Indian Journal of Animal Research

  • Chief EditorK.M.L. Pathak

  • Print ISSN 0367-6722

  • Online ISSN 0976-0555

  • NAAS Rating 6.50

  • SJR 0.263

  • Impact Factor 0.4 (2024)

Frequency :
Monthly (January, February, March, April, May, June, July, August, September, October, November and December)
Indexing Services :
Science Citation Index Expanded, BIOSIS Preview, ISI Citation Index, Biological Abstracts, Scopus, AGRICOLA, Google Scholar, CrossRef, CAB Abstracting Journals, Chemical Abstracts, Indian Science Abstracts, EBSCO Indexing Services, Index Copernicus
Indian Journal of Animal Research, volume 56 issue 8 (august 2022) : 933-940

Character-based Diagnostic Keys, Molecular Identification and Phylogenetic Relationships of Fishes based on Mitochondrial Gene from Pulicat Lake, India: A Tool for Conservation and Fishery Management Purposes

A. Rathipriya1, A. Kathirvelpandian1,2,*, S.A. Shanmugam1, A. Uma3, E. Suresh1, N. Felix4
1Institute of Fisheries Postgraduate Studies, Tamil Nadu Dr. J. Jayalalithaa Fisheries University, OMR Campus, Vaniyanchavadi-603 103, Chennai, Tamil Nadu, India.
2Presently at ICAR-National Bureau of Fish Genetic Resources, PMFGR Centre, Kochi-682 018, Kerala, India.
3Dr. M.G.R. Fisheries College and Research Institute, Tamil Nadu Dr. J. Jayalalithaa Fisheries University, Ponneri-601 204, Chennai, Tamil Nadu, India.
4Directorate of Incubation and Vocational Training in Aquaculture, Tamil Nadu Dr. J. Jayalalithaa Fisheries University, ECR-Muttukadu- 603 112, Chennai, Tamil Nadu, India.
Cite article:- Rathipriya A., Kathirvelpandian A., Shanmugam S.A., Uma A., Suresh E., Felix N. (2022). Character-based Diagnostic Keys, Molecular Identification and Phylogenetic Relationships of Fishes based on Mitochondrial Gene from Pulicat Lake, India: A Tool for Conservation and Fishery Management Purposes . Indian Journal of Animal Research. 56(8): 933-940. doi: 10.18805/IJAR.B-4905.
Background: Pulicat Lake is one of India’s most valuable ecosystems. Correct species identification is essential for the conservation and management of finite resources. Molecular signatures and character-based keys could be used for faster identification. This will be a powerful tool to obtain a significant amount of accurate information quickly and conveniently and could be used for forensic applications and the conservation of fish in Indian waters.

Methods: In addition to morphological identification, the species were identified using various approaches, such as Neighbour-joining (NJ), Maximum Likelihood (ML) trees and the Character-based method. For each species, the LOGic (BLOG 2.0) DNA Barcode analysis method was used to identify different positions of key diagnostic nucleotides in a given set.

