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Chapter-III
Materials and methods
3.1 The study area
The present study focuses on the molecular characterization of the freshwater eel
species of the genus Monopterus in different states of Northeast India (Assam and
Manipur) including bordering area of Assam-Meghalaya, part of the two hotspot
region, viz. Indo-Burma and Himalayan hotspots (CEPF, 2005). The GPS location of
the study area is 26°10'22.79''- 27°39'32.79''N Latitude and 91°26'39.74''-
96°15'39.84''E Longitude.
3.1.1 Assam
The major part of the present study was conducted in the state of Assam, located in
the tropical Latitudes (24.30
N and 280
N) and eastern longitudes (89.50 E and 96.1
0E)
within the territorial jurisdiction of Northeast India. The entire state is drained by the
dense networks of two river system, viz. the Brahmaputra and Barak system.
3.1.2 Manipur
Manipur in northeast India is covering the border area of Myanmar to its east, falls
under Northeast India at the Indo-Malayan biodiversity hotspot (Roach, 2005).
Situated between latitudes 23.80oN to 25.68
oN and longitudes 93.03
oE to 94.78
oE, it
covers a total geographical area of 22,327 Sq. Km. It is unique for providing
profusion of habitats with characteristic diverse biota and high level of endemism.
Northeast India is regarded as a mega-biodiversity centre (De and Medhi, 2014). The
region of Indo-Burma biodiversity is known to be one of the most threatened
biodiversity hotspots due to the high rate of resource exploitation and habitat loss of
the total area of Manipur; about nine-tenths constitute the hills which surrounds the
remaining one-tenth valley.
3.1.3 Meghalaya
Some of the sampling locations of the present study also fall under Assam-Meghalaya
border area in Kulshi River. These study sites were situated near the Loharghat range
under West Kamrup forest division bordering Meghalaya (26.03°N; 91.26° E).
17
3.1.4 Beels and paddy fields
Sampling also carried out in beels (wetlands) and paddy fields of the study area too.
The study area includes following wetlands -
(i) Chandubi beel, situated within the Loharghat range of Kamrup, West division
(25° 51' N; 91° 21' E), which is surrounded by natural forest and hilly terrain of
Rajapara and Mayang hill range on its North-West and South-West respectively. In
the West there is Kulsia River.
(ii) Deepor beel Ramsar site, located between 91° 37′ 6.46″ E to 91° 40′ 48.82″ E
and 26° 5′ 40.02″ N to 26° 9′ 5.18″ N, south of Brahmaputra River in Kamrup
District, 18 km south west of Guwahati city in Assam .It lies at an altitude of 53 meter
above MSL and covers an area of about 4,000 ha. The wetland is surrounded by the
Bharalu basin on the east, Basistha basin in the south East, Kalmani River on the
west, Jhalukbari Beel on the north and Rani and Garbhanga Reserve forests on the
south.
(iii) Loktak Lake of Manipur, located in Bishnupur district of Manipur (24.25° -
24.42° N; 93.46°- 93.55°E).
The sampling sites were selected based on distribution of selected genera in
each region. At least five individuals were studied and sampled from each sampling
site.
3.2 Sample collection
Field work was carried out during January, 2012 to February, 2014 in paddy- field
and swamps of different parts of Assam and Manipur to collect M. cuchia and M.
albus samples (Table 3.1; Figure 3.1). From each sampling site 5-15 samples were
collected. A total of 230 Monopterus individuals were sampled. Geographically, the
Monopterus individuals collected were allocated into four populations based on the
geographical proximity of the water bodies of each sampling area. Each Monopterus
population was sampled at a gap of about 100-400 km away from other populations
(Table 3.1). Taxonomy and nomenclature were followed after Rosen and Greenwood
(1976) and Talwar and Jhingran (1992). The eels were released at their points of
capture.
18
Table 3.1. GPS coordinates of the sampling sites of Monopterus cuchia and Monopterus
albus.
