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1 Metagenomic analysis of bacterial community composition among the cave sediments of Indo-
2 Burman biodiversity hotspot region
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4 Surajit De Mandal, Zothansanga and Nachimuthu Senthil Kumar*
5 Department of Biotechnology, Mizoram University, Aizawl-796004, Mizoram, India.
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17 *Corresponding author:
18 Email: [email protected]
19 Mobile: +91-9436352574
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1 ABSTRACT
2 Caves in Mizoram, Northeast India are potential hotspot diversity regions due to the historical
3 significance of the formation of Indo-Burman plateau and also because of their unexplored and
4 unknown diversity. High throughput paired end illumina sequencing of V4 region of 16S rRNA
5 was performed to systematically evaluate the bacterial community of three caves situated in
6 Champhai district of Mizoram, Northeast India. A total of 10,643 operational taxonomic units
7 (based on 97% cutoff) comprising 21 bacterial phyla and 21 candidate phyla with a sequencing s t n
i 8 depth of 11, 40013 were found in this study. The overall taxonomic profile obtained by BLAST r
P 9 against RDP classifier and Greengene OTU database revealed high diversity within the bacterial e r 10 communities, dominated by Planctomycetes, Actinobacteria, Proteobacteria, Bacteroidetes, and P
11 Firmicutes, while members of archea were less diverse and mainly comprising of eukaryoarchea.
12 Analysis revealed that Farpuk (CFP) cave has low diversity and is mainly dominated by
13 actinobacteria (80% reads), whereas diverse communities were found in the caves of Murapuk
14 (CMP) and Lamsialpuk (CLP). Analysis of rare and abundant species also revealed that a major
15 portion of the identified OTUs were falling under rare biosphere. Significantly, all these caves
16 recorded a high number of unclassified OUTs which might represent novel species. Further,
17 analysis with whole genome sequencing is needed to validate the novel species as well as to
18 determine their functional significance.
19
20 Subjects Biodiversity, Cave Ecology, Microbiology
21 Keywords Cave, Indo-Burman plateau, Bacterial diversity, illumina sequencing
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1 INTRODUCTION
2 Indo-Burma region, a part of the 25 global biodiversity hotspots, is one of the richest biomes of
3 the world with high species diversity (Myers et al., 2000). This region is spread over 2, 62, 379
4 sq. kms and represents the transition zone between the Indian and Indochinese subregions of the
5 Oriental biogeographic region (Mani, 1974). This region contains an estimated 9.7% of the
6 world’s known endemic plant species and 8.3% of the endemic vertebrate species (Brook et al.,
7 2003). Interestingly, not many reports are available on the microbial diversity, particularly from s t n
i 8 Caves, from the Indo-Burma region. r P
e 9 Caves represent subsurface habitat and are less explored in terms of biodiversity and r
P 10 community composition due to environmental and geographical constrains. Lack of
11 photosynthesis and limited nutrient source makes the caves an extreme environment to sustain
12 life. However, alternative energy in the form of allochthonous organic materials transported from
13 the surface through bat, rodents and human activities or by percolating water is utilized by
14 certain groups of microorganisms (Barton, 2006). These ecosystems with extreme temperature,
15 osmolarity, pressure, and pH forces the inhabitants to undertake diverse and novel metabolic
16 pathways for oxidizing reduced metals, fixing gases and for utilization of various aromatic
17 compounds. Organic matter helps the formation of secondary microbial communities - usually
18 multicolored yellow, grey, white or pink cloddy coadings on carbonate or clay coated walls in
19 the form of bioflim with unusual coloration, precipitates, corrosion residues (Barton, 2006).
20 Caves also act as long-term reservoirs for endemic as well as allochthonous
21 microorganisms (Engel et al., 2010). Earlier studies reported diverse group of microorganisms
22 associated with different geological and environmental factors (Adetutu et al., 2011; 2012) and
23 have already been implicated in astrobiology, drug discovery and cave conservation studies
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1 (Northup et al., 2011; Saiz-Jimenez, 2012). These microbial communities also influence the
2 formation and preservation of cave deposits by constructive and destructive processes. Cave
3 microbes are also important since they act as primary producers, which sustain populations of
4 more complex organisms (Barton and Northup 2007).