Result: DNA barcodes were generated from twenty-nine species of fish representing seven orders, 26 genera and 21 families in this study. The current study’s findings show that the 16S rRNA barcodes facilitated successful fish species identification and also provided phylogenetic information to differentiate the fishes. The character-based molecular diagnostic keys for fishes were also developed in-silico from the data set comprising 393 mitochondrial 16S rRNA sequences including the sequences generated in the present study and other published resources.
Pulicat lake in India is the second-largest brackish-water lake running almost parallel to the Bay of Bengal. Govindan and Ravichandran (2016) identified 83 species in Pulicat Lake, categorising them into 14 orders and 50 families. The biodiversity details from this area have been reported by several researchers. So far, no recent consolidated data is available. DNA-based markers, particularly mitochondrial DNA (mtDNA) markers, are commonly used to improve the resolution of systematic connections between species. According to Finizio et al., (2006), particular morphological characteristics of fish are sufficient for identifying fish species; however, there is an issue when processed food is involved. Mitochondrial DNA is a useful tool for identifying fish species (Teletchea, 2009) maintaining fish stocks and monitoring seafood (Rubinoff et al., 2006). These genes are non-coding and conserved and therefore played an important part in determining phylogenetic relationships. In several fishes, the mitochondrial 16S rRNA gene was employed for molecular phylogenetic analysis (Li et al., 2013).
Species categorization with DNA Barcode is a technique for assigning an unknown specimen to a recognised species by evaluating its DNA Barcode sequence and it has been shown to work on a variety of organisms (Schindel and Miller, 2005). A supervised machine learning method termed BLOG (Barcoding with LOGic) is one of the ad-hoc DNA Barcodes classification tools (Weitschek et al., 2013). It’s a character-based method for classifying specimens into species by using classification criteria that compactly identify species in terms of DNA Barcode positions of essential diagnostic nucleotides. This study will give a DNA barcode database for fish species found in Pulicat lake, which will aid in the preservation of native diversity and ecological stability for the long-term use of the country’s fish genetic resources.
The study was conducted during March 2020-May 2021 at the Institute of Fisheries Post Graduate Studies, TNJFU, Chennai, Tamil Nadu. The tissue and voucher specimens of twenty-nine species from 21 families were collected from different locations of Pulicat Lake. All of the specimens were morphologically identified using the FAO Fish Identification taxonomy keys. Total DNA was isolated from the muscle tissues that were preserved in 99% ethanol using the salting-out procedure (Miller et al., 1988). The PCR amplification of the partial segment of the gene was carried out using primers 16S FP 5¢-CGC CTG TTT ATC AAA AAC AT-3¢ 16S RP 5¢-CCG GTC TGA ACT CAG ATC ACG T-3¢ (Palumbi, 1996). Bidirectional sequencing of PCR products was carried out using the Sanger sequencing methodology in ABI 3730 DNA sequencer.
The raw sequences were edited and aligned using BIOEDIT sequence alignment version (Hall, 1999). Firstly, BLAST searches were conducted for each sample to identify highly similar sequences on GenBank. The highly similar sequences were then used to generate a Neighbour-Joining (NJ) and Maximum Likelihood (ML) tree for each of the studied species separately using based on Kimura two-parameter distance model (K2P) as implemented in MEGA version 6.0 (Tamura et al., 2013). Sequences of Litopenaeus vannamei were selected as the out-group for phylogenetic tree construction. BLOG 2.0 (Weitschek et al., 2013) applies a supervised machine learning approach, which involves the usage of test and training data sets. mtDNA 16S rRNA gene sequences of fishes were obtained directly from the Fish Barcode Information system and GenBank Nucleotide Database. For this purpose, the datasets of 393 sequences were divided into a training set (314 sequences, 80% of total) and test set (79 sequences, 20% of total) for the identification of nucleotide positions and classification of species with the help of character-based identification keys. The probe sequences were identified using Gene Runner version 6.2.07.
Nucleotide sequence and genetic divergence analysis
The study represented 29 species of 26 genera, 21 families and 7 orders. The species list and the GenBank accession numbers are provided in Table 1. According to the current study, many researchers utilised the BLASTn similarity approach to check sequences (Rathipriya et al., 2019; Lee and Kim, 2020; Das and Choudhury, 2021; Saravanan et al., 2021). They studied 16 rRNA gene sequences showed both conserved and variable regions. This could be responsible for the differences in sequence length generated. This may also enable the identification of appropriate taxon-specific primers for phylogenetic applications. The average GC content was (48.1%) similar to the one reported by Ward et al., (2005) in teleost (47.10%). The 16S rRNA sequences of fishes were aligned to yield a final size of 523 bp. A total of 260 sites were constant (Fig 1), 263 sites exhibited variable, of which 210 were parsimony informative and 53 were singletons. The estimated transition/transversion bias (R) was 1.297. The sequence analysis revealed average nucleotide frequencies as 28.85% (A), 21.82% (T/U), 25.18% (C) and 24.15% (G). The average overall mean distance was 0.103. The highest distance was observed between Cynoglossus arel and Stolephorus commersonnii (0.184) and the lowest distance was observed between Lutjanus fulviflamma and L. johnii (0.020). In this study, K2P% was used to assign an unknown specimen to a known species, to detect novel sequences and to determine whether an unknown specimen is a distinct new species. The average genetic distances between species, genera, families and orders were 18.4%, 21.4%, 23.2% and 25.6%, respectively (Table 2), which was comparable with patterns observed in several fish barcoding studies (Bingpeng et al., 2018; Wang et al., 2018).

