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http://dx.doi.org/10.4014/jmb.1804.04005

Spatial Physicochemical and Metagenomic Analysis of Desert Environment  

Sivakala, Kunjukrishnan Kamalakshi (School of Biotechnology, Madurai Kamaraj University)
Jose, Polpass Arul (Department of Agricultural Microbiology, Agricultural College and Research Institute, Tamil Nadu Agricultural University)
Anandham, Rangasamy (Department of Agricultural Microbiology, Agricultural College and Research Institute, Tamil Nadu Agricultural University)
Thinesh, Thangathurai (School of Life sciences, Pondicherry University)
Jebakumar, Solomon Robinson David (School of Biotechnology, Madurai Kamaraj University)
Samaddar, Sandipan (Department of Environmental and Biological Chemistry, Chungbuk National University)
Chatterjee, Poulami (Department of Environmental and Biological Chemistry, Chungbuk National University)
Sivakumar, Natesan (School of Biotechnology, Madurai Kamaraj University)
Sa, Tongmin (Department of Environmental and Biological Chemistry, Chungbuk National University)
Publication Information
Journal of Microbiology and Biotechnology / v.28, no.9, 2018 , pp. 1517-1526 More about this Journal
Abstract
Investigating bacterial diversity and its metabolic capabilities is crucial for interpreting the ecological patterns in a desert environment and assessing the presence of exploitable microbial resources. In this study, we evaluated the spatial heterogeneity of physicochemical parameters, soil bacterial diversity and metabolic adaptation at meter scale. Soil samples were collected from two quadrats of a desert (Thar Desert, India) with a hot, arid climate, very little rainfall and extreme temperatures. Analysis of physico-chemical parameters and subsequent variance analysis (p-values < 0.05) revealed that sulfate, potassium and magnesium ions were the most variable between the quadrats. Microbial diversity of the two quadrats was studied using Illumina bar-coded sequencing by targeting V3-V4 regions of 16S rDNA. As for the results, 702504 high-quality sequence reads, assigned to 173 operational taxonomic units (OTUs) at species level, were examined. The most abundant phyla in both quadrats were Actinobacteria (38.72%), Proteobacteria (32.94%), and Acidobacteria (9.24%). At genus level, Gaiella represented highest prevalence, followed by Streptomyces, Solirubrobacter, Aciditerrimonas, Geminicoccus, Geodermatophilus, Microvirga, and Rubrobacter. Between the quadrats, significant difference (p-values < 0.05) was found in the abundance of Aciditerrimonas, Geodermatophilus, Geminicoccus, Ilumatobacter, Marmoricola, Nakamurella, and Solirubrobacter. Metabolic functional mapping revealed diverse biological activities, and was significantly correlated with physicochemical parameters. The results revealed spatial variation of ions, microbial abundance and functional attributes in the studied quadrats, and patchy nature in local scale. Interestingly, abundance of the biotechnologically important phylum Actinobacteria, with large proposition of unclassified species in the desert, suggested that this arid environment is a promising site for bioprospection.
Keywords
Desert; arid soil; spatial heterogeneity; microbial diversity; functional mapping; actinobacteria;
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1 Arocha-Garza HF, Castillo RC-D, Eguiarte LE, Souza V, Torre-Zavala SD. 2017. High diversity and suggested endemicity of culturable Actinobacteria in an extremely oligotrophic desert oasis. PeerJ 5: e3247.   DOI
2 Peel MC, Finlayson BL, McMahonTA. 2007. Updated world map of the Koppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 11: 1633-1644.   DOI
3 Shyampura RL, Sehgal J. 1995. Soils of Rajasthan for optimizing land use. Soils of India Series, NBSS Pub. 51.
4 Bower CA, Reitemeier RF, Fireman R. 1972. Exchangeable cations of saline and alkaline soils. Soil Sci. 73: 251-257.
5 Walkley A, Black JA. 1934. An estimation of digestion method for determining soil organic matter and a proposed modification of chromic acid titration method. Soil Sci. 37: 29-38.   DOI
6 Jackson ML. 1973. Soil chemical analysis. Prentice Hall of India Private Limited, New Delhi.
7 Keeney DR, Nelson DW. 1982. Nitrogen-inorganic forms, pp. 643-698. In Page AL, Miller RH, Keeney DR (eds.), Methods of soil analysis, Part 2, chemical and microbiological methods. ASA-SSSA, Madison, USA.
