Microbial community characterization in Red Sea-derived samples using a field-deployable DNA extraction system and nanopore sequencing

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Jiménez, Tahira Jamil, Georgios Miliotis, Júnia Schultz, and 23 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5928577/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background In this study, xTitan, a field-deployable, automated, and versatile nucleic acid extraction system was employed to characterize microbial communities in Red Sea-derived samples, including coral colonies, mangrove sediments, and seawater. The use of the xTitan in the field was intended to minimize sample transport bias, obtaining data that may be closer to “ground truth” for microbial diversity. The observed microbial communities from DNA extracted in the field using the xTitan system were compared to DNA extractions performed in a laboratory setting using both xTitan and a commercial Qiagen kit after approximately 24 h of samples transfer and storage. Results Microbial community analyses conducted on DNA extracted using the xTitan system and the Qiagen kit yielded similar alpha diversity metric values, with a trend toward higher diversity observed in most samples extracted with the xTitan. The microbial community structure in samples from a Pocillopora verrucosa colony, mangrove sediments, and seawater was affected by the DNA extraction system. In the P. verrucosa colony, an amplicon sequence variant (ASV) identified as Endozoicomonas acroporae was preferentially abundant when DNA was extracted in the field with the xTitan system rather than in the lab. In mangrove sediments, significant differences ( P -value < 0.05) in beta diversity and functional gene profiles were observed when comparing in-field to in-lab xTitan DNA extracts. In seawater, a pronounced decrease in the relative abundance of cyanobacterial populations was observed when DNA was extracted with both methods after samples were transported to the lab on ice. In addition, hundreds of ASVs from mangrove-associated samples were differentially abundant when DNA was extracted on-site with xTitan system compared to in-lab extractions. Balneolaceae was one of the most abundant taxa in mangrove sediments and several genera from this family were detected in all replicates across all DNA extraction systems. Conclusions The usability of different field-deployable instruments for microbial community characterization in marine-derived samples was demonstrated. Moreover, differences in beta diversity were observed when DNA was extracted in-field versus in-lab using the xTitan system, particularly for mangrove-associated samples. These results highlight the value of on-site nucleic acid extraction for enhancing the detection of microbial taxa that can be sensitive to cold storage. This study enabled the testing of the xTitan on Red Sea-derived samples, generating comprehensive information on the effects of DNA extraction systems and transportation of samples on coral and mangrove-associated microbiomes. Balneolaceae Corals Endozoicomonadaceae Halophiles Mangroves Marine microbiomes Samples storage Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Traditional cultivation-independent approaches to evaluating microbial diversity in host-associated and environmental microbiomes mainly involve six steps: 1) sampling, 2) storage and transportation of samples under cold conditions (e.g., at 4°C, -20°C, or in liquid nitrogen) or in a stabilizing reagent to protect biomolecules from degradation, 3) pre-processing (e.g., sieving, filtration, concentration, maceration, enzymatic and/or mechanical cell lysis), 4) nucleic acid extraction and purification, 5) DNA sequencing, and 6) computational analysis (e.g., raw data processing, bioinformatics pipeline execution, and statistical tests). Each of these steps can introduce bias, affecting the characterization of microbial community diversity [ 1 ] and ultimately influencing conclusions and biological interpretations. The timing of sample collection and its preservation are crucial for reproducibility in microbiome analyses [ 2 ], but the effects of sample storage and transportation, from field to laboratory, on the observed microbial communities from marine ecosystems remain poorly explored. Recently, it was demonstrated that storing marine sediment samples at refrigerated (4°C) and frozen temperatures (-20°C) for several weeks prior to DNA isolation, had a significant impact on their microbiomes [ 3 ], affecting mostly relative abundance values of Pseudomonadota populations. Microbial diversity, interactions, and metabolic activity can change while transporting samples from the natural ecosystem to the laboratory, and depending on the storage conditions and transportation time, the resulting data can give a distorted view of the original microbial community structure [ 4 ]. To overcome sample storage and transport issues, a handful of solutions are possible, including on-site sample processing strategies, nucleic acid extraction, library preparation, and DNA sequencing using portable devices, such as the MinION from Oxford Nanopore Technologies (ONT). On-site generation of marker genes (e.g., bacterial 16S rRNA and fungal ITS) and/or metagenome sequencing data may allow for more accurate characterization of the structural and functional profiles of microbial communities in real-time [ 5 , 6 ]. In addition, the use of nanopore DNA sequencing technologies allows for full-length 16S rRNA gene amplicon sequence data, which are useful in accurately assessing bacterial communities at genus or species level [ 7 ]. Unfortunately, utilizing these on-site microbial monitoring strategies can be challenging due to the size and number of instruments needed, as well as the complexity involved in various steps associated with environmental DNA extraction and library preparation. In 2023, Patin and Goodwin [ 8 ] have reviewed the primary issues of sampling, preservation, and extraction of environmental DNA for field microbial monitoring. Despite these issues, the on-site characterization of coral-associated microbial communities was successfully carried out during the Tara Pacific expedition using a ONT MinION device [ 9 ]. However, comprehensive and robust studies with appropriate controls comparing field-derived microbial community structures to those generated in the laboratory after transport are still needed. Assessment of on-site DNA extractions from environmental-derived samples is becoming a priority for marine microbiome studies as increasingly remote sites are explored and as different devices are developed for automated sampling and total DNA isolation [ 10 , 11 ]. Recently, a field-deployable and automated DNA extraction system (named µTitan) was developed and validated by a start-up supported by the National Aeronautics and Space Administration (NASA) to perform molecular microbiology on the International Space Station [ 12 ]. This innovative system has been used to extract DNA and characterize hot springs-inhabiting microbial communities at Yellowstone National Park [ 13 ]. A new version of this instrument, named xTitan, has been developed to include 96-well plates for high-throughput assays with a single DNA extraction run. The automated miniaturized xTitan hardware, compact in size (22 cm W x 22 cm D x 29 cm H) and lightweight (2.73 kg), is capable of extracting DNA or RNA due to its methodological flexibility and has the unique capability to use interchangeable extraction chemistries. The xTitan prototype (µTitan) underwent vibration testing during DNA extraction while operating in a moving vehicle at 85 miles per hour. This preliminary test [ 12 , 13 ] demonstrated the robustness of the device under dynamic conditions, but further trials in distinct real-world environments are necessary to validate its use across a broader range of applications. Thus, a major goal of this study was to evaluate the performance of the field-deployable xTitan system for the recovery of total DNA from marine-derived samples and to explore the microbial community structure of two distinct coral colonies, mangrove sediments, and seawater. This study was performed in the Kingdom of Saudi Arabia (KSA), which hosts unique marine biodiversity hotspots, such as coral reefs and mangroves, along the Red Sea shore. These two interconnected ecosystems provide significant economic, ecological, and social benefits [ 14 , 15 ], making them a global priority for conservation and restoration. The Red Sea is characterized by high salinity, oligotrophic waters, and temperatures ranging from 22°C to over 31°C [ 16 , 17 ]. These conditions have shaped resilient coral reefs and mangroves hosting distinctive microbial communities. Thus, the on-site extraction of total DNA from samples collected in these marine ecosystems can provide valuable insights into their associated microbiomes, enhancing our understanding of their ecology, functions, and biotechnological potential. To address the project aims, the same xTitan system was employed twice—first in the field and then in the laboratory—both equipped with reagents from the ZymoBIOMICS MagBead DNA/RNA kit. These systems were compared with each other and with a standard commercial lab-based DNA extraction method (i.e., DNeasy PowerSoil Pro Kit, Qiagen). Specifically, DNA was extracted on-site (onboard a research vessel for coral-associated samples and at the seashore for mangrove-associated samples) using the xTitan system, and the same raw samples were processed in laboratory settings (through xTitan and Qiagen kit) after approximately 24 h of sample storage and transportation on ice, for mangrove sediments and seawater, or in liquid nitrogen, for coral fragments. After total DNA extraction and quantification, bacterial full-length 16S rRNA gene amplicon and shotgun metagenome sequencing were performed on all samples using the ONT PromethION platform. Methods Study sites Two natural ecosystems (coral reefs and mangroves) located on the shores of the Red Sea (Thuwal, KSA) were selected. Specifically, corals and their surrounding seawater were sampled in the “Coral Probiotic Village” (CPV) (22°18.3071'' N, 38°57.8741''E) developed by the King Abdullah University of Science and Technology (KAUST) on 5th November 2023. The CPV is a multidisciplinary underwater laboratory consisting of a shallow (8–10 m) sheltered area of about 500 m 2 roughly 15 km off-shore from KAUST [ 18 ]. In addition, mangrove ( Avicennia marina ) sediments (pH 6.3, 32°C) and their surrounding seawater (pH 7.4, 30°C) were sampled (on November 6, 2023) on the periphery of KAUST at the King Abdullah monument (22° 20' 23.9" N, 39° 05' 17.5" E). Sampling corals, mangrove sediments, and seawater Two visually healthy coral colonies ( Pocillopora verrucosa and Xenia sp.) were sampled by scuba divers according to the established guidelines available in Voolstra et al . [ 19 ]. From each coral species, several fragments (~ 1–2 cm 3 ) were aseptically collected from different parts of coral colonies using sterile gloves and pliers, and then stored in Whirl-Pak® sterile bags (see photos in Fig. 1 ). Biological replicates (n = 3) from each coral colony were processed on the boat and set aside for analysis. On the boat, one set of coral fragments (n = 7, including a negative control) was immediately processed (see below on-site sample processing section) and the remaining two sets (n = 14) were placed in sterile 5 mL cryovials and snap-frozen in liquid nitrogen until processed in the laboratory (Fig. 1 and Suppl. Table 1). Approximately 2 L of seawater (n = 6) surrounding each coral colony was collected and placed in sterilized Whirl-Pak® plastic bags. For the mangrove sampling, sites were divided into four locations along a five-meter transect (biological quadruplicates). Seawater surrounding the mangrove area (~ 2 L) and sediments (~ 200 g) were collected and placed in sterilized Whirl-Pak® plastic bags (see photos in Fig. 1 ). Mangrove sediments were aseptically collected using an autoclaved spatula. Three sets of these samples were collected: one was processed on-site (see below) and the remaining two were stored on ice until processing in the laboratory (Fig. 1 ). Sample characteristics, including the DNA extraction method, are presented in Suppl. Table 1. In total, 60 samples (48 samples and 12 controls) were processed during this study. On-site sample processing - Coral biopulverization : For samples processed onboard the research vessel, each coral colony fragment was macerated using a sterile and DNA-free stainless-steel pestle biopulverizer (BioSpec, Cat # 59013N, https://www.biospec.com/products/biopulverizer ). Whenever needed, the biopulverizer was sterilized with 95% ethanol and decontaminated by flame sterilization. The biopulverizer assembly was wrapped in aluminum foil and placed in an ice bath prior to each sample processing. The coral fragments were aseptically placed into the biopulverizer and macerated by striking with a hammer until completely disassociated. With each strike, the piston was rotated a quarter turn to ensure homogeneous maceration (see photos in Suppl. Figure 1). - Homogenization and enzymatic digestion with MetaPolyzyme : Each pulverized coral fragment (~ 0.5 g) or mangrove sediment sample (~ 0.25 g) was aseptically transferred to a 2 mL tube containing 1 mL of sterile 1X phosphate-buffered saline (PBS, no Ca or Mg) and ¼ volume of DNA-free sterilized beater beads which included a combination of 2 mm steel balls, 1 mm garnet beads, and 0.1 mm of AlO 2 beads (Ciro co., Deerfield, Fl., USA). Samples were subjected to mechanical homogenization for 1 min using the handheld SoniBeast bead beater (SBJ319, Biospec Products) at a setting of 3 (see photos in Fig. 1 ). Samples were allowed to settle, and the supernatant was transferred to a new 2 mL tube. Cellular enzymatic digestion of microbial cell walls was accomplished using the certified DNA-free version of the microbiome enzyme cocktail MetaPolyzyme (MAC4-DF, Millipore Sigma, St. Louis, MO, USA). Each sample tube was treated with 25 µL of 10 U/µL of MetaPolyzyme and incubated (on-site or in the lab) for 1 h at 35°C [ 13 , 20 ]. An aliquot of 100 µL of the enzyme-treated sample was transferred to the xTitan system for DNA extraction (see below). - InnovaPrep concentrating pipette system (for seawater samples only) : In the field, 1.5 L of seawater samples were concentrated using an InnovaPrep CP Select™ system equipped with a 0.2 µm hollow fiber Concentrating Pipette Tip (CC08022-10, InnovaPrep, Drexel, MO, USA) and eluted using FluidPrep™ Elution Buffer PBS (PBS HC0800, InnovaPrep) following manufacturer's procedures (see photo in Fig. 1 and Suppl. Figure 1). Specifically, 1.5 L seawater samples were supplemented with 15 mL of sterile 10% Tween 20 and then processed on the CP Select prior to performing an automated wash step, using 50 mL of 25 mM Tris/1 mM EDTA pH 9, and automated elution into nominal 1 mL concentrated volume. The resulting concentrated cell biomass was subject to the same bead-beating and MetaPolyzyme treatment outlined above. Each sample’s DNA was then extracted using the xTitan device (see below). - DNA extraction using the xTitan system : The xTitan system was operated offshore, onboard the research vessel in the CPV for the coral reef samples, and in a parking area near the shore for the mangrove samples (see photos in Suppl. Figure 1). It is important to note that the xTitan is a reagent agnostic instrument capable of using a variety of manufacturer reagents, including Promega, Zymo, and Biomerieux, as well as other magnetic bead-based nucleic acid extraction kits [ 12 , 13 ]. In this study, the system was equipped with commercially available reagents from the ZymoBIOMICS MagBead DNA/RNA kit (Zymo Research, Irvine, CA, USA) in prefilled 96-deep well plates (A48305, ThermoFisher Scientific). The xTitan extraction protocol was as follows: 100 µL of pre-processed samples was added to the first row (Row A) of the 96-well plate, which was pre-filled with 300 µL of lysis buffer. The device then mixed the sample and lysis buffer mixture for 10 min by moving the extraction tip-comb up and down. The tip-comb moves up and down 195 times per min. Next, 400 µL of 100% ethanol and 30 µL of magnetic beads were added to Row A containing the lysed sample to facilitate the binding of nucleic acids to the magnetic