Comparative transcriptome analysis of Ethiopian indigenous chickens from

low and high altitudes under heat stress condition reveals differential

immune response

Woncheoul Park1†, Krishnamoorthy Srikanth1†, Dajeong Lim1, Mina Park2, Taiyoung Hur1,

Steve Kemp3, Tadelle Dessie4, Min Seok Kim1, Sang-Ryong Lee5, Marinus F. W. te Pas6, Jun-

Mo Kim7*and Jong-Eun Park1*

1 Animal Genomics and Bioinformatics Division, National Institute of Animal Science, RDA, Wanju 55365, Republic of Korea2 Animal breeding and genomics Division, National Institute of Animal Science, RDA, Wanju 55365, Republic of Korea 3 Animal Biosciences, International Livestock Research Institute (ILRI), 30709, Nairobi 00100, Kenya 4 Animal Biosciences, International Livestock Research Institute (ILRI), 5689, Addis Ababa, Ethiopia5 Department of Agro-biotechnology Convergence, Jeonju University, 55069, Republic of Korea.6Animal Breeding and Genomics, Wageningen UR Livestock Research, 6700AH Wageningen, The Netherlands7 Department of Animal Science and Technology, Chung-Ang University, Anseong, Gyeonggi-do, 17546, Republic of Korea

† These authors equally contributed.

CORRESPONDING AUTHORS:

Prof. Jun-Mo Kim (junmosss@korea.kr)

Dr. Jong-Eun Park (jepark0105@korea.kr)

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Abstract

Ethiopia is an ecologically diverse country; the low altitude regions are hot and

humid while the high altitude regions are cooler. In this study we analyzed the transcriptome

response of high altitude (Addis Ababa) and low altitude chickens (Awash) to heat stress

(HS) conditions that are prevalent in the low altitude regions. The chickens were free ranged

for 20 hours in an enclosure in Awash, and then the heart, breast muscle and spleen tissues

were collected at 6AM, 12PM and 6PM to follow a daily circadian cycle. Through RNA-seq

analysis, we identified differentially expressed genes (DEGs) to be significant (q < 0.05).

These DEGs were subjected to Protein-Protein interaction (PPI) network and Gene Co-

Expression network (GCN) analysis to understand their role. KEGG pathway analysis and

GO analysis of all the identified DEGs and the genes identified from the PPI network

analysis and GCN analysis revealed that several immune related pathways such as

Proteasome, Focal adhesion, Influenza A, ErbB signaling pathway and Glycerophospholipid

metabolism were enriched in response to HS. These results suggest that the high altitude

chicken was under HS and might be immunologically susceptible. Our findings will help in

developing a genetic approach to mitigate production loss due to HS.

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Introduction

Ethiopia has an ecologically diverse climatic condition. The country's ecologically

diverse region is roughly divided into two major parts. The highlands are at an altitude of

over 1,500 meters above mean sea level (MSL) and have a climate which is generally

considerably cooler and less humid, while the lowlands are at an altitude of less than 1,500

meters above MSL and have a climate which is generally hot and humid.

Environmental changes have a significant effect on growth, development,

physiology, immunity, productivity and might eventually result in the death of the animals

(Renaudeau et al. 2012) Especially, temperature change causes deleterious effect on domestic

animals. Temperatures above normal are felt as heat stress (HS) by all living organisms

(Fuquay 1981; Phillips & Piggins 1992; Kotak et al. 2007). HS can have significant effects

on most aspects of reproductive function in mammals (Fuquay 1981). In addition, HS has a

serious effect on maintaining the thermophilicity of the domestic ruminants such as cattle,

sheep and goats as well as has a high impact on productivity (Lu 1989; Silanikove 2000;

West 2003; Marai et al. 2007), particularly in the tropical belt and arid areas (Silanikove

1992). HS in chicken causes specific behavioral and physiological responses (Harrison &

Biellier 1969; Altan et al. 2003). In laying hens, HS is noxious to body weight and egg

production, weight and shell quality, and also leads to decrease in feed intake resulting in a

decline in the productivity (EMERY et al. 1984; Scott & Balnave 1988; Muiruri & Harrison

1991; Mahmoud et al. 1996; Balnave & Muheereza 1997). Moreover, HS increased the

percentage of mortality which could be due to inhibition of immune responses (Mashaly et

al. 2004). In broiler chicken, HS causes various metabolic and physiological changes such as

increased body temperature, panting and respiratory alkalosis (Deyhim & Teeter 1991; Lin et

al. 2006) as well as having serious effects on the growth rate, body weight gain, feed

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consumption and mortality rate (HAN & BAKER 1993; Al-Fataftah & Abu-Dieyeh 2007).

