Microbial Populations under fluoride Stress: a metagenomic exploration from Indian soil

Microbial Populations under fluoride Stress: a metagenomic exploration from Indian soil | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Microbial Populations under fluoride Stress: a metagenomic exploration from Indian soil Krishnendu Pramanik, Arup Sen, Subrata Dutta, Gouranga Sundar Mandal, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6243086/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Jun, 2025 Read the published version in World Journal of Microbiology and Biotechnology → Version 1 posted 11 You are reading this latest preprint version Abstract Fluoride exposure, even at a low concentration, significantly impairs crop growth and productivity by inhibiting metabolic enzymes and disrupting photosynthesis. Addressing this challenge, microbial de-fluoridation emerges as a vital strategy to improve soil health, enhance crop growth, and ensure agricultural sustainability. This study analyzed topsoil samples (0–0.2 m depth) from rice fields in three blocks of Purulia district, West Bengal— Arsha, Jhalda-I , and Joypur . Fluoride content in the samples ranged from 58.76 ± 0.76 mg/kg to 282.9 ± 4.9 mg/kg (total) and 1.57 ± 0.02 mg/kg to 2.97 ± 0.03 mg/kg (available). The Whole metagenomic analysis of the collected soil samples (BioSample Accession Number: PRJNA1154823) revealed diverse microbial communities comprising archaea, bacteria, fungi, and viruses, with Actinobacteria (phylum), Hyphomicrobiales (order), and Nocardioidaceae (family) being the dominant prokaryotes. Arsha soil with comparatively low fluoride contamination exhibited the highest microbial diversity (11,891 taxa), followed by Joypur (11,528 taxa) and Jhalda-I (11,358 taxa), with Arsha showing nearly double the unique microbial taxa compared to the other locations. Clusters of Orthologous Groups of proteins functional analysis identified 60,898 genes in Arsha , 63,403 genes in Jhalda-I , and 73,334 genes in Joypur , while Kyoto Encyclopedia of Genes and Genomes analysis revealed 9,385, 9,104, and 10,633 genes, respectively. Key genes associated with fluoride metabolism— inorganic pyrophosphatase , divalent metal cation transporter MntH , and putative fluoride ion transporter CrcB —were abundant across all sites, highlighting the influence of fluoride on microbial community structure. This study provides the first comprehensive report on soil microbial communities in fluoride-rich areas, highlighting the potential of native fluoride-tolerant microbes to mitigate fluoride toxicity in agricultural soils and offer sustainable, microbe-based solutions to fluoride contamination. De-fluoridation Fluoride tolerant bacteria Metagenomics Bioremediation Fluoride tolerant genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Fluoride, the very first and the lightest member of the halogen group, is a widely available mineral in nature. Out of 85 million tonnes of fluoride deposits on the Earth’s surface, 12 million tonnes are reported in India (Teotia et al. , 1994). The World Health Organization (WHO) and the Indian Bureau of Standards (BIS) have set the maximum permissible limit of fluoride in drinking water as 1.5 mg/l (WHO, 1997; WHO, 2004 and BIS, 2012). Bioaccumulation of fluoride in food and feed crops may lead to its introduction into the food chain. However, there is no available data regarding the recommended fluoride content in plants that is considered safe for human health. Consumption of fluoride beyond permissible limits leads to serious health hazards like diarrhoea, abdominal pain, vomiting, dehydration, and excessive salivation etc. Long-term fluoride intake above 8 mg/l causes skeletal fluorosis, arthritis, cancer, osteoporosis, infertility, thyroid disease, Alzheimer's disease, and brain damage (Whitford et al. , 2005; Yadav et al. , 2007; Bibiet al., 2017 and Singh et al. , 2018). Fluoride toxicity in cattle has also been reported to inhibit thyroxin secretion (Cinar et al. , 2021). In plants, excessive fluoride accumulation causes phytotoxicity, leading to chlorosis, leaf necrosis, reduced growth, and lower crop yields(Weinstein et al. , 2004). It induces oxidative stress, i.e., the production of Reactive Oxygen Species (ROS) and Reactive Nitrogen Species (RON), disrupting natural antioxidant defence mechanisms (Maritim et al. , 2003). Excess fluoride in the soil can impair nutrient uptake, resulting in nutrient imbalances in plants, inhibits seed germination and cellular enzymatic activity (Mackowiak et al ., 2001; Yadu et al ., 2018; and Baunthiyal et al ., 2014). In bacteria, direct inhibition of enzymes by fluoride ion or hydrogen fluoride is observed due to the formation of phosphate analogues that inhibit adenosine triphosphate (ATP) synthesis (Kalyani et al ., 2009 and Chouhan et al ., 2012). Most studies to date have primarily concentrated on hydrological investigations. However, fluoride contamination extends beyond groundwater, affecting agricultural soils, infiltrating cultivated crops, and ultimately entering the food chain. The average soil fluoride concentrations in India range from 90 to 500 mg/kg of soil (Bhattacharya et al. , 2018) with 19 states severely affected by fluoride contamination in both groundwater and soil. In West Bengal, about 12% of the population across 8 districts is affected by fluoride exposure (PHED, 2007; Chatterjee et al., 2008 ). Long-term use of fluoride-contaminated groundwater for irrigation, particularly in paddy cultivation, significantly increases soil fluoride levels. This raises concerns about increased fluoride bioaccumulation in rice and other cereals grown in these soils. Fluoride accumulates in plant tissues in the following order: roots > leaves > stems > seeds (Rao et al. , 1993 and Chakrabartiet al., 2013 ). High fluoride levels in straw pose risks to livestock health and indirectly affect human health through contaminated meat and milk. Microbes offer a promising alternative to fluoride scavenging due to cell wall compositions, which include functional groups like amines, sulfhydryl carboxylates and phosphates. These groups not only reduce fluoride ions but also facilitate their adherence to microbial surfaces(Juwarkar et al. , 2010; Kleinübing et al. , 2011 andChouhan et al. , 2012). Earlier, many microorganisms have been isolated and characterized from fluoride-contaminated water sources. It has further been used to remove fluoride ions from wastewater such as Acinetobacter species . (GU566361) (Shanker et al. , 2020), Providenciavermicola (KX926492) (Mukherjee et al. , 2017), Cyanobacteria (Biswaset al., 2018 ), Aspergillusniger (Annaduraiet al., 2019 ). However, the microbiome structure of highly fluoride-contaminated soils remains largely unexplored. Understanding microbial communities that influence the biogeochemical cycling of fluoride in agricultural soils is a significant challenge due to the diversity and underexplored status of these systems for varying fluoride levels. In this study, we hypothesized that fluoride, due to its toxic effects on microbes, could exert a stronger disruptive influence and selection pressure on the microbial community, affecting its diversity and structure at varying fluoride concentrations in the soil. Recent studies have reported that agricultural soils in the Purulia District of West Bengal have, on average, a very high fluorine content i.e.126 ± 65 mg/kg (range: 47.9–297 mg/kg, n = 47) (De et al. , 2021). To test our hypothesis, typical areas in Purulia district, characterized by low, medium, and high concentrations of natural soil fluoride, were selected as the model system. The effects of these fluoride concentrations on dynamic shifts in the microbiome were examined using a whole metagenome sequencing approach. Additionally, the predicted functions of microbiome genes related to fluoride cycling processes were also investigated. Thus, this study aims to provide novel insights into microbial adaptation and biogeochemical interactions in fluoride-contaminated agricultural ecosystems. 2. Materials and Methods 2.1 Experimental site description Purulia district, located in the eastern part of India, is recognized as the Land of Red and Laterite Soil. Positioned between latitude 22°42ʹ–23°42ʹ N and longitude 85°49′–86°54ʹ E, the district spans an area of 6,259 km² and had a population of 2,930,115 as per the 2011 Census. Situated at an average altitude of 748 feet, Purulia experiences a subtropical climate, with extreme temperatures ranging from scorching summers of up to 52°C to chilly winters as low as 3.8°C. Despite its aridity, the district receives limited rainfall from the Southwest monsoons. This study focuses on three selected blocks viz., Arsha , Jhalda-I and Jaipur , out of the district's 20 administrative blocks. 2.2 Soil sampling The soil samples were collected in February 2023, before the onset of the rainy season. Samples of rhizospheric soil were collected from the top layer (0-0.2 m) of the rice field of three different blocks of Purulia distinct namely Arsha (23°17 '42'' N, 86°12 '26''E), Jhalda-I (23°21 '55'' N, 85°59 '50''E) and Joypur (23°24 '45'' N, 86°6 '55''E)to conduct the soil metagenomic studies (Fig. 1 ). Approximately 500 g of soil was collected from three different locations of each block and then immediately the samples were transferred to sterile plastic bags also kept in icebox. Half of the soils were stored at − 80°C for subsequent molecular analysis. The remaining half was air-dried at room temperature subsequently sieved through a 100-mesh sifter and then stored at 4°C for further analysis of soil physico-chemical properties. 2.3 Soil physico-chemical properties The basic physicochemical properties of the soils were determined to give a preliminary idea about the fluoride and fertility status of the experimental soils. The parameters evaluated included soil organic carbon (SOC) using the wet digestion method, pH and EC (1:2 soil-to-water ratio), available phosphorus (AP) via sodium bicarbonate extraction with spectrophotometric analysis, and available nitrogen (AN) using the alkaline permanganate method (Jackson, 1973). Soil texture was determined following Piper (1966), and soil order classification was based on Soil Survey Staff (2010). Cation exchange capacity (CEC) was measured using the method by Sumner and Miller (1996), and available potassium (AK) was assessed using ammonium acetate extraction and flame photometry (Muhr et al ., 1965). Available zinc (Zn) was estimated via DTPA extraction and atomic absorption spectrophotometry (Lindsay and Norvell, 1978). Available fluoride (AF) was determined using the 0.01 M calcium chloride extraction method using Jenway 3040 model ion-meter in combination with a fluoride-specific electrode (Orion ISE 940900)(Larsen and Widdowson, 1971). Total fluoride (TF) was extracted using the alkali fusion method (McQuaker et al ., 1977). All the soil samples were tested in triplicate, and the results are presented as the mean ± SEM of the data. 2.4 DNA extraction, library construction, and metagenomic sequencing The metagenomic DNA from soil samples of fluoride-rich areas were isolated using DNeasy® PowerSoil® Pro Kit (Qiagen, Hilden, Germany) according to the manufacturer’s protocols. The DNA quality was evaluated with a 0.8% agarose gel. DNA concentration was quantified by Qubit DNA HS Assay Kit (Invitrogen) as per the manufacturer’s instruction. To minimize DNA extraction bias, three replicate DNA isolations of each sample were pooled. The genomic library was prepared using the TruSeq® Nano DNA Library Prep kit as per the manufacturer’s instructions. The quantification of the final libraries was done using the Qubit DNA HS Assay Kit (Invitrogen) following the manufacturer’s protocol. DNA 5000 Screen Tapes, Tapestation (Agilent) was used to identify library size following the manufacturer’s protocol. Following libraries were loaded onto an Illumina (Novaseq 6000), 150PE sequencing platform for sequencing according to the standard protocol. Paired-end sequencing allowed the template fragments to be sequenced in both the forward and reverse directions. The average insert sizes of the libraries were 364 bp, 380 bp and 395 bp, for the samples Arsha , Jhalda-I and Joypur blocks, respectively. Read count summaries and assembly statistics are provided in Tables 1 and 2 . All raw metagenomics datasets have been submitted to the NCBI Sequence Read Achieve database (BioProject Accession Number: PRJNA1154823). Table 1 Summary of raw data (Sequencing) S.No Locations No. of raw reads No QC passed reads Data in GBs Read length GC % %Q30 1. Arsha 94046382 92286214 14.2 151 64.82 94.2 2. Jhalda-I 73882296 72474562 11.2 151 64.53 93.8 3. Joypur 76547322 74999816 11.6 151 65.87 93.7 Table 2 Summary of assembled metagenomic data Assembly Arsha Jhalda-I Joypur #contigs( > = 0bp) 827553 907335 855834 #contigs( > = 1000bp) 16856 31420 21932 #contigs( > = 5000bp) 330 133 224 #contigs( > = 10000bp) 92 6 16 #contigs( > = 25000bp) 4 0 0 #contigs( > = 50000bp) 0 0 0 Largestcontig 34528 20146 23493 Total length 375629184 443670980 404091808 GC(%) 64.82 64.53 65.87 N50 435 476 458 N75 362 380 373 L50 305395 317172 308902 2.5 Metagenomic sequence assembly, gene prediction, taxonomy, and functional annotation After generating the raw sequencing data, the data quality of the whole metagenomic sequence was checked for base call quality distribution, %GC, % bases above Q30and sequencing adapter using FastQCand MultiQC software (Andrews et al ., 2010 and Ewels et al ., 2016). The raw sequence reads were processed to remove adapter sequences and low-quality bases using fastp v0.12.4 (Chen et al ., 2018) with default parameters. The pre-processed reads were submitted to Kraken2 v2.1.2 (Wood et al ., 2019), which classifies the reads using k-mer-based homology against the complete NCBI nt database of sequences. The Kraken2 results were visualized using pavian v1.2.0 (Breitwieser et al ., 2020) to generate Sankey plots. The pre-processed reads were assembled to contigs using Megahit v1.2.9 (Li et al ., 2015) with k-mer sizes --k-min 31, --k-max141 --k-step 28. Contigs < 200 bp were not considered for the genome assembly. The assembled genome statistics were assessed using QUAST v5.0.2 (Gurevich et al ., 2013). The quality of the assembly was verified by mapping the reads back onto the assembled contigs using bowtie2 v2.4.5 (Langmead et al ., 2012). Using BLASTn, the annotation of the assembled contigs was carried out against the NCBI (nt) custom metagenome database that included all nucleotide entries for the taxa belonging to fungi, bacteria, archaea, and viruses. Taxonomic distribution of BLAST hit contigs was recognized with taxon kit v0.12.0 (Shen et al ., 2021). The annotation of the assembled contigs and prediction of the gene were done using PROKKA v1.13 with the metagenome option (Seemann et al ., 2014). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Clusters of Orthologous Groups of Proteins (COG) annotations were obtained by submitting the protein sequences of each sample to KAAS (KEGGAutomatic Annotation Server) and WebMGA, respectively (Moriya et al ., 2007 andWu et al ., 2011). After the taxonomic classification, OTU abundance was estimated.Kraken2 output was converted into an OTU table using a kraken2 script and a house script. Krona plot was created using kraken2 results. Gene classification for predicted resistance was done against the COG database by BLASTx on COG classification. 2.6 Statistical analysis Phyloseq v1.38.0 (McMurdie et al ., 2013), vegan R package and Phyloseqwasused to perform the Alpha diversity indexes, Principal coordinates analysis (PCoA), Rarefaction curve, bar plots and heatmaps. Beta Diversity was estimated by Fisher's exact test using STAMP v2.1.3 (Parks et al ., 2014). The Analysis of Variance (ANOVA), mean, DMRT (Duncan’s Multiple Range Test), and Bar diagram with Standard error of mean were performed using SPSS software and Microsoft Excel. 3. Results and discussion 3.1 Physico-chemical characterization of fluoride-contaminated soil All the Physicochemical properties of the soil of three locations are represented in Table 3 . The pH of the soil samples was ranged from 4.77 ± 0.14 to 5.61 ± 0.16, indicating that the soil samples were moderate to strong acidic. As all three locations fall under the red and laterite zones of West Bengal, soil acidity is influenced by the composition of the parent material and the extent of weathering. The CEC of the soils ranged from 0.031 ± 0.005 to 0.076 ± 0.013 dS/m, levels which were generally conducive to crop growth. The oxidizable SOC was 1.68 ± 0.09% in Arsha soil, 1.36 ± 0.05% in Jhalda-I soil, and 2.04 ± 0.42% in Joypur soil. The A(N content was 162.58 ± 5.8kg/ha in Arsha , 167.63 ± 3.37 kg/ha in Jhalda-I , and 151.7 ± 4.65 kg/ha in Joypur . The AP content was 9.72 ± 0.3 kg/ha in Arsha , 13.18 ± 0.39kg/ha in Jhalda-I , and 11.48 ± 0.3 kg/ha in Joypur . The AK content was 162.27 ± 3.66 kg/ha in Arsha , 171.41 ± 3.38 kg/ha in Jhalda-I , and 188.33 ± 4.08 kg/ha in Joypur . The available Zn content was 2.14 ± 0.2mg/kg in Arsha , 2.26 ± 0.08 mg/kgin Jhalda-I , and 1.96 ± 0.07 mg/kg in Joypur . Most importantly, TF content was 58.76 ± 0.76 mg/kg in Arsha , 147.32 ± 1.87 mg/kg in Jhalda-I , and 282.9 ± 4.9 mg/kg in Joypur while the AF content was 1.57 ± 0.02 mg/kg in Arsha , 2.31 ± 0.05 mg/kg in Jhalda-I , and 2.97 ± 0.03 mg/kg in Joypur . The textural class of Arsha and Joypur soils generally fell within the sandy clay loam category, whereas Jhalda-I soil was identified as sandy loam. Table 3 Physico-chemical properties of the fluoride-contaminated soil samples Soil Parameter Jhalda-I ( J hI ) Joypur ( Joy ) Arsha ( Ar ) Latitude 23°21 '55'' N 23°24 '45'' N 23°17 '42'' N Longitude 85°59 '50''E 86°6 '55''E 86°12 '26''E Soil Order Alfisol Alfisol Alfisol Soil Texture Sandy loam Sandy clay loam Sandy- clay loam pH 4.77 ± 0.14 4.84 ± 0.28 5.61 ± 0.16 CEC (cmol(p+)/kg soil) 8.12 ± 0.33 10.25 ± 0.25 9.57 ± 0.32 EC (dS/m) 0.058 ± 0.007 0.031 ± 0.005 0.076 ± 0.013 Organic Carbon (%) 1.36 ± 0.05 2.04 ± .042 1.68 ± 0.09 Available N (kg/ha) 167.63 ± 3.37 151.7 ± 4.65 162.58 ± 5.8 Available P (kg/ha) 13.18 ± 0.39 11.48 ± 0.3 9.72 ± 0.3 Available K(kg/ha) 171.41 ± 3.38 188.33 ± 4.08 162.27 ± 3.66 Available Zn (mg/kg) 2.26 ± 0.08 1.96 ± 0.07 2.14 ± 0.2 Available F (mg/kg) 2.31 ± 0.05 2.97 ± 0.03 1.57 ± 0.02 Total fluoride (mg/kg) 147.32 ± 1.87 282.9 ± 4.9 58.76 ± 0.76 3.2 Comparison of the microbiome profiles among the fluoride-rich soils The annotations of the assembled contigs revealed that archaea were more abundant in Arsha , while bacteria and viruses were more abundant in Joypur . The summary of the taxonomic distribution of NCBI nt annotated contigs is exhibited in Table 4 . The maximum numbers of genes were predicted in Joypur as compared to Arsha and Jhalda-I. The summary of Prokka annotations is exhibited in Table 5 . Table 4 Abundance of different microbial populations in three locations Locations Archaea Bacteria Viruses Arsha 3171 332968 63 Jhalda-I 1455 305995 46 Joypur 2104 353968 68 Table 5 Summary of Prokka annotation Locations CDS gene misc_RNA tmRNA tRNA rRNA Arsha 135672 141075 1915 40 2927 521 Jhalda-I 139286 144087 1847 37 2529 388 Joypur 158765 163835 1710 52 2840 468 The most abundant taxonomic community under fluoride stress condition obtained was bacteria (~ 96%) followed by Archaea (~ 1%) and Eukaryota (3% in Arsha and Jhalda-I except 2% in Joypur ) and the rest remained virus and uncategorized. The distribution of reads and complete microbial diversity from Arsha , Jhalda-I and Joypur has been summarized by the Sankey plot [Fig. 2 (a-c)] and Krona graph in [Fig. 3 (a-c)] respectively. This study revealed that the most abundant prokaryotic organisms in the soil samples belonged phylogenetically to the Actinobacteria phylum, accounting for 44.75% (in Arsha ), 47.3% (in Jhalda-I ) and 40.84% (in Joypur ) followed by Proteobacteria (41.59% in Arsha ), 39.68% (in Jhalda-I ) and 47.07% (in Joypur ), and Planctomycetota (2.67%, 3.18%, 2.28%) of the total OTUs in all libraries. 3.3 Relative abundance of microbial order, family and Species Kiritimatiellaeota, Peploviricota, Tenericutes, Thermotogae, Chlamydiae, Fusobacteria, Candidatus_Bipolaricaulota and Thermodesulfobacteria was found to be the minor phyla in all the three samples. Hyphomicrobiales, Streptomycetales, Burkholderiales, Micrococcales, Corynebacteriales, Myxococcales, and Propionibacteriales were the most abundant orders in all three samples. Figure 4 (a) shows the relative abundances of 20 bacterial orders across the fluoride concentration. The microbial community, at the phylum level, was almost similar among all the soil samples. Higher relative abundances of the families Nocardioidaceae, Anaeromyxobacteraceae, Comamonadaceae, Sphingomonadaceae, Methylobacteriaceae, Sphaerotilaceae, Intrasporangiaceae, Xanthobacteraceae , and Geobacteraceae were observed in the high fluoride congaing soil ( Joypur ), while lower relative abundances of these families were observed in sample with medium to low fluoride concentrations ( Jhalda-I and Arsha ). On contrary, lower relative abundances of the families Streptomycetaceae, Nitrobacteraceae, Mycobacteriaceae, Pseudonocardiaceae, Microbacteriaceae, Pseudomonadaceae, Micrococcaceae, Conexibacteraceae, Thermomonosporaceae, Streptosporangiaceae, Acidobacteriaceae, Corynebacteriaceae, Paenibacillaceae, Planctomycetaceae and Nocardiopsaceae were observed in the high fluoride congaing soil of Joypur , while higher relative abundances of these families were observed in sample with medium to low fluoride concentrations. Therefore, it was cautiously inferred that the decrease in the relative abundance of these families may be attributed to the increase in fluoride concentration in the Joypur sample. Obviously; the richness of the predominant families revealed a significant diversity in soil samples with different fluoride concentration levels. Figure 4 (b) shows the relative abundances of 20 bacterial genera across the fluoride concentration. At the genus level Desulforamulus, Cohaesibacter, Syntrophus, Anaerohalosphaera, Aurantimicrobium, Muribaculum, Methanosphaerula, Alkalihalobacillus, Pseudochrobactrum, Syntrophus, Providencia, Desulforamulus, Terrihabitans, Mammaliicoccus, Mesobacillus, Salinibacter, Olivibacter and Methanoregula , was observed only in Arsha whereas Endozoicomonas, Oxynema, Vespertiliibacter, Halomicronema, Microcoleus, Lacticaseibacillus, Terrihabitans, Sphaerochaeta, Acaryochloris, Acetivibrio, Desulfonema, Thermanaerovibrio, Oxynema, Slackia, Endozoicomonas, Oceanimonas, Pseudovibrio, Brasilonema were observed only in Jhalda-I and Woeseia, Muricauda, Thermobaculum, Filimonas, Phnomibacter, Aquisediminimonas, Microvenator, Salinibacter, Rhodocaloribacter, Rhodothermus, Pseudorhodoplanes only in Joypur. Streptomyces, Bradyrhizobium, Nocardioides, Anaeromyxobacter, Mycobacterium, Pseudomonas, Micromonospora, Mycolicibacterium, Microbacterium, Sphingomonas, Conexibacter , Burkholderia and Methylobacterium werethe most abundant genera irrespective of locations. Figure 4 (c) reflects the relative abundances of 20 bacterial families across the fluoride concentration. The analysis of taxonomic abundance within the dominant bacterial groups across soil samples with varying fluoride concentrations yielded significant insights regarding the presence of specific fluoride-tolerant rhizospheric bacteria. This study clearly demonstrated that variations in fluoride concentrations play a decisive role in demonstrating soil microbiome diversity in the soil environment. 3.4 Alpha and Beta Diversity and PoCA Estimated alpha diversity indices values were higher in all three samples indicating higher microbial diversity in individual samples but very low or no significant differences existed in the values of each indices estimated here among the samples. Shannon index value was highest in Arsha (7.76) followed by Jhalda-I (7.68) and Joypur (7.61). Simpson index values also followed the same trend with higher values in Arsha (0.9984) followed by Jhalda-I (0.9981) and Joypur (0.9979). Based on the alpha diversity analysis a moderate change in soil microbiome diversity was apparent across the fluoride concentration gradient (Table 5 ). All the standard alpha diversity indices estimated here represented similar patterns of diversity and richness among the samples. Species richness in terms of observed richness value in the Arsha (118910) sample showed the highest richness followed by Joypur (11528) and Jhalda-I (11358). The Chao1 Index indicated that the Arsha sample has the highest number of observed species followed by Joypur and Jhalda-I . The refraction curve has formed a plateau, indicating that an adequate amount of sequencing has been carried out, and further sequencing will unearth the limited number of new OTUs. These results indicate that Arsha soil has higher microbial diversity than Jhalda-I followed by Joypur as depicted by the rarefaction curve of the OTUs. Alpha diversity measurements of microbial communities and refraction curves are represented in Figs. 5 and 6 respectively. In PCoA ( Fig. 7 ) , the two principal coordinates explained 100% variation in the sample matrix data out of which, the horizontal axis explained 64% variation and the vertical axis explained 36% variation. The heterogeneity concerning microbial population diversity among the locations Arsha , Jhalda-I and Joypur was evident from the PCoA where these three locations have been plotted in three different ordinated of the PCoA graph. Following the metagenomic analysis, to find out the unique as well as the common, taxonomical units/ microbes of three soil samples viz., Arsha , Jhalda-I and Joypur , Fisher’s exact test, between 2 Samples was performed. The result is presented in Table 6 . For the unique taxonomical unit, approximately twice the number of unique microbes was found in the soil sample of Arsha in in comparison to both Jhalda-I and Joypur whereas no variation was found between Jhalda-I and Joypur . Common microbes were found maximum in between Arsha and Joypur , closely followed by Arsha and Jhalda-I (Table 7 ). This finding was also in agreement with the findings of the PCoA which usually plotted objects in the graph as points. The differences in sample distances in the graph being further apart, exhibited stronger heterogeneity. Table 5 Summary of Alpha diversity indices Locations Observed richness Chao1 ACE Shannon Simpson InvSimpson Fisher Arsha 11891 12750.39 12637.49 7.77 0.998 654.63 1305.79 Jhalda-I 11358 12181.18 12009.61 7.68 0.998 549.43 1294.46 Joypur 11528 12257.92 12147.69 7.62 0.998 498.56 1281.89 Table 6 Fisher's exact test to compare two samples Sl.No Comparison Significant OTUs (p-value < = 0.05) 1 Ar vs. JhI 252 2 Ar vs. Joy 267 3 Jh vs. Joy 302 Table 7 Number of Unique and common taxonomical units present in the soil Sl.No Comparison Unique in Sample1 Unique in Sample2 Common 1 Ar vs. JhI 624 340 9073 2 Ar vs. Joy 612 324 9085 3 Jh1 vs. Joy 457 453 8956 3.5 Comparison of functional profiles for the microbial metagenomes The number of hits was the maximum against COG, trailed by the KEGG. The functional analysis of COG, assigned 60,898 genes in total in Arsha , 63,403 genes in Jhalda- I and 73334 genes in Joypur while KEGG functional analysis revealed that 9,385 genes in Arsha , 9,104 genes in Jhalda I and 10,633 genes in Joypur KEGG classes. Relative loads of the top 50 KEGG Orthology (KO) groups that were commonly identified in all three samples and a relative heat map were generated (Fig. 8 ). The assigned KO was the highest in the category of the metabolism followed by genetic information processing, cellular process and organismal systems in all the three samples, and these were relatively higher in Joypur than others. It was observed that among the KO groups acetyl-CoA acetyltransferase, pyruvate ferredoxin oxidoreductase, methyl malonyl-CoA mutase, alcohol dehydrogenase, and acetyl-CoA synthetase, flavin prenyltransferase, glycine hydroxymethyl transferase, acetolactate synthase, 6-phosphofructokinase, Shydroxymethyl, branched-chain amino acid, aminotransferase, glutathione dehydrogenase was more abundant in all the three soil samples. Acetyl-CoAacetyltransferase was found as the most copious KO group in Arusha followed by Joypur and Jhalda-I w hereas, glutamine synthetase was the most abundant KO group in Joypur followed by Arsha and Jhalda-I . Functional COG analysis exhibited that the assigned COG function was the highest for the amino acid transport and metabolism category followed by energy production and conversion, translation, ribosomal structure and biogenesis, general function, lipid transport and metabolism, transcription category in all three samples, and these were relatively higher in Joypur than others (Fig. 9 ). 