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. 2022 Sep 30;12(11):300. doi: 10.1007/s13205-022-03369-y

Population genetic structure of fringe-lipped carp, Labeo fimbriatus from the peninsular rivers of India

Subrat Kumar Swain 1,2, Basanta Pravas Sahu 2,3, Sofia Priyadarsani Das 2,4, Lakshman Sahoo 2, Pratap Chandra Das 2, Paramananda Das 2,✉
PMCID: PMC9525529  PMID: 36276442

Abstract

Labeo fimbriatus is a medium carp species found throughout India's peninsular river basins and is regarded as a valuable aquaculture resource alongside Indian major carps due to its taste and nutritional value. This species has recently declined dramatically due to habitat degradation and overfishing. Because of its enormous economic importance, a selective breeding programme is likely to be in place to improve performance traits. Knowledge of genetic variation among the base population from which the broodstock will be selected is an important step in this process. A diverse genetic base of broodstock is required to achieve the best response to selection for long-term aquaculture management practices. Consequently, using mitochondrial DNA (ATPase 6 and Control region) and microsatellite markers, we have made the first step toward estimating the level of genetic variation and how it is distributed among the four populations of L. fimbriatus found in peninsular rivers in India. The ATPase 6 gene analysis in four populations revealed 15 haplotypes and 51 variable sites, in contrast to the Control region, which had 60 haplotypes together with 73 variable sites and a haplotype diversity of 0.941. Twelve microsatellite loci displayed estimated allele numbers (NA) ranging from 3 to 19, observed heterozygosity (HO), and expected heterozygosity (HE), respectively, of 0.705 to 0.753 and 0.657 to 0.914. Each marker type showed a significant FST value, indicating the presence of low to moderate genetic differentiation across entire wild populations. The Godavari, Kaveri, and Mahanadi populations formed one cluster according to the UPGMA, which was based on genetic distance matrix, while the Krishna population formed a separate cluster. The comparative genetic analysis of data from different markers utilized in the current study would enable the identification of the genetic stocks of L. fimbriatus and facilitate conservation measures and selective breeding.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03369-y.

Keywords: Labeo fimbriatus, Microsatellite, mtDNA, Genetic diversity, Population structure

Introduction

The Indian aquaculture industry is primarily reliant on three Indian major carps, such as Labeo rohita (L. rohita), Catla catla (C. catla), and Cirrhinus mrigala (C. mrigala). Aside from these, common carp, medium carp, and minor carp account for 5% of net freshwater aquaculture production. A focus on species diversity is being made because of the growing demand for aquaculture products, and as a result, new species have been added to the candidate species list, including Labeo fimbriatus, Labeo calbasu, and Labeo gonius (Jena et al. 2011). Labeo fimbriatus (L. fimbriatus), also known as podosi, is endemic to the southern peninsular region of India, including the rivers Krishna, Kaveri, Mahanadi and Godavari. The riverine regions of Pakistan, Nepal, Myanmar and Bangladesh are also home to this species (Molur 1998). However, due to overharvesting, habitat destruction, fragmentation and river pollution, the natural populations of these species are continuously declining and are in danger of going extinct (Dahanukar 2011). Understanding the genetic diversity of brood stocks is essential to managing wild populations in a sustainable and efficient manner. The genetic enhancement of commercial fish and shellfish species through the adaptation of selective breeding methods will support that information (Dinesh et al. 1993; Garcia and Benzie 1995; Tassanakajon et al. 1997). Microsatellites, also known as simple sequence repeats (SSRs), are found in almost all known eukaryotic and prokaryotic organisms and have a widespread distribution throughout the genome (Waber and Wong 1993). This is a codominant nuclear marker with a high mutation rate that results in extensive polymorphism within a genome (Tautz and Renz 1984). As a result, it has the potential to be used as a genetic tool to decipher genetic diversity, molecular mapping, parentage analysis, marker-assisted selection and molecular breeding (Jarne and Lagoda 1996; Bruford and Wayne 1993). However, it has been continuously used for the genetic improvement of several aquaculture species (Abdul-Muneer 2014). Mitochondrial DNA (mtDNA) markers are also regarded as an excellent tool for analyzing population structure, which cannot be deciphered using nuclear markers (Bowen et al. 1992). Because of its maternal inheritance and relatively faster evolutionary rate than nuclear DNA, mtDNA has been used as a preferred marker for population genetics and phylogenetic studies (Avise 1986). Thus, the population genetics of several economically important species, including L. rohita (Behera et al. 2018), C. catla (Sahoo et al. 2019), and C. mrigala (Das et al. 2018), have been revealed using this marker.

