- Open Access
Molecular characterization and the effect of salinity on cyanobacterial diversity in the rice fields of Eastern Uttar Pradesh, India
© Srivastava et al; licensee BioMed Central Ltd. 2009
- Received: 13 January 2009
- Accepted: 06 April 2009
- Published: 06 April 2009
Salinity is known to affect almost half of the world's irrigated lands, especially rice fields. Furthermore, cyanobacteria, one of the critical inhabitants of rice fields have been characterized at molecular level from many different geographical locations. This study, for the first time, has examined the molecular diversity of cyanobacteria inhabiting Indian rice fields which experience various levels of salinity.
Ten physicochemical parameters were analyzed for samples collected from twenty experimental sites. Electrical conductivity data were used to classify the soils and to investigate relationship between soil salinity and cyanobacterial diversity. The cyanobacterial communities were analyzed using semi-nested 16S rRNA gene PCR and denaturing gradient gel electrophoresis. Out of 51 DGGE bands selected for sequencing only 31 which showed difference in sequences were subjected to further analysis. BLAST analysis revealed highest similarity for twenty nine of the sequences with cyanobacteria, and the other two to plant plastids. Clusters obtained based on morphological and molecular attributes of cyanobacteria were correlated to soil salinity. Among six different clades, clades 1, 2, 4 and 6 contained cyanobacteria inhabiting normal or low saline (having EC < 4.0 ds m-1) to (high) saline soils (having EC > 4.0 ds m-1), however, clade 5 represented the cyanobacteria inhabiting only saline soils. Whilst, clade 3 contained cyanobacteria from normal soils. The presence of DGGE band corresponding to Aulosira strains were present in large number of soil indicating its wide distribution over a range of salinities, as were Nostoc, Anabaena, and Hapalosiphon although to a lesser extent in the sites studied.
Low salinity favored the presence of heterocystous cyanobacteria, while very high salinity mainly supported the growth of non-heterocystous genera. High nitrogen content in the low salt soils is proposed to be a result of reduced ammonia volatilization compared to the high salt soils. Although many environmental factors could potentially determine the microbial community present in these multidimensional ecosystems, changes in the diversity of cyanobacteria in rice fields was correlated to salinity.
- Paddy Field
- Rice Field
- Cyanobacterial Community
- Cyanobacterial Genus
- Heterocystous Cyanobacterium
The Indian agriculture is suffering with many man-made problems like canal irrigation, pesticide and chemical fertilization application. However, the former is responsible for salt accumulation in the soil which is further expanding due to water-logging in paddy fields. Salinization is predicted to result in 30% of farmable land loss globally within the next 25 years, and up to 50% by the year 2050 . In developing countries like India and China, the problem could be more serious due to the increasing demand for rice as a staple food. If water-logged conditions prevail for lengthy durations salinization of the soil occurs and, in India, this is commonly known as the formation of Usar land . High salt concentrations lead to a decline in soil fertility by adversely affecting the soil microbial flora, including nitrogen-fixing cyanobacteria and therefore further decreasing rice productivity.
Cyanobacteria, the ancient oxygen-evolving photoautotrophs, are the dominant microbial inhabitants of rice fields. Members of the orders Nostocales and Stigonematales assume a special significance in this environment . Salinity adversely affects photosynthesis and therefore productivity , the functioning of plasma membranes , ionic balance in the cells  and protein profiles [7, 8] of some phototrophs including cyanobacteria. However, salinity does not affect all cyanobacteria to the same extent due to their morphological and genomic diversity [9, 10], and therefore the distribution of cyanobacterial communities in natural habitats is not uniform. The adaptive ability of cyanobacteria to salinity makes them the subject of intense biochemical and ecological investigation .
The classical methods for cyanobacterial identification and community assessment involve microscopic examination [3, 12, 13]. This assessment has, however, been criticized on the grounds that morphology can vary considerably in response to fluctuations in environmental conditions . In addition, the perennating bodies of cyanobacteria such as hormogonia, akinetes and heterocysts may be difficult to characterize by microscopy and thus the actual diversity can be underestimated . In view of the above, cyanobacterial diversity assessments and community analysis should be investigated by microscopic observation supplemented with a molecular taxonomy. Therefore, cyanobacterial diversity assessments using molecular tools have been widely applied . The application of denaturing gradient gel electrophoresis (DGGE) along with PCR for studying natural cyanobacterial assemblages has increased our understanding of their complexity in environmental samples . Among the various gene sequences used to assess cyanobacterial biodiversity, 16S rRNA gene has been applied most often .
