Gene expression changes leading extreme alkaline tolerance in Amur ide (Leuciscus waleckii) inhabiting soda lake
- Jian Xu†1,
- Qiang Li†1,
- Liming Xu1,
- Shaolin Wang2,
- Yanliang Jiang1,
- Zixia Zhao1,
- Yan Zhang1,
- Jiongtang Li1,
- Chuanju Dong1, 3,
- Peng Xu1Email author and
- Xiaowen Sun1
© Xu et al.; licensee BioMed Central Ltd. 2013
Received: 9 August 2013
Accepted: 2 October 2013
Published: 4 October 2013
Amur ide (Leuciscus waleckii) is an economically and ecologically important cyprinid species in Northern Asia. The Dali Nor population living in the soda lake Dali Nor can adapt the extremely high alkalinity, providing us a valuable material to understand the adaptation mechanism against extreme environmental stress in teleost.
In this study, we generated high-throughput RNA-Seq data from three tissues gill, liver and kidney of L. waleckii living in the soda lake Dali Nor and the fresh water lake Ganggeng Nor, then performed parallel comparisons of three tissues. Our results showed that out of assembled 64,603 transcript contigs, 28,391 contigs had been assigned with a known function, corresponding to 20,371 unique protein accessions. We found 477, 2,761 and 3,376 differentially expressed genes (DEGs) in the gill, kidney, and liver, respectively, of Dali Nor population compared to Ganggeng Nor population with FDR ≤ 0.01and fold-change ≥ 2. Further analysis revealed that well-known functional categories of genes and signaling pathway, which are associated with stress response and extreme environment adaptation, have been significantly enriched, including the functional categories of “response to stimulus”, “transferase activity”, “transporter activity” and “oxidoreductase activity”, and signaling pathways of “mTOR signaling”, “EIF2 signaling”, “superpathway of cholesterol biosynthesis”. We also identified significantly DEGs encoding important modulators on stress adaptation and tolerance, including carbonic anhydrases, heat shock proteins, superoxide dismutase, glutathione S-transferases, aminopeptidase N, and aminotransferases.
Overall, this study demonstrated that transcriptome changes in L. waleckii played a role in adaptation to complicated environmental stress in the highly alkalized Dali Nor lake. The results set a foundation for further analyses on alkaline-responsive candidate genes, which help us understand teleost adaptation under extreme environmental stress and ultimately benefit future breeding for alkaline-tolerant fish strains.
Amur ide (Leuciscus waleckii) belongs to the family of cyprinid, inhabiting the Heilongjiang (Amur) River basin in Russia, Mongolia, China and Korea. Although L. waleckii inhabits fresh water in rivers, streams and lakes, it also has great tolerance on high salinity and alkalinity (http://www.fishbase.org). As an extreme instance, L. waleckii inhabiting Dali Nor lake, Inner Mongolia (E116o25′-116o45′,N43o13′-43o23′) can survive in water of ultra-high alkalinity up to pH 9.6. Dali Nor lake is a typical saline-alkaline lake with high concentrations of carbonate salts. It locates in an endorheic basin on eastern Inner Mongolia Plateau. The evaporation is greater than precipitation and inflows, making the lake shrink consistently from 1600 to less 200 km2 since early Holocene (11,500–7,600 cal yr BP). The alkalinity and salinity are increasing steadily . Currently the pH value ranged from 8.25 to 9.6, with the alkaline content (ALK) over 50 mg/L and the salinity around 6 ‰. Combining geological and biological evidence, it’s commonly believed that L. waleckii population in Dali Nor lake were used to be fresh water fish that evolved fast in the past several thousand years and developed great tolerance on high alkalinity [2, 3].