Because of the resulting poor growth performance, immunosuppression, and high mortality

HS in poultry industry in poor tropical countries is a major concern (Bottje & Harrison 1985;

Young 1990; Yahav et al. 1995).

Generations of specialized selection in commercial chicken breeds and exposure to

only controlled environment have made them susceptible to environmental extremes

(McMichael et al. 2007; Berry et al. 2011; Ciscar et al. 2011; Kantanen et al. 2015) and this

will be a hindrance for the expansion of the poultry industry to regions such as Africa where

the local environment are considerably different to the environment where the commercial

chickens were selected, and might lead to significant environmental stresses (Canario et al.

2013; Lawrence & Wall 2014; Loyau et al. 2014; Rothschild & Plastow 2014; Fleming et al.

2017). Due to increasing population and prosperity the demand for quality poultry products

will increase in regions where climatic changes have made environment undesirable to

commercial livestocks, which lacks the genetic potential to adapt (McMichael et al. 2007;

Thornton et al. 2009; Rothschild & Plastow 2014; Fleming et al. 2017). Fleming et al., 2017

suggested that in order to mitigate the impact of environmental stresses through genetic

approaches, it is necessary to analyze livestock species that have evolved under those

environments.

Our objective was to understand the response of two Ethiopian indigenous chickens

from low and high altitudes to HS conditions prevalent in the low altitude environment, by

profiling the transcriptome of three important tissues (heart, breast muscle and spleen), before

(Morning), during (Afternoon) and after (Evening) hot day time temperature . It has been

reported that a 1 to 1.5°C increase in temperature during incubation of eggs can induce an

adaptive response (Moraes et al. 2003; Janke et al. 2004; Loyau et al. 2014; Loyau et al.

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2015; Loyau et al. 2016; Fleming et al. 2017), based on this we hypothesized that the high

altitude chickens that were hatched and raised at relatively lower temperature will be

relatively more affected by HS due to the increased temperature prevalent in the low altitude

regions. transcriptomes. We identified differentially expressed genes (DEGs) for each tissue

at each time point and identified hub genes that are regulated in response to HS. We then

focused on identifying genes molecular functions and biological processes in the heart, breast

muscle and spleen tissues of the treatment high altitude chicken group under the HS

condition.

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Materials and Methods

Experimental Ethiopia chicken and care

Eighteen chickens obtained from Ethiopian hatcheries (Awash, low altitude (950 m

above sea level, 370C in summer) (n = 9); Addis Ababa, high altitude (2400 m above sea

level, 160C in summer) (n = 9)) were used in this study. The care and experimental use of the

chickens were reviewed and approved by the Institutional Animal Care and Use Committee at

International Livestock Research Institute (ILRI, Ethiopia) (IACUC No.: 2016-216). All

animal experiments were performed in accordance with the relevant guidelines and

regulations in either the National Institute of Animal Science in South Korea or at ILRI.

Experimental design and sample collection.

A total of 18 Ethiopian chickens (Sex: male, Body weight: average 1.0 kg, Age: 5~6

months) were sampled from the two groups, high altitude (Addis Ababa) chickens and low

altitude (Awash) chickens. The birds had ad-libitum access to water and feed. Experimental

environment such as temperature and humidity and samples such as body weight (before and

after heat stress) and body temperature was recorded (Table 3 and S1). The experiments were

carried out at Awash (low altitude). The chickens were housed in individual cages throughout

the experimental period. The animals were kept in the cages one day prior to the start of the

experiment for acclimation. The experiments began at 6AM the next day with the first set of

samples being collected; samples were collected at 6 hour interval (at 12 PM and 6 PM).

Heart, breast muscle and spleen tissue samples were collected at the aforementioned time

points, six chickens (n=3 per line) were sacrificed at each time point. A total of 54 samples

were collected, and they were immediately frozen in liquid nitrogen, and stored in a freezer at

-80°C until further use.