Cyanobacterial diversity has been assessed from a variety of geographical locations, including the Colorado plateau [18, 19], exposed dolomite in central Switzerland , hot springs , the McMurdo Ice Self , and Southern Baltic Sea  using a combination of 16S rRNA gene PCR and DGGE. A considerable number of studies have been done on DGGE based identification and phylogenetic characterization of toxic cyanobacteria [24–26]. In contrast to above, cyanobacteria have been characterized only at morphological level in rice fields of India [27, 28], Bangladesh , Chile , Pakistan , Korea  and Uruguay . However, the work of Song et al.  constitutes the only known report on the biodiversity assessment of cyanobacteria in rice paddy fields (Fujian, China) during September 2001 to January 2002 using molecular tools.
Despite the considerable negative impact of salinity on physiology of pure cultured cyanobacterium as observed under laboratory conditions, nothing is known regarding its effect on the biodiversity of cyanobacteria in rice fields having different salt levels. Thus there is a need to examine how salinity-induced changes among other physicochemical properties of soil affect the distribution of cyanobacteria in paddy fields. In view of the reports by Stal [35, 10] that cyanobacteria have a remarkable yet varying flexibility to adapt to a wide range of environmental conditions, we propose that the resilient physiologies of certain cyanobacteria, including exopolysaccharide production, afford resistance to higher salinity compared to strains with relatively simpler morphologies. Further, high salinity inhibits ammonia volatilization , and this would result in soils with high nitrogen content and favor the proliferation of non-heterocystous cyanobacterial genera. This study was undertaken to provide first hand data on cyanobacterial diversity using PCR-DGGE, and correlate it to different salt levels of soil to investigate salinity-induced changes in the distribution of cyanobacteria in Indian rice fields. Further, how far the salinity affects the agriculturally important cyanobacteria was also examined.
Physicochemical analyses of soil
The sampling sites showed a wide range of Na+ concentrations (2.12 – 9.15 ppm) and EC (1.89 – 7.55 ds m-1), thereby indicating a saline-sodic nature of the soils . However, the highest EC (7.55 ds m-1) and Na+ (9.15 ppm) were observed in the soil of Rauri. In contrast to this, the soils of Madhopur and Parasurampur had the lowest EC (1.89 ds m-1) and Na+ (2.12 ppm) levels, respectively. The regression analysis between Na+ and EC (P < 0.05) also showed a wide distribution of soil samples (Figure 1B). Further, K+ content in saline soils was very low; lowest in the Rajatalab soil samples. This probably contributes to high Na+/K+ and thus the sparse population of cyanobacteria observed since K+ is essential for maintenance of cellular homeostasis, cell turgor and protein synthesis . K+ also plays a vital role in extreme environments, both as an extracellular signal and as an intracellular metabolic regulator  essential for growth and metabolism. Microscopically, the lower Na+/K+ ratio was shown to support luxuriant growth of cyanobacterial mats. Although regression analysis revealed that cyanobacterial diversity decreased with an increase in Na+/K+ ratio, a significant correlation between the number of cyanobacterial phylotypes (in terms of DGGE bands) and Na+/K+ ratio was not confirmed. This result is in contrast to that reported by Parker et al.  who demonstrated K+ toxicity to Microcystis in natural ponds. The soil of Makara also had a low Na+/K+ ratio but was associated with a sparse cyanobacterial population, however, this could be attributed to a high pH in this case.
Physicochemical properties related to salinity level of the soils collected from respective sites.