Population Name/
Sampling area
Population
Code
Sampling Site(s) Coordinates
Manipur
1
Loktak Lake 24.3°N, 93.5°E
Ijei River, Longmai, Tamenglong 24.5°N, 93.4°E
Tuivel River, Manipur 24.08°N , 93.2°E
Assam
2
Wetlands, Hallawgaon, Sadiya,
Tinsukia
27.50°N, 95.45°E
Rice field, Mariani, Jorhat 27.52°N, 95.37°E
Bhogdoi River, Jorhat 26.77°N, 94.22°E
Kakodonga River, Golaghat 26.43°N, 94.3 °E
Dhansiri River, Golaghat 26.35°N, 93.35°E
3
Kolong River, Nagaon 26.36°N, 92.69°E
Jia Bhorali River, Sonitpur 26.69°N, 92.87°E
Bishwanath Ghat, Sonitpur 26.66°N, 93.17°E
4
Chandubi beel, Kamrup 25.51°N; 91.21° E
Kulshi river, Kamrup 26.03°N; 91.26° E
Rice field, Nalbari 26.14°N, 91.08°E
Puthimari river, Kamrup 26.22°N, 91.4°E
Rice field, Hajo, Kamrup 26.14°N, 91.32°E
Wetland, Manikpur, Bangaigaon 26.45°N, 90.80°E
Rice field, Bilaishipara, Dhubri 26.11°N, 90.16°E
Rice field, Dhubri 26.01°N, 89.6°E
Urpad Beel, Goalpara 26.06°N, 90.35°E
Rice field, Dudhnoi, Goalpara 25.98°N, 90.79°E
3.3 General protocol followed for numbering of samples
All codes start with M followed by an identifier C or A respectively.
Numbering methodology:
1. The target samples were collected from each sampling site from their potential
distribution range.
2. Morphological character and measurements were performed. The juveniles
and abnormal individuals (such as specimen having broken tail) were avoided
from the analysis.
19
3. Coloration of specific markings (stripes, spots and other body markings) on
the body of Monopterus individuals was recorded.
4. Tissue samples were collected following the non-invasive tail tip clipping
process and preserved in 95% alcohol.
5. Live specimens were released in same location treating with antiseptic.
6. GPS location of the place was recorded by GPS (Garmin 72) and other web
based database.
Figure 3.1. Map of study area showing the sampling sites.
3.4 Study of phenotypic variations
The phenotypic variations were captured and used for the analysis of molecular data.
Twenty three (23) reliably measurable morphometric characters were selected for the
study (Table 3.2).
20
Table 3.2. The morphological characters measured
Sl
No. Characters Description
1 Total length (TL) Distance from the tip of the snout to the longest
caudal fin ray
2 Pre dorsal length (PDL) Distance from the snout tip to the anterior base of
the dorsal fin
3 Post dorsal length (PoDL) Distance from posterior base of the dorsal fin to
last part of the caudal fin
4 Pre anal length (PAL) Distance from the tip of the snout to the anal base
5 Post anal length (PoAL) Distance from the anal base to the last part of the
caudal fin
6 Length of lateral line (LAL) Distance from the first base to the last base of
lateral line
7 Head length (HL) Distance from the tip of the snout to the posterior
margin of the opercula
8 Snout length (SnL) Distance from tip of mouth to nostril
9 Upper jaw length (UJL) Upper jaw length (UJL)
10 Lower jaw length (LJL) Length of lower jaw
11 Mouth gape (MG) Length of mouth gape
12 Eye diameter (ED) Distance between margins of the eye ball across
the cornea
13 Head depth (HD)
The perpendicular distance from the midline at
the back of head vertically downward to the
vertical contour of the head
14 Head width (HW) It is the greatest distance across the head
15 Pre orbital length (POL) Distance from tip of mouth to anterior base of
eye
16 Post orbital length (PoOrL) Distance from posterior base of eye to last hard
part of head
17 Greatest body depth (GBD) Greatest body depth of the body
18 Least body depth (LBD) Least body depth of the body
19 Greatest width of body (GWB) Width of body at greatest part
20 Highest body diameter (HBD) Diameter of the highest body part
21 Width of body at vent (WBV) At vent base width of the upper part of the body
22 Depth of body at vent (DBV) Depth of body at vent base
23
Distance between vent and
commencement of dorsal fin
(DBCB)
Distance between vent base to dorsal fin base
21
3.5 Mapping of distribution pattern
As a major objective of the present work, sampling locations were recorded using
handheld Garmin 72 GPS and were plotted on habitat map using the software
ARCVIEW (Ormsby and Alvi, 1999) to find out the phylogeographic relations.