Table 1: GenBank accession numbers of 16S rRNA gene for the fishes


Fig 1: Conserved sites of 16S rRNA sequences.


Table 2: Summary of 16S rRNA gene sequences genetic divergences (K2P per cent) between various taxonomic levels.

Phylogenetic tree analysis
The species phylogenetic relationships were established, with comparable genera clustered under the same nodes and different genera clustered under separate nodes. High bootstrap values (90-99%) supported the nodes. Basheer et al., (2015), Bineesh et al., (2015) and Lakra et al., (2011) all found similar findings in marine fish species in Indian waters. Several researchers have employed 16S rRNA gene sequences to investigate the phylogeny of various groups, including fish. With a revised classification of the Epinepelini using 12S and 16S rRNA sequences, Craig and Hastings (2007) explained the molecular phylogeny of groupers of the subfamily Epinephelinae (Serranidae). Sparks and Smith (2004) on cichlid fishes, Vinson et al., (2004) on Sciaenid fishes, Wiley et al., (1998) on lampridiform fishes and Ilves and Taylor (2009) on Osmeridae are some other examples of phylogenetic studies. The summary form of the NJ tree and ML tree is given in Fig 2 and Fig 3.

Fig 2: Summary form of Neighbour joining tree of 16S rRNA gene sequences derived from 29 fish species using K2P distances, out-group Litopenaeus vannamei sequence.


Fig 3: Summary form of maximum likelihood tree of 16S rRNA gene sequences derived from 29 fish species using K2P distances, out-group Litopenaeus vannamei sequence.

Character-based species classification of species
In terms of DNA barcode sites, the character-based method aims to identify specimens of essential diagnostic nucleotides for species. In this study, we developed 112 positions of character-based keys using 16S rRNA gene sequences (Table 3). After the species formula was identified, the Primer 3.0 tool was used to check the possible secondary structures to finalise the species-specific probes for each species under study (Table 4). The melting temperature (Tm) of primers in the 55-60°C range and 5°C below the annealing Temperature give the best results, the primers were selected within the range. Paine et al., (2007) developed a character-based key for identifying the 17 members of the Scombridae family which is common to the western Atlantic Ocean. Lowenstein et al., (2009) described 40 distinguishing locations for blue-fin tuna. Puncher et al., (2015) developed a character-based key for identifying Atlantic blue fin tuna larvae (Thunnas thynnus). Vargheese et al., (2019) utilised BLOG 2.0 to distinguish 82 species of elasmobranchs and found 214 diagnostic nucleotides, which is similar to Rathipriya et al., (2021). Mahapatra et al., (2020) developed 25 positions of character-based keys for scombrid identification in Indian waters. These diagnostic molecular keys could be translated into a customised DNA chip for precise species identification.

Table 3: Species formula for fishes of pulicat lake using BLOG2.0.


Table 4: Species-specific probes for the fishes of pulicat lake identified through CBIS (16S rRNA).

Incorrect identification could result in inaccurate estimates of spawning stock biomass and management. Genetic markers are a valid tool for unambiguous identification that is both reliable and rapid and they play a significant role in fisheries management. The probes created could be used to detect substitution in fish and fish products not only for complete specimens but also for products and damaged specimens for fish authenticity.

