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Article

Identification of Novel mRNA Isoforms Associated with Acute Heat Stress Response Using RNA Sequencing Data in Sprague Dawley Rats

by
Jinhuan Dou
1,2,
Abdul Sammad
2,
Angela Cánovas
3,
Flavio Schenkel
3,
Tahir Usman
4,
Maria Malane Magalhães Muniz
3,
Kaijun Guo
1,* and
Yachun Wang
2,*
1
Animal Science and Technology College, Beijing University of Agriculture, Beijing 102206, China
2
Key Laboratory of Animal Genetics, Breeding and Reproduction, MARA, National Engineering Laboratory of Animal Breeding, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
3
Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
4
College of Veterinary Sciences and Animal Husbandry, Abdul Wali Khan University, Mardan 23200, Pakistan
*
Authors to whom correspondence should be addressed.
Biology 2022, 11(12), 1740; https://doi.org/10.3390/biology11121740
Submission received: 25 October 2022 / Revised: 17 November 2022 / Accepted: 21 November 2022 / Published: 29 November 2022
(This article belongs to the Section Genetics and Genomics)
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Abstract

:

Simple Summary

Global warming and events of heat waves even in temperate climatic zones signify the importance of the genetic makeup of heat stress. Our earlier acute heat stress exposure study (from 30 min to 120 min treatments) in rats led to the designation of aberrant differentially expressed genes (DEGs) and pathways through RNA-sequencing of the three most relevant tissues, i.e., blood, liver, and adrenal glands. However, the causes and mechanisms of differential expression of genes associated with heat stress are still unclear. Using the same RNA-sequencing data, this study identified the differentially expressed mRNA isoforms and narrowed down the most reliable DEG markers and molecular pathways that underlie the thermoregulatory mechanisms. The spatial-temporal differential expression pattern of heat stress-responsive differential mRNA isoforms may be ultimately used in marker-assisted selection for improved thermotolerance.

Abstract

The molecular mechanisms underlying heat stress tolerance in animals to high temperatures remain unclear. This study identified the differentially expressed mRNA isoforms which narrowed down the most reliable DEG markers and molecular pathways that underlie the mechanisms of thermoregulation. This experiment was performed on Sprague Dawley rats housed at 22 °C (control group; CT), and three acute heat-stressed groups housed at 42 °C for 30 min (H30), 60 min (H60), and 120 min (H120). Earlier, we demonstrated that acute heat stress increased the rectal temperature of rats, caused abnormal changes in the blood biochemical parameters, as well as induced dramatic changes in the expression levels of genes through epigenetics and post-transcriptional regulation. Transcriptomic analysis using RNA-Sequencing (RNA-Seq) data obtained previously from blood (CT and H120), liver (CT, H30, H60, and H120), and adrenal glands (CT, H30, H60, and H120) was performed. The differentially expressed mRNA isoforms (DEIs) were identified and annotated by the CLC Genomics Workbench. Biological process and metabolic pathway analyses were performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. A total of 225, 5764, and 4988 DEIs in the blood, liver, and adrenal glands were observed. Furthermore, the number of novel differentially expressed transcript lengths with annotated genes and novel differentially expressed transcript with non-annotated genes were 136 and 8 in blood, 3549 and 120 in the liver, as well as 3078 and 220 in adrenal glands, respectively. About 35 genes were involved in the heat stress response, out of which, Dnaja1, LOC680121, Chordc1, AABR07011951.1, Hsp90aa1, Hspa1b, Cdkn1a, Hmox1, Bag3, and Dnaja4 were commonly identified in the liver and adrenal glands, suggesting that these genes may regulate heat stress response through interactions between the liver and adrenal glands. In conclusion, this study would enhance our understanding of the complex underlying mechanisms of acute heat stress, and the identified mRNA isoforms and genes can be used as potential candidates for thermotolerance selection in mammals.

1. Introduction

As the global temperature increases, heat stress has become a big challenge for human health and animal survival. An assessment report released by the Intergovernmental Panel on Climate Change (IPCC) predicts that by 2100, the global average surface temperature will rise by 0.3–4.8 °C [1], suggesting that it is more urgent to explore the body’s adaptation mechanism to heat stress and develop strategies to resist the adverse effects of heat stress. Heat stress is a major stressor affecting the health of animals, and body homeostasis is disturbed while employing thermoregulation [2]. The heat stress response is carried out through the hypothalamic–pituitary–adrenal (HPA) axis, causing hormonal changes including an increase in adrenocorticotropic hormones [3,4]. Homeotherms, upon heat stress, accelerate the respiratory rate, and sweating ensues, among other physiological responses [5,6]. Additional changes induced by heat stress cause spatiotemporal temperature distribution in various tissues [7]. Moreover, being a public health concern [8], heat stress causes physiological and biochemical changes in the body of rodents [9] and livestock [10,11,12]. These changes end up in the morbidity and decline of production and reproduction [10,13]. The negative effects of heat stress are characterized by high oxidative stress [14], molecular and transcriptional changes [15,16], along with post-transcriptional [17,18] and epigenetic changes [19] at the cellular and tissue levels. However, there is limited information about the molecular mechanisms of heat stress response in mammals.
Heat stress is a complex regulatory process combining the neurohormonal, oxidative, and immune responses [20,21,22]. Next-generation sequencing technologies, such as RNA sequencing (RNA-Seq), allow the large-scale, rapid, and accurate identification of heat stress-responsive genes, contributing to elucidating the molecular mechanisms of heat stress more deeply [23,24,25,26,27]. Moreover, numerous studies have demonstrated that the activity of some heat stress-responsive genes is not only regulated by their post-translational modifications [28,29,30] but also affected by changes in mRNA expression. Alternative splicing profoundly plays a vital role in many physiological processes. A study has shown that 60% of disease-causing point mutations are induced by interference with normal mRNA splicing during transcription [31]. In addition, alternative splicing was found to be involved in the process of biotic and abiotic stress responses [32,33,34]. As early as 1994, Takechi et al. reported that alternatively spliced mRNA with 169 additional nucleotides in the 5′ noncoding region was found after heat shock in comparison with mRNA transcribed under non-heat shock conditions [35]. Ju et al. found that in heat-stressed Bama miniature pigs, the second exon of the TLR4 gene was spliced and 167 bp shorter in the alternative splicing variant [36]. In heat-stressed Drosophila, Nobuhiro et al. identified three new isoforms of heat shock transcription factor mRNA, including HSFb, HSFc, and HSFd, and found that the ratio of HSFb increased upon heat stress [28]. Although numerous studies reveal hundreds or thousands of heat stress-related genes in animals [9,37,38,39,40], studies focusing on large-scale mRNA isoforms in heat stressed-animals are still scarce in the literature.
In our previous study, the mRNA expression profiles of the blood, liver, and adrenal glands of rats in the control group (CT, 22 ± 1 °C, relative humidity 50%) and three heat-stressed groups fed at 42 °C for 30 min (H30), 60 min (H60) and 120 min (H120) (relative humidity 50%) were investigated using RNA-Seq [9,17,41]. In this context, the main hypothesis of the current study is that heat stress may change the number of mRNA isoforms involved in the expression levels of transcripts that are transcribed by genes involved in thermotolerance mechanisms in different tissues under different heat stress durations. Therefore, the objective of the present study was to identify the novel mRNA isoforms differentially expressed in blood, liver, and adrenal gland tissues of rats under H30, H60, and H120 conditions using RNA-Seq data obtained previously [9,17]. In addition, the mRNA isoforms identified in this study were integrated into different functional analyses in order to identify active physiological pathways and transcripts involved in the process of the heat stress response.

2. Materials and Methods

2.1. Animals and Sample Collection

Twenty 8-week-old female Sprague Dawley (SD) rats (Beijing Vital River Laboratory, Animal Technology Co., Ltd., Beijing, China) weighing 205 ± 7.16 g were to be used as subjects. During the whole experiment, water and feed were provided ad libitum. Based on changes in rectal temperature and 17 biochemical indicators of blood housed at H30, H60, and H120 conditions [41], a heat-stressed rat model was established in which 48 samples including blood (n = 4 each in the CT and H120 groups), liver (n = 5 each in the CT, H30, H60, and H120 groups), and adrenal glands (n = 5 each in the CT, H30, H60, and H120 groups) were collected. The blood, liver, and adrenal gland tissues in the same treatment group were collected from the same rat. Briefly, the experimental rats were anesthetized with an injection of 1%, 1.2 mL pentobarbital sodium (0.6 mL/100 g body weight), sacrificed, and quickly dissected by sterile surgical scissors and forceps (Shinva Medical Instrument Co. Ltd., Zibo, China) [42]. About 4 mL of blood was collected using Vacutainer® tubes (Becton Dickinson, Plymouth, UK) containing Ethylenediaminetetraacetic acid (EDTA) from each rat and immediately placed on ice. Then, the peripheral blood mononuclear cells (PBMC) were collected by RNase-free spear via centrifugation (10 min, 3000 rpm) in a 2 mL centrifuge tube containing 1 mL Trizol (Invitrogen 15596018, Thermo Fisher Scientific Inc., Waltham, MA, USA) and stored at −80 °C for RNA extraction. The liver and adrenal glands were collected, washed in ice-cold phosphate-buffered solution, and snap-frozen immediately in liquid nitrogen.

