Gene 546 (2014) 200–205

Contents lists available at ScienceDirect

Gene journal homepage: www.elsevier.com/locate/gene

Exploring the molecular mechanism of acute heat stress exposure in broiler chickens using gene expression profiling Q.B. Luo a,b, X.Y. Song b, C.L. Ji c, X.Q. Zhang a,b, D.X. Zhang a,c,⁎ a b c

College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, South China Agricultural University, Guangzhou 510642, Guangdong, China Guangdong Wens Food Group Limited Company, Xinxing 527439, Guangdong, China

a r t i c l e

i n f o

Article history: Received 28 April 2014 Received in revised form 29 May 2014 Accepted 9 June 2014 Available online 13 June 2014 Keywords: Chicken Heat-stress response Gene expression Gene module

a b s t r a c t The process of heat regulation is complex and its exact molecular mechanism is not fully understood. In this study, to investigate the global gene regulation response to acute heat exposure, gene microarrays were exploited to analyze the effects of heat stress on three tissues (brain, liver, leg muscle) of the yellow broiler chicken (Gallus gallus). We detected 166 differentially expressed genes (DEGs) in the brain, 219 in the leg muscle and 317 in the liver. Six of these genes were differentially expressed in all three tissues and were validated by qRT-PCR, and included heat shock protein genes (HSPH1, HSP25), apoptosis-related genes (RB1CC1, BAG3), a cell proliferation and differentiation-related gene (ID1) and the hunger and energy metabolism related gene (PDK). All these genes might be important factors in chickens suffering from heat stress. We constructed gene co-expression networks using the DEGs of the brain, leg muscle and liver and two, four and two gene co-expression modules were identified in these tissues, respectively. Functional enrichment of these gene modules revealed that various functional clusters were related to the effects of heat stress, including those for cytoskeleton, extracellular space, ion binding and energy metabolism. We concluded that these genes and functional clusters might be important factors in chickens under acute heat stress. Further in-depth research on the newly discovered heat-related genes and functional clusters is required to fully understand their molecular functions in thermoregulation. © 2014 Elsevier B.V. All rights reserved.

1. Introduction The yellow broiler chicken (Gallus gallus), an important animal in both agricultural and biomedical research, can maintain its body temperature within a narrow range through physiological self-regulation. Nevertheless, excessive heat stress beyond this range may lead to serious damage or even death (Halevy et al., 2001). This can cause huge economic loss to chicken farms (Rozenboim et al., 2007; St-Pierre et al., 2003). Moreover, as global warming worsens and poultry production is increasingly centralized, the problem of heat stress for poultry has become more severe. At present, adjusting diet and cooling by ventilation are used in poultry production to relieve the effects of heat stress (Lin et al., 2006). Recently, heat stress has been shown to change metabolism, affect the immune system, and consequently decrease growth and layer performance (Donkoh, 1989; Mashaly et al., 2004; Wallis and Balnave, 1984). However, heat regulation is a complicated process and its exact molecular mechanism is not fully understood.

Abbreviations: DEGs, differentially expressed genes; HSPs, heat shock proteins; DAVID, the Database for Annotation, Visualization and Integrated Discovery; GO, gene ontology. ⁎ Corresponding author at: College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China. E-mail address: [email protected] (D.X. Zhang).

http://dx.doi.org/10.1016/j.gene.2014.06.017 0378-1119/© 2014 Elsevier B.V. All rights reserved.

Microarray analysis has been used to explore variation of mRNA expression profiles of chickens under heat stress (Li et al., 2011; Wang et al., 2013). Using gene microarrays, Li et al. (2011) detected hundreds of differentially expressed genes (DEGs) in the breast muscle of chickens under heat stress. These DEGs were involved in mitogen-activated protein kinase, ubiquitin–proteasome and nuclear factor κB pathways. In this study, to investigate global gene profiles in response to acute heat exposure, we detected the global gene expression in the brain, liver and leg muscle of the yellow broiler chicken using affymetrix microarray technology. Gene co-expression network analysis was used to explore the molecular mechanism of the heat stress. 2. Materials and methods 2.1. Animals and treatments Twenty-four 16-day-old yellow broiler chickens (12 male and 12 female) were used in this study (purchased from Wens, GuangDong, China). The experiments were reviewed and approved by the Institutional Animal Care and Use Committee of South China Agricultural University (GuangDong, China). These chickens were randomized and divided into two experimental groups [control group (12) and treatment group (12)], with equal numbers of males and females in each

Q.B. Luo et al. / Gene 546 (2014) 200–205

group. The control group was kept at 28 °C ± 1 and 50% relative humidity (RH) for 3 h, and the treatment group at 40 °C ± 1 and 50% RH for 3 h to induce acute heat stress. All chickens were then killed and the brain, liver and leg muscle tissues were collected. The tissues were immediately frozen in liquid nitrogen and kept at −80 °C.