L. waleckii is economically important to local Mongolian who live around the Dali Nor lake, and ecologically important to wild birds on their migration journeys from Siberia to the south which feed on L. waleckii as major food source . In spite of economic and ecological importance, the mechanism of its high tolerance on alkalinity is still a puzzle. Very limited physiological and genetic studies had been performed, and rare genetic resources had been developed. So far, only a few genetic markers had been developed for population genetics evaluation and phylogeny analysis [3, 5]. Mitochondrial genome had been completely sequenced and annotated, providing basic molecular tools for ecological and genetic study . Scientists are paying more attention to L. waleckii with gradually recognized importance. Recently, high throughput transcriptome sequencing was performed on Illumina platform and analyzed, providing the genomic basis for further investigation of the mechanism of its alkaline tolerance . L. waleckii has been recently developed as potential aquaculture species in the widely distributed saline and alkaline water in northern China. The breeding program also eagerly desires better understanding of its physiological and genetic basis of the tolerance adaptation and stress resistant on alkaline environment. Besides, scientists are also interested on the mechanism of microevolution on L. waleckii which evolved fast to adapt paleoenvironmental changes since early Holocene.
Comparative study between organisms inhabiting distinct environments could provide insight into the mechanism that responding to the environmental difference. In some cases, scientists apply artificial treatments to create the difference in the experiments, and facilitate the comparison [8, 9]. To better understand the physiological and genetic changes and mechanism of alkaline tolerance and adaptation in L. waleckii, comparative analysis between the fish living in alkaline water and fresh water is the efficient method. Fortunately, there is a sister lake of Dali Nor called Ganggeng Nor, which is fresh water lake and connected to Dali Nor through the short Shali river. L. waleckii also inhabits the fresh water of Ganggeng Nor lake. There is frequent genetic communication between the population in Dali Nor lake (alkaline water type, AW) and those in Ganggeng Nor lake (fresh water type, FW) through anadromous spawning migration annually. Both types of L. waleckii are derived from same ancestors and have consistent genetic background, which provide us unique natural samples to explore gene expression changes in response to high alkaline environment.
Transcriptome profiling and differential gene expression analyses traditionally use microarray technology, which requires cDNA library, Expressed Sequence Tags (EST) dataset and array hybridization. With the emerging of the next generation sequencing, RNA sequencing (RNA-Seq) is relatively new technology for transcriptomic study across the whole genome. Comparing to traditional cDNA microarray, RNA-Seq provides deep sequencing data for direct quantification of transcripts, which is more sensitive to detect all expressed genes without the hassles of EST collection, probe synthesis, microarray design and hybridization [10, 11]. In the past several years, RNA-Seq has been widely used in many teleost for differential gene expression analysis in various organisms. For instance, RNA-Seq were used to unveil gene expression differences in response to various pathogenic challenge in Lateolabrax japonicas, catfish (Ictalurus punctatus) [13, 14], Grouper (Epinephelus spp.) , European sea bass (Dicentrarchus labrax)  and Asian sea bass (Lates calcarifer) . It was even used to quantify the gene expression changes in Fundulus grandis in the Gulf of Mexico to evaluate the impact of oil contamination after the disaster of Deepwater Horizon drilling platform . Gene expression changes responding to abiotic stress are generally very significant comparing to those control counterparts. Thus, RNA-Seq was also used to profile DEGs and pathways under certain environmental stress. For instance, drought-responsive genes were identified and analyzed using RNA-Seq to compare drought-treated and well-watered fertilized ovary and basal leaf meristem tissue . Gene expression changes in response to extreme dehydration on Belgica Antarctica were characterized using RNA-Seq, unveiling the tolerance mechanisms on dehydration in Antarctic insect . RNA-Seq results also revealed gene expression changes in various metabolic pathways in response to osmotic stress and exogenous abscisic acid challenge, providing global gene expression overview of drought stress sorghum .
In this study, we use RNA-Seq to investigate the genome-wide gene expression differences in L. waleckii population inhabiting soda water of Dali Nor lake and their sister population inhabiting fresh water of Ganggeng Nor lake. Gene expression changes are identified from whole transcriptome background. Our study highlights those reactive pathways in response to high alkaline stress by using gene ontology and pathway analysis. This study provides us useful information to explain mechanism of alkaline stress tolerance in teleost.