In comparison to Indian major carps, L. fimbriatus has received less attention in terms of genomic resources such as molecular markers, transcriptome, and complete genome analysis. Only preliminary information on its population genetics using heterologous amplification (Swain et al. 2013) and microsatellite marker characterization is available (Swain et al. 2012). It prevents the development of an appropriate framework for the management of the broodstocks, followed by captive breeding and the preservation of this species. Using mitochondrial and microsatellite markers, we examined the genetic composition of four L. fimbriatus populations and discovered that there was substantial genetic heterogeneity among these populations. In addition, we found that a clade is formed by three groups that are geographically close to one another. All wild populations studied showed low to moderate genetic differentiation. In most populations, the expected heterozygosity (HE) was lower than the observed heterozygosity (HO), indicating that inbreeding effects had a lower impact within the populations. The current study’s findings can be used as molecular evidence for the selection of a base population to help with brood stock management, followed by genetic improvement of this underutilized species.

Materials and methods

Sample collection

A total of 192 samples of L. fimbriatus were collected from 2009 to 2012 from Mahanadi (n = 48), Godavari (n = 48), Krishna (n = 48), and Kaveri (n = 48). Fin clippings from all the samples were stored at − 20 °C in 95% ethanol. Detailed sampling information is shown in Table 1 and Fig. 1.

Table 1.

Place and year of collected samples from four different rivers of India and its accession numbers of respective markers utilized during the study

Peninsular riverine population Place of collection Coordinates Time of collection ATPse 6 gene (Accession numbers) Control region (CR) (Accession numbers) Microsatellite (Accession numbers)
Mahanadi Burla, Sambalpur 21.4888° N, 83.8844° E September 2009 KC734902-KC734927 KC734984-KC735013 JQ838157-JQ838171
Kaveri KRS dam, Mysore 12.4255° N, 76.5724° E August 2010 KC734957-KC734983 KC735040-KC735069
Godavari Dhavaleshwaram, Rajahmundry 16.9558° N, 81.7927° E April 2011 KU541892-KU541922 KU541923-KU541949
Krishna

Ibrahimpatnam,

Vijayawada

 16°36′20″N 80°22′3″E October 2012 KC734929-KC734956 KC735014-KC735039
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Fig. 1.

Fig. 1

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Partial map of India showing the rivers from where the samples were collected

Genomic DNA isolation

High molecular weight genomic DNA was isolated using the standard phenol–chloroform extraction method (Sambrook et al. 2001). The isolated genomic DNA was resuspended in TE buffer followed by estimation of its concentration using a UV spectrophotometer (Nanodrop, Eppendorf, Germany) and stored at − 20 °C for further use.

Microsatellite genotyping and data analysis

Swain et al. (2012) developed a total of 12 microsatellite loci for multiplexing and marker characterization. For 5′ end labelling, fluorescent dyes such as HEX and FAM were used (Supplementary file-Table 1). The twelve loci were divided into six groups based on the size of the product and the colour of the labelled dye. Polymerase chain reactions were performed in a 20 μl volume with 20 ng of template DNA, 5 pmol of each primer, 1X Taq buffer containing 1.5 mM MgCl2, 200 M of each dNTP, and 0.25U of Taq DNA polymerase. PCR amplification was performed using a GeneAmp 9700 thermocycler (Applied Biosystems, USA) with the following cyclic conditions: initial denaturation at 94 °C for 5 min, followed by 30 cycles at 94 °C for 30 s, 30 s for annealing at specific temperatures, 45 s for elongation at 72 °C, and 5 min for final extension at 72 °C.