2.2. RNA Isolation and Library Construction

The RNA reagent (HUAYUEYANG Biotechnology Co. Ltd., Beijing, China) was used to extract the total RNA from 48 samples of 20 SD rats according to the manufacturer’s protocol. The purity of the RNA samples was assessed at A260/230 nm and A260/280 nm ratios using the NanoDrop 2100 (Thermo Fisher Scientific Inc., Waltham, MA, USA). The integrity of the RNA (RIN) was determined using an RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA), and the RNA concentration and genomic DNA contamination were determined using a Qubit® 2.0 Fluorometer (Thermo Fisher Scientific Inc., Waltham, USA). The OD260/OD280 ratio varied from 1.8 to 2.0 for all samples, and RIN values > 7.0, indicating good RNA quality [43].
A total of 3 µg RNA from each sample was used to prepare the cDNA libraries using the NEBNext® UltraTM Directional RNA Library Prep Kit from Illumina® (NEB, San Diego, CA, USA), following the manufacturer’s recommendation. Paired-end (2 × 150 pb) reads of samples in the CT and H120 groups were sequenced using a HiSeq 2000 sequencer (Illumina, San Diego, CA, USA) in a previous study [9], and the reads of H30 and H60 groups were sequenced using the HiSeq 2500 sequencer (Illumina, San Diego, CA, USA) with 2 × 150 pb reads paired-end [17]. Both of the RNA-Seq protocols were performed in the Novogene Technology Co., Ltd., located in Tianjin of China.

2.3. Sequence Assembly and Quantification

All the transcriptomic analyses of the reads generated in CT, H30, H60, and H120 were performed using the CLC Genomics Workbench software 12.0 (CLC Bio, Aarhus, Denmark). Quality control analyses were performed following the parameters described in Cánovas et al. [44]. The paired-end sequenced reads were mapped to the annotated reference genome Rattus norvegicus 6.0 (rn6, ftp://ftp.ensembl.org/pub/release-105/genbank/rattus_norvegicus/, accessed on 11 February 2022) using the “Large Gap Read Mapping” tool. Briefly, this tool allows to map the RNA-Seq reads that span introns without requiring prior transcript annotations, which helps to conduct the transcript discovery analysis [45].
The transcript discovery analysis was performed using the “Transcript Discovery” tool implemented in the CLC Genomics Workbench environment (CLC Bio, Aarhus, Denmark) [45,46], which takes large gap read mapping tracks, uses gene and transcript annotations as an input, and produces optimized gene and transcript track annotations as outputs. Therefore, for each gene region, there was a set of transcript annotations that can explain the observed exons and splice sites in this region (data files available but not attached here to this manuscript) [46,47]. Then, the sequences of the samples were mapped to the created tracks (genes and transcripts tracks) using the rat reference genome as a map via the “RNA-Sequencing analysis tool” implemented in the CLC Genomics Workbench. The reads per kilobase per million mapped reads (RPKM) were used to quantify the expression levels for different transcripts across RNA-Seq libraries and were transformed by log10 [48,49]. Transcripts with RPKM ≥ 0.2 were defined as expressed. The expression levels of transcripts identified in each sample were classified as lowly expressed, moderately expressed, and highly expressed according to the criteria of RPKM ≤ 50, 50 < RPKM < 500, and RPKM ≥ 500 [44]. Principal component analysis (PCA, an unsupervised machine learning technique that seeks to find principal components) was performed on the gene expression by using the prcomp R function. The pair-wise Pearson correlation coefficient (PCC, r) between any two of the four or five biological replicates within each treatment of each comparison was also calculated by correlation procedure in the R package (version 4.2.0, https://cran.rproject.org/src/base/R-3/, accessed on 22 April 2022), where the Pearson’s correlation significance was computed using the Hmisc procedure.

2.4. Identification of Differential mRNA Isoform Expression

The CLC Genomics Workbench software 12.0 was used to conduct the differential mRNA isoform expression analyses [45,50]. Therefore, a two-stage experiment was carried out, applying empirical statistical analysis that implements an “Exact Test” developed by [51] and incorporated into the edgeR [52]. The statistical test was performed for each set up experiment, using the original count values and two parameters for the estimation of the dispersion: 1—“total count filter cut-off > 5, which specifies which features should be considered when estimating the common dispersion component”. 2—estimate tag-wise dispersions, which allow a weighted combination of the tag-wise and common dispersion for each transcript [45]. In addition, the original expression values were transformed based on a logarithm (log10) and normalized using scaling normalization described by [53] to ensure that the samples are comparable and assumptions on the data for analysis are met. The isoforms in the blood that met the thresholds of p < 0.05, |fold change (FC)| ≥ 2, and in the liver and adrenal glands met with p ≤ 0.001, FDR ≤ 0.05, and |FC| ≥ 2 were identified as differentially expressed (DEIs) [54], respectively.

2.5. Functional Enrichment Analysis and Gene Annotation

The main category biological process (BP) was performed using gene ontology (GO) enrichment analysis [55]. The analysis was performed using the list of genes associated with the DEIs in each tissue under various heat stress conditions. Functional evidence of the relationship between the significant GO terms (FDR ≤ 0.05) and the target phenotypes (e.g., response to heat stress) was identified. The metabolic pathway analysis was performed by the Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg, accessed on 30 April 2022) database. The non-redundant biological terms for large clusters of genes in functionally related groups network were analyzed by ClueGO [56] and visualized by Cytoscape 3.8.2 [57].
To annotate novel mRNA isoforms of unknown genes, their coding sequence was obtained from the Genome Data Viewer tool, which is implemented in the National Center of Biotechnology Information (NCBI) database (https://www.ncbi.nlm.nih.gov/genome/gdv/browser/genome/, accessed on 6 May 2022). Thereafter, the ‘Nucleotide BLAST’ method available on the Basic Local Alignment Search Tool (BLAST; https://blast.ncbi.nlm.nih.gov/Blast.cgi, accessed on 6 May 2022) was used to detect similarities between the nucleotide sequences from the novel mRNA isoforms and the nucleotide (nt) collection available in the database. The nt collection consists of GenBank, EMBL, DDBJ, PDB, and RefSeq sequence data, but it excludes EST, STS, GSS, WGS, TSA, patent sequences, phase 0, 1, and 2 HTGS sequences, and sequences longer than 100 Mb. The database is non-redundant; i.e., identical sequences are merged into one entry (but it keeps the accession, GI, title, and taxonomy information from each entry).
Draw Venn Diagram (http://bioinformatics.psb.ugent.be/webtools/Venn/, accessed on 10 May 2022) was used to analyze the common and specific genes identified under various heat stress conditions and in different tissues. Finally, we searched our previous list of differentially expressed genes [41] for the genes associated with the differentially expressed known transcripts and the novel transcript lengths, defined them as DEIDEGs, and summarized the common DEIDEGs involved in the process of thermoregulation.

3. Results and Discussion

3.1. Global Landscape of the Rat Transcriptome

A total of 45 billion reads of 150 bp paired-end RNA-Seq data were produced, corresponding to an average of ~66 million reads per sample (Table S1). High-quality clean reads were mapped to the rn6 genome. On average, 54,042, 86,680, and 102,927 transcripts were identified in the blood, liver, and adrenal glands, mapping to 34,239, 37,043, and 38,689 genes, respectively. The PCA results between any treatment samples within each tissue showed that the samples were divided mainly by various treatments (Figure S1A). Then, the pair-wise PCC analysis yielded 6 or 10 pair-wise r values per sample group. For example, the mean r value in the CT of the blood was 0.9978 (ranging from 0.9971–0.9996), and the mean r value in the H120 group was 0.9995 (ranging from 0.9989–0.9998) (Figure S1B). The results of all the PCCs indicated a high level of measurement consistency among the biological replicates (Figure S1B).
Differences in the numbers of transcripts expressed among tissues were observed in the current study. On average, 11,288 (20.89%) of 54,042, 38,890 (46.02%), of 86,680, and 48,798 (47.10%) of 102,927 transcripts were defined as expressed (RPKM ≥ 0.2) in the blood, liver, and adrenal gland tissues, respectively (Figure 1). Among all the identified transcripts, a large proportion of lesser expressed transcripts (0.2 ≤ RPKM < 50) were identified in the blood (20.51%), liver (ranging from 43.67–45.31%), and adrenal glands (44.73–48.82%), followed by moderately expressed transcripts and highly expressed transcripts (Figure 1).