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RNA sequencing and library preparation

Total RNA was isolated from all the heart, breast muscle and spleen tissues (50–100

mg) collected using TRIzol (Invitrogen, USA) reagent following the manufactures

instructions. The quantity and quality of the isolated RNAs were checked using a 2100

Bioanalyzer and RNA 6000 Nano Labchip kit (Agilent Technologies, USA) and only samples

that had RIN values of ≥ 8 were used for the construction of the RNA-seq library. For RNA-

seq, the Illumina TruSeq® RNA Sample Pre Kit was used following the manufacturer’s

guidelines. Agilent Technologies Human UHR total RNA was used as a positive control

sample. The library was constructed according to the standard protocol provided by Illumina,

Inc. Libraries with different indexes were pooled and sequenced in one lane using an Illumina

HiSeq2000 high-throughput sequencing instrument with 100 paired-end reads. Total RNA-

seq data have been deposited in the Gene Expression Omnibus (GEO) database (accession

number GSE119387).

Alignment of raw reads to the chicken genome.

After RNA sequencing (RNA-seq), the reads were trimmed to remove the adapter,

the low quality sequence, and reads less than 80 base pairs (bp), using the Trimmomatic ver.

0.36 tool (Bolger et al. 2014). Subsequently, using Hisat2 ver. 2.0.5 alignment (Kim et al.

2015), the reads were aligned with the chicken reference genome (gallus gallus) that was

downloaded from the Ensembl website

(ftp://ftp.ensembl.org/pub/release-89/fasta/gallus_gallus/dna/). When using Hisat2 alignment,

we basically used the default options, but we added the --dta-cufflinks option to tailor the

alignments for subsequent use with Cufflinks.

Differentially expressed genes analysis

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Differentially expressed genes (DEGs) were identified by Cufflinks ver. 2.2.1

(Trapnell et al. 2012). We used the Cufflinks tool, following the default options, and added

the -G/--GTF flags, which quantitate against reference transcript annotations. Next, Cuffdiff

in the Cufflinks package was used to find significant changes in gene expression. Fragments

per kilobase of exon per million fragments mapped (FPKM) of each sample were counted to

estimate the expression levels of the transcripts. Cuffdiff was used to estimate the differential

expression of transcripts across condition and identify significant changes in gene expression.

CummeRbund (Goff et al. 2012) R package was used for visualization and exploration of

cuffdiff results.

Functional annotation clustering, gene co-expression and protein/protein interaction

network analysis.

First chicken gene IDs (Ensembl) without official gene symbol, were converted to its

orthologous human official gene symbol. These official gene symbols were used for

functional clustering and enrichment analyses by DAVID, a web based GeneOntology (GO)

tool (Dennis et al. 2003) with Expression Analysis Systematic Explorer (EASE) (Hosack et

al. 2003). To identify enriched GO term and KEGG pathways, functionally clustered genes

were filtered by EASE value < 0.1. We summarized the GO term information for each of the

nine groups, into a reduced amount of GOslim using the online tool REVIGO (Supek et al.

2011). To identify the gene expression patterns and clustering in each DEGs group, we used

the MultiExperiment Viewer (MeV) tool (Howe et al. 2011), k-means clustering and figure of

merit (FOM). We performed the gene co-expression network analysis using the partial

correlation coefficient with information theory (PCIT) algorithm (Watson-Haigh et al. 2009).

Only those significant DEGs (n=1069) that had a stringent significant level in at least one of

the nine groups were employed, to enhance the efficiency of network construction of co-

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expressed genes. The gene co-expression network was visualized with Cytoscape ver. 3.4.1

(Shannon et al. 2003). The analysis only included genes with significant partial correlation |r|

≥ 0.99. We also performed the protein-protein interaction network analysis using STRING

database following the default options (Szklarczyk et al. 2014).

Quantitative real-time PCR analysis

cDNA was synthesized using the SuperscriptTM II RT-PCR System (Invitrogen,

Karlsruhe, Germany) according to the manufacture’s recommendations for oligo(dT)20

primed cDNA-synthesis. cDNA synthesis was performed on 500ng of RNA, at 42°C Finally,

cDNA was diluted 1:2 prior to use for qPCR analysis. PCR was performed on an ABI PRISM

7900HT Sequence Detection System (Applied Biosystems, Foster City, Calif., U.S.A.) in

384-well microtiter plates at a final volume of 10ul. Optimum reaction conditions were

obtained with 5ul of Universal Master Mix (Applied Biosystems, Foster City, Calif., U.S.A.)

