Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Local adaptation of Gymnocypris przewalskii (Cyprinidae) on the Tibetan Plateau


Divergent selection among environments affects species distributions and can lead to speciation. In this article, we investigated the transcriptomes of two ecotypes of scaleless carp (Gymnocypris przewalskii przewalskii and G. p. ganzihonensis) from the Tibetan Plateau. We used a transcriptome sequencing approach to screen approximately 250,000 expressed sequence tags (ESTs) from the gill and kidney tissues of twelve individuals from the Ganzi River and Lake Qinghai to understand how this freshwater fish has adapted to an ecological niche shift from saline to freshwater. We identified 9,429 loci in the gill transcriptome and 12,034 loci in the kidney transcriptome with significant differences in their expression, of which 242 protein-coding genes exhibited strong positive selection (Ka/Ks > 1). Many of the genes are involved in ion channel functions (e.g., Ca2+-binding proteins), immune responses (e.g., nephrosin) or cellular water absorption functions (e.g., aquaporins). These results have potentially broad importance in understanding shifts from saline to freshwater habitats. Furthermore, this study provides the first transcriptome of G. przewalskii, which will facilitate future ecological genomics studies and aid in the identification of genes underlying adaptation and incipient ecological speciation.


The causes of speciation have long been one of the central questions in evolutionary biology1,2,3,4,5,6. When one population colonizes a new habitat or niche, they often diverge phenotypically and genetically to increase the fitness of the population7. Adaptation to different environments is underpinned by both regulatory variation and structural protein variation8,9. Therefore, linking fitness to genotypic variation is key to understanding local adaptation and ecological speciation10,11. However, the molecular mechanisms underlying adaptation in gene expression and coding sequence divergence to various environmental conditions in natural populations remain relatively poorly understood.

In the past, elucidating the genomic basis of adaptation and speciation has been a challenge, especially in natural systems, because of the lack of genomic resources for non-model organisms. Next generation sequencing (NGS) techniques provide new opportunities for studying the genomics of non-model organisms. In particular, transcriptome sequencing promises to reveal numerous aspects of ecological speciation12,13, such as discovering genes previously unaffiliated with ecological environments and examining the relative contributions of gene expression and coding sequence divergence to phenotypic changes apparent during ecological speciation.

Many of the salt lakes in China are located in the Tibetan Plateau, of which Lake Qinghai is the most prominent representative. Lake Qinghai is the biggest inland salt lake in China, characterized by high salt and alkali content (salinity 14 parts per thousand by weight; pH 9.3). Historically, Lake Qinghai was a freshwater lake connected to the Yellow River. Geologic data indicates that approximately 1.2 Ma BP the Yellow River emerged at the edge of the plateau and approximately 0.15 Ma BP the “Gonghe Movement” of the Tibetan Plateau led to the separation of Lake Qinghai from the upper reaches of the Yellow River, after which Lake Qinghai became an occlusion lake14,15. Originally the fauna of both Yellow River and Lake Qinghai was similar, including many fish species (e.g., Gymnocypris eckloni, Schizopygopsis pylzovi, Chuanchia labiosa, Platypharodon extremus, Gymnodiptychus pachycheilus, Triplophysa stoliczkai, T. dorsonotatus, T. scleroptera and T. siluroides). As the climate turned colder and drier at the dawn of the Holocene (10,500 to 7,500 yr BP), the salinity and alkalinity of Lake Qinghai increased and few fish species survived (G. przewalskii, N. T. stoliczkai, T. dorsonotatus, T. scleroptera and T. alticeps)16,17,18. Currently, the dominant species is the endemic, scaleless carp, Gymnocypris przewalskii (Kessler) (Cyprinidae: Schizothoracinae). The scaleless carp is a cold-water-adapted freshwater fish, narrowly distributed in Lake Qinghai Basin in China19. A subspecies or ecotype (G. p. ganzihonensis) of the scaleless carp has been identified in the Ganzi River (Fig. 1) based on the shape and number of gill rakers16. While historical sources indicate that the Ganzi River once flowed into Lake Qinghai, an additional survey, conducted in 1964, revealed that the lake lacked a connection to the Ganzi River, likely reflecting the shrinking of the lake shoreline and low flows in the upper reaches16. The upper reaches have evolved into a subterranean river where the water peters out and the scaleless carp colonized this small freshwater habitat16. Therefore, G. przewalskii is an excellent system to study adaptational differences between saline and freshwater habitats, especially when phenotypic divergence and speciation are the results of divergent natural selection.

Figure 1

Sampling sites

Samples of Gymnocypris przewalskii przewalskii were collected from Lake Qinghai (salt water, salinity 14 parts per thousand by weight) and samples of G. p. ganzihonensis were collected from the Ganzi River (fresh water). The map was created using ArcGIS version 10.0. Original photographs of fishes by Kai Zhao.