The amplified products were loaded onto an ABI PRISM 3700 DNA automated sequencer (Applied Biosystems, USA) and separated through capillary electrophoresis. The fragment size was evaluated using the GeneScan™–500 LIZ™ size standards (ABI) as a reference using GeneMapper v.3.7 software (Applied Biosystems, USA). The GMCONVERTER was utilized to convert the genotyping data into the GENEPOP input format (Faircloth 2006). The presence of any null allele was evaluated using Micro‐Checker (Van Oosterhout et al. 2004).

To evaluate the genetic diversity of four L. fimbriatus populations, the number of alleles per locus (NA), observed (HO) and expected (HE) heterozygosity, and the Hardy–Weinberg equilibrium (HWE) at each marker for each population were estimated using Genetic Data Analysis (GDA) software, version 1.1 (Lewis and Zaykin 2001). Pairwise FST and P values were evaluated by utilizing ARLEQUIN version 3.1 (Excoffier et al. 2006). The variance between populations and among individuals within a population was estimated through AMOVA using ARLEQUIN 3.1. F-statistics were calculated by utilizing 1000 bootstrap replicates, having a 95% confidence interval of GDA. Furthermore, by implementing GDA, Nei’s (1987) genetic distances were calculated followed by the construction of an UPGMA dendrogram to test population grouping. A Bayesian clustering analysis among samples was performed to investigate the population structure through STRUCTURE 2.3 (Pritchard et al. 2000). The admixture model was used by correlating allele frequency with 20 replicates of each run from K = 2 to K = 7 (K is the number of genetic clusters), 20,000 iterations (burn-in), and 20,000 Markov chain Monte Carlo (MCMC) generations. The Evanno method of STRUCTURE HARVESTER was applied to detect the value of K that best fitted the data (Earl and vonHoldt, 2012; Evanno et al. 2005).

ATPase 6 gene and Control region amplification and sequencing

Partial gene sequences of ATPase 6 and the Control region were amplified from DNA samples using universal primers (Supplementary file-Table 2). A thermal cycler was used to perform PCR amplification in a 25 µl reaction volume with 1X PCR buffer, 0.25 mM of dNTP mix, 10 pmol of each primer, 0.25 U of Taq polymerase, and 50 ng/µl genomic DNA. The PCR profile was used as one cycle initial denaturation at 94 °C for 4 min, followed by 34 cycles (denaturation: 94 °C, 30 s, annealing: 50 °C, 1 min and extension: 72 °C 2 min) and a 7 min final extension at 72 °C. In the case of ATPase 6, similar conditions were applied except for the annealing temperature (55 °C). PCR products were checked on 1% agarose gels followed by the selection of the most specific and intense products for sequencing. The amplicons were purified using a PCR purification kit (Qiagen). The Sanger sequencing was accomplished with an automated DNA sequencer (ABI Prism 310 genetic analyzer) by employing universal primers. Raw sequence data was checked and aligned using the Bioedit software version 7.0.5.3 to obtain a consensus sequence of an individual gene fragment.

Data analysis

All sequences derived during this study were submitted to GenBank for a unique accession number (Table 1). The Kimura's two parameters implemented in MEGA 6.0 were used to calculate pairwise distances between nucleotide and amino acid sequences (Tamura et al. 2007). The polymorphic sites, as well as nucleotide diversity (π) and haplotype diversity (Hd) were identified using DnaSP version 5.10.01 (Librado and Rozas 2009).

The pairwise FST values were evaluated using Arlequin version 3.11 (Excoffier et al. 2006).

Results

Microsatellite polymorphism estimation

The microsatellite loci used in this study yielded 347 alleles across all four populations. The number of alleles per locus ranged between 3 and 19 (Table 2). MicroChecker results indicate the presence of null alleles at locus LF-2 in the Mahanadi population and LF-6 in the Godavari and Kaveri populations. The average number of alleles across loci, ranging from 6.82 (Krishna) to 15 (Godavari), suggests that all populations have relatively high genetic variation. Expected heterozygosity ranged from 0.560 to 0.909, but observed heterozygosity across all loci ranged from 0.230 to 0.978 (Table 2). After Bonferroni correction, exact tests for Hardy–Weinberg equilibrium showed that every population had two loci that were out of equilibrium.