3.6 DNA extraction
From each captured specimen, approximately 1 cm of tail tissue was removed with
forceps and was placed it in a sterile 1.5 ml microtube containing 95% ethanol and
was stored at –20 oC until processing. The eels were released at their points of
capture. The samples were placed in an ice chest during transport to the laboratory.
The samples were used simultaneously for genomic DNA extraction, SSR analysis,
sequencing of targeted regions of genome.
3.6.1 Chemicals and Reagents prepared
A. Homogenizing buffer (100ml)
a. 0.4M Nacl-2.33g
b. 10 mM Tris(PH8)-0.12114g
c. 2 mM EDTA(PH8)-0.05845g
B. Preparation of 5XTBE electrophoresis buffer (stock solution):
a. 0.445 M Tris borate
b. 0.01 M EDTA
(PH
was adjusted to 8.2-8.4 at 25oC)
C. 1XTBE electrophoresis buffer (working solution):
a. 89 mM Tris borate
b. 2 mM EDTA
(PH
was adjusted to 8.2-8.4 at 25oC)
D. 10X Proteinase K buffer
a. 100 mM Tris-Cl(PH
8)
b. 50 mM EDTA(PH
8)
c. 500 mM NaCl
E. Tris Chloride (1 M)
a. Tris-Cl base-121.1g
b. Double distilled water-800ml
22
(PH
was adjusted to 8)
c. Sterilized the solution by autoclaving.
F. 6X Gel-Loading Buffer
a.0.25 % (w/v) bromophenol blue
b. 0.25% (w/v) xylene cyanol FF
c. 40% (w/v) sucrose in water
3.6.2 DNA Extraction by using Chloroform-Octanol method
Genomic DNA was isolated from the tissue using the Chloroform-Octanol method
(Salah and Iciar, 1997; Cabe et al., 2007).
The steps followed in the Chloroform-Octanol method of DNA isolation:
1. Around 100 mg (0.1gm) of tissue was incised from the specimen using surgical
sterile blade and used for DNA extraction.
2. The pieces were chopped into fine bits and transferred into a sterile 2ml micro
centrifuge tube.
3. Then, added 500 μl homogenizing buffer to the sample and homogenized using
polytron tissue homogenizer for 10-15 s.
4. 50μl of 20% SDS (2% final concentration) + 11μl of 20mg/ml proteinase K(400
μg /ml final concentration) was added and mixed well.
5. Samples were incubated at 500C for overnight, mixed periodically by inversion.
6. Cooled the samples to 370C.
7. Added with 2.29 μl of 4 μg /μl RNase to the sample (s) for a final volume of 550
μl.
8. Samples were incubated at 370C for 30min. and added with equal volume of
Chloroform: Octanol (24:1).
9. Allowed to shake for 1h and centrifuged the sample at 14000g for 10 minutes.
10. Removed about 90% of the upper aqueous layer to a clean tube, carefully
avoiding proteins and added ¾ volume i.e. 400 μl of Isopropanol and mixed by
inverting the tubes and placed the same at -200C for 2 h.