3.2. Identification of Various Types of the Differentially Expressed mRNA Isoforms

An overview of the DEIs between the treatments in each tissue is shown in Table 1. A total of 225, 2072, 2086, 1606, 1130, 1994, and 1864 DEIs were detected in the CT vs. H120 of blood, CT vs. H30, CT vs. H60, and CT vs. H120 comparisons of the liver and adrenal glands, respectively. A larger number of DEIs was identified in the liver compared with the adrenal glands and blood. Over all of the samples, the total number of novel differentially expressed transcript lengths of the annotated genes (6763) in all tissues was 1.75 and 19.43 times higher than the known DEIs (3866) and novel differentially expressed transcripts of non-annotated genes (348).

3.2.1. Differentially Expressed mRNA Isoforms Annotated in the Rat Genome (Rattus Norvegicus 6.0)

A total of 81 known mRNA isoforms were differentially expressed in the blood in the CT vs. H120 comparison; 70.37% of them were up-regulated, and 29.63% were down-regulated. The Rpl11-201 (ribosomal protein L11) was the top one annotated DEI with the highest FC of 206.84 (p = 8.34 × 105), followed by Rpl36al-201 (ribosomal protein L36a-like) (p = 1.75 × 104) (Table S2). In proliferating cells, the ribosomal stress response can be triggered by both increased and decreased ribosomal biogenesis, accompanied by the activation of the p53 pathway [58]. Rpl11 may suppress cell growth via p53-dependent and/or independent mechanisms by interacting with other proteins and RNAs [59]. There is no research related to the role of Rpl11 in heat stress. In addition, previous studies have reported that the transcription-independent p53 can trigger heat stress-induced apoptosis [60]. Therefore, it is necessary to further study whether Rpl11 regulates cell activity during heat stress.
In the liver (Table S3), an average of 698 known DEIs were detected in each comparison, and about 69.12% (482) of them were significantly down-regulated (p ≤ 0.001, FDR ≤ 0.05 and |FC| ≥ 2). Among all the comparisons of the liver, the most significantly changed known DEI was Dbi-202 (diazepam-binding inhibitor, acyl-CoA-binding protein), with FCs of −6671.54 (FDR = 8.37 × 1087), −4386.08 (FDR = 1.54 × 1085), and −4386.08 (FDR = 1.54 × 1085) in CT vs. H30, CT vs. H60, and CT vs. H120 comparisons, respectively. The Dbi gene plays a role in the regulation of mitochondrial steroidogenesis and acyl-CoA metabolism. Previous studies have indicated that the DBI-related peptide can protect neurons and astrocytes from oxidative stress-induced apoptosis through its metabotropic receptor [61]. It is well known that oxidative stress occurs as a consequence of the imbalance between antioxidant defense and reactive oxygen species (ROS) production, and heat stress can stimulate the production of ROS [20], which suggests that Dbi may play an indirect role in the heat stress response. The Venn diagram analysis of all DEIs identified in three comparisons of the liver has shown that 97 isoforms were shared among all comparisons, including 83 significantly up-regulated isoforms and 14 significantly down-regulated isoforms (Figure 2A).
In adrenal glands, a total of 1690 isoforms were differentially expressed; 70.23%, 71.08%, and 65.83% of them were significantly up-regulated, which were 2.36, 2.46, and 1.93 times higher than down-regulated isoforms identified in CT vs. H30, CT vs. H60, and CT vs. H120 comparisons, respectively (Table 1). The Rps15a-201 (ribosomal protein S15a) was the top one isoform that was differentially expressed (FDR = 2.43 × 1060) when H30 was compared to CT, with an FC of 3225.49 (Table S4). The primary structure of Rps15a was firstly investigated in 1994, and it was shown that Rps15a has 129 amino acids [62]. Previous studies reported that Rps15a is engaged in the metabolism of RNA and proteins, rRNA processing, and translation [63,64,65]. In the comparison of CT vs. H60 (Table S4), the most significantly changed isoform was Hspe1-201 (heat shock protein family E (Hsp10) member 1), which is a stress-induced mitochondrial matrix protein and molecular chaperone [66]. However, no study was conducted on the function of Hspe1 during the heat stress response. The Giot1 (gonadotropin inducible ovarian transcription factor 1) was the top one significantly changed isoform in CT vs. H120 which was up-regulated (FC = 547.48, FDR = 2.96 × 1030). No shared annotated DEIs were detected among the three comparisons in the adrenal glands (Figure 2B).

3.2.2. Differentially Expressed Novel Transcript Lengths for Genes Annotated in the Rat Genome Reference (Rattus Norvegicus 6.0)

In total, 60.44% (136), 61.57% (3549), and 61.71% (3078) of all DEIs in the blood (225), liver (5764), and adrenal glands (4988) were novel differentially expressed transcript lengths of annotated genes (Table 1). In the blood, transcript (ENSRNOG00000032844.4) with a 1198 bp length annotated to the known gene RT1-Da (RT1 class II, locus Da) was changed the most (FC = −115.88) and was significantly down-regulated at H120 (Table S2). Furthermore, a large number of novel transcript lengths annotated for known genes that were identified as differentially expressed in blood were involved in the immune response, such as RT1-Bb (RT1 class II, locus Bb), RT1-Da, and Crip1 (cysteine-rich protein 1).
In the liver, the novel transcript lengths of the annotated genes were found to be down-regulated (72.33% of 3549 in three comparisons), and 187 novel transcript lengths of the annotated known genes were shared among the CT vs. H30, CT vs. H60, and CT vs. H120 comparisons (Table S5). Moreover, several shared genes were related to the response to the fatty acid metabolic process [such as Hacl1 (2-hydroxyacyl-CoA lyase 1), Acaca (acetyl-CoA carboxylase alpha), Fads1 (fatty acid desaturase 1), etc.]; the response to cold [such as Gk (glycerol kinase), Hspd1 (heat shock protein family D member 1) and Vegfa (vascular endothelial growth factor A)]; the response to oxidative stress [such as Abcb11 (ATP binding cassette subfamily B member 11), Btg1 (BTG anti-proliferation factor 1), Atrn (attractin), Gclc (glutamate-cysteine ligase, catalytic subunit), Ppif (peptidylprolyl isomerase F), Rcan1 (regulator of calcineurin 1), Rps3 (ribosomal protein S3), and cell motility [such as Slc9a3r1 (SLC9A3 regulator 1) and Ctnna1 (catenin alpha 1)].
In adrenal glands (Table S6), a total of 3078 novel transcript lengths annotated for known genes were differentially expressed depending on the thresholds of p ≤ 0.001, FDR ≤ 0.05, and |FC| ≥ 2, and 156 genes were commonly identified in three comparisons, including 63 genes that were up-regulated and 93 genes that were down-regulated when heat stress occurred. We also found that most shared genes were engaged in the response to hormones, the response to heat, and metabolic processes, such as Btg1 (BTG anti-proliferation factor 1), Lrp5 (LDL receptor-related protein 5), Cyp11b3 (cytochrome P450, family 11, subfamily b, polypeptide 3), Nr4a3 (nuclear receptor subfamily 4, group A, member 3), Dnaja1 (DnaJ heat shock protein family (Hsp40) member A1), Dnaja4 (DnaJ heat shock protein family (Hsp40) member A4), Hspd1 and Hsf1 (heat shock transcription factor 1), etc.

3.2.3. Annotation of Differentially Expressed Novel Transcript Lengths Associated with Non-Annotated Genes in the Rat Genome Reference (Rattus Norvegicus 6.0)

The differentially expressed novel transcripts associated with non-annotated genes are shown in Table 2, Table 3 and Table 4, for blood (Table 2), liver (Table 3), and adrenal glands (Table 4), respectively. In total, 6, 48, and 66 differentially expressed novel transcripts with non-annotated genes were detected in the blood, liver, and adrenal glands, respectively. Furthermore, it was observed that those transcripts presented lengths between 248–16045 bp. Only the sequences of novel mRNA isoforms that had 100% similarity to the nucleotides in the NCBI database were retained.
Among the differentially expressed novel transcripts in the blood, Gene_338_1 and Gene_786_1 were down-regulated and presented 100% similarity with Calm2 and Gypa (Table 2). Calm2 is a kind of Ca2+-binding protein; it can modulate cell survival, apoptosis [67], and autophagy [68] via different pathways. A study has proved that Calm2 is involved in cell stress regulation, especially endoplasmic reticulum stress [69]. Gypa is a major erythrocyte membrane sialoglycoprotein with a potential contribution to the development of diabetic complications [70]. In the liver (Table 3), 27, 15, and 6 differentially expressed novel transcripts associated with non-annotated genes were annotated in the rat genome (Rattus norvegicus 6.0), when H30, H60, and H120 were compared to the CT groups, respectively. The Gene_541, located at the genome position “3:163817141-16381790”, had the greatest degree of change among the H30 (FC = −46.82), H60 (FC = −44.03), and H120 (FC = −43.04) treatment groups. A total of 35 differentially expressed novel transcripts were obtained in the H120 conditions in the adrenal glands, which is 1.46 and 5 times more than those transcripts in the H60 and H30 conditions (Table 4). Under H120, the gene encoding the heat shock protein 86 (Hsp86), also known as Hsp90aa1, was annotated by Gene_4216. Numerous studies have revealed the Hsp90aa1 gene can be significantly up-regulated in a tissue-specific and time-dependent manner after heat stress [71]. Moreover, our previous study also confirmed the important role of HSP70AA1 in dairy cattle [72]. Thus, the Hsp90aa1 plays a pivotal role in the heat stress response.

