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. 2019 Aug 5;9(9):320. doi: 10.1007/s13205-019-1851-0

Concordant expression pattern across multiple immune tissues of commercial broilers in response to avian pathogenic Escherichia coli (APEC)

Hongyan Sun 1,✉, Huan Li 2
PMCID: PMC6682839  PMID: 31406642

Abstract

In this study, RNA-seq gene expression data were obtained for three different immune tissues (bone marrow, thymus, and bursa) at two time points from birds with three phenotypes (non-infected, resistant, and susceptible birds). A total of 380 significant differentially expressed genes (DEGs) were commonly expressed in each of the three tissues in the contrast of susceptible vs. non-infected birds at 5 days post-infection (dpi) and 106 significant DEGs were shared in susceptible vs. resistant birds at 5 dpi. For the co-expressed DEGs, a relatively high consistency in expression pattern was identified in the three tissues for the two contrasts. These co-expressed DEGs were involved in the biological process of response to stimulus, regulation of cell proliferation, signal transduction, and immune system. Moreover, three gene networks were identified for the co-expressed DEGs, showing the activation of the humoral immune response, cell survival, growth and death were the concordant response for the three lymphocyte tissues. Several potential biomarker genes, HPGD, PROX1, FOSL2, E2F1, and ANP32A were found, which may have critical functions in resistant birds to resist APEC infection. Taken together, this study provides a novel insight that may elucidate the molecular mechanisms underlying host response to systemic APEC infection, as well as enhances the probability of advancement in biomedical research.

Electronic supplementary material

The online version of this article (10.1007/s13205-019-1851-0) contains supplementary material, which is available to authorized users.

Keywords: Meta-analyses, APEC, Bone marrow, Bursa, Thymus

Introduction

Avian pathogenic Escherichia coli (APEC) are a group of gram-negative, flagellated, rod-shaped bacteria infecting chicken, turkeys, and ducks, as well as threatening human health (Dho-Moulin and Fairbrother 1999). It can cause various diseases such as colibacillosis, septicemia, chronic respiratory disease, and peritonitis (Dho-Moulin and Fairbrother 1999). Economic loss associated with APEC in birds is estimated to reach millions of dollars worldwide annually, attributed to condemnation of products, along with mortality and production contamination (Barnes et al. 2003; Johnson et al. 2007). Contaminated retail poultry meat and eggs are identified as the major sources of infection threatening human health (Johnson et al. 2009). Moreover, APEC strains show similar genomic structure and virulence traits with some human extra-intestinal pathogenic E. coli (ExPEC), further indicating its zoonotic potential (Johnson et al. 2007, 2008, 2009). The enormous consequences of APEC on the quality of animal and human life have prompted the need to elucidate the host mechanisms of resistance involved.

Estimation of global gene expression patterns across different conditions is a principal method of investigating molecular mechanism in both microbial pathogens and their hosts via a single tissue (mostly immune-related) (Sandford et al. 2011, 2012; Sun et al. 2015a, b, 2016), which can only provide limited fundamental biological information on a single tissue. Few meta-analyses across multiple tissues were conducted to identify host responses in different conditions via the same individuals as complex interactions between tissues are the major mechanisms used by hosts against diseases. Thus, integrated meta-data from different immune tissues can complement and refine traditional expression analysis, yielding a more refined assessment of gene action and encouraging improved understanding of the functional genomics of hosts.

In this study, we aimed to conduct meta-analyses in multiple specific tissues, including three primary lymphoid tissues (bone marrow, bursa, and thymus) at two time points on birds with three phenotypes to evaluate the effects of APEC infection on gene expression patterns in chicken.

Materials and methods

Experiment and data acquisition

Commercial male broilers were raised to 4 weeks of age on wire-floor cages with ad libitum access to food and water. Then, infected birds were raised separately from non-infected birds. Infected birds were challenged with 0.1 mL APEC O1 (108 colony-forming units) via the left intra-air sac injection. Non-infected birds were injected with 0.1 mL phosphate-buffered saline (PBS) via the same route. Samples were collected at 1 and 5 days post-infection (dpi). The infected birds were categorized into resistant and susceptible based on internal lesion scores. The internal lesion scores were measured by the lesion of air sacs, pericardium, and liver according to the study of Peighambari et al. (2000). The lesion score of pericardium and liver was 0–2, and air sacs was 0–3. Consequently, the resistant birds were the sum of lesion score with 0–1, while the susceptible birds were the sum of lesion score with 6–7. In total, six treatment groups were generated: non-infected birds at 1 and 5 dpi, resistant birds at 1 and 5 dpi, and susceptible birds at 1 and 5 dpi.

