Table 2: Co-ordinates of Sampling Sites
Figure 4: Images of some date palm sites visited for Sampling.
Figure 5: Sample Collection and Storage.
(a) Fine roots chosen for DNA extraction (b) Roots stored in falcon tubes (c) Roots and soil samples prepared to be stored in -20°C.
3.2 Materials and Equipments
The biotechnological laboratories were well equipped with all the basic instruments that were needed for plant molecular and cell biology experiments. The materials such as the refrigerated centrifuge, heating block, vortex, DNA plant kit (Qiagen), Nanodrop spectrophotometer (Thermo Fisher Scientific), gel documentation, electrophoresis systems, freezers (-20°C and -80°C), PCR machines, and all the other small equipment's needed for the study were available in the laboratories.
3.3 Soil and Water Chemistry Analyses
The soil collected from the roots of the date palm was analyzed in order to get the various metadata to correlate with the composition of the bacterial community.
3.3.1 EC and pH
Initially, the soil was sieved through a 2 mm sieve, and then 3 grams of soil was dissolved in 7 ml of distilled water; the mixture was then placed on a shaker for 1 hour at 200 rpm. The soil mixture was then filtered through a 90 mm filter paper, and then the filtered water was analyzed with (i) EC meter (ii) pH meter to analyze the Electrical conductivity and pH of the soil (Figure: 6). The irrigation water was also analyzed with an EC meter and pH meter, the procedure was taken from (FAO, 2021b).
Figure 6: Preparation of Soil Samples for EC and pH testing
3.3.2 Soil Organic Matter
For analyzing the soil organic matter (SOM), the used method is mass loss on ignition (LOI). In the SOM estimation method, the SOM is heat decomposed with the use of a muffle furnace (Nelson & Sommers, 1996; Schulte & Hopkins, 1996). For the measurements of SOM, approximately 5 g of air-dry soil (Sullivan et al., 2019) was placed in a 30 mL porcelain crucible which was placed in a sealed desiccator, the moisture was removed by drying it at 105°C in a convection oven. Following this, a muffle furnace was preheated to 360°C, and the measured soil was heated at that temperature for four hours. After 4 hours, the crucible with soil was removed from the furnace and placed in a desiccator to cool before being weighed. The difference
between the mass of the crucible and soil before heating and after heating was calculated as the SOM of the soil.
Weight before mass loss (W1) = soil + crucible Weight after mass loss (W2) = ash + crucible
% SOM =
3.4 DNA Extraction and Gel Electrophoresis
The root tissue was ground in liquid nitrogen with sterile mortar and pestle as the ultimate objective is to access the nuclear material without degrading it (Sahu et al., 2012). Subsequently, the total DNA extraction of the root tissues was performed using one gram of the ground tissue and the DNeasy Plant Mini Kit (Qiagen, Germany). To avoid any cross-contamination of the samples, the process was performed with sterile equipment.
The protocol recommended by the manufacturer for DNA extraction was followed accordingly in order to successfully achieve DNA extraction from the roots. The quantity and quality of the extracted DNA were evaluated by using a Nanodrop spectrophotometer (Thermo Fisher Scientific). After extraction, the DNA
°C until further use.
The quality of some DNA extracted was also analyzed using agarose gel documentation (Figure: 7). A 1% of agarose gel was prepared by mixing 0.5 g of agarose and 50 ml of 1x TBE buffer, melted and cast to the corresponding tray. Around 4 µl of ethidium bromide was added to the crystal solution for the visualization of DNA strands. The agarose was left for solidifying for 15 20 minutes. 10 µl of the
extracted DNA was mixed with blue dye and ran through the gel at 100 V for 60 minutes, the protocol was a modified version of P. Y. Lee et al. (2012). The run was visualized by a (BioRad gel imager, USA).
Figure 7: Gel Image of Quality Analysis of Samples.
3.5 PCR process using 16s rRNA
The process of Illumina sequencing was outsourced by Integrated Microbiome Resource (IMR, Dalhousie University, Canada). Before sending the samples for sequencing, the samples were analyzed by performing PCRs with corresponding primers targeting the 16S rDNA (V3 and V4 regions) (Table: 3).
Table 3: Primer Target used for Illumina Sequencing.
The protocols for polymerase chain reaction (PCR) amplification and 16S rRNA sequencing were taken from Klindworth et al. (2013).
For each sample, PCR reactions were run in duplicate. The reaction was carried out in 50 µl volumes containing 0.3 mg/ml BSA (Bovine Serum Albumin), 250 µM dNTPs, 0.5 µM of each primer (341F = CCTACGGGNGGCWGCAG; 805R = GACTACHVGGGTATCTAATCC), 0.02 U of Phusion High-Fidelity DNA Polymerase (Finnzymes OY, Espoo, Finland) and 5x Phusion HF Buffer containing 1.5 mM MgCl2. The following PCR conditions were used: initial denaturation at 95°C for 5 min, followed by 25 cycles consisting of denaturation (95°C for 40 s), annealing 55°C (2 min) and extension (72°C for 1 min), and a final extension step at 72°C for 7 min. The annealing temperature for primer pair (i) was set at 55°C and for (ii) at 44°C. PCR products were purified with a QiaQuick PCR purification kit (QIAGEN, Hilden, Germany). The quantity and quality of the extracted DNA were analyzed by spectrophotometry using an ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE) and by agarose gel electrophoresis. The PCR products
°C for sequencing (Klindworth et al., 2013).