  1. Basheer, V.S., Mohitha, C., Vineesh, N., Divya, P.R., Gopalakrishnan, A., Jena, J.K. (2015). Molecular phylogenetics of three species of the genus Rastrelliger using mitochondrial DNA markers. Mol. Biol. Rep. 42: 873- 879.

  2. Bineesh, K.K., Mohitha, C., Vineesh, N., Basheer, V.S., Joselet, M., Pillai, N.K., Jena, J.K., Gopalakrishnan, A. (2015). Molecular identification of three deep-sea fish species of the genus Chelidoperca (Perciformes: Serranidae) from Indian waters. Indian J. Fish. 62: 104-108.

  3. Bingpeng, X., Heshan, L., Zhilan, Z., Chunguang, W., Yanguo, W., Jianjun, W., 2018. DNA barcoding for identification of fish species in the Taiwan Strait. PLoSOne. 13:e0198109.

  4. Craig, M.T., Hastings, P.A. (2007). A molecular phylogeny of the groupers of the subfamily Epinephelinae (Serranidae) with a revised classification of the Epinephelini. Ichthyological Research 54: 1-17. 

  5. Das, P. and Choudhury, R. (2021). Mitochondrial D-loop Genetic Diversity Studies of Rhinoceros  unicornis in Assam, India. Indian Journal of Animal Research. 55(1): 6-10.

  6. Finizio, D., Guerriero, A., Russo, G., Gian, L., Ciarcia, G. (2006). Identification of gadoid species (Pisces, Gadidae) by sequencing and PCR-RFLP analysis of mitochondrial 12S and 16S rRNA gene fragments. Eur. Food Res. Technol. 225(3): 337-344

  7. Govindan, S.,  Ravichandran, R. (2016). Fish Fauna Diversity and Conservation Status of Pulicat Lagoon in Tamil Nadu. Annals of Aquaculture and Research. 3(2):1018.

  8. Hall, T.A. (1999). BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symposium Series. 41: 95-98. 

  9. Lakra, W.S., Verma, M.S., Goswami, M., Lal, K.K., Mohindra, V., Punia, P.,  Gopalakrishnan, A., Singh, K.V., Ward, R.D., Hebert, P. (2011). DNA barcoding Indian marine fishes. Mol Ecol Resour. 11 (1):60-71.

  10. Lee, D.J. and Kim, Il-C. (2020). The new freshwater crab belongs to the genus Geothelphusa as inferred from mitochondrial and nuclear DNA markers. Indian Journal of Animal Research. 54(4): 424-429.

  11. Li, Y., Ren, Z., Shedlockm, A.M., Wu, J., Sang, L., Tersing, T. (2013). High altitude adaptation of the Schizothoracine fishes (Cyprinidae) revealed by the mitochondrial genome analysis. Gene 517:169-178.

  12. Lowenstein, J.H., Amato, G., Kolokotronis, S.O. (2009). The real maccoyii: Identifying tuna sushi with DNA barcodes - contrasting characteristics attributes and genetic distances. PLoS ONE 4: e7866.

  13. Mahapatra, S., Rathipriya, A., Priyadarshini, D., Suresh, E., Shanmugam, S.A., Kathivelpandian, A. (2020). Character-based identification system of scombrids from Indian waters for authentication and conservation purposes, Mitochondrial DNA Part B. 5(3): 3239-3242.

  14. Paine, M.A., McDowell, J.R., Graves, J.E. (2007). Specific identification of western Atlantic Ocean scombrids using mitochondrial DNA cytochrome c oxidase subunit I (COI) gene region sequences. Bull Marine Sci. 80:353-367. 