Table 1 Primers and annealing temperatures used for quantitative real-time PCR. Gene symbol

primers

Annealing temperature

Beta-actin

5′-CATGCCATCCTCCGTCTG-3′ 5′-AGGACTCCATACCCAAGAA-3′ 5′-TGCATTGTTTTGGGAACTATTTATA-3′ 5′-ACAGGAATCTGAAAGTTACCAAGGC-3′ 5′-AGCCCTAGACTCGGTGGA-3′ 5′-CTCCCTTGCTCTTGACTACC-3′ 5′-TCGTCGCTATGAAGGTCGC-3′ 5′-GCAGGTCCCAGATGTAGTCG-3′ 5′-GGGCTTCTAACAATCCAA-3′ 5′-TCCTCCACAGTTTCAGCNAA-3′ 5′-CATAATATACTTGAATACTTTGACC-3′ 5′-TGCAAGCACTATTACAGACATTCAT-3′ 5′-CCGTCTTCTGCTGAGAGGAGTG-3′ 5′-ACCGTTGTTCCGTCCCATCAC-3′

55–60 °C

HSPH1

2.2. RNA extraction BAG3

Three biological replicates for all tissues were prepared. Total RNA was isolated by Trizol reagent (Invitrogen, Breda, Netherlands) and purified using a Qiagen RNeasy Micro kit (Qiagen, Venlo, Netherlands). RNA quality was verified using an Agilent 2100 bioanalyzer (Agilent Technologies, Amsterdam, Netherlands).

ID1 RB1CC1 PDK4 HSP25

2.3. Microarray processing RNA labeling and microarray hybridization were carried out according to the Affymetrix Expression Analysis Technical Manual (Capitalbio, Beijing, China). The arrays were scanned using the Affymetrix Scanner 3000, and the raw data were submitted to the Gene Expression Omnibus database (GSE23592). The GeneChip Chicken Genome Array used in this study was created by Affymetrix (Santa Clara, CA, USA), and included over 38,000 probe sets representing comprehensive coverage of 32,773 transcripts corresponding to over 28,000 chicken genes (Chicken Genome Sequencing Consortium 2.1). Sequence information for this array is provided by the following public data sources: GenBank, UniGene and Ensembl. 2.4. Data preprocessing The normalization of raw data is needed to eliminate dye-related artifacts. Consecutive filtering procedures were performed to normalize the data, and remove noise derived from absent genes, background and non-specific hybridizations. Then, genes with the most significant differential expression (P b 0.05) were screened. DEGs between control and treatment chickens were identified using the significance analysis of microarrays (SAM) algorithm (change ≥ 2, Q values b 0.05). Gene co-expression networks were constructed using Pearson's correlation algorithm (|r| N 0.9, P b 0.01) by R language, and network visualization was performed with cytoscape software (http://www. cytoscape.org/). We classify independent gene co-expression networks with more than five genes as a gene module. The Database for Annotation, Visualization and Integrated Discovery (DAVID) (http://david.abcc. ncifcrf.gov/webcite) (Huang et al., 2009) was used for gene function enrichment analysis and functional annotation clustering. Enriched functional clusters were identified with Enrichment Scores N 1. Both gene ontology (GO) and KEGG pathways were incorporated in function enrichment analysis.