beads. The DNA bound to the magnetic beads was captured by the magnets inside the tip-comb and transferred to successive rows for the washing steps and the final DNA elution step. The washing and elution buffers were prefilled into the 96-well plate as follows: Row D contained 500 µL of ZymoBIOMICS MagWash 1 buffer, Row E contained 500 µL of ZymoBIOMICS MagWash 2 buffer, Rows F and G contained 500 µL of 95% ethanol, and Row H contained 100 µL of elution buffer (DNase/RNase-free DI water). For each of the two MagWash steps, the magnetic beads were released into MagWash buffers and mixed by the tip-comb for 2 min. The beads were then washed for 2 min in 95% ethanol twice and air dried for 10 min. Finally, purified DNA was recovered by eluting in 100uL of DNase/RNase-free DI water for 5 min at 65°C to release the nucleic acids. In-laboratory sample processing To evaluate the potential impact of storage and transportation on microbial recovery, all on-site sample processing steps were replicated in the laboratory within approximately 24 hours of sample transportation, followed by using the same xTitan system and Qiagen method (see below). - Millipore seawater filtration in the laboratory : Seawater samples were transported on ice and stored at 4°C for ~ 24 h until processing. In the laboratory, 1.5 L of each seawater sample was filtered through a 0.2 µm Isopore™ PC membrane (Cat # GTTP04700, Merck Millipore, Burlington, MA, USA) with a peristaltic vacuum pump. After filtration, filters were snap-frozen in liquid nitrogen and stored at − 80°C during ~ 12 h until total DNA extraction using the DNeasy PowerSoil Pro Kit (see below). - DNA extractions using a standard commercial kit in the laboratory : A set of coral fragment samples was transported from the CPV to the KAUST laboratories in liquid nitrogen. In the laboratory, coral samples were pulverized following the methods described above, and ~ 0.5 g was used for DNA extraction. For mangrove sediments, rocks, and plant biomass material were removed, and ~ 0.25 g was used for DNA extraction. For seawater samples, Millipore filter membranes (see above) were cut into small pieces (approximately 1 × 1 cm) using a sterilized scalpel and transferred into a sterile single-use Petri dish with the cell-coated surface facing up. Membrane fragments were then transferred into a sterilized 2 mL tube to start the DNA extraction protocol. DNA extraction was performed using the DNeasy PowerSoil Pro Kit (Cat #47016, Qiagen), following the manufacturer’s instructions. Controls Negative controls (blanks) were used to identify background DNA, including extracellular DNA and microbial contaminants associated with kits (“kitomes”) and secondary contamination during sample processing steps. In this study, negative controls (n = 12) included 2 ml of the same sterilized 1X PBS employed for rinsing either the biopulverizer used in processing coral (n = 3) or the spatula used for collecting mangrove sediments (n = 3), along with 1.5 L of sterile 3.5% saline solution filtered using either the InnovaPrep (n = 4) or the Millipore filtration system (n = 2). Positive controls (n = 12) included a microbial reference standard from the American Type Culture Collection (ATCC MSA-2003™, Manassas, VA USA) composed of ten well-characterized intact whole cells spiked into marine samples (i.e., 3 corals, 3 mangrove sediments, and 6 seawater samples). The negative and positive controls are detailed in Suppl. Table 2. In addition, an ATCC genomic DNA reference standard representing a ten bacterial strain even mixture (ATCC MSA-1000™) was subjected to sequencing in triplicate to determine the efficacy and accuracy of the ONT platform. All controls were processed using methods identical to those used for the samples and subjected to full-length 16S rRNA gene amplicon and shotgun metagenome sequencing (see below). Oxford Nanopore DNA sequencing Extracted DNA was quantified using the dsDNA HS Assay Kit (Q33231, Life Technologies, USA) on a Qubit 4.0 device (quantification range: 0.1 to 120 ng). Appropriate aliquots of purified DNA samples were prepared for 16S rRNA gene amplicon and shotgun metagenomic using ONT PromethION platform following the manufacturer’s protocols. Bacterial 16S rRNA gene amplicons were generated with standard full-length primers (27F: 5’-AGR GTT YGA TYM TGG CTC AG-3’ and 1492R: 5’-RGY TAC CTT GTT ACG ACT T-3’) over 25 cycles using KAPA HiFi Hotstart ReadyMix (KK2601, Roche) followed by library preparation using the ligation-based native barcoding kit (SQK-NBD114.96; ONT, UK). Shotgun metagenomics libraries were prepared using the same library kit without prior bacterial 16S rRNA gene amplification. Libraries were pooled according to the manufacturer's recommendations and loaded (30–50 fmol for 16S rRNA gene and 20 fmol for metagenome sequencing) onto ONT PromethION flowcells (FLO-PRO114M, R10.4.1) for DNA sequencing. Analysis of full-length 16S rRNA gene amplicon sequencing data Taxonomic classification of bacterial 16S rRNA gene sequences was performed using the EPI2ME Labs wf-metagenomics pipeline (Nextflow) v2.0.8, utilizing the NCBI 16S rRNA and 18S rRNA gene databases for reference sequences ( https://github.com/epi2me-labs/wf-metagenomics ). Prior to classification, raw sequence reads were filtered based on length, with a minimum of 1,200 bp and a maximum 1,800 bp, as well as a minimum per read Phred quality score of 15. Amplicon sequence variants (ASVs) were generated from the filtered sequences. Low-abundance ASVs, defined as those with 10 or fewer reads, were excluded from further analysis. Potential contaminant ASVs were identified and removed using the decontam R package (v1.12) [ 21 ] by assigning a probability to each ASV of it being a contaminant based on its prevalence in negative control samples versus true samples. Those ASVs with a contaminant probability greater than 0.5 were classified as contaminants and subsequently removed from the dataset. Analysis of nanopore metagenomic sequencing data Raw sequence reads were trimmed to remove adapter and barcode sequences using Porechop (v0.2.4). Quality filtering was subsequently performed with fastp (v0.23.2) [ 22 ], retaining reads with a Phred quality score > 12. Filtered reads were then analyzed using SqueezeMeta (v1.6.3) [ 23 ] through an assembly-free (i.e., gene-centric) approach implemented by the Perl script "sqm_longreads.pl," which performs taxonomic classifications of open reading frames (ORFs) by executing DIAMOND searches, assigning taxonomy using the lowest common ancestor (LCA) method, and applies KEGG functional annotations. For downstream data processing, tables were generated using the Python script "qmreads2tables.py." Statistical analysis Alpha diversity of the microbial communities was estimated using the Shannon diversity index and observed species values to assess species evenness and richness, respectively, within each sample. The Shapiro–Wilk test was used to assess normality, and the Kruskal–Wallis test was used to evaluate statistical significance, with P -values < 0.01 accepted as indicating significance. Beta diversity was calculated using the Bray–Curtis dissimilarity metric to evaluate differences in community composition between samples and non-metric multidimensional scaling (NMDS) to visualize differences. Group differences in microbial community composition were assessed using Permutational Multivariate Analysis of Variance (PERMANOVA) tests based on the Bray–Curtis dissimilarity matrix with 999 permutations and a significance threshold set at P- values < 0.05. Differentially abundant ASVs between groups were identified based on the negative binomial distribution using the DESeq2 package [ 24 ]. All statistical analyses and data visualizations were performed in R (v4.4.1) using these packages: tidyverse (v1.3.0), for data manipulation and visualization; phyloseq (v1.48.0), for microbiome data analysis; decontam (v1.24.0), for contaminant identification and removal; vegan (v2.6.8) for ecological diversity analyses; and metagMisc (v0.5.0) for metagenomic data processing. Results Microbial diversity and community structure based on 16S rRNA gene amplicon data After total DNA extraction and full-length 16S rRNA gene amplicon sequencing, on average 347,884 ± 281,671 raw sequences were generated per sample with an average read length of 1,236 bases (Suppl. Table 1), resulting in a total of 5,940 ASVs after quality control and “kitomes” taxa removal (see below). Regarding alpha diversity metrics, mangrove sediments and seawater samples showed higher numbers of observed species and Shannon index values than coral-derived samples (Fig. 2 AB). No significant differences in Shannon values were observed among the three DNA extraction systems (i.e., in-field xTitan, in-lab xTitan, and in-lab Qiagen); however, observed species-level richness in mangrove sediments and seawater extracts from the xTitan system (in-field and in-lab) trended higher than those from Qiagen extracts (Fig. 2 A). In beta diversity (i.e., Bray–Curtis dissimilarity NMDS plots), significant differences (PERMANOVA; P -value < 0.05) were observed between the three DNA extraction systems in all sample types except the soft coral colony (i.e., Xenia sp., coral 2) and seawater surrounding coral samples (Fig. 2 C). Additionally, significant differences ( P -value < 0.05) were found between the in-field and in-lab xTitan extraction systems in samples from the mangrove ecosystem (including sediments and seawater). The lack of significant differences between seawater samples near corals may be attributed to the lower number of respective biological replicates (n = 2), influencing the statistical analyses. Based on this, these data were excluded from further comparisons. However, slight differences in the relative abundances of taxa in the near-coral seawater bacterial communities were observed, especially when comparing the Qiagen and xTitan extraction systems, with Oceanospirillaceae conspicuously reduced in Qiagen extracts (Fig. 3 A). The microbial compositions of the hard coral ( P. verrucosa , coral 1), mangrove sediments, and seawater surrounding mangroves were highly influenced by the DNA extraction method (i.e., Qiagen versus xTitan), followed by the place of DNA isolation (i.e., in-field xTitan versus in-lab xTitan). Such influences were evidenced by pronounced changes in the relative abundances of the most abundant bacterial families (for coral 1 and seawater surrounding mangroves) or phyla (in the case of mangrove sediments) (Fig. 3 B, Fig. 4 B, and Suppl. Figure 2). These findings were confirmed by the DESeq2 analysis, in which 381 and 322 ASVs were differentially abundant between in-lab xTitan- and Qiagen-extracted mangrove sediment and seawater samples, respectively. Conversely, 122 and 233 ASVs were differentially abundant between in-field and in-lab xTitan-extracted mangrove sediment and seawater samples, respectively (Suppl. Table 4). Taxonomic composition of coral-associated samples based on 16S rRNA gene amplicon data Based on the DNA extracted in-lab (Qiagen and xTitan), the bacterial community of the coral 1 colony ( P. verrucosa ) was dominated by members of the family Fulvivirgaceae (i.e., Fulvivirga imtechensis ), which represented approximately 80% of relative abundance in Qiagen extracts (Fig. 3 C). However, the relative abundance of Fulvivirgaceae was much lower when DNA was extracted in-boat using the xTitan system, with a concomitant significant increase in the relative abundance of Endozoicomonas acroporae (DEseq2 P < 0.001; Log 2 [fold change] = 12.8) (Fig. 3 C). Although bacterial communities in coral 1 samples were affected by the nucleic acid extraction protocol, the observed bacterial community structure of Xenia sp. (coral 2) was not significantly affected by the DNA extraction systems (Fig. 2 C). Endozoicomonadaceae , Spiroplasmataceae , Oxalobacteraceae , and Francisellaceae were the primary family level taxa observed in the samples obtained from a single colony of Xenia sp. (Fig. 3 B). Within them, three bacterial species showed high relative abundance values across most technical replicates: Endozoicomonas coralli , Endozoicomonas gorgoniicola , and Spiroplasma lampyridicola (a small symbiont of the Mollicutes group), accounting for a considerable proportion (~ 70%) of the bacterial community in these samples (Fig. 3 C). Allofrancisella frigidaquae was also abundant (20–25%) in two of three replicates extracted with xTitan in the field (Fig. 3 C). In both coral colonies, some samples (e.g., C1, C27, C18, and C30) revealed high proportion of sequences belonging to families Prochlorococcaceae, Flavobacteriaceae, Pelagibacteraceae , and Bacteroidaceae (Fig. 3 B). These families were also abundant in near-coral seawater samples, indicating that coral colonies could include taxa that are not obligately associated with the hosts (Fig. 3 A). Taxonomic composition of mangrove-associated samples based on 16S rRNA gene amplicon data Microbiomes in mangrove-associated samples were characterized with sediments maintaining more diverse bacterial communities compared to seawater (Fig. 2 A). Moreover, the results indicated that the relative abundance of bacterial sequences from the phylum Pseudomonadota was highest when DNA was extracted using the xTitan system, whereas bacterial sequences from the phylum Bacteroidota were more abundant when DNA was extracted using the Qiagen kit (Suppl. Figure 2). In addition, the relative abundance of the family Prochlorococcaceae was significantly lower (DEseq2 P < 0.001; Log 2 [fold change] = 1.8) in seawater when DNA was extracted in the laboratory using both systems (Fig. 4 B), suggesting that storage and transportation on ice may affect cyanobacterial populations. As mentioned, 122 ASVs were differentially abundant between on-site and in-lab xTitan-extracted DNA from mangrove sediments. This included bacterial ASVs belonging to the genera Enterovibrio , Vibrio , Alkalibacillus , Enterococcus , Photobacterium , and Skermanella , among others, that were highly enriched when DNA was extracted in the field (DEseq2 P 8) (Suppl. Table 4). The relative abundance of the family Balneolaceae (primarily composed of moderately halophilic species) was higher in mangrove sediments (5–10%) and showed similar values across the three DNA extraction strategies (Fig. 4 A). Analyzing the four most abundant bacterial families, it was noted that some families (e.g., Balneolaceae and Pirellulaceae ) were preferentially found in mangrove sediments compared to seawater, while others (e.g., Flavobacteriaceae and Rhodobacteraceae ) were predominant in seawater (Fig. 4 C). Identification and removal of “kitomes” from 16S rRNA gene amplicon data A challenge in environmental microbiome analyses (especially in samples with low microbial biomass) is the presence of background DNA contamination inherent to reagents from DNA extraction kits (i.e., “kitomes”) and/or PCR reagents. Identifying these contaminants necessitates the use of appropriate negative controls. These contaminants must be detected and removed before any microbial diversity analyses. In our study, DNA from sterilized 2 mL of 1X PBS solution and ~ 1.5 L of sterilized and filtered saline solution were processed as negative controls (n = 12) through extraction, library preparation, and DNA sequencing. The amounts of DNA were below the detection limit (0.1 ng) in these negative controls using Qubit fluorometric measurement (Suppl. Table 2), except for one sample (M24; 1.5 L saline solution; 0.1 ng/µL), which was sterilized and filtered through a Millipore filter before undergoing extraction using the Qiagen method. PCR amplification of the 16S rRNA gene yielded positive results for all negative controls, indicating the presence of background contaminating DNA. In this study, 104 ASVs (1.72% of the total ASVs) were identified as contaminants using the prevalence method in the decontam pipeline [ 21 ] (Suppl. Table 3) and removed from the ASVs table prior to microbial diversity analyses (see above). The most frequent and abundant contaminant ASVs were annotated as belonging to the genera Herbaspirillum , Janthinobacterium, Pseudomonas , Sphingomonas , and Citrobacter (Suppl. Figure 3A; Suppl. Table 3). To illustrate the biases generated by the presence of DNA contaminants in coral-associated bacterial communities, relative abundance taxa plots (at the family level) with (Fig. 3 B) and without (Suppl Fig. 3B) removal of ASV contaminants were generated. These plots revealed that sequences belonging to the families Oxalobacteraceae and Pseudomonadaceae were found in high proportions in samples without the removal of ASV contaminants, especially when DNA was extracted using the xTitan system, indicating that one source of DNA contaminants is the ZymoBIOMICS MagBead DNA/RNA kit reagents. Nanopore metagenome sequencing output After ONT sequencing, total sequence yields (i.e., the number of metagenomic reads) obtained from libraries generated with Qiagen-extracted DNA were significantly higher than those generated from field and laboratory xTitan extracts, except for when looking at the coral 2 ( Xenia sp.) samples (Suppl. Table 1; Suppl. Figure 4). This appears to be directly correlated with the total DNA yield from extractions, which was higher for Qiagen, and the equal volume pooling of libraries before loading on the ONT sequencing flow cell. As mentioned, one exception to this was the in-field xTitan-extracted samples from coral 2 (Suppl. Table 1; Suppl. Figure 4), which generated approximately 2.5 to 15 million raw reads with read lengths of 1,327 ± 646, 1,059 ± 17, and 1,552 ± 1,016 bases for Qiagen, in-field xTitan, and in-lab xTitan, respectively (Suppl. Table 1). Like the coral-associated samples, in mangrove-associated samples, a larger number of reads were generated from libraries made from Qiagen extracted DNA (an average of ~ 5 million reads per sample) than from xTitan-extracted DNA (an average of < 1 million reads per sample; Suppl. Table 1; Suppl. Figure 4). Additionally, average read lengths were higher for Qiagen libraries than for xTitan libraries from mangrove sediments, with average reads lengths of 2,362 ± 677 bases for Qiagen, and 1,033 ± 144 bases for in-lab xTitan ( P -value 0.043). The lowest average read lengths were obtained from in-field xTitan-extracted samples, where read lengths of 839 ± 331 and 927 ± 191 bases were obtained from sediments and seawater, respectively (Suppl. Table 1). Characterization of microbial communities based on nanopore metagenome data Taxonomic and functional affiliations of metagenomic sequences were based on an assembly-free gene-centric approach using the software package SqueezeMeta [ 23 ]. A high proportion (~ 80% for corals and ~ 45% for mangrove sediment and seawater) of metagenomic-derived genes were unclassified at the kingdom level, likely due to the limitations of the reference databases used and the large unknown genetic diversity of the sampled environments. Unclassified sequences were removed from downstream metagenomic analyses. In coral-derived metagenomes, the majority (> 90%) of the remaining sequences were associated with eukaryotic organisms, most known from coral hosts (Suppl. Figure 4A). Taxonomy-based clustering of metagenome-derived genes within Bacteria , Archaea , and Viruses indicated that microbial communities derived from both coral colonies were highly similar across the DNA extraction systems (Fig. 5 A). Taxonomic affiliation of these genes showed that bacteria from the phyla Pseudomonadota , Bacteroidota , and unclassified Bacteria were predominant in both coral colonies (Fig. 5 B). The relative abundance of phylum Bacteroidota was higher in coral 1 ( P. verrucosa ) than in coral 2 ( Xenia sp.), especially in the Qiagen extracts. Archaea from the phylum Euryarchaeota were found in both coral colonies (Fig. 5 B). Despite the limited characterization of virus-derived genes in our metagenome data from corals, viral sequences were relatively abundant (~ 10%) in Xenia sp. but nearly absent in P. verrucosa samples. Specifically, unclassified and Preplasmiviricota -like viruses (eukaryotic viruses) were annotated in Xenia -derived metagenomes (Fig. 5 B), regardless of DNA extraction method. A targeted analysis of the most abundant bacterial genera identified in the full-length 16S rRNA gene amplicon data (Fig. 3 C) revealed that metagenome-derived genes from Fulvivirga and Endozoicomonas were also highly abundant in coral 1 and coral 2, respectively (Fig. 5CD). In the metagenomic analysis of mangrove-associated samples, a greater number of total raw reads were produced by the Qiagen extracts than the xTitan extractions, while the proportions of Bacteria and Archaea (prokaryotic) genes were similar following both DNA extraction methods (Suppl. Figure 4B). The taxonomic and functional profiles of the mangrove-associated microbial communities differed depending on the DNA extraction system employed (PERMANOVA; P -value < 0.05), with pronounced differences between the Qiagen and xTitan (Fig. 6AB). In mangrove sediments, significant differences (PERMANOVA, P -value = 0.033) in functional profiles were observed between in-field and in-lab xTitan-extracted samples. Based on the taxonomic classification of genes detected in the mangrove-associated metagenomes, bacteria from the phyla Pseudomonadota and Bacteroidota were the most abundant. However, approximately 30% of sequenced data from the sediments was cataloged as unclassified Bacteria- derived genes (Fig. 6 C). About 20% of metagenome-derived genes from mangrove-associated seawater were assigned to the phylum Cyanobacteria , and the average relative abundance of Cyanobacteria was visually higher on in-field xTitan extracts than in Qiagen extracts. A slight reduction in the relative abundance of Cyanobacteria in in-lab xTitan extracts compared to in-field xTitan extracts was also observed (Fig. 6 B). A small proportion of genes from unclassified Archaea and Uriviricota taxa (bacterial and archaeal viruses with head-tail morphology) were found in seawater surrounding mangroves (Fig. 6 C). Since 16S rRNA gene amplicon sequencing results showed a large number of sequences belonging to Balneolaceae in mangrove sediments (Fig. 4 A), a specific analysis of genes assigned to the Balneolaceae family was performed (Fig. 6 D). These targeted analyses indicate that a considerable fraction of genes derived from mangrove sediments were affiliated with unclassified Balneolaceae (e.g., 33,639 and 1,041 sequences, on average, from the Qiagen and in-lab xTitan extracts, respectively), Aliifodinibius , and Gracilimonas genera. In seawater, a high proportion of genes belonged to Balneola (e.g., 700 and 514 sequences, on average, from the Qiagen and in-lab xTitan extracts, respectively) and unclassified Balneolaceae taxa (Fig. 5 D). Discussion In this study, although the Qiagen method extracted a significantly higher quantity of total DNA than the xTitan system across coral, mangrove sediment, and seawater samples (Suppl. Table 1), the impact on microbial community profiling outcomes or the predicted proportion of prokaryotic DNA extracted was not consistently different. Both methods (i.e., Qiagen and xTitan coupled with Zymo chemistry) effectively captured similar microbial diversity and taxonomic composition, demonstrating that DNA yield alone is not a determining factor for accurate community characterization. Based on our metagenomic analysis performed in mangrove sediments and seawater, the percentage of prokaryote-assigned genes was similar among the three DNA extraction systems (Suppl. Figure 4B). This suggests that the xTitan system can extract a similar proportion of prokaryotic DNA when compared with a “gold standard” commercial kit, which is consistent with the findings of a previous study that employed the use of the µTitan equipped with the BioMérieux NucliSENS® chemistry [ 12 , 13 ]. Moreover, it is important to highlight that the use of a field-deployable biopulverizer, hand-held bead beater, and the portable filtration Innovaprep CP Select™ system, were important for successful in-field DNA extractions using the xTitan device (photos in Fig. 1 and Suppl. Figure 1). The quantity, quality, and integrity of on-site-extracted DNA are key factors for the success of subsequent processes carried out in the field or in the laboratory (e.g., ONT metagenome sequencing). In our study, the lowest average read length (a proxy for DNA integrity) in the nanopore metagenomic data was observed in DNA extracted using the xTitan in the field, though this difference was not statistically significant (Suppl. Table 1). Thus, while there were differences, this suggests that on-site extraction using the xTitan system can effectively maintain DNA integrity and may vary with alternative chemistries. Although no significant differences were found in alpha diversity metrics among the three DNA extraction systems (Fig. 2AB), the xTitan tended to produce higher numbers of observed species, suggesting that the combination of MetaPolyzyme pretreatment, bead-beating, and the use of the automated xTitan device was capable of extracting DNA from a greater fraction of the prokaryotic community. Moreover, the beta diversity results suggested that biases introduced by different DNA extraction methods and sample storage and transportation are sample type-specific, with some environmental microbial communities (e.g., coral-associated microbiome) being less susceptible. Therefore, the use of liquid nitrogen to preserve the coral fragments was a crucial factor in maintaining microbial community structure between samples extracted in the field and in the lab. The effects of sample storage and transportation have been well-documented in fecal-derived microbiomes [ 25 , 26 ]. However, there is limited information about their impacts on coral- and mangrove-associated prokaryotic communities. Recently, studies tackling this topic have been carried out in marine sediment, algae-associated, and seawater samples [ 3 , 27 , 28 ]. Overall, our results indicated that microbial community structure characterization was primarily affected by the DNA extraction method (Qiagen versus xTitan), but significant differences were found between the in-field and in-lab xTitan extractions, especially when applied to mangrove sediments and their surrounding seawater (Fig. 2 C and Fig. 6AB). For instance, the relative abundance of Prochlorococcaceae was significantly lower when DNA was extracted in the lab using the xTitan system rather than in the field. This could be due to the continued growth of heterotrophic bacteria during storage and transportation, leading to a reduced relative abundance of Prochlorococcaceae . Alternatively, rapid preferential lysis of Prochlorococcaceae during storage and transport, followed by DNA degradation, could similarly lead to altered observed bacterial community structures. These processes can lead to an underestimation of cyanobacterial populations when characterizing such marine ecosystems. These findings indicate that transportation on ice, even during short-term storage, can alter these microbial communities, potentially introducing biases in bacterial diversity and community structure analyses and affecting biological interpretation. In this study, the use of different systems for extracting total DNA provided us with comprehensive information to characterize microbial communities from two coral species in the Red Sea. To highlight this, two bacterial species were highly abundant in the P. verrucosa colony (i.e., F. imtechensis and E. acroporae ; Fig. 3BC). It is well known that Endozoicomonadaceae is a dominant family in the microbiome of Pocillopora species, regardless of their health status [ 29 , 30 ]. This coral can retain a large fraction of its microbial symbionts after being exposed to environmental stress (e.g., bleaching), suggesting that its microbiome is predictable and rather inflexible [ 29 , 31 ]. Our findings allowed us to formulate these open questions: i) Why was Fulvivirgaceae found in high relative abundance compared to Endozoicomonadaceae when using two (i.e., Qiagen and xTitan in-lab) of the three total DNA extraction systems with P. verrucosa colony fragments? ii) Are these differences caused mainly by the preferential extraction of DNA and/or the restructuring of prokaryotic communities during short-term storage and transportation in liquid nitrogen? Moreover, as the same coral fragments were subjected to distinct DNA extraction systems, differences caused by spatial heterogeneity in the coral colony can be discarded. Regarding the symbionts of Xenia sp., a high diversity of Endozoicomonas ASVs (Fig. 3 C), similar to that seen in deep-sea corals [ 32 ], was found. Recently, a high proportion of Endozoicomonas species has been found in samples obtained from Xenia sp. colonies around 170 km to the north of our sampling point (in Al Rayyis White Head, KSA) [ 33 ]. In our study, a small symbiont of the Mollicutes group ( Spiroplasma lampyridicola ) was detected in high relative abundance on the Xenia sp. colony (Fig. 3 C). This microorganism is a cell-wall-deficient helical bacterium that has been found in abundance in other soft corals (i.e., Veretillum cynomorium ) in the Sea of Marmara [ 34 ]. However, its physical location (e.g., intracellular or not) and metabolic roles as a symbiont of Xenia sp. have yet to be elucidated. It is important to mention that our findings were obtained from single coral colonies, from two genotypes, and that further extensive biological replication and experimentation will be necessary to answer the above questions and support our claims about coral microbiome structure and ecology. As mentioned, a significant challenge in marine microbiome analyses is the presence of exogenous DNA contaminants that come from DNA extraction kits (“kitomes”) or PCR reagents [ 35 ]. These “kitomes,” if not removed prior to analysis, could bias diversity analyses, inflate alpha diversity metrics, and affect beta diversity clustering and taxonomy plots. In addition, they are particularly problematic in samples with low prokaryotic biomass [ 36 , 37 , 38 ], low quantity of prokaryotic DNA, and/or low prokaryotic diversity (e.g., corals). In this study, mostly ASVs belonging to the family Oxalobacteraceae were detected as DNA contaminants (Suppl. Figure 2A; Suppl. Table 3). Oxalobacteraceae taxa have frequently been found as contaminants in several commercial DNA isolation kits, including ZymoBIOMICS MagBead DNA/RNA kit, and PCR reagents [ 35 ]. Although Oxalobacteraceae species have been found in high abundance in coral surface mucus [ 39 ], other studies have pinpointed that this taxon is a reagent contaminant [ 40 ], which is corroborated by our data. This finding highlights the need for proper negative controls, including multiple extraction blanks, trip blanks, and sequencing library preparation blanks, on the characterization of coral-associated microbiomes. Characterization of coral-derived microbiomes using nanopore metagenome sequencing was particularly challenging, as much of the extracted DNA was derived from the coral itself rather than its associated prokaryotic species. In this study, the majority (> 90%) of the sequences from most of the coral metagenome samples were associated with the domain Eukaryota (Suppl Fig. 4B). Thus, the development of new field-deployable host-depleting methods as well as high performance reagents to increase cell lysis are essential for advancing the study of coral-associated microbial communities using metagenomic approaches [ 41 ]. In this regard, MetaPolyzyme, a multi-lytic enzyme mixture targeting bacterial cell walls, may be used to enhance microbial cell lysis and increase the relative yield of prokaryotic-derived DNA [ 42 ]. Moreover, high proportions of unclassified sequences are another issue in microbiome analysis. In our study, a high percentage (e.g., ~ 45% in mangrove sediments and seawater) of metagenomic-derived genes were designated as unclassified at the kingdom level (Suppl. Figure 4A), indicating that these sequences are lacking contextual sequences (i.e., references sequences with > 40% similarity) in established non-redundant databases (e.g., the GenBank-nr database) [ 23 ]. This could be due to incomplete reference databases lacking representation of unique environmental microorganisms [ 43 ], high sequence divergence from known organisms, or limitations in taxonomic classification algorithms, hindering accurate classification in complex and understudied marine ecosystems like the Red Sea. It is also important to mention that ONT metagenome sequencing can produce high-quality “noise reads” that pass quality and length filters but appear to be artifacts from a subset of flow cell pores [ 44 ]. Moreover, the