11. Spined the tube at 14000g for 10min.
12. DNA pallets were taken and mixed with 500μl of 76% ethanol and washed the
pallet.
23
13. The tubes were placed in an oven at 500C and dried the DNA pallet for 5-10
minutes.
14. The dried DNA pallets were dissolved in 1XTBE buffer and kept in deep
freezer.
3.6.3 Preparation of 1% Agarose Gel for genomic DNA
1. 1g of Agarose was dissolved in 100ml of 1X TBE buffer by mixing thoroughly in
a clean conical flask.
2. Carefully warmed the solution to dissolve the Agarose. Kept the solution to cool
down to about 60 °C at room temperature, or water bath. Stirred the solution
while cooling.
3. 5 μl of 1X Ethidium Bromide was added into the conical flask containing
Agarose solution and swirled gently.
4. The solution was sufficiently cooled and poured the molten gel into the gel cast
which should be washed immediately with water and alcohol prior to use and
comb was fixed.
5. The gel was allowed to cool for solidification for 30-45 minutes.
6. The comb was removed and transferred the gel into an Electrophoretic tank filled
with 1X TBE buffer sufficient enough to just immerse the gel fully.
3.6.4 Running the genomic DNA Sample on the Agarose Gel
1. 4 μl of DNA sample and 1 μl of 6X Loading dye was mixed on a paraffin strip
and loaded this into each well. The same was done for each sample.
2. The loaded sample(s) in the well were allowed to run at 90 volts for 30-40
minutes or till the dye has run to 3/4th of the length of the gel.
3. The DNA bands in gel were visualized under UV using the Gel Doc.
Quantification of the DNA was performed using Evolution 201- UV-Visible
spectrophotometer (Thermo Scientific, Mumbai), used 20 ng DNA for PCR. DNA
was diluted to 1:5 or 1:10 of the original concentration obtained after isolation for
amplification in a PCR.
24
3.7 Selection of molecular markers
The present study has used SSR markers for genotyping of different populations of
Monopterus. The study also employed both mitochondrial and nuclear DNA for PCR
amplification. Sequencing and molecular characterization were carried out for two
mitochondrial (COI and D loop region) and two nuclear (gst: Glutathione s
transferase and gs: Glutamine Synthetase) genes, to infer differences and relationships
of Monopterus species complex.
Table 3.3. PCR Primers used for amplified from microsatellite loci
#Sl.
No.
SSR
Locus
GenBank
Accession
no.
Repeat Motif PCR Primers Tm
(°C)
1 Mal01 DQ987569 (TG)2CA(TG)4(TG)11 F: ATATTTGCAAGCGAGCCTGT
R: CTCTTCAATGCAGGCACAAA
57
2 Mal02 DQ987574 (AC)23 F: TTGAAGAGCTCCGAGAGCAG
R: TTTGGTGCTGGAAAACTGGT
55
3 Mal04 DQ987576 (CA)21 F: CCACTGAGATTCACCCTCGT
R: GCTCTGTCTGTGTCGTCTGG
64
4 Mal05 DQ987579 (GT)10 F:TATTTCTCCAAGGGCTGGTG
R:CACATGCACACAAACAGCA
48
5 Mal07 DQ987581 (CA)4(TA)6(CA)13 F:CCCATACAGACATTTGCCACT
R: CCAGACAACCTCTTTCCACA
55
6 Mal09 DQ987583 (TC)21(CA)20 F: GCCCAGATGATACAAGTC
R: TGGGAGAAAGGAAGCACAAG
55
7 Mal10 DQ987584 (TC)10(AGG)6 F: GTGGGCCTTTATCTGCATGTG
R: TGCTTGTCTGTCCCTTACGA
62
8 Mal11 DQ987590 (TG)11 F: GTGGGCCTTTATCTGCATGT
R: CTGTCCCTTACGCACCTCTC
65
9 Mal13 DQ987593 (TG)19 F: CAGCAAAAGTAAAGCCGACTA
R: TGTCGCATTTCTGCAAGTTT
65
10 Mal008 DQ987582 (AC)13 F:AGCTCACTATTCCCGTCTGTCTGA
R:CCTGCCCTTTCTCACATTACAAAC
55
11 Mal043 JN712692 (TG)20 F: CAGCCGCAGAGTTAAACATCACCA
R: CCTAAACCCCAAAGGTCCCAGAAA
55
#Primer Reference: Li et al. (2007): Sl No.1- 9; Lei et al. (2012): Sl No.10- 11.