Bone marrow, bursa, and thymus were collected after the birds were euthanized. RNA samples were isolated from the three tissues of non-infected, resistant, and susceptible birds at 1 dpi and 5 dpi using the Ambion MagMax-96 Kit (AM1839). Quality and quantity of RNA were measured by Nanodrop (NanoDrop Technologies Inc., Wilmington, DE, USA) and Agilent 2100 Bioanalyser (Agilent Technologies). Then, cDNA libraries were constructed by using the Illumina TruSeq® RNA Sample Preparation v2 Kit, and sequenced by the Illumina® HiSeq 2000. Detailed RNA-seq experiments can be found in previous studies (Sun et al. 2015a, b, 2016). RNA-seq data on the bone marrow, bursa, and thymus can be downloaded from Gene Expression Omnibus (GEO) with accession numbers GSE67302, GSE6901, and GSE70334, respectively.

Differentially expressed genes (DEGs) and biological activities

edgeR method (McCarthy et al. 2012) was used to identify DEGs in different contrasts. Nine two-way contrasts were generated among the six treatment groups in each of the three tissues (Table 1). Only two contrasts had large numbers of DEGs in each of the three tissues, which were used for meta-analyses: susceptible vs. non-infected birds at 5 dpi and susceptible vs. resistant birds at 5 dpi (Table 1). VennDiagram in R package was used to identify the overlapped and unique DEGs across the three tissues in each of the two contrasts. The online tool DAVID was used to identify the enriched gene ontology (GO) categories and significantly changed pathways with the cutoff FDR < 0.05 among the co-expressed DEGs. Gene network and functional characterization of the co-expressed concordant DEGs in the three tissues from each of the two contrasts were performed using IPA (Ingenuity Systems, Redwood City, CA, USA). Statistically significant networks were considered with adjusted P value cut-off of 0.001 to identify gene interactions within the networks. The DESeq2 method (Love et al. 2014) was also used in this study to detect the DEGs in different contrasts to verify the reliability of edgeR results.

Table 1.

Number of significantly, differentially expressed genes in each contrast of interest for each tissue

Contrast of interest Bone marrow Bursa Thymus
Total Up Down Total Up Down Total Up Down
Susceptible vs. non-infected birds at 5 dpi 2804 1739 1165 3165 1743 1422 3061 2162 899
Susceptible vs. resistant birds at 5 dpi 1873 965 908 748 456 292 3816 2640 1176
Resistant vs. non-infected birds at 5 dpi 2 2 0 1 1 0 3 0 3
Susceptible vs. non-infected birds at 1 dpi 885 463 422 25 6 19 158 89 69
Susceptible vs. resistant birds at 1 dpi 50 10 40 2 2 0 23 19 4
Resistant vs. non-infected birds at 1 dpi 189 116 73 2 0 2 4 2 2
5 dpi vs. 1 dpi susceptible birds 1371 957 414 99 91 8 2563 2089 474
5 dpi vs. 1 dpi resistant birds 105 37 68 1 0 1 2 2 0
5 dpi vs. 1 dpi non-infected birds 9 0 9 2 2 0 3 3 0
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dpi day post-infection

qRT-PCR analysis

Independent samples (challenged and non-challenged birds at 5 dpi) were analyzed to identify the relative expression level of DEGs in each of the three tissues by using qRT-PCR. The total RNA was extracted from birds with different phenotypes using RNAqueous® RNA isolation kit (Thermo Fisher, CA, USA). Isolated RNA was treated with RNase-free DNase I to remove DNA contamination. A Nanodrop spectrophotometer was used to measure the quantity and quality of purified RNA. Eight genes, HPGD, PROX1, E2F1, ANP32A, TLR5, TLR15, PDK4, and IL18, were selected to confirm the RNA-seq results, and the housekeeping gene 28S was used as the control gene to normalize the starting concentration of RNA. The primer sequences for the eight selected genes are listed in Table S1. Then, QuantiTect SYBR Green RT-PCR kit (Qiagen Inc., Valencia, CA, USA) was used to detect gene expression. The adjusted cycle threshold (Ct) values were calculated using the equation 2−∆∆CT (Livak and Schmittgen 2001). The qPCR data were analyzed using JMP 8.0.2 statistical software (SAS Institute Inc., Cary, NC, USA).