201

55 °C 60 °C 55 °C 55 °C 58 °C 58 °C

each RT-PCR run. The fold expression or repression of the target gene relative to the internal control gene, β-actin, was then calculated for each sample using the following formulas: Cttarget

gene –Ctbeta‐actin

ΔCt−ΔCtstandardized

Fold change ¼ 2

¼ ΔCt

value

−ΔΔCt

¼ ΔΔCt

:

ð1Þ

ð2Þ

ð3Þ

To be consistent with the microarray analysis, the cutoff value for DEGs was set at a two-fold change. 3. Results 3.1. Identification of DEGs in the brain, liver and leg muscle To investigate the effects of heat stress on global gene expression in chicken, we randomly chose six chickens from the two experimental

2.5. Validation of gene expression by qRT-PCR Six DEGs in response to heat stress were discovered by microarray analysis, including HSPH1, BAG3, ID1, HSP25 and PDK4. They were validated by quantitative (q) real-time (RT) polymerase chain reaction (PCR) assays using the same samples used for microarray analysis. Primers for the qRT-PCR and annealing temperatures are shown in Table 1. The qRT-PCR reactions were performed on a Roche LightCycler Instrument 1.5, using a LightCycler Fast Start DNA Master PLUS SYBR Green I kit (Roche Cat. 03515885001, Castle Hill, Australia). Briefly, 15 μL reactions: 7.5 μL Master Mix, 0.1 μL forward primer and reverse primer, 1 μL cDNA sample, and 6.3 μL ddH2O were prepared. Each sample was run in triplicate. The RT-PCR program was set to 95 °C for 5 min, and then 45 cycles of 95 °C for 10 s, 55–60 °C for 35 s, and 72 °C for 40 s. At the end of each program, a melting curve analysis was performed. Also, the data were automatically analyzed by the system, and an amplification plot was generated for each cDNA sample at the end of

Fig. 1. Venn diagram of the DEGs in the three tissues. Blue circle: brain; yellow circle: liver; green circle: leg muscle.

202

Q.B. Luo et al. / Gene 546 (2014) 200–205

groups (three chickens from the control group and three chickens from the treatment group that had suffered from heat stress for 3 h). We then collected the brain, liver and leg muscle tissues for gene expression analysis. We detected 166 DEGs in the brain, 219 in the leg muscle, and 317 in the liver (|log ratio| N 1; P b 0.05; Fig. 1; Table S1). There were 141, 167, and 261 genes differentially expressed specifically in the brain, leg muscle and liver, respectively (Fig. 1). As shown in Fig. 1, there were six genes that were differentially expressed in all three tissues, including PDK4, ID1, HSP25, HSPH1, RB1CC1 and BAG3. HSP25 and HSPH1 are heat shock proteins (HSPs) (Table 2), which are related to heat stress (Goldbaum et al., 2009; Hendrick and Hartl, 1993; Katoh et al., 2004; Samali and Orrenius, 1998; Vertii et al., 2006; Yamashita et al., 2007). They are known as biomarker proteins for the heat-stress response. RB1CC1 and BAG3 are related to the negative regulation of apoptosis and programmed cell death (Antoku et al., 2001; Chano et al., 2002; Doong et al., 2000). PDK4 belongs to the pyruvate

dehydrogenase kinase isozyme family, which is related to diabetes, hunger and energy metabolism (Wu et al., 1998, 1999, 2000). ID1, which belongs to the inhibitor of differentiation or inhibitor of DNA binding family, is related to cell proliferation and differentiation (Martinsen and Bronner-Fraser, 1998; Norton et al., 1998). 3.2. Gene co-expression networks and functional enrichment analysis for DEGs To gain understanding of the biological implications of the three DEG lists, we constructed co-expression networks (|r| N 0.9, P b 0.01). As shown in Fig. 2, two, four and two highly co-expressed gene networks (modules) were identified for the brain, liver and leg muscle, respectively. We then investigated the functional significance of the modules by performing functional enrichment and clustering analysis using DAVID (Huang et al., 2009). One of DAVID's features is functional

Fig. 2. Co-expression gene modules of the three tissues and enriched functional clusters in each module. Blue node: DEGs in the brain; green node: DEGs in the liver; red node: DEGs in the leg muscle; string: correlation relationship.