25
3.8 Microsatellite Genotyping
The sequences of 11 Monopterus–specific microsatellite primers (Table 3.3) were
obtained from Li et al. (2007), Lei et al. (2012). Polymerase Chain Reactions (PCRs)
were carried out in 25 μL of reaction volume containing 20 ng of genomic DNA, 1.25
U Taq polymerase, 200 μM dNTP, 0.2 μM primer, and 1X amplification buffer
(containing 2 mM MgCl2). The condition for amplification was an initial denaturation
temperature 94 °C for 5 min, followed by 36 cycles of 94
°C for 30 s, then by 30 s at
appropriate annealing temperature (Table 3.3) followed by 72°C for 45 s followed by
a final extension at 72 °C for 10 min. Amplicons and DNA size marker (HiMedia)
were resolved on 7.5% nondenaturing polyacrylamide gels containing acrylamide and
N,N’-methylenebisacrylamide in 19:1 ratio. Electrophoresis was done at 8 V/cm for
120 min. DNA bands were stained using ethidium bromide 10 mg/ml solution for 30
min and visualized with Ultraviolet Gel Document System (UVIdoc) (Tong and Liao,
2005; Zhu et al., 2005). The sizes of the microsatellite bands were measured by using
the software UVItec ver 12.8.
3.8.1 Data analysis in Microsatellite Genotyping
The 230 Monopterus samples used in SSR studies were allocated into four
populations based on the geographical proximity of the water bodies. Microsateliite
allele size permutation test was performed as described by Hardy et al. (2003) using
the software SPAGeDi (Hardy and Vekemans, 2002) to determine whether allele size-
based or allele identity-based statistics were appropriate for the analysis of
microsatellite dataset. The test revealed that allele identity-based analyses were more
suitable for the dataset of the present study.
Agreement of genotype frequencies with the expected Hardy-Weinberg
equilibrium, and existence of linkage disequilibrium were analyzed using exact test
implemented by Genepop (Raymond and Rousset, 1995).
Estimates of identity-based gene diversity parameters, He (expected
heterozygosity or gene diversity corrected for sample size, Nei, 1978) and NAe
(effective number of alleles, Nielsen et al., 2003) were computed using SPAGeDi.
F-statistics, FIT and FIS, were computed with help of Genepop 4.2.2 (Rousett,
2008). The F-statistics, were defined according to Rousset (2002) and Cocherham and
Weir (1987). The ANOVA approach of Weir and Cockerham (1984) was
26
implemented to compute single locus estimates of FIS and FIT.
Polymorphism Information Content (PIC) was calculated according to
Botstein et al. (1980) who defined PIC values as a probability of a given marker being
informative at a random mating.
The programs Structure 2.3.4 (Pritchard et al., 2000), Structure Harvester
(Earl and vonHoldt, 2012) and CLUMPP 1.1.2 (Jackobson and Rosenberg, 2007)
were utilized to determine the most probable number (K) of genetic clusters
represented by sampled individuals irrespective of their geographical locations.