In this study, we sequenced the transcriptomes of the two ecotypes of G. przewalskii inhabiting the freshwater Ganzi River and the saltwater Lake Qinghai to study the changes in their transcriptomes that may be adaptations to the different environments in which they live. These fish ecotypes are a unique opportunity to study the process of divergence and speciation in the harsh climate of the Tibetan Plateau.


Sequencing and assembly

We constructed four cDNA libraries derived from gill and kidney tissues from both salt and freshwater ecotypes of G. przewalskii. Each sample resulted in a library containing an average of 80 million reads equal to 6 Gb and a total data of 24 Gb were generated. Two different strategies (separate or pooled assembly) were used to guarantee appropriate sequencing depth data for each assembly. The pooled assembly was used for the final analyses and the average transcript length was 952 bp, with an N50 read length of 1,836 bp. The characteristics of the assembly are provided in Table S1.

Functional annotation

To annotate the G. przewalskii transcriptome, all unigene sets were annotated based on similarity to sequences in several public databases. A total of 158,087 unigenes were queried against the Nr protein database, Swiss-Prot, KEGG, COG and nt database using BLASTX and BLASTN with an E-value cut-off of < 10−5. A search of the Nr protein database yielded 77,313 unigenes (48.9%; Table S2). The E-value distribution of the top hits in the Nr database ranged from 0 to 1.0E−5 (Fig. 2a). The similarity distribution of the top BLAST hits for each sequence ranged from 17–100% (Fig. 2b). The top BLAST hits matched annotated unigenes from Danio rerio, Oreochromis niloticus, Tetraodon nigroviridis, Salmo salar and Xenopus tropicalis (Fig. 2c). The genome annotations for these species are comprehensive and largely accepted, suggesting that the G. przewalskii sequences were correctly assembled and annotated.

Figure 2

Homology search of assembled sequences against the Nr database.

(a) E-value distribution of best BLASTX hits for each sequence with a cut-off E-value of 1.0E-5. (b) Similarity distribution of the best BLAST hits for each sequence. (c) The species distribution is shown as a percentage of the total homologous sequences with an E-value of at least 1.0E-5.

Gene Ontology (GO) assignments were used to classify the functions of the predicted G. przewalskii genes. Based on sequence homology, 51,671 sequences were categorized into 52 functional groups in three functional divisions (Fig. 3). Many of the unigenes were classified into “cellular process”, “cell” and “cell part”, whereas only a few genes belonged to “carbohydrate utilization”, “nitrogen utilization”, “protein tag”, “morphogen activity” and “metallochaperone activity”. Intriguingly, 157 unigenes were classified into “channel regulator activity”, while only 44 unigenes were classified into “translation regulator activity” categories.

Figure 3

GO putative functions of the transcripts expressed in

Gymnocypris przewalskii przewalskii and G. p. ganzihonensis. The transcripts are categorized in three main categories: biological process, cellular component and molecular function. In total, 51,671 unigenes with BLAST matches to known proteins were assigned to a particular gene ontology.

To further evaluate the integrity of the transcriptome library and determine the effectiveness of the annotation process, unigene sequences were subjected to COG classification. Among 77,313 Nr hits, 25,272 sequences were assigned a COG classification (Fig. 4). Among the 25 COG categories, the cluster for “general function prediction only” was the largest group (10,768 unigenes), followed by “replication, recombination and repair” (6,313 unigenes), “transcription” (5,086 unigenes), “signal transduction mechanisms” (3,962 unigenes) and “translation, ribosomal structure and biogenesis” (3,778 unigenes). The categories of “nuclear structure” (31 unigenes) and “extracellular structures” (50 unigenes) had the fewest genes.

Figure 4

COG classification of the putative proteins.

All 25,272 putative proteins with significant similarity to those in the COG database were functionally classified into 25 molecular families. The y-axis indicates the number of unigenes in a specific functional cluster.

The 77,313 annotated sequences were mapped to the reference canonical pathways in the KEGG database. Among these, 56,270 unigenes were assigned to 256 KEGG pathways. The pathways most strongly represented were “metabolic pathways” (6,277 unigenes), “regulation of actin cytoskeleton” (2,722 unigenes) and “pathways in cancer” (2,648 unigenes). Moreover, 78,790 unigene coding sequences (CDSs) were predicted in the transcriptome of scaleless carp, of which 76,208 unigenes were generated through BLAST analysis and 2,582 unigenes were annotated using the ESTscan program.