Q.B. Luo et al. / Gene 546 (2014) 200–205 Table 2 Expression levels of the six genes differentially expressed in all three tissues. Gene symbol

Gene name

Fold change Brain

HSPH1 BAG3 ID1 RB1CC1 PDK4 HSP25

Heat shock 105 kDa/110 kDa protein 1 BCL2-associated athanogene 3 Inhibitor of DNA binding 1, dominant negative HLH protein RB1-inducible coiled-coil 1 Pyruvate dehydrogenase kinase, isozyme 4 Heat shock protein 25

Liver

Table 4 Expression fold-changes of the six genes differentially expressed in the three examined tissues using microarray and qRT-PCR analyses. Fold change

Leg muscle

2.16

3.31

5.75

4.02 0.42

2.63 0.42

2.00 0.38

2.16 3.36

2.08 4.80

2.00 4.87

33.24

3.18

Brain

14.9

203

Liver Leg muscle

qRT-PCR Microarray qRT-PCR Microarray qRT-PCR Microarray

Gene HSPH1

BAG3

ID1

RB1CC1

PDK4

HSP25

3.14 2.16 4.37 3.31 4.85 5.75

3.03 4.02 4.41 2.63 2.11 2.00

0.41 0.42 0.23 0.42 0.30 0.38

2.58 2.16 2.69 2.08 2.82 2.00

4.58 3.36 4.42 4.80 4.25 4.87

12.34 14.90 22.48 33.24 5.23 3.18

genes involved in three clusters: enzyme inhibitor activity, extracellular space and ion binding (Fig. 2, Table S2). annotation clustering that places similar annotations with GO categories, based on parent/child GO term associations and the number of shared genes in a functional cluster. The GO cluster enrichment score is based on a geometric mean of a member's P-values and is used to rank their biological significance; therefore, DAVID reports the significance of enriched clusters. We used this feature to estimate the relationship between the GO terms and the KEGG pathways. For the two brain modules: module 1 contains seven genes involved in biological processes related to the muscle system such as muscle contraction, sarcomere, myofibril function, etc., with P-values ranging from 3.14E− 05 to 0.01987 and enriched up to 175.69-fold. DAVID joined these into a single functional cluster with an enrichment score of 3.09 (Tables 3, S2). Moreover, DAVID also identified a second GO cluster containing cytoskeleton, cytoskeletal components, actin cytoskeleton GO terms, etc. (P-values range from 0.003646 to 0.053466). Module 1 involved two clusters: muscle system and cytoskeleton (Fig. 2, Table S2). Similarly, brain module 2 contains 18 genes classified into two clusters: hemostasis and extracellular space (Fig. 2, Table S2). For the four liver modules: module 1 contains 18 genes classified into two clusters: neuron projection and ion binding (Fig. 2, Table S2). Module 2 contains 16 genes in one cluster, which is energy metabolism (Fig. 2, Table S2). Module 3 contains 12 genes involved in three clusters: muscle system, cytoskeleton and ion binding (Fig. 2, Table S2). Module 4 contains 12 genes involved in one cluster, which is cell fraction (Fig. 2, Table S2). For the two leg muscle modules: module 1 contains 14 genes that were not enriched in any functional cluster but were assigned to two GO terms: synapse and axon (Fig. 2, Table S2). Module 2 contains 50

3.3. Validation of representative DEGs To verify microarray data, six genes differentially expressed in all three tissues were validated with qRT-PCR. Fold change of target genes was normalized to the expression level of the housekeeping gene, β-actin. The mRNA levels of HSPH1, BAG3, PDK4 and HSP25 were increased under heat stress, which confirmed the results of the microarray analysis (Table 4). Both qRT-PCR and microarray analysis also revealed that ID1 was down-regulated under heat stress (Table 4). Moreover, as shown in Fig. 3, the correlation coefficients of the expression levels of the six genes between qRT-PCR and microarray were calculated using Pearson's correlation algorithm (brain: r = 0.91, P b 0.0325; liver: r = 0.91, P b 0.0414; leg muscle: r = 0.88, P b 0.0481). The results show high consistency between these two methods. 4. Discussion Gene co-expression network analysis has recently emerged as a new data analysis field that presents an opportunity to extract gene interactions from the large number of gene expression datasets (Stanley et al., 2013). This new system biological approach can complement traditional differential gene expression analysis. Hence, instead of exclusively defining differentially expressed genes, the identification of groups of highly co-expressed (CE) genes or gene modules may facilitate gene research in terms of regulation (Choi et al., 2005). In this study, we combined gene co-expression and gene differential expression to analyze gene microarray datasets, enabling us to explore genes with similar

Table 3 Enriched functional clusters and terms of brain module 1. Annotation cluster 1

Enrichment score: 3.09

Muscle system

Category

Term

P value

Fold enrichment

GOTERM_BP_FAT GOTERM_BP_FAT GOTERM_CC_FAT GOTERM_CC_FAT GOTERM_CC_FAT GOTERM_CC_FAT GOTERM_MF_FAT GOTERM_MF_FAT GOTERM_CC_FAT GOTERM_CC_FAT