containing dNUTPs, MgCl2, reaction buffer and AmpliTaq Gold RNA polymerase, 90 nM of

primer(s) and 250nM fluorescence -labeled TaqMan probe (Schmittgen 2001). Finally, 2ul

template cDNA was added to the reaction mixture. The primer/TaqMan probe combinations

were optimized for each target sequence. Amplifications were performed, starting with a

10min template denaturation step at 95°C, followed by 40 cycles at 95°C for 15 s and 60°C

for 1min. All samples were amplified on triplicates and data were analyzed with Sequence

Detector software (Applied Biosystems). The gene expression fold change was calculated

after normalization to chicken glyceraldehyde-3-phosphate dehydrogenase gene (GAPDH),

and using the 2-ΔΔCt method (Schmittgen & Livak 2008). Primers are listed in Table S2

(GAPDH and FOS gene: TaqMan® Gene Expression Assays, Thermo Fisher Scientific).

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Results and Discussion

Overview of mRNA libraries

To identify the different transcriptome changes between the two Ethiopian chickens

in the heart, breast muscle and spleen in response to heat stress (HS), we produced 54 cDNA

libraries that included the low altitude chicken group (Control) and high altitude chicken

group (Treatment) at 3 time points from 3 tissues. Thus for each group we had 3 biological

repeats. The libraries were sequenced using Illumina Hiseq 2000 sequencing platform, 33.4,

30.3 and 35.3 million raw reads were generated from heart, breast muscle and spleen tissues,

respectively (Table 1). After trimming for removing the adapters, low-quality and N-based

reads, over 99% of the sequence reads survived for each sample. Then, these reads were

aligned to the chicken reference genome. The results showed that the alignment rate were

91~94%, 63~89% and 89~93% for heart, breast muscle and spleen, respectively (Table 1).

Especially in breast muscle, which had a lower alignment rate than other tissues, two high

altitudes (H8, H5) and one low altitude (L8) samples had alignment rate less than 80%. This

result might be due to sample specific sequencing errors, but we did not exclude these

samples because this alignment rate is sufficient for further analysis (Zhang et al. 2013; Yang

et al. 2016; Zambonelli et al. 2016). The reads mapped to each gene were calculated and

normalized to FPKM to estimate the gene expression levels. Table 2 shows the FPKM

interval for all genes for each group. Of the 18 groups (from 2 regions at 3 time points from 3

tissues), 34.80–48.86% of the gene had FPKM > 1. Multi-dimensional scaling (MDS; Figure

1a and 1b) and Principal component analysis (PCA; Figure 1c) showed that the samples

grouped according to tissues, and the ecotype of the chickens (low and high altitudes).

However, we found that in some condition like Muscle morning (Figure 1b) the high and low

altitude chickens did not separate perfectly. Overall, we conclude that there is sufficient

variation between samples and condition to identify DEGs.

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Differentially expressed genes (DEGs) between low and high altitude Ethiopian indigenous chickens breeds under low altitude climatic conditions

After aligning the RNA-seq data to the chicken genome, Cuffdiff within

Cufflinks was used to identify DEGs between low and high altitude chickens under low

altitude climate (hot and humid environment), at 3 time points from 3 tissues (Table S3),

Genes that had a false discovery rate (FDR) adjusted q-value less than 0.05 were considered

significantly differentially expressed (Figure S1). In all 358, 1087 and 742 significant DEGs

were identified from heart, breast muscle and spleen tissues, respectively. In addition, 28, 24

and 14 DEGs were identified to be significantly differentially expressed at all 3 time points in

heart, breast muscle and spleen, respectively (Figure 1d). We did not identify any genes to be

significantly differentially expressed in all three tissues, however we found 2 DEGs S100

Calcium Binding Protein A8 (S100A8) associated with immune and inflammation response

(Hwang et al. 2013) and Cytidine/Uridine Monophosphate Kinase 2 (CMPK2) involved in

terminal differentiation of monocytic cells and regulation of immune response to various

virus infections (Ma et al. 2016; Zhang & Cao 2016; Zhang et al. 2017) to be significantly

differentially expressed between heart and breast muscle and heart and spleen respectively.

CMPK2 is also correlated with macrophage activation and inflammatory response. All the 9

significant DEGs sets identified in this study are included in the supplementary data 1.

Functional annotation clustering, gene co-expression and protein/protein interaction network analysis

1) Functional Annotation Clustering

We assumed that the significant DEGs identified above represent candidate genes

involved in regulation of Heat stress (HS). To further elucidate the functional roles of these

significant DEGs under HS, we performed GO enrichment and KEGG pathway enrichment

analysis using DAVID web tool. Supplementary data 2 shows the number of significant GO’s

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and KEGG pathways at each time point in each tissue. In the list of the significant KEGG

pathways (Figure 2a), several heat stresses related immune response pathways were enriched.