EC (dS m-1)
8.53 ± 0.37
4.05 ± 0.28
2.91 ± 0.15
2.45 ± 0.16
1.18 ± 0.14
4.72 ± 0.55
0.49 ± 0.01
0.27 ± 0.01
8.76 ± 0.24
3.57 ± 0.21
7.48 ± 0.21
5.19 ± 0.23
1.44 ± 0.11
9.77 ± 0.82
0.79 ± 0.01
0.38 ± 0.02
8.12 ± 0.21
2.44 ± 0.10
2.93 ± 0.16
3.87 ± 0.11
0.75 ± 0.09
3.73 ± 0.21
0.69 ± 0.01
0.54 ± 0.01
7.93 ± 0.13
4.12 ± 0.23
3.31 ± 0.15
2.01 ± 0.09
1.64 ± 0.17
4.56 ± 0.11
0.72 ± 0.03
0.33 ± 0.02
8.68 ± 0.26
4.62 ± 0.28
2.37 ± 0.11
1.10 ± 0.05
2.15 ± 0.20
5.92 ± 0.21
0.17 ± 0.01
0.15 ± 0.01
7.83 ± 0.15
1.89 ± 0.11
2.80 ± 0.21
3.69 ± 0.12
0.75 ± 0.07
6.03 ± 0.42
0.32 ± 0.01
0.11 ± 0.01
7.76 ± 0.11
1.97 ± 0.24
2.80 ± 0.13
2.99 ± 0.15
0.93 ± 0.04
4.42 ± 0.20
0.29 ± 0.02
0.51 ± 0.02
7.45 ± 0.32
2.11 ± 0.13
3.07 ± 0.22
2.21 ± 0.16
1.38 ± 0.12
3.82 ± 0.19
0.66 ± 0.01
0.63 ± 0.02
8.81 ± 0.24
5.81 ± 0.14
7.92 ± 0.35
4.01 ± 0.24
1.97 ± 0.15
9.82 ± 0.64
0.83 ± 0.04
0.47 ± 0.01
7.95 ± 0.17
3.12 ± 0.12
3.42 ± 0.17
2.33 ± 0.11
1.46 ± 0.18
5.40 ± 0.32
0.47 ± 0.02
0.33 ± 0.01
8.69 ± 0.21
3.54 ± 0.13
5.58 ± 0.16
7.14 ± 0.38
0.78 ± 0.05
6.55 ± 0.21
1.13 ± 0.12
0.32 ± 0.01
8.19 ± 0.15
3.86 ± 0.18
2.98 ± 0.19
2.69 ± 0.23
1.10 ± 0.09
5.07 ± 0.47
0.54 ± 0.02
0.15 ± 0.01
7.40 ± 0.18
4.37 ± 0.16
2.12 ± 0.14
1.51 ± 0.05
1.40 ± 0.11
5.99 ± 0.38
0.14 ± 0.01
0.11 ± 0.01
8.54 ± 0.16
4.13 ± 0.20
3.37 ± 0.12
2.67 ± 0.17
1.26 ± 0.13
5.65 ± 0.32
0.62 ± 0.01
0.09 ± 0.005
8.06 ± 0.27
3.35 ± 0.11
4.47 ± 0.15
1.17 ± 0.11
3.82 ± 0.23
3.64 ± 0.12
0.65 ± 0.02
2.35 ± 0.09
9.04 ± 0.35
7.55 ± 0.27
9.15 ± 0.39
5.32 ± 0.21
1.71 ± 0.11
6.70 ± 0.52
2.03 ± 0.11
1.69 ± 0.05
7.40 ± 0.29
3.06 ± 0.21
2.13 ± 0.17
0.91 ± 0.01
2.34 ± 0.19
3.26 ± 0.11
0.60 ± 0.02
0.25 ± 0.01
8.06 ± 0.17
3.22 ± 0.10
3.11 ± 0.14
2.24 ± 0.23
1.38 ± 0.13
4.44 ± 0.31
0.81 ± 0.01
0.17 ± 0.01
8.62 ± 0.28
5.69 ± 0.17
4.16 ± 0.16
0.58 ± 0.01
7.17 ± 0.22
6.23 ± 0.24
0.83 ± 0.01
0.06 ± 0.001
8.39 ± 0.13
5.12 ± 0.22
5.61 ± 0.16
3.15 ± 0.12
1.78 ± 0.12
5.96 ± 0.36
1.58 ± 0.03
0.19 ± 0.01
EC, the most appropriate parameter to characterize soil salinity , was employed to classify the soil samples into two categories, normal (hereafter low) (< 4.0 ds m-1) and saline (hereafter high salinity) (> 4.0 ds m-1) soil . This classification divided the sample soils into the following: (i) low salinity: Anei, Bardah, Bakesh, BHU, Jaddopur, Kataka, Madhopur, Maharupur, Makara, Misirpura, and Phootia, and (ii) high salinity: Aswania, Bithwal, Chauki, Kartihan, Parsurampur, Rajatalab, Rauri, Sewapuri and Teduababa. The regression analysis showed a significant negative correlation (P < 0.05) between the cyanobacterial population and EC (Figure 2A). Further, the influence of EC on cyanobacterial population was found highest among other parameters as reflected by a high r value (0.75) in regression analysis.