3.3. Functional Enrichment Analysis

In order to annotate the DEIs further, GO and metabolic pathway analyses were performed on all of the annotated genes associated with DEIs (Tables S7 and S8). In summary, 185 genes were obtained in the blood (CT vs. H120) and 3189 genes were annotated in the liver, including 178, 1672, and 1339 genes in the CT vs. H30, CT vs. H60, and CT vs. H120 comparisons. In addition, a total of 3876 genes were annotated in the adrenal glands when the three heat treatment groups were compared to the CT.

3.3.1. GO Analysis

Based on FDR ≤ 0.05, 77 BP terms were found in the blood for the CT vs. H120 comparison, and most of them were related to the immune response, such as the regulation of adaptive immune response (GO:0002819) and the regulation of T cell-mediated immunity (GO:0002709) (Table S7). Furthermore, 48 genes were significantly enriched in terms of the response to stress (GO: 0006950) (FDR = 1.91 × 102), including Fn1, Rpl11, Dnajb2, etc., which also have the functions of the positive regulation of T cell migration, the negative regulation of the apoptotic process, as well as the negative regulation of I-kappaB kinase/NF-kappaB (NF-κB) signaling. Heat stress is well documented to have a negative impact on the immune system through humoral immune responses and cell-mediated responses [73,74]. In the current research, all of the 77 significantly enriched BPs are directly or indirectly related to the immune response (Table S7), suggesting that blood regulates heat stress by activating the immune response.
In the liver, totals of 139, 693, and 646 significantly (FDR ≤ 0.05) BP terms were detected in the CT vs. H30, CT vs. H60, and CT vs. H120 comparisons (Table S7), respectively. Eighty of them were shared in all comparisons in the liver. The most significantly enriched term was the small molecule metabolic process (GO: 0044281). There were 395 BP terms significantly enriched by genes that were in common in at least two comparisons. Among them, the process of the response to stress (GO: 0006950) was significantly enriched by 339 and 279 genes in the CT vs. H60 and CT vs. H120 comparisons (Table S7); 24 and 20 genes of them were also significantly (FDR ≤ 0.05) enriched in the process of the response to heat. Venn diagram analysis indicated that 17 genes were commonly identified in the H60 and H120 groups (Figure 3A), and they also play critical roles (p ≤ 0.05) in the response to arsenic-containing substances (Figure 3C). In addition, the positive regulation of the cold-induced thermogenesis (GO: 0120162) term was significantly (FDR = 3.91 × 102) clustered by 14 genes that were specifically identified in the CT vs. H120 comparison (Figure 3B), and the other functions of these 14 genes are shown in Figure 3D.
In adrenal glands, 114, 649, and 567 BP terms were significantly (FDR ≤ 0.05) detected in the CT vs. H30, CT vs. H60, and CT vs. H120 comparisons, respectively (Table S7). Among them, 353 terms were commonly detected in these three comparisons (marked in green color in Table S7). In addition, the significant terms (FDR ≤ 0.05) related to the response to heat (GO: 0009408, Figure 4A) and cold-induced thermogenesis (GO: 0106106, Figure 4B) were enriched, and six genes including Igfbp7, Dnaja1, LOC680121 (similar to heat shock protein 8), Bag3, Hsp90aa1, and Hspd1 were shared in the CT vs. H30, CT vs. H60, and CT vs. H120 comparisons (Figure 4A). There were 19, 19, and 17 genes of the H30, H60, and H120 groups clustered in the process of cold-induced thermogenesis, and 5 genes were commonly identified among the CT vs. H30, CT vs. H60, and CT vs. H120 comparisons (Figure 4B). Furthermore, the Hsf1 and Trpv2 genes were significantly enriched both in the response to heat- and cold-induced thermogenesis processes at H60 and H120 (Figure 4A,B). Further functional annotation analysis was performed on genes that engaged in response to heat (n = 30) and cold-induced thermogenesis (n = 36). The results show that genes clustered in response to heat also play significant (p ≤ 0.01) roles in protein folding and the positive regulation of reactive oxygen species metabolic processes (Figure 4C). Genes clustered in the cold-induced thermogenesis process were also significantly (p ≤ 0.01) involved in the regulation of transcription from the RNA polymerase H promoter in response to stress, sterol homeostasis, and the regulation of DNA-template transcription in response to stress (Figure 4D).
It is widely known that changes in gene expression may be triggered by thermal stress; in turn, the expression of genes may help cells to defend themselves against thermal stress. Some genes were both activated by heat and cold stress, such as the HSP70 (HSP72), HSP90, HSF-1, and TRP channels [75]. A previous study showed that the trimerization of HSF-1 was induced by cold shock at 4 °C, and then increased the expression of HSP70 and HSP90 by binding to the heat response element [76]. This study also reported that HSF-1 not only mediated the expression of HSP during the period of heat stress but also after rewarming to 37 °C. The TRP channels are often activated by a variety of stressors (e.g., heat, cold, pH). TRPV1-4 and TRPM2 are activated by heat stress, and TRPA1 and TRPM8 are activated by cold stress [77]. In the present study, HSF-1 and Trpv2 were found to be involved in the process of the response to heat- and cold-induced thermogenesis at H60 and H120, suggesting that both HSF-1 and Trpv2 played important roles in thermoregulation, but further validation experiments are needed to confirm their functions. The Trpv2 gene can be considered as a novel target for a subsequent heat stress study.

3.3.2. Metabolic Pathway Analysis

Thirty-three metabolic pathways were significantly detected in the blood, which were divided into five categories, including the cellular process, the environment information process, human diseases, metabolism, and organismal systems (Table S8). A total of 57.58% (n = 19) of the 33 pathways were related to human disease, containing cardiovascular disease, immune disease, and energy metabolism. The most significantly enriched pathway was viral myocarditis (FDR = 8.73 × 1010), which clustered 13 genes, including RT1-Ba, Prf1, RT1-CE5, RT1-CE7, RT1-T24-1, RT1-Bb, RT1-Da, RT1-Db1, Actb, Actg1, RT1-CE4, RT1-CE10, and LOC108348139 (Table S8). Kanda et al. proved that heat stress can aggravate viral myocarditis in mice by inducing viral infection [78].
In the liver, 31, 46, and 51 pathways were significantly enriched in the CT vs. H30, CT vs. H60, and CT vs. H120 comparisons, respectively (Table S8). Eleven pathways were commonly shared in the three comparisons, including fatty acid metabolism (ko01212) and the PPAR signaling pathway (ko03320) (Table S8). Fatty acids can regulate immune homeostasis by the activation of fatty acid receptors involved in inflammation and oxidative stress during heat stress [79,80]. Heat stress increases the expression levels of certain genes involved in promoted fatty acid metabolism, such as PPARγ, CCAAT-enhancer-binding protein α (CEBPα), and fatty acid synthase (FAS) [80]. Importantly, the PPAR signaling pathway activated by fatty acids regulates inflammatory responses and lipid metabolism [79,81,82]. The fatty acid metabolism-related genes and pathways identified in this study show the complexity of heat stress response and its possible implications for the welfare and production traits of livestock in summer. There were also 11 and 67 genes in the H30 and H60 groups significantly clustered in the thermogenesis pathway (ko04714), which help animals adapt to environmental changes. In adrenal glands, 47, 20, and 19 pathways were significantly detected by 1234, 1416, and 1226 genes in the CT vs. H30, CT vs. H60, and CT vs. H120 comparisons (Table S8). Only two pathways, including protein processing in the endoplasmic reticulum (ko04141) and ferroptosis (ko04216), were commonly detected by genes in all three comparisons. The pathway of thermogenesis (ko04714) was detected by 38 and 35 genes in the H30 and H60 groups, respectively. (Table S8).