Later, the PCR amplified DNA fragment was checked on a quality 2% agarose gel. The sequences were sequenced using the standard Miseq illumina sequencing process (2x300 bp). All the files were stored in fastq.gz format.
3.6 Bioinformatic Analyses
After receiving total no. of 550566 reads of demultiplexed R1 and R2 data from IMR, these reads were kept separate for the analyses using dada2version 1.12 (Callahan et al., 2016) using R programming.
The first step was to perform quality filtering and trimming of the sequences, followed by dereplication, generating error models and denoising, further merging them in contigs, creating the table of amplicon sequence variants (ASVs) and then finally removing of chimeras. As recommended
et al., 2021), additional clustering of ASVs in operational taxonomic units (OTUs), was done using vsearch version 2.15.1 (Rognes et al., 2016) at 98% similarity, total 88 cluster/OTUs (877 reads) were removed due to chimera analyses. After Chimera analyses total 3047 non-chimeric cluster (OTUs) comprising of 357313 reads were detected.
OTUs that contained only one read (singletons) were removed after clustering.
To correct for potential over-splitting of OTUs due to remaining sequencing errors.
Total 3040 OTUs (355724 reads) in dataset after analyses.
Taxonomic assignment of the OTUs was carried out using vsearch against the Silva database v 138.1 (downloaded from https://zenodo.org/record/4587955), and a taxonomical table containing Kingdom, phylum, class, order, family, the genus was created, and also species-level resolution was added if the database was available.
Double-checking of these OTU was done using RDP databases (version 11.5).
The final OTU table was curated were, 3 OTUs (50 reads) were unidentified at kingdom level hence removed, 7 OTUs (34 reads) were identified as Archaea hence removed, 10 OTUs (169512 reads) were identified as Chloroplast (plant) sequence hence removed, 3 OTUs (32008 reads) were identified as mitochondrial (plant) sequence hence removed. 17 OTUs (179 reads) were identified as non-bacterial origin hence removed, leaving 3000 OTUs in the dataset. Then a total of 9 samples with low reads were removed. 148 OTUs (0 reads now) strictly associated with those samples
were also removed. The final OTUs table contains 2852 OTUs (148904 reads) in total 33 samples root samples from 6 saline and 7 non-saline farms with a minimum read number of 625 and maximum read number of 16252.
3.7 Statistical Analyses
Unless stated otherwise, statistical analyses were performed in R v4.0.3 (R Core Development Team, 2020). To increase variance homogeneity, OTUs data were arcsine-transformed. Water and soil chemistry variables including pH, EC, and OM were standardized using Z transformation and expressed on a 0 1 scale prior to analyses.
To examine effects of irrigation water source (non-saline vs. saline water) on soil chemistry (pH, EC, and OM) and also to check differences in irrigation water chemistry (pH and EC), we employed analyses of variance (ANOVA) test followed by Tukey's HSD posthoc test (package agricolae), and results were illustrated using box plots together with strip charts. Similar ANOVA analyses followed by Tukey's HSD posthoc test were also performed to investigate effects of irrigation water source on bacterial richness, Shannon diversity index, Pielou'evenness index, and relative abundance of bacterial phyla. To investigate the relationship between environmental variables (soil and water chemistry) and bacterial richness, Shannon diversity index, and Pielou'evenness index, linear regression analyses were performed using vegan package (Oksanen et al., 2020), and significance was determined at p <0.05 level.
To perform multivariate analyses, we calculated dissimilarities in OTUs matrices using Bray-Curtis distances. The importance of irrigation water source, water chemistry (pH and EC), and soil chemistry (pH, EC, and OM) variables in explaining community structural patterns for bacteria was assessed by permutational analysis of
variance (PERMANOVA) using Adonis function of vegan. We calculated pseudo-F statistics following 9999 times permutation of the OTUs matrices. A forward selection procedure was exercised to optimize the final model for PERMANOVA analyses.
First, we tested single factor models and thereafter, including significant factors in the final model in order of their R2 values. The bacterial OTUs matrices was also subjected to Nonmetric Multi-Dimensional Scaling (NMDS) ordination analyses in vegan with following settings: dimensions (k) = 2; maximum iterations = 1000; initial configurations = 100; minimum stress improvement in each iteration cycle = 10-5. To perform NMDS analyses, we used metaMDS function to find a stable solution with minimum stress values. The envfit function of vegan package was used to investigate the significance of the above-mentioned factor and vector variables with community structural patterns where R2 and p-values were calculated by regression individual variables with NMDS1 and NMDS2 axes.