Identification and annotation of potentially differently expressed genes

A total of 8,551 unigenes were identified in the gill libraries of both ecotypes including 4,722 up-regulated and 3,829 down-regulated unigenes (Fig. S1). Among these, 327 unigenes were only expressed in the saltwater ecotype (G. p. przewalskii) and 551 unigenes were exclusively detected in the freshwater ecotype (G. p. ganzihonensis). In the kidney, 10,910 unigenes were identified in both ecotype libraries, including 4,470 up-regulated and 6,440 down-regulated unigenes (Fig. S2). Among these, 433 unigenes were only observed in the saltwater ecotype, whereas 691 unigenes were only detected in the freshwater ecotype.

An enrichment analysis of the gill was conducted to clarify the biological functions of the differentially-expressed loci. The results indicated that 9,429 loci were enriched in 57 GO terms (P-value ≤ 0.05). Among these GO categories, “response to stimulus” (1,207 unigenes), “catalytic activity” (1,110 unigenes) and “immune system process” (289 unigenes) were significantly enriched among the unigenes compared to the whole transcriptome background. In total, 4,026 unigenes were enriched in 248 metabolic pathways (P-value ≤ 0.05). The most enriched pathways were “metabolic pathways” (459 unigenes, 11.4%), “focal adhesion” (324 unigenes, 8.05%) and “amoebiasis” (295 unigenes, 7.33%). The same analysis was conducted for the unigenes identified in the kidney. The results indicated that 12,034 unigenes were enriched in 60 GO terms (P-value ≤ 0.05). Among these GO categories, “response to stimulus” (4,646 unigenes), “catalytic activity” (4,325 unigenes) and “immune system process” (1,021 unigenes) were significantly enriched among the unigenes compared to the whole transcriptome background. In total, 4,921 unigenes were enriched in 253 pathways (P-value ≤ 0.05). The most enriched pathways were “metabolic pathways” (601 unigenes, 12.21%), “focal adhesion” (240 unigenes, 4.9%) and “regulation of actin cytoskeleton” (241 unigenes, 4.9%).

Identification of putative orthologs between G. p. przewalskii and G. p. ganzihonensis

We recovered 80,916 CDSs for G. p. przewalskii, which was slightly higher than the number of CDSs (80,305 CDSs) recovered from G. p. ganzihonensis. The average CDS length for G. p. przewalskii and G. p. ganzihonensis was 613 and 602 bp, respectively. We identified 56,629 pairs of orthologs using a reciprocal best hit BLAST search. A total of 25,631 pairs of orthologous ESTs matched the ORFs of characterized and uncharacterized proteins. The median length of these transcripts was 1,238 bp, ranging from 163 to 16,276 bp.

Ka/Ks between pairs of orthologs

The Ka/Ks ratio is used as an indicator of selection acting on a protein-coding gene20. Among the 25,631 pairs of orthologs identified, divergence was sufficiently high for 1,546 orthologs (6.0%), facilitating the calculation of both Ka and Ks rates. Among the orthologous pairs for which Ka/Ks values could be calculated, 242 pairs (15.7%) had a Ka/Ks > 1, indicating positive selection and 357 pairs (23.1%) had a Ka/Ks between 0.5 and 1, indicating weak purifying selection (Fig. 5). The vast majority of EST pairs with Ka/Ks > 1 were associated with the GO category of “immune response”. For the remaining orthologous ESTs, we could only calculate either Ka (2,628 orthologous ESTs, 10.3%) or Ks (6,349 orthologous ESTs, 24.8%), or the orthologous ESTs were identical (8,344, 32.6%).

Figure 5

Distribution of Ka and Ks

Orthologous pairs with Ka/Ks ratios > 1 are shown above the gray line, whereas pairs with Ka/Ks ratios between 0.5-1 are shown between the gray and black lines.

Experimental validation

PCR amplification showed that all qPCR primers produced only single fragments of the expected lengths. The qPCR results for 24 selected unigenes were consistent with the transcript abundance determined using RNA-seq, supporting the reliability of the EST data (Figs. S3 and S4).


A central goal of evolutionary genetics is to identify the genes involved in adaptation to new or changing environments, but the difficulty is that adaptation typically involves many (interacting) genes involved in a multitude of biological functions (signaling pathways, cellular and bodily functions, the immune system, etc.). The introduction of NGS techniques has provided a promising tool to assess expression across the genome and address the goal of linking genes with adaptation to particular habitats. In the present study, the transcriptomes of two ecotypes of scaleless carp were sequenced to investigate the adaptations necessary shift from saline to freshwater in these species. Many candidate genes were identified in several of pathways/functions (e.g., calcium signaling pathway(s), aquaporins (AQPs), the neuroendocrine system, immune defense and apoptosis).