Results and discussion
RNA-Seq data processing, reference assembly and alignment
Summary of samples and RNA-Seq data
Mapping ratio (%)
Statistics of transcriptome reference assembly and annotation
Number of contigs
Maximum contig length
Minimum contig length
Average contig length
Median contig length
Contigs with blast hits on NR
Contigs with blast hits on Uniprot
Contigs with blast hits on D.rerio protein
Unigenes with blast results
Contigs with GO terms
Unigenes with GO terms
GO enrichment analysis of genes up- or down-regulated in response to AW stress
No. up- or down- regulated
Total in category
enzyme activator activity
translation regulator activity
immune system process
electron carrier activity
enzyme regulator activity
electron carrier activity
enzyme regulator activity
response to stimulus
enzyme regulator activity
transcription factor activity
transcription regulator activity
response to stimulus
response to chemical stimulus
Identification of differentially expressed genes
Functional analysis on differential expressed genes in gill
In response to AW stress, we observed significant gene enrichment of several Gene Ontology (GO) terms in gill that related to stress response. These GO terms include “transcription regulator activity (GO:0030528)”, “metabolic process (GO:0008152)” and “cell communication (GO:0007154)” in up-regulated genes, and “response to stimulus (GO:0050896)” in the down-regulated genes. Notably, there are a total of 26 DEGs in the category of “response to stimulus”. Detailed analysis revealed that 12 genes are related to “response to stress (GO:0006950)”, 12 genes are related to “response to chemical stimulus (GO:0042221)”, 7 genes are related to “response to external stimulus (GO:0009605)” and 6 genes are related to “cellular response to stimulus (GO:0051716)”.
We further investigated those highly DEGs in gill, and observed that 7 heat shock protein genes, 4 Cathepsin genes and 3 proteasome subunit genes are highly up-regulated in gill of AW (Additional file 1: Table S1). Heat shock proteins target damaged proteins to the proteasome to prevent accumulation of dysfunctional proteins and to recycle peptides and amino acids . This result suggested that the high level of autophagy occurred in gill under AW stress.
In the IPA analysis, we observed significant Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of the differential expressed genes in gill in response to AW environment stress. “Eukaryotic Initiation Factor 2 (EIF2) Signaling”, “Regulation of eIF4 (Eukaryotic Initiation Factor 4) and p70S6K (P70S6 kinase) Signaling” and “mTOR (Mammalian Target of Rapamycin) Signaling” were listed in the top enriched pathways, and all of them had been reported that played essential roles on stress response and tolerance. Eukaryotic Initiation Factor 2 (EIF2) is a GTP (Guanosine Triphosphate)-binding protein that escorts the initiation-specific form of Met-tRNA (Met-tRNAi) onto the ribosome, it also plays a role in identifying the translational start site. EIF2 signaling is the protein synthesis pathway in eukaryotic organisms. Because protein synthesis is energetically costly, stressed cells usually inhibit this process to devote resources to stress responses. In many cases EIF2α phosphorylation is a biological response that facilitates cells to cope with stressful environments by down-regulation of protein synthesis [24, 25]. mTOR is a serine/threonine kinase distributed within two protein complexes (mTORC1 and mTORC2) in the cell , which plays important roles in response to stress, including activation of the autophagy  and modulation of protein synthesis . These responses can conserve energy and promote survival during prolonged periods of stress . “Regulation of eIF4 and p70S6K Signaling” pathway has similar functions on protein synthesis regulation. p70s6k has been considered an mTOR activation mirror and a marker of increased protein synthesis induced by stress and stimulation .
According to above evidences from GO and KEGG pathway analysis, we hypothesized that high alkaline stress suppressed protein synthesis and increased the level of autophagy in gill of L. waleckii in Dali Nor, which could conserve energy and provide sufficient amino acids and macromolecules for surviving in high alkaline environment.