Table 2.

Genetic variability at 12 microsatellite loci in four L. fimbriatus populations

Microsatellite Locus
Population LF-01 LF-03 LF-04 LF-07 LF-08 LF -09 LF -10 LF -11 LF -12 LF -13 LF -15 LF-16 Average across loci
Mahanadi
 NA 8 15 5 12 8 12 8 6 19 14 12 7 12
 HO 0.704 0.931 0.772 1.00 1.000 0.976 0.825 0.553 0.883 1.000 0.733 1.000 0.865
 HE 0.623 0.863 0.571 0.887 0.705 0.751 0.675 0.560 0.909 0.892 0.825 0.775 0.753
 P 0.149 0.195 0.147 0.007 0.128 0.042 0.037 0.010 0.036 0.00* 0.191 0.032
 1 − HO/HE 0.132 − 0.07 − 0.35 − 0.12 − 0.42 − 0.30 − 0.22 0.01 0.02 − 0.12 0.11 − 0.29 − 0.15
Godavari
 NA 10 10 11 4 6 9 1 9 15 6 12 9 15
 HO 1.00 1.00 1.00 1.000 1.000 1.000 0.00 1.000 0.978 0.765 1.000 1.000 0.895
 HE 0.853 0.864 0.846 0.673 0.698 0.846 0.00 0.767 0.853 0.628 0.865 0.805 0.725
 P 0.002 0.549 0.408 0.000 0.004 0.113 1.000 0.081 0.035 0.505                 0.067 0.010
 1 − HO/HE 0.174 − 0.15 − 0.18 − 0.49 − 0.43 − 0.18 0.00 − 0.30 − 0.14 − 0.22 − 0.15 − 0.24 − 0.23
Krishna
 NA 7 6 7 4 5 5 4 4 6 5 12 5 6.82
 HO 0.736 0.609 0.526 0.428 0.454 0.760 0.777 0.590 0.595 0.446 0.869 0.708 0.657
 HE 0.802 0.797 0.785 0.657 0.696 0.738 0.594 0.590 0.825 0.699 0.801 0.687 0.729
 P 0.105 0.093 0.043 0.001 0.035 0.030 0.003 0.661 0.563 0000* 0.003 0.134
 1 − HO/HE 0.083 − 0.23 0.33 0.350 0.349 − 0.03 − 0.31 0.00 0.28 0.36 − 0.08 − 0.03 0.099
Kaveri
 NA 6 6 5 3 8 7 1 7 10 6 7 7 14.37
 HO 1.00 0.230 1.00 1.000 1.000 1.000 0.000 1.000 1.000 0.975 1.000 0.708 0.914
 HE 0.821 0.756 0.771 0.669 0.823 0.814 0.000 0.825 0.847 0.697 0.750 0.687 0.705
 P 0.011 0.011 0.001 0.000 0.058 0.014 1.000 0.113 0.001 0.096 0.013 0.035
 1 − HO/HE − 0.22 0.178 − 0.29 − 0.50 − 0.21 − 0.23 0.000 − 0.21 − 0.18 − 0.40 − 0.33 − 0.03 -0.30
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The data presented as no. of alleles (NA), expected heterozygosity (HE), observed heterozygosity (Ho), the p value for testing Hardy–Weinberg equilibrium, and fixation index (FST) = 1 − HO/HE in four populations of L. fimbriatus

NA number of allele, HO and HE observed and expected heterozygosity, f inbreeding co-efficient

*Significant deviation from HWE after Bonferroni correction (p < 0.0001)

Population differentiation

The pairwise FST values of the four populations of L. fimbriatus ranged from 0.082 to 0.258. (Supplementary file-Table 3). Based on FST values (FST—0.258), an elevated genetic differentiation was observed between the rivers Godavari–Kaveri populations, whereas a nominal differentiation was observed between the Krishna and Kaveri populations (FST—0.082). AMOVA determined that genetic variation within and between populations were 83.01% and 16.99%, respectively (P < 0.05) (Supplementary file-Table 4). The UPGMA dendrogram revealed that the Mahanadi, Godavari, and Kaveri populations were all in one cluster, with Krishna having only one branch (Fig. 2).