Microscopic observation of cyanobacterial community
The name of experimental sites, their location, date of collection, the studied nutritional properties of the soil collected from respective sites and the microscopically observed cyanobacterial genera.
Date of collection
(ppm × 10-1)
(ppm × 10-1)
Microscopically observed cyanobacteria
1.98 ± 0.25
1.97 ± 0.07
Aulosira sp., Gloeotrichia sp.* Phormidium sp.
3.72 ± 0.06
3.08 ± 0.12
Aulosira sp., Fischerella sp., Hapalosiphon sp.,
3.02 ± 0.13
2.38 ± 0.09
Anabaena sp. (2 genera), Aulosira sp.
1.37 ± 0.01
2.15 ± 0.10
Anabaena sp.*, Nostoc sp.
3.38 ± 0.13
3.14 ± 0.13
Aulosira sp., Nostoc sp.
3.20 ± 0.15
1.47 ± 0.08
Anabaena sp., Cylindrospermum sp., Nostoc sp.
3.74 ± 0.37
1.22 ± 0.03
Aulosira sp.*, Gloeotrichia sp.
2.59 ± 0.03
0.98 ± 0.02
Anabaena sp., Cylindrospermum sp.
5.89 ± 0.12
3.60 ± 0.12
6.73 ± 0.25
3.20 ± 0.15
Anabaena sp., Phormidium sp., Rivularia sp.
2.20 ± 0.17
1.13 ± 0.06
Aulosira sp., Nostoc sp. (2 genera*)
2.16 ± 0.13
1.96 ± 0.05
Aulosira sp., Hapalosiphon sp., Lyngbya sp.
10.37 ± 0.47
2.32 ± 0.12
Anabaena sp., Aphanothece sp.
4.63 ± 0.27
2.04 ± 0.13
Aphanothece sp.*, Nostoc sp., Tolypothrix sp.
3.23 ± 0.17
2.32 ± 0.09
Nostoc sp., Hapalosiphon sp.
5.67 ± 0.69
3.78 ± 0.08
5.47 ± 0.37
1.76 ± 0.04
Aulosira sp., Nostoc sp., Phormidium sp.
4.59 ± 0.15
2.38 ± 0.14
Aphanothece sp., Aulosira sp., Hapalosiphon sp., Lyngbya sp., Tolypothrix sp.
3.53 ± 0.11
3.18 ± 0.14
Anabaena sp.*, Aulosira sp., Lyngbya sp.
3.22 ± 0.21
2.96 ± 0.08
Fischerella sp., Hapalosiphon sp.
DGGE and molecular diversity
Selected DGGE bands showing similarity after sequencing and NCBI-BLAST search.
No. of bases sequenced
GenBank accession number
Nostoc sp. CCG3
Tolypothrix sp. PCC7415
Anabaena variabilis NIES23
Anabaena doliolum LCR1
Nostoc endophytum IAMM267
Fischerella muscicola SAG1427
Aulosira sp. PP615
Aulosira fertilissima LCR4
Uncultured Lyngbya sp. (Phormidium corium)
Uncultured Lyngbya sp.
Nostoc muscorum CENA18
Anabaena anomala LCR5
Anabaena oryzae LCR2
Hapalosiphon sp. CCG6
Uncultured Oscillatoria sp. clone BME114
Phormidium inundatum SAG79.79
Aphanothece sp. OES3853
Uncultured Gloeothece sp.