8 Pamberton H. 1945. Estimation of total phosphorus. J Amer. Chem. Soc. 15: 383-395.
9 Sharma R, Manda R, Gupta S, Kumar S, Kumar V. 2013. Isolation and characterization of osmotolerant bacteria from Thar desert of western Rajasthan (India). Rev. Biol. Trop. 61: 1551-1562.
10 Bachar A, Soares MIM, Gillor O. 2012. The effect of resource islands on abundance and diversity of bacteria in arid soils. Microbial Ecol. 63: 694-700.   DOI
11 Su LJ, Yang LL, Huang S, Su XQ, Li Y, Wang FQ, et al. 2016. Comparative gut microbiomes of four species representing the higher and the lower termites. J. Insect. Sci. 16: 97.   DOI
12 Llorens-Mares T, Yooseph S, Goll J, Hoffman J, Vila-Costa M, Borrego CM, et al. 2015. Connecting biodiversity and potential functional role in modern euxinic environments by microbial metagenomics. ISME J. 9: 1648-1661.   DOI
13 Arndt D, Xia J, Liu Y, Zhou Y, Guo AC, Cruz JA, et al. 2012. METAGENassist: A comprehensive web server for comparative metagenomics. Nucleic Acids Res. 40: W88-W95.   DOI
14 Edgar RC. 2013. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10: 996-998.   DOI
15 Huson DH, Beier S, Flade I, Górska A, El-Hadidi M, Mitra S, et al. 2016. MEGAN community edition-interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput. Biol. 12: e1004957.   DOI
16 Colwell RK, Chao A, Gotelli NJ, Lin S-Y, Mao CX, Chazdon RL, et al. 2012. Models and estimators linking individual-based and sample-based rarefaction, extrapolation, and comparison of assemblages. J. Plant Ecol. 5: 3-21.   DOI
17 Hackstadt AJ, Hess AM. 2009. Filtering for increased power for microarray data analysis. BMC Bioinformatics 10: 11.   DOI
18 Rao S, Chan Y, Bugler-Lacap DC, Bhatnagar A, Bhatnagar M, Stephen B. 2016. Pointing microbial diversity in soil, sand dune and rock substrates of the Thar monsoon desert, India. Indian J. Microbiol. 56: 35-45.   DOI
19 Perry RA, Goodall DW. 2009. Arid Land Ecosystems: structure, functioning and management. Cambridge UK: Cambridge University Press
20 Yasir M, Azhar EI, Khan I, Bibi F, Baabdullah R, Al-Zahrani IA, et al. 2015. Composition of soil microbiome along elevation gradients in southwestern highlands of Saudi Arabia. BMC Microbiol. 15: 65.   DOI
21 Elbert T, Pantev C, Wienbruch C, Rockstroh B, Taub E. 1995. Increased cortical representation of the fingers of the left hand in string players. Science 270: 305-307.   DOI
22 Ronca S, Ramond J-B, Jones BE, Seely M, Cowan DA. 2015. Namib desert dune/interdune transects exhibit habitat-specific edaphic bacterial communities. Front Microbiol. 6: 845.
23 Wei STS, Lacap-Bugler DC, Lau MCY, Caruso T, Rao S, de los Rios A, et al. 2016. Taxonomic and functional diversity of soil and hypolithic microbial communities in Miers Valley, McMurdo Dry Valleys, Antarctica. Front Microbiol. 7: 1642.
24 Cowan DA, Makhalanyane TP, Dennis PG, Hopkins DW. 2014. Microbial ecology and biogeochemistry of continental Antarctic soils. Front. Microbiol. 5: 154.