generation of gene and genome catalogs [ 45 , 46 ] from the Red Sea-derived microbial systems will be essential in improving future microbiome characterization using metagenomic methods. Mangrove ecosystems harbor immense and yet underexplored prokaryotic diversity [ 14 ]. In our study, this was confirmed by alpha diversity metrics (Fig. 2AB) and the high proportion of unclassified bacterial genes (~ 30%) found in mangrove sediment metagenomes (Fig. 6 B). These ecosystems receive a constant input of nutrients and marine microorganisms via seawater dispersal [ 47 ]. In particular, high salinity conditions on the Red Sea mangroves are ideal for halophilic prokaryotes, which have yet to be fully characterized. In our study, the family Balneolaceae (belonging to the new phylum Balneolota ) [ 48 ] was the most abundant family in mangrove sediments, showing similar relative abundance values across all three DNA extraction systems (Fig. 4 A). This family of halophilic bacteria is composed of six genera, Aliifodinibius, Balneola , Fodinibius , Gracilimonas , Halalkalibaculum , and Rhodohalobacter [ 49 , 50 ]. Elsewhere, these microorganisms have been found in the rhizosphere of plants growing in saline soils [ 51 – 53 ] and in volcanic alkaline soils [ 54 ]. Currently, they are still poorly explored, and their ecological and functional roles in mangrove ecosystems remain unknown. In our study, genes derived from Gracilimonas , Aliifodinibius , and Halalkalibaculum and from undetermined members of the family Balneolaceae predominated mangrove sediments metagenomes, while Balneola -derived genes were more abundant in seawater metagenomes (Fig. 6 D). Targeted and in-depth metagenomic analyses of Balneolota phylum in mangrove microbiome surveys will generate more information about their lifestyles, their roles in nature, and their biotechnological potential. Conclusions This study introduced a strategy that utilized innovative sample processing instruments, techniques (e.g., biopulverizer, handheld bead beater, concentrating pipette system and MetaPolyzyme treatment), and a portable system (i.e., xTitan) to perform on-site automated total DNA extraction for investigation of microbiomes. We have demonstrated the usability of such field-deployable technologies for microbial community characterization in Red Sea-derived ecosystems, including corals, mangrove sediments and seawater. In this study, the three DNA extraction systems (i.e., in-field xTitan, in-lab xTitan, and in-lab Qiagen) performed comparably, capturing consistent microbial diversity and taxonomic composition across distinct marine-associated samples. However, our results indicated that differences in DNA extraction systems, as well as short-term sample storage and transportation at cold temperatures, influenced microbial diversity, community structure, and functional profiles in a sample-specific manner, with pronounced effects in mangrove-associated samples. Given that xTitan system supports flexible chemistries for DNA and RNA extraction, future metatranscriptomic analyses incorporating on-site and in-lab RNA extractions can help determine what functions, metabolic activities, and active microbial taxa will be more affected by sample storage and transportation. Finally, this study highlights the effectiveness of novel sample processing methods, and on-site DNA extraction systems as reliable tools for microbial monitoring, particularly in remote, extreme, and marine ecosystems. These deployable technologies address challenges associated with sample storage and transportation, enabling in-field assessments that provide critical insights into the dynamics of microbial communities as they respond to real-time environmental conditions. Declarations Ethics approval and consent to participate Not applicable Consent for publication Consent for publication have been obtained from co-authors Availability of data and materials The bacterial 16S rRNA gene amplicon and metagenome sequences of all samples characterized in this study were deposited in NCBI under BioProject no. PRJNA1171851. All the codes, figures, and data related to this project are available at https://github.com/Tahiraj/iMMC. Competing interests A.K. was employed by AI Biosciences, Inc., and S.W. is a co-founder of AI Biosciences, Inc., the company that developed the xTitan device used in this manuscript. AP was employed by InnovaPrep LLC and CTO of the company that developed the Innovaprep CP Select TM system used in this study. All other authors declare no conflict of interest. Funding This work was financially supported by KAUST Grant BAS/1/1096-01-0 (assigned to Prof. A. S. Rosado). Author's contributions. Diego J. Jiménez: Sampling, DNA extraction, computational analyses, advising, figures design, and drafting of the first version of the manuscript. Tahira Jamil: Computational analyses, figures design, and writing. Georgios Miliotis: Sampling, experimental design, computational analyses, molecular procedures, advising, and writing. Júnia Schultz: Sampling, DNA extraction, and writing. Niketan Patel: Administration. Lila Aldakhee: Sampling and DNA extraction. Nicholas Kontis: Sampling and DNA extraction. Francisca C. García: DNA extraction, advising, and writing. Helena Villela: Sampling, advising, and writing. Gustavo A.S. Duarte: Advising, DNA extraction, and writing. Adam R. Barno: Sampling and DNA extraction. Ayman Farran : Sampling. Ahmed Alsagaaf: Sampling. Erika Santoro: Advising and sampling. Anna Tumeo: Bioinformatic analysis and writing. Andy Page: DNA extraction and writing. Season Wong: Sampling, experimental design, DNA extraction and writing. Adam Kabza: Sampling and DNA extraction. Alexander Putra: DNA sequencing. Park Changsook: DNA sequencing. Angel Angelov: DNA sequencing and writing. Patrick Driguez: DNA sequencing and writing. Raquel S. Peixoto: Sampling, advising, resource acquisition, and writing. Stefan J. Green: Sampling, experimental design, advising, and writing. Scott Tighe: Sampling, experimental design, DNA extraction, advising, and writing. Alexandre S. Rosado: Sampling, experimental design, advising, resource acquisition, financial support and coordination. Kasthuri Venkateswaran: Sampling, experimental design, advising, writing, and the coordination of all parties involved in the project. Acknowledgments We thank Neus Garcias-Bonet for her comments on the first version of the methods. We thank Ping Xu for excellent administrative assistance and Morgan Bennett-Smith for photography. 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Aliifodinibius halophilus sp. nov., a moderately halophilic member of the genus Aliifodinibius , and proposal of Balneolaceae fam. nov. Int J Syst Evol Microbiol. 2016;66(6):2225–33. Xia J, Xie ZH, Dunlap CA, Rooney AP, Du ZJ. Rhodohalobacter halophilus gen. nov., sp. nov., a moderately halophilic member of the family Balneolaceae . Int J Syst Evol Microbiol. 2017;67(5):1281–7. Jalal RS, Sheikh HI, Alotaibi MT, Shami AY, Ashy RA, Baeshen NN, et al. The Microbiome of Suaeda monoica and Dipterygium glaucum From Southern Corniche (Saudi Arabia) Reveals Different Recruitment Patterns of Bacteria and Archaea. Front Mar Sci. 2022;9:865834. Muwawa EM, Obieze CC, Makonde HM, Jefwa JM, Kahindi JHP, Khasa DP. 16S rRNA gene amplicon-based metagenomic analysis of bacterial communities in the rhizospheres of selected mangrove species from Mida Creek and Gazi Bay, Kenya. PLoS ONE. 2021;16(3):e0248485. Wu S, Wang J, Wang J, Du X, Ran Q, Chen Q et al. Corrigendum: Halalkalibacterium roseum gen. nov., sp. nov., a new member of the family Balneolaceae isolated from soil. Int J Syst Evol Microbiol. 2022;72(6). Dos Santos A, Schultz J, Almeida Trapp M, Modolon F, Romanenko A, et al. Investigating Polyextremophilic Bacteria in Al Wahbah Crater, Saudi Arabia: A Terrestrial Model for Life on Saturn's Moon Enceladus. Astrobiology. 2024;24(8):824–38. Additional Declarations Competing interest reported. A.K. was employed by AI Biosciences, Inc., and S.W. is a co-founder of AI Biosciences, Inc., the company that developed the xTitan device used in this manuscript. AP was employed by InnovaPrep LLC and CTO of the company that developed the Innovaprep CP SelectTM system used in this study. All other authors declare no conflict of interest. Supplementary Files SupplTable1Samples.xlsx Table S1. Information related to the coral- and mangrove-associated samples used in this study: codes, microbiome type, pretreatment, DNA extraction strategy, DNA yield, number of 16S rRNA gene sequences, number of metagenomic reads, average read length of metagenomic reads, and Gbp or Mbp of metagenomic information per sample. SupplTable2DNAControls.xlsx Table S2. Control sample IDs and the quantity of DNA extracted per sample SupplTable3Kitomes.xlsx Table S3. ASVs detected as contaminants and their abundance across the samples SupplTable4DESeq.xlsx Table S4. ASVs with significantly different abundances in the DESeq analysis SupplFiguresIMMC.pptx Supplementary Fig. 1. Photos taken during the sampling expeditions. Supplementary Fig. 2. Taxon relative abundance (%) plot at the phylum level based on 16S rRNA gene sequencing data from mangrove sediments. Supplementary Fig. 3. Taxon relative abundance (%) plot at the A) family and B) genus levels based on 16S rRNA gene amplicon sequencing data from the coral 1 and coral 2 samples. These results were obtained before running the decontamination pipeline. Supplementary Fig 4. Taxonomic classification of metagenomic genes in each microbiome, coral 1, coral 2, mangrove sediments, and their surrounding seawater, across three extraction systems: in-field xTitan (F), in-lab xTitan (L), and Qiagen. A) The number of metagenomic reads (in millions). B) Relative abundance (%) values at the kingdom level after removing all unclassified sequences. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5928577","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":426235059,"identity":"de40e8ea-e68d-4f89-b7bc-14a333fb8174","order_by":0,"name":"Diego J. 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Santoro","email":"","orcid":"","institution":"King Abdullah University of Science and Technology (KAUST)","correspondingAuthor":false,"prefix":"","firstName":"Érika","middleName":"P.","lastName":"Santoro","suffix":""},{"id":426235082,"identity":"d768b2ff-cf31-482b-8631-7410d0b5c5d6","order_by":14,"name":"Anna Tumeo","email":"","orcid":"","institution":"University of Galway","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Tumeo","suffix":""},{"id":426235083,"identity":"d99ba3e9-3d86-4be1-8089-4b720c4a5d09","order_by":15,"name":"Andy Page","email":"","orcid":"","institution":"InnovaPrep LLC","correspondingAuthor":false,"prefix":"","firstName":"Andy","middleName":"","lastName":"Page","suffix":""},{"id":426235084,"identity":"6da4d9d0-d6f8-4ca3-b91b-c6396c2a5546","order_by":16,"name":"Season Wong","email":"","orcid":"","institution":"AI Biosciences, Inc","correspondingAuthor":false,"prefix":"","firstName":"Season","middleName":"","lastName":"Wong","suffix":""},{"id":426235085,"identity":"5ebeeec6-1b77-4776-b0ba-1b8e130bf896","order_by":17,"name":"Adam Kabza","email":"","orcid":"","institution":"AI Biosciences, Inc","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"","lastName":"Kabza","suffix":""},{"id":426235086,"identity":"19f34ccf-695f-49b9-b9ec-7cc5c544a770","order_by":18,"name":"Alexander Putra","email":"","orcid":"","institution":"KAUST Core Laboratories, King Abdullah University of Science and Technology (KAUST)","correspondingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Putra","suffix":""},{"id":426235087,"identity":"13dd52f9-27bc-4734-871c-03f314f9711d","order_by":19,"name":"Changsook Park","email":"","orcid":"","institution":"KAUST Core Laboratories, King Abdullah University of Science and Technology (KAUST)","correspondingAuthor":false,"prefix":"","firstName":"Changsook","middleName":"","lastName":"Park","suffix":""},{"id":426235088,"identity":"f13c1ce7-dfb8-4780-8179-634502413af6","order_by":20,"name":"Angel Angelov","email":"","orcid":"","institution":"KAUST Core Laboratories, King Abdullah University of Science and Technology (KAUST)","correspondingAuthor":false,"prefix":"","firstName":"Angel","middleName":"","lastName":"Angelov","suffix":""},{"id":426235089,"identity":"6a837882-11c7-4a0b-b861-0a293b22bc4b","order_by":21,"name":"Patrick Driguez","email":"","orcid":"","institution":"KAUST Core Laboratories, King Abdullah University of Science and Technology (KAUST)","correspondingAuthor":false,"prefix":"","firstName":"Patrick","middleName":"","lastName":"Driguez","suffix":""},{"id":426235090,"identity":"dbacebac-18b6-423d-9549-dafd2415a778","order_by":22,"name":"Raquel S. Peixoto","email":"","orcid":"","institution":"King Abdullah University of Science and Technology (KAUST)","correspondingAuthor":false,"prefix":"","firstName":"Raquel","middleName":"S.","lastName":"Peixoto","suffix":""},{"id":426235091,"identity":"ca9c1675-aa29-4c9f-bee2-2b3365d52f86","order_by":23,"name":"Stefan J. Green","email":"","orcid":"","institution":"Rush University","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"J.","lastName":"Green","suffix":""},{"id":426235092,"identity":"6cf41bd3-7e89-4714-9f77-d0603edd33af","order_by":24,"name":"Scott Tighe","email":"","orcid":"","institution":"University of Vermont","correspondingAuthor":false,"prefix":"","firstName":"Scott","middleName":"","lastName":"Tighe","suffix":""},{"id":426235093,"identity":"2f70995f-4875-4d65-abf1-0318c5f18932","order_by":25,"name":"Alexandre S. Rosado","email":"data:image/png;base64,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","orcid":"","institution":"King Abdullah University of Science and Technology (KAUST)","correspondingAuthor":true,"prefix":"","firstName":"Alexandre","middleName":"S.","lastName":"Rosado","suffix":""},{"id":426235094,"identity":"3cd0444a-ca82-48ca-b8bb-659c4fb13adc","order_by":26,"name":"Kasthuri Venkateswaran","email":"","orcid":"","institution":"California Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Kasthuri","middleName":"","lastName":"Venkateswaran","suffix":""}],"badges":[],"createdAt":"2025-01-30 08:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5928577/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5928577/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78699885,"identity":"2762b493-957d-45a1-8732-e92ab98cb8d4","added_by":"auto","created_at":"2025-03-17 18:40:47","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":659096,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic representation of the experimental design. \u003c/strong\u003eSamples were obtained from two coral colonies (\u003cem\u003eP. verrucosa\u003c/em\u003e and \u003cem\u003eXenia \u003c/em\u003esp.), mangrove sediments, and associated seawater (i.e., seawater surrounding the corals and mangroves sediments). Each sample was subjected to in-field and in-lab DNA extraction using the xTitan system. During this process, several pretreatments were carried out (e.g., coral pulverization, seawater filtration, beat beating, and enzymatic cell lysis; see methods). Photos of some specific processes were displayed at the left of the figure: from top to bottom, coral sampling, bead beating, in-lab xTitan extraction, mangrove sampling, and seawater filtration using InnovaPrep CP Select. Two sets of samples were stored in liquid nitrogen (corals) or ice (seawater and sediments) and processed in the laboratory around 24 h after sampling. These samples were used for DNA extraction using a lab-based commercial kit (DNeasy PowerSoil Pro Kit, Qiagen). DNA was extracted from a sterilized PBS and saline solution to serve as negative controls. All DNA samples were quantified using Qubit and subjected to full-length 16S rRNA gene amplicon and shotgun metagenome sequencing (using Oxford Nanopore Technology, ONT) at KAUST core lab. Figure partially created using bioRender (https://BioRender.com).