Muscle contraction Muscle system process Sarcomere Contractile fiber part Myofibril Contractile fiber Actin binding Cytoskeletal protein binding Non-membrane-bounded organelle Intracellular non-membrane-bounded organelle

3.14E−05 3.96E−05 3.80E−04 4.58E−04 5.14E−04 5.44E−04 0.001441 0.003426 0.01987 0.01987

175.6969697 156.7027027 73.76129032 67.25294118 63.51666667 61.8 38.13461538 24.63354037 4.463836018 4.463836018

Annotation cluster 2

Enrichment score: 1.73

Cytoskeleton

Category

Term

P value

GOTERM_CC_FAT GOTERM_CC_FAT GOTERM_CC_FAT GOTERM_CC_FAT GOTERM_CC_FAT

Actin cytoskeleton Non-membrane-bounded organelle Intracellular non-membrane-bounded organelle Cytoskeletal part Cytoskeleton

0.003646 0.01987 0.01987 0.027657 0.053466

Fold enrichment 23.81875 4.463836018 4.463836018 8.406617647 5.923834197

204

Q.B. Luo et al. / Gene 546 (2014) 200–205

Fig. 3. Correlation of expression between microarray and qRT-PCR for the six genes differentially expressed in all three tissues.

expression patterns and reduce the false positive rate in the DEG lists (Eisen et al., 1998). We identified six genes differentially expressed in all three tissues in response to heat stress. HSPH1 is a biomarker for heat-stress response, and has a similar function to that of HSP70 and HSP90, that is to suppress apoptosis by negatively regulating the P38-MAPK pathway (Yamagishi et al., 2008; Yamashita et al., 2007). This indicates that during heat stress, the expression level of HSPH1 increases to negatively regulate cell death and maintain cell survival. HSP25, which also belongs to the HSPs' family, suppresses apoptosis and maintains cytoskeleton stabilization by preventing aggregation of protein folding intermediates (Goldbaum et al., 2009; Vertii et al., 2006). RB1CC1 and BAG3 are related to negative regulation of apoptosis and programmed cell death (Antoku et al., 2001; Chano et al., 2002; Doong et al., 2000). Jacobs and Marnett (2009) have revealed that BAG3 is HSF1-inducible and has a unique role in facilitating cancer cell survival during pro-apoptotic stress by stabilizing the level of Bcl-2 family proteins. We therefore suggest that HSF1 can increase the expression of BAG3 and maintain cell survival during heat stress. PDK, which belongs to pyruvate dehydrogenase kinase isozyme family, is related to cell proliferation and differentiation (Wu et al., 1998, 1999). PDK is a key enzyme for fatty acid oxidation, and high expression can reduce glucose oxidation activity (LeBlanc et al., 2007; Sugden, 2003). In brief, chickens will stop eating when exposed to heat stress and the expression level of PDK will increase. Induction of ID1 results in suppressing differentiation and increasing cell proliferation (Martinsen and Bronner-Fraser, 1998; Norton et al., 1998). Taken together, these genes described above may be important in the response of chickens suffering from heat stress. Therefore, identification of these six genes by analyzing differential expression in all three tissues is a powerful way to identify key genes involved in the heat-stress response in chickens. To explore the mechanism underlying heat stress in the three tissues, we constructed gene co-expression network using these DEGs. We identified two, four and two modules in the different tissues, respectively. Specifically, two brain modules were related to the muscle system, cytoskeleton, hemostasis and extracellular space, four liver modules were related to neuron projection, ion binding, energy metabolism, muscle system, cytoskeleton and cell fraction, and two leg muscle modules were related to synapse, axon, enzyme inhibitor activity and extracellular space. The functional clusters mentioned above may be important for the heat-stress response in chickens, especially those of the cytoskeleton and extracellular space, which were found in more than one tissue. Gavrilova et al. (2012) have shown that cytoskeleton reorganization can be of vital importance in cell protection against temperature stress. Moreover, proteins in the extracellular space (especially extracellular HSPs) may contribute to the physiological responses to severe heat stress (Zuo et al., 2000).