Results

Meta-analyses of the gene expression pattern across primary lymphoid tissues

The venn diagram showed the overlapped and unique DEGs across the three tissues in different contrasts (Fig. 1). A total of 380 significant DEGs were commonly expressed in each of the three tissues in the contrast of susceptible vs. non-infected birds at 5 dpi; and 106 significant DEGs were shared in susceptible vs. resistant birds at 5 dpi (Fig. 1). Regarding the shared DEGs, the expression direction concordance was more than 60% (263/380 and 65/106) in each contrast across the three tissues (Table 2).

Fig. 1.

Fig. 1

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The shared and unique number of differentially expressed genes in bone marrow, bursa, and thymus, in the contrast of a susceptible vs. non-infected birds at 5 days post-infection (dpi) and b susceptible vs. resistant birds at 5 dpi

Table 2.

The percent of concordant co-expressed significantly differentially expressed genes in the two contrast across two or three tissues

Contrast of interest BM and Bu and T BM and T Bu and T BM and Bu
Susceptible vs. non-infected birds at 5 dpi 69.21% (263/380) 78.40% (392/500) 95.98% (954/994) 62.17% (235/378)
Susceptible vs. resistant birds at 5 dpi 61.32% (65/106) 73.22% (462/631) 91.67% (341/372) 68.75% (22/32)
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BM bone marrow, Bu bursa, T thymus, dpi day post-infection

Moreover, 500 and 631 DEGs were shared in both the bone marrow and the thymus in each of the two contrasts; 378 and 32 DEGs in the bone marrow and the bursa, and 994 and 372 DEGs in the thymus and the bursa (Fig. 1). The percent of expression concordances also reach about 60% in both the bone marrow and either the thymus or the bursa (Table 2). However, a high level of concordant expression pattern (> 90%) for DEGs was exhibited in both the thymus and the bursa (Table 2).

Compared with the non-infected and resistant birds at 5 dpi, the susceptible birds at 5 dpi had more up-regulated than down-regulated genes in the concordant co-expressed DEGs (Fig. 2). Thousands of DEGs were uniquely expressed in each of the three tissues in each of the two contrasts, indicating the presence of a tissue-specific pattern.

Fig. 2.

Fig. 2

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The number of concordant differentially expressed genes (DEGs) in the shared DEGs in the three tissues or each of the two tissues in the contrast of susceptible vs. non-infected birds at 5 days post-infection (dpi) and susceptible vs. resistant birds at 5 dpi. BM bone marrow, Bu, bursa, T thymus

GO terms assignments and KEGG pathway analysis

The two contrasts with the largest numbers of the concordant co-expressed DEGs were used to analyze the gene ontology (GO) category and pathways with the online tool DAVID with FDR < 0.05 as the cutoff for significance. In contrast susceptible vs. resistant birds at 5 dpi, the concordant DEGs across the three tissues were involved in response to stimulus (N = 34), regulation of cell communication (N = 17), and regulation of signaling (N = 17) biological activities (Table S2). Analysis of the concordant DEGs in the susceptible vs. non-infected birds at 5 dpi contrast revealed the biological processes were response to stimulus (N = 110), regulation of cell proliferation (N = 32), signal transduction (N = 76), and immune system process (N = 38) (Table S3).

Meanwhile, many canonical pathways were identified as significant in the two contrasts with FDR < 0.05. Only one pathway, lysosome, was significantly changed in susceptible vs. resistant birds at 5 dpi (Table S4). Besides the lysosome pathway, the significantly changed pathways that those concordant DEGs involved included cytokine–cytokine receptor interaction for the contrast susceptible vs. non-infected birds at 5 dpi (Table S4).

Gene network analysis of concordant co-expressed DEGs

To identify the common or universal regulatory mechanism, we focused on the concordant co-expressed 263 and 65 DEGs in the three tissues in the contrasts of susceptible vs. non-infected birds at 5 dpi and susceptible vs. resistant birds at 5 dpi, respectively, to perform a gene network analysis. The gene network referred to as “cell death and survival” was identified in the contrast of susceptible vs. non-infected birds at 5 dpi via IPA (Fig. 3). Meanwhile, the “humoral immune response, protein synthesis, cellular development” and “cellular growth and proliferation, cell death and survival, cell signaling” networks were identified in susceptible vs. resistant birds at 5 dpi (Figs. S1 and S2).