Fig. 2.

Fig. 2

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UPGMA dendrogram based on genetic distance among four wild populations of L. fimbriatus

STRUCTURE HARVESTER suggested that K = 5 be used to build the admixture models (Evanno et al. 2005). The STRUCTURE analysis revealed that the four riverine populations studied in this study formed four distinct clusters (Fig. 3).

Fig. 3.

Fig. 3

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Bar plots from STRUCTURE suggesting four clusters (K = 5) based on 12 polymorphic microsatellite loci

Mitochondrial DNA variation

A partial ATPase 6 gene (612 bp) analysis in four L. fimbriatus populations revealed 15 haplotypes, 51 variable positions, 2 singletons and 50 parsimony informative sites. The Godavari had the most haplotypes, with eight, followed by Krishna, which had three. In each Mahanadi and Kaveri population, two haplotypes were found. For Godavari, Krishna, Mahanadi and Kaveri, the haplotype diversity (Hd) was 0.8482, 0.6402, 0.3589, 0.1424, and the nucleotide diversity (π) was 0.0025, 0.0065, 0.0264, and 0.0004, respectively. Godavari, Krishna, and Kaveri shared Hap-4; Mahanadi, Godavari, and Kaveri shared Hap-2.

AMOVA estimated 24% genetic variation between populations and 76% within populations (Supplementary file-Table 4). The pairwise FST values ranged from 0.157 to 0.343. (Supplementary file-Table 5). Godavari–Kaveri and Krishna–Kaveri had the highest (0.343) and lowest (0.157) pairwise FST values (Supplementary file-Table 5).

A partial Control region (710 bp) analysis in four L. fimbriatus populations revealed 60 haplotypes with 83 variable positions, 3 singleton variable sites, and 127 Parsimony informative sites. The Godavari had the most haplotypes, with 24, followed by Krishna, which had eight. Each Mahanadi and Kaveri population had two haplotypes. For Godavari, Kaveri, Mahanadi, and Krishna, the haplotype diversity (Hd) was 0.9841, 0.8917, 0.8482, 0.6758, and the nucleotide diversity (π) was 0.0340, 0.00275, 0.0387, and 0.0020, respectively. Krishna and Kaveri only had hap-13 in common. According to AMOVA, there is 34.40 percent genetic variation among the population and 65.60 percent within the population (Supplementary file-Table 4). FST values for pairs ranged from 0.074 to 0.539 (Supplementary file-Table 6). The highest (0.539) and lowest (0.074) pairwise FST values were obtained between Godavari and Kaveri, and Krishna and Kaveri, respectively (Supplementary file-Table 6).

Discussion

Genetic variation is regarded as an important factor in the development of a diverse range of genotypes through selection, and one can improve the traits of interest while maintaining optimal genetic resources for the species’ long-term management (Abdul-Muneer 2014). This study used data from microsatellite and mitochondrial DNA sequences to better understand the genetic diversity of the L. fimbriatus population found in India's peninsular areas. The observation of a wide range of allelic richness per locus (NA = 3–19) in this study revealed that the populations studied retain a significant amount of genetic variety. In our previous preliminary investigation, we discovered three alleles per locus (Swain et al. 2013). However, because that study only used three heterologous primers and had a small population, drawing a good picture of the genetic diversity present was impossible. The current study demonstrated the advantages of species-specific primers over heterologous primers for genetic analysis. When compared to the previous study's results (HO: 0.214–0.833 and HE: 0.472–0.645), the evidence of high levels of observed and expected heterozygosity (HO: 0.230–0.978 and HE: 0.560–0.909) further supports this idea (Swain et al. 2013). However, the present study’s heterozygosity value, which was higher than that of L. rohita and other Indian big carp (HO: 0.500–0.870 and HE: 0.389–0.878), adds to the evidence for the occurrence of a high diversity within the wild population of L. fimbriatus (Sahoo et al. 2014).