25 Makhalanyane TP, Valverde A, Gunnigle E, Frossard A, Ramond JB, Cowan DA. 2015. Microbial ecology of hot desert edaphic systems. FEMS Microbiol. Rev. 39: 203-221.   DOI
26 Jones FT, Wineland MJ, Parsons JT, Hagler WM. 1996. Degradation of aflatoxin by poultry litter. Poult. Sci. 75: 52-58.   DOI
27 Belap J, Welter JR, Grimm, NB, Barger N, Ludwig JA. 2005. Linkages between microbial and hydrologic processes in arid and semiarid watersheds. Ecology 86: 298-307.   DOI
28 Pajares S, Escalante AE, Noguez AM, Garcia-Oliva F, Martinez-Piedragil C, Cram SS, et al. 2016. Spatial heterogeneity of physicochemical properties explains differences in microbial composition in arid soils from CuatroCienegas, Mexico. PeerJ 4: e2459.   DOI
29 Housman DC, Yeager CM, Darby BJ, Sanford RL, Kuske CR, Neher DA, et al. 2007. Heterogeneity of soil nutrients and subsurface biota in a dryland ecosystem. Soil Biol. Biochem. 39: 2138-2149.   DOI
30 Geyer KM, Altrichter AE, Van Horn DJ, Takacs-Vesbach CD, Gooseff MN, Barrett JE. 2013. Environmental controls over bacterial communities in polar desert soils. Ecosphere 4: 127.
31 Garcia-Pichel F, Loza V, Marusenko Y, Mateo P, Potrafka RM. 2013. Temperature drives the continental-scale distribution of key microbes in topsoil communities. Science 340: 1574-1577.   DOI
32 Jose PA, Jha B. 2017. Intertidal marine sediment harbours Actinobacteria with promising bioactive and biosynthetic potential. Sci. Rep. 7: 10041.   DOI
33 Le PT, Makhalanyane TP, Guerrero LD, Vikram S, Peer YV, Cowan DA. 2016. Comparative metagenomic analysis reveals mechanisms for stress response in hypoliths from extreme hyperarid deserts. Genome Biol. Evol. 8: 2737-2747.   DOI
34 Vikram S, Guerrero LD, Makhalanyane TP, Le PT, Seely M, Cowan DA. 2015. Metagenomic analysis provides insights into functional capacity in a hyperarid desert soil niche community. Environ. Microbiol. 18: 1875-1888.
35 Barka EA, Vatsa P, Sanchez L, Gaveau-Vaillant N, Jacquard C, Klenk HP, et al. 2016. Taxonomy, physiology, and natural products of Actinobacteria. Microbiol. Mol. Biol. Rev. 80: 1-43.   DOI
36 Parrot D, Antony-Babu S, Intertaglia L, Grube M, Tomasi S, Suzuki MT. 2015. Littoral lichens as a novel source of potentially bioactive Actinobacteria. Sci. Rep. 5: 15839.   DOI
37 Guo X, Liu N, Li X, Ding Y, Shang F, Gao Y, et al. 2015. Red soils harbour diverse culturable actinomycetes that are promising sources of novel secondary metabolites. Appl. Environ. Microbiol. 81: 3086-3103.   DOI
38 Maestre FT, Delgado-Baquerizo M, Jeffries TC, Eldridge DJ, Ochoa V, Gozalo B, et al. 2015. Increasing aridity reduces soil microbial diversity and abundance in global drylands. P. Natl. Acad. Sci. USA 112: 15684-15689.
39 Lupatini M, Suleiman AK, Jacques RJ, Antoniolli ZI, Kuramae EE, de Oliveira Camargo FA, et al. 2013. Soil-borne bacterial structure and diversity does not reflect community activity in Pampa biome. PLoS One 8: e76465.   DOI
40 Gorlach-Lira K, Coutinho HDM. 2007. Population dynamics and extracellular enzymes activity of mesophilic and thermophilic bacteria isolated from semi-arid soil of Northeastern Brazil. Braz. J. Microbiol. 38: 135-141.   DOI
41 Mohammadipanah F, Wink J. 2016.Actinobacteria from arid and desert habitats: diversity and biological activity. Front Microbiol. 6: 1541.
42 Ben-David EA, Zaady E, Sher Y, Nejidat A. 2011. Assessment of the spatial distribution of soil microbial communities in patchy arid and semi-arid landscapes of the Negev Desert using combined PLFA and DGGE analyses. FEMS Microbiol. Ecol. 76: 492-503.   DOI
43 Maestre FT, Escudero A, Martínez I, Guerrero C, Rubio A. 2005. Does spatial pattern matter to ecosystem functioning? Insights from biological soil crusts. Funct. Ecol. 19: 566-573.   DOI