For example, “Proteasome” is the most significantly enriched pathway in muscle evening

dataset, and plays a role in response to cellular stress caused by heat shock, infection or

oxidative damage. “Focal adhesion” enriched in muscle morning dataset plays an important

role in the immune system, in which white blood cells migrate along the connective

endothelium following cellular signals to damaged biological tissue. “Influenza A” enriched

in heart evening dataset, is directly related to innate immunity due to heat stress (Jin et al.

2011).

2) Co-Expression Network Analysis

We performed a co-expression network analysis using a modified version of PCIT,

that computes the co-expression correlation using all the DEGs datasets, and then used MeV

tool to check the expression clustering pattern of the co-expressed genes. Cytoscape was used

to visualize the PCIT analysis results, it resulted in two networks (Figure 2b). We named

them the “muscle specific gene expression network (MSGEN)” and the “Time specific gene

expression network (TSGEN)” based upon the expression pattern of the genes in the network

(Genes were up or down regulated based on tissue in MSGEN while it was regulated based

on Time in TSGEN). The MSGEN which included 435 DEGs were clustered into 3 groups by

the K-means clustering algorithm in MeV, while the 58 DEGs in the TSGEN network were

grouped into 2 clusters (Figure 2b). In the MSGEN network, the most significant KEGG

pathway was “ErbB signaling pathway” containing 5 DEGs, “Proteasome” containing 11

DEGs and “Glycerophospholipid metabolism” containing 2 DEGs in first, second and third

clusters, respectively. “ErbB signaling pathway” was also found to be associated with heat

stress in duck (Kim et al. 2017). “Glycerophospholipid metabolism” a lipid metabolism

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pathways, has an important role in maintaining the energy homeostasis in thermoregulatory

responses and is a fundamental biological process in the immune system under heat stress

(Renaudeau et al. 2012; Das et al. 2016). In the TSGEN network, “Influenza A” was the most

significant KEGG pathway in the first and second clusters and included 2 and 6 DEGs,

respectively. This pathway is directly related to avian immune response under heat stress.

(Qureshi et al. 2000; Hu et al. 2007; Jin et al. 2011; Morera & MacKenzie 2011).

3) HS specific HUB gene identification

To identify the hub genes from DEGs that responded to HS, we constructed protein-

protein interaction (PPI) network of the 28, 24 and 14 common DEGs identified in the heart

muscle, breast muscle and spleen tissues (Figure 1d) respectively using STRING (Figure 3a).

The genes were grouped into one expression set and subjected to K-Means clustering with

MeV. Two clusters were formed according to the gene expression patterns, the genes in each

of these clusters were used to construct the PPI network (Figure 3a). The hub genes were

“GADD45B”, “S100A8”. “FOS”, “CEBPD”, “CBFB”, “SAT1”, “MPP1” and “F8” in cluster

1, and “NMI”, “USP18”, “CMPK2”, “IFI27L2” and “ENSGALG00000023821” in cluster 2 .

In our results, most of hub genes were related with heat stress or immune response. Among

these hub genes, N-myc and Stat interactor (NMI) was recently identified to interact with the

Hsp105β-binding protein that enhances Heat shock proteins 70 (Hsp70) promoter activity

through Stat3 signaling pathway (Saito et al. 2014). Fos Proto-Oncogene and AP-1

Transcription Factor Subunit (FOS) are also related to the Hsp70 gene (He et al. 2000) and is

associated with heat shock (Sonna et al. 2002). Hsp70 gene expression is further induced

under stress conditions such as heat shock (Mosser et al. 1997). In addition,

Spermidine/Spermine N1-Acetyltransferase 1 (SAT1) interacts with Heat shock proteins 10

(Hsp10) that inhibits lipopolysaccharide-induced inflammatory mediator production and

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interacts with the Hsp70 gene (Johnson et al. 2005; Czarnecka et al. 2006). Membrane

Palmitoylated Protein 1 (MPP1) interacts with the cytoskeleton and regulates cell

proliferation, signaling pathways, and intercellular junctions. This gene is required for

maintenance of the apical protein complex and adherens junctions and for stratification and

proper migration of neurons during the development of the cortex (Dudok et al. 2013). Core-