(Gloeothece sp. SK40)
Rivularia sp. PCC7116
Gloeotrichia echinulata URA3
Gloeotrichia echinulata URA3
Cylindrospermum sp. A1345
Cylindrospermum sp. CENA33
Uncultured diatom clone 100M1
Aulosira, Phormidium and Lyngbya formed clade 4 with sparse occurrence of Anabaena oryzae, Anabaena anomala and uncultured cyanobacteria (correspond to plastid 16S rRNA gene). All of these species were found within sites having a wide range of salinities suggesting that the species in this clade (clade 4) are salt tolerant. Of these Aulosira emerged most widely distributed among the sample sites. Densely aggregated trichomes, macroscopic structure and the presence of a thick exopolysaccharide layer are the mechanisms that could permit this ecological adaptation . Further, Zulpa de Caire et al.  have reported that salinity induces the synthesis of exopolysaccharides, which may help to tolerate high salinity. This molecular data highlights the role of Aulosira in the nitrogen budget of this region and in the potential reclamation of Usar (saline) land by aggregating the soil particles . Sequences of clade 5 were present in high saline soil in this study and included Oscillatoria and Gloeothece. However, presence of cyanobacteria belonging to Chroococcales and Oscillatoriales in same clade may be due to their polyphyletic origin . Rest of the species fell in clade 6 consisting of DGGE band similar to Aphanothece and uncultured cyanobacteria.
The nucleotide diversity was measured using Tajima-Nei model, which assumes equal substitution rates among character positions and between transitions and transversions. This model revealed the minimum evolutionary distance among members of Stigonematales. However, maximum genetic diversity was observed among the members of Nostocales. This may be due to the lower prevalence of Stigonematales compared to Nostocales in this phylogeny. Genetic distances were highest between Nostocales and Oscillatoriales and minimum between Chroococcales and Oscillatoriales.
Based on these observations, salinity tolerance in cyanobacteria would appear to be an adaptive trait that has evolved in parallel to speciation. Since this observation is based on 16S rRNA gene, a highly conserved gene, the better picture of salinity tolerance would probably emerged using the gene sequences not much conserved so may represent the effect of environmental variables on diversity of cyanobacteria. This finds support with the work of Jaspers and Overmann  that microorganisms vary considerably in their genomes and thus ecophysiologies even with similar ribosomal gene sequences. Thus the mechanism for salinity tolerance may well be conserved in closely related cyanobacteria but differs considerably across this group of prokaryotes and may be attributed to genome plasticity in cyanobacteria.
These results demonstrated that the morphological characters and molecular phylogeny were almost congruent for these populations that contained a large number of filamentous and heterocystous species either with or without branching. It is established that the morphology of unicellular cyanobacteria is not as well defined and thus there is a considerable difference between morphology and the 16S rRNA gene based phylogeny for this group. Therefore, the morphological characters for the identification of cyanobacteria and its agreement with the phylogenetic classification depends largely on the type of cyanobacteria in question.
Salinity-induced changes in cyanobacterial community
Relatively low salinity levels favored the growth of heterocystous cyanobacteria while high salinity (more than 4 ds m-1) appeared to select for non-heterocystous species (Figure 6). Since high salinity reduces ammonia volatilization , the high nitrogen content in saline soil would be detrimental for heterocystous cyanobacteria . Likewise, Staal et al.  demonstrated that in less saline conditions, the glycolipid envelop of a heterocyst provides a selective advantage over non-heterocystous cyanobacteria. The distribution of phylogenetic relationships across environmental gradients is not well understood. However, here we obtained a distinct relationship between cyanobacterial occurrence and salinity levels using both morphological and molecular data. This ecosystem is characterized by numerous overlapping factors other than salinity. Therefore, the community structure that was described here may vary with other environmental perturbations.
Soil salinity is one of the major determinants of cyanobacterial distribution and diversity in the rice fields of Eastern Uttar Pradesh. This study has shown that salinity influences cyanobacterial species distribution in rice fields, whereby high salinity soils selectively support the growth of non-heterocystous cyanobacterial populations. Threats imposed by ever-increasing salinity have resulted in thin cyanobacterial populations that lead to a reduction in biological nitrogen fixation and increased demand of chemical fertilizers in the paddy fields.
