small blood sample (&lt;10 ul) is a
 minimally invasive option in non-mammalian vertebrates for quantifying
 expression of a large number of ecologically relevant genes longitudinally
 and in protected populations.
description: <i>Taxon
 Sampling</i> Six reptile species (3 snakes, 2
 lizards, and a turtle) were included in this study (Table 1) for
 development of blood transcriptomes. These were chosen based on reptile
 taxonomic diversity and interest to our research groups. Blood was taken
 using a heparinized needle (U-100 BD Micro-Fine™ IV Insulin Syringes 28
 Gauge, 1 mL 12.7 mm
 (<sup>1</sup>/<sub>2</sub>")). For all
 animals &lt;1% of body weight of blood was taken from the caudal vein
 in the tail, this was (20 ml to 300 ml for these animals). Blood was
 either (1) put immediately into RNAlater (Ambion) (&lt;1:5 ratio of
 blood to RNAlater) in a 2 ml screw-cap tube and kept on ice as would be
 typical in field settings until stored 4°C, or (2) centrifuged (1000xG for
 5 min) to separate blood components, which were flash frozen in liquid
 nitrogen as plasma and red blood cells in 2 ml screw-cap tubes and stored
 at -80°C as is common in laboratory settings. These are two approaches
 (RNAlater versus snap-freeze in liquid nitrogen) that are commonly used to
 preserve the RNA in tissue, and this study demonstrates that both produce
 high quality RNA and RNAseq data when used on blood. Typically, the blood
 cell pellet (hematocrit) is approximately one half of the whole blood cell
 volume, thus 10 ml of blood cells is obtainable from 20 ml of whole blood.
 Typically, the blood cell pellet from reptiles is almost all red blood
 cells with a fine layer of white blood cells at the interface between the
 red blood cells and the plasma, and thereby is referred to as the red
 blood cell (RBC) pellet. All procedures were approved by the IACUC at the
 respective universities or agency of the individual collecting the
 sample. <i>RNA Isolation
 </i> Blood
 that was in RNAlater was centrifuged at 1000xG for 5 minutes to pellet the
 RBC and the RNAlater was pipetted off. From either the snap-frozen RBC
 pellet, or the RNAlater RBC pellet, we used 10ml of pelleted blood cells
 for RNA isolation using the Ambion RiboPure Kit, with DNAse digestion as
 described by the manufacturer. Purified RNA was analyzed on a Bioanalyzer
 (Agilent) to validate the quality and to quantify of RNA. From these 10 ml
 of blood cells we obtained between 4.5 mg and 8.9 mg of RNA, far more than
 was needed for RNAseq. All samples had a RIN &gt;7.5. <i>RNA-seq Library
 Preparation and Sequencing</i> We sent 1 mg of total RNA to the
 Heflin Genomic Center at the University of Alabama at Birmingham. Barcoded
 libraries were prepared using the Agilent SureSelect Stranded library kit
 (Agilent Technologies, Santa Clara, CA) as described by the manufacturers.