Salinity change is a key driving force for the adaptation of fishes11,21,22,23 and ion channels and aquaporins are proteins that are thought to play a role as salinity stress effectors24. Ion channels mediate the ionic steady state not only for Na+, K+ and Cl but also for divalent cations, such as Ca2+. Notably, we observed that the expression of Ca2+-binding proteins was significantly down-regulated in the lake ecotype. Calcium-binding proteins participate in many cellular regulatory processes, including metabolism, apoptosis, nerve growth, the immune response and other biological processes25. In addition to maintaining ionic homeostasis under salt and alkaline stress, the fish from Lake Qinghai showed improved water homeostasis through the use of AQPs. The functions of AQPs have been studied in various animals, plants and bacteria26, but little is known about the role of these proteins in adaptation to salinity and alkalinity in fish. We observed the decreased expression of three AQPs in the gill of the lake ecotype (Table 1). In addition, inositol monophosphatase 1 (IMPA1) was under positive selection in the lake ecotype (Table 2),suggesting an important role for this protein after ecological transitions from saline to freshwater habitats. IMPA1 is essential for the synthesis of inositol, a polyol that plays a protective role in salinity adaptation27.

Table 1 Aquaporins and immune-related gene expression in salt and fresh water ecotypes of G. przewalskii. PG = the gill of G. p. przewalskii. GG = the gill of G. p. ganzihonensis. PK = kidney of G. p. przewalskii. GK = kidney of G. p. ganzihonensis.
Table 2 List of selected candidate genes under positive selection.

The neuroendocrine system also plays a crucial role in adaptation to different salinity environments. Cortisol is a vital hormone for seawater adaptation in teleost species and prolactin plays an important role in the adaptation of anadromous (e.g., salmon) and catadromous (e.g., eel) fish28. A recent study has demonstrated the involvement of the growth hormone (GH)/insulin-like growth factor I (IGFI) axis in salinity adaptation in euryhaline fish29,30. A previous study in scaleless carp showed that both plasma GH and IGFI levels transiently increased after transfer from river-water to lake-water31. In the present study, the expression levels of GH, IGFI and their receptors significantly differed, providing further evidence that these hormones are involved in the regulation of ion homeostasis and salinity acclimation.

The immune functions of fishes are known to correlate with habitat structure32,33,34. After the colonization of novel habitats or upon the exploitation of vacant ecological niches, selection on immune-related genes can be particularly strong when fishes encounter novel parasites33. In the present study, the vast majority of genes under balancing or positive selection (Ka/Ks > 1) were involved in the immune response, including the interferon regulatory factor 5 and complement component C6 genes. Stress can suppress immune system functions35,36 and these data suggest a dramatic stress-related immune response. For example, the up-regulated genes involved in the immune response (e.g., acute phase response proteins, inflammatory response and chemotaxis) have been associated with signaling pathways in innate immunity (e.g., up-regulation of C-type lectin gene and complement C4) or implicated in apoptosis (e.g., up-regulation of caspase 3; Table 1). In contrast to these up-regulated genes, the genes associated with antimicrobial immunity (e.g., rhamnose-binding lectin and Jun B) and apoptosis induction (e.g., caspases and programmed cell death 1 ligand 1) were down-regulated.

Although most of the genes under positive selection were immune-related, no significant differences were detected in nephrosin expression. However, strong evolutionary forces were acting on the nephrosin precursor (Table 2). Nephrosin, a newly discovered member of the astacin family, is a gene potentially involved in the regulation of the immune system37. Nephrosin is primarily synthesized in the lymphohematopoietic tissues of teleosts, which include the head kidney, kidney and spleen38. Boutet et al.39 used suppression subtractive hybridization (SSH) to show that the immune system (nephrosin) in the gill and intestine is regulated through salinity and our results support the importance of nephrosin in habitat shifts between salt and fresh water.

In summary, we compared transcriptome sequence divergence between freshwater and saltwater ecotypes of scaleless carp (G. przewalskii) and these results provide insight into local adaptation and potentially incipient ecological speciation. In addition, these results suggest that a few core genes, involved in immune defense, the neuroendocrine system, the calcium signaling pathway, apoptosis, ion exchange and water absorption, play crucial roles in fish habitat shifts from saline to freshwater. Thus, the present study shows the potential use of NGS transcriptomics to identify the underlying genetics of adaptive traits in wild species, a research field of steadily increasing importance.