\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5928577/v1/00c0749a6365e1adc38890e8.jpg"},{"id":78699543,"identity":"3358432e-d1c0-4b51-b98e-98c816f38402","added_by":"auto","created_at":"2025-03-17 18:32:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":501471,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlpha and Beta diversity analysis. \u003c/strong\u003eA) Observed species and B) Shannon diversity index values (mean ± standard deviation) obtained using three DNA extraction systems, in-field xTitan, in-lab xTitan, and Qiagen, across five distinct marine-derived microbiomes: coral seawater, coral 1 (\u003cem\u003eP. verrucosa\u003c/em\u003e), coral 2 (\u003cem\u003eXenia \u003c/em\u003esp.), mangrove sediments, and mangrove-associated seawater. C) Bray–Curtis dissimilarity NMDS plots. PERMANOVA analysis showed that microbial communities differed significantly (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) between the three DNA extraction systems in the coral 1, mangrove sediment, and mangrove seawater samples. Triangles represent pairwise comparisons, displaying PERMANOVA \u003cem\u003eP\u003c/em\u003e-values.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5928577/v1/37ab6cd6f1e70849b6598435.jpg"},{"id":78699541,"identity":"d9ae5977-c28e-4b45-9f54-917fc48f7fcd","added_by":"auto","created_at":"2025-03-17 18:32:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":574119,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTaxon relative abundance plots based on 16S rRNA gene amplicon sequencing data from coral-associated microbiomes. \u003c/strong\u003eRelative abundance (%) values at family level for A) coral-associated seawater and B) two coral colonies (\u003cem\u003eP. verrucosa\u003c/em\u003e and \u003cem\u003eXenia \u003c/em\u003esp.). The figures display the top ten most abundant families, comparing the microbiomes as assessed by three extraction systems: in-field xTitan (F), in-lab xTitan (L), and Qiagen. The top four most abundant families are highlighted in bold. C) The relative abundance values (%) of the top five most abundant species within the top four most abundant bacterial families. In the figure, a comparison between coral 1 and coral 2 is shown. Asterisks represent species with significantly different abundances in the in-field and in-lab xTitan extracts.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5928577/v1/4116d79c11e3c7a8412c6698.jpg"},{"id":78699549,"identity":"673edfbe-348b-4e01-8482-3697dbab9ec6","added_by":"auto","created_at":"2025-03-17 18:32:47","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":609784,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTaxon relative abundance plots based on 16S rRNA gene amplicon sequencing data from mangrove-associated microbiomes. \u003c/strong\u003eRelative abundance values (%) at family level for mangrove-associated A) sediments and B) seawater. The figures display the top ten most abundant families, comparing the microbiomes as assessed by three extraction systems: in-field xTitan (F), in-lab xTitan (L), and Qiagen). The most abundant families are highlighted in bold. C) The relative abundance (%) values of the top five most abundant species within the top four most abundant bacterial families. In the figure, a comparison between mangrove-associated seawater and sediments is shown.\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5928577/v1/84adb610b145901e25722930.jpg"},{"id":78699886,"identity":"85ecab5d-b8f7-47d0-8e0f-9916598e11d7","added_by":"auto","created_at":"2025-03-17 18:40:47","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":558235,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetagenomic analysis of coral colony-derived microbiomes. \u003c/strong\u003eClustering of coral colony-derived microbial communities assessed using three total DNA extraction systems, in-field xTitan (F), in-lab xTitan (L), and Qiagen, based on A) taxonomic profiles at the species level (only \u003cem\u003eBacteria\u003c/em\u003e-,\u003cem\u003e Archaea-\u003c/em\u003e,\u003cem\u003e \u003c/em\u003eand\u003cem\u003e Virus\u003c/em\u003e-derived genes are included). B) Taxon relative abundance (%) plot at the phylum level using metagenome sequencing data from coral 1 and coral 2. This data only includes genes associated with \u003cem\u003eBacteria\u003c/em\u003e, \u003cem\u003eArchaea\u003c/em\u003e, and \u003cem\u003eViruses\u003c/em\u003e. C) and D) Chord diagrams of the number of metagenome-derived genes associated with the abundant genera identified by 16S rRNA gene amplicon sequencing across the three DNA extraction systems.\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5928577/v1/7cb4ca2283312926b70475e7.jpg"},{"id":78700063,"identity":"c8ca1d62-f32e-4860-8524-c0e5daa8a9b9","added_by":"auto","created_at":"2025-03-17 18:48:47","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":536548,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetagenomic analysis of mangrove-associated microbiomes. \u003c/strong\u003eClustering of mangrove-associated microbial communities from sediments and seawater assessed using three total DNA extraction systems, in-field xTitan (F), in-lab xTitan (L), and Qiagen, based on A) taxonomic profiles at the species level (only \u003cem\u003eBacteria\u003c/em\u003e-,\u003cem\u003e Archaea\u003c/em\u003e-, and\u003cem\u003e Virus\u003c/em\u003e-derived genes are included) and B) functional profiles based on KEGG Orthology (KO) annotations. Triangles represent pairwise comparisons, displaying PERMANOVA \u003cem\u003eP\u003c/em\u003e-values. C) Taxon relative abundance (%) plots at the phylum level for mangrove-associated sediments and seawater using a gene-centric metagenomic approach. D) A chord diagram of the number of metagenome-derived genes associated with taxa belonging to the \u003cem\u003eBalneolaceae \u003c/em\u003efamily across the three DNA extraction systems.\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5928577/v1/de05875ccf8c0e93983a0c85.jpg"},{"id":82361202,"identity":"0b41a07b-d245-492c-894d-483c0a2987dd","added_by":"auto","created_at":"2025-05-09 11:47:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5097885,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5928577/v1/093f9fc6-d850-44e9-8156-79c78e209db2.pdf"},{"id":78700060,"identity":"5dc400b3-b48a-4306-999f-de08fdcff190","added_by":"auto","created_at":"2025-03-17 18:48:47","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":45599,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1. \u003c/strong\u003eInformation related to the coral- and mangrove-associated samples used in this study: codes, microbiome type, pretreatment, DNA extraction strategy, DNA yield, number of 16S rRNA gene sequences, number of metagenomic reads, average read length of metagenomic reads, and Gbp or Mbp of metagenomic information per sample.\u003c/p\u003e","description":"","filename":"SupplTable1Samples.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5928577/v1/75ef002ea4e316caf679eb73.xlsx"},{"id":78699546,"identity":"3c30f3ce-8afe-4584-8aeb-f875374f39f8","added_by":"auto","created_at":"2025-03-17 18:32:47","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14004,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S2. \u003c/strong\u003eControl sample IDs and the quantity of DNA extracted per sample\u003c/p\u003e","description":"","filename":"SupplTable2DNAControls.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5928577/v1/48431f23652c4aab482dc65c.xlsx"},{"id":78699888,"identity":"63fadefd-565d-4ab5-b62f-38f4d1630d37","added_by":"auto","created_at":"2025-03-17 18:40:47","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":41216,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S3. \u003c/strong\u003eASVs detected as contaminants and their abundance across the samples\u003c/p\u003e","description":"","filename":"SupplTable3Kitomes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5928577/v1/80af34b4d007b7f563e3fab9.xlsx"},{"id":78699894,"identity":"fea57151-1465-46eb-bd5a-f00f0267fb18","added_by":"auto","created_at":"2025-03-17 18:40:47","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":303469,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S4. \u003c/strong\u003eASVs with significantly different abundances in the DESeq analysis\u003c/p\u003e","description":"","filename":"SupplTable4DESeq.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5928577/v1/6f0d25443851974d5d238a5f.xlsx"},{"id":78699568,"identity":"42cdef86-119f-44da-86fb-199768398bcd","added_by":"auto","created_at":"2025-03-17 18:32:48","extension":"pptx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":17673908,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Fig. 1. \u003c/strong\u003ePhotos taken during the sampling expeditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Fig. 2. \u003c/strong\u003eTaxon relative abundance (%) plot at the phylum level based on 16S rRNA gene sequencing data from mangrove sediments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Fig. 3. \u003c/strong\u003eTaxon relative abundance (%) plot at the A) family and B) genus levels based on 16S rRNA gene amplicon sequencing data from the coral 1 and coral 2 samples. These results were obtained before running the decontamination pipeline\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Fig 4. \u003c/strong\u003eTaxonomic classification of metagenomic genes in each microbiome, coral 1, coral 2, mangrove sediments, and their surrounding seawater, across three extraction systems: in-field xTitan (F), in-lab xTitan (L), and Qiagen. A) The number of metagenomic reads (in millions). B) Relative abundance (%) values at the kingdom level after removing all unclassified sequences.\u003c/p\u003e","description":"","filename":"SupplFiguresIMMC.pptx","url":"https://assets-eu.researchsquare.com/files/rs-5928577/v1/87aa549ac36249493a9fdc6c.pptx"}],"financialInterests":"Competing interest reported. A.K. was employed by AI Biosciences, Inc., and S.W. is a co-founder of AI Biosciences, Inc., the company that developed the xTitan device used in this manuscript. AP was employed by InnovaPrep LLC and CTO of the company that developed the Innovaprep CP SelectTM system used in this study. All other authors declare no conflict of interest.","formattedTitle":"Microbial community characterization in Red Sea-derived samples using a field-deployable DNA extraction system and nanopore sequencing","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTraditional cultivation-independent approaches to evaluating microbial diversity in host-associated and environmental microbiomes mainly involve six steps: 1) sampling, 2) storage and transportation of samples under cold conditions (e.g., at 4\u0026deg;C, -20\u0026deg;C, or in liquid nitrogen) or in a stabilizing reagent to protect biomolecules from degradation, 3) pre-processing (e.g., sieving, filtration, concentration, maceration, enzymatic and/or mechanical cell lysis), 4) nucleic acid extraction and purification, 5) DNA sequencing, and 6) computational analysis (e.g., raw data processing, bioinformatics pipeline execution, and statistical tests). Each of these steps can introduce bias, affecting the characterization of microbial community diversity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and ultimately influencing conclusions and biological interpretations. The timing of sample collection and its preservation are crucial for reproducibility in microbiome analyses [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], but the effects of sample storage and transportation, from field to laboratory, on the observed microbial communities from marine ecosystems remain poorly explored. Recently, it was demonstrated that storing marine sediment samples at refrigerated (4\u0026deg;C) and frozen temperatures (-20\u0026deg;C) for several weeks prior to DNA isolation, had a significant impact on their microbiomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], affecting mostly relative abundance values of \u003cem\u003ePseudomonadota\u003c/em\u003e populations. Microbial diversity, interactions, and metabolic activity can change while transporting samples from the natural ecosystem to the laboratory, and depending on the storage conditions and transportation time, the resulting data can give a distorted view of the original microbial community structure [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo overcome sample storage and transport issues, a handful of solutions are possible, including on-site sample processing strategies, nucleic acid extraction, library preparation, and DNA sequencing using portable devices, such as the MinION from Oxford Nanopore Technologies (ONT). On-site generation of marker genes (e.g., bacterial 16S rRNA and fungal ITS) and/or metagenome sequencing data may allow for more accurate characterization of the structural and functional profiles of microbial communities in real-time [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition, the use of nanopore DNA sequencing technologies allows for full-length 16S rRNA gene amplicon sequence data, which are useful in accurately assessing bacterial communities at genus or species level [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Unfortunately, utilizing these on-site microbial monitoring strategies can be challenging due to the size and number of instruments needed, as well as the complexity involved in various steps associated with environmental DNA extraction and library preparation. In 2023, Patin and Goodwin [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] have reviewed the primary issues of sampling, preservation, and extraction of environmental DNA for field microbial monitoring. Despite these issues, the on-site characterization of coral-associated microbial communities was successfully carried out during the \u003cem\u003eTara\u003c/em\u003e Pacific expedition using a ONT MinION device [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, comprehensive and robust studies with appropriate controls comparing field-derived microbial community structures to those generated in the laboratory after transport are still needed.\u003c/p\u003e \u003cp\u003eAssessment of on-site DNA extractions from environmental-derived samples is becoming a priority for marine microbiome studies as increasingly remote sites are explored and as different devices are developed for automated sampling and total DNA isolation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Recently, a field-deployable and automated DNA extraction system (named \u0026micro;Titan) was developed and validated by a start-up supported by the National Aeronautics and Space Administration (NASA) to perform molecular microbiology on the International Space Station [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This innovative system has been used to extract DNA and characterize hot springs-inhabiting microbial communities at Yellowstone National Park [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A new version of this instrument, named xTitan, has been developed to include 96-well plates for high-throughput assays with a single DNA extraction run. The automated miniaturized xTitan hardware, compact in size (22 cm W x 22 cm D x 29 cm H) and lightweight (2.73 kg), is capable of extracting DNA or RNA due to its methodological flexibility and has the unique capability to use interchangeable extraction chemistries. The xTitan prototype (\u0026micro;Titan) underwent vibration testing during DNA extraction while operating in a moving vehicle at 85 miles per hour. This preliminary test [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] demonstrated the robustness of the device under dynamic conditions, but further trials in distinct real-world environments are necessary to validate its use across a broader range of applications.\u003c/p\u003e \u003cp\u003eThus, a major goal of this study was to evaluate the performance of the field-deployable xTitan system for the recovery of total DNA from marine-derived samples and to explore the microbial community structure of two distinct coral colonies, mangrove sediments, and seawater. This study was performed in the Kingdom of Saudi Arabia (KSA), which hosts unique marine biodiversity hotspots, such as coral reefs and mangroves, along the Red Sea shore. These two interconnected ecosystems provide significant economic, ecological, and social benefits [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], making them a global priority for conservation and restoration. The Red Sea is characterized by high salinity, oligotrophic waters, and temperatures ranging from 22\u0026deg;C to over 31\u0026deg;C [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These conditions have shaped resilient coral reefs and mangroves hosting distinctive microbial communities. Thus, the on-site extraction of total DNA from samples collected in these marine ecosystems can provide valuable insights into their associated microbiomes, enhancing our understanding of their ecology, functions, and biotechnological potential. To address the project aims, the same xTitan system was employed twice\u0026mdash;first in the field and then in the laboratory\u0026mdash;both equipped with reagents from the ZymoBIOMICS MagBead DNA/RNA kit. These systems were compared with each other and with a standard commercial lab-based DNA extraction method (i.e., DNeasy PowerSoil Pro Kit, Qiagen). Specifically, DNA was extracted on-site (onboard a research vessel for coral-associated samples and at the seashore for mangrove-associated samples) using the xTitan system, and the same raw samples were processed in laboratory settings (through xTitan and Qiagen kit) after approximately 24 h of sample storage and transportation on ice, for mangrove sediments and seawater, or in liquid nitrogen, for coral fragments. After total DNA extraction and quantification, bacterial full-length 16S rRNA gene amplicon and shotgun metagenome sequencing were performed on all samples using the ONT PromethION platform.