The observed heterozygosity (HO) was higher in most populations than the expected heterozygosity (HE), indicating that most populations agree with the Wright–Fisher model assumptions and no evidence of genetic drift was found (Nesa et al. 2018). In most of the loci, both positive and negative values of 1 − HO/HE were found in all of the L. fimbriatus populations studied. Low heterozygosity can be caused by a species' genetic bottleneck (Tonny et al., 2014), which results in a sudden decrease in effective population size or a high frequency of assortative mating. At locus LF-13, the Mahanadi and Krishna populations deviated from HWE (p < 0.001) (Table 2). Evolutionary forces, such as random genetic drift, migration, mutation, and natural selection may shape the genetic differentiation pattern (Hartl and Clark 1997). The majority of other loci agree with the HWE, and demographic forces cause genetic variation that affects the entire genome rather than a single locus. As a result, this locus may have been subjected to natural selection in the two populations mentioned above. A low to moderate (Supplementary file-Table 3) level of population differentiation was observed in this study (0.082 to 0.258). Godavari–Kaveri stocks had the highest genetic differentiation (FST—0.258) and Krishna–Kaveri stocks had the lowest (FST—0.082). The two rivers (Krishna and Kaveri) are linked by man-made channels. As a result, gene flow between these rivers may have occurred recently (Das et al. 2021).

The results of the microsatellite study and the examination of mitochondrial markers were consistent. The ATPase 6 and Control region obtained FST values of 0.157–0.281 and 0.074–0.539, respectively, which indicated the presence of moderate to low genetic differentiation. Similar to microsatellite marker analyses, both the ATPase 6 and Control region showed the maximum genetic divergence between Godavari and Kaveri stocks and the lowest between Krishna and Kaveri. As measures of variation within populations, haplotype and nucleotide diversity of mitochondrial markers, as well as average allelic richness of microsatellites, were used. Interestingly, all of the analyzed data revealed that the Godavari population had the most variation, followed by Kaveri, Mahanadi, and Krishna.

In conclusion, assessing the genetic diversity of this commercially important carp species will assist breeders in developing the best selective breeding programmes possible, fostering a broad genetic base and allowing them to make well-informed decisions about how to manage brood stocks in a sustainable manner.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOC 136 KB) (69.5KB, doc)

Acknowledgements

The authors are thankful to Indian Council of Agricultural Research (ICAR)—Central Institute of Freshwater Aquaculture (CIFA), for the laboratory facilities provided during the work.

Author contributions

PD contributed to the conceptualization of this study. SKS, SPD, LS and PCD collected samples. SKS, BPS, SPD, and LS carried out the genomic DNA isolation; PCR carried out the genotyping and population genetic analysis. SKS and BPS compiled the results and prepared the first draft. PD, SPD, LS and PCD supervised the study and edited the final draft and gave final approval of this version to be published. All authors read and approved the final manuscript.

Funding

No funding was received for conducting this study.

Data availability

The nucleotide sequences of present study are available in the GenBank repository (https://www.ncbi.nlm. nih.gov/genbank/) with the accession number KC734902-KC735069, KU541892-KU541949, and JQ838157-JQ838171.

Declarations

Conflict of interest

The authors declare that they have no conflicts of interest.

Research involving animals

All the experiments in the present study were carried out in accordance with the guidelines for control and supervision of experiments on animals by the Government of India and approved by Institutional Animal Ethics Committee (AEC).

Informed consent

Not applicable.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (DOC 136 KB) (69.5KB, doc)

Data Availability Statement

The nucleotide sequences of present study are available in the GenBank repository (https://www.ncbi.nlm. nih.gov/genbank/) with the accession number KC734902-KC735069, KU541892-KU541949, and JQ838157-JQ838171.

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