5 Majority of the cave microbial diversity studies have been done using culture dependent
6 techniques which can reveal only 1% of the total microorganisms. In recent years, a novel s
t 7 methodology is being developed to detect the environmental microorganisms, independently of a n i
r 8 need for culture based screening. Molecular microbial ecology tools such as denaturing gradient P
e 9 gel electrophoresis (DGGE) and clone library analysis are being used by many researchers to r
P 10 characterize these uncultured microbes, but these techniques are also not sufficient to analyze the
11 entire population in the community (Adetutu et al., 2012). With the advancement of Next
12 Generation Sequencing, cave microbial ecology research has also expanded which allows us to
13 use culture-independent techniques to reveal further the hidden biodiversity and key process
14 happening inside the caves.
15 This study involves the use of high throughput illumina sequencing of sediment samples
16 collected from caves situated in Indo-Burmese border of Champhai district, Mizoram, Northeast
17 India to contribute to better understanding of their microbial community.
18
19 MATERIALS AND METHODS
20 Three caves namely, Murapuk (CMP), Lamsialpuk (CLP) and Farpuk (CFP) were
21 selected for the present study based on the fact these caves are devoid of any human influence
22 and have not been studied yet. Sediment samples were collected from different locations of the
23 caves and upon collection; the samples were sieved and preserved at 4°C. No specific permit was
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1 taken for the sampling since it did not involve any endangered species or protected area.
2 Sediment samples were analyzed for carbon and nitrogen content with a CHNS/O analyzer
3 (Perkin Elmer, USA) and pH of the sample were measured by pH meter (Table 1).
4 Soil community DNA was extracted from 0.5 g of soil sample using the Fast DNA spin
5 kit (MP Biomedical, Solon, OH, USA) following the manufacturer’s protocol. DNA
6 concentration was quantified using a microplate reader (Molecular device Spectromax 2E). V4 s
t 7 hypervariable region of the 16S rRNA gene was amplified using 2 µl of each 10 pmol/µl forward n i
r 8 and reverse primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′- P
e 9 GGACTACHVGGGTWTCTAAT-3′). The amplification mix contained 5 μL of 40mM dNTP, 5 r
P 10 μL of 5X Phusion HF reaction buffer, 0.2 μL of 2U/ µl F-540Special Phusion HS DNA
11 Polymerase, 5ng input DNA and water to make up the total volume to 25 μL. High throughput
12 Illumina Mi-seq sequencing was performed at Scigenome Labs, Cochin, India (Table 2).
13
14 Sequence quality was analyzed according to base quality score distributions, average base
15 content per read and GC distribution in the reads. Singletons, the unique OTU that did not cluster
16 with other sequence were removed as it might be a result of sequencing errors and can be
17 resulted to spurious OTUs. Chimeras were also removed using UCHIME method and pre-
18 processed consensus V4 sequences were clustered into Operational Taxonomic Units (OTUs)
19 based on their sequence similarity using Uclust program (similarity cutoff=0.97). All the pre-
20 processed reads were used to identify the OTUs using QIIME program for constructing a
21 representative sequence for each OTUs. The representative sequence was finally aligned to the
22 Greengenes core set reference databases using PyNAST program (Caporaso et al., 2010;
23 DeSantis et al., 2006). Representative sequence for each OTU was classified using RDP
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1 classifier and Greengenes OTUs database. Sequences which are not classified were categorized
2 as unknown.