3.4. Summary of Differential Isoform Corresponding Genes That Were also Differentially Expressed in Tissues

A total of 43, 786, and 1149 DEIDEGs were found in blood, liver, and adrenal gland tissues under heat stress, respectively (Table 5). Genes enriched in the heat stress process and thermogenesis are shown in Table 6. A total of 35 genes were enriched in the liver and adrenal glands, including 10 shared genes, such as Dnaja1, LOC680121, Chordc1, AABR07011951.1, Hsp90aa1, Hspa1b, Cdkn1a, Hmox1, Bag3, and Dnaja4, suggesting that these genes may regulate the heat stress response through crosstalk between the liver and adrenal glands. Combining with the expression profiles from the RNA-Seq and the validation results of the RT-qPCR experiments performed in our previous study [9], which demonstrated the high reliability of the RNA-Seq technology, we preferentially suggest that these 35 genes could be valuable candidates for underlying mechanisms to cope with heat stress.
Understanding mechanisms of heat stress response and discovering indicators to quantify the degree of heat stress are challenging for researchers [82,83]. Numerous studies have evidenced that gene expression analysis is a great strategy to identify positional and functional candidate genes associated with an organism’s response to heat stress [45]. Furthermore, the development of RNA-Seq technology has greatly improved the accuracy and comprehensiveness of gene expression analyses and accelerated the progress of heat stress-related research [84,85]. In the current study, RNA-Seq analysis was performed on three tissues (blood, liver, and adrenal glands) and four treatment groups (CT, H30, H60, and H120) and was not only able to identify the known mRNA isoforms annotated to known genes but also detected some novel transcripts associated with non-annotated genes. Our study also indicated that the expression of heat stress-responsive genes has spatial and temporal differences [17]. Moreover, the novel mRNA isoforms identified in this study would contribute to further studies, providing candidate mechanisms related to heat stress response, and to enriching the rat transcriptome map. If these novel mRNA isoforms are common to different mammalians, these regions could be used to identify universal markers that could be used for breeding for heat-resistant livestock mammals.

4. Conclusions

This research described the transcriptome profiles in the blood, liver, and adrenal glands of Sprague Dawley rats under various heat stress conditions, showing that the expression of heat stress-responsive genes in rats has spatiotemporal differences. In total, 35 genes were involved in the process of thermal stress regulation annotated by DEIs and were also differentially expressed in the liver and adrenal gland tissues, which could be preferentially considered as candidate markers for heat stress in rats. Furthermore, the identification of novel mRNA isoforms related to heat stress response allows for new insights into a better understanding of transcripts functionally playing important roles in heat stress, which may in the future be used to select for thermotolerance. The evident role of the inflammation–metabolism nexus appears to be central in the acute-heat-stressed rats through mRNA isoform identification, and hence this study indirectly emphasizes this intervention nexus for heat stress management. The magnitude of transcriptional changes uncovered in this study points towards the need for pathway-level biological investigation, along with further studies on the essential crosstalk between different biological pathways. Future GWAS studies focusing on the thermotolerance breeding of livestock can benefit from this study in the selection of genes, and a careful approach taking into account the spatiotemporal and post-transcriptional changes can be adopted.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biology11121740/s1, Figure S1. Principal components analysis (PCA) (A) and Pearson correlation analysis (B) between the CT and heat stress groups based on the expression levels of genes in blood, liver, and adrenal gland tissues. The sample groups were manually preset in the R code for plotting PCA charts with such designed clusters. No unsupervised clustering method was used on the PCA results. Table S1. Overview of the mapping information in blood, liver, and adrenal gland tissues under various heat stress conditions. Table S2. The differentially expressed mRNA isoforms (p < 0.05, |FC| > 2) identified in blood in CT vs. H120. Table S3. The known differentially expressed mRNA isoforms (p < 0.001, FDR < 0.05, and |FC| > 2) identified in the liver under various heat stress conditions. Table S4. The known differentially expressed mRNA isoforms (p < 0.001, FDR < 0.05, and |FC| > 2) identified in adrenal glands under various heat stress conditions. Table S5. The novel differentially expressed transcripts (p < 0.001, FDR < 0.05 and |FC| > 2) length associated with annotated genes identified in the liver under various heat stress conditions. Table S6. The novel differentially expressed transcripts (p < 0.001, FDR < 0.05 and |FC| > 2) length associated with annotated genes identified in adrenal glands under various heat stress conditions. Table S7. Significantly enriched biological process (BP) terms from differentially expressed transcripts corresponding to known genes identified in blood, liver, and adrenal glands. Table S8. Significantly enriched pathways from differentially expressed transcripts corresponding to known genes identified in blood, liver, and adrenal glands. Table S9. Statistics of differentially expressed genes that were also annotated by differential mRNA isoforms (DEIDEGs) in blood, liver, and adrenal glands under heat stress conditions.

Author Contributions

Y.W. and J.D. participated in the design of the study. J.D. collected samples, performed all the experiments, analyzed the data, and drafted the manuscript. Y.W. and K.G. guided the experimental implementation. F.S., A.S., T.U. and A.C. provided training to the first author and contributed to the analyses and results interpretation. M.M.M.M. and A.S. helped with the writing and gave editorial assistance. F.S., Y.W., M.M.M.M., A.C. and K.G. edited the final version of the manuscript and provided valuable suggestions. All authors reviewed and approved the final draft. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the fund of the 2022 Research and Innovation Ability Improvement Plan for Young Teachers of Beijing University of Agriculture, grant number QJKC2022027, the Modern Agroindustry Technology Research System of China (No. CARS-36), the program for the Changjiang Scholar and Innovation Research Team in University of China (No. IRT_15R62), Beijing Livestock Innovation Consortium (BAIC05-2022) and the 2022 Beijing Municipal Education Commission Classification Development Project (5076516023/003).

Institutional Review Board Statement

The protocol of animal experiments was reviewed according to the China Physiological Society’s guidelines and approved by the Institutional Animal Care and Use Committee at China Agricultural University, China (DK996). The in vivo rat experiments were performed at the College of Animal Science and Technology, China Agricultural University. The Institutional Animal Care and Use Committee approved all the experimental procedures, which complied with the China Physiological Society’s guiding principles for research involving animals and adhered to the high standard (best practice) of veterinary care as stipulated in the Guide for Care and Use of Laboratory Animals.

Informed Consent Statement

Not applicable.

Data Availability Statement

The RNA-Sequencing datasets of the CT and H120 groups (blood, liver, and adrenal glands) and datasets of the H30 and H60 groups (liver and adrenal glands) used in this work are available in the Sequence Read Archive (SRA) at the National Center for Biotechnology Information (NCBI) with BioProject IDs PRJNA589869 and PRJNA690189, respectively.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationship that could be constructed as a potential conflict of interest.