  15. Puncher, G.N., Arrizabalaga, H., Alemany, F., Cariani, A., Oray, I.K., Karakulak, F.S., Basilone, G., Cuttitta, A., Mazzola, S., Tinti, F. (2015). Molecular identification of Atlantic Blue- fin tuna (Thunnus thynnus, Scombridae) larvae and development of a DNA character-based identification key for Mediterranean scombrids. PLOS One. 10(7):e0130407.

  16. Rathipriya, A., Karal Marx, K., Jeyashakila, R. (2019). Molecular identification and phylogenetic relationship of flying fishes of Tamil Nadu coast for fishery management purposes. Mitochondrial DNA Part A. 1-11 .DOI: 10.1080/24701394.2018. 1558220.

  17. Rathipriya, A., Kathirvelpandian, A., Shanmugam, S.A., Suresh, E., Felix, N. (2021). Character-based identification key for commercially important fishes of Pulicat lake: Tool for conservation and management. Mitochondrial DNA Part A. 32:4, 120-125. DOI: 10.1080/24701394.2021.1883009.

  18. Rubinoff, D., Cameron, S., Will, K. (2006). A genomic perspective on the shortcomings of mitochondrial DNA for ‘barcoding’ identification. J. Hered. 97: 581-594.

  19. Saravanan, K., Kumar, S., Praveenraj, J., Meena, B.L., Kasinath, B.L., Kiruba-Sankar, R. and Roy, S.D. (2021). Molecular Characterization of Muscle Infecting Myxobolus sp. Causing Outbreak in Labeo rohita, Rohu: First Report from Andaman Islands, Indian Journal of Animal Research. 55(2): 211- 216.

  20. Schindel, D., Miller, S. (2005). DNA barcoding a useful tool for taxonomists. Nature. 435: 17-17.

  21. Sparks, J.S., Smith, W.L. (2004). Phylogeny and biogeography of the Malagasy and Australasian rainbow fishes (Teleostei: Atherinoidei): Gondwanan vicariance and evolution in freshwater. Mol. Phylogenet. Evol. 33: 719-734 

  22. Tamura, K., Stecher, G., Peterson, D., Filipski, A., Kumar, S. (2013). MEGA6: Molecular Evolutionary Genetics Analysis version 6.0. Molecular Biology and Evolution. 30: 2725-2729. 

  23. Teletchea, F. (2009). Molecular identification methods of fish species: Reassessment and possible applications. Rev. Fish Biol. Fish 19: 265-293.

  24. Vargheese, S., Chowdhury, L.M., Shijin Ameri, Anjela Mercy, A., Kathirvelpandian, A. (2019). Character based identification system for Elasmobranchs for conservation and forensic applications. Mitochondrial DNA Part A. 30(4): 651-656.

  25. Vinson, C., Grazielle, G., Schneider, H., Sampaio, I. (2004). Sciaenidae fish of the Caete river estuary, Northern Brazil: Mitochondrial DNA suggests explosive radiation for the Western Atlantic assemblage. Genet Mol Biol. 27: 174-80.

  26. Ward, R.D., Zemlak, T.S., Innes, B.H., Last, P.R., Hebert, P.D. (2005). DNA barcoding Australia’s fish species. Philos Trans R Soc Lond B Biol Sci. 360: 1847-57. 

  27. Wang, L., Wu, Z., Liu, M., Liu, W., Zhao, W., Liu, H., You, F., (2018). DNA barcoding of marine fish species from Rongcheng Bay, China. PeerJ. 6: e5013.

  28. Weitschek, E., van Velzen, R., Felici, G., Bertolazzi, P. (2013). BLOG 2.0: A software system for character based species classification with DNA Barcode sequences: what it does, how to use it. Mol Ecol Resour. 13(6): 1043-1046.

  29. Wiley, E.O., Johnson, G.D., Dimmick, W.W. (1998). The phylogenetic relationships of lampridiform fishes (Teleostei: Acanthomorpha), based on a total evidence analysis of morphological and molecular data. Mol Phylo Evol. 10: 417-425.

Editorial Board

View all (0)