Materials and methods

Fish sampling and RNA extraction

Using gill nets, we collected 3 male and 3 female scaleless carp from Lake Qinghai (37°03´N, 100°26´E) and the Ganzi River (37°04´N, 100°27´E) (Fig. 1). The length and weight of the fish were measured (Table S3). Immediately after collection, the fish were dissected and portions of the gill and kidney were steeped in liquid nitrogen and stored at −80°C. Total RNA was extracted from the frozen gill and kidney tissues using Trizol Reagent (Invitrogen, Carlsbad, CA, USA), according to the manufacturer’s instructions and analyzed for integrity and purity using agarose gel electrophoresis and a NanoDrop 2000 spectrophotometer, respectively.

Library construction and Illumina sequencing

RNA quality and quantity were verified using an Agilent 2100 Bioanalyzer prior to further processing. Total RNA from each fish was mixed in equal proportions for each ecotype group (n = 6 per group). Subsequently, beads coated with oligo (dT) were used to isolate poly (A) mRNA. Fragmentation buffer (Ambion, Austin, TX, USA) was added to generate short mRNA fragments through digestion. The first strand cDNA was synthesized using random hexamer primers, followed by synthesis of the second strand. The short fragments were purified using the QiaQuick PCR Purification kit (Qiagen, Valencia, CA, USA) for both end repair and poly (A) addition reactions. The purified cDNA libraries were PCR amplified using 18 cycles. The libraries were then sequenced on an Illumina HiSeq™ 2000 platform. The four cDNA libraries were prepared and sequenced independently in the Beijing Genomics Institute in Shenzhen, China.

De novo assembly of mRNA-seq data and functional annotation

The adapter sequences were removed and reads containing ambiguous bases ‘N’ > 5% or showing low-quality (more than 20% Q ≤ 10 bases) were removed. The de novo assembly of the clean reads was performed using the Trinity assembly program40. For each library, short reads were first assembled into contigs. Subsequently, the reads with overlapping regions were mapped back to contigs, using the distance of the paired-end reads as a guide. Unknown sequences were represented with ‘N’s and the unigenes were generated. Subsequently, the unigenes from four libraries (two libraries for each ecotype) were further spliced and assembled to obtain individual non-redundant unigenes through TGICL, with a minimum overlap length of 100 bp41.

The unigene functions were annotated through BLAST analysis (with an E-value threshold of 10−5) to protein databases, including the NCBI non-redundant (Nr) database, the Swiss-Prot protein database, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database42, the Clusters of Orthologous Groups of proteins (COG) database and nucleotide (nt) database. The unigene sequences were also translated into amino acid sequences using ESTScan43. Blast2GO44 was used to obtain GO annotation of the unigenes based on BLASTX hits against the NCBI Nr database (E-value < 10−5). WEGO45 was used for GO functional analyses. The unigene sequences were also aligned to the COG database to predict and classify functions. Pathway assignments were generated using the KEGG database and the BLASTX algorithm with an E-value threshold of 10−5.

Identification of differentially expressed genes

The gene expression levels were measured in the RNA-seq analyses as the number of reads per kilobase of the exons in a given gene per million mapped reads (RPKM)46. To identify genes and alleles associated with a saline niche, we determined the number of reads for each coding region in the four sample libraries and subsequently calculated the ratio of reads in the same tissue between the two ecotypes. The statistical significance of the differential expression value for each gene was determined using the methods of Audic and Claverie47 and the results of the statistical tests were corrected for multiple testing using the Benjamini–Hochberg false discovery rate (FDR). The expression of a particular sequence was considered significantly different when the adjusted P-value obtained using this method was ≤0.001 and there was at least a two-fold change (≥1 or ≤−1 in log2 ratio value) in the sequence count between two libraries. After screening differentially expressed genes (DEGs), GO function and KEGG pathway analyses were performed.

Identification of orthologous genes

We used the bidirectional best-hit method in BLAST, with a bit score threshold of ≥300, to identify putatively orthologous ESTs between the two ecotypes. This bidirectional best hit threshold requires that the alignment of any two unigenes is longer than 150 bp. The bidirectional best hit method has been widely used to identify putative orthologous genes between closely related species48,49. Open reading frames (ORF) for the putatively orthologous ESTs were determined through a BLASTX analysis (NCBI blast version, 2.2.19)50 against all known vertebrate proteins from the Universal Protein Resource (UniProt Consortium 2008) and protein data sets for six fishes (Danio rerio, Latimeria chalumnae, Tetraodon nigroviridis, Oryzias latipes, Gadus morhuaand and Branchiostoma floridae) in the Ensembl database (Ensembl 52) using an E-value <10−5. If both orthologous ESTs could be annotated, then the coding regions were extracted according to the BLASTX results. The coding sequences were aligned using ClustalW 2.051. As de novo transcriptome assemblies do not generally distinguish members of gene families, using bidirectional best hits to identify the orthologous genes might also generate large numbers of false positive orthologous pairs52. Therefore, these putative orthologous genes were further matched to the ORFs of known or unknown proteins to remove potential paralogs. If two sequences were identified as paralogous in the BLAST search, then these sequences will likely match to different ORFs. Only pairs of sequences unambiguously mapped to the same ORF with an E-value <10−5 were selected as orthologous genes.