Fig. 3.

Fig. 3

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Cell death and survival in the contrast of susceptible vs. non-infected birds at 5 days post-infection (dpi). Pathway analysis of gene functions in response to systemic avian pathogenic Escherichia coli (APEC) infection. Red color shows up-regulation and green color shows down-regulation. White molecules are not differentially expressed, but are included to illustrate associations with significantly up- and down-regulated genes

The “cell death and survival” network included 23 DEGs, 16 of which were up-regulated (ADAP1, ATXN1, EPS8, FOSL2, GINM1, HPGD, MSMO1, NACC2, NRP1, PDE5A, PDK4, PLEKHA5, PROX1, TPST1, TRIO, and WDFY3), and 7 were down-regulated (ANP32A, IFRD1, NRF1, NUSAP1, STMN1, TFDP1, and WEE1) in the contrast of susceptible vs. non-infected birds at 5 dpi (Fig. 3).

Fifteen DEGs were involved in the “cellular growth and proliferation, cell death and survival, cell signaling” network, fourteen of which were up-regulated (ARHGEF28, ARID5B, ATP9A, CDCP1, CDH5, CIDEA, F13A1, FKBP5, IGF1R, PDE5A, PIM3, PLOD2, PSD3, and TRIO), and 1 was down-regulated (ANP32A) in the contrast of susceptible vs. resistant birds at 5 dpi (Fig. S1).

All of the 13 DEGs in the “humoral immune response, protein synthesis, cellular development” network were up-regulated in the contrast of susceptible vs. resistant birds at 5 dpi: ABCG1, ANXA5, ATF3, CEBPB, ETS2, HPGD, IL1R1, IL6ST, JAG1, NFIL3, SCARB2, SH3BP2, and UGP2 (Fig. S2).

qRT-PCR assay of concordant co-expressed DEGs

qRT-PCR was performed to detect the expression of the eight concordant co-expressed DEGs. The direction of fold change and significance in qRT-PCR were in accordance with those in edgeR and DESeq2 although the fold change magnitude was slightly different. Figure 4 displays the correlation between RNA-seq and qRT-PCR, and the correlation coefficient was about 96%.

Fig. 4.

Fig. 4

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qRT-PCR validation of HPGD and ANP32A expression level in bone marrow, thymus, and bursa at 5 days post-infection. BM bone marrow, T thymus, Bu bursa. *p < 0.05; **p < 0.01

Discussion

Gene expression profiling is regarded as an excellent way to study organismal biology, development, and functions under complex diseases (Ciraci et al. 2010; Ross et al. 2000; Alizadeh et al. 2000). Many researchers were limited to using specific tissues (often immune tissue) to explore molecular knowledge via gene expression (Sandford et al. 2011, 2012; Sun et al. 2015a, b, 2016), which can only provide a single aspect of an organism responding to complex disease. Organisms always use the interactive effects of the dynamic immune system to resist infections, which suggests the importance of combining information on multiple tissues. Meta-analyses of multiple tissues from the same individuals can broaden and deepen insights into the responses of the organism as a whole to a disease, as well as provide a standard of understanding universal biological function and common regulatory mechanisms. Moreover, conducting meta-analyses can reduce the cost of analysis and increase reliability and generalizability (Ramasamy et al. 2008; Hong and Breitling 2008) using the publicly available data of related studies from the same platform.

We conducted a meta-analyses of the genetic patterns of multiple immune tissues in non-infected, resistant, and susceptible birds. The popular software, edgeR, was used in this study as it can detect the largest numbers of DEGs (Seyednasrollah et al. 2013). Large numbers of DEGs at 5 dpi were found in the overlapped of tissues in susceptible vs. non-infected and resistant birds. The concordant co-expressed DEGs comprised a low-to-medium percentage (10–55%) of total DEGs in each of the three tissues, which is likely due to tissue-specific responses to APEC. Nie et al. (2010) reported that about half (49%) of the assessed genes commonly expressed in eight different chicken tissues (including the thymus and the bursa), which is in good agreement with our current results, to a certain extent. However, a relatively high consistency of co-expressed DEGs across the bone marrow, thymus, and bursa was identified in the expression patterns in two or three tissues in response to the systemic APEC infection (60–90%, Table 2), indicating the high consistency immune response among the three tissues to resist APEC infection and a common basic mechanism of immune regulation in susceptible birds at 5 dpi.