3.6 Contrast of the fluoride cycling genes of bacterial communities In the present study, the functional profiling, emphasizing genes predicted to be linked with fluoride cycling, of soil samples from Arsha , Jhalda-I and Joypur were further analyzed based on KO group assignments and a comparative heat map was generated (Fig. 10 ). Amongst the gene families, enolase, inorganic pyrophosphatase, divalent metal cation transporter MntH were the most abundant fluoride metabolism-related gene families in the bacterial communities found in the soil samples of all three locations. The abundance of enolase was higher in Jhalda-I and Joypur as compared to Arsha . The richness of putative fluoride iontransporterCrcB was more in Arsha and Joypur in comparison to Jhalda-I where the Putativefluoride in transporter CrcB1 was more in Jhalda-I than the Arsha and Joypur . Indian states are severely affected by fluoride contamination in both groundwater and soil. Fluoride toxicity is significantly higher in the western part of West Bengal, mainly in the districts of Bankura, Purulia and Birbhum (Ghosh et al., 2024 ). Unfortunately, in India, fluoride research is specifically restricted to groundwater. De-fluoridation of agricultural soil is crucial for improving soil health, promoting crop growth, and ensuring overall agricultural sustainability. The rapid advancements in Next Generation Sequencing (NGS) technologies have made it possible to gain a more comprehensive understanding of the richness and diversity within the rhizospheric niche by examining both culturable microbes (less than 1% of the total population) and non-culturable microbes (Hugenholtz et al 1998; Pramanik et al. , 2020). Studies on microbial communities from several environments viz., low-temperature acid-mine drainage (Maria et al. , 2015), marine water and sediments (Cabello et al. , 2021 and Mason et al. , 2014) including arsenic-contaminated soils (Zhang et al. , 2021) have revealed novel insights on the microbiome structure, function along with their evolution pattern also led to the discovery of novel genes involved in bioremediation of toxic substances. In recent years, there has been increasing concern about the impact of fluoride contamination on agricultural products (De et al. , 2021). The impact of the problem as well as a dearth of information prompted this area of research to gain detailed insight into the microbial communities that control the biogeochemical cycling of fluoride in agricultural soils. This study utilized high-throughput metagenomic sequencing as an effective approach to explore the microbiome structure and functional potential in fluoride-rich soil.The estimated TF content of the soil samples was 58.76 ± 0.76 mg/kg in Arsha , 147.32 ± 1.87 mg/kg in Jhalda -1, and 282.9 ± 4.9 mg/kg in Joypur while the AF content was 1.57 ± 0.02 mg/kg in Arsha , 2.31 ± 0.05 mg/kg in Jhalda -1, and 2.97 ± 0.03 mg/kg in Joypur . The diversity and structure of microbial communities were influenced by various environmental factors, including pH, as well as the concentrations of different elements, electron donors, and acceptors in the soil (Fierer et al. ,2006).Fluoride exhibits antibacterial properties and has been shown in vitro to inhibit bacterial cell growth by interfering with glycolysis and energy metabolism-related pathways (Johnston and Strobel, 2020). Research has shown that higher fluoride concentrations improve its antibacterial and antibiofilm efficacy against S. aureus (Xue et al , 2023 and Liu, J et al , 2024).Fluoride also inhibits essential bacterial enzymes, including enolase, F-ATPase etc. thus disrupting bacterial growth and metabolism (Liao et al , 2017). For more than 50 years, fluoride has been extensively utilized in dental care worldwide. Earlier studies suggest that exposure to 100 mg/L fluoride enhances the diversity and richness of the gut microbiome in mice (Liu, J et al 2019 and Fu, R et al 2020). The study revealed that the use of NaF-containing mouthwash in children could significantly reduce S. mutans counts (Gedam and Katre, 2022). The evolution of bacterial resistance genes against potential selective pressures or evolutionary forces (such as drugs, therapeutics, or pathogens) is driven by genetic changes, via allelic variation generated through mutation, recombination between alleles, and occasionally through horizontal gene transfer, such as transformation, conjugation, and transduction.Microorganisms that exhibit fluoride tolerance are believed to possess either altered enzymes or antiporters or mutations in regulatory sequences that alter the level of expression of fluoride tolerance-related genes (Waugh et al ,, 2019, Thesai et al . 2018, Li et al . 2022). Studies on the effects of various fluoride dosages on the human gut microbiota in vitro showed that low fluoride dosages (1 and 2 mg/L) had a minimal impact on the structure and functional Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. In contrast, high fluoride dosages (10 and 15 mg/L) significantly modified the composition and functional KEGG pathways of the gut microbiota (Chen, et al, 2021 ). The degree of fluoride tolerance in bacteria varies depending on the nature and position of mutations in the genome, as well as the number of mechanisms working together to alleviate the fluoride stress.The knockout and complementation of the crcB, eno, GI3, gpmA, orf5249, ppaC, and uspA genes, which code for fluoride transporters, fluoride-inhibited enzymes, and a universal stress protein, residing in an operon and activated by fluoride exposure within a fluoride riboswitch, revealed that these genes collaborate to confer high fluoride resistance to E. cloacae FRM (Liu et al , 2017). Depending on their level of fluoride tolerance, different groups of bacteria migrate from areas with higher fluoride concentrations to regions with lower fluoride concentrations, and vice versa.Already, it has been demonstrated that fluoride plays a significant role in shaping the microbiome structure in groundwater systems (Zhang et al 2019).The annotated assembled contigs revealed that the organisms belonged to the taxa Archaea, Bacteria, Fungi, and Viruses.The abundance of archaea was higher in Arsha while the abundance of bacteria and viruses was higher in Joypur . The most abundant prokaryotic organisms in the soil samples belonged phylogenetically to the Actinobacteria phylum, accounting for 44.75% (in Arsha ), 47.3% (in Jhalda 1) and 40.84% (in Joypur ) followed by Proteobacteria and Planctomycetota of the total OTUs in all libraries.These findings were also congruent with fluoride-contaminated groundwater samples studied by Zhang et al 2019.The phylum Actinobacteria was a major taxonomic group within the domain Bacteria, encompassing five subclasses, six orders, and fourteen suborders (Barka et al ., 2016). Species of Actinobacteria are commonly found in soil and are well-known for their ability to produce antibiotics (Madigan et al ., 2009). Among the most abundant orders were Hyphomicrobiales, Streptomycetales, Burkholderiales, Micrococcales, Corynebacteriales, Myxococcales , and Propionibacteriales .Additionally, the most abundant families in fluoride-rich soil included, Anaeromyxobacteraceae, Comamonadaceae, Sphingomonadaceae, Methylobacteriaceae, Sphaerotilaceae, Intrasporangiaceae, Xanthobacteraceae, and Geobacteraceae were most abundant family in the fluoride-rich soil. At the genus level, Streptomyces , Bradyrhizobium , Nocardioides , Anaeromyxobacter , Mycobacterium , Pseudomonas , Micromonospora , Mycolicibacterium , Amycolatopsis , and Microbacterium are the most abundant across all three samples.The genera Pseudomonas, Bacillus, Acinetobacter , and Streptococcus have been reported to be resistant to fluoride due to an ancient system involving fluoride-specific riboswitches and associated proteins such as CrcB (Baker et al. 2012 , Banerjee et al .2016, Praveen et al . 2013, Men et al. 2016). A higher fluoride concentration in the Joypur soil exhibited a significant impact on the soil microbiota at the genus level. The abundance of Streptomyces and Bradyrhizobium was reduced compared to the Arsha and Jhalda1 soils. Bradyrhizobium has been reported to be positively associated with biological nitrogen fixation in legumes (Han et al. 2024). Based on the alpha diversity analysis and the rarefaction curve a moderate change in soil microbial community diversity was apparent across the fluoride concentration gradient. The highest number of observed taxa was found in the soil sample of Arsha followed by Joypur and Jhalda-1 . Beta diversity and the Principle Coordinate analysis (PCoA) analysis exhibited that approximately twice the number of unique microbe was found in the soil sample of “ Arsha ” in comparison to both Jhalda-1 and where no variation was found between Jhalda-1 and Joypur , likely due to the lower fluoride content in the Arsha soil compared to the soils of Jhalda-1 and Joypur . Common microbes were found maximum in between Arsha and Joypur , closely followed by Arsha and Jhalda-1. In all the samples, enolase 2 was detected as the least abundant followed by universal stress protein A. The richness of putative fluoride ion transporter CrcB was more in Arsha and Joypur in comparison to Jhalda-I where the Putativefluoride ion transporter CrcB1 was more in Jhalda-I than the Arsha and Joypur.In bacteria generally, two fundamentally different types of fluoride exporter proteins have been identified so far where CLCFs are annotated as such, eriC, clcA, or clcB and Flucs which may be annotated as crcB or fluC) (McIlwain et. al 2021). Previous studies have shown that fluoride inhibited the activity of soil enzymes, including dehydrogenase, arylsulfatase and alkaline phosphatase (Wilke et al 1987). Therefore, it can be inferred that fluoride is likely to harm the function of the microbial community. In our study, enolase, acetyl-CoA acetyltransferase, pyruvate ferredoxin oxidoreductase, methylmalonyl-CoA mutase, alcohol dehydrogenase, acetyl-CoA synthetase, flavinprenyl transferase, glycine hydroxyl methyltransferase, acetolactate synthase, 6-phosphofructokinase, S-hydroxymethyl, branched-chain amino acid aminotransferase, and glutathione dehydrogenase were found to be more abundant in all three soil samples, likely as a response to mitigate fluoride toxicity. The involvement of the fluoride-sensitive enolase enzyme in fluoride resistance in Streptococcus mutans has already been demonstrated attributed to a point mutation in the gene encoding this enzyme (Mitsuhata et. al 2014 ). Transcriptome analysis under fluoride stress conditions in Enterobacter cloacae FRM revealed that the transcripts of the enolase gene eno (orf5255), the universal stress protein gene uspA (orf5256) and Divalent metal cation transporter increased 176-fold 120-fold and 15-fold, respectively (Liu et. al 2017 ). Thus, our findings suggest that fluoride may be a critical factor influencing microbial diversity in soil. 4. Conclusions Soil fluoride toxicity is a major problem affecting both the natural flora and fauna. Our study revealed a strong link between fluoride concentration and the composition of bacterial communities in the soil of Purulia district, West Bengal, India. The extent of fluoride levels in soil significantly influences the structure of bacterial communities. The PCoA analysis highlighted heterogeneity in microbial population diversity across the testing locations. Functional profiling of soil samples revealed that the most abundant fluoride metabolism-related genes across all three locations were enolase, inorganic pyrophosphatase, and divalent metal cation transporter MntH.These findings offer valuable insights into the biogeochemical processes involving fluoride, emphasizing the need to consider fluoride concentrations when evaluating microbial responses to fluoride-rich soils. This study explores the potential of soil microbial communities to mitigate fluoride toxicity, providing a foundation for sustainable management practices in fluoride-contaminated agricultural systems. Declarations CRediT authorship contribution statement Krishnendu Pramanik: Conceptualization, Investigation, Formal analysis, Data curation, Writing – original draft. Arup Sen: Writing – review & editing, Resources, Methodology. Subrata Dutta: Writing – review & editing. Gouranga Sundar Mandal: Writing – review & editing, Resources. Bappa Paramanik: Analysis, Resources. Arpita Das: Writing – review & editing, resources. Nitin Chatterjee: Analysis and Resources. Ankit Kumar Ghorai: Resources. Md. Nasim Ali: Conceptualization, Project administration, Supervision, Writing-Editing and finalization of the paper. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose Ethics approval No ethical issue is involved in the study as it does not involve humans as experimental material. References Andrews, S., 2017. FastQC: a quality control tool for high throughput sequence data. 2010. Annadurai, S. T., Arivalagan, P., Sundaram, R., Mariappan, R., Munusamy, A. P., 2019. Batch and column approach on biosorption of fluoride from aqueous medium using live, dead and various pretreated Aspergillusniger (FS18) biomass. Surf.Interfaces . 15, 60-69. Baker, J. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6243086","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435941597,"identity":"7d6176f2-6485-4601-9b2d-478a6fec9747","order_by":0,"name":"Krishnendu Pramanik","email":"","orcid":"","institution":"Bidhan Chandra Krishi Viswavidyalaya","correspondingAuthor":false,"prefix":"","firstName":"Krishnendu","middleName":"","lastName":"Pramanik","suffix":""},{"id":435941598,"identity":"07a6a04e-e558-4a18-b2b1-b7398799ddf7","order_by":1,"name":"Arup Sen","email":"","orcid":"","institution":"Bidhan Chandra Krishi Viswavidyalaya","correspondingAuthor":false,"prefix":"","firstName":"Arup","middleName":"","lastName":"Sen","suffix":""},{"id":435941601,"identity":"7c7b1976-c2b1-4c89-b312-3027a38ba425","order_by":2,"name":"Subrata Dutta","email":"","orcid":"","institution":"Bidhan Chandra Krishi Viswavidyalaya","correspondingAuthor":false,"prefix":"","firstName":"Subrata","middleName":"","lastName":"Dutta","suffix":""},{"id":435941602,"identity":"c9f874c5-8a42-4c42-b2fc-4dacee431104","order_by":3,"name":"Gouranga Sundar Mandal","email":"","orcid":"","institution":"Bidhan Chandra Krishi Viswavidyalaya","correspondingAuthor":false,"prefix":"","firstName":"Gouranga","middleName":"Sundar","lastName":"Mandal","suffix":""},{"id":435941604,"identity":"b7911526-f468-4500-ab0e-bb00c00f7ddb","order_by":4,"name":"Bappa Paramanik","email":"","orcid":"","institution":"Uttar Banga Krishi Viswavidyalaya","correspondingAuthor":false,"prefix":"","firstName":"Bappa","middleName":"","lastName":"Paramanik","suffix":""},{"id":435941605,"identity":"895e6938-d732-4f36-a1b9-a67d0f37d877","order_by":5,"name":"Arpita Das","email":"","orcid":"","institution":"Bidhan Chandra Krishi Viswavidyalaya","correspondingAuthor":false,"prefix":"","firstName":"Arpita","middleName":"","lastName":"Das","suffix":""},{"id":435941607,"identity":"a078fb44-6f4f-4ea0-b980-12695f43767c","order_by":6,"name":"Nitin Chatterjee","email":"","orcid":"","institution":"Bidhan Chandra Krishi Viswavidyalaya","correspondingAuthor":false,"prefix":"","firstName":"Nitin","middleName":"","lastName":"Chatterjee","suffix":""},{"id":435941609,"identity":"18484732-4a1a-433e-b482-8128c6399889","order_by":7,"name":"Ankit Kumar Ghorai","email":"","orcid":"","institution":"Bidhan Chandra Krishi Viswavidyalaya","correspondingAuthor":false,"prefix":"","firstName":"Ankit","middleName":"Kumar","lastName":"Ghorai","suffix":""},{"id":435941610,"identity":"5b08c8ae-b3f1-4c1f-addf-4382f483a130","order_by":8,"name":"Md. Nasim Ali","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYJCCAwwMFjz8zIztPz4AeWzsxGmR4JFsbz4gOQOkhZk4iyQYDM4cS5DmAbEJaTFnP/7w0I0aCRmGGzkGxja/tsnzMTMwfviYg1uLZU+OweGcYxI8jDNyDJJz+24btjEzMEvO3IZbi8GBHIbDOWwSPMwSQL25PbcZgVrYmHnxaTn//MHhnH8SPGwSOYbNlj237QlruZEANLxNgoeH51gyM8OP24lEaHkD1NInwSPB3nyMsbfhdnIbM2Mzfr+cT3/8Oeebjb39YcY2hh9/btvOb28++OEjHi2oAKgLRDYQqx4E/pCieBSMglEwCkYKAABp71DK8McMigAAAABJRU5ErkJggg==","orcid":"","institution":"Bidhan Chandra Krishi Viswavidyalaya","correspondingAuthor":true,"prefix":"","firstName":"Md.","middleName":"Nasim","lastName":"Ali","suffix":""}],"badges":[],"createdAt":"2025-03-17 09:23:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6243086/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6243086/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11274-025-04408-5","type":"published","date":"2025-06-25T15:57:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79609990,"identity":"96c71872-3724-4c3a-abc5-dea7997e26fc","added_by":"auto","created_at":"2025-03-31 17:18:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":182658,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Sampling Sites\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6243086/v1/baa1e40eeb57d346771bf7c7.png"},{"id":79609991,"identity":"fb9c3c3c-cefd-4ed4-8b9a-31df65b7f193","added_by":"auto","created_at":"2025-03-31 17:18:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6538356,"visible":true,"origin":"","legend":"\u003cp\u003e(a-c): Sankey plot illustrating the proportion of bacterial reads in F-rich soil sampled from\u003cem\u003e \u003c/em\u003e\u003cstrong\u003e(a)\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e Arsha (Ar) \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(b)\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e Jhalda I (Jh1) \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eand (c)\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eJoypur (Joy)\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6243086/v1/45307d41605a96406ec1911f.png"},{"id":79611022,"identity":"fa55234f-07e3-4e40-b42c-5d78d64866b4","added_by":"auto","created_at":"2025-03-31 17:34:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":32576350,"visible":true,"origin":"","legend":"\u003cp\u003e(a-c): Krona graph showing the abundance of the taxonomic groups in F-rich soil sampled from \u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e(a)\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e Arsha (Ar) \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(b)\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e Jhalda I (Jh1) \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eand (c)\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e Joypur (Joy)\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6243086/v1/8f057574706782bd5ab8d739.png"},{"id":79611019,"identity":"954b7f4d-e8b7-40e8-bc5c-910ba5bd5a8f","added_by":"auto","created_at":"2025-03-31 17:34:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1150545,"visible":true,"origin":"","legend":"\u003cp\u003e(a-c): Relative richness of the top 20 common bacterial \u003cstrong\u003e(a) order (b) genera and (c) family \u003c/strong\u003ewithin microbial communities in F-rich soil sampled from\u003cem\u003e Arsha (Ar), Jhalda I (Jh1) and Joypur (Joy)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6243086/v1/57590134c65e0172aeba4265.png"},{"id":79610712,"identity":"b8ce72ec-dcae-4e83-a671-95597d1397ba","added_by":"auto","created_at":"2025-03-31 17:26:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1812754,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha diversity among microbial communities in F-rich soil sampled from\u003cem\u003e Arsha (Ar), Jhalda I (Jh1) and Joypur (Joy)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-6243086/v1/bcb75fe468db353be3e7f20a.png"},{"id":79609996,"identity":"ddf5a3d5-5fc9-408d-a3f7-cd624add7963","added_by":"auto","created_at":"2025-03-31 17:18:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":254838,"visible":true,"origin":"","legend":"\u003cp\u003eRarefaction curve showing diversity among microbiota in F-rich soil sampled from\u003cem\u003eArsha (Ar), Jhalda I (Jh1) and Joypur (Joy)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-6243086/v1/ec0ad128b0c883ad682cac02.png"},{"id":79611538,"identity":"8defca63-c67f-4e10-9977-78e1685651ee","added_by":"auto","created_at":"2025-03-31 17:42:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":741713,"visible":true,"origin":"","legend":"\u003cp\u003ePCoA-based diversity among microbiota in F-rich soil sampled from\u003cem\u003e Arsha (Ar), Jhalda I (Jh1) and Joypur (Joy)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-6243086/v1/a871c45bb1578585bd02fb10.png"},{"id":79610001,"identity":"e862b0f5-7f39-4b5d-a672-3d7bab7044c5","added_by":"auto","created_at":"2025-03-31 17:18:42","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":281235,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map of the relative abundance of the top 50 common KO groups based on the KEGG (Kyoto Encyclopedia of Genes and Genomes) database in F-rich soil sampled from\u003cem\u003e Arsha (Ar), Jhalda I (Jh1) and Joypur (Joy)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-6243086/v1/bc2fbd9f312b4909886e67a7.png"},{"id":79610013,"identity":"47d9f396-2d72-45ae-8932-e6660dfe1a2a","added_by":"auto","created_at":"2025-03-31 17:18:42","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":7260799,"visible":true,"origin":"","legend":"\u003cp\u003e(a-c): The COG orthology classification of the annotated proteins found in F-rich soil sampled from \u003cstrong\u003e(a)\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e Arsha (Ar) \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(b)\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e Jhalda I (Jh1) \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eand (c)\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e Joypur (Joy)\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6243086/v1/f63162fca64cb7ea7a00d45b.png"},{"id":79610716,"identity":"39036d6c-5ce7-491b-bf12-b7cd4df7d066","added_by":"auto","created_at":"2025-03-31 17:26:42","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":158452,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map of the relative abundance of fluoride metabolism-related genes found in F-rich soil sampled from\u003cem\u003e Arsha (Ar), Jhalda I (Jh1) and Joypur (Joy)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-6243086/v1/9d140e6430696ced3efda625.png"},{"id":85686457,"identity":"a6370a6f-0dd4-42e2-8fd2-559b84a9928e","added_by":"auto","created_at":"2025-06-30 16:05:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":62525854,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6243086/v1/d1a73553-fe80-4c6e-95ce-78bed5a7120c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Microbial Populations under fluoride Stress: a metagenomic exploration from Indian soil","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFluoride, the very first and the lightest member of the halogen group, is a widely available mineral in nature. Out of 85\u0026nbsp;million tonnes of fluoride deposits on the Earth\u0026rsquo;s surface, 12\u0026nbsp;million tonnes are reported in India (Teotia \u003cem\u003eet al.\u003c/em\u003e, 1994). The World Health Organization (WHO) and the Indian Bureau of Standards (BIS) have set the maximum permissible limit of fluoride in drinking water as 1.5 mg/l (WHO, 1997; WHO, 2004 and BIS, 2012). Bioaccumulation of fluoride in food and feed crops may lead to its introduction into the food chain. However, there is no available data regarding the recommended fluoride content in plants that is considered safe for human health. Consumption of fluoride beyond permissible limits leads to serious health hazards like diarrhoea, abdominal pain, vomiting, dehydration, and excessive salivation etc. Long-term fluoride intake above 8 mg/l causes skeletal fluorosis, arthritis, cancer, osteoporosis, infertility, thyroid disease, Alzheimer's disease, and brain damage (Whitford \u003cem\u003eet al.\u003c/em\u003e, 2005; Yadav\u003cem\u003eet al.\u003c/em\u003e, 2007; Bibiet al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e and Singh \u003cem\u003eet al.\u003c/em\u003e, 2018). Fluoride toxicity in cattle has also been reported to inhibit thyroxin secretion (Cinar\u003cem\u003eet al.\u003c/em\u003e, 2021). In plants, excessive fluoride accumulation causes phytotoxicity, leading to chlorosis, leaf necrosis, reduced growth, and lower crop yields(Weinstein \u003cem\u003eet al.\u003c/em\u003e, 2004). It induces oxidative stress, i.e., the production of Reactive Oxygen Species (ROS) and Reactive Nitrogen Species (RON), disrupting natural antioxidant defence mechanisms (Maritim\u003cem\u003eet al.\u003c/em\u003e, 2003). Excess fluoride in the soil can impair nutrient uptake, resulting in nutrient imbalances in plants, inhibits seed germination and cellular enzymatic activity (Mackowiak\u003cem\u003eet al\u003c/em\u003e., 2001; Yadu\u003cem\u003eet al\u003c/em\u003e., 2018; and Baunthiyal \u003cem\u003eet al\u003c/em\u003e., 2014). In bacteria, direct inhibition of enzymes by fluoride ion or hydrogen fluoride is observed due to the formation of phosphate analogues that inhibit adenosine triphosphate (ATP) synthesis (Kalyani \u003cem\u003eet al\u003c/em\u003e., 2009 and Chouhan\u003cem\u003eet al\u003c/em\u003e., 2012).\u003c/p\u003e \u003cp\u003eMost studies to date have primarily concentrated on hydrological investigations. However, fluoride contamination extends beyond groundwater, affecting agricultural soils, infiltrating cultivated crops, and ultimately entering the food chain. The average soil fluoride concentrations in India range from 90 to 500 mg/kg of soil (Bhattacharya \u003cem\u003eet al.\u003c/em\u003e, 2018) with 19 states severely affected by fluoride contamination in both groundwater and soil. In West Bengal, about 12% of the population across 8 districts is affected by fluoride exposure (PHED, 2007; Chatterjee et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Long-term use of fluoride-contaminated groundwater for irrigation, particularly in paddy cultivation, significantly increases soil fluoride levels. This raises concerns about increased fluoride bioaccumulation in rice and other cereals grown in these soils. Fluoride accumulates in plant tissues in the following order: roots\u0026thinsp;\u0026gt;\u0026thinsp;leaves\u0026thinsp;\u0026gt;\u0026thinsp;stems\u0026thinsp;\u0026gt;\u0026thinsp;seeds (Rao\u003cem\u003eet al.\u003c/em\u003e, 1993 and Chakrabartiet al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). High fluoride levels in straw pose risks to livestock health and indirectly affect human health through contaminated meat and milk.\u003c/p\u003e \u003cp\u003eMicrobes offer a promising alternative to fluoride scavenging due to cell wall compositions, which include functional groups like amines, sulfhydryl carboxylates and phosphates. These groups not only reduce fluoride ions but also facilitate their adherence to microbial surfaces(Juwarkar \u003cem\u003eet al.\u003c/em\u003e, 2010; Klein\u0026uuml;bing\u003cem\u003eet al.\u003c/em\u003e, 2011 andChouhan\u003cem\u003eet al.\u003c/em\u003e, 2012). Earlier, many microorganisms have been isolated and characterized from fluoride-contaminated water sources. It has further been used to remove fluoride ions from wastewater such as \u003cem\u003eAcinetobacter species\u003c/em\u003e. (GU566361) (Shanker \u003cem\u003eet al.\u003c/em\u003e, 2020), \u003cem\u003eProvidenciavermicola\u003c/em\u003e(KX926492) (Mukherjee \u003cem\u003eet al.\u003c/em\u003e, 2017), \u003cem\u003eCyanobacteria\u003c/em\u003e (Biswaset al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), \u003cem\u003eAspergillusniger\u003c/em\u003e(Annaduraiet al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the microbiome structure of highly fluoride-contaminated soils remains largely unexplored. Understanding microbial communities that influence the biogeochemical cycling of fluoride in agricultural soils is a significant challenge due to the diversity and underexplored status of these systems for varying fluoride levels. In this study, we hypothesized that fluoride, due to its toxic effects on microbes, could exert a stronger disruptive influence and selection pressure on the microbial community, affecting its diversity and structure at varying fluoride concentrations in the soil. Recent studies have reported that agricultural soils in the Purulia District of West Bengal have, on average, a very high fluorine content i.e.126\u0026thinsp;\u0026plusmn;\u0026thinsp;65 mg/kg (range: 47.9\u0026ndash;297 mg/kg, n\u0026thinsp;=\u0026thinsp;47) (De \u003cem\u003eet al.\u003c/em\u003e, 2021). To test our hypothesis, typical areas in Purulia district, characterized by low, medium, and high concentrations of natural soil fluoride, were selected as the model system. The effects of these fluoride concentrations on dynamic shifts in the microbiome were examined using a whole metagenome sequencing approach. Additionally, the predicted functions of microbiome genes related to fluoride cycling processes were also investigated. Thus, this study aims to provide novel insights into microbial adaptation and biogeochemical interactions in fluoride-contaminated agricultural ecosystems.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental site description\u003c/h2\u003e \u003cp\u003ePurulia district, located in the eastern part of India, is recognized as the Land of Red and Laterite Soil. Positioned between latitude 22\u0026deg;42ʹ\u0026ndash;23\u0026deg;42ʹ N and longitude 85\u0026deg;49\u0026prime;\u0026ndash;86\u0026deg;54ʹ E, the district spans an area of 6,259 km\u0026sup2; and had a population of 2,930,115 as per the 2011 Census. Situated at an average altitude of 748 feet, Purulia experiences a subtropical climate, with extreme temperatures ranging from scorching summers of up to 52\u0026deg;C to chilly winters as low as 3.8\u0026deg;C. Despite its aridity, the district receives limited rainfall from the Southwest monsoons. This study focuses on three selected blocks viz., \u003cem\u003eArsha\u003c/em\u003e, \u003cem\u003eJhalda-I\u003c/em\u003eand \u003cem\u003eJaipur\u003c/em\u003e, out of the district's 20 administrative blocks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Soil sampling\u003c/h2\u003e \u003cp\u003eThe soil samples were collected in February 2023, before the onset of the rainy season. Samples of rhizospheric soil were collected from the top layer (0-0.2 m) of the rice field of three different blocks of Purulia distinct namely \u003cem\u003eArsha\u003c/em\u003e(23\u0026deg;17 '42'' N, 86\u0026deg;12 '26''E), \u003cem\u003eJhalda-I\u003c/em\u003e(23\u0026deg;21 '55'' N, 85\u0026deg;59 '50''E) and \u003cem\u003eJoypur\u003c/em\u003e(23\u0026deg;24 '45'' N, 86\u0026deg;6 '55''E)to conduct the soil metagenomic studies (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Approximately 500 g of soil was collected from three different locations of each block and then immediately the samples were transferred to sterile plastic bags also kept in icebox. Half of the soils were stored at \u0026minus;\u0026thinsp;80\u0026deg;C for subsequent molecular analysis. The remaining half was air-dried at room temperature subsequently sieved through a 100-mesh sifter and then stored at 4\u0026deg;C for further analysis of soil physico-chemical properties.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Soil physico-chemical properties\u003c/h2\u003e \u003cp\u003eThe basic physicochemical properties of the soils were determined to give a preliminary idea about the fluoride and fertility status of the experimental soils. The parameters evaluated included soil organic carbon (SOC) using the wet digestion method, pH and EC (1:2 soil-to-water ratio), available phosphorus (AP) via sodium bicarbonate extraction with spectrophotometric analysis, and available nitrogen (AN) using the alkaline permanganate method (Jackson, 1973). Soil texture was determined following Piper (1966), and soil order classification was based on Soil Survey Staff (2010). Cation exchange capacity (CEC) was measured using the method by Sumner and Miller (1996), and available potassium (AK) was assessed using ammonium acetate extraction and flame photometry (Muhr\u003cem\u003eet al\u003c/em\u003e., 1965). Available zinc (Zn) was estimated via DTPA extraction and atomic absorption spectrophotometry (Lindsay and Norvell, 1978). Available fluoride (AF) was determined using the 0.01 M calcium chloride extraction method using Jenway 3040 model ion-meter in combination with a fluoride-specific electrode (Orion ISE 940900)(Larsen and Widdowson, 1971). Total fluoride (TF) was extracted using the alkali fusion method (McQuaker\u003cem\u003eet al\u003c/em\u003e., 1977). All the soil samples were tested in triplicate, and the results are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM of the data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 DNA extraction, library construction, and metagenomic sequencing\u003c/h2\u003e \u003cp\u003eThe metagenomic DNA from soil samples of fluoride-rich areas were isolated using DNeasy\u0026reg; PowerSoil\u0026reg; Pro Kit (Qiagen, Hilden, Germany) according to the manufacturer\u0026rsquo;s protocols. The DNA quality was evaluated with a 0.8% agarose gel. DNA concentration was quantified by Qubit DNA HS Assay Kit (Invitrogen) as per the manufacturer\u0026rsquo;s instruction. To minimize DNA extraction bias, three replicate DNA isolations of each sample were pooled. The genomic library was prepared using the TruSeq\u0026reg; Nano DNA Library Prep kit as per the manufacturer\u0026rsquo;s instructions. The quantification of the final libraries was done using the Qubit DNA HS Assay Kit (Invitrogen) following the manufacturer\u0026rsquo;s protocol. DNA 5000 Screen Tapes, Tapestation (Agilent) was used to identify library size following the manufacturer\u0026rsquo;s protocol. Following libraries were loaded onto an Illumina (Novaseq 6000), 150PE sequencing platform for sequencing according to the standard protocol. Paired-end sequencing allowed the template fragments to be sequenced in both the forward and reverse directions. The average insert sizes of the libraries were 364 bp, 380 bp and 395 bp, for the samples \u003cem\u003eArsha\u003c/em\u003e, \u003cem\u003eJhalda-I\u003c/em\u003e and \u003cem\u003eJoypur\u003c/em\u003e blocks, respectively. Read count summaries and assembly statistics are provided in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. All raw metagenomics datasets have been submitted to the NCBI Sequence Read Achieve database (BioProject Accession Number: PRJNA1154823).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of raw data (Sequencing)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. of raw reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo QC passed reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eData in GBs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRead length\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGC %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e%Q30\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eArsha\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94046382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92286214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e94.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eJhalda-I\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73882296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72474562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e93.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eJoypur\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76547322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74999816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e65.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e93.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of assembled metagenomic data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssembly\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eArsha\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eJhalda-I\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eJoypur\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#contigs(\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e827553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e855834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#contigs(\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;1000bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#contigs(\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;5000bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#contigs(\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;10000bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#contigs(\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;25000bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#contigs(\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;50000bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLargestcontig\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e375629184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e443670980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e404091808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGC(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e305395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e317172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e308902\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Metagenomic sequence assembly, gene prediction, taxonomy, and functional annotation\u003c/h2\u003e \u003cp\u003eAfter generating the raw sequencing data, the data quality of the whole metagenomic sequence was checked for base call quality distribution, %GC, % bases above Q30and sequencing adapter using FastQCand MultiQC software (Andrews \u003cem\u003eet al\u003c/em\u003e., 2010 and Ewels\u003cem\u003eet al\u003c/em\u003e., 2016). The raw sequence reads were processed to remove adapter sequences and low-quality bases using fastp v0.12.4 (Chen \u003cem\u003eet al\u003c/em\u003e., 2018) with default parameters. The pre-processed reads were submitted to Kraken2 v2.1.2 (Wood \u003cem\u003eet al\u003c/em\u003e., 2019), which classifies the reads using k-mer-based homology against the complete NCBI nt database of sequences. The Kraken2 results were visualized using pavian v1.2.0 (Breitwieser\u003cem\u003eet al\u003c/em\u003e., 2020) to generate Sankey plots. The pre-processed reads were assembled to contigs using Megahit v1.2.9 (Li \u003cem\u003eet al\u003c/em\u003e., 2015) with k-mer sizes --k-min 31, --k-max141 --k-step 28. Contigs\u0026thinsp;\u0026lt;\u0026thinsp;200 bp were not considered for the genome assembly. The assembled genome statistics were assessed using QUAST v5.0.2 (Gurevich\u003cem\u003eet al\u003c/em\u003e., 2013). The quality of the assembly was verified by mapping the reads back onto the assembled contigs using bowtie2 v2.4.5 (Langmead\u003cem\u003eet al\u003c/em\u003e., 2012). Using BLASTn, the annotation of the assembled contigs was carried out against the NCBI (nt) custom metagenome database that included all nucleotide entries for the taxa belonging to fungi, bacteria, archaea, and viruses. Taxonomic distribution of BLAST hit contigs was recognized with taxon kit v0.12.0 (Shen\u003cem\u003eet al\u003c/em\u003e., 2021). The annotation of the assembled contigs and prediction of the gene were done using PROKKA v1.13 with the metagenome option (Seemann \u003cem\u003eet al\u003c/em\u003e., 2014). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Clusters of Orthologous Groups of Proteins (COG) annotations were obtained by submitting the protein sequences of each sample to KAAS (KEGGAutomatic Annotation Server) and WebMGA, respectively (Moriya \u003cem\u003eet al\u003c/em\u003e., 2007 andWu\u003cem\u003eet al\u003c/em\u003e., 2011). After the taxonomic classification, OTU abundance was estimated.Kraken2 output was converted into an OTU table using a kraken2 script and a house script. Krona plot was created using kraken2 results. Gene classification for predicted resistance was done against the COG database by BLASTx on COG classification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003ePhyloseq v1.38.0 (McMurdie\u003cem\u003eet al\u003c/em\u003e., 2013), vegan R package and Phyloseqwasused to perform the Alpha diversity indexes, Principal coordinates analysis (PCoA), Rarefaction curve, bar plots and heatmaps. Beta Diversity was estimated by Fisher's exact test using STAMP v2.1.3 (Parks \u003cem\u003eet al\u003c/em\u003e., 2014). The Analysis of Variance (ANOVA), mean, DMRT (Duncan\u0026rsquo;s Multiple Range Test), and Bar diagram with Standard error of mean were performed using SPSS software and Microsoft Excel.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Physico-chemical characterization of fluoride-contaminated soil\u003c/h2\u003e \u003cp\u003eAll the Physicochemical properties of the soil of three locations are represented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The pH of the soil samples was ranged from 4.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 to 5.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16, indicating that the soil samples were moderate to strong acidic. As all three locations fall under the red and laterite zones of West Bengal, soil acidity is influenced by the composition of the parent material and the extent of weathering. The CEC of the soils ranged from 0.031\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005 to 0.076\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013 dS/m, levels which were generally conducive to crop growth. The oxidizable SOC was 1.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09% in \u003cem\u003eArsha\u003c/em\u003e soil, 1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05% in \u003cem\u003eJhalda-I\u003c/em\u003e soil, and 2.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42% in \u003cem\u003eJoypur\u003c/em\u003e soil. The A(N content was 162.58\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8kg/ha in\u003cem\u003eArsha\u003c/em\u003e, 167.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.37 kg/ha in \u003cem\u003eJhalda-I\u003c/em\u003e, and 151.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.65 kg/ha in \u003cem\u003eJoypur\u003c/em\u003e. The AP content was 9.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 kg/ha in\u003cem\u003eArsha\u003c/em\u003e, 13.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39kg/ha in \u003cem\u003eJhalda-I\u003c/em\u003e, and 11.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 kg/ha in \u003cem\u003eJoypur\u003c/em\u003e. The AK content was 162.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.66 kg/ha in \u003cem\u003eArsha\u003c/em\u003e, 171.41\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38 kg/ha in \u003cem\u003eJhalda-I\u003c/em\u003e, and 188.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.08 kg/ha in \u003cem\u003eJoypur\u003c/em\u003e. The available Zn content was 2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2mg/kg in \u003cem\u003eArsha\u003c/em\u003e, 2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 mg/kgin\u003cem\u003eJhalda-I\u003c/em\u003e, and 1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 mg/kg in\u003cem\u003eJoypur\u003c/em\u003e. Most importantly, TF content was 58.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76 mg/kg in \u003cem\u003eArsha\u003c/em\u003e, 147.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87 mg/kg in \u003cem\u003eJhalda-I\u003c/em\u003e, and 282.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9 mg/kg in \u003cem\u003eJoypur\u003c/em\u003ewhile the AF content was 1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 mg/kg in \u003cem\u003eArsha\u003c/em\u003e, 2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 mg/kg in \u003cem\u003eJhalda-I\u003c/em\u003e, and 2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 mg/kg in \u003cem\u003eJoypur\u003c/em\u003e. The textural class of \u003cem\u003eArsha\u003c/em\u003eand \u003cem\u003eJoypur\u003c/em\u003esoils generally fell within the sandy clay loam category, whereas \u003cem\u003eJhalda-I\u003c/em\u003e soil was identified as sandy loam.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysico-chemical properties of the fluoride-contaminated soil samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil Parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eJhalda-I (\u003c/em\u003eJ\u003cem\u003ehI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eJoypur\u003c/em\u003e(\u003cem\u003eJoy\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eArsha\u003c/em\u003e(\u003cem\u003eAr\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u0026deg;21\u0026nbsp;'55'' N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u0026deg;24\u0026nbsp;'45'' N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u0026deg;17\u0026nbsp;'42'' N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85\u0026deg;59\u0026nbsp;'50''E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86\u0026deg;6\u0026nbsp;'55''E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86\u0026deg;12\u0026nbsp;'26''E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil Order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlfisol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlfisol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlfisol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil Texture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandy loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSandy clay loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSandy- clay loam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEC (cmol(p+)/kg soil)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC (dS/m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.058 \u0026plusmn; 0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.031 \u0026plusmn; 0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.076\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganic Carbon (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04\u0026thinsp;\u0026plusmn;\u0026thinsp;.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailable N (kg/ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162.58\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailable P (kg/ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailable K(kg/ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171.41\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailable Zn (mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailable F (mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal fluoride (mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e282.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Comparison of the microbiome profiles among the fluoride-rich soils\u003c/h2\u003e \u003cp\u003eThe annotations of the assembled contigs revealed that archaea were more abundant in \u003cem\u003eArsha\u003c/em\u003e, while bacteria and viruses were more abundant in \u003cem\u003eJoypur\u003c/em\u003e. The summary of the taxonomic distribution of NCBI nt annotated contigs is exhibited in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The maximum numbers of genes were predicted in \u003cem\u003eJoypur\u003c/em\u003e as compared to \u003cem\u003eArsha\u003c/em\u003eand\u003cem\u003eJhalda-I.\u003c/em\u003e The summary of Prokka annotations is exhibited in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAbundance of different microbial populations in three locations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArchaea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eViruses\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eArsha\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e332968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eJhalda-I\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e305995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eJoypur\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e353968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Prokka annotation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003egene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emisc_RNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003etmRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003etRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003erRNA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eArsha\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eJhalda-I\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e139286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eJoypur\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e158765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e163835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e468\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe most abundant taxonomic community under fluoride stress condition obtained was bacteria (~\u0026thinsp;96%) followed by Archaea (~\u0026thinsp;1%) and Eukaryota (3% in \u003cem\u003eArsha\u003c/em\u003eand \u003cem\u003eJhalda-I\u003c/em\u003e except 2% in \u003cem\u003eJoypur\u003c/em\u003e) and the rest remained virus and uncategorized. The distribution of reads and complete microbial diversity from \u003cem\u003eArsha\u003c/em\u003e, \u003cem\u003eJhalda-I\u003c/em\u003e and \u003cem\u003eJoypur\u003c/em\u003e has been summarized by the Sankey plot [Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003e(a-c)]\u003c/b\u003e and Krona graph in [Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003e(a-c)]\u003c/b\u003e respectively. This study revealed that the most abundant prokaryotic organisms in the soil samples belonged phylogenetically to the \u003cem\u003eActinobacteria\u003c/em\u003e phylum, accounting for 44.75% (in \u003cem\u003eArsha\u003c/em\u003e), 47.3% (in \u003cem\u003eJhalda-I\u003c/em\u003e) and 40.84% (in \u003cem\u003eJoypur\u003c/em\u003e) followed by \u003cem\u003eProteobacteria\u003c/em\u003e(41.59% in \u003cem\u003eArsha\u003c/em\u003e), 39.68% (in \u003cem\u003eJhalda-I\u003c/em\u003e) and 47.07% (in \u003cem\u003eJoypur\u003c/em\u003e), and\u003cem\u003ePlanctomycetota\u003c/em\u003e(2.67%, 3.18%, 2.28%) of the total OTUs in all libraries.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Relative abundance of microbial order, family and Species\u003c/h2\u003e \u003cp\u003e \u003cem\u003eKiritimatiellaeota, Peploviricota, Tenericutes, Thermotogae, Chlamydiae, Fusobacteria, Candidatus_Bipolaricaulota and Thermodesulfobacteria\u003c/em\u003ewas found to be the minor phyla in all the three samples. \u003cem\u003eHyphomicrobiales, Streptomycetales, Burkholderiales, Micrococcales, Corynebacteriales, Myxococcales, and Propionibacteriales\u003c/em\u003e were the most abundant orders in all three samples. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003e(a)\u003c/b\u003e shows the relative abundances of 20 bacterial orders across the fluoride concentration. The microbial community, at the phylum level, was almost similar among all the soil samples. Higher relative abundances of the families \u003cem\u003eNocardioidaceae, Anaeromyxobacteraceae, Comamonadaceae, Sphingomonadaceae, Methylobacteriaceae, Sphaerotilaceae, Intrasporangiaceae, Xanthobacteraceae\u003c/em\u003e, and\u003cem\u003eGeobacteraceae\u003c/em\u003ewere observed in the high fluoride congaing soil (\u003cem\u003eJoypur\u003c/em\u003e), while lower relative abundances of these families were observed in sample with medium to low fluoride concentrations (\u003cem\u003eJhalda-I\u003c/em\u003e and \u003cem\u003eArsha\u003c/em\u003e). On contrary, lower relative abundances of the families \u003cem\u003eStreptomycetaceae, Nitrobacteraceae, Mycobacteriaceae, Pseudonocardiaceae, Microbacteriaceae, Pseudomonadaceae, Micrococcaceae, Conexibacteraceae, Thermomonosporaceae, Streptosporangiaceae, Acidobacteriaceae, Corynebacteriaceae, Paenibacillaceae, Planctomycetaceae\u003c/em\u003e and \u003cem\u003eNocardiopsaceae\u003c/em\u003e were observed in the high fluoride congaing soil of \u003cem\u003eJoypur\u003c/em\u003e, while higher relative abundances of these families were observed in sample with medium to low fluoride concentrations. Therefore, it was cautiously inferred that the decrease in the relative abundance of these families may be attributed to the increase in fluoride concentration in the \u003cem\u003eJoypur\u003c/em\u003e sample. Obviously; the richness of the predominant families revealed a significant diversity in soil samples with different fluoride concentration levels. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003e(b)\u003c/b\u003e shows the relative abundances of 20 bacterial genera across the fluoride concentration. At the genus level \u003cem\u003eDesulforamulus, Cohaesibacter, Syntrophus, Anaerohalosphaera, Aurantimicrobium, Muribaculum, Methanosphaerula, Alkalihalobacillus, Pseudochrobactrum, Syntrophus, Providencia, Desulforamulus, Terrihabitans, Mammaliicoccus, Mesobacillus, Salinibacter, Olivibacter and Methanoregula\u003c/em\u003e, was observed only in \u003cem\u003eArsha\u003c/em\u003ewhereas\u003cem\u003eEndozoicomonas, Oxynema, Vespertiliibacter, Halomicronema, Microcoleus, Lacticaseibacillus, Terrihabitans, Sphaerochaeta, Acaryochloris, Acetivibrio, Desulfonema, Thermanaerovibrio, Oxynema, Slackia, Endozoicomonas, Oceanimonas, Pseudovibrio, Brasilonema\u003c/em\u003ewere observed only in\u003cem\u003eJhalda-I\u003c/em\u003eand\u003cem\u003eWoeseia, Muricauda, Thermobaculum, Filimonas, Phnomibacter, Aquisediminimonas, Microvenator, Salinibacter, Rhodocaloribacter, Rhodothermus, Pseudorhodoplanes\u003c/em\u003e only in \u003cem\u003eJoypur. Streptomyces, Bradyrhizobium, Nocardioides, Anaeromyxobacter, Mycobacterium, Pseudomonas, Micromonospora, Mycolicibacterium, Microbacterium, Sphingomonas, Conexibacter\u003c/em\u003e, \u003cem\u003eBurkholderia and Methylobacterium\u003c/em\u003ewerethe most abundant genera irrespective of locations. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003e(c)\u003c/b\u003e reflects the relative abundances of 20 bacterial families across the fluoride concentration. The analysis of taxonomic abundance within the dominant bacterial groups across soil samples with varying fluoride concentrations yielded significant insights regarding the presence of specific fluoride-tolerant rhizospheric bacteria. This study clearly demonstrated that variations in fluoride concentrations play a decisive role in demonstrating soil microbiome diversity in the soil environment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e3.4 Alpha\u003c/em\u003e and \u003cem\u003eBeta\u003c/em\u003e Diversity and PoCA\u003c/h2\u003e \u003cp\u003eEstimated alpha diversity indices values were higher in all three samples indicating higher microbial diversity in individual samples but very low or no significant differences existed in the values of each indices estimated here among the samples. Shannon index value was highest in \u003cem\u003eArsha\u003c/em\u003e(7.76) followed by \u003cem\u003eJhalda-I\u003c/em\u003e (7.68) and \u003cem\u003eJoypur\u003c/em\u003e(7.61). Simpson index values also followed the same trend with higher values in \u003cem\u003eArsha\u003c/em\u003e (0.9984) followed by \u003cem\u003eJhalda-I\u003c/em\u003e (0.9981) and \u003cem\u003eJoypur\u003c/em\u003e (0.9979). Based on the alpha diversity analysis a moderate change in soil microbiome diversity was apparent across the fluoride concentration gradient (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). All the standard alpha diversity indices estimated here represented similar patterns of diversity and richness among the samples. Species richness in terms of observed richness value in the \u003cem\u003eArsha\u003c/em\u003e (118910) sample showed the highest richness followed by \u003cem\u003eJoypur\u003c/em\u003e (11528) and \u003cem\u003eJhalda-I\u003c/em\u003e (11358). The Chao1 Index indicated that the \u003cem\u003eArsha\u003c/em\u003e sample has the highest number of observed species followed by \u003cem\u003eJoypur\u003c/em\u003e and \u003cem\u003eJhalda-I\u003c/em\u003e. The refraction curve has formed a plateau, indicating that an adequate amount of sequencing has been carried out, and further sequencing will unearth the limited number of new OTUs. These results indicate that \u003cem\u003eArsha\u003c/em\u003e soil has higher microbial diversity than \u003cem\u003eJhalda-I\u003c/em\u003e followed by \u003cem\u003eJoypur\u003c/em\u003eas depicted by the rarefaction curve of the OTUs. Alpha diversity measurements of microbial communities and refraction curves are represented in Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e respectively. In PCoA \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, the two principal coordinates explained 100% variation in the sample matrix data out of which, the horizontal axis explained 64% variation and the vertical axis explained 36% variation. The heterogeneity concerning microbial population diversity among the locations \u003cem\u003eArsha\u003c/em\u003e, \u003cem\u003eJhalda-I\u003c/em\u003e and \u003cem\u003eJoypur\u003c/em\u003ewas evident from the PCoA where these three locations have been plotted in three different ordinated of the PCoA graph.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFollowing the metagenomic analysis, to find out the unique as well as the common, taxonomical units/ microbes of three soil samples viz., \u003cem\u003eArsha\u003c/em\u003e, \u003cem\u003eJhalda-I\u003c/em\u003e and \u003cem\u003eJoypur\u003c/em\u003e, Fisher\u0026rsquo;s exact test, between 2 Samples was performed. The result is presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e6\u003c/span\u003e. For the unique taxonomical unit, approximately twice the number of unique microbes was found in the soil sample of \u003cem\u003eArsha\u003c/em\u003ein in comparison to both \u003cem\u003eJhalda-I\u003c/em\u003e and \u003cem\u003eJoypur\u003c/em\u003e whereas no variation was found between \u003cem\u003eJhalda-I\u003c/em\u003e and \u003cem\u003eJoypur\u003c/em\u003e. Common microbes were found maximum in between \u003cem\u003eArsha\u003c/em\u003e and \u003cem\u003eJoypur\u003c/em\u003e, closely followed by \u003cem\u003eArsha\u003c/em\u003e and \u003cem\u003eJhalda-I\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e7\u003c/span\u003e). This finding was also in agreement with the findings of the PCoA which usually plotted objects in the graph as points. The differences in sample distances in the graph being further apart, exhibited stronger heterogeneity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Alpha diversity indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003cp\u003erichness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChao1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eACE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSimpson\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInvSimpson\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFisher\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eArsha\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12750.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12637.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e654.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1305.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eJhalda-I\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12181.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12009.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e549.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1294.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eJoypur\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12257.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12147.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e498.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1281.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFisher's exact test to compare two samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSl.No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSignificant OTUs\u003c/p\u003e \u003cp\u003e(p-value\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.05)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAr\u003c/em\u003e vs. \u003cem\u003eJhI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAr\u003c/em\u003e vs. \u003cem\u003eJoy\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eJh\u003c/em\u003e vs. \u003cem\u003eJoy\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of Unique and common taxonomical units present in the soil\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSl.No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnique in Sample1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnique in Sample2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCommon\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAr\u003c/em\u003e vs. \u003cem\u003eJhI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAr\u003c/em\u003e vs. \u003cem\u003eJoy\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eJh1\u003c/em\u003e vs. \u003cem\u003eJoy\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Comparison of functional profiles for the microbial metagenomes\u003c/h2\u003e \u003cp\u003eThe number of hits was the maximum against COG, trailed by the KEGG. The functional analysis of COG, assigned 60,898 genes in total in \u003cem\u003eArsha\u003c/em\u003e, 63,403 genes in \u003cem\u003eJhalda-\u003c/em\u003eI and 73334 genes in \u003cem\u003eJoypur\u003c/em\u003e while KEGG functional analysis revealed that 9,385 genes in \u003cem\u003eArsha\u003c/em\u003e, 9,104 genes in \u003cem\u003eJhalda\u003c/em\u003eI and 10,633 genes in \u003cem\u003eJoypur\u003c/em\u003e KEGG classes. Relative loads of the top 50 KEGG Orthology (KO) groups that were commonly identified in all three samples and a relative heat map were generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The assigned KO was the highest in the category of the metabolism followed by genetic information processing, cellular process and organismal systems in all the three samples, and these were relatively higher in \u003cem\u003eJoypur\u003c/em\u003ethan others. It was observed that among the KO groups acetyl-CoA acetyltransferase, pyruvate ferredoxin oxidoreductase, methyl malonyl-CoA mutase, alcohol dehydrogenase, and acetyl-CoA synthetase, flavin prenyltransferase, glycine hydroxymethyl transferase, acetolactate synthase, 6-phosphofructokinase, Shydroxymethyl, branched-chain amino acid, aminotransferase, glutathione dehydrogenase was more abundant in all the three soil samples. Acetyl-CoAacetyltransferase was found as the most copious KO group in \u003cem\u003eArusha\u003c/em\u003e followed by \u003cem\u003eJoypur\u003c/em\u003eand \u003cem\u003eJhalda-I w\u003c/em\u003ehereas, glutamine synthetase was the most abundant KO group in \u003cem\u003eJoypur\u003c/em\u003e followed by \u003cem\u003eArsha\u003c/em\u003e and \u003cem\u003eJhalda-I\u003c/em\u003e. Functional COG analysis exhibited that the assigned COG function was the highest for the amino acid transport and metabolism category followed by energy production and conversion, translation, ribosomal structure and biogenesis, general function, lipid transport and metabolism, transcription category in all three samples, and these were relatively higher in \u003cem\u003eJoypur\u003c/em\u003e than others (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Contrast of the fluoride cycling genes of bacterial communities\u003c/h2\u003e \u003cp\u003eIn the present study, the functional profiling, emphasizing genes predicted to be linked with fluoride cycling, of soil samples from \u003cem\u003eArsha\u003c/em\u003e, \u003cem\u003eJhalda-I\u003c/em\u003e and \u003cem\u003eJoypur\u003c/em\u003e were further analyzed based on KO group assignments and a comparative heat map was generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Amongst the gene families, enolase, inorganic pyrophosphatase, divalent metal cation transporter MntH were the most abundant fluoride metabolism-related gene families in the bacterial communities found in the soil samples of all three locations. The abundance of enolase was higher in \u003cem\u003eJhalda-I\u003c/em\u003e and \u003cem\u003eJoypur\u003c/em\u003eas compared to \u003cem\u003eArsha\u003c/em\u003e. The richness of putative fluoride iontransporterCrcB was more in \u003cem\u003eArsha\u003c/em\u003e and \u003cem\u003eJoypur\u003c/em\u003e in comparison to \u003cem\u003eJhalda-I\u003c/em\u003e where the Putativefluoride in transporter CrcB1 was more in \u003cem\u003eJhalda-I\u003c/em\u003e than the \u003cem\u003eArsha\u003c/em\u003e and \u003cem\u003eJoypur\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIndian states are severely affected by fluoride contamination in both groundwater and soil. Fluoride toxicity is significantly higher in the western part of West Bengal, mainly in the districts of \u003cem\u003eBankura, Purulia\u003c/em\u003eand \u003cem\u003eBirbhum\u003c/em\u003e(Ghosh\u003cem\u003eet al., 2024\u003c/em\u003e). Unfortunately, in India, fluoride research is specifically restricted to groundwater. De-fluoridation of agricultural soil is crucial for improving soil health, promoting crop growth, and ensuring overall agricultural sustainability. The rapid advancements in Next Generation Sequencing (NGS) technologies have made it possible to gain a more comprehensive understanding of the richness and diversity within the rhizospheric niche by examining both culturable microbes (less than 1% of the total population) and non-culturable microbes (Hugenholtz\u003cem\u003eet al\u003c/em\u003e1998; Pramanik \u003cem\u003eet al.\u003c/em\u003e, 2020). Studies on microbial communities from several environments viz., low-temperature acid-mine drainage (Maria \u003cem\u003eet al.\u003c/em\u003e, 2015), marine water and sediments (Cabello \u003cem\u003eet al.\u003c/em\u003e, 2021 and Mason \u003cem\u003eet al.\u003c/em\u003e, 2014) including arsenic-contaminated soils (Zhang \u003cem\u003eet al.\u003c/em\u003e, 2021) have revealed novel insights on the microbiome structure, function along with their evolution pattern also led to the discovery of novel genes involved in bioremediation of toxic substances. In recent years, there has been increasing concern about the impact of fluoride contamination on agricultural products (De \u003cem\u003eet al.\u003c/em\u003e, 2021). The impact of the problem as well as a dearth of information prompted this area of research to gain detailed insight into the microbial communities that control the biogeochemical cycling of fluoride in agricultural soils.\u003c/p\u003e \u003cp\u003eThis study utilized high-throughput metagenomic sequencing as an effective approach to explore the microbiome structure and functional potential in fluoride-rich soil.The estimated TF content of the soil samples was 58.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76 mg/kg in \u003cem\u003eArsha\u003c/em\u003e, 147.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87 mg/kg in \u003cem\u003eJhalda\u003c/em\u003e-1, and 282.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9 mg/kg in \u003cem\u003eJoypur\u003c/em\u003ewhile the AF content was 1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 mg/kg in \u003cem\u003eArsha\u003c/em\u003e, 2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 mg/kg in \u003cem\u003eJhalda\u003c/em\u003e-1, and 2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 mg/kg in \u003cem\u003eJoypur\u003c/em\u003e. The diversity and structure of microbial communities were influenced by various environmental factors, including pH, as well as the concentrations of different elements, electron donors, and acceptors in the soil (Fierer\u003cem\u003eet al.\u003c/em\u003e,2006).Fluoride exhibits antibacterial properties and has been shown in vitro to inhibit bacterial cell growth by interfering with glycolysis and energy metabolism-related pathways (Johnston and Strobel, 2020). Research has shown that higher fluoride concentrations improve its antibacterial and antibiofilm efficacy against \u003cem\u003eS. aureus\u003c/em\u003e (Xue\u003cem\u003eet al\u003c/em\u003e, 2023 and Liu, J\u003cem\u003eet al\u003c/em\u003e, 2024).Fluoride also inhibits essential bacterial enzymes, including enolase, F-ATPase etc. thus disrupting bacterial growth and metabolism (Liao \u003cem\u003eet al\u003c/em\u003e, 2017). For more than 50 years, fluoride has been extensively utilized in dental care worldwide. Earlier studies suggest that exposure to 100 mg/L fluoride enhances the diversity and richness of the gut microbiome in mice (Liu, J \u003cem\u003eet al\u003c/em\u003e2019 and Fu, R\u003cem\u003eet al\u003c/em\u003e2020). The study revealed that the use of NaF-containing mouthwash in children could significantly reduce \u003cem\u003eS. mutans\u003c/em\u003e counts (Gedam and Katre, 2022). The evolution of bacterial resistance genes against potential selective pressures or evolutionary forces (such as drugs, therapeutics, or pathogens) is driven by genetic changes, via allelic variation generated through mutation, recombination between alleles, and occasionally through horizontal gene transfer, such as transformation, conjugation, and transduction.Microorganisms that exhibit fluoride tolerance are believed to possess either altered enzymes or antiporters or mutations in regulatory sequences that alter the level of expression of fluoride tolerance-related genes (Waugh\u003cem\u003eet al\u003c/em\u003e,, 2019, Thesai\u003cem\u003eet al\u003c/em\u003e. 2018, Li\u003cem\u003eet al\u003c/em\u003e. 2022). Studies on the effects of various fluoride dosages on the human gut microbiota in vitro showed that low fluoride dosages (1 and 2 mg/L) had a minimal impact on the structure and functional Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. In contrast, high fluoride dosages (10 and 15 mg/L) significantly modified the composition and functional KEGG pathways of the gut microbiota (Chen, et al, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The degree of fluoride tolerance in bacteria varies depending on the nature and position of mutations in the genome, as well as the number of mechanisms working together to alleviate the fluoride stress.The knockout and complementation of the crcB, eno, GI3, gpmA, orf5249, ppaC, and uspA genes, which code for fluoride transporters, fluoride-inhibited enzymes, and a universal stress protein, residing in an operon and activated by fluoride exposure within a fluoride riboswitch, revealed that these genes collaborate to confer high fluoride resistance to \u003cem\u003eE. cloacae\u003c/em\u003e FRM (Liu\u003cem\u003eet al\u003c/em\u003e, 2017). Depending on their level of fluoride tolerance, different groups of bacteria migrate from areas with higher fluoride concentrations to regions with lower fluoride concentrations, and vice versa.Already, it has been demonstrated that fluoride plays a significant role in shaping the microbiome structure in groundwater systems (Zhang \u003cem\u003eet al\u003c/em\u003e2019).The annotated assembled contigs revealed that the organisms belonged to the taxa Archaea, Bacteria, Fungi, and Viruses.The abundance of archaea was higher in \u003cem\u003eArsha\u003c/em\u003ewhile the abundance of bacteria and viruses was higher in \u003cem\u003eJoypur\u003c/em\u003e. The most abundant prokaryotic organisms in the soil samples belonged phylogenetically to the \u003cem\u003eActinobacteria\u003c/em\u003e phylum, accounting for 44.75% (in \u003cem\u003eArsha\u003c/em\u003e), 47.3% (in \u003cem\u003eJhalda\u003c/em\u003e1) and 40.84% (in \u003cem\u003eJoypur\u003c/em\u003e) followed by \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003ePlanctomycetota\u003c/em\u003eof the total OTUs in all libraries.These findings were also congruent with fluoride-contaminated groundwater samples studied by Zhang \u003cem\u003eet al\u003c/em\u003e 2019.The phylum \u003cem\u003eActinobacteria\u003c/em\u003e was a major taxonomic group within the domain Bacteria, encompassing five subclasses, six orders, and fourteen suborders (Barka\u003cem\u003eet al\u003c/em\u003e., 2016). Species of \u003cem\u003eActinobacteria\u003c/em\u003e are commonly found in soil and are well-known for their ability to produce antibiotics (Madigan \u003cem\u003eet al\u003c/em\u003e., 2009). Among the most abundant orders were \u003cem\u003eHyphomicrobiales, Streptomycetales, Burkholderiales, Micrococcales, Corynebacteriales, Myxococcales\u003c/em\u003e, and \u003cem\u003ePropionibacteriales\u003c/em\u003e.Additionally, the most abundant families in fluoride-rich soil included, \u003cem\u003eAnaeromyxobacteraceae, Comamonadaceae, Sphingomonadaceae, Methylobacteriaceae, Sphaerotilaceae, Intrasporangiaceae, Xanthobacteraceae, and Geobacteraceae\u003c/em\u003ewere most abundant family in the fluoride-rich soil. At the genus level, \u003cem\u003eStreptomyces\u003c/em\u003e, \u003cem\u003eBradyrhizobium\u003c/em\u003e, \u003cem\u003eNocardioides\u003c/em\u003e, \u003cem\u003eAnaeromyxobacter\u003c/em\u003e, \u003cem\u003eMycobacterium\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eMicromonospora\u003c/em\u003e, \u003cem\u003eMycolicibacterium\u003c/em\u003e, \u003cem\u003eAmycolatopsis\u003c/em\u003e, and\u003cem\u003eMicrobacterium\u003c/em\u003eare the most abundant across all three samples.The genera \u003cem\u003ePseudomonas, Bacillus, Acinetobacter\u003c/em\u003e, and \u003cem\u003eStreptococcus\u003c/em\u003e have been reported to be resistant to fluoride due to an ancient system involving fluoride-specific riboswitches and associated proteins such as CrcB (Baker et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Banerjee \u003cem\u003eet al\u003c/em\u003e.2016, Praveen \u003cem\u003eet al\u003c/em\u003e. 2013, Men \u003cem\u003eet al.\u003c/em\u003e 2016).\u003cb\u003eA\u003c/b\u003e higher fluoride concentration in the \u003cem\u003eJoypur\u003c/em\u003e soil exhibited a significant impact on the soil microbiota at the genus level. The abundance of \u003cem\u003eStreptomyces\u003c/em\u003e and \u003cem\u003eBradyrhizobium\u003c/em\u003e was reduced compared to the \u003cem\u003eArsha\u003c/em\u003e and \u003cem\u003eJhalda1\u003c/em\u003e soils.\u003cem\u003eBradyrhizobium\u003c/em\u003e has been reported to be positively associated with biological nitrogen fixation in legumes (Han\u003cem\u003eet al.\u003c/em\u003e 2024).\u003c/p\u003e \u003cp\u003eBased on the alpha diversity analysis and the rarefaction curve a moderate change in soil microbial community diversity was apparent across the fluoride concentration gradient. The highest number of observed taxa was found in the soil sample of \u003cem\u003eArsha\u003c/em\u003efollowed by\u003cem\u003eJoypur\u003c/em\u003eand \u003cem\u003eJhalda-1\u003c/em\u003e. Beta diversity and the Principle Coordinate analysis (PCoA) analysis exhibited that approximately twice the number of unique microbe was found in the soil sample of \u0026ldquo;\u003cem\u003eArsha\u003c/em\u003e\u0026rdquo; in comparison to both \u003cem\u003eJhalda-1\u003c/em\u003e and \u003cem\u003ewhere\u003c/em\u003e no variation was found between \u003cem\u003eJhalda-1\u003c/em\u003e and \u003cem\u003eJoypur\u003c/em\u003e, likely due to the lower fluoride content in the \u003cem\u003eArsha\u003c/em\u003e soil compared to the soils of \u003cem\u003eJhalda-1\u003c/em\u003e and \u003cem\u003eJoypur\u003c/em\u003e. Common microbes were found maximum in between \u003cem\u003eArsha\u003c/em\u003eand \u003cem\u003eJoypur\u003c/em\u003e, closely followed by \u003cem\u003eArsha and Jhalda-1.\u003c/em\u003e In all the samples, enolase 2 was detected as the least abundant followed by universal stress protein A. The richness of putative fluoride ion transporter CrcB was more in \u003cem\u003eArsha\u003c/em\u003e and \u003cem\u003eJoypur\u003c/em\u003e in comparison to \u003cem\u003eJhalda-I\u003c/em\u003e where the Putativefluoride ion transporter CrcB1 was more in Jhalda-I than the Arsha and Joypur.In bacteria generally, two fundamentally different types of fluoride exporter proteins have been identified so far where CLCFs are annotated as such, eriC, clcA, or clcB and Flucs which may be annotated as crcB or fluC) (McIlwain\u003cem\u003eet. al\u003c/em\u003e2021).\u003c/p\u003e \u003cp\u003ePrevious studies have shown that fluoride inhibited the activity of soil enzymes, including dehydrogenase, arylsulfatase and alkaline phosphatase (Wilke\u003cem\u003eet al\u003c/em\u003e 1987). Therefore, it can be inferred that fluoride is likely to harm the function of the microbial community. In our study, enolase, acetyl-CoA acetyltransferase, pyruvate ferredoxin oxidoreductase, methylmalonyl-CoA mutase, alcohol dehydrogenase, acetyl-CoA synthetase, flavinprenyl transferase, glycine hydroxyl methyltransferase, acetolactate synthase, 6-phosphofructokinase, S-hydroxymethyl, branched-chain amino acid aminotransferase, and glutathione dehydrogenase were found to be more abundant in all three soil samples, likely as a response to mitigate fluoride toxicity. The involvement of the fluoride-sensitive enolase enzyme in fluoride resistance in \u003cem\u003eStreptococcus mutans\u003c/em\u003e has already been demonstrated attributed to a point mutation in the gene encoding this enzyme (Mitsuhata \u003cem\u003eet. al 2014\u003c/em\u003e). Transcriptome analysis under fluoride stress conditions in \u003cem\u003eEnterobacter cloacae FRM\u003c/em\u003e revealed that the transcripts of the enolase gene eno (orf5255), the universal stress protein gene uspA (orf5256) and Divalent metal cation transporter increased 176-fold 120-fold and 15-fold, respectively (Liu \u003cem\u003eet. al 2017\u003c/em\u003e). Thus, our findings suggest that fluoride may be a critical factor influencing microbial diversity in soil.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eSoil fluoride toxicity is a major problem affecting both the natural flora and fauna. Our study revealed a strong link between fluoride concentration and the composition of bacterial communities in the soil of Purulia district, West Bengal, India. The extent of fluoride levels in soil significantly influences the structure of bacterial communities. The PCoA analysis highlighted heterogeneity in microbial population diversity across the testing locations. Functional profiling of soil samples revealed that the most abundant fluoride metabolism-related genes across all three locations were enolase, inorganic pyrophosphatase, and divalent metal cation transporter MntH.These findings offer valuable insights into the biogeochemical processes involving fluoride, emphasizing the need to consider fluoride concentrations when evaluating microbial responses to fluoride-rich soils. This study explores the potential of soil microbial communities to mitigate fluoride toxicity, providing a foundation for sustainable management practices in fluoride-contaminated agricultural systems.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKrishnendu Pramanik: Conceptualization, Investigation, Formal analysis, Data curation, Writing \u0026ndash; original draft. Arup Sen: Writing \u0026ndash; review \u0026amp; editing, Resources, Methodology. Subrata Dutta: Writing \u0026ndash; review \u0026amp; editing. Gouranga Sundar Mandal: Writing \u0026ndash; review \u0026amp; editing, Resources. Bappa Paramanik: \u0026nbsp;Analysis, Resources. Arpita Das: Writing \u0026ndash; review \u0026amp; editing, resources. \u0026nbsp; Nitin Chatterjee: Analysis and Resources. Ankit Kumar Ghorai: Resources. Md. Nasim Ali: Conceptualization, Project administration, Supervision, Writing-Editing and finalization of the paper.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors have no relevant financial or non-financial interests to disclose\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo ethical issue is involved in the study as it does not involve humans as experimental material.\u0026nbsp;\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAndrews, S., 2017. 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Rep. \u003cem\u003e9\u003c/em\u003e, 14488.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-microbiology-and-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wibi","sideBox":"Learn more about [World Journal of Microbiology and Biotechnology](https://www.springer.com/journal/11274)","snPcode":"11274","submissionUrl":"https://submission.nature.com/new-submission/11274/3","title":"World Journal of Microbiology and Biotechnology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"De-fluoridation, Fluoride tolerant bacteria, Metagenomics, Bioremediation, Fluoride tolerant genes","lastPublishedDoi":"10.21203/rs.3.rs-6243086/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6243086/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFluoride exposure, even at a low concentration, significantly impairs crop growth and productivity by inhibiting metabolic enzymes and disrupting photosynthesis. Addressing this challenge, microbial de-fluoridation emerges as a vital strategy to improve soil health, enhance crop growth, and ensure agricultural sustainability. This study analyzed topsoil samples (0\u0026ndash;0.2 m depth) from rice fields in three blocks of Purulia district, West Bengal\u0026mdash;\u003cem\u003eArsha, Jhalda-I\u003c/em\u003e, and \u003cem\u003eJoypur\u003c/em\u003e. Fluoride content in the samples ranged from 58.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76 mg/kg to 282.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9 mg/kg (total) and 1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 mg/kg to 2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 mg/kg (available). The Whole metagenomic analysis of the collected soil samples (BioSample Accession Number: PRJNA1154823) revealed diverse microbial communities comprising archaea, bacteria, fungi, and viruses, with \u003cem\u003eActinobacteria\u003c/em\u003e (phylum), \u003cem\u003eHyphomicrobiales\u003c/em\u003e (order), and \u003cem\u003eNocardioidaceae\u003c/em\u003e (family) being the dominant prokaryotes. \u003cem\u003eArsha\u003c/em\u003e soil with comparatively low fluoride contamination exhibited the highest microbial diversity (11,891 taxa), followed by \u003cem\u003eJoypur\u003c/em\u003e (11,528 taxa) and \u003cem\u003eJhalda-I\u003c/em\u003e (11,358 taxa), with Arsha showing nearly double the unique microbial taxa compared to the other locations. Clusters of Orthologous Groups of proteins functional analysis identified 60,898 genes in \u003cem\u003eArsha\u003c/em\u003e, 63,403 genes in \u003cem\u003eJhalda-I\u003c/em\u003e, and 73,334 genes in \u003cem\u003eJoypur\u003c/em\u003e, while Kyoto Encyclopedia of Genes and Genomes analysis revealed 9,385, 9,104, and 10,633 genes, respectively. Key genes associated with fluoride metabolism\u0026mdash;\u003cem\u003einorganic pyrophosphatase\u003c/em\u003e, \u003cem\u003edivalent metal cation transporter MntH\u003c/em\u003e, and \u003cem\u003eputative fluoride ion transporter CrcB\u003c/em\u003e\u0026mdash;were abundant across all sites, highlighting the influence of fluoride on microbial community structure. This study provides the first comprehensive report on soil microbial communities in fluoride-rich areas, highlighting the potential of native fluoride-tolerant microbes to mitigate fluoride toxicity in agricultural soils and offer sustainable, microbe-based solutions to fluoride contamination.\u003c/p\u003e","manuscriptTitle":"Microbial Populations under fluoride Stress: a metagenomic exploration from Indian soil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 17:18:37","doi":"10.21203/rs.3.rs-6243086/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-03T12:35:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-03T09:25:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-02T11:43:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194940922776958175439479967940597039600","date":"2025-03-25T10:21:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-24T05:41:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313291792872968644571200029891706415487","date":"2025-03-22T14:42:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120596322026027081135496088777930470163","date":"2025-03-21T14:28:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-20T14:31:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-20T09:08:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-19T01:19:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"World Journal of Microbiology and Biotechnology","date":"2025-03-17T09:16:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-microbiology-and-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wibi","sideBox":"Learn more about [World Journal of Microbiology and Biotechnology](https://www.springer.com/journal/11274)","snPcode":"11274","submissionUrl":"https://submission.nature.com/new-submission/11274/3","title":"World Journal of Microbiology and Biotechnology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9edd3d7c-50b9-4d08-a5cf-783ccc8d173a","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-30T16:04:05+00:00","versionOfRecord":{"articleIdentity":"rs-6243086","link":"https://doi.org/10.1007/s11274-025-04408-5","journal":{"identity":"world-journal-of-microbiology-and-biotechnology","isVorOnly":false,"title":"World Journal of Microbiology and Biotechnology"},"publishedOn":"2025-06-25 15:57:28","publishedOnDateReadable":"June 25th, 2025"},"versionCreatedAt":"2025-03-31 17:18:37","video":"","vorDoi":"10.1007/s11274-025-04408-5","vorDoiUrl":"https://doi.org/10.1007/s11274-025-04408-5","workflowStages":[]},"version":"v1","identity":"rs-6243086","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6243086","identity":"rs-6243086","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}