Binding Factor Beta Subunit (CBFB) play essential roles in skeletal development (Komori

2003). Moreover, two genes, MPP1 and CBFB genes are associated with temperature

response under heat stress in fish and chicken, respectively (Smith et al. 2013; Tu et al. 2016;

Gates et al. 2017). CCAAT/Enhancer Binding Protein Delta (CEBPD) is an important

transcriptional activator in the regulation of genes involved in immune and inflammatory

responses and is also associated with heat stress in cattle and fish (Kassahn et al. 2007; Kolli

2012). It has been reported that the Ubiquitin Specific Peptidase 18 (USP18) is involved in

cytokine signaling within the immune system, which is implicated in heat stress response in

chicken and catfish (Kaltenboeck & Liu 2013; Kamineni 2015). Growth Arrest and DNA

Damage Inducible Beta (GADD45B) gene respond to various environmental and genotoxic

stresses by mediating activation of the p38/JNK pathway that control the balance of

apoptosis. The function of this gene is related to the regulation of growth and apoptosis

(Zumbrun et al. 2009). Moreover, this gene can function cooperatively in inhibiting cell

growth and overexpression of GADD45B inhibited avian leukosis virus subgroup J

replication in chicken (Zhang et al. 2016). In our study, this gene is down-regulated in the

high altitude chicken, suggesting that heat stress can negatively affect the immune response

mechanism of high altitude chicken making them susceptible to various pathogens. Interferon

Alpha Inducible Protein 27 Lee 2 (IFI27L2) and Coagulation Factor VII (F8) is immune-

related gene that responds to inflammation in a variety of routs (Chen et al. 2011;

Tassanakajon et al. 2013; Monson et al. 2015). These results suggest that most of the hub

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genes are directly or indirectly related to heat stress and immune response.

4) Validation of hub genes expression

We selected 2 hub DEGs: GADD45B which is involved in immune response and

FOS which is a heat shock responsive gene from the 13 hub genes identified (Figure 3a) to

verify their heat specific response. We validated the expression of these two genes by qRT-

PCR on a different group of chickens from high (Naivasha) and low altitude (Mombasa)

region of Kenya. Unlike this study the Kenyan chickens were subjected to targeted heat stress

at 35°C for five hours (Treatment group, n = 4 each) and another control group (n = 4 each)

was maintained at room temperature. We checked the expression of the two selected hub

genes by qRT-PCR and compared them with the Ethiopian chickens RNAseq expression. The

expression pattern was found to be similar (Figure 3b). This, biological and technical

validation, confirmed that our RNA-seq analysis was successfully and accurately performed.

This confirms that these genes may be directly regulated by HS.

In summary, we analyzed the response of high altitude chickens (Treatment) relative

to the low altitude chickens (Control), to the high temperature found in the lowland area of

Ethiopia at three time points (Morning, Afternoon and Evening). Though the body

temperature between the low and high altitude chickens did not differ significantly, the

transcriptome results suggest that the high altitude chickens suffered significant heat stress

which was evident from the activation of many heat stress related genes suggesting the that

the animal was struggling to maintain its core body temperature, moreover there was also

activation of many immune related pathways, under heat stress condition. The various genes

identified as hub genes and the genes in the various pathway found to be significantly

enriched can be good candidates for further targeted studies for improving thermal tolerance

in the high altitude chicken group and for developing genetic strategies to raise thermo

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tolerant commercial chickens. 1

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Acknowledgements

This study was carried out with the support of "Cooperative Research Program for

Agriculture Science & Technology Development (Project title: Biomarker investigation for

livestock animal productivity through combinatory analysis with big-data related to genetic

elements affecting economic traits, Project No. PJ01206301)" Rural Development

Administration, Republic of Korea. And this study was supported by the Chung-Ang

University Research Grants 2018.