Functional analysis on differential expressed genes in liver
In response to AW stress, we observed significant gene enrichment under several GO terms in liver, including “oxidoreductase activity (GO:0016491) (52 genes)” and “transporter activity (GO:0005215) (139 genes)” in up-regulated genes, and “transferase activity (GO:0016740) (201 genes)”, “electron carrier activity (GO:0009055)” and “oxidoreductase activity (GO:0016491) (132 genes)” in the down-regulated genes. The genes in the molecular function category of “oxidoreductase activity” and “electron carrier activity” are widely studied and recognized to associate with oxidative stress and adaptation on environmental stimuli in coupling with mitochondrial functions. For instance, Mitchell et al. performed genome-wide gene expression profiling on two model microorganisms, Escherichia coli and Saccharomyces cerevisiae, in response to environmental stimuli, showed significant functional enrichment of oxidative stress categories, including oxidoreductase activity . The expression profiling studies on teleost species also showed similar response that a cluster of genes of oxidoreductase activity differential expressed in response to environmental stress, such as temperature stress and confinement stress [31, 32]. Here we identified 6 oxidoreductase genes differentially expressed in AW and FW environment. Genes in the category of “transporter activity” are in charge of the movement of substances, such as macromolecules, small molecules and ions, etc. They were significantly enriched in the sub-terms of “substrate-specific transmembrane transporter activity” and “ion transmembrane transporter activity” in this study, suggesting their important roles in regulating homeostasis of various substrates in response to environmental stress, which were consistent with those previous reports on stress adaptation and resistance of many organisms. For instance, amino acid and ion transmembrane transporters were reported to be essential factors to salt and osmotic stress response in many plants [33, 34], as well as in many aquatic animals including mollusks [35, 36] and teleosts [37, 38] etc. We further inspected those highly DEGs in liver and confirmed that 128 accessions encoding various transporter proteins or solute carrier (SLC) family members, suggesting they were important regulators in response to alkaline stress in liver of L. waleckii. IPA pathway enrichment analysis on those DEGs in liver showed significant pathway enrichment on “Superpathway of cholesterol biosynthesis”, suggesting the cholesterol synthesis had been significantly induced under the severe environmental stress in the liver of L. waleckii.
Functional analysis on differential expressed genes in kidney
Kidney is the essential organ which serves homeostatic functions such as the regulation of electrolytes, maintenance of acid–base balance, and salt and water balance in the body. From those 2,761 DEGs in kidney, we identified significant enrichment on several GO terms, including “oxidoreductase activity (GO:0016491) (150 genes)”, “transferase activity (GO:0016740) (165 genes)”, “transporter activity (GO:0005215) (77 genes)”, “electron carrier activity (GO:0009055) (32 genes)”, “enzyme regulator activity (GO:0030234) (56 genes)”, “response to stimulus (GO:0050896) (91 genes)” in the up-regulated genes, and “oxidoreductase activity (GO:0016491) (41 genes)”, “transporter activity (GO:0005215) (124 genes)”, “enzyme regulator activity (GO:0030234) (93 genes)”, “transcription factor activity (GO:0003700) (75 genes)” in the down-regulated genes. The enrichment profile is similar to those DEGs in liver on “oxidoreductase activity”, suggesting that both tissues were facing oxidative stress caused by environmental stimuli, and the genes with oxidoreductase activity changed their expression to adapt the changes. The genes of “transporter activity” were enriched in both up- and down-regulated genes. A significant portion of these transporters are substrate-specific transmembrane transporters (GO:0022891). The active transmembrane transporters are much more than those passive transmembrane transporters, which indicate that active transporters play the essential roles to transport specific substrates such as ion and organic acid across membranes under severe osmotic and alkaline stress with great energy consumption than those in FW environment. The increased energy requirement leads to active proteolysis in the kidney, which can be demonstrated by observed up-regulation of genes encoding various aminotransferases, including tyrosine aminotransferase, aspartate aminotransferase, ornithine aminotransferase, and alanine aminotransferase, etc. Other than aminotransferases, abundant genes under the term of “transferase activity” are significantly enriched in up-regulated genes, which are mainly comprised of “transferring one-carbon groups”, “transferring acyl groups”, “transferring glycosyl groups”, “transferring phosphorus-containing groups”, suggesting their important roles in response to environmental stress in AW. One of well-studied transferase families is glutathione S-transferase (GST) gene family, which have been confirmed their essential functions in protection against oxidative stress caused by various stress from toxic heavy metal ions [39–41], osmotic imbalance , salinity  and pH change . GSTs have been even used as biomarkers for environmental pollution and toxins monitoring recently [45, 46]. In the biological process, we identified 47 DEGs that belong to the subcategory of “response to chemical stimulus” in those 91 up-regulated genes of “response to stimulus” in kidney, corresponding to the essential roles of kidney in response to external and endogenous chemical stresses in AW environment.