The gene ontology terms found within the co-expressed DEGs were enriched in response to stimulus, regulation of cell communication and signal transduction in susceptible birds compared to non-infected and resistant birds at 5 dpi, demonstrating that the initial response to bacterial infection was started. Moreover, the immune response was also induced in susceptible birds at 5 dpi. The aforementioned results showed the response to APEC infection was step by step. Meanwhile, changes in the lysosome pathway and cytokine–cytokine receptor signaling highlight the most important and common signaling cascades to fight APEC infection in the three immune tissues in coordination. The concordant co-expressed DEGs across the three tissues indicate their universal biological function in response to systemic APEC infection and can thus be considered as potential biomarkers.

Moreover, the gene network among the co-expressed DEGs showed the “cell death and survival” significantly changed, indicating the cells of susceptible birds experienced a marked struggle to live during systemic APEC infection compared to non-infected birds at 5dpi. HPGD, PROX1, and FOSL2 were up-regulated and directly connected, whereas E2F1, TFDP1, STMN1, and WEE1 were down-regulated. Hydroxyprostaglandin dehydrogenase 15-(NAD) (HPGD) was a hub gene in this network. This biochemical enzyme can regulate inflammation or proliferation via inactivation of prostaglandins (Nielsen et al. 2010). Moreover, Myung et al. (2006) reported that this key enzyme was also a tumor suppressor. Compared with that in normal tissues, its expression was greatly reduced in human cancer (Tseng-Rogenski et al. 2010; Thiel et al. 2009; Wolf et al. 2006).

In the current study, HPGD exhibited extremely high fold changes in susceptible birds compared with non-infected (fold change = 92.49) and resistant birds (fold change = 33.94) (Fig. 3, Table 3, Fig. S2), suggesting its importance in host response to infection. Meanwhile, resistant birds may use this gene to successfully resist the systemic APEC infection.

Table 3.

Fold changes of discussed differentially expressed genes in different tissues and contrasts via different calculation methods

Gene name HPGD PROX1 FOSL2 WEE1 TFDP1 E2F1 ANP32A
edgeR Susceptible vs. non-infected birds at 5 dpi Bone marrow 92.49 3.33 2.16 − 1.53 − 1.79 − 2.31 − 1.80
Thymus 4.45 1.99 2.62 − 2.09 − 2.17 − 3.08 − 1.81
Bursa 4.10 2.57 2.72 − 1.63 − 1.64 − 3.06 − 2.16
Susceptible vs. resistant birds at 5 dpi Bone marrow 33.94 4.30 1.56 NA NA − 2.37 − 1.52
Thymus 5.88 2.41 2.43 NA NA − 3.28 − 1.70
Bursa 2.50 NA NA NA NA − 2.10 − 1.85
DESeq2 Susceptible vs. non-infected birds at 5 dpi Bone marrow 14.02 2.31 1.95 − 1.68 − 1.59 − 4.18 − 1.58
Thymus 5.49 3.12 2.19 − 2.22 − 2.76 − 2.98 − 3.29
Bursa 6.82 3.09 2.94 − 1.74 − 1.68 − 3.49 − 2.64
Susceptible vs. resistant birds at 5 dpi Bone marrow 15.28 3.47 2.36 − 1.59 NA − 2.86 − 1.89
Thymus 4.69 2.88 1.95 NA NA − 4.08 − 2.07
Bursa 3.85 NA NA NA NA NA − 2.39
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NA none available, dpi day post-infection

FOS like antigen 2 (FOSL2) was involved in lymphocyte differentiation and maturation and its expression correlated with the relative proportions of naïve, central, and effector memory subsets (Costa et al. 2015). In the network, this up-regulated gene can directly interact with HPGD (Fig. 3) in the contrast of susceptible vs. non-infected birds at 5 dpi, showing that inflammation occurred in the host during APEC infection. Moreover, the up-regulated prospero homeobox1 (PROX1) may indirectly interact with HPGD (Fig. 3). This gene was reported to influence tumor progression by regulating angiogenesis and tumor cell proliferation (Park et al. 2015).