 Briefly, 100ng of total RNA was subjected to two rounds of poly A+
 selection using oligo dT magnetic beads. The mRNA was randomly fragmented,
 and first strand cDNA synthesis was performed in the presence of random
 hexamers and 2.4ng/µL (final concentration) of Actinomycin D using
 standard techniques. After second strand synthesis was complete, the cDNA
 was adenylated and used in a ligation reaction to add primary adaptors for
 flow cell attachment with bar code information. The sequencing libraries
 were mixed to equal molar amounts and run on the Illumina HiSeq2500 using
 a Rapid Run flow cell with paired-end 100 bp sequencing reads, aiming for
 20 million reads/sample. Following completion of the run, the .bcl files
 were converted to FASTQ file format using BCL2FASTQ 1.8.4 from
 Illumina. <i>Liver
 Transcriptomes</i> For comparison to our blood
 transcriptomes, we downloaded the liver transcriptomes from Dryad (McGaugh
 et al., 2015a) for two species that overlap with our blood transcriptomes
 (<i>T. elegans, </i><i>E. multicarinata</i>), and
 a third species that shared a genus (<i>Sceloporus
 undulatus</i>) (Table 1). <i>Blood Transcriptome
 Assembly</i> The bioinformatic pipeline is
 represented in Figure 1. FASTQ files were assessed using FastQC
 (http://bioinformatics.babraham.ac.uk/projects/fastqc/) to assess quality
 control before cleaning. Using Trimmomatic (Bolger, Lohse, &
 Usadel, 2014), low quality base pairs were removed from raw reads. To
 reduce biases the first 10 base pairs of each read were removed from each
 read, and any sequences shorter than 30 base pairs were removed. Quality
 of the reads was assessed again using FastQC. Transcriptomic reads were
 <i>de novo</i> assembled using Trinity 2.2.0 (Haas et al.,
 2013) with the default parameters, we refer to this as the Raw
 Assembly. <i>Metagenomic
 Contamination Screening</i> Contamination screening was
 performed on the contigs from each assembled blood transcriptome and the
 liver transcriptomes from McGaugh <i>et al. </i>(2015a). We
 performed DIAMOND (Buchfink, Xie, & Huson, 2015) blastp searches
 against NCBI’s non-redundant nucleotide database (e-value cutoff of 1E-10)
 and sorted the resulting reports by bitscore, then e-value, then percent
 identity and isolated any sequences whose top hit matched to a
 non-vertebrate from the reports using a custom perl script. The
 contaminate non-vertebrate contigs were cleaned (removed) from the raw
 transcriptomes, and we refer to these as the “cleaned
 transcriptomes”. <i>Reference Blood
 Transcriptomes</i> After removing the non-vertebrate
 sequences from the original blood transcriptome assemblies, we used
 TransDecoder (https://github.com/TransDecoder/) to generate longest open reading frames and peptide files. The longest open reading frames were passed to the UCLUST algorithm implemented in usearch7 (Edgar, 2010) to cluster the transcripts within each transcriptome using an identity threshold of 90%. Resulting centroids were kept as representative sequences for each cluster. These centroids from the cleaned-clustered assemblies we refer to as the Reference Blood Transcriptomes. <i>Functional Annotation</i> Both the raw and the reference transcriptome assemblies were annotated with the Trinotate annotation pipeline version 3.0.  (Bryant et al., 2017), which used TransDecoder to identify the longest open reading frame peptide candidates and compares them to Swiss-Prot (Bateman et al., 2017), PFAM (Finn et al., 2016), SignalP (Petersen, Brunak, Heijne, & Nielsen, 2011), TMHMM (Sonnhammer, von Heijne, & Krogh, 1998) databases. We also did custom BLAST searches (blastx and blastp, e-value cutoff of 1E-10) (Altschul, Gish, Miller, Myers, & Lipman, 1990) to genomes: <i>Anolis carolinensis </i>2.0; <i>Gallus gallus</i> 5.0; and <i>Homo sapiens</i> GRCh38.p12 from ENSEMBL release 92 (Zerbino et al., 2018). These transcriptomes and annotation files are provided as a Dryad Repository. Additionally, translated transcripts were checked for completeness using the BUSCO tetrapoda database (Simao, Waterhouse, Ioannidis, Kriventseva, & Zdobnov, 2015). TransDecoder peptide files from both cleaned blood transcriptomes and the cleaned liver transcriptomes<i> </i>were passed to OrthoFinder (Emms & Kelly, 2015) for orthology inference using all-vs-all blastp searches. To assign similarity-based protein identifications of resulting putative homologous proteins, we performed BLAST searches to the three genomes noted above.
description: This package contains the
 assmbled transcriptomes from blood (.fastq) and annotation files for the
 blood transcriptomes and liver transcriptomes from McGaugh et al. 2015
 PNAS (.xls), and two supplemental .xls files. Supplemental File 1 is an excel
 file of the Candidate Functional Pathway Genes and the transcripts IDs
 (from translated raw transcriptomes) from each species. Supplemental File 2 serves as an
 excel database listing the genes found in each transcriptome as a resource
 for researchers considering using blood transcriptomes to investigate
 which genes and candidate gene groups are being expressed and thereby may
 be assayed in their reptile system.  Raw Blood RNAseq data has been
 deposited in the NCBI SRA database. Accession SRP135786: Runs SRR6841717
 to SRR6841722