Structure version 2.3.4 was set to output the likelihood Ln [P(X/K)] for values
of K from 1 to 12; both the burn-in period and data collection were set at 10,000 steps,
20 runs were performed for each value of K. The admixture model and correlated
allele frequencies were used for every run. The output files of ‘Structure software’
were submitted to the online tool Structure Harvester (Earl and vonHoldt, 2012) for
further analysis, which followed the procedure of Evanno et al. (2005) to identify the
values of K. CLUMPP was then used to perform permutations of the 20 Q matrices
associated with the best value of K and to output a mean of permuted matrices. This
matrix was then input into the cluster visualization program distruct (Rosenberg,
2004) to obtain the bar plot.
3.9 PCR amplification of COI, D-loop and gst genes
Selected samples (6 each from M. cuchia and M. albus) were used for sequencing of
targeted regions of mitochondrial genome (D-Loop and COI gene) and nuclear
genome (gst gene). The informative regions of selected genes (D-Loop, COI and gst
genes) were PCR amplified from multiple individuals per species using taxa specific
primers. The PCR primer pairs used for amplification of D loop region were F-
TTCCAATGGAGGGATGGTGC and R- AACCACCGAAAAGCGAAAGC (Metz
et al., 1998). The COI gene was amplified using primers F-TCAACCAACCACAT
TGGCAC and R-TAGACTTCTGGGTGGCCAAAGAATCA (Chandra et al., 2012).
[Table 3.4].The gst gene was PCR amplified using primer pairs GST1-F-
GATGTGTTCCTGCCTTCTCCACA and GST1-R-GAAGACCAATCACCACAAA
GGGAA (Fu and Xie, 2006) (Table 3.4).
27
Table 3.4. PCR primer sequences used for amplification and for sequencing of DNA
Gene Name Primer
Name
5'<-----Sequence----->3' Tm (0C) Reference
Cytochrome c
oxidase
COI-F TGATCCGGCTTAGTCGGAACTGC Chandra et
al., 2012 COI-R GATGTGTTGAAATTACGGTCGGT 50
D loop region D loop-F TTGTTCGTTACCCACCAAGC 54 Metz et
al.,1998 D loop-R TGCCTGTGGGACTTTTTAGG
Glutathione S
transferase-1
GST1-F GATGTGTTCCTGCCTTCTCCACA 55 Fu and Xie,
2006 GST1-R GAAGACCAATCACCACAAAGGGAA
All PCR amplifications were carried out in 25 μL reaction volume, with 1.5
units of Taq DNA Polymerase (Bangalore Genei, Bangalore, India), 0.25 mM of
dNTPs (Bangalore Genei), 2.0 mM of MgCl2, 0.1 M (Sigma) of each primer and 20
ng of genomic DNA (Tables 3.4 & 3.5). The condition for amplification was an initial
denaturation temperature 94 °C for 5 min, followed by 35 cycles of 94
°C denaturing
temperature for 50 sec , then by 45 sec at appropriate annealing temperature (Table
3.3; Figure 3.2) followed by extension temperature of 72°C for 90 sec and then by a
final extension at 72 °C for 10 min. The annealing temperature used for amplification
mitochondrial (COI, D-loop region) genes were Tm = 500C for COI gene, 54
0C for D-
loop region and the Tm = 550C for nuclear (gst) gene (Figure 3.2 A-D; Table 3.4).
Table 3.5. Constitution of PCR mixture
Reagents Concentration in final reaction mixture
10X 1X
dNTPs (2.5 mM) 0.25 mM
MgCl2 (25 mM) 1.5-2 mM
Primer (10 μM) 0.1+0.1 μM (forward + reverse primers)
Taq (3U/μl) 1.5-2.0 U/tube
Template DNA 1 μl / sample (20 ng)
MQ H2O Added to make the volume up to 25 μl
28
A. Stages of PCR cycle for COI gene
B. Stages of PCR cycle for D-loop region
C. Stages of PCR cycle for Nuclear genes (gst )
Figure 3.2 (A-C): Thermocycler profiles in which mitochondrial and nuclear
DNA were amplified.