The down-regulated genes—E2F1, TFDP1, STMN1, and WEE1—in this network were involved in the cell cycle pathway, which is consistent with the results of previously published single-tissue studies (Sun et al. 2015b). These results indicated that comparing with non-infected birds at 5 dpi, cell proliferation and differentiation in susceptible birds at 5 dpi were extensively decreased in any of the three primary lymphoid tissues.

Notably, the down-regulated gene, acidic (leucine-rich) nuclear phosphoprotein 32 family member A (ANP32A) in contrast of susceptible vs. non-infected birds at 5 dpi and susceptible vs. resistant birds at 5 dpi (Fig. 3 Fig. S1). ANP32A is a multifunctional protein involved in cell differentiation, transcription, apoptosis, and cell cycle progression (Adegbola and Pasternack 2005; Theodosiou and Ashworth 2002; Millward et al. 1999). The increased expression of ANP32A in three primary lymphoid tissues of resistant birds might be a special mechanism of resistant birds against systemic APEC infection.

Considering that the edgeR method was often too liberal (Seyednasrollah et al. 2013; Nookaew et al. 2012), we also used the relatively conservative method, DESeq2, to verify the reliability of the aforementioned genes. In the study of Seyednasrollah et al. (2013), the researchers compared several softwares, edgeR, DESeq2, NOIseq, SAMseq, Limma, and Cuffdiff2, to analyze the real RNA-seq data. They demonstrated that DESeq2 is more conservative and with high precision compared to edgeR (Seyednasrollah et al. 2013) although edgeR and DESeq2 showed the same underlying statistical model and their results correlation is more than 80% (Nookaew et al. 2012). DESeq2 results showed that all aforementioned genes were significantly differentially expressed and in the same expression direction with edgeR (Table 3). Moreover, qRT-PCR results of the DEGs were also consistent with edgeR and DESeq2 in both fold change and significance. These results confirmed that the aforementioned genes performed important functions during systemic APEC infection.

The elucidation of the co-expressed concordant DEGs indicates the presence of common regulatory mechanisms or participation in a similar biological process in the three primary lymphoid tissues. In particular, many potentially important genes were identified in the current study, which may complement previously published single-tissue study. The findings in this study can add greater depth to the knowledge base about chicken host response to APEC and help improve the understanding of the conserved aspect of immune tissues in response to systemic APEC infection.

Conclusion

Integrated meta-analyses is an effective method of investigating the dataset in a biologically relevant manner in multiple immune tissues from the same individual, which generates a more comprehensive picture about the genes and genetic networks that were previously unidentified and potentially involved in host response to systemic APEC infection. The activation of the humoral immune response, cell survival, growth, and death were the concordant response for the three lymphocyte tissues. Several potential biomarker genes, HPGD, PROX1, FOSL2, E2F1, and ANP32A were found, which may have critical functions in resistant birds to resist APEC infection. Taken together, this study provides a novel insight that may elucidate the molecular mechanisms underlying host response to systemic APEC infection, as well as enhances the probability of advancement in biomedical research.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 2192 kb) (2.1MB, docx)
Supplementary material 2 (DOCX 2327 kb) (2.3MB, docx)
Supplementary material 3 (DOCX 12 kb) (12.6KB, docx)
Supplementary material 4 (DOCX 12 kb) (12.2KB, docx)
Supplementary material 5 (DOCX 13 kb) (13.6KB, docx)
Supplementary material 6 (DOCX 12 kb) (12KB, docx)

Author contributions

HS designed the experiments, performed the experiments, analyzed the data, and wrote the manuscript. HL analyzed the data and wrote the manuscript.

Funding

This work was supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (17KJB230006), the Yangzhou Municipal Natural Science Foundation for Youths (YZ2017104), the Innovation Cultivation Foundation of Yangzhou University (2017CXJ077), the Lvyangjinfeng Talent Program of Yangzhou (yzlyjfjh2016YB133), the National Natural Science Foundation of China (31802053), and the Jiangsu Province Natural Science Foundation for Youths (BK20180907).

Compliance with ethical standard

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethical approval

For the RNA-seq experiment, all animal care and experimental procedures were reviewed and approved by the Institutional Animal Care and Use Committee of Iowa State University (Log #11-07-6460-G). For the independent validated experiment, all animal protocols were approved by the animal welfare committee of the Yangzhou University (permission number: SYXK(Su) IACUC 2012-0036).

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