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Int.J.Curr.Microbiol.App.Sci (2021) 10(02): 141-149 International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 10 Number 02 (2021) \ Journal homepage: http://www.ijcmas.com Original Research Article https://doi.org/10.20546/ijcmas.2021.1002.018 Microbiome of Groundnut (Arachis hypogaea L.) Rhizosphere Infected with Macrophomina pheseolina Root Rot M. V. Parakhia* and B. A. Golakia Department of Biotechnology, Junagadh Agricultural University, Junagadh, Gujarat, India *Corresponding author ABSTRACT Keywords Microbiome, Groundnut, Arachis hypogaea, Macrophomina pheseolina Article Info Accepted: 12 January 2021 Available Online: 10 February 2021 This study reports the analyses of the rhizospheric microbiome of the groundnut (Arachis hypogaea L.) from Gujarat, India. Samples were collected using standard protocols and 16S rRNA gene V3–V4 region amplicon sequencing was performed to identify the microbial communities prevalent in the rhizosphere. A total of 9527329 quality checked reads with the average read length 232 bp were analysed using QIIME to reveal the abundance of Acidobacteria. Proteobacteria, and Planctomycetes. The diversity analysis for the community highest 116 spices were observed in infected sample. years also has resulted in adverse effects on the environment and non-target organisms. The Groundnut & other crops in India often suffer from various type of root rot & wilt diseases caused by fungi. Among these the dry root rot caused by Macrophomina phaseolina (Tassi) Goid has been noticed to cause 33.33 per cent seed rotting and 23.80 per cent post emergence mortality (Gupta and Kolte, 1981). Introduction In the world where the population keeps increasing at an alarming rate while food is inadequate, it is compulsory to new alternatives to feed the growing population need to be identified. The answer lies in harnessing and exploiting the beneficial interactions between plants and their rhizospheric microbiome to increase plant health and productivity. An understanding of the mechanisms that govern such interactions is essential to increase plant health and productivity. To study the dry root rot disease conventional microbiological methods were used. in these method only cultivable microbes and its interaction can be studied. it is identified that 98% microbes are not cultivable means cannot grown in laboratory media. it is To amplify the food production over use of conventional chemical pesticides from the 141 Int.J.Curr.Microbiol.App.Sci (2021) 10(02): 141-149 require to study the role of uncultivable microbe's on infection to the plant. The rhizosphere zone around the root is the largest microorganisms ecology in the world. The rhizomicrobiome has the capability to help fight numerous biotic and abiotic stresses as well as enhance plant health and productivity. rhizomicrobiome study of microorganisms cultivable as well as uncultivable to understand the interaction effect of microorganisms on plant productivity and health. communities and their hosts. For prokaryotes, sequencing of 16S rRNA gene is the most common method used for the identification of different species and strains. By analyzing on 16S rRNA gene region, it is possible to taxonomically group different bacteria. Along with increasing plant productivity, the soil microbiome also provides an important role in disease-suppressive soils. The ability of a soil to suppress disease is of key importance in measuring soil productivity (Janvier et al., 2007). There are many PGPRs that aid in disease suppression via the release of antimicrobial or antifungal compounds that deter plant pathogens (Garbeva et al., 2004; Weller et al., 2002). Microbiome is defined as the totality (Cultivable and Uncultivable) of microorganisms and their collective genetic material present in or on the plant or in another environment. Elevated levels in species richness and diversity produce high functional redundancy within the soil microbiome, allowing it to quickly recover during stress (Nannipieri et al., 2003; Yin et al., 2000). The high functional redundancy in soil microbial diversity also confers protection against soilborne diseases (Brussaard et al., 2007; Garbeva et al., 2004; Mendes et al., 2011; Nannipieri et al., 2003). Consequences of interaction between rhizosphere microbiome, especially the dominant microbial community and dry root rot fungus, Macrophomina have not been systematically analyzed. Unraveling the composition and function of the