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Biology 11 01740 g001 550
Figure 1. Statistics of the number of transcripts identified in blood, liver, and adrenal glands under various heat stress conditions. High, medium, and low mean the high, moderate, and low expressed transcripts. The statistic numbers marked in white, orange, and red means the ratio of transcripts with different expression levels to all transcripts identified in blood, liver, and adrenal glands under various heat stress conditions. B, L, and A mean blood, liver, and adrenal gland tissues, respectively. CT means rats housed at 22 ± 1 °C and relative 50% humidity; the H30, H60, and, H120 mean the rats exposed to 42 °C for 30 min, 60 min, and 120 min with a relative humidity of 50%.
Figure 1. Statistics of the number of transcripts identified in blood, liver, and adrenal glands under various heat stress conditions. High, medium, and low mean the high, moderate, and low expressed transcripts. The statistic numbers marked in white, orange, and red means the ratio of transcripts with different expression levels to all transcripts identified in blood, liver, and adrenal glands under various heat stress conditions. B, L, and A mean blood, liver, and adrenal gland tissues, respectively. CT means rats housed at 22 ± 1 °C and relative 50% humidity; the H30, H60, and, H120 mean the rats exposed to 42 °C for 30 min, 60 min, and 120 min with a relative humidity of 50%.
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Biology 11 01740 g002 550
Figure 2. Venn diagrams of the known differentially expressed mRNA isoforms identified in all comparisons. (A) Venn diagram of the shared DEIs in the liver among three comparisons. (B) Venn diagram of the shared DEIs in adrenal glands among three comparisons. The blue, red, and green circles refer to the comparisons of CT vs. H30, CT vs. H60, and CT vs. H120, respectively.
Figure 2. Venn diagrams of the known differentially expressed mRNA isoforms identified in all comparisons. (A) Venn diagram of the shared DEIs in the liver among three comparisons. (B) Venn diagram of the shared DEIs in adrenal glands among three comparisons. The blue, red, and green circles refer to the comparisons of CT vs. H30, CT vs. H60, and CT vs. H120, respectively.
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Biology 11 01740 g003 550
Figure 3. Genes of liver involved in the heat stress process and their functional annotations. (A) Venn diagram of genes that engaged in terms of response to heat (GO: 0009408). (B) Venn diagram of genes that engaged in terms of the positive regulation of cold-induced thermogenesis (GO: 0120162). (C) The functional annotation of genes that were significantly clustered in response to heat and were shared between the CT vs. H60 and CT vs. H120 comparisons. (D) The functional annotation of genes in the CT vs. H120 comparison of the liver that were significantly clustered in the positive regulation of cold-induced thermogenesis. Only the GO-BP terms with p ≤ 0.05 are shown in colored words.
Figure 3. Genes of liver involved in the heat stress process and their functional annotations. (A) Venn diagram of genes that engaged in terms of response to heat (GO: 0009408). (B) Venn diagram of genes that engaged in terms of the positive regulation of cold-induced thermogenesis (GO: 0120162). (C) The functional annotation of genes that were significantly clustered in response to heat and were shared between the CT vs. H60 and CT vs. H120 comparisons. (D) The functional annotation of genes in the CT vs. H120 comparison of the liver that were significantly clustered in the positive regulation of cold-induced thermogenesis. Only the GO-BP terms with p ≤ 0.05 are shown in colored words.
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Biology 11 01740 g004a 550Biology 11 01740 g004b 550
Figure 4. Analysis of genes in adrenal glands engaged in thermal regulation processes. (A) Venn diagram of the genes involved in process of response to heat (GO: 0009408). (B) Venn diagram of the genes involved in the process of cold-induced thermogenesis (GO: 0106106). (C) Functional annotation of genes enriched in the process of the response to heat. The GO-BP terms with p ≤ 0.01 were regarded as significant. (D) Functional annotation of genes enriched in the process of cold-induced thermogenesis. The GO-BP terms with p ≤ 0.01 were regarded as significant.
Figure 4. Analysis of genes in adrenal glands engaged in thermal regulation processes. (A) Venn diagram of the genes involved in process of response to heat (GO: 0009408). (B) Venn diagram of the genes involved in the process of cold-induced thermogenesis (GO: 0106106). (C) Functional annotation of genes enriched in the process of the response to heat. The GO-BP terms with p ≤ 0.01 were regarded as significant. (D) Functional annotation of genes enriched in the process of cold-induced thermogenesis. The GO-BP terms with p ≤ 0.01 were regarded as significant.
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Table
Table 1. Summary of DEIs identified in blood, liver, and adrenal glands of rats under heat stress.
Table 1. Summary of DEIs identified in blood, liver, and adrenal glands of rats under heat stress.
ComparisonsDEIs Annotated with Associated Known GenesNovel Differentially Expressed Transcript Lengths of Annotated GenesNovel Differentially Expressed Transcripts with Non-Annotated Associated Genes
TotalUpDownTotalUpDownTotalUpDown
B_CT vs. H1208157241368650862
L_CT vs. H30812997131214413801461630
L_CT vs. H6073133239913113011010441430
L_CT vs. H1205522163361024268756301218
A_CT vs. H3030921792788492296331716
A_CT vs. H607024992031208709499845727
A_CT vs. H12067944723210827253571038320
Note: CT means control group, rats housed at 22 ± 1 °C and relative humidity of 50%; H30, H60, and H120 mean 42 °C heat stress for 30 min (H30), 60 min (H60), and 120 min (H120) with a relative humidity of 50%; DEIs means differentially expressed mRNA isoforms; Up means the expression of the mRNA isoforms was up-regulated in the heat treatment group compared to the CT; Down means the expression of the mRNA isoforms was down-regulated in the heat treatment group compared to the CT. B, L, and A refer to blood, liver, and adrenal gland tissues.
Table
Table 2. Differentially expressed novel transcripts associated with non-annotated genes in blood.
Table 2. Differentially expressed novel transcripts associated with non-annotated genes in blood.
Feature IDPositionmRNA Length (bp)p-ValueFCE-ValueIdentity (%)Pred. Gene AccessionPredicted Gene
Gene_338_16:11067663-1106981012253.97 × 102−2.520.00 × 100100.00BC058485.1Calm2
Gene_45_21:148454137-1484575784143.52 × 1039.198.49 × 10−29100.00BC091223.1Mpp1
Gene_45_31:148454178-1484575783739.40 × 1032.868.49 × 10−29100.00BC091223.1Mpp1
Gene_739_217:36393064-363958635533.48 × 1026.230.00 × 100100.00XR_598144.1LOC103694081
Gene_750_117:85715378-857490248331.18 × 1024.623.44 × 10−121100.00AB032899.1Pip4k2a
Gene_786_119:31115483-3113108514052.48 × 102−16.840.00 × 100100.00FQ225003.1Gypa
Notes: FC = fold change; Identify = the highest percent identity for a set of aligned segments to the same subject sequence; Pred. gene accession = accession number of predicted genes in the NCBI database.
Table
Table 3. Differentially expressed novel transcripts associated with non-annotated genes in the liver.
Table 3. Differentially expressed novel transcripts associated with non-annotated genes in the liver.
Feature IDPositionmRNA Length (bp)p-ValueFCE-ValueIdentity
(%)		Pred. Gene AccessionPredicted Gene
Gene_2181:227110795-22711394810238.98 × 104−2.152.23 × 1014100.00FQ229791.1TL0ADA46YK21
Gene_5443:165405586-1654123148362.18 × 109−2.324.86 × 104100.00AC091536.2clone RP31-547A5
Gene_6684:180315560-1803171308109.84 × 104−15.112.40 × 106100.00AC091503.2clone RP31-446A15
Gene_3602:225812754-2258270112912.70 × 104−7.317.98 × 105100.00AC087067.3RP31-40H10
Gene_451:77219372-7722280234306.65 × 1045.426.86 × 105100.00AC091514.2RP31-223K12
Gene_2772:34923108-349404103823.54 × 1012−6.061.00 × 103100.00AC091514.2clone RP31-223K12
Gene_11328:127840013-12784570612587.93 × 104−5.856.83 × 107100.00AC087722.2RP31-198L13