Sampling sites, sampling and biochemicals
Chemical composition of soil samples
For the analysis of pH and electrical conductivity (EC), 50 ml of double-distilled deionized Milli-Q water was added to 10 g of soil and homogenized. The suspension was subjected to centrifugation at 10,000 g for 10 min. The supernatant was used for the measurement of pH and EC using a pH (Systronics, India) and EC (Hanna Instruments, Portugal) meters respectively.
Available phosphorus was measured using the method of Olsen et al. . One gram of soil was mixed with 20 ml of 0.5 M NaHCO3 (pH 8.5) and 200 mg activated charcoal. This was shaken for 30 min at 200 g in an environmental shaker (Model-3597-ICOGMPR, USA) maintained at 25°C followed by filtration through Whatmann No. 1 filter paper. The pH of extract was maintained to 5.0 using concentrated H2SO4. The extract was then quantified for phosphorus content using molybdophosphoric acid .
Ammonia- and nitrate-nitrogen were measured by extracting 10 g of soil in 50 ml 2 M KCl and Morgan's Reagent (pH 4.8) respectively. In each sample 250 mg activated charcoal was added to obtain the clear supernatant. These were subjected to filtration through Whatmann No. 1 filter paper and used for ammonia-nitrogen measurement by phenate method  and nitrate-nitrogen estimation using the procedure described by Jackson . Data were presented in terms of total nitrogen (combination of ammonia- and nitrate-nitrogen).
The samples were microscopically analyzed using a trinocular microscope (Kyowa, Getner, Japan). The morphological characteristics of the cyanobacteria were compared with those in the literature of Desikachary  and Geitler . Photo-documentation was performed with a digital camera and 40× magnification (Olympus).
Genomic DNA isolation and PCR amplification of 16S rRNA gene
Total genomic DNA from the natural samples (paddy field soil and cyanobacterial mat) was isolated using the phenol and lysozyme-free method of Srivastava et al. . The DNA thus obtained was passed through a spin column containing Sepharose 4B for the removal of salts and humic acids. Soil and mat samples were selected for DNA isolation from perennating bodies and growing cyanobacteria respectively. The primers CYA106F (CGC ACG GGT GAG TAA CGC GTG A) and CYA359F (GGG GAA TYT TCC GCA ATG GG) with a 40 nucleotide GC clamp (5'-CGC CCG CCG CGC CCC GCG CCG GTC CCG CCG CCC CCG CCC G-3') on the 5' end (forward primer) and CYA781R (equimolar mixture of CYA781Ra (GAC TAC TGG GGT ATC TAA TCC CAT T) and CYA781Rb (GAC TAC AGG GGT ATC TAA TCC CTT T)) (reverse primers) for amplification of a segment of cyanobacterial 16S rRNA gene  were synthesized (Sigma Chemical Co., USA). A semi-nested PCR reaction was carried out with the first reaction using primers CYA106F and CYA781R followed by a reaction with primers CYA359F and CYA781R. PCR was performed in a 25 μl final volume of reaction mixture containing 100 ng of DNA, 2.5 μl of 10× PCR buffer with 15 mM MgCl2, 200 μM dNTPs, 10 pmol of each primer, 200 μg bovine serum albumin (nuclease free) and 0.2 U Taq DNA polymerase (Bangalore Genei, India) in an Icycler (Bio-Rad, USA). The thermal cycling profile was as follows: initial denaturation for 3 min at 94°C, followed by 35 amplification cycles each consisting of 1.5 min denaturation at 94°C, 1 min annealing at 59°C, and a 2 min elongation at 72°C, with a final 5 min elongation at 72°C.