5. Conclusions We have identified six genes differentially expressed under heat stress in all three tissues examined, including two HSP family genes (HSPH1, HSP25), two genes related to negative regulation of apoptosis and programmed cell death (RB1CC, BAG3), one gene related to hunger and regulation of energy metabolism (PDK), and one gene related to differentiation and cell proliferation (ID1). These genes might be important factors for chickens' suffering from heat stress. The gene coexpression network of DEGs indicates that different tissues showed various responses to heat stress. These responses mainly involved the cytoskeleton, extracellular space, ion binding and energy metabolism. The roles that these genes play in chicken thermotolerance require further investigation. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.gene.2014.06.017. Conflict of interest There is no conflict of interest for this manuscript. Acknowledgments This research was supported by the National Natural Science Foundation of China (#30972093) and the National Science and Technology Support Program of China (#2014BAD08B08). References Antoku, K., Maser, R.S., Scully, W.J., et al., 2001. Isolation of Bcl-2-binding proteins that exhibit homology with BAG1 and suppressor of death domains protein. Biochem. Biophys. Res. Commun. 286 (5), 1003–1010. Chano, T., Ikegawa, S., Kontani, K., Okabe, H., Baldini, N., Saeki, Y., 2002. Identification of RB1CC1, a novel human gene that can induce RB1 in various human cells. Oncogene 21 (8), 1295–1298 (Feb 14). Choi, J.K., Yu, U., Yoo, O.J., Kim, S., 2005. Differential coexpression analysis using microarray data and its application to human cancer. Bioinformatics 21 (24), 4348–4355. Donkoh, A., 1989. Ambient temperature: a factor affecting performance and physiological response of broiler chickens. Int. J. Biometeorol. 33, 259–265. Doong, H., Price, J., Kim, Y.S., et al., 2000. CA IR21/BA G23 forms an EGF-regulated ternary complex with phospholipase C-gamma and Hsp70/Hsc70. Oncogene 19 (38), 4385–4395. Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D., 1998. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. U.S.A. 95 (25), 14863–14868 (Dec 8). Gavrilova, L.P., Korpacheva, I.I., Semushina, S.G., Iashin, V.A., 2012. Heat shock induces simultaneous rearrangements of all known cytoskeletal filaments in normal interphase fibroblasts. Tsitologiia 54 (11), 837–846. Goldbaum, O., Riedel, M., Stahnke, T., Richter-Landsberg, C., 2009. The small heat shock protein HSP25 protects astrocytes against stress induced by proteasomal inhibition. Glia 57 (14), 1566–1577.

Q.B. Luo et al. / Gene 546 (2014) 200–205 Halevy, O., Krispin, A., Leshem, Y., McMurtry, J.P., Yahav, S., 2001. Early-age heat exposure affects skeletal muscle satellite cell proliferation and differentiation in chicks. Am. J. Physiol. Regul. Integr. Comp. Physiol. 281, R302–R309. Hendrick, J.P., Hartl, F., 1993. Molecular chaperone functions of heat-shock proteins. Annu. Rev. Biochem. 62, 349–384. Huang, D.W., Sherman, B.T., Lempicki, R.A., 2009. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4 (1), 44–57. Jacobs, A.T., Marnett, L.J., 2009. HSF1-mediated BAG3 expression attenuates apoptosis in 4-hydroxynonenal-treated colon cancer cells via stabilization of anti-apoptotic Bcl2 proteins. J. Biol. Chem. 284 (14), 9176–9183 (Apr 3). Katoh, Y., Fujimoto, M., Nakamura, K., Inouye, S., Sugahara, K., Izu, H., Nakai, A., 2004. Hsp25, a member of the Hsp30 family, promotes inclusion formation in response to stress. FEBS Lett. 565 (1–3), 28–32. LeBlanc, P.J., Harris, R.A., Peters, S.J., 2007. Skeletal muscle fiber type comparison of pyruvate dehydrogenase phosphatase activity and isoform expression in fed and fooddeprived rats. Am. J. Physiol. Endocrinol. Metab. 292, 571–576. Li, C., Wang, X., Wang, G., Li, N., Wu, C., 2011. Expression analysis of global gene response to chronic heat exposure in broiler chickens (Gallus gallus) reveals new reactive genes. Poult. Sci. 90 (5), 1028–1036 (May). Lin, H., Jiao, H.C., Buyse, J., et al., 2006. Strategies for preventing heat stress in poultry. Worlds Poult. Sci. J. 62, 71–85. Martinsen, B.J., Bronner-Fraser, M., 1998. Neural crest specification regulated by the helix–loop–helix repressor Id2. Science 281 (5379), 988–991. Mashaly, M.M., Hendricks, G.L., Kalama, M.A., Gehad, A.E., Abbas, A.O., Patterson, P.H., 2004. Effect of heat stress on production parameters and immune responses of commercial laying hens. Poult. Sci. 83, 889–894. Norton, J.D., Deed, R.W., Craggs, G., et al., 1998. Id helix–loop–helix proteins in cell growth and differentiation. Trends Cell Biol. 8 (2), 58–65. Rozenboim, I., Tako, E., Gal-Garber, O., et al., 2007. The effect of heat stress on ovarian function of laying hens. Poult. Sci. 86, 1760–1765. Samali, A., Orrenius, S., 1998. Heat shock proteins: regulators of stress response and apoptosis. Cell Stress Chaperones 3 (4), 228–236. Stanley, D., Watson-Haigh, N.S., Cowled, C.J., Moore, R.J., 2013. Genetic architecture of gene expression in the chicken. BMC Genomics 14, 13 (Jan 16).