Test for positive selection

We estimated the rate of nonsynonymous substitutions per nonsynonymous site (Ka) to the number of synonymous substitutions per synonymous site (Ks) among putatively orthologous coding regions using the maximum-likelihood method53 implemented in yn00 in PAML (ver. 4.0)54. Orthologous ESTs with a Ks rate > 0.005 were excluded from further analyses to avoid analyzing potentially paralogous genes55. The genes with Ka/Ks > 1 were subjected to GO functional and KEGG pathway analyses. Statistical significance was determined using the chi square test (Fisher’s exact test was used when the expected value was less than 5) and the results of all statistical analyses were corrected for multiple testing using FDR.

Real-time quantitative RT–PCR validation

RNA samples from three biological replicates were used for a real-time quantitative reverse transcriptase PCR (qRT-PCR) validation of the transcriptome data. RNAs used in transcriptome sequencing were converted into cDNAs using the PrimeScript™ RT reagent Kit (Takara, Dalian, China) according to the manufacturer’s instructions. qRT-PCR was performed with SYBR green fluorescent dye using an ABI ViiA™7 (Applied Biosystems, Foster City, CA, USA). The fold-induction values were calculated using the 2−ΔΔCt method according to the manufacturer’s instructions. The primers used in the qRT-PCR analysis are listed in Table S4 and β-actin served as the internal control for the expression analyses.

Ethics statements

All necessary permits for collection and experimentation were acquired for the described field study from the Agriculture Department of Qinghai Province, China. All samples of fish used in this study followed the guidelines of the regulations of experiments on animals and was approved by China Zoological Society.

Additional Information

Accession codes: Raw sequencing reads are available at NCBI SRA under accession numbers: SRX673786, SRX673788, SRX673790 and SRX673793.

How to cite this article: Zhang, R. et al. Local adaptation of Gymnocypris przewalskii (Cyprinidae) on the Tibetan Plateau. Sci. Rep. 5, 09780; doi: 10.1038/srep09780 (2015).


  1. Darwin, C. On the origin of species by means of natural selection, or the preservation of favoured races in the struggle for life. (John murray: London, 1859).

  2. Dobzhansky, T. Genetics and the origin of species. Columbia University Press: New York, 1937).

  3. Dobzhansky, T. Speciation as a stage in evolutionary divergence. Am Nat. 74, 312–321 (1940).

    Google Scholar 

  4. Mayr, E. Ecological factors in speciation. Evolution 1, 263–288 (1947).

    Google Scholar 

  5. Mayr, E. Animal species and evolution. Harvard University Press: Cambridge, MA, 1963).

  6. Schluter, D. Ecology and the origin of species. Trends Ecol Evol. 16, 372–380 (2001).

    CAS  PubMed  Google Scholar 

  7. Crawford, J. E. & Nielsen, R. Detecting adaptive trait loci in nonmodel systems: divergence or admixture mapping? Mol Ecol. 22, 6131–6148 (2013).

    PubMed  Google Scholar 

  8. Rice, A. M., Rudh, A., Ellegren, H. & Qvarnström, A. A guide to the genomics of ecological speciation in natural animal populations. Ecol Lett. 14, 9–18 (2011).

    PubMed  Google Scholar 

  9. Kelley, J. L. et al. Genomic resources for a model in adaptation and speciation research: characterization of the Poecilia mexicana transcriptome. BMC genomics 13, 652 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Shafer, A. B. A. & Wolf, J. B. W. Widespread evidence for incipient ecological speciation: a meta-analysis of isolation-by-ecology. Ecol Lett. 16, 940–950 (2013).

    PubMed  Google Scholar 

  11. Kozak, G. M., Brennan, R. S., Berdan, E. L., Fuller, R. C. & Whitehead, A. Functional and population genomic divergence within and between two species of killifish adapted to different osmotic niches. Evolution 68, 63–80 (2014).

    CAS  PubMed  Google Scholar 

  12. Faria, R. et al. Advances in ecological speciation: an integrative approach. Mol Ecol. 23, 513–521 (2014).

    PubMed  Google Scholar 

  13. Li, J., Fang, X., Pan, B., Zhao, Z. & Song, Y. Late Cenozoic intensive uplift of Qinghai-Xizang Plateau and its impacts on environments in surrounding area. Quaternary Sci. 21, 381–391 (2001).