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Figures

Figure 1. Comparative analysis of RNA-seq data from the Ethiopia chickens breeds: a)

clustering of each tissue by MDS plot. b) Clustering of each group by MDS plot. c) PCA plot

using each group. d )Venn diagram of number of DEGs

Figure 2. visualization of fucntional anotation clcustering , gene co-expressiong pattern

and network analysis: a)histogram of KEGG pathway using all of DEGs set. b) Summary of

co-expressiong network set by using PCIT, MeV, DAVID and RAVIGO tools

Figure 3. Gene co-expressiong and protein-protein interaction network analysis using

common significant DEGs set and validation using qRT-PCR: a) common significant

DEGs list among 3 time points each tissue (heart, breast muscle and spleen), expression

pattern and PPI network of these DEGs. b) Validation of GADD45B and FOS gene using

qRT-PCR

Supplementary Figure 1. Volcano plot of statistical significance against fold change

between low land and high land in each time point (6 AM, 12 PM and 6 PM) in each

tissue (heart, breast muscle and spleen): FDR < 0.05 and FC >2

Supplementary Figure 2. Figure of merit (FOM) vs. no. of clusters graph for the k-

means cluster algorithm: a) hub genes in muscle specific gene expression network

(MSGEN). b) Hub genes in time specific gene expression network (TSGEN)

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Table1

Table 1. Summary of sequencing reads, trimming and alignment to chicken genome

Treatments samples

Tissues

Heart Muscle Spleen

Region Time Read

Pairs Both Surviving Dropped Alignment rate

Read Pairs Both Surviving Dropped Alignmen

t rateRead Pairs Both Surviving Dropped Alignmen

t rate

High

Morning

H1 17958469 17853340 (99.41%) 16697 (0.09%) 94.25% 13783476 13701710 (99.41%) 16101 (0.12%) 89.80% 20861756 20751692 (99.47%) 15197 (0.07%) 92.27%

H2 20074349 19950961 (99.39%) 26136 (0.13%) 94.45% 16786683 16702826 (99.50%) 17321 (0.10%) 85.94% 20011316 19921424 (99.55%) 11131 (0.06%) 93.19%

H3 17771440 17677553 (99.47%) 18540 (0.10%) 93.69% 16922650 16839018 (99.51%) 18499 (0.11%) 88.35% 19687566 19583243 (99.47%) 14415 (0.07%) 91.98%

Afternoon

H4 16847831 16766718 (99.52%) 20177 (0.12%) 92.56% 17680967 17576733 (99.41%) 28568 (0.16%) 89.13% 19378320 19297499 (99.58%) 14460 (0.07%) 93.44%

H5 17688433 17601181 (99.51%) 20949 (0.12%) 93.35% 15919624 15837059 (99.48%) 14818 (0.09%) 76.85% 18228740 18173664 (99.70%) 9663 (0.05%) 93.05%

H6 19275793 19169791 (99.45%) 25696 (0.13%) 93.10% 18305878 18199521 (99.42%) 24332 (0.13%) 85.91% 19111735 19034789 (99.60%) 14811 (0.08%) 92.88%

Evening

H7 17878977 17781619 (99.46%) 18803 (0.11%) 92.81% 17576723 17454881 (99.31%) 27360 (0.16%) 88.37% 18288860 18202579 (99.53%) 16133 (0.09%) 90.33%

H8 17537522 17435188 (99.42%) 23641 (0.13%) 93.06% 18623366 18493810 (99.30%) 32228 (0.17%) 63.76% 18480711 18393343 (99.53%) 16038 (0.09%) 89.59%

H9 17489193 17341467 (99.16%) 28946 (0.17%) 92.94% 14215825 14116355 (99.30%) 26307 (0.19%) 86.90% 18663086 18578346 (99.55%) 14764 (0.08%) 92.18%

Low

Morning

L1 19096206 19017566 (99.59%) 15166 (0.08%) 94.59% 16770900 16676742 (99.44%) 22070 (0.13%) 87.29% 20610548 20507242 (99.50%) 18059 (0.09%) 92.35%

L2 19278470 19188655 (99.53%) 17204 (0.09%) 94.38% 17579681 17476464 (99.41%) 31084 (0.18%) 86.67% 19253542 19157697 (99.50%) 21241 (0.11%) 92.29%

L3 18066129 17992655 (99.59%) 18257 (0.10%) 94.27% 16132907 16048310 (99.48%) 18343 (0.11%) 86.62% 20263333 20155792 (99.47%) 18443 (0.09%) 92.72%

Afternoon

L4 17156458 17073666 (99.52%) 17914 (0.10%) 91.83% 16171322 16081700 (99.45%) 18829 (0.12%) 87.30% 19953492 19852022 (99.49%) 15706 (0.08%) 91.99%

L5 20029142 19934953 (99.53%) 16364 (0.08%) 94.50% 18198714 18097770 (99.45%) 24095 (0.13%) 88.28% 19353480 19258969 (99.51%) 15603 (0.08%) 92.64%