3 QIIME software was used to calculate Shannon index and Observed species metrices.
4 Shannon metric represents observed OTU abundance and estimates for both richness and
5 evenness, whereas observed species metric detects unique OTUs present in the sample. In this
6 study, the comparison of beta diversity between three bacterial communities (CLP, CMP and
7 CFP) was done by calculating the distance matrix using UniFrac approach (Lozupone et al., s t n
i 8 2005). Weighted UPGMA tree was constructed by performing jackknife test A with 10 replicates r
P 9 and each sub-sample containing 1, 00,000 random reads selected from each sample. e r 10 P
11 RESULTS AND DISCUSSION
12 With an unsuitable geology, caves are the most remote and inaccessible environment for
13 research, but are now being considered as a potential biodiversity hotspot due to its unique
14 ecological significance. Most of the caves present in Mizoram are of tectonic origin which was
15 caused due to tension cleavage of the compact host rock (Gebauer et al., 2001). Since these
16 caves are present in extreme conditions, it is assumed that microorganisms living in these caves
17 would be mostly novel and undisturbed. Studying this unique habitat provides an opportunity to
18 understand global microbial diversity, novel population assemblages, energy dynamics and
19 metabolism (Ortiz et al., 2013). Previous study based on caves across the world revealed
20 heterotrophic interaction and carbon turnover by Alpha and Betaproteobacteria, Firmicutes and
21 Actinobacteria (Macalady et al., 2008; Barton, 2014), although application of next generation
22 sequencing technology on these environment suggested to have more information about
23 microbial physiology in these caves (Tetu et al., 2013; Ortiz et al., 2014).
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1 In the present study, we have used paired end illumina sequencing generating 585,434
2 raw sequences with 90% of reads having a phred score greater than 30. Illumina method is cost
3 effective and provides more detailed taxonomic profiles between samples to be determined
4 ( Nelson el al., 2014). After quality checking of V4 region of 16s rRNA, reads were clustered
5 into Operational Taxonomic Units (OTUs) based on their sequence similarity using Uclust
6 program (97% similarity level). A total of 1,140,013 preprocessed reads were clustered into
7 10,643 OTUs (operational taxonomical units). Sample library ranges from 259,895(CFP) to s t n
i 8 470,260 (CLP) sequence reads (Table 2). Identification of this huge number of sequence reads is r
P 9 a common phenomenon for underground microbial community compared to surface environment e r 10 (Moss et al., 2011; Epure et al., 2014). P
11
12 The number of OTUs and Shannon diversity indicates are summarized in Table 3. On the
13 basis of the OTUs, CMP has the highest diversity followed closely by CLP. Shannon index also
14 showes a high diversity among CMP bacterial community. Rarefaction curve for Observed
15 species and Shannon metric are shown in Fig.1. The Observed species metric is only the count of
16 unique OTUs identified in the sample. This analysis shows that sample from CMP is more
17 diverse than the other two samples. Beta diversity represents the explicit comparison of
18 microbial communities based on their composition. Unweighted UniFrac reveals a close
19 relationship between these three communities with no difference in distance matrix (Table 4).
20 Consensus UPGMA tree with weighted Unifac approach bring the two communities CLP and
21 CMP together showing the existing of similar bacteria, while the bacterial community in CFP is
22 different from others (Fig.2). This difference may be due to the remote location of the Farpuk
23 (CFP) than the other two caves, which probably causes CFP to retain its unique bacterial
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1 community without any disturbance. The sequences from CFP sample had more than 50%
2 singletons in the consensus reads, which are believed to possess no taxonomic information and
3 hence deleted for further sequence analysis leading to less diversity representation of the
4 bacterial species.