    Google Scholar 

  14. Pan, B. A study on the geomorphic evolution and development of the upper reaches of Yellow River in Guide Basin. Arid Land Geogr. 17, 43–50 (1994).

    Google Scholar 

  15. Li, J., Fang, X., Ma, H., Zhu, J., Pan, B. & Chen, H. Geomorphological and environmental evolution in the upper reaches of the Yellow River during the late Cenozoic. Sci China Ser D. 39, 380–390 (1996).

    Google Scholar 

  16. Zhu, S. & Wu, F. Study of fish fauna in Qinghai Lake. Science Press: Beijing, 1975).

  17. Zhang, P., Zhang, B., Qian, G., Li, H. & Xu, L. The study of paleoclimatic parameter of Qinghai lake since Holocene. Quaternary Sci. 3, 225–238 (1994).

    Google Scholar 

  18. Shen, J., Liu, X., Wang, S. & Matsumoto, R. Palaeoclimatic changes in the Qinghai Lake area during the last 18,000 years. Quatern Int. 136, 131–140 (2005).

    Google Scholar 

  19. Xiong, F., Chen, D. & Duan, X. Threatened fishes of the world: Gymnocypris przewalskii (Kessler, 1876) (Cyprinidae: Schizothoracinae). Environ Biol Fish. 87, 351–352 (2010).

    Google Scholar 

  20. Elmer, K. R. et al. Rapid evolution and selection inferred from the transcriptomes of sympatric crater lake cichlid fishes. Mol Ecol. 19 Suppl 1, 197–211 (2010).

    CAS  PubMed  Google Scholar 

  21. Lee, C. E. & Bell, M. A. Causes and consequences of recent freshwater invasions by saltwater animals. Trends Ecol Evol. 14, 284–288 (1999).

    CAS  PubMed  Google Scholar 

  22. Smith, S. A. & Bermingham, E. The biogeography of lower Mesoamerican freshwater fishes. J Biogeogr. 32, 1835–1854 (2005).

    Google Scholar 

  23. Laverty, G. & Skadhauge, E. Adaptation of teleosts to very high salinity. Comp Biochem Phys A. 163, 1–6 (2012).

    CAS  Google Scholar 

  24. Meng, J. et al. Genome and transcriptome analyses provide insight into the euryhaline adaptation mechanism of Crassostrea gigas. PloS ONE 8, e58563 (2013).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  25. Zhuang, Z., Zhao, X., Li, H., Wang, S. & Peng, X. Gut CaVP is an innate immune protein against bacterial challenge in amphioxus Branchiostoma belcheri. Fish Shellfish Immun. 31, 217–223 (2011).

    CAS  Google Scholar 

  26. Borgnia, M., Nielsen, S., Engel, A. & Agre, P. Cellular and molecular biology of the aquaporin water channels. Annu Rev Biochem. 68, 425–458 (1999).

    CAS  PubMed  Google Scholar 

  27. Kalujnaia, S., McVee, J., Kasciukovic, T., Stewart, A. J. & Cramb, G. A role for inositol monophosphatase 1 (IMPA1) in salinity adaptation in the euryhaline eel (Anguilla anguilla). Faseb J. 24, 3981–3991 (2010).

    CAS  PubMed  Google Scholar 

  28. Shul′man, G. E. & Love, R. M. The biochemical ecology of marine fishes. Academic Press: San Diego, 1999).

  29. McCormick, S. D. Endocrine control of osmoregulation in teleost fish. Am Zool. 41, 781–794 (2001).

    CAS  Google Scholar 

  30. Sakamoto, T. & McCormick, S. D. Prolactin and growth hormone in fish osmoregulation. Gen Comp Endocr. 147, 24–30 (2006).

    CAS  PubMed  Google Scholar 

  31. Cao, Y. et al. Growth hormone and insulin-like growth factor of naked carp (Gymnocypris przewalskii) in Lake Qinghai: Expression in different water environments. Gen Comp Endocr. 161, 400–406 (2009).

    ADS  CAS  PubMed  Google Scholar 

  32. Bowden, T. J. Modulation of the immune system of fish by their environment. Fish Shellfish Immun. 25, 373–383 (2008).

    CAS  Google Scholar 

  33. Birrer, S. C., Reusch, T. B. & Roth, O. Salinity change impairs pipefish immune defence. Fish Shellfish Immun. 33, 1238–1248 (2012).

    CAS  Google Scholar 

  34. Diepeveen, E. T., Roth, O. & Salzburger, W. Immune-related functions of the Hivep gene family in East African cichlid fishes. G3: Genes Genom Genet. 3, 2205–2217 (2013).