29
3.10 Agarose gel electrophoresis of amplified PCR products
The amplified products were separated in 2% agarose gel (i.e. 2g Agarose in 100 ml
1X TBE or 0.6 g agarose in 30 ml 1x TBE) by electrophoresis. Low Range DNA
Ruler or 1 kb ladder was used as MW markers. 4 μl PCR products with 1μl of 6X
Loading dye were loaded in each of the wells. Electrophoresis was performed at 90 V
till the tracking dye; bromophenol blue reached the anodal end of the gel. The results
of electrophoresis was observed and recorded in a gel documentation system
(UVIdoc). The amplified PCR products were purified using QIAquick PCR
Purification kit (Qiagen).
3.11 Sequencing of mitochondrial and nuclear genes
Sequencing of mitochondrial (COI and D-loop region) and nuclear (gst: Glutathione s
transferase and gs: Glutamine Synthetase) genes were performed by ABI PRISM®
377 DNA sequencer (at BioAxis DNA Research Centre, Hyderabad). After
verification the nucleotide sequences were deposited to GenBank (NCBI) public
sequence repository (Benson et al., 2013).
3.12 In silico analysis
3.12.1 Acquisition of additional nucleotide sequences
In addition to the sequences generated from the present study, the study also included
the seqences of coi, D-loop, gst and gs genes (gs01, gso2 and gso3), extracted from
GenBank (NCBI) (Benson et al., 2013) by database keyword search and by BLASTp
(Altschul et al., 1997) and FASTA (Pearson, 1991) searches. The existing GenBank
sequences of closely related species was used as outgroup of phylogenetic analysis.
3.12.2 Translation of coding region and acquisition of protein sequences
The coding regions for COI, gs01 and GST genes were translated into amino acids of
Cytochrome C Oxydase, Glutamine synthetase-1 and Glutathione-s-tranferese
proteins (using EMBOSS Transeq; Rice et al., 2000) for structural and evolutionary
studies. Apart from these, amino acid sequences of Cytochrome C Oxydase,
Glutamine synthetase-1 and Glutathione-s-tranferese were also extracted from Protein
30
Knowledgebase (UniProtKB, Apweiler et al., 2004) both by database keyword search
and by BLASTp (Altschul et al., 1997) and FASTA (Pearson, 1991) searches.
3.12.3 Sequence Alignment and Comparative sequence analysis
The in silico analysis were carried out in the Bioinformatics Centre (DBT-BIF),
Gauhati University. Data mining and sequence analyses of mitochondrial (COI and D
loop region) and nuclear (gst and gs) genes along with COI, Glutamine synthetase-1
and Glutathione-S-tranferese proteins were performed using the CLC Genomics
Workbench 7.0.3 (CLC Bio, Hydarabad, India). The nucleotide and protein sequences
were simultaneously aligned using CLUSTAL-W (Higgins et al., 1994) and Modeller
version 9.12 (Fiser et al., 2000) programs.
The physico-chemical parameters of COI, Glutamine synthetase-1 and
Glutathione-S-tranferese proteins were computed using CLC Genomics Workbench
7.0.3 (CLC Bio, Hydarabad, India) and ProtParam (Gasteiger et al., 2005). The
important calculations for the amino acid composition, atomic composition,
theoretical pI, molecular weight, Formula, extinction coefficients, half-life, instability
index, aliphatic index, hydrophobicity, charge vs. pH were carried out under
sequence analysis.