205

St-Pierre, N.R., Cobanov, B., Schnitkey, G., 2003. Economic losses from heat stress by US livestock industries. J. Dairy Sci. 86, E52–E77. Sugden, M.C., 2003. PDK4: a factor in fatness? Obes. Res. 11, 167–169. Vertii, A., Hakim, C., Kotlyarov, A., Gaestel, M., 2006. Analysis of properties of small heat shock protein Hsp25 in MAPK-activated protein kinase 2 (MK2)-deficient cells— MK2-dependent insolubilization of Hsp25 oligomers correlates with susceptibility to stress. J. Biol. Chem. 281 (37), 26966–26975. Wallis, I.R., Balnave, D., 1984. The influence of environmental temperature, age and sex on the digestibility of amino acids in growing broiler chickens. Br. Poult. Sci. 25, 401–407. Wang, S.H., Cheng, C.Y., Tang, P.C., Chen, C.F., Chen, H.H., Lee, Y.P., Huang, S.Y., 2013. Differential gene expressions in testes of L2 strain Taiwan country chicken in response to acute heat stress. Theriogenology 79 (2) (Jan 15, 374–82.e1–7). Wu, P., Sato, J., Zhao, Y., et al., 1998. Starvation and diabetes increase the amount of pyruvate dehydrogenase kinase isoform 4 in rat heart. Biochem. J. 329, 197–201. Wu, P., Inskeep, K., Bowker-Kinley, M.M., et al., 1999. Mechanism responsible for inactivation of skeletal muscle pyruvate dehydrogenase complex in starvation and diabetes. Diabetes 48, 1593–1599. Wu, P., Blair, P.V., Sato, J., et al., 2000. Starvation increases the amount of pyruvate dehydrogenase kinase in several mammalian tissues. Arch. Biochem. Biophys. 381, 1–7. Yamagishi, N., Saito, Y., Hatayama, T., 2008. Mammalian 105 kDa heat shock family proteins suppress hydrogen peroxide-induced apoptosis through a p38 MAPKdependent mitochondrial pathway in HeLa cells. FEBS J. 275 (18), 4558–4570. Yamashita, H., Kawamata, J., Okawa, K., Kanki, R., Nakamizo, T., Hatayama, T., Yamanaka, K., Takahashi, R., Shimohama, S., 2007. Heat-shock protein 105 interacts with and suppresses aggregation of mutant Cu/Zn superoxide dismutase: clues to a possible strategy for treating ALS. J. Neurochem. 102 (5), 1497–1505. Zuo, L., Christofi, F.L., Wright, V.P., Liu, C.Y., Merola, A.J., Berliner, L.J., Clanton, T.L., 2000. Intra- and extracellular measurement of reactive oxygen species produced during heat stress in diaphragm muscle. Am. J. Physiol. Cell Physiol. 279 (4), C1058–C1066 (Oct).

Exploring the molecular mechanism of acute heat stress exposure in broiler chickens using gene expression profiling.

The process of heat regulation is complex and its exact molecular mechanism is not fully understood. In this study, to investigate the global gene reg...
838KB Sizes 1 Downloads 3 Views