Gene_11328:127837857-12784570924383.37 × 104−3.229.43 × 107100.00AC087722.2clone RP31-198L13
Gene_289311:81514108-815167602782.02 × 104−23.145.29 × 105100.00AC087722.2clone RP31-198L13
Gene_124110:14087347-140880014193.00 × 104−5.455.83 × 109100.00AC087262.2RP31-151E23
Gene_181416:70876536-7089670212301.00 × 104−9.085.22 × 108100.00AC087262.2clone RP31-151E23
Gene_691:90859863-908891962262.87 × 104−2.841.64 × 104100.00AC079389.2RP31-263K14
Gene_189418:3426564-343747412055.46 × 104−2.277.89 × 104100.00AC079389.2clone RP31-263K14
Gene_8126:60958385-610553434346.73 × 104−2.662.39 × 1042100.00AC079378.2clone RP31-7L11
Gene_2622:22812846-228196915248.38 × 1044.764.74 × 1034100.00AC079378.2clone RP31-7L11
Gene_6614:175847044-17588230469637.15 × 10137.199.13 × 108100.00AC079378.2clone RP31-7L11
Gene_10007:127445500-12744724217427.06 × 1046.960.00100.00XM_017595283.1LOC678774
Gene_2063KL568103.1:3964-45544041.18 × 107−5.111.13 × 104100.00AB002169.1RT1
Gene_1021:137413556-13742247789216.05 × 1055.791.39 × 104100.00AC105654.4Renin BAC CH230-201P14
Gene_1021:137413555-137427301128128.50 × 10511.952.14 × 104100.00AC105654.4Renin BAC CH230-201P14
Gene_202720:5353181-53540608404.67 × 105−4.907.89 × 104100.00AJ314857.1Atp6G
Gene_8867:3051669-305389515691.79 × 1010−5.631.59 × 104100.00AB218617.1clone CH230-65K18
Gene_6624:176559544-17656509136184.23 × 1047.856.56 × 1017100.00AC092530.35clone rp32-28p17
Gene_2451:266346460-26635596927629.70 × 109−5.971.48 × 105100.00AC094697.7BAC CH230-5F4
Gene_2086X:74324189-7432990057111.34 × 10164.714.12 × 104100.00AC107611.610 BAC CH230-195I24
Gene_2086X:74324189-7433043511434.22 × 1073.324.51 × 104100.00AC107611.610 BAC CH230-195I24
Gene_5413:163817141-1638179052967.88 × 104−44.030.00100.00FO181541.11clone bRB-233C6
Gene_8596:125861248_1258701773635.82 × 1010−2.426.45 × 104100.00AC091503.2clone RP31-446A15
Gene_8596:125861248_1258701152981.27 × 109−2.656.41 × 104100.00AC091503.2clone RP31-446A15
Gene_4093:23268250_2327288632118.75 × 104−7.904.08 × 1043100.00AC091503.2clone RP31-446A15
Gene_3602:225812754_2258270112913.59 × 104-6.177.98 × 105100.00AC087067.3clone RP31-40H10
Gene_2772:34923108_349404103827.82 × 1011−5.161.00 × 103100.00AC091514.2clone RP31-223K12
Gene_309911:81514105_815167592804.30 × 104−12.035.29 × 105100.00AC087722.2clone RP31-198L13
Gene_8867:3051672_305389515666.61 × 1011−5.931.59 × 104100.00AB218617.1clone CH230-65K18
Gene_22151:187792023_18785381496683.79 × 10414.363.27 × 1044100.00AB218617.1clone CH230-65K18
Gene_6795:33659016_336965064648.46 × 1058.859.70 × 108100.00AC094583.7BAC CH230-4M16
Gene_7385:150489343_1504921824791.52 × 1042.123.22 × 1042100.00AC094583.7BAC CH230-4M16
Gene_7976:21900746_2191581920452.64 × 106−6.863.03 × 104100.00AC096165.10BAC CH230-11H2
Gene_8386:102105282_1021343025415.13 × 106−4.444.52 × 105100.00AB049248.2Atrn gene
Gene_4133:36034086_360355013342.21 × 104−4.577.77 × 106100.00AC091353.6BAC CH230-1B20
Gene_451:77213725_77228134144091.75 × 10410.132.90 × 104100.00AC096051.7BAC CH230-21G1
Gene_5413:163817141_1638179052967.23 × 104−46.820.00100.00FO181541.11clone bRB-233C6
Gene_7385:150489343-1504921824792.02 × 1052.303.22 × 1042100.00AC094583.7BAC CH230-4M16
Gene_441:77203585-7721363310063.68 × 1054.391.21 × 106100.00AC106169.5BAC CH230-105D14
Gene_8386:102105282-1021343025413.86 × 105−3.694.52 × 105100.00AB049248.2Atrn
Gene_8867:3051668-305362515232.53 × 104−2.241.40 × 104100.00AB218617.1clone CH230-65K18
Gene_451:77214418-7722812967593.84 × 1047.732.76 × 104100.00AC096051.7BAC CH230-21G1
Gene_5413:163817141-1638179052968.31 × 104−43.040.00100.00FO181541.11clone bRB-233C6
Notes: FC = fold change; Identify = the highest percent identity for a set of aligned segments to the same subject sequence; Pred. gene accession = accession number of predicted genes in the NCBI database. The blue, orange, and green backgrounds mean results generated in CT vs. H30, CT vs. H60, and CT vs. H120 comparisons.
Table
Table 4. Differentially expressed novel transcripts associated with non-annotated genes in adrenal glands.
Table 4. Differentially expressed novel transcripts associated with non-annotated genes in adrenal glands.
Feature IDPositionmRNA Length (bp)p-ValueFCE-ValueIdentity (%)Pred. Gene
Accession		Predicted Gene
Gene_7543:154869698-15487243927418.27 × 10734.476.69 × 1044100.00AC079378.2clone RP31-7L11
Gene_36375:157946908-15795656716274.62 × 10416.081.14 × 1016100.00AC109542.6BAC CH230-270O15
Gene_4812:183542130-1835827024114.78 × 104−21.503.78 × 107100.00AC087262.2clone RP31-151E23
Gene_881:81050735-810586563397.50 × 105−27.629.51 × 107100.00AC095281.6BAC CH230-10M16
Gene_36375:157939068-15795656727064.61 × 1046.993.52 × 104100.00AC105515.5BAC CH230-13H11
Gene_36335:155905289-15591227018031.83 × 105−32.285.04 × 104100.00AB294577.1chromosome 13q11-q12
Gene_37486:133793172-13380142032522.34 × 104−2.365.96 × 104100.00AC079389.2clone RP31-263K14
Gene_9164:157118179-15712202617964.73 × 105−2.722.77 × 104100.00AC241808.7BAC RNECO-49K24
Gene_4442:112717826-1127230088425.03 × 10410.763.71 × 109100.00AC094963.9BAC CH230-6L20
Gene_1871:146659430-14670692725317.03 × 1042.727.35 × 1010100.00AC087775.2clone RP31-464J4
Gene_81:15748741-1576049944561.67 × 1046.878.51 × 104100.00AC087722.2clone RP31-198L13
Gene_249915:51276012-513037862481.39 × 1046.511.56 × 104100.00AC087112.2clone RP31-162L19
Gene_180810:16149054-1615511449741.88 × 10724.503.38 × 105100.00AC090529.2clone RP31-160L19
Gene_180810:16149054-1615511450495.93 × 10614.633.38 × 105100.00AC090529.2clone RP31-160L19
Gene_180810:16149054-1615511456397.33 × 1067.733.38 × 105100.00AC090529.2clone RP31-160L19
Gene_180810:16149054-1615511442414.31 × 1044.533.38 × 105100.00AC090529.2clone RP31-160L19
Gene_9915:100650478-10068817814051.36 × 104−2.561.26 × 106100.00AC087262.2clone RP31-151E23
Gene_5392:205516170-2055252976142.60 × 1059.343.05 × 107100.00AC079389.2clone RP31-263K14
Gene_3631:236356294-23646699364857.56 × 10512.312.87 × 107100.00AC079378.2clone RP31-7L11
Gene_4242:33937181-3398327813911.89 × 105−31.701.14 × 1042100.00AC079378.2clone RP31-7L11
Gene_4242:33935270-3398327814819.16 × 1043.331.18 × 1042100.00AC079378.2clone RP31-7L11
Gene_7543:154870205-15487243911603.20 × 108−6.075.44 × 1044100.00AC079378.2clone RP31-7L11
Gene_222912:40266483-4033261238322.48 × 104−21.323.50 × 1044100.00AB218617.1clone CH230-65K18
Gene_248715:39865097-398735208972.51 × 105−6.527.76 × 1013100.00AC095845.8CH230-10C24
Gene_222412:38974186-389752387874.17 × 1058.869.47 × 1014100.00AC097039.8BAC CH230-61E1
Gene_444916:83872552-8387519320701.68 × 10517.285.27 × 105100.00AC095195.6BAC CH230-5J23
Gene_399310:10510094-1051574225195.79 × 10616.556.77 × 107100.00AC132013.4BAC CH230-269G5
Gene_399310:10510094-1051574256487.01 × 10421.756.77 × 107100.00AC132013.4BAC CH230-269G5
Gene_204310:108207735-10820941316789.77 × 1064.162.57 × 106100.00AC128611.4BAC CH230-249K23
Gene_881:81050735-810586563392.99 × 105−28.249.51 × 107100.00AC095281.6BAC CH230-10M16
Gene_180710:16140312-1614838522962.30 × 102032.721.62 × 104100.00AC094950.6BAC CH230-6H12
Gene_38378:61949091-619608956453.15 × 1048.021.04 × 1042100.00AC091537.2clone RP31-78C13
Gene_225712:52289596-5230797632922.35 × 1053.292.21 × 106100.00AC091000.2clone RP31-485F9
Gene_197510:88721220-887314249152.13 × 10815.045.51 × 1030100.00AC091514.2clone RP31-223K12
Gene_204410:108197514-10820791589782.35 × 1073.873.45 × 1012100.00AC087722.2clone RP31-198L13
Gene_426614:5550182-55562144241.32 × 1042.645.59 × 108100.00AC087722.2clone RP31-198L13
Gene_213712:992649-100881345783.66 × 104−7.575.41 × 107100.00AC087722.2clone RP31-198L13
Gene_213712:992649-100881343178.03 × 104−6.965.41 × 107100.00AC087722.2clone RP31-198L13
Gene_180810:16149065-1615483053443.31 × 101710.223.22 × 105100.00AC090529.2clone RP31-160L19
Gene_180810:16149065-1615483057659.16 × 101513.413.22 × 105100.00AC090529.2clone RP31-160L19
Gene_381:52785873-5281039555989.61 × 10158.832.28 × 107100.00AC087262.2clone RP31-151E23
Gene_15828:104634314-1046565369618.57 × 1056.792.07 × 107100.00AC087262.2clone RP31-151E23
Gene_38128:33136091-3315319574561.48 × 10412.631.59 × 107100.00AC087262.2clone RP31-151E23