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy sites\u003c/h2\u003e\n \u003cp\u003eTwo natural ecosystems (coral reefs and mangroves) located on the shores of the Red Sea (Thuwal, KSA) were selected. Specifically, corals and their surrounding seawater were sampled in the \u0026ldquo;Coral Probiotic Village\u0026rdquo; (CPV) (22\u0026deg;18.3071\u0026apos;\u0026apos; N, 38\u0026deg;57.8741\u0026apos;\u0026apos;E) developed by the King Abdullah University of Science and Technology (KAUST) on 5th November 2023. The CPV is a multidisciplinary underwater laboratory consisting of a shallow (8\u0026ndash;10 m) sheltered area of about 500 m\u003csup\u003e2\u003c/sup\u003e roughly 15 km off-shore from KAUST [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. In addition, mangrove (\u003cem\u003eAvicennia marina\u003c/em\u003e) sediments (pH 6.3, 32\u0026deg;C) and their surrounding seawater (pH 7.4, 30\u0026deg;C) were sampled (on November 6, 2023) on the periphery of KAUST at the King Abdullah monument (22\u0026deg; 20\u0026apos; 23.9\u0026quot; N, 39\u0026deg; 05\u0026apos; 17.5\u0026quot; E).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eSampling corals, mangrove sediments, and seawater\u003c/h3\u003e\n\u003cp\u003eTwo visually healthy coral colonies (\u003cem\u003ePocillopora verrucosa\u003c/em\u003e and \u003cem\u003eXenia\u003c/em\u003e sp.) were sampled by scuba divers according to the established guidelines available in Voolstra \u003cem\u003eet al\u003c/em\u003e. [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. From each coral species, several fragments (~\u0026thinsp;1\u0026ndash;2 cm\u003csup\u003e3\u003c/sup\u003e) were aseptically collected from different parts of coral colonies using sterile gloves and pliers, and then stored in Whirl-Pak\u0026reg; sterile bags (see photos in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Biological replicates (n\u0026thinsp;=\u0026thinsp;3) from each coral colony were processed on the boat and set aside for analysis. On the boat, one set of coral fragments (n\u0026thinsp;=\u0026thinsp;7, including a negative control) was immediately processed (see below on-site sample processing section) and the remaining two sets (n\u0026thinsp;=\u0026thinsp;14) were placed in sterile 5 mL cryovials and snap-frozen in liquid nitrogen until processed in the laboratory (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Suppl. Table 1). Approximately 2 L of seawater (n\u0026thinsp;=\u0026thinsp;6) surrounding each coral colony was collected and placed in sterilized Whirl-Pak\u0026reg; plastic bags. For the mangrove sampling, sites were divided into four locations along a five-meter transect (biological quadruplicates). Seawater surrounding the mangrove area (~\u0026thinsp;2 L) and sediments (~\u0026thinsp;200 g) were collected and placed in sterilized Whirl-Pak\u0026reg; plastic bags (see photos in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Mangrove sediments were aseptically collected using an autoclaved spatula. Three sets of these samples were collected: one was processed on-site (see below) and the remaining two were stored on ice until processing in the laboratory (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Sample characteristics, including the DNA extraction method, are presented in Suppl. Table 1. In total, 60 samples (48 samples and 12 controls) were processed during this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOn-site sample processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e- Coral biopulverization\u003c/strong\u003e: For samples processed onboard the research vessel, each coral colony fragment was macerated using a sterile and DNA-free stainless-steel pestle biopulverizer (BioSpec, Cat # 59013N, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.biospec.com/products/biopulverizer\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Whenever needed, the biopulverizer was sterilized with 95% ethanol and decontaminated by flame sterilization. The biopulverizer assembly was wrapped in aluminum foil and placed in an ice bath prior to each sample processing. The coral fragments were aseptically placed into the biopulverizer and macerated by striking with a hammer until completely disassociated. With each strike, the piston was rotated a quarter turn to ensure homogeneous maceration (see photos in Suppl. Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e- Homogenization and enzymatic digestion with MetaPolyzyme\u003c/strong\u003e: Each pulverized coral fragment (~\u0026thinsp;0.5 g) or mangrove sediment sample (~\u0026thinsp;0.25 g) was aseptically transferred to a 2 mL tube containing 1 mL of sterile 1X phosphate-buffered saline (PBS, no Ca or Mg) and \u0026frac14; volume of DNA-free sterilized beater beads which included a combination of 2 mm steel balls, 1 mm garnet beads, and 0.1 mm of AlO\u003csub\u003e2\u003c/sub\u003e beads (Ciro co., Deerfield, Fl., USA). Samples were subjected to mechanical homogenization for 1 min using the handheld SoniBeast bead beater (SBJ319, Biospec Products) at a setting of 3 (see photos in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Samples were allowed to settle, and the supernatant was transferred to a new 2 mL tube. Cellular enzymatic digestion of microbial cell walls was accomplished using the certified DNA-free version of the microbiome enzyme cocktail MetaPolyzyme (MAC4-DF, Millipore Sigma, St. Louis, MO, USA). Each sample tube was treated with 25 \u0026micro;L of 10 U/\u0026micro;L of MetaPolyzyme and incubated (on-site or in the lab) for 1 h at 35\u0026deg;C [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. An aliquot of 100 \u0026micro;L of the enzyme-treated sample was transferred to the xTitan system for DNA extraction (see below).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e- InnovaPrep concentrating pipette system (for seawater samples only)\u003c/strong\u003e: In the field, 1.5 L of seawater samples were concentrated using an InnovaPrep CP Select\u0026trade; system equipped with a 0.2 \u0026micro;m hollow fiber Concentrating Pipette Tip (CC08022-10, InnovaPrep, Drexel, MO, USA) and eluted using FluidPrep\u0026trade; Elution Buffer PBS (PBS HC0800, InnovaPrep) following manufacturer\u0026apos;s procedures (see photo in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Suppl. Figure 1). Specifically, 1.5 L seawater samples were supplemented with 15 mL of sterile 10% Tween 20 and then processed on the CP Select prior to performing an automated wash step, using 50 mL of 25 mM Tris/1 mM EDTA pH 9, and automated elution into nominal 1 mL concentrated volume. The resulting concentrated cell biomass was subject to the same bead-beating and MetaPolyzyme treatment outlined above. Each sample\u0026rsquo;s DNA was then extracted using the xTitan device (see below).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e- DNA extraction using the xTitan system\u003c/strong\u003e: The xTitan system was operated offshore, onboard the research vessel in the CPV for the coral reef samples, and in a parking area near the shore for the mangrove samples (see photos in Suppl. Figure\u0026nbsp;1). It is important to note that the xTitan is a reagent agnostic instrument capable of using a variety of manufacturer reagents, including Promega, Zymo, and Biomerieux, as well as other magnetic bead-based nucleic acid extraction kits [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. In this study, the system was equipped with commercially available reagents from the ZymoBIOMICS MagBead DNA/RNA kit (Zymo Research, Irvine, CA, USA) in prefilled 96-deep well plates (A48305, ThermoFisher Scientific). The xTitan extraction protocol was as follows: 100 \u0026micro;L of pre-processed samples was added to the first row (Row A) of the 96-well plate, which was pre-filled with 300 \u0026micro;L of lysis buffer. The device then mixed the sample and lysis buffer mixture for 10 min by moving the extraction tip-comb up and down. The tip-comb moves up and down 195 times per min. Next, 400 \u0026micro;L of 100% ethanol and 30 \u0026micro;L of magnetic beads were added to Row A containing the lysed sample to facilitate the binding of nucleic acids to the magnetic beads. The DNA bound to the magnetic beads was captured by the magnets inside the tip-comb and transferred to successive rows for the washing steps and the final DNA elution step. The washing and elution buffers were prefilled into the 96-well plate as follows: Row D contained 500 \u0026micro;L of ZymoBIOMICS MagWash 1 buffer, Row E contained 500 \u0026micro;L of ZymoBIOMICS MagWash 2 buffer, Rows F and G contained 500 \u0026micro;L of 95% ethanol, and Row H contained 100 \u0026micro;L of elution buffer (DNase/RNase-free DI water). For each of the two MagWash steps, the magnetic beads were released into MagWash buffers and mixed by the tip-comb for 2 min. The beads were then washed for 2 min in 95% ethanol twice and air dried for 10 min. Finally, purified DNA was recovered by eluting in 100uL of DNase/RNase-free DI water for 5 min at 65\u0026deg;C to release the nucleic acids.\u003c/p\u003e\n\u003ch3\u003eIn-laboratory sample processing\u003c/h3\u003e\n\u003cp\u003eTo evaluate the potential impact of storage and transportation on microbial recovery, all on-site sample processing steps were replicated in the laboratory within approximately 24 hours of sample transportation, followed by using the same xTitan system and Qiagen method (see below).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e- Millipore seawater filtration in the laboratory\u003c/strong\u003e: Seawater samples were transported on ice and stored at 4\u0026deg;C for ~\u0026thinsp;24 h until processing. In the laboratory, 1.5 L of each seawater sample was filtered through a 0.2 \u0026micro;m Isopore\u0026trade; PC membrane (Cat # GTTP04700, Merck Millipore, Burlington, MA, USA) with a peristaltic vacuum pump. After filtration, filters were snap-frozen in liquid nitrogen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C during ~\u0026thinsp;12 h until total DNA extraction using the DNeasy PowerSoil Pro Kit (see below).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e- DNA extractions using a standard commercial kit in the laboratory\u003c/strong\u003e: A set of coral fragment samples was transported from the CPV to the KAUST laboratories in liquid nitrogen. In the laboratory, coral samples were pulverized following the methods described above, and ~\u0026thinsp;0.5 g was used for DNA extraction. For mangrove sediments, rocks, and plant biomass material were removed, and ~\u0026thinsp;0.25 g was used for DNA extraction. For seawater samples, Millipore filter membranes (see above) were cut into small pieces (approximately 1 \u0026times; 1 cm) using a sterilized scalpel and transferred into a sterile single-use Petri dish with the cell-coated surface facing up. Membrane fragments were then transferred into a sterilized 2 mL tube to start the DNA extraction protocol. DNA extraction was performed using the DNeasy PowerSoil Pro Kit (Cat #47016, Qiagen), following the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\n\u003ch3\u003eControls\u003c/h3\u003e\n\u003cp\u003eNegative controls (blanks) were used to identify background DNA, including extracellular DNA and microbial contaminants associated with kits (\u0026ldquo;kitomes\u0026rdquo;) and secondary contamination during sample processing steps. In this study, negative controls (n\u0026thinsp;=\u0026thinsp;12) included 2 ml of the same sterilized 1X PBS employed for rinsing either the biopulverizer used in processing coral (n\u0026thinsp;=\u0026thinsp;3) or the spatula used for collecting mangrove sediments (n\u0026thinsp;=\u0026thinsp;3), along with 1.5 L of sterile 3.5% saline solution filtered using either the InnovaPrep (n\u0026thinsp;=\u0026thinsp;4) or the Millipore filtration system (n\u0026thinsp;=\u0026thinsp;2). Positive controls (n\u0026thinsp;=\u0026thinsp;12) included a microbial reference standard from the American Type Culture Collection (ATCC MSA-2003\u0026trade;, Manassas, VA USA) composed of ten well-characterized intact whole cells spiked into marine samples (i.e., 3 corals, 3 mangrove sediments, and 6 seawater samples). The negative and positive controls are detailed in Suppl. Table\u0026nbsp;2. In addition, an ATCC genomic DNA reference standard representing a ten bacterial strain even mixture (ATCC MSA-1000\u0026trade;) was subjected to sequencing in triplicate to determine the efficacy and accuracy of the ONT platform. All controls were processed using methods identical to those used for the samples and subjected to full-length 16S rRNA gene amplicon and shotgun metagenome sequencing (see below).\u003c/p\u003e\n\u003ch3\u003eOxford Nanopore DNA sequencing\u003c/h3\u003e\n\u003cp\u003eExtracted DNA was quantified using the dsDNA HS Assay Kit (Q33231, Life Technologies, USA) on a Qubit 4.0 device (quantification range: 0.1 to 120 ng). Appropriate aliquots of purified DNA samples were prepared for 16S rRNA gene amplicon and shotgun metagenomic using ONT PromethION platform following the manufacturer\u0026rsquo;s protocols. Bacterial 16S rRNA gene amplicons were generated with standard full-length primers (27F: 5\u0026rsquo;-AGR GTT YGA TYM TGG CTC AG-3\u0026rsquo; and 1492R: 5\u0026rsquo;-RGY TAC CTT GTT ACG ACT T-3\u0026rsquo;) over 25 cycles using KAPA HiFi Hotstart ReadyMix (KK2601, Roche) followed by library preparation using the ligation-based native barcoding kit (SQK-NBD114.96; ONT, UK). Shotgun metagenomics libraries were prepared using the same library kit without prior bacterial 16S rRNA gene amplification. Libraries were pooled according to the manufacturer\u0026apos;s recommendations and loaded (30\u0026ndash;50 fmol for 16S rRNA gene and 20 fmol for metagenome sequencing) onto ONT PromethION flowcells (FLO-PRO114M, R10.4.1) for DNA sequencing.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eAnalysis of full-length 16S rRNA gene amplicon sequencing data\u003c/h2\u003e\n \u003cp\u003eTaxonomic classification of bacterial 16S rRNA gene sequences was performed using the EPI2ME Labs wf-metagenomics pipeline (Nextflow) v2.0.8, utilizing the NCBI 16S rRNA and 18S rRNA gene databases for reference sequences (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/epi2me-labs/wf-metagenomics\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Prior to classification, raw sequence reads were filtered based on length, with a minimum of 1,200 bp and a maximum 1,800 bp, as well as a minimum per read Phred quality score of 15. Amplicon sequence variants (ASVs) were generated from the filtered sequences. Low-abundance ASVs, defined as those with 10 or fewer reads, were excluded from further analysis. Potential contaminant ASVs were identified and removed using the \u003cem\u003edecontam\u003c/em\u003e R package (v1.12) [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e] by assigning a probability to each ASV of it being a contaminant based on its prevalence in negative control samples versus true samples. Those ASVs with a contaminant probability greater than 0.5 were classified as contaminants and subsequently removed from the dataset.