The PCR products of mat and soil samples obtained after the second PCR reaction were subjected to DGGE analysis using the DGGE-2001 system (C.B.S. Scientific Company, Inc. USA). An aliquot of 25 μl of PCR product was mixed with 5 μl of 10× gel loading solution (100% glycerol, 0.25% bromophenol blue and 0.25% xylenecyanole) and applied directly onto a 6% polyacryamide gel (acrylamide/bis 38.93/1.07) (w/v) in 1× Tris-acetate-EDTA (TAE) buffer with a linear 35–55% denaturant gradient (100% denaturant solution contained 7 M urea and 40% (v/v) deionized formamide). A gradient dye solution (0.5% bromophenol blue, 0.5% xylenecyanole and 1× TAE buffer) was used to check the gradient formation. DGGE was carried out at 60°C (constant temperature) for 16 h at 100 V (35 mA). The gel was stained for 15 min with ethidium bromide (1 μg ml-1 in 1× TAE buffer) and visualized by UV transillumination and photographed. The PCR (16S rRNA gene) products of cultured Anabaena PCC7120, Anabaena doliolum LCR1 and Hapalosiphon intricatus BHULCR1 were included as genetic markers on each gel alongside the environmental samples (data not shown).
Sequencing of 16S rRNA gene
A total of 51 bands were carefully excised from the DGGE gels using an autoclaved surgical scalpel and re-suspended in sterile Milli-Q water for 3 h to elute DNA from the gel matrix . The eluted PCR products were used as template for re-amplification of the corresponding DGGE bands using the primer set CYA359F (with GC clamps) and CYA781R, and subsequently followed by another DGGE as described above. Only reactions that resulted in a single band with the predicted mobility were processed further. The specific bands were again excised and re-amplified. PCR conditions were the same as mentioned above for 16S rRNA gene amplification except the primers did not have the GC clamp and 0.5 μl template DNA was used. PCR products were sequenced commercially (Bangalore Genei, India) with the same amplification primers in separate reactions. However, only 31 DGGE bands which showed significant difference in their sequences were selected for further analysis.
A multiple alignment was produced using the CLUSTAL_X ver. 2  and manually corrected using JalView. Bands with identical mobility on DGGE gel were considered to have identical sequences. Sequence similarity between the 31 different partial 16S rRNA gene sequences resulting from DGGE analysis were deposited in GenBank and assessed by BLASTN  homology searches using the nonredundant NCBI GenBank database. In addition to this, 43 16S rRNA gene sequences from GenBank, which showed the closest similarity with the different DGGE-PCR products, were also included in the multiple alignment. Pair-wise distance matrices were calculated using the Tajima-Nei method . Character positions with gaps were deleted. The 16S rRNA gene sequences of cyanobacteria were classified into phylogenetic groups as proposed by Desikachary  for the determination of genetic variability within and between the groups. Phylogenetic trees were constructed using the neighbor-joining algorithm  provided in MEGA4 . One thousand bootstrap replicates of the alignment data were also performed and the consensus tree was constructed.
Results of the soil analysis were statistically analyzed using one-way ANOVA followed by correlation coefficient (r) analysis using SPSS 10.0. Principal component analysis was performed using Statistica 8.0. For PCA analysis, the soil analysis data presented in Tables 1 and 2 were considered. The clustering in the PCA was performed as per Coeyne et al.  using cluster analysis (similarity measure: Pearson or product-moment correlation coefficient; clustering method: UPGMA). Further, to correlate cyanobacterial abundance and the physicochemical properties of soil (EC and available nitrogen), each band on a DGGE gel was treated as an individual species. The total number of bands present in any individual lane was considered to be the cyanobacterial diversity in that soil sample. Further, for accurate estimation of diversity, microscopic observations were also compared with the molecular data. Three independent variables were used for each experiment.
Nucleotide accession numbers
The 31 partial 16S rRNA gene sequences which showed significant difference in their sequence were analyzed and taxonomically assigned using the BLAST program of NCBI. The sequences were deposited in the database under the accession numbers [GenBank: EF619446] to [GenBank: EF619472] and [GenBank: EF624387] to [GenBank: EF624390].
AKS is Assistant Professor in Mizoram University, India. LCR and BAN are Professors in Banaras Hindu University, India and University of New South Wales, Australia, respectively.
Financial support for this study was provided through megabiodiversity project of ICAR to LCR. AKS and PB are thankful to CSIR and UGC for senior research fellowships. BAN is supported by the Australian Research Council. AKS is thankful to Mizoram University, Aizawl. We are thankful to the Head and program coordinator, CAS in Botany, Banaras Hindu University, Varanasi for facilities. The financial help of BAN for publication of the MS is also gratefully acknowledged.
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