Gene_235414:5744018-57469232984.82 × 104−2.667.45 × 109100.00AC087262.2clone RP31-151E23
Gene_861:80618017-8063021814025.20 × 1042.633.15 × 108100.00AC087262.2clone RP31-151E23
Gene_397710:16154557-1616246579084.19 × 101515.412.04 × 108100.00AC079389.2clone RP31-263K14
Gene_16268:129175363-12918071132649.80 × 101112.504.96 × 108100.00AC079389.2clone RP31-263K14
Gene_17529:113942674-1139485285761.84 × 1059.051.87 × 1037100.00AC079389.2clone RP31-263K14
Gene_17539:113952791-1140014478171.14 × 1098.601.60 × 1021100.00AC080157.26RP32-475K22
Gene_1721:137413500-13742730627036.43 × 104−2.012.15 × 105100.00AC105654.4BAC CH230-201P14
Gene_421613:6344942-634757914056.94 × 1056.023.98 × 1021100.00AJ297736.1hsp86
Gene_254116:7199973-720370637331.76 × 104−10.967.47 × 105100.00AC111654.6BAC CH230-108G17
Gene_208611:39085999-3909358775334.30 × 104−3.065.48 × 104100.00AC095195.6BAC CH230-5J23
Gene_394710:10509959-1051603258364.23 × 10773.207.28 × 107100.00AC132013.4BAC CH230-269G5
Gene_394710:10509959-1051603231814.60 × 10726.597.28 × 107100.00AC132013.4BAC CH230-269G5
Gene_394710:10509959-1051603255125.63 × 10624.267.28 × 107100.00AC132013.4BAC CH230-269G5
Gene_394710:10509959-1051603260734.05 × 10559.937.28 × 107100.00AC132013.4BAC CH230-269G5
Gene_394710:10509959-1051603257497.53 × 10427.747.28 × 107100.00AC132013.4BAC CH230-269G5
Gene_204310:108207735-10820941316781.84 × 10178.432.57 × 106100.00AC128611.4BAC CH230-249K23
Gene_16178:122685629-1226897814179.80 × 104−17.872.99 × 104100.00AC120734.5BAC CH230-220D1
Gene_8464:61876180-61892396102294.68 × 1085.651.51 × 107100.00AC095876.6BAC CH230-10G12
Gene_8464:61876180-61892397102301.58 × 1075.681.51 × 107100.00AC095876.6BAC CH230-10G12
Gene_8464:61876180-61892396147262.31 × 1057.131.51 × 107100.00AC095876.6BAC CH230-10G12
Gene_8464:61876180-61892397154135.10 × 1043.361.51 × 107100.00AC095876.6BAC CH230-10G12
Gene_180710:16140312-1614838522963.86 × 104147.781.62 × 104100.00AC094950.6BAC CH230-6H12
Gene_180710:16132421-16148466160459.80 × 10416.993.23 × 104100.00AC094950.6BAC CH230-6H12
Notes: FC = fold change; Identify = the highest percent identity for a set of aligned segments to the same subject sequence; Pred. gene accession = accession number of predicted genes in the NCBI database. The blue, orange, and green backgrounds mean results generated in the CT vs. H30, CT vs. H60, and CT vs. H120 comparisons.
Table
Table 5. Summary of differential isoform-corresponding genes that were also differentially expressed (DEIDEGs).
Table 5. Summary of differential isoform-corresponding genes that were also differentially expressed (DEIDEGs).
ComparisonsNo. DEIDEGsNo. Up-Regulated DEIDEGsNo. Down-Regulated DEIDEGs
B_CT vs. H120432518
L_CT vs. H30811566
L_CT vs. H60452187265
L_CT vs. H120253134119
A_CT vs. H3013210725
A_CT vs. H6045135398
A_CT vs. H120566405161
Note: DEIDEGs refer to the differential isoform corresponding genes that were also differentially expressed. B, L, and A mean blood, liver, and adrenal glands.
Table
Table 6. Summary of the DEIDEGs enriched in processes of response to heat- and cold-induced thermogenesis under heat stress.
Table 6. Summary of the DEIDEGs enriched in processes of response to heat- and cold-induced thermogenesis under heat stress.
ComparisonsGene NamePositionFCFDR
Response to heat
L_CT vs. H60		Bag31:199941160-19996519113.181.13 × 1016
Hsp90aa16:135107270-1351127753.731.13 × 108
Dnaja15:57028466-570393782.961.23 × 108
Hmox119:14508615-145154563.281.85 × 107
Dnaja48:59278261-592940037.244.61 × 107
Pklr2:188449209-188459592−3.561.27 × 105
Chordc18:17421556-174461652.531.02 × 103
Cdkn1a20:6351457-63588649.401.45 × 103
LOC68012111:13499163-135012632.221.53 × 103
Hspa1b20:4877323-48797791281.271.05 × 1059
Tp53inp15:24410862-2441688811.162.54 × 104
LOC103694877AABR07024106.1:11533-12195−2.322.99 × 103
Mif20:13732197-13732859−2.343.12 × 103
Atp2a212:39553902-396033262.275.63 × 103
Response to heat
L_CT vs. H120		Hsp90aa16:135107270-1351127753.842.99 × 1010
Pklr2:188449209-188459592−3.063.85 × 109
AABR07011951.12:177651240-1776532883.741.61 × 108
Abcc21:263554452-2636132522.284.77 × 108
Hmox119:14508615-145154563.616.85 × 106
Dnaja48:59278261-592940038.299.12 × 106
Bag31:199941160-1999651915.563.04 × 104
Dnaja15:57028466-570393782.353.40 × 104
Cdkn1a20:6351457-63588645.821.51 × 103
Chordc18:17421556-174461652.452.00 × 103
Cold-induced thermogenesis
L_CT vs. H120		Fabp52:93981655-93985378−6.246.83 × 1014
Prkab112:46316235-46326790−3.179.30 × 109
Lpin16:41799748-418700464.094.31 × 104
Scd1:264160128-264172729−3.072.11 × 103
Response to heat
A_CT vs. H30		Hspa1b20:4877323-487977935.001.84 × 1013
Bag31:199941160-1999651914.207.50 × 106
Response to heat
A_CT vs. H60		Bag31:199941160-19996519135.792.05 × 1047
Hspa1b20:4877323-4879779182.128.50 × 1038
Dnaja15:57028466-570393789.436.77 × 1032
Hsp90aa16:135107270-1351127759.562.95 × 1030
LOC68012111:13499163-135012635.444.21 × 1027
Dnaja48:59278261-5929400316.658.66 × 1023
Chordc18:17421556-174461655.157.58 × 1014
AABR07011951.12:177651240-1776532883.682.71 × 1011
Hmox119:14508615-145154565.291.57 × 109
Hspd19:61680529-616909562.021.72 × 108
Cdkn1a20:6351457-63588644.731.95 × 104
Hspa53:13838303-138427622.056.98 × 105
Response to heat
A_CT vs. H120		LOC68012111:13499163-135012637.371.50 × 1053
Hsp90aa16:135107270-13511277511.352.46 × 1037
Dnaja15:57028466-570393789.241.55 × 1025
Dnaja48:59278261-5929400322.413.09 × 1023
AABR07011951.12:177651240-1776532884.018.87 × 1018
Chordc18:17421556-174461655.061.14 × 1015
Vcp5:58426548-584459532.128.30 × 1013
Bag31:199941160-19996519113.502.75 × 1011
Hspd19:61680529-616909562.044.29 × 1010
Hmox119:14508615-145154565.192.18 × 108
Cdkn1a20:6351457-63588645.952.58 × 106
Trpv210:48903539-48925030−3.022.33 × 105
Cold-induced thermogenesis
A_CT vs. H30		AABR07033324.111:17336443-173403733.413.09 × 103
Gadd45g17:13391466-133932435.606.99 × 107
Arntl1:178039062-1781374657.452.22 × 106
Gnas3:172374956-1724284834.594.77 × 106
Cebpb3:164424514-1644259102.489.99 × 105
Cold-induced thermogenesis
A_CT vs. H60		Atf47:121480722-1214827723.161.50 × 1012
Arntl1:178039062-1781374659.735.39 × 108
Gnas3:172374956-1724284834.026.04 × 108
Gadd45g17:13391466-133932437.059.29 × 108
Acsl116:48937455-49003246−2.097.51 × 103
Igf1r1:128924965-1292065162.762.00 × 102
Cold-induced thermogenesis
A_CT vs. H120		Arntl1:178039062-17813746517.471.72 × 1021
Stk117:12440750-124575133.069.21 × 1015
Atf47:121480722-1214827723.561.21 × 103
Vegfa9:17340340-173556812.192.37 × 1013
Gadd45g17:13391466-133932437.374.47 × 1012
Trpv210:48903539-48925030−3.022.33 × 105
Dync1h16:134958853-1350857692.051.10 × 103
Acsl116:48937455-49003246−2.111.33 × 103
Igf1r1:128924965-1292065163.161.67 × 103
Note: FC means fold change, FDR means the false discovery rate, and L and A mean the liver and adrenal glands.
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Dou, J.; Sammad, A.; Cánovas, A.; Schenkel, F.; Usman, T.; Muniz, M.M.M.; Guo, K.; Wang, Y. Identification of Novel mRNA Isoforms Associated with Acute Heat Stress Response Using RNA Sequencing Data in Sprague Dawley Rats. Biology 2022, 11, 1740. https://doi.org/10.3390/biology11121740

AMA Style

Dou J, Sammad A, Cánovas A, Schenkel F, Usman T, Muniz MMM, Guo K, Wang Y. Identification of Novel mRNA Isoforms Associated with Acute Heat Stress Response Using RNA Sequencing Data in Sprague Dawley Rats. Biology. 2022; 11(12):1740. https://doi.org/10.3390/biology11121740

Chicago/Turabian Style

Dou, Jinhuan, Abdul Sammad, Angela Cánovas, Flavio Schenkel, Tahir Usman, Maria Malane Magalhães Muniz, Kaijun Guo, and Yachun Wang. 2022. "Identification of Novel mRNA Isoforms Associated with Acute Heat Stress Response Using RNA Sequencing Data in Sprague Dawley Rats" Biology 11, no. 12: 1740. https://doi.org/10.3390/biology11121740

APA Style

Dou, J., Sammad, A., Cánovas, A., Schenkel, F., Usman, T., Muniz, M. M. M., Guo, K., & Wang, Y. (2022). Identification of Novel mRNA Isoforms Associated with Acute Heat Stress Response Using RNA Sequencing Data in Sprague Dawley Rats. Biology, 11(12), 1740. https://doi.org/10.3390/biology11121740

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