5 A total of 21 bacterial phyla and 21 candidate phyla were identified from all the cave
6 sediments and were mostly dominated by Actinobacteria, Planctomycetes, Chloroflexi,
7 Acidobacteria and Proteobacteria. Relative abundance among the top ten dominated phylum are s t n
i 8 represented in Fig. 3 and Fig. 4. Previous studies recorded diverse groups of actinobacteria in r
P 9 caves and their role in colored crystal formation in cave walls and therefore also in constructive e r 10 biomineralization processes (Barton et al., 2001). Our study also detected actinobacteria as the P
11 most dominating phyla (35.97% of total sequence) with majority of them (244 OTU) falling
12 under the order actinomycelales, followed by solirubrobacterales and acidimicrobiales. Other
13 orders identified include thermoleophilia, rubrobacterraceae and MMB-A2-108. In our study,
14 twelve actinobacteria were identified upto species level: Streptomyces radiopugnans,
15 Virgisporangium ochraceum, Actinomadura vinacea, Streptomyces lanatus, Rhodococcus
16 fascians, R. fascians, Saccharopolyspora hirsute, Virgisporangium ochraceum, S. mirabilis,
17 Actinomadura vinacea and Mycobacterium celatum. In all the cave samples, denovo 3283 were
18 most dominated phylotype and BLAST result shows a 100% similarity with the species
19 Mycobacterium. Other dominated phylotype include denovo5355, which is closely related with
20 Arthrobacter - a member of the GC rich ‘actinomycete’ capable of utilizing a wide and diverse
21 range of organic substances as carbon and energy sources such as nicotine, nucleic acids, various
22 herbicides and pesticides. Phylum Chloroflexi had the second largest number of sequence
23 (13.96%) with 1999 OTUs dominated by the class Ktedonobacteria. Identified genera under
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1 these phyla include Chloroflexus, FFCH10602, Caldilinea, Ardenscatena, Chloronema and
2 Oscillochloris. Most dominated OTUs under this phylum were denovo1827 and denovo 9830
3 which were classified under the order thermogemmatisporales and TK10, respectively. Members
4 of these phyla were commonly found in most of the caves from other environments such as
5 anaerobic thermophiles, filamentous anoxygenic phototrophs, and anaerobic organohalide
6 respirers. Our study detects on an average value of 13.76% of all sequences belonging to
7 planctomycetes, a distinct phylum of the domain bacteria having intracellular s t n
i 8 compartmentalization and lack of peptidoglycan in their cell walls. Most of the dominant OUTs r
P 9 within this group were classified under the order WD2101 and Gemmatales. This phyla is the e r 10 major abundant members in CMP (22.82% of all read) and CLP (18.43% of all read) samples, P
11 whereas in CFP it is only 0.03%. Although this phylum is a common member of the cave
12 bacterial community, its role in cave is not clear due to limited cultural representative. Few study
13 showed their involvement in metabolism of sulfated polysaccharides as well as oxidation of
14 ammonia (Schmid et al., 2000; Jetten et al., 2003). Proteobacteria was found to be diverse in all
15 the three bacterial communities. A total of 46154 sequences with 497 OTUs were found under
16 the subphylum alpha proteobacteria. Major OTUs in this subphylum were classified under the
17 order Rhizobiales. Dominant genera within this subphylum were Sphingomonadaceae
18 kaistobacter, Bradyrhizobiaceae bradyrhizobium and Hyphomicrobiaceae rhodoplanes.
19 Betaproteobacteria were less diverse, only 19 OTUs (2792 sequence) was detected among all the
20 samples. Most dominant OTUs within Betaproteobacteria were denovo 360, 2244 and 8071 all
21 classified under the genus Burkholderia. Fourty nine OTUs from 3534 sequences grouped under
22 gamma proteobacteria, dominated by the genus Dyella. 323 sequences clustering into 37 OUTs
23 were classified under the subphyla Deltaproteobacteria. All of OTUs were present in less
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1 numbers. The phylum acidobacteria was moderately abundant among the cave samples and
2 represented by 11.44% of the total sequence obtained. This phylum consisted of family
3 solibacteraceae, koribacteraceae and acidobacteriaceae. Most dominant OTUs under this order
4 were denovo7994 and denovo 9544, belonging to the class chloracidobacteria and acidobacteria-
5 6 respectively. Two OTUs within this phylum - denovo 6901 and denovo 5227 demonstrate
6 close sequence similarity with Candidatus Solibacter usitatus Ellin6076, which are adopted to
7 survive under low-nutrient conditions (Ward et al., 2009). s t n
i 8 r
P 9 A total of 33411 reads (CLP=8, CFP=11583 & CMP =21820) comprising 361 OTUs e r 10 were classified within the phylum Armatimonadetes (formerly known as ‘candidate division P
11 OP10’), a dominant and globally-distributed lineage within this ‘uncultured majority’. All the
12 OTUs were classified under the genus fimbriimonas except denovo 1709 which is classified
13 under the genus chthonomonas. Only one OTU (denovo 4733) was found in CMP classified
14 under the genus Gemmatimonas. Seven OTUs containing 89 reads (CFP53, CLP2, and CMP 34)
15 were affiliated with phylum nitrospira with only two detected genus- JG37-AG-70 and
16 nitrospira. Members of this group are obligate chemolithoautotroph, obtaining energy by
17 oxidation in nitrite and detected previously in Mexican anchialine caves and Tito Bustillo caves
18 (Pohlman et al., 1997; Schabereiter-Gurtner et al., 2002). Our analysis reveals 45 OTUs under
19 the phylum bacteroidetes. Taxa classified under the species level were Cytophaga xylanolytica,
20 Flavobacterium succinicans, Bacteroides plebeius, Sphingobacterium multivorum and
21 Fontibacter flavus. Most dominant OTU classified were under flavisolibacter (denovo1336 and
22 denovo3516) and adhaeribacter (denovo8478). There were 334 sequences comprising 19 OTUs
23 classified under the phylum Euryarchaeota, dividing into four classes methanomicrobia,
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1 thermoplasmata, halobacteria and methanobacteria. Identified genera in this phylum include
2 Methanocella, Methanocorpusculum, Halolamina, Methanoculleus, Methanoculleus,
3 Methanosarcina, Haloquadratum, Methanobacterium, Methanosaeta, and Methanoplanus.
4 However within the phylum Euryarchaeota, none of the OTUs were classified upto the species
5 level in all the three cave communities and were found to be present in rare numbers. The
6 phylum Crenarchaeota is also present in very low quantity and was clustered into 21 OTUs (total
7 read 1698). All the Crenarchaeota were assigned into two classes- MBGA and Thaumarchaeota. s t n
i 8 Our analysis identified twenty one candidate phyla, also known as a bacterial lineage, r
P 9 mostly falling within the rare biosphere. The most dominant OTU among the candidate phyla is e r 10 denovo 1407 (read=3094), classified under the phylum AD3, having close sequence similarity P
11 with the environmental clone LuqGS470001 (Minyard et al., 2012). This clone was originally
12 isolated from deep saprolite and saprock which is believed to play a role in weathered minerals
13 in deep tropical saprolite and is found to be a common inhabitant of all the analyzed caves
14 (Minyard et al., 2012). Other candidate phyla identified in our study includes LD1, MPV, NKB,
15 OD, OD1, OD3, TM6, TM7, WS1, WS2, WS3, WWE1, ZB3, BH1, BRC1, FCPU, GAL, GN,
16 ZB3 and Kazan3b. Top ten bacterial genera based on OTU number and top ten OTU’s based on
17 total read count number among the cave samples is represented in Supplementary Tables S1 and
18 S2, respectively. Relative abundance of bacterial diversity from phylum to species is shown in
19 Supplementary Fig.’s S1 to S3.
20 Illumina sequencing reveals a huge number of phylotype among these caves samples
21 belonging to the rare biosphere, which are microorganisms with extremely low abundance (Reid
22 et al., 2011). We have selected the criteria for rare (<0.01% of total community) and abundant
23 (other than rare species) species based on the previous study (Aravindraja et al., 2013) and
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1 according to this distribution, the rare species was 75.72-83.01% among the samples, whereas
2 the abundant species was 16.98-24.27 % (Fig. 5). Ratio of rare and abundant OTUs among all
3 the three samples were similar and within a range of 3.11- 4.88. The most abundant phylotype
4 was denovo 6722 classified under actinobacteria and were present in all the three cave samples.
5 Fig. 6 shows the unique and shared species among the rare biosphere in all the three samples.
6 Venn diagram shows only 171 rare OTUs (1.78%) being shared among the three communities,
7 but majority of the rare species in CFP are unique, whereas many common species was observed s t n
i 8 between CFP and CMP. Among abundant species 3.94% is shared by all the three samples. r
P 9 Many OTUs among the cave samples were rare in one community but showing as an abundant in e r 10 other community. This showes that different environmental factors prevalent among the caves P
11 which makes some group to be dormant and become a member of the rare biosphere. These
12 members can be active at favorable environmental conditions and become abundant. Common
13 identified species among the cave samples were members of the phylum Actinobacteria,
14 Firmicutes and Proteobacteria (Table 5). Further analysis with whole genome sequencing will
15 reveal the actual role of these rare and abundant phyla present in the cave samples.
16 This study provides an in-depth study of unexplored bacterial diversity in cave samples
17 of Mizoram with a large number of classified phyla (twenty), candidate phyla (twenty one) and a
18 large portion of unclassified bacteria, indicates the possibility the presence of novel species. It is
19 found that the classified reads were simultaneously decreased from phylum to species level. The
20 two most dominant phylotype were denovo 6722 (11.72%) and denovo 4035 (5.72%) belonging
21 to Actinobacteria and Verrucomicrobia, respectively. The remaining phyla present in the
22 communities had low (<4%) abundance. The present study revealed a unique bacterial
23 community in Farpuk which was mostly classified under uncultured actinobacteria. These
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1 uncultured species could be a major source of new antibiotics. This analysis also revealed that
2 the bacterial diversity is higher in CMP and CFP samples compared to CLP samples. This might
3 be due to the fact that CLP is situated in extremely remote place and their diversity is not
4 influenced by an exogenous source compared to other or each cave environment might have
5 different nutrient composition or ecological condition for specific bacterial taxa.
6 s t
n 7 ACKNOWLEDGEMENTS i r P
e 8 This research was funded by a grant from the State Biotech Hub sponsored by Department of r
P 9 Biotechnology, Govt. of India, New Delhi. We would like to thank Mr. Lalrinhlua for his help in
10 sampling.
11 Competing Interests
12 The authors declare there are no competing interests.
13
14 Author Contributions
15 • Surajit De Mandal, Zothansanga and Nachimuthu Senthil Kumar conceived and designed the
16 experiments, analyzed the data, wrote the paper, prepared Figures and Tables. Surajit De Mandal
17 performed the experiments.
18
19 DNA Deposition
20 The following information was supplied and is under process regarding the deposition of DNA
21 sequences: EBI Sequence Read Archive, Project Number PRJEB7730 and ERP008676.
22
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1 Supplemental Information
2 Supplemental information for this article is attached. The file contains 2 Tables and 3 Figures.
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1
2 3 Table 1 Details of the cave samples used in the present study. 4
Name of the Place and Latitude Longitude Elevation Humidity Temperature pH C N H
cave Year of o s
collectiont (MSL) (%) ( C) (%) (%) (%)
(Sample n i Name) r P
e Murapuk r N23°44.295' E92°39'770' 4927 44 22 7.2 110.50 13.46 30.90
(CMP) P Champhai, Lamsialpuk Mizoram, N23°08.019' E93°16'896' 4446 40 24 7.2 126.79 13.96 9.96 (CLP) Northeast India (2014) Farpuk N23°06.055' E92°17'911' 4645 44 23 6.8 40.58 3.19 9.07 (CFP)
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Table 2 Summary statistics of illumina paired-end reads (V4 region of 16S rRNA gene) used in this study.
Sample Total Passed Passed Passed Passed Consensus Chimeric Pre- Name Reads Conserved Spacer Read Mismatch Reads After Sequences processed Singleton (bp) s Region (bp) Quality Filter (bp) (bp) (bp) Reads t Filter (bp) Filter (bp) Removal (bp) n i
r (bp)
CLP 674,406P 617,278 616,828 616,738 568,149 568,149 470,943 683 470,260 e
CMP 635,210r 583,165 582,750 582,628 538,641 538,641 406,640 1,252 405,388
CFP 690,975P 621,772 620,134 619,973 560,239 538,641 262,430 2,535 259,895
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Table 3 Summary of illumina operational taxonomical units (OTUs) and alpha diversity estimates using QIIME tool.