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAnalysis of nanopore metagenomic sequencing data\u003c/h3\u003e\n\u003cp\u003eRaw sequence reads were trimmed to remove adapter and barcode sequences using \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePorechop\u003c/span\u003e (v0.2.4). Quality filtering was subsequently performed with fastp (v0.23.2) [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e], retaining reads with a Phred quality score\u0026thinsp;\u0026gt;\u0026thinsp;12. Filtered reads were then analyzed using SqueezeMeta (v1.6.3) [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] through an assembly-free (i.e., gene-centric) approach implemented by the Perl script \u0026quot;sqm_longreads.pl,\u0026quot; which performs taxonomic classifications of open reading frames (ORFs) by executing DIAMOND searches, assigning taxonomy using the lowest common ancestor (LCA) method, and applies KEGG functional annotations. For downstream data processing, tables were generated using the Python script \u0026quot;qmreads2tables.py.\u0026quot;\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eAlpha diversity of the microbial communities was estimated using the Shannon diversity index and observed species values to assess species evenness and richness, respectively, within each sample. The Shapiro\u0026ndash;Wilk test was used to assess normality, and the Kruskal\u0026ndash;Wallis test was used to evaluate statistical significance, with \u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.01 accepted as indicating significance. Beta diversity was calculated using the Bray\u0026ndash;Curtis dissimilarity metric to evaluate differences in community composition between samples and non-metric multidimensional scaling (NMDS) to visualize differences. Group differences in microbial community composition were assessed using Permutational Multivariate Analysis of Variance (PERMANOVA) tests based on the Bray\u0026ndash;Curtis dissimilarity matrix with 999 permutations and a significance threshold set at \u003cem\u003eP-\u003c/em\u003evalues\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Differentially abundant ASVs between groups were identified based on the negative binomial distribution using the \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eDESeq2\u003c/span\u003e package [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. All statistical analyses and data visualizations were performed in R (v4.4.1) using these packages: \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003etidyverse\u003c/span\u003e (v1.3.0), for data manipulation and visualization; \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003ephyloseq\u003c/span\u003e (v1.48.0), for microbiome data analysis; \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003edecontam\u003c/span\u003e (v1.24.0), for contaminant identification and removal; \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003evegan\u003c/span\u003e (v2.6.8) for ecological diversity analyses; and \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003emetagMisc\u003c/span\u003e (v0.5.0) for metagenomic data processing.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMicrobial diversity and community structure based on 16S rRNA gene amplicon data\u003c/h2\u003e \u003cp\u003eAfter total DNA extraction and full-length 16S rRNA gene amplicon sequencing, on average 347,884\u0026thinsp;\u0026plusmn;\u0026thinsp;281,671 raw sequences were generated per sample with an average read length of 1,236 bases (Suppl. Table\u0026nbsp;1), resulting in a total of 5,940 ASVs after quality control and \u0026ldquo;kitomes\u0026rdquo; taxa removal (see below). Regarding alpha diversity metrics, mangrove sediments and seawater samples showed higher numbers of observed species and Shannon index values than coral-derived samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eAB). No significant differences in Shannon values were observed among the three DNA extraction systems (i.e., in-field xTitan, in-lab xTitan, and in-lab Qiagen); however, observed species-level richness in mangrove sediments and seawater extracts from the xTitan system (in-field and in-lab) trended higher than those from Qiagen extracts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In beta diversity (i.e., Bray\u0026ndash;Curtis dissimilarity NMDS plots), significant differences (PERMANOVA; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed between the three DNA extraction systems in all sample types except the soft coral colony (i.e., \u003cem\u003eXenia\u003c/em\u003e sp., coral 2) and seawater surrounding coral samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Additionally, significant differences (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were found between the in-field and in-lab xTitan extraction systems in samples from the mangrove ecosystem (including sediments and seawater). The lack of significant differences between seawater samples near corals may be attributed to the lower number of respective biological replicates (n\u0026thinsp;=\u0026thinsp;2), influencing the statistical analyses. Based on this, these data were excluded from further comparisons. However, slight differences in the relative abundances of taxa in the near-coral seawater bacterial communities were observed, especially when comparing the Qiagen and xTitan extraction systems, with \u003cem\u003eOceanospirillaceae\u003c/em\u003e conspicuously reduced in Qiagen extracts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The microbial compositions of the hard coral (\u003cem\u003eP. verrucosa\u003c/em\u003e, coral 1), mangrove sediments, and seawater surrounding mangroves were highly influenced by the DNA extraction method (i.e., Qiagen versus xTitan), followed by the place of DNA isolation (i.e., in-field xTitan versus in-lab xTitan). Such influences were evidenced by pronounced changes in the relative abundances of the most abundant bacterial families (for coral 1 and seawater surrounding mangroves) or phyla (in the case of mangrove sediments) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, and Suppl. Figure\u0026nbsp;2). These findings were confirmed by the DESeq2 analysis, in which 381 and 322 ASVs were differentially abundant between in-lab xTitan- and Qiagen-extracted mangrove sediment and seawater samples, respectively. Conversely, 122 and 233 ASVs were differentially abundant between in-field and in-lab xTitan-extracted mangrove sediment and seawater samples, respectively (Suppl. Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTaxonomic composition of coral-associated samples based on 16S rRNA gene amplicon data\u003c/h2\u003e \u003cp\u003eBased on the DNA extracted in-lab (Qiagen and xTitan), the bacterial community of the coral 1 colony (\u003cem\u003eP. verrucosa\u003c/em\u003e) was dominated by members of the family \u003cem\u003eFulvivirgaceae\u003c/em\u003e (i.e., \u003cem\u003eFulvivirga imtechensis\u003c/em\u003e), which represented approximately 80% of relative abundance in Qiagen extracts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). However, the relative abundance of \u003cem\u003eFulvivirgaceae\u003c/em\u003e was much lower when DNA was extracted in-boat using the xTitan system, with a concomitant significant increase in the relative abundance of \u003cem\u003eEndozoicomonas acroporae\u003c/em\u003e (DEseq2 \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Log\u003csub\u003e2\u003c/sub\u003e[fold change]\u0026thinsp;=\u0026thinsp;12.8) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Although bacterial communities in coral 1 samples were affected by the nucleic acid extraction protocol, the observed bacterial community structure of \u003cem\u003eXenia\u003c/em\u003e sp. (coral 2) was not significantly affected by the DNA extraction systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). \u003cem\u003eEndozoicomonadaceae\u003c/em\u003e, \u003cem\u003eSpiroplasmataceae\u003c/em\u003e, \u003cem\u003eOxalobacteraceae\u003c/em\u003e, and \u003cem\u003eFrancisellaceae\u003c/em\u003e were the primary family level taxa observed in the samples obtained from a single colony of \u003cem\u003eXenia\u003c/em\u003e sp. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Within them, three bacterial species showed high relative abundance values across most technical replicates: \u003cem\u003eEndozoicomonas coralli\u003c/em\u003e, \u003cem\u003eEndozoicomonas gorgoniicola\u003c/em\u003e, and \u003cem\u003eSpiroplasma lampyridicola\u003c/em\u003e (a small symbiont of the \u003cem\u003eMollicutes\u003c/em\u003e group), accounting for a considerable proportion (~\u0026thinsp;70%) of the bacterial community in these samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). \u003cem\u003eAllofrancisella frigidaquae\u003c/em\u003e was also abundant (20\u0026ndash;25%) in two of three replicates extracted with xTitan in the field (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In both coral colonies, some samples (e.g., C1, C27, C18, and C30) revealed high proportion of sequences belonging to families \u003cem\u003eProchlorococcaceae, Flavobacteriaceae, Pelagibacteraceae\u003c/em\u003e, and \u003cem\u003eBacteroidaceae\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). These families were also abundant in near-coral seawater samples, indicating that coral colonies could include taxa that are not obligately associated with the hosts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTaxonomic composition of mangrove-associated samples based on 16S rRNA gene amplicon data\u003c/h2\u003e \u003cp\u003eMicrobiomes in mangrove-associated samples were characterized with sediments maintaining more diverse bacterial communities compared to seawater (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Moreover, the results indicated that the relative abundance of bacterial sequences from the phylum \u003cem\u003ePseudomonadota\u003c/em\u003e was highest when DNA was extracted using the xTitan system, whereas bacterial sequences from the phylum \u003cem\u003eBacteroidota\u003c/em\u003e were more abundant when DNA was extracted using the Qiagen kit (Suppl. Figure\u0026nbsp;2). In addition, the relative abundance of the family \u003cem\u003eProchlorococcaceae\u003c/em\u003e was significantly lower (DEseq2 \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Log\u003csub\u003e2\u003c/sub\u003e[fold change]\u0026thinsp;=\u0026thinsp;1.8) in seawater when DNA was extracted in the laboratory using both systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), suggesting that storage and transportation on ice may affect cyanobacterial populations. As mentioned, 122 ASVs were differentially abundant between on-site and in-lab xTitan-extracted DNA from mangrove sediments. This included bacterial ASVs belonging to the genera \u003cem\u003eEnterovibrio\u003c/em\u003e, \u003cem\u003eVibrio\u003c/em\u003e, \u003cem\u003eAlkalibacillus\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003ePhotobacterium\u003c/em\u003e, and \u003cem\u003eSkermanella\u003c/em\u003e, among others, that were highly enriched when DNA was extracted in the field (DEseq2 \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Log\u003csub\u003e2\u003c/sub\u003e[fold change]\u0026thinsp;\u0026gt;\u0026thinsp;8) (Suppl. Table\u0026nbsp;4). The relative abundance of the family \u003cem\u003eBalneolaceae\u003c/em\u003e (primarily composed of moderately halophilic species) was higher in mangrove sediments (5\u0026ndash;10%) and showed similar values across the three DNA extraction strategies (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Analyzing the four most abundant bacterial families, it was noted that some families (e.g., \u003cem\u003eBalneolaceae\u003c/em\u003e and \u003cem\u003ePirellulaceae\u003c/em\u003e) were preferentially found in mangrove sediments compared to seawater, while others (e.g., \u003cem\u003eFlavobacteriaceae\u003c/em\u003e and \u003cem\u003eRhodobacteraceae\u003c/em\u003e) were predominant in seawater (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and removal of \u0026ldquo;kitomes\u0026rdquo; from 16S rRNA gene amplicon data\u003c/h2\u003e \u003cp\u003eA challenge in environmental microbiome analyses (especially in samples with low microbial biomass) is the presence of background DNA contamination inherent to reagents from DNA extraction kits (i.e., \u0026ldquo;kitomes\u0026rdquo;) and/or PCR reagents. Identifying these contaminants necessitates the use of appropriate negative controls. These contaminants must be detected and removed before any microbial diversity analyses. In our study, DNA from sterilized 2 mL of 1X PBS solution and ~\u0026thinsp;1.5 L of sterilized and filtered saline solution were processed as negative controls (n\u0026thinsp;=\u0026thinsp;12) through extraction, library preparation, and DNA sequencing. The amounts of DNA were below the detection limit (0.1 ng) in these negative controls using Qubit fluorometric measurement (Suppl. Table\u0026nbsp;2), except for one sample (M24; 1.5 L saline solution; 0.1 ng/\u0026micro;L), which was sterilized and filtered through a Millipore filter before undergoing extraction using the Qiagen method. PCR amplification of the 16S rRNA gene yielded positive results for all negative controls, indicating the presence of background contaminating DNA. In this study, 104 ASVs (1.72% of the total ASVs) were identified as contaminants using the prevalence method in the decontam pipeline [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] (Suppl. Table\u0026nbsp;3) and removed from the ASVs table prior to microbial diversity analyses (see above). The most frequent and abundant contaminant ASVs were annotated as belonging to the genera \u003cem\u003eHerbaspirillum\u003c/em\u003e, \u003cem\u003eJanthinobacterium, Pseudomonas\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e, and \u003cem\u003eCitrobacter\u003c/em\u003e (Suppl. Figure\u0026nbsp;3A; Suppl. Table\u0026nbsp;3). To illustrate the biases generated by the presence of DNA contaminants in coral-associated bacterial communities, relative abundance taxa plots (at the family level) with (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) and without (Suppl Fig.\u0026nbsp;3B) removal of ASV contaminants were generated. These plots revealed that sequences belonging to the families \u003cem\u003eOxalobacteraceae\u003c/em\u003e and \u003cem\u003ePseudomonadaceae\u003c/em\u003e were found in high proportions in samples without the removal of ASV contaminants, especially when DNA was extracted using the xTitan system, indicating that one source of DNA contaminants is the ZymoBIOMICS MagBead DNA/RNA kit reagents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eNanopore metagenome sequencing output\u003c/h2\u003e \u003cp\u003eAfter ONT sequencing, total sequence yields (i.e., the number of metagenomic reads) obtained from libraries generated with Qiagen-extracted DNA were significantly higher than those generated from field and laboratory xTitan extracts, except for when looking at the coral 2 (\u003cem\u003eXenia\u003c/em\u003e sp.) samples (Suppl. Table\u0026nbsp;1; Suppl. Figure\u0026nbsp;4). This appears to be directly correlated with the total DNA yield from extractions, which was higher for Qiagen, and the equal volume pooling of libraries before loading on the ONT sequencing flow cell. As mentioned, one exception to this was the in-field xTitan-extracted samples from coral 2 (Suppl. Table\u0026nbsp;1; Suppl. Figure\u0026nbsp;4), which generated approximately 2.5 to 15\u0026nbsp;million raw reads with read lengths of 1,327\u0026thinsp;\u0026plusmn;\u0026thinsp;646, 1,059\u0026thinsp;\u0026plusmn;\u0026thinsp;17, and 1,552\u0026thinsp;\u0026plusmn;\u0026thinsp;1,016 bases for Qiagen, in-field xTitan, and in-lab xTitan, respectively (Suppl. Table\u0026nbsp;1). Like the coral-associated samples, in mangrove-associated samples, a larger number of reads were generated from libraries made from Qiagen extracted DNA (an average of ~\u0026thinsp;5\u0026nbsp;million reads per sample) than from xTitan-extracted DNA (an average of \u0026lt;\u0026thinsp;1\u0026nbsp;million reads per sample; Suppl. Table\u0026nbsp;1; Suppl. Figure\u0026nbsp;4). Additionally, average read lengths were higher for Qiagen libraries than for xTitan libraries from mangrove sediments, with average reads lengths of 2,362\u0026thinsp;\u0026plusmn;\u0026thinsp;677 bases for Qiagen, and 1,033\u0026thinsp;\u0026plusmn;\u0026thinsp;144 bases for in-lab xTitan (\u003cem\u003eP\u003c/em\u003e-value 0.043). The lowest average read lengths were obtained from in-field xTitan-extracted samples, where read lengths of 839\u0026thinsp;\u0026plusmn;\u0026thinsp;331 and 927\u0026thinsp;\u0026plusmn;\u0026thinsp;191 bases were obtained from sediments and seawater, respectively (Suppl. Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCharacterization of microbial communities based on nanopore metagenome data\u003c/h2\u003e \u003cp\u003eTaxonomic and functional affiliations of metagenomic sequences were based on an assembly-free gene-centric approach using the software package SqueezeMeta [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A high proportion (~\u0026thinsp;80% for corals and ~\u0026thinsp;45% for mangrove sediment and seawater) of metagenomic-derived genes were unclassified at the kingdom level, likely due to the limitations of the reference databases used and the large unknown genetic diversity of the sampled environments. Unclassified sequences were removed from downstream metagenomic analyses. In coral-derived metagenomes, the majority (\u0026gt;\u0026thinsp;90%) of the remaining sequences were associated with eukaryotic organisms, most known from coral hosts (Suppl. Figure\u0026nbsp;4A). Taxonomy-based clustering of metagenome-derived genes within \u003cem\u003eBacteria\u003c/em\u003e, \u003cem\u003eArchaea\u003c/em\u003e, and \u003cem\u003eViruses\u003c/em\u003e indicated that microbial communities derived from both coral colonies were highly similar across the DNA extraction systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Taxonomic affiliation of these genes showed that bacteria from the phyla \u003cem\u003ePseudomonadota\u003c/em\u003e, \u003cem\u003eBacteroidota\u003c/em\u003e, and unclassified \u003cem\u003eBacteria\u003c/em\u003e were predominant in both coral colonies (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The relative abundance of phylum \u003cem\u003eBacteroidota\u003c/em\u003e was higher in coral 1 (\u003cem\u003eP. verrucosa\u003c/em\u003e) than in coral 2 (\u003cem\u003eXenia\u003c/em\u003e sp.), especially in the Qiagen extracts. \u003cem\u003eArchaea\u003c/em\u003e from the phylum \u003cem\u003eEuryarchaeota\u003c/em\u003e were found in both coral colonies (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Despite the limited characterization of virus-derived genes in our metagenome data from corals, viral sequences were relatively abundant (~\u0026thinsp;10%) in \u003cem\u003eXenia\u003c/em\u003e sp. but nearly absent in \u003cem\u003eP. verrucosa\u003c/em\u003e samples. Specifically, unclassified and \u003cem\u003ePreplasmiviricota\u003c/em\u003e-like viruses (eukaryotic viruses) were annotated in \u003cem\u003eXenia\u003c/em\u003e-derived metagenomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), regardless of DNA extraction method. A targeted analysis of the most abundant bacterial genera identified in the full-length 16S rRNA gene amplicon data (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) revealed that metagenome-derived genes from \u003cem\u003eFulvivirga\u003c/em\u003e and \u003cem\u003eEndozoicomonas\u003c/em\u003e were also highly abundant in coral 1 and coral 2, respectively (Fig.\u0026nbsp;5CD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the metagenomic analysis of mangrove-associated samples, a greater number of total raw reads were produced by the Qiagen extracts than the xTitan extractions, while the proportions of \u003cem\u003eBacteria\u003c/em\u003e and \u003cem\u003eArchaea\u003c/em\u003e (prokaryotic) genes were similar following both DNA extraction methods (Suppl. Figure\u0026nbsp;4B). The taxonomic and functional profiles of the mangrove-associated microbial communities differed depending on the DNA extraction system employed (PERMANOVA; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with pronounced differences between the Qiagen and xTitan (Fig.\u0026nbsp;6AB). In mangrove sediments, significant differences (PERMANOVA, \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.033) in functional profiles were observed between in-field and in-lab xTitan-extracted samples. Based on the taxonomic classification of genes detected in the mangrove-associated metagenomes, bacteria from the phyla \u003cem\u003ePseudomonadota\u003c/em\u003e and \u003cem\u003eBacteroidota\u003c/em\u003e were the most abundant. However, approximately 30% of sequenced data from the sediments was cataloged as unclassified \u003cem\u003eBacteria-\u003c/em\u003ederived genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). About 20% of metagenome-derived genes from mangrove-associated seawater were assigned to the phylum \u003cem\u003eCyanobacteria\u003c/em\u003e, and the average relative abundance of \u003cem\u003eCyanobacteria\u003c/em\u003e was visually higher on in-field xTitan extracts than in Qiagen extracts. A slight reduction in the relative abundance of \u003cem\u003eCyanobacteria\u003c/em\u003e in in-lab xTitan extracts compared to in-field xTitan extracts was also observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). A small proportion of genes from unclassified \u003cem\u003eArchaea\u003c/em\u003e and \u003cem\u003eUriviricota\u003c/em\u003e taxa (bacterial and archaeal viruses with head-tail morphology) were found in seawater surrounding mangroves (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Since 16S rRNA gene amplicon sequencing results showed a large number of sequences belonging to \u003cem\u003eBalneolaceae\u003c/em\u003e in mangrove sediments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), a specific analysis of genes assigned to the \u003cem\u003eBalneolaceae\u003c/em\u003e family was performed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). These targeted analyses indicate that a considerable fraction of genes derived from mangrove sediments were affiliated with unclassified \u003cem\u003eBalneolaceae\u003c/em\u003e (e.g., 33,639 and 1,041 sequences, on average, from the Qiagen and in-lab xTitan extracts, respectively), \u003cem\u003eAliifodinibius\u003c/em\u003e, and \u003cem\u003eGracilimonas\u003c/em\u003e genera. In seawater, a high proportion of genes belonged to \u003cem\u003eBalneola\u003c/em\u003e (e.g., 700 and 514 sequences, on average, from the Qiagen and in-lab xTitan extracts, respectively) and unclassified \u003cem\u003eBalneolaceae\u003c/em\u003e taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, although the Qiagen method extracted a significantly higher quantity of total DNA than the xTitan system across coral, mangrove sediment, and seawater samples (Suppl. Table\u0026nbsp;1), the impact on microbial community profiling outcomes or the predicted proportion of prokaryotic DNA extracted was not consistently different. Both methods (i.e., Qiagen and xTitan coupled with Zymo chemistry) effectively captured similar microbial diversity and taxonomic composition, demonstrating that DNA yield alone is not a determining factor for accurate community characterization. Based on our metagenomic analysis performed in mangrove sediments and seawater, the percentage of prokaryote-assigned genes was similar among the three DNA extraction systems (Suppl. Figure\u0026nbsp;4B). This suggests that the xTitan system can extract a similar proportion of prokaryotic DNA when compared with a \u0026ldquo;gold standard\u0026rdquo; commercial kit, which is consistent with the findings of a previous study that employed the use of the \u0026micro;Titan equipped with the BioM\u0026eacute;rieux NucliSENS\u0026reg; chemistry [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, it is important to highlight that the use of a field-deployable biopulverizer, hand-held bead beater, and the portable filtration Innovaprep CP Select\u0026trade; system, were important for successful in-field DNA extractions using the xTitan device (photos in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Suppl. Figure\u0026nbsp;1). The quantity, quality, and integrity of on-site-extracted DNA are key factors for the success of subsequent processes carried out in the field or in the laboratory (e.g., ONT metagenome sequencing). In our study, the lowest average read length (a proxy for DNA integrity) in the nanopore metagenomic data was observed in DNA extracted using the xTitan in the field, though this difference was not statistically significant (Suppl. Table\u0026nbsp;1). Thus, while there were differences, this suggests that on-site extraction using the xTitan system can effectively maintain DNA integrity and may vary with alternative chemistries.\u003c/p\u003e \u003cp\u003eAlthough no significant differences were found in alpha diversity metrics among the three DNA extraction systems (Fig.\u0026nbsp;2AB), the xTitan tended to produce higher numbers of observed species, suggesting that the combination of MetaPolyzyme pretreatment, bead-beating, and the use of the automated xTitan device was capable of extracting DNA from a greater fraction of the prokaryotic community. Moreover, the beta diversity results suggested that biases introduced by different DNA extraction methods and sample storage and transportation are sample type-specific, with some environmental microbial communities (e.g., coral-associated microbiome) being less susceptible. Therefore, the use of liquid nitrogen to preserve the coral fragments was a crucial factor in maintaining microbial community structure between samples extracted in the field and in the lab. The effects of sample storage and transportation have been well-documented in fecal-derived microbiomes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, there is limited information about their impacts on coral- and mangrove-associated prokaryotic communities. Recently, studies tackling this topic have been carried out in marine sediment, algae-associated, and seawater samples [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Overall, our results indicated that microbial community structure characterization was primarily affected by the DNA extraction method (Qiagen versus xTitan), but significant differences were found between the in-field and in-lab xTitan extractions, especially when applied to mangrove sediments and their surrounding seawater (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and Fig.\u0026nbsp;6AB). For instance, the relative abundance of \u003cem\u003eProchlorococcaceae\u003c/em\u003e was significantly lower when DNA was extracted in the lab using the xTitan system rather than in the field. This could be due to the continued growth of heterotrophic bacteria during storage and transportation, leading to a reduced relative abundance of \u003cem\u003eProchlorococcaceae\u003c/em\u003e. Alternatively, rapid preferential lysis of \u003cem\u003eProchlorococcaceae\u003c/em\u003e during storage and transport, followed by DNA degradation, could similarly lead to altered observed bacterial community structures. These processes can lead to an underestimation of cyanobacterial populations when characterizing such marine ecosystems. These findings indicate that transportation on ice, even during short-term storage, can alter these microbial communities, potentially introducing biases in bacterial diversity and community structure analyses and affecting biological interpretation.\u003c/p\u003e \u003cp\u003eIn this study, the use of different systems for extracting total DNA provided us with comprehensive information to characterize microbial communities from two coral species in the Red Sea. To highlight this, two bacterial species were highly abundant in the \u003cem\u003eP. verrucosa\u003c/em\u003e colony (i.e., \u003cem\u003eF. imtechensis\u003c/em\u003e and \u003cem\u003eE. acroporae\u003c/em\u003e; Fig.\u0026nbsp;3BC). It is well known that \u003cem\u003eEndozoicomonadaceae\u003c/em\u003e is a dominant family in the microbiome of \u003cem\u003ePocillopora\u003c/em\u003e species, regardless of their health status [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This coral can retain a large fraction of its microbial symbionts after being exposed to environmental stress (e.g., bleaching), suggesting that its microbiome is predictable and rather inflexible [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Our findings allowed us to formulate these open questions: i) Why was \u003cem\u003eFulvivirgaceae\u003c/em\u003e found in high relative abundance compared to \u003cem\u003eEndozoicomonadaceae\u003c/em\u003e when using two (i.e., Qiagen and xTitan in-lab) of the three total DNA extraction systems with \u003cem\u003eP. verrucosa\u003c/em\u003e colony fragments? ii) Are these differences caused mainly by the preferential extraction of DNA and/or the restructuring of prokaryotic communities during short-term storage and transportation in liquid nitrogen? Moreover, as the same coral fragments were subjected to distinct DNA extraction systems, differences caused by spatial heterogeneity in the coral colony can be discarded. Regarding the symbionts of \u003cem\u003eXenia\u003c/em\u003e sp., a high diversity of \u003cem\u003eEndozoicomonas\u003c/em\u003e ASVs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), similar to that seen in deep-sea corals [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], was found. Recently, a high proportion of \u003cem\u003eEndozoicomonas\u003c/em\u003e species has been found in samples obtained from \u003cem\u003eXenia\u003c/em\u003e sp. colonies around 170 km to the north of our sampling point (in Al Rayyis White Head, KSA) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In our study, a small symbiont of the \u003cem\u003eMollicutes\u003c/em\u003e group (\u003cem\u003eSpiroplasma lampyridicola\u003c/em\u003e) was detected in high relative abundance on the \u003cem\u003eXenia\u003c/em\u003e sp. colony (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). This microorganism is a cell-wall-deficient helical bacterium that has been found in abundance in other soft corals (i.e., \u003cem\u003eVeretillum cynomorium\u003c/em\u003e) in the Sea of Marmara [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, its physical location (e.g., intracellular or not) and metabolic roles as a symbiont of \u003cem\u003eXenia\u003c/em\u003e sp. have yet to be elucidated. It is important to mention that our findings were obtained from single coral colonies, from two genotypes, and that further extensive biological replication and experimentation will be necessary to answer the above questions and support our claims about coral microbiome structure and ecology.\u003c/p\u003e \u003cp\u003eAs mentioned, a significant challenge in marine microbiome analyses is the presence of exogenous DNA contaminants that come from DNA extraction kits (\u0026ldquo;kitomes\u0026rdquo;) or PCR reagents [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These \u0026ldquo;kitomes,\u0026rdquo; if not removed prior to analysis, could bias diversity analyses, inflate alpha diversity metrics, and affect beta diversity clustering and taxonomy plots. In addition, they are particularly problematic in samples with low prokaryotic biomass [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], low quantity of prokaryotic DNA, and/or low prokaryotic diversity (e.g., corals). In this study, mostly ASVs belonging to the family \u003cem\u003eOxalobacteraceae\u003c/em\u003e were detected as DNA contaminants (Suppl. Figure\u0026nbsp;2A; Suppl. Table\u0026nbsp;3). \u003cem\u003eOxalobacteraceae\u003c/em\u003e taxa have frequently been found as contaminants in several commercial DNA isolation kits, including ZymoBIOMICS MagBead DNA/RNA kit, and PCR reagents [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Although \u003cem\u003eOxalobacteraceae\u003c/em\u003e species have been found in high abundance in coral surface mucus [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], other studies have pinpointed that this taxon is a reagent contaminant [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], which is corroborated by our data. This finding highlights the need for proper negative controls, including multiple extraction blanks, trip blanks, and sequencing library preparation blanks, on the characterization of coral-associated microbiomes.\u003c/p\u003e \u003cp\u003eCharacterization of coral-derived microbiomes using nanopore metagenome sequencing was particularly challenging, as much of the extracted DNA was derived from the coral itself rather than its associated prokaryotic species. In this study, the majority (\u0026gt;\u0026thinsp;