3.12.4 Molecular Phylogenetic analysis
The sequences of outgroup species for each gene (COI and D-loop region, gs and gst)
were downloaded from GenBank and UniProtKB. For COI gene-based phylogeny, the
nucleotide sequence of COI of other eel shaped fishes belonging to the families
Anguillidae (Anguilla bengalensis), Mastacembelidae (Mastacembelus armatus,
Macrognathus pancalus, Macrognathus aral, Macrognathus aculeatus) were included
to establish the evolutionary relationships of Monopterus albus and Monopterus
cuchia with other eel species. The sequences for the mitochondrial (COI and D-loop
region) and nuclear markers [gs (gs01,gs02, gs03) and gst genes] were aligned using
ClustalW 1.6 (Thompson et al., 1994) integrated in software MEGA (Tamura et al.,
2007), using default parameters. The datasets were subjected to phylogenetic analyses
carried out in MEGA6 (Tamura et al., 2013). The evolutionary history was inferred
by using two different methods namely the Maximum Parsimony (Eck and Dayhoff,
1966) and Maximum Likelihood (Jones et al., 1992) methods. Maximum parsimony
31
(MP) tree was estimated using the Close-Neighbor-Interchange algorithm (Nei and
Kumar, 2000). The percentage of replicate trees in which the associated taxa clustered
together in the bootstrap test (1000 replicates) (Felsenstein, 1985). The branch lengths
calculated using the average pathway method (Nei and Kumar, 2000).
Nucleotide substitution model that best fits each dataset and the model
parameters were estimated using Akaike information criterion implemented in the
program MODELTEST version 3.7 (Posada and Crandall, 1998). The Maximum
Likelihood analysis was carried out on the Hasegawa-Kishino-Yano model
(Hasegawa et al., 1985).
3.12.5 Three-dimensional structure prediction
Comparative modeling of important proteins namely Cytochrome C Oxydase,
Glutamine synthetase-1 and Glutathione-S-tranferese were conducted by using
Modeller9v12 program (Marti-Renom et al., 2000).
BlastP (Altschul et al., 1997) and FASTA (Pearson, 1991) searches were
performed independently with PDB (Berman et al., 2007) for obtaining a suitable
template. The significance of the BLAST results was assessed by expect values (e-
value) generated by BLAST family of search algorithm (Altschul et al., 1991). The
target-template alignment (Lassmann and Sonnhammer, 2005) was carried out using
ClustalW version 2.1 (Higgins et al., 1994) and Modeller version 9.12 (Fiser et al.,
2000) programmes. Comparative modelling was conducted by the Modeller version
9.12 programme (Marti-Renom et al., 2002). The loop regions were modeled using
MODLOOP server (Fiser and Sali, 2003). The final 3D structures with all the
coordinates for COI, GS and GST were obtained by optimization of a molecular
probability density function (pdf) of Modeller (Eswar et al., 2006). The molecular pdf
for homology modelling was optimized with the variable target function procedure in
Cartesian space that employed the method of conjugate gradients and molecular
dynamics with simulated annealing (Sali and Blundell, 1993).
The computational protein structures were evaluated (Giorgetti et al., 2005) by
ERRAT (Colovos and Yeates, 1993) and ProCheck (Laskowski et al., 2003)
programmes. After fruitful verification, the coordinate files were successfully
deposited to PMDB (Protein Model Database) (Tiziana et al., 2006). All the graphic
presentations of the 3D structures were prepared using Chimera version 1.8.1
(Pettersen et al., 2004).
32
3.12.6 Molecular evolution of proteins
Evolutionary analyses of proteins (COI, GS and GST) were conducted in MEGA6
(Tamura et al., 2013). The evolutionary history was inferred by using the Maximum
Parsimony (Eck and Dayhoff, 1966) and Maximum Likelihood (Jones et al., 1992)
methods.
3.12.7 Data mining and sequence analysis and functional annotation
Modern bioinformatics tools including the Expasy Proteomics tools were used for
sequence analysis, transcription, translation, structural analysis, functional annotation
etc. The proteomics analyses were carried out using CLC Genomics Workbench 7.0.3
(CLC Bio, Hydarabad, India). ProFunc server (Laskowski et al., 2005) was used to
identify the likely biochemical function of proteins from the predicted three-
dimensional structure. Databases like PFam, PROSITE, PRINTS, ProDom,
InterProScan (Zdobnov and Apweiler, 2001) were used for functional
characterization.
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