    CAS  Google Scholar 

  35. Wendelaar Bonga, S. E. The stress response in fish. Physiol Rev. 77, 591–625 (1997).

    CAS  PubMed  Google Scholar 

  36. Bly, J. E., Quiniou, S. M. & Clem, L. W. Environmental effects on fish immune mechanisms. Dev Biol Stand 90, 33–43 (1997).

    CAS  PubMed  Google Scholar 

  37. Tsai, P. L., Chen, C. H., Huang, C. J., Chou, C. M. & Chang, G. D. Purification and cloning of an endogenous protein inhibitor of carp nephrosin, an astacin metalloproteinase. J Biol Chem. 279, 11146–11155 (2004).

    CAS  PubMed  Google Scholar 

  38. Hung, C. H., Huang, H. R., Huang, C. J., Huang, F. L. & Chang, G. D. Purification and cloning of carp nephrosin, a secreted zinc endopeptidase of the astacin family. J Biol Chem. 272, 13772–13778 (1997).

    CAS  PubMed  Google Scholar 

  39. Boutet, I., Ky, C. L. & Bonhomme, F. A transcriptomic approach of salinity response in the euryhaline teleost, Dicentrarchus labrax. Gene 379, 40–50 (2006).

    CAS  PubMed  Google Scholar 

  40. Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 29, 644–652 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Pertea, G. et al. TIGR Gene Indices clustering tools (TGICL): a software system for fast clustering of large EST datasets. Bioinformatics 19, 651–652 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y. & Hattori, M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 32, D277–D280 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Iseli, C., Jongeneel, C. V. & Bucher, P. ESTScan: a program for detecting, evaluating and reconstructing potential coding regions in EST sequences. Proc Int Conf Intell Syst Mol Biol. 99, 138–148 (1999).

    Google Scholar 

  44. Conesa, A. et al. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674–3676 (2005).

    CAS  Google Scholar 

  45. Ye, J. et al. WEGO: a web tool for plotting GO annotations. Nucleic Acids Res 34, W293–W297 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Mortazavi, A., Williams, B. A., Mccue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5, 621–628 (2008).

    CAS  PubMed  Google Scholar 

  47. Audic, S. & Claverie, J. M. The significance of digital gene expression profiles. Genome Res. 7, 986–995 (1997).

    CAS  PubMed  Google Scholar 

  48. Kunstner, A. et al. Comparative genomics based on massive parallel transcriptome sequencing reveals patterns of substitution and selection across 10 bird species. Mol Ecol. 19, 266–276 (2010).

    PubMed  PubMed Central  Google Scholar 

  49. Barreto, F. S., Moy, G. W. & Burton, R. S. Interpopulation patterns of divergence and selection across the transcriptome of the copepod Tigriopus californicus. Mol Ecol. 20, 560–572 (2011).

    PubMed  Google Scholar 

  50. Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Larkin, M. A. et al. Clustal W and clustal X version 2.0. Bioinformatics 23, 2947–2948 (2007).

    CAS  Google Scholar 

  52. Wang, X. et al. Transcriptome analysis and comparison reveal divergence between two invasive whitefly cryptic species. BMC Genomics 12, 458 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Yang, Z. & Nielsen, R. Estimating synonymous and nonsynonymous substitution rates under realistic evolutionary models. Mol Biol Evol. 17, 32–43 (2000).

    CAS  PubMed  Google Scholar 

  54. Yang, Z. PAML 4: Phylogenetic analysis by maximum likelihood. Mol Biol Evol. 24, 1586–1591 (2007).

    CAS  Google Scholar 

  55. Bustamante, C. D. et al. Natural selection on protein-coding genes in the human genome. Nature 437, 1153–1157 (2005).

    ADS  CAS  PubMed  Google Scholar 

Download references


We thank Daniel Förster for helpful comments. This research was partially supported by grants from the Chinese Academy of Sciences (Key Innovation Plan: KSCX2-YW-N-101 and KSCX2-1W-N-004) and from the National Natural Science Foundation of China (30970341, 31172070). ZP is supported by the grant from the National Natural Science Foundation of China (31272283) and the Program for New Century Excellent Talents in University (2013).

Author information




R.Z., Z.P. and K.Z. designed the experiment; R.Z., G.L., Y.T. and K.Z. performed the experiment; R.Z., A.L., C.Z., C.T. and Z.P. analysed and interpreted the data; and R.Z., A.L., Z.P. and K.Z. wrote the paper. All co-authors participated in the scientific discussions and commented on the manuscript.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Electronic supplementary material

Rights and permissions

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, R., Ludwig, A., Zhang, C. et al. Local adaptation of Gymnocypris przewalskii (Cyprinidae) on the Tibetan Plateau. Sci Rep 5, 9780 (2015).

Download citation

Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing