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Metagenomic analysis of bacterial community composition among the cave sediments of Indo-1

Burman biodiversity hotspot region 2

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Surajit De Mandal, Zothansanga and Nachimuthu Senthil Kumar* 4

Department of Biotechnology, Mizoram University, Aizawl-796004, Mizoram, India. 5

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*Corresponding author: 17

Email: nskmzu@gmail.com 18

Mobile: +91-9436352574 19

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ABSTRACT 1

Caves in Mizoram, Northeast India are potential hotspot diversity regions due to the historical 2

significance of the formation of Indo-Burman plateau and also because of their unexplored and 3

unknown diversity. High throughput paired end illumina sequencing of V4 region of 16S rRNA 4

was performed to systematically evaluate the bacterial community of three caves situated in 5

Champhai district of Mizoram, Northeast India. A total of 10,643 operational taxonomic units 6

(based on 97% cutoff) comprising 21 bacterial phyla and 21 candidate phyla with a sequencing 7

depth of 11, 40013 were found in this study. The overall taxonomic profile obtained by BLAST 8

against RDP classifier and Greengene OTU database revealed high diversity within the bacterial 9

communities, dominated by Planctomycetes, Actinobacteria, Proteobacteria, Bacteroidetes, and 10

Firmicutes, while members of archea were less diverse and mainly comprising of eukaryoarchea. 11

Analysis revealed that Farpuk (CFP) cave has low diversity and is mainly dominated by 12

actinobacteria (80% reads), whereas diverse communities were found in the caves of Murapuk 13

(CMP) and Lamsialpuk (CLP). Analysis of rare and abundant species also revealed that a major 14

portion of the identified OTUs were falling under rare biosphere. Significantly, all these caves 15

recorded a high number of unclassified OUTs which might represent novel species. Further, 16

analysis with whole genome sequencing is needed to validate the novel species as well as to 17

determine their functional significance. 18

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Subjects Biodiversity, Cave Ecology, Microbiology 20

Keywords Cave, Indo-Burman plateau, Bacterial diversity, illumina sequencing 21

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INTRODUCTION 1

Indo-Burma region, a part of the 25 global biodiversity hotspots, is one of the richest biomes of 2

the world with high species diversity (Myers et al., 2000). This region is spread over 2, 62, 379 3

sq. kms and represents the transition zone between the Indian and Indochinese subregions of the 4

Oriental biogeographic region (Mani, 1974). This region contains an estimated 9.7% of the 5

world’s known endemic plant species and 8.3% of the endemic vertebrate species (Brook et al., 6

2003). Interestingly, not many reports are available on the microbial diversity, particularly from 7

Caves, from the Indo-Burma region. 8

Caves represent subsurface habitat and are less explored in terms of biodiversity and 9

community composition due to environmental and geographical constrains. Lack of 10

photosynthesis and limited nutrient source makes the caves an extreme environment to sustain 11

life. However, alternative energy in the form of allochthonous organic materials transported from 12

the surface through bat, rodents and human activities or by percolating water is utilized by 13

certain groups of microorganisms (Barton, 2006). These ecosystems with extreme temperature, 14

osmolarity, pressure, and pH forces the inhabitants to undertake diverse and novel metabolic 15

pathways for oxidizing reduced metals, fixing gases and for utilization of various aromatic 16

compounds. Organic matter helps the formation of secondary microbial communities - usually 17

multicolored yellow, grey, white or pink cloddy coadings on carbonate or clay coated walls in 18

the form of bioflim with unusual coloration, precipitates, corrosion residues (Barton, 2006). 19

Caves also act as long-term reservoirs for endemic as well as allochthonous 20

microorganisms (Engel et al., 2010). Earlier studies reported diverse group of microorganisms 21

associated with different geological and environmental factors (Adetutu et al., 2011; 2012) and 22

have already been implicated in astrobiology, drug discovery and cave conservation studies 23

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(Northup et al., 2011; Saiz-Jimenez, 2012). These microbial communities also influence the 1

formation and preservation of cave deposits by constructive and destructive processes. Cave 2

microbes are also important since they act as primary producers, which sustain populations of 3

more complex organisms (Barton and Northup 2007). 4

Majority of the cave microbial diversity studies have been done using culture dependent 5

techniques which can reveal only 1% of the total microorganisms. In recent years, a novel 6

methodology is being developed to detect the environmental microorganisms, independently of a 7

need for culture based screening. Molecular microbial ecology tools such as denaturing gradient 8

gel electrophoresis (DGGE) and clone library analysis are being used by many researchers to 9

characterize these uncultured microbes, but these techniques are also not sufficient to analyze the 10

entire population in the community (Adetutu et al., 2012). With the advancement of Next 11

Generation Sequencing, cave microbial ecology research has also expanded which allows us to 12

use culture-independent techniques to reveal further the hidden biodiversity and key process 13

happening inside the caves. 14

This study involves the use of high throughput illumina sequencing of sediment samples 15

collected from caves situated in Indo-Burmese border of Champhai district, Mizoram, Northeast 16

India to contribute to better understanding of their microbial community. 17

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MATERIALS AND METHODS 19

Three caves namely, Murapuk (CMP), Lamsialpuk (CLP) and Farpuk (CFP) were 20

selected for the present study based on the fact these caves are devoid of any human influence 21

and have not been studied yet. Sediment samples were collected from different locations of the 22

caves and upon collection; the samples were sieved and preserved at 4°C. No specific permit was 23

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taken for the sampling since it did not involve any endangered species or protected area. 1

Sediment samples were analyzed for carbon and nitrogen content with a CHNS/O analyzer 2

(Perkin Elmer, USA) and pH of the sample were measured by pH meter (Table 1). 3

Soil community DNA was extracted from 0.5 g of soil sample using the Fast DNA spin 4

kit (MP Biomedical, Solon, OH, USA) following the manufacturer’s protocol. DNA 5

concentration was quantified using a microplate reader (Molecular device Spectromax 2E). V4 6

hypervariable region of the 16S rRNA gene was amplified using 2 µl of each 10 pmol/µl forward 7

and reverse primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-8

GGACTACHVGGGTWTCTAAT-3′). The amplification mix contained 5 μL of 40mM dNTP, 5 9

μL of 5X Phusion HF reaction buffer, 0.2 μL of 2U/ µl F-540Special Phusion HS DNA 10

Polymerase, 5ng input DNA and water to make up the total volume to 25 μL. High throughput 11

Illumina Mi-seq sequencing was performed at Scigenome Labs, Cochin, India (Table 2). 12

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Sequence quality was analyzed according to base quality score distributions, average base 14

content per read and GC distribution in the reads. Singletons, the unique OTU that did not cluster 15

with other sequence were removed as it might be a result of sequencing errors and can be 16

resulted to spurious OTUs. Chimeras were also removed using UCHIME method and pre-17

processed consensus V4 sequences were clustered into Operational Taxonomic Units (OTUs) 18

based on their sequence similarity using Uclust program (similarity cutoff=0.97). All the pre-19

processed reads were used to identify the OTUs using QIIME program for constructing a 20

representative sequence for each OTUs. The representative sequence was finally aligned to the 21

Greengenes core set reference databases using PyNAST program (Caporaso et al., 2010; 22

DeSantis et al., 2006). Representative sequence for each OTU was classified using RDP 23

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classifier and Greengenes OTUs database. Sequences which are not classified were categorized 1

as unknown. 2

QIIME software was used to calculate Shannon index and Observed species metrices. 3

Shannon metric represents observed OTU abundance and estimates for both richness and 4

evenness, whereas observed species metric detects unique OTUs present in the sample. In this 5

study, the comparison of beta diversity between three bacterial communities (CLP, CMP and 6

CFP) was done by calculating the distance matrix using UniFrac approach (Lozupone et al., 7

2005). Weighted UPGMA tree was constructed by performing jackknife test A with 10 replicates 8

and each sub-sample containing 1, 00,000 random reads selected from each sample. 9

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RESULTS AND DISCUSSION 11

With an unsuitable geology, caves are the most remote and inaccessible environment for 12

research, but are now being considered as a potential biodiversity hotspot due to its unique 13

ecological significance. Most of the caves present in Mizoram are of tectonic origin which was 14

caused due to tension cleavage of the compact host rock (Gebauer et al., 2001). Since these 15

caves are present in extreme conditions, it is assumed that microorganisms living in these caves 16

would be mostly novel and undisturbed. Studying this unique habitat provides an opportunity to 17

understand global microbial diversity, novel population assemblages, energy dynamics and 18

metabolism (Ortiz et al., 2013). Previous study based on caves across the world revealed 19

heterotrophic interaction and carbon turnover by Alpha and Betaproteobacteria, Firmicutes and 20

Actinobacteria (Macalady et al., 2008; Barton, 2014), although application of next generation 21

sequencing technology on these environment suggested to have more information about 22

microbial physiology in these caves (Tetu et al., 2013; Ortiz et al., 2014). 23

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In the present study, we have used paired end illumina sequencing generating 585,434 1

raw sequences with 90% of reads having a phred score greater than 30. Illumina method is cost 2

effective and provides more detailed taxonomic profiles between samples to be determined 3

( Nelson el al., 2014). After quality checking of V4 region of 16s rRNA, reads were clustered 4

into Operational Taxonomic Units (OTUs) based on their sequence similarity using Uclust 5

program (97% similarity level). A total of 1,140,013 preprocessed reads were clustered into 6

10,643 OTUs (operational taxonomical units). Sample library ranges from 259,895(CFP) to 7

470,260 (CLP) sequence reads (Table 2). Identification of this huge number of sequence reads is 8

a common phenomenon for underground microbial community compared to surface environment 9

(Moss et al., 2011; Epure et al., 2014). 10

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The number of OTUs and Shannon diversity indicates are summarized in Table 3. On the 12

basis of the OTUs, CMP has the highest diversity followed closely by CLP. Shannon index also 13

showes a high diversity among CMP bacterial community. Rarefaction curve for Observed 14

species and Shannon metric are shown in Fig.1. The Observed species metric is only the count of 15

unique OTUs identified in the sample. This analysis shows that sample from CMP is more 16

diverse than the other two samples. Beta diversity represents the explicit comparison of 17

microbial communities based on their composition. Unweighted UniFrac reveals a close 18

relationship between these three communities with no difference in distance matrix (Table 4). 19

Consensus UPGMA tree with weighted Unifac approach bring the two communities CLP and 20

CMP together showing the existing of similar bacteria, while the bacterial community in CFP is 21

different from others (Fig.2). This difference may be due to the remote location of the Farpuk 22

(CFP) than the other two caves, which probably causes CFP to retain its unique bacterial 23

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community without any disturbance. The sequences from CFP sample had more than 50% 1

singletons in the consensus reads, which are believed to possess no taxonomic information and 2

hence deleted for further sequence analysis leading to less diversity representation of the 3

bacterial species. 4

A total of 21 bacterial phyla and 21 candidate phyla were identified from all the cave 5

sediments and were mostly dominated by Actinobacteria, Planctomycetes, Chloroflexi, 6

Acidobacteria and Proteobacteria. Relative abundance among the top ten dominated phylum are 7

represented in Fig. 3 and Fig. 4. Previous studies recorded diverse groups of actinobacteria in 8

caves and their role in colored crystal formation in cave walls and therefore also in constructive 9

biomineralization processes (Barton et al., 2001). Our study also detected actinobacteria as the 10

most dominating phyla (35.97% of total sequence) with majority of them (244 OTU) falling 11

under the order actinomycelales, followed by solirubrobacterales and acidimicrobiales. Other 12

orders identified include thermoleophilia, rubrobacterraceae and MMB-A2-108. In our study, 13

twelve actinobacteria were identified upto species level: Streptomyces radiopugnans, 14

Virgisporangium ochraceum, Actinomadura vinacea, Streptomyces lanatus, Rhodococcus 15

fascians, R. fascians, Saccharopolyspora hirsute, Virgisporangium ochraceum, S. mirabilis, 16

Actinomadura vinacea and Mycobacterium celatum. In all the cave samples, denovo 3283 were 17

most dominated phylotype and BLAST result shows a 100% similarity with the species 18

Mycobacterium. Other dominated phylotype include denovo5355, which is closely related with 19

Arthrobacter - a member of the GC rich ‘actinomycete’ capable of utilizing a wide and diverse 20

range of organic substances as carbon and energy sources such as nicotine, nucleic acids, various 21

herbicides and pesticides. Phylum Chloroflexi had the second largest number of sequence 22

(13.96%) with 1999 OTUs dominated by the class Ktedonobacteria. Identified genera under 23

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these phyla include Chloroflexus, FFCH10602, Caldilinea, Ardenscatena, Chloronema and 1

Oscillochloris. Most dominated OTUs under this phylum were denovo1827 and denovo 9830 2

which were classified under the order thermogemmatisporales and TK10, respectively. Members 3

of these phyla were commonly found in most of the caves from other environments such as 4

anaerobic thermophiles, filamentous anoxygenic phototrophs, and anaerobic organohalide 5

respirers. Our study detects on an average value of 13.76% of all sequences belonging to 6

planctomycetes, a distinct phylum of the domain bacteria having intracellular 7

compartmentalization and lack of peptidoglycan in their cell walls. Most of the dominant OUTs 8

within this group were classified under the order WD2101 and Gemmatales. This phyla is the 9

major abundant members in CMP (22.82% of all read) and CLP (18.43% of all read) samples, 10

whereas in CFP it is only 0.03%. Although this phylum is a common member of the cave 11

bacterial community, its role in cave is not clear due to limited cultural representative. Few study 12

showed their involvement in metabolism of sulfated polysaccharides as well as oxidation of 13

ammonia (Schmid et al., 2000; Jetten et al., 2003). Proteobacteria was found to be diverse in all 14

the three bacterial communities. A total of 46154 sequences with 497 OTUs were found under 15

the subphylum alpha proteobacteria. Major OTUs in this subphylum were classified under the 16

order Rhizobiales. Dominant genera within this subphylum were Sphingomonadaceae 17

kaistobacter, Bradyrhizobiaceae bradyrhizobium and Hyphomicrobiaceae rhodoplanes. 18

Betaproteobacteria were less diverse, only 19 OTUs (2792 sequence) was detected among all the 19

samples. Most dominant OTUs within Betaproteobacteria were denovo 360, 2244 and 8071 all 20

classified under the genus Burkholderia. Fourty nine OTUs from 3534 sequences grouped under 21

gamma proteobacteria, dominated by the genus Dyella. 323 sequences clustering into 37 OUTs 22

were classified under the subphyla Deltaproteobacteria. All of OTUs were present in less 23

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numbers. The phylum acidobacteria was moderately abundant among the cave samples and 1

represented by 11.44% of the total sequence obtained. This phylum consisted of family 2

solibacteraceae, koribacteraceae and acidobacteriaceae. Most dominant OTUs under this order 3

were denovo7994 and denovo 9544, belonging to the class chloracidobacteria and acidobacteria-4

6 respectively. Two OTUs within this phylum - denovo 6901 and denovo 5227 demonstrate 5

close sequence similarity with Candidatus Solibacter usitatus Ellin6076, which are adopted to 6

survive under low-nutrient conditions (Ward et al., 2009). 7

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A total of 33411 reads (CLP=8, CFP=11583 & CMP =21820) comprising 361 OTUs 9

were classified within the phylum Armatimonadetes (formerly known as ‘candidate division 10

OP10’), a dominant and globally-distributed lineage within this ‘uncultured majority’. All the 11

OTUs were classified under the genus fimbriimonas except denovo 1709 which is classified 12

under the genus chthonomonas. Only one OTU (denovo 4733) was found in CMP classified 13

under the genus Gemmatimonas. Seven OTUs containing 89 reads (CFP53, CLP2, and CMP 34) 14

were affiliated with phylum nitrospira with only two detected genus- JG37-AG-70 and 15

nitrospira. Members of this group are obligate chemolithoautotroph, obtaining energy by 16

oxidation in nitrite and detected previously in Mexican anchialine caves and Tito Bustillo caves 17

(Pohlman et al., 1997; Schabereiter-Gurtner et al., 2002). Our analysis reveals 45 OTUs under 18

the phylum bacteroidetes. Taxa classified under the species level were Cytophaga xylanolytica, 19

Flavobacterium succinicans, Bacteroides plebeius, Sphingobacterium multivorum and 20

Fontibacter flavus. Most dominant OTU classified were under flavisolibacter (denovo1336 and 21

denovo3516) and adhaeribacter (denovo8478). There were 334 sequences comprising 19 OTUs 22

classified under the phylum Euryarchaeota, dividing into four classes methanomicrobia, 23

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thermoplasmata, halobacteria and methanobacteria. Identified genera in this phylum include 1

Methanocella, Methanocorpusculum, Halolamina, Methanoculleus, Methanoculleus, 2

Methanosarcina, Haloquadratum, Methanobacterium, Methanosaeta, and Methanoplanus. 3

However within the phylum Euryarchaeota, none of the OTUs were classified upto the species 4

level in all the three cave communities and were found to be present in rare numbers. The 5

phylum Crenarchaeota is also present in very low quantity and was clustered into 21 OTUs (total 6

read 1698). All the Crenarchaeota were assigned into two classes- MBGA and Thaumarchaeota. 7

Our analysis identified twenty one candidate phyla, also known as a bacterial lineage, 8

mostly falling within the rare biosphere. The most dominant OTU among the candidate phyla is 9

denovo 1407 (read=3094), classified under the phylum AD3, having close sequence similarity 10

with the environmental clone LuqGS470001 (Minyard et al., 2012). This clone was originally 11

isolated from deep saprolite and saprock which is believed to play a role in weathered minerals 12

in deep tropical saprolite and is found to be a common inhabitant of all the analyzed caves 13

(Minyard et al., 2012). Other candidate phyla identified in our study includes LD1, MPV, NKB, 14

OD, OD1, OD3, TM6, TM7, WS1, WS2, WS3, WWE1, ZB3, BH1, BRC1, FCPU, GAL, GN, 15

ZB3 and Kazan3b. Top ten bacterial genera based on OTU number and top ten OTU’s based on 16

total read count number among the cave samples is represented in Supplementary Tables S1 and 17

S2, respectively. Relative abundance of bacterial diversity from phylum to species is shown in 18

Supplementary Fig.’s S1 to S3. 19

Illumina sequencing reveals a huge number of phylotype among these caves samples 20

belonging to the rare biosphere, which are microorganisms with extremely low abundance (Reid 21

et al., 2011). We have selected the criteria for rare (<0.01% of total community) and abundant 22

(other than rare species) species based on the previous study (Aravindraja et al., 2013) and 23

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according to this distribution, the rare species was 75.72-83.01% among the samples, whereas 1

the abundant species was 16.98-24.27 % (Fig. 5). Ratio of rare and abundant OTUs among all 2

the three samples were similar and within a range of 3.11- 4.88. The most abundant phylotype 3

was denovo 6722 classified under actinobacteria and were present in all the three cave samples. 4

Fig. 6 shows the unique and shared species among the rare biosphere in all the three samples. 5

Venn diagram shows only 171 rare OTUs (1.78%) being shared among the three communities, 6

but majority of the rare species in CFP are unique, whereas many common species was observed 7

between CFP and CMP. Among abundant species 3.94% is shared by all the three samples. 8

Many OTUs among the cave samples were rare in one community but showing as an abundant in 9

other community. This showes that different environmental factors prevalent among the caves 10

which makes some group to be dormant and become a member of the rare biosphere. These 11

members can be active at favorable environmental conditions and become abundant. Common 12

identified species among the cave samples were members of the phylum Actinobacteria, 13

Firmicutes and Proteobacteria (Table 5). Further analysis with whole genome sequencing will 14

reveal the actual role of these rare and abundant phyla present in the cave samples. 15

This study provides an in-depth study of unexplored bacterial diversity in cave samples 16

of Mizoram with a large number of classified phyla (twenty), candidate phyla (twenty one) and a 17

large portion of unclassified bacteria, indicates the possibility the presence of novel species. It is 18

found that the classified reads were simultaneously decreased from phylum to species level. The 19

two most dominant phylotype were denovo 6722 (11.72%) and denovo 4035 (5.72%) belonging 20

to Actinobacteria and Verrucomicrobia, respectively. The remaining phyla present in the 21

communities had low (<4%) abundance. The present study revealed a unique bacterial 22

community in Farpuk which was mostly classified under uncultured actinobacteria. These 23

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uncultured species could be a major source of new antibiotics. This analysis also revealed that 1

the bacterial diversity is higher in CMP and CFP samples compared to CLP samples. This might 2

be due to the fact that CLP is situated in extremely remote place and their diversity is not 3

influenced by an exogenous source compared to other or each cave environment might have 4

different nutrient composition or ecological condition for specific bacterial taxa. 5

6

ACKNOWLEDGEMENTS 7

This research was funded by a grant from the State Biotech Hub sponsored by Department of 8

Biotechnology, Govt. of India, New Delhi. We would like to thank Mr. Lalrinhlua for his help in 9

sampling. 10

Competing Interests 11

The authors declare there are no competing interests. 12

13

Author Contributions 14

• Surajit De Mandal, Zothansanga and Nachimuthu Senthil Kumar conceived and designed the 15

experiments, analyzed the data, wrote the paper, prepared Figures and Tables. Surajit De Mandal 16

performed the experiments. 17

18

DNA Deposition 19

The following information was supplied and is under process regarding the deposition of DNA 20

sequences: EBI Sequence Read Archive, Project Number PRJEB7730 and ERP008676. 21

22

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Supplemental Information 1

Supplemental information for this article is attached. The file contains 2 Tables and 3 Figures. 2

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1

2

Table 1 Details of the cave samples used in the present study. 3 4

Name of the

cave

(Sample

Name)

Place and

Year of

collection

Latitude Longitude Elevation

(MSL)

Humidity

(%)

Temperature

(oC)

pH C

(%)

N

(%)

H

(%)

Murapuk

(CMP)

Champhai,

Mizoram,

Northeast India

(2014)

N23°44.295' E92°39'770'

4927 44 22 7.2 110.50 13.46 30.90

Lamsialpuk

(CLP)

N23°08.019' E93°16'896'

4446 40 24 7.2 126.79 13.96 9.96

Farpuk

(CFP)

N23°06.055' E92°17'911'

4645 44 23 6.8 40.58 3.19 9.07

5

6

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Table 2 Summary statistics of illumina paired-end reads (V4 region of 16S rRNA gene) used in this study.

Sample

Name

Total

Reads

(bp)

Passed

Conserved

Region

Filter (bp)

Passed

Spacer

(bp)

Passed

Read

Quality

Filter (bp)

Passed

Mismatch

Filter (bp)

Consensus

Reads

(bp)

After

Singleton

Removal

(bp)

Chimeric

Sequences

(bp)

Pre-

processed

Reads

(bp)

CLP 674,406 617,278 616,828 616,738 568,149 568,149 470,943 683 470,260

CMP 635,210 583,165 582,750 582,628 538,641 538,641 406,640 1,252 405,388

CFP 690,975 621,772 620,134 619,973 560,239 538,641 262,430 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

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Table 4 Unweighted UniFrac distance matrix among the cave samples.

Sample

name

CLP CMP CFP

CLP 0 0.761516 0.550632

CMP 0.761516 0 0.779452

CFP 0.550632 0.779452 0

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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*

Clostridium bowmanii* Bacillus badius Afipia felis

Clostridium butyricum* Bacteroides plebeius Bacillus badius

Glaciecola polaris Brevundimonas diminuta Burkholderia tuberum

Mycobacterium celatum Burkholderia tuberum Clostridium acetobutylicum

Peredibacter starrii* Caenispirillum salinarum Clostridium bifermentans

Rhodococcus fascians Clostridium bifermentans Clostridium bowmanii*

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*

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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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2

3

4

Figure 2 Phylogenetic tree based on the distances between sample CLP, CMP and CFP 5

with weighted UniFrac approach. 6

7

8

9

10

11

12

13

14

15

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1

Figure 3 Taxonomy classifications of reads at phylum level for the cave samples. Only top 2

10 enriched class categories are shown in the figure. Classification is performed using RDP 3

classifier and Greengenes OTUs database. 4

5

6

7

8

9

10

11

12

13

14

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1

2

3

Figure 4 Taxonomy classifications of OTUs at phylum level for the cave samples. Only 4

top 10 enriched class categories are shown in the figure. Classification is performed using 5

RDP classifier and Greengenes OTUs database. 6

7

8

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Figure 5 Percentage of abundant and rare OTUs among the cave samples 2 3

4

5

6

7

8

9

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Figure 6 Venn diagram showing the unique and shared species among the rare and 2

abundant OTUs among of the cave samples 3

4

5

6

7

8

9

10

11

12

13

14

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