Sample name Total OTU Shannon index
CMP 6555 9.30 CLP 4968 9.05 CFP 3108 4.75 s
t n i r
P e
r
P
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Table 4 Unweighted UniFrac distance matrix among the cave samples.
Sample CLP CMP CFP name CLP 0 0.761516 0.550632
CMP 0.761516 0 0.779452
CFP 0.550632 0.779452 0
s t n i r
P e
r
P
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Table 5 Shared and unic taxa (identified upto species level) in the cave samples. Species with * is present in all the samples
Species in CFP Species in CLP Species in CMP Actinomadura vinacea* Actinomadura vinacea* Actinomadura vinacea* s t Clostridium bowmanii* Bacillus badius Afipia felis n i Clostridium butyricum* Bacteroides plebeius Bacillus badius r Glaciecola polaris Brevundimonas diminuta Burkholderia tuberum P Mycobacterium celatum Burkholderia tuberum Clostridium acetobutylicum e
r Peredibacter starrii* Caenispirillum salinarum Clostridium bifermentans Rhodococcus fascians Clostridium bifermentans Clostridium bowmanii* P Saccharopolyspora hirsuta* Clostridium bowmanii* Clostridium butyricum* Sphingomonas azotifigens* Clostridium butyricum* Coccomyxa subellipsoidea Stenotrophomonas acidaminiphila Clostridium perfringens Corallococcus exiguus Streptomyces mirabilis* Clostridium tetani Desulfosporosinus meridiei Thermomonas fusca Clostridium venationis Escherichia coli Virgisporangium ochraceum* Coccomyxa subellipsoidea Flavobacterium succinicans* Cytophaga xylanolytica Glaciecola polaris Escherichia coli Leptolyngbya frigida Flavobacterium succinicans* Luteibacter rhizovicinus Fontibacter flavus Nevskia ramosa Inquilinus limosus Paenibacillus chondroitinus Kosmotoga mrcj Paenibacillus ginsengarvi Luteibacter rhizovicinus Peredibacter starrii* Megamonas hypermegale Propionispira arboris Methylobacterium organophilum Pseudomonas viridiflava Mycobacterium celatum Rhodococcus fascians Paenibacillus chondroitinus Roseomonas mucosa Paenibacillus curdlanolyticus Saccharopolyspora hirsuta* Peredibacter starrii* Shewanella algae Pseudomonas viridiflava Sphingobacterium multivorum Saccharopolyspora hirsuta* Sphingomonas azotifigens* Shewanella algae Streptomyces lanatus Singulisphaera rosea Streptomyces mirabilis* Sphingobacteria multivorum Syntrichia ruralis Sphingomonas azotifigens* Virgisporangium ochraceum* Sphingomonas wittichii
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Streptomyces mirabilis* Streptomyces radiopugnans Veillonella dispar Virgisporangium ochraceum* s t n i r P e r P
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s t n i r P e r P
Figure 1 Rarefaction analysis of alpha diversity among CMP, CLP and CMP samples. Three different diversity matrix were used a) Observed number of species, b) Shannon diversity index.
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1
2 3 s t
n 4 i r 5 Figure 2 Phylogenetic tree based on the distances between sample CLP, CMP and CFP P
e 6 with weighted UniFrac approach. r 7 P 8 9 10 11 12 13 14 15
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2 Figure 3 Taxonomy classifications of reads at phylum level for the cave samples. Only top 3 10 enriched class categories are shown in the figure. Classification is performed using RDP 4 classifier and Greengenes OTUs database.
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2 s t n i r P e r P
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4 Figure 4 Taxonomy classifications of OTUs at phylum level for the cave samples. Only 5 top 10 enriched class categories are shown in the figure. Classification is performed using 6 RDP classifier and Greengenes OTUs database.
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P 2 Figure 5 Percentage of abundant and rare OTUs among the cave samples e 3 r
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