groundnut rhizosphere microbiome is not only important for our basic understanding of host-microbe interactions but also of great agronomic and ecological importance as well. Analysis of groundnut rhizosphere microbiome is of great commercial importance for groundnut farming as the crop is susceptible to several pathogens. Therefore, there is a need to decipher the microbial composition of groundnut rhizosphere and their influence on economically important diseases like dry root rot. Metagenomics combines the power of genomics, bioinformatics, and systems biology. Operationally, it is novel in that it involves study of the genomes of many organisms simultaneously. It provides new access to the microbial world; the vast majority of microbes cannot be grown in the laboratory and therefore cannot be studied with the classical methods of microbiology. Although community ecology is not new to microbiology, the ability to bring to bear the power of genomics in the study of communities initiates an unparallel opportunity. Normally the plant associated microbes are studied by culture based methods. Unfortunately this culture based methods could not represent the actual microbial composition of the environment. Culture based methods are, most often, known to have bias towards few bacterial groups. Microbes that cannot grow on artificial media can study using direct nucleic acid extracted from samples. In the recent times high-throughput technologies are developed to expose the interactions between complex microbial 142 Int.J.Curr.Microbiol.App.Sci (2021) 10(02): 141-149 With these background a thesis project on “Utilizing groundnut (Arachis hypogaea L.) rhizosphere microbiome for biological control of dry root rot" is envisaged with the following objectives. 16S region. Binning process were carry out in MG-RAST (https://www.mg-rast.org/) and QIIME (http://qiime.org/). QIIME software was used for analysis of 16S rDNA sequencing data. QIIME is comprehensive software comprising of tools and algorithms such as Fast Tree for heuristic based maximum-likelihood phylogeny inference (Price et al., 2010). Materials and Methods Sample collections Rhizosphere soil of groundnut variety GG-20 was collected. Samples were collected from selected sites having different type of soils (table no.3.1) with grown GG-20 groundnut variety having healthy and/or dry root rot infected plant for microbiome study. Ion Reporter (https://ionreporter.thermofisher. com/ir/) tool were used to identify the genus or species level of microbes present in complex polybacterial research samples, using both Greengenes and premium MicroSEQ ID 16S rRNA reference databases. Ion Reporter metagenomics workflow: Unaligned binary data files (Binary Alignment Map, BAM) generated by the Ion Torrent Ion S5 were uploaded to Ion Reporter and analysed using default settings. Calls were made by assessing the most specific unique taxonomic level identified using the mapped sequences. DNA Extractions and sequencing DNA was extracted from all 10 samples using The Power Soil® DNA Isolation Kit with minor modification and accordance to manufacture's instruction by MoBio laboratories Inc. Sample library preparation and sequencing were carry out as per ion torrent S4 sequencing kit guideline. Reads obtained from sequencer were converted to bam file and Fastq format in ion torrent server The Ion Reporter metagenomics workflow was developed for primer information, classification information, percent ID, and mapping information. Also identified population diversity for whole metagenome at taxonomic level with the interactive display Quality control for reads Data process and visualisation Primary quality control by the machine inbuild software to reduce ambiguous reads and final fastq file used for the secondary quality checking using the CLC genomics workbench 20.0. In that CLC processed for the adapter, barcodes, ambiguity and primer, duplicate reads are screened and removed. Data of QIIME and MG RAST were processed using MEGAN (https://bio.tools/megan) and STAMP (https://beikolab.cs.dal.ca/software/STAMP) tools and data was further process and selection for OTU were narrow down manually. Selected OTU were shorted on the base of abundance and uniqueness. Data analysis Binning process Selected Data/OTU were compared on the basis of sample site variation and status of groundnut (Healthy/Infected). Data also Reads are used for mapping with using databases RDP, Greengen, and SILVA for 143 Int.J.Curr.Microbiol.App.Sci (2021) 10(02): 141-149 analyzed manually for selected genus and species richness in the samples/sites. was healthy sample and one was root rot infected sample. Samples were collected from five different type of soil location. So total 10 samples in which 5 healthy plant rhizosphere and 5 rppt rot infected plant rhizosphere soil. Total DNA was extracted direct from all 10 rhizosphere soil using The Power Soil® DNA Isolation Kit. to check the DNA quality performed the gel electrophoresis and got the intect DNA as shown in Figure 1. Diversity analysis Using QIME (Observed-species, Shannon, Simpson and Good-coverage) alpha diversity through 8 indices was analyzed. Beta diversity was also calculated by QIIME (Version 1.9.1) on unweighted Jacknifed. Cluster analysis was preceded by principal component analysis (PCA). Beta-diversity provides a compute of similarity, or dissimilarity, of one microbial composition to another. Beta-diversity is typically calculated on the OTU/ASV/species composition tables directly (after normalization), but can be calculated using abundances at higher taxonomic levels. Microbial population was studied using the metagenomics approach. after the DNA was extracted, prepare the amplicon using the primer set (16S primer set V2-4-8 16S primer set V3-6,7-9) provided in the 16S™ Metagenomics Kit from thermo fisher. using these primer set prepared the sequencing libraries. sequencing was performed using the Next generation sequencer ION S5 (Iontorrent) of the prepared libraries of all 10 samples. during the sequencing samples were separated by using the IonExpress barcode_001 to Ion Express barcode_010. observations were recorded from the NGS as Figure 2. While alpha diversity is a measure of the diversity (or complexity) within samples, beta diversity refers to the diversity between samples. This is essentially a measure of how similar or dissimilar the samples are, and is usually represented by a distance matrix which is then used to do Principal Coordinates Analysis (PCOA). The result of this is an ordination plot of multiple dimensions, where each sample is a point and the distance between the points represents the similarity of those samples (closer together = more similar). I used these plots to explore the data and see which variables explain the similarity or dissimilarity of groups of samples (Aitchison and Greenacre, 2002). Sequencing data was extracted from the the Sequencer. Total 2.5 GB data in fastQ format. this data included unprocess total reads 1,07,68,140 with the mean length was 233bp. debarcoding of the sample data gave the following data reads (Table 1). All sequenced reads were analyzed using the MG-RAST (Metagenomics RAST: metagenomics.anl.gov) server version 3.5, which is an open source service made up of a high-throughput pipeline built for highperformance computing of metagenomes. Screening against a large group of protein and nucleotide databases, MG-RAST allows for the automated assignment of metagenomic sequences to their respective phylogenetic and functional groups for ease of data interpretation, in addition to providing tools Results and Discussion To study microbial diversity and population associated with healthy and root rot infected groundnut GG20 plant rhizosphere. Rhizspheric soil samples were collected fom the 5 different sites. From each site collected 10 healthy and 10 root rot infected plants. out of them 20 sample make two samples, one 144 Int.J.Curr.Microbiol.App.Sci (2021) 10(02): 141-149 for comparative metagenomics. The pipeline, implemented in Perl, utilizes a combination of open source components which include the SEED framework (Anderson et al., 2005), Sun Grid Engine, SQLite, and NCBI Blast (Gopalakrishnan et al., 2011). It is important to check the quality and coverage of the sequencing data using the CLC Genomics Workbench 20.0. analysed the data obtained from the sequencer. Table.1 Untrimmed raw data representing No. of reads and Mean length present in samples S.N. 1 2 3 4 5 6 7 8 9 10 Sample Healthy-1 Infected-1 Healthy-2 Infected-2 Healthy-3 Infected-3 Healthy-4 Infected-4 Healthy-5 Infected-5 Bases in MB 241.32 507.05 149.48 155.23 275.30 242.10 264.10 186.61 198.11 211.36 Reads 1022740 1245313 635376 658089 1125961 1073193 1154472 827429 863458 921298 Mean Read length 235 236 235 235 244 225 228 225 229 229 Table.2 Family, Genus and Species no. of (operational taxonomic unit) OTU presented in all 10 metagenomes Sr. No. 1 2 3 4 5 6 7 8 9 10 Sample H1 I1 H2 I2 H3 I3 H4 I4 H5 I5 Family OTU 336361 817965 201247 215198 478039 420035 481866 325684 366968 325480 Genus OTU 113208 271234 60035 81489 225603 163045 207818 136267 156751 129548 Species OTU 38262 90693 19225 26290 99773 53588 78772 46115 55638 41767 Fig.1 Gel of DNA extracted from all 10(H1-H5 and I1-I5) samples 145 Int.J.Curr.Microbiol.App.Sci (2021) 10(02): 141-149 Fig.2 Sequencing run summary representing chip image (Ion 530) and read length Fig.3 The α-diversity of samples representing the species numbers and standard deviation H1 The α-diversity of 65 species I1The α-diversity of 87 species H2 The α-diversity of 70 species I2 The α-diversity of 109 species H3 The α-diversity of 77 species I3 The α-diversity of 66 species H4 The α-diversity of 92 species I4 The α-diversity of 97 species H5 The α-diversity of 99 species I5The α-diversity of 116 species Fig.4 OTU numbers representing Family, Genus and Species of groundnut variety GG-20 Rhizospheric metagenomes of 5 different location of Gujarat at healthy and infection stage. (Xaxis represent sample and Y-axis represent OTU) 146 Int.J.Curr.Microbiol.App.Sci (2021) 10(02): 141-149 Fig.5 Principle component analysis based on unweighted unifrac 2d reveals sequence based grouping of Family, Genus and species level in A) Healthy H1, H2, H3, H4 and H5. B) Infected I1, I2,I3,I4 and I5 C) Healthy and Infected samples of this metagenome is shown in red. Alpha diversity summarizes the diversity of organisms in a sample with a single number. The α-diversity of annotated samples can be estimated from the distribution of the specieslevel annotations. according to result the infected soil sample show more diversity then the healthy soil sample (Figure 3). GC Content of sequencing data GC content of any sequencing data is specific for strains and the the GC content of a gene region can impact its coverage, with regions having 50–60% GC content receiving the highest coverage while regions with high (70– 80%) or low (30–40%) GC content having significantly decreased coverage. our result based on CLC analysis all the samples sequencing data had GC content 50-65% so we can said that our sequencing data have sufficient coverage. The PHERD score of all the samples were between 17-32 and most of the sequences score was 30. So it was indicated that the quality of data was good and have 99.99% accuracy. Analysis using QIIME pipeline Sequence data further processed using the Quantitative Insights Into Microbial Ecology (QIIME), It is an open-source bioinformatics pipeline for performing microbiome analysis from raw DNA sequencing data (McPherson et al., 2018) (Caporaso et al., 2010). from all the 10 metagenomes, no. of valid reads were uploaded in QIIME. the low quality reads were removed before the uploading. second the raw seuences data also uploaded in MGRAST open source pipeline to analyse metagenome (http://api.metagenomics. anl.gov/ api.html). in this software reads were blast against the ribosomal database, genebank data base and converted to OTU (Operational Taxonomic Unit). In MGRAST the OTU analysed up to genus while in QIIME analysed up to the specceies. from the QIIME pipeline we got no. of OTU for Bacterial diversity analysis Alpha-diversity The alpha-diversity of the microbial metagenomes in each site was calculated to assess both species richness and evenness. The below image shows the range of αdiversity values in the study 'Groundnut metagenome'. The min, max, and mean values are shown, with the standard deviation ranges (σ and 2σ) in different shades. The α-diversity 147 Int.J.Curr.Microbiol.App.Sci (2021) 10(02): 141-149 family, genus and species as Table 2. We observed that no. of OTU for family, genus and species were different from location to location mens different in H1, H2, H3, H4 and H5 samples. while the no. of family OTU high in H4 samples and low in H2 samples. no. of genus OTU was found high in H3 samples and low in H2 sample. same trend was observed for no. of species OTU (Fugure4). activities. while some bacterial species have characteristics to suppress the growth of plant pathogen or to protect the plant from the pathogen. microbiome of rhizosphere manipulation will be useful to control the plant disease. microbiome manipulated by adding of the some beneficial microbes into the rhizosphere. 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Microbiome of Groundnut (Arachis hypogaea L.) Rhizosphere Infected with Macrophomina pheseolina Root Rot. Int.J.Curr.Microbiol.App.Sci. 10(02): 141-149. doi: https://doi.org/10.20546/ijcmas.2021.1002.018 149