Expression of the genes under positive selection
Differentially expression of positively selected genes
Differentially expression tissues
Polymeric immunoglobulin receptor
T-cell receptor beta chain T17T-22
Cytokine receptor common subunit gamma
Transposable element Tcb1 transposase
B-cell antigen receptor complex-associated protein alpha chain
Carbonic anhydrase 4
Extracellular superoxide dismutase [Cu-Zn]
Stonustoxin subunit beta
Ig mu chain C region membrane-bound form
Transposable element Tc1 transposase
Disabled homolog 2-interacting protein
Complement factor H
Glutathione S-transferase A
Complement factor B
Myb-binding protein 1A-like protein
Interferon-induced very large GTPase 1
RNA-directed DNA polymerase from mobile element jockey
Ig heavy chain V region 5A
UPF0577 protein KIAA1324-like
UHRF1-binding protein 1-like
Nuclear factor 7, ovary
Ceramide synthase 2
Laminin subunit alpha-3
We performed comparative transcriptome profiling study on L. waleckii inhibiting in alkaline water of Dali Nor lake and in fresh water of Ganggeng Nor lake, and identified a relatively large number of genes that displayed distinct differences on their expression in gill, liver and kidney. Further analysis revealed that several well-known functional categories of genes and signaling pathway, which are associated with stress response and extreme environment adaptation, had been significantly enriched, including the functional categories of “response to stimulus”, “transferase activity”, “transporter activity” and “oxidoreductase activity”, etc., and signaling pathways of “mTOR signaling”, “EIF2 signaling”, “superpathway of cholesterol biosynthesis”, etc. We also identified significantly DEGs in three tissues, encoding important modulators on stress adaptation and tolerance, including carbonic anhydrases, heat shock proteins, superoxide dismutase, glutathione S-transferases, aminopeptidase N, and aminotransferases. Overall, this study demonstrated that transcriptome changes in L. waleckii played a role in adaptation to complicated environmental stress in the highly alkalized Dali Nor lake. The results set a foundation for further analyses on alkaline-responsive candidate genes, which would help us understand teleost adaptation under extreme environmental stress and ultimately benefit future breeding for alkaline-tolerant fish strains.
This study was approved by the Animal Care and Use committee of Centre for Applied Aquatic Genomics at Chinese Academy of Fishery Sciences.
Ten individuals (five males, five females) of L. waleckii inhabiting alkaline water were sampled at north shore of Dali Nor lake, Inner Mongolia, China (43o22′43"N, 116o39'24"E). Nine individuals (five males, four females) inhabiting fresh water were sampled at west shore of Ganggeng Nor lake (43o17'48"N, 116o53'27"E). Both groups weighted ranging from 130 grams to 150 grams. Liver, kidney and gill were dissected and collected, due to previous report that these organs play important role in hyperosmotic and hypersaline conditions . Tissue samples were stored in RNAlater (Qiagen, Hilden, Germany) and transported to laboratory in Beijing at room temperature, then stored at −20°C prior to RNA extraction.
RNA extraction and quality control
Total RNA was extracted from each tissue using TRIZOL Kit (Invitrogen, Carlsbad, CA, USA) with manufacturer’s instructions. RNA samples were then digested by DNase I to eliminate potential genomic DNA. Integrity and size distribution were checked on Bioanalyzer 2100 with RNA 6000 Nano Labchips (Agilent technologies, Santa Clara, CA, USA). Equal amounts of the high quality RNA samples from each tissue were then pooled for RNA-Seq.
cDNA library construction and sequencing
RNA-Seq library preparation and sequencing was carried out by HudsonAlpha Genomic Services Lab (Huntsville, AL, USA) as previously described . cDNA libraries were prepared with ∼2.5 μg of starting total RNA following the protocols of the Illumina TruSeq RNA Sample Preparation Kit (Illumina). The final library had an average fragment size of 270 bp and final yields of 400 ng. After KAPA quantitation and dilution, the library was sequenced on an Illumina HiSeq 2000 with 101 bp paired-end reads.