L6 19904978 19805211 (99.50%) 20470 (0.10%) 94.36% 16835237 16722829 (99.33%) 30192 (0.18%) 87.53% 19165569 19083123 (99.57%) 16224 (0.08%) 92.22%

Evening

L7 19404574 19311034 (99.52%) 23031 (0.12%) 94.18% 17505136 17402668 (99.41%) 20885 (0.12%) 87.76% 20729401 20624211 (99.49%) 21039 (0.10%) 91.67%

L8 20011420 19902757 (99.46%) 24044 (0.12%) 94.11% 17321111 17212505 (99.37%) 22717 (0.13%) 78.65% 18662492 18579381 (99.55%) 17758 (0.10%) 92.44%

L9 19137073 19022815 (99.40%) 26263 (0.14%) 93.86% 16619096 16518472 (99.39%) 29002 (0.17%) 84.94% 22548322 22439143 (99.52%) 21314 (0.09%) 93.00%

1

2

3

Table 2. FPKM interval and the total genes of each group

Tissues Timelines

Regions

FPKM Interval

0~1 1~5 5~15 15~60 60<

Heart

MorningLow 13637(56.21%

)3695(15.23%

)2990(12.32%

)2602(10.73%

)1336(5.51%

)

High 13794(56.85%)

3710(15.29%)

2862(11.80%)

2543(10.48%)

1355(5.58%)

AfterLow 13693(56.43%

)3621(14.92%

)3120(12.86%

)2605(10.74%

)1225(5.04%

)

High 13873(57.16%)

3608(14.87%)

2752(11.34%)

2549(10.51%)

1482(6.11%)

EveningLow 13311(54.86%

)3457(14.25%

)3238(13.34%

)2874(11.84%

)1384(5.70%

)

High 13292(54.78%)

3540(14.59%)

3291(13.56%)

2814(11.60%)

1327(5.47%)

Muscle

MorningLow 15368(63.33%

)3237(13.34%

) 2270(9.35%) 2132(8.79%) 1257(5.18%)

High 15591(64.26%)

3147(12.97%) 2158(8.89%) 2059(8.49%) 1309(5.39%

)

AfterLow 15820(65.20%

)3052(12.58%

) 2119(8.73%) 1970(8.12%) 1303(5.37%)

High 15751(64.92%)

3197(13.18%) 2219(9.15%) 1981(8.16%) 1116(4.60%

)

EveningLow 15414(63.53%

)3058(12.60%

) 2188(9.02%) 2205(9.09%) 1399(5.77%)

High 15321(63.14%)

3145(12.96%) 2237(9.22%) 2236(9.22%) 1325(5.46%

)

Spleen

MorningLow 12861(53.00%

)2950(12.16%

)2998(12.36%

)3685(15.19%

)1770(7.29%

)

High 12825(52.86%)

2907(11.98%)

3078(12.69%)

3668(15.12%)

1786(7.36%)

AfterLow 12707(52.37%

)2827(11.65%

)3001(12.37%

)3927(16.18%

)1802(7.43%

)

High 12646(52.12%)

2913(12.01%)

3104(12.79%)

3809(15.70%)

1792(7.39%)

EveningLow 12414(51.16%

)2921(12.04%

)3102(12.78%

)3991(16.45%

)1836(7.57%

)

High 12408(51.14%)

2822(11.63%)

3133(12.91%)

4112(16.95%)

1789(7.37%)

1

2

3

4

Table 3. Body Weight and temperature with High and low land chicken

Region Time ID Tube code

Body weight before Heat stress (kg)

Body weight after Heat stress (kg)

Body temperatur

e ( )℃

High land (Addis Ababa)

6 AM1074 H1 1.1 1.05 41980 H2 1.09 1.06 41156 H3 1.12 1.12 41

12 PM884 H4 1.07 1.05 42

1669 H5 1.07 1.03 421175 H6 1.16 1.14 42

6 PM1775 H7 1.08 1.06 421422 H8 1.03 9.96 4249 H9 1.07 1.16 41

low land (Awash)

6 AM1933 L1 1.3 1.31 41789 L2 1.0 1.06 40

1087 L3 1.32 1.25 41

12 PM1226 L4 1.15 1.12 421519 L5 1.06 1.06 41704 L6 1.2 1.18 41

6 PM805 L7 1.18 1.18 41

1048 L8 1.0 1.03 4243 L9 1.24 1.28 39

1

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