Sequence data processing and de novo assembly
Adaptor sequences were trimmed and low quality reads were removed. Then read length less than 10 were removed. TRINITY was used to assemble all cleaned reads with default parameters  and generate reference sequences for comparative transcriptome study.
Functional annotation of assembled contigs
The assembled transcriptome contigs were subjected to similarity search against NCBI non-redundant (nr) protein database using BLASTx with e-value cutoff of 1E-10. Gene name and description was assigned to each contig based on the top BLASTx hit with the highest score. Gene ontology (GO) analysis was conducted on assembled transcriptome using InterProScan (http://www.ebi.ac.uk/Tools/pfa/iprscan/) and integrated protein databases with default parameters. The GO terms associated with transcriptome contigs were then obtained for describing their biological processes, molecular functions and cellular components.
Read mapping and differential gene expression analysis
All the cleaned reads were mapped to the assembled reference transcriptome by Bowtie , and about 84.1% of the reads can be mapped to the reference for each sample (Table 1). RSEM was then used to estimate and quantify the gene and isoform abundances according to the trinity assembled transcriptome. Finally, we used edgeR to normalize the expression levels in each of these samples and obtain the differentially expressed transcripts by pairwise comparisons .
Quantitative reverse transcription-PCR (qRT-PCR)
qRT-PCR was used to validate the RNA-Seq results on randomly selected 30 gene accessions. The beta-actin gene was used as an internal reference, and primers were designed as below, forward primer: 5′- TGCAAAGCCGGATTCGCTGG -3′; reverse primer: 5′- AGTTGGTGACAATACCGTGC -3′. Briefly, qRT-PCR was performed in the optical 96-well plates with an ABI PRISM 7500 Real-time Detection System (Life Technology). The amplification was performed in a total volume of 15 μl, containing 7.5 μl 2X SYBR Green Master Mix reagent (Life Technology), 1 μl of cDNA (100 ng/μl), and 0.3 μl of 10 μM of each gene-specific primer. The PCR cycle was 50°C for 2 min, 95°C for 10 min, 40 cycles of 95°C for 15 s and 60°C for 1 min. All reactions were set up in triplicate including the negative controls with no template. To assess PCR efficiency, five 10-fold serial dilutions of a randomly selected cDNA sample were used on both the target genes and the reference gene to assess the PCR efficiency. After the PCR, data were analyzed with ABI 7500 SDS software. The comparative CT method (2-∆∆CT method) was used to analyze the expression of the target genes. All data were given at levels relative to the expression of the beta-actin gene.
The genes differentially expressed in 3 tissues were further analyzed using the Ingenuity Pathway Analysis program (IPA; https://analysis.ingenuity.com) in order to identify the biochemical pathways affected. The IPA software contains the biological function, interaction, and information of a curated gene set and many biochemical pathways, identifying global canonical pathways, dynamical biological networks and functions from a given list of genes. Basically, the accession numbers of DEGs were uploaded into the IPA and compared with the genes included in each canonical pathway using the whole gene set as the background. All the pathways with one or more genes overlapping the candidate genes were extracted. During IPA analyses, each of the pathways was assigned a P value from Fisher’s exact test, denoting the probability of overlap between the pathway and the input genes.
Availability of supporting data
All supporting files have been deposited to LabArchives entitled ‘amur ide DGE’ with a DOI of 10.6070/H4PZ56RJ (https://mynotebook.labarchives.com/share/xujian/Mi42fDIxOTY2LzIvVHJlZU5vZGUvMzc5NDY5ODU2MHw2LjY=).
This study was supported by the grants from National Natural Science Foundation of China (No. 31101893), National High-tech R&D Program of China (2011AA100401), China Ministry of Agriculture “948” Program (No. 2013-Z12) and Research Foundation of Chinese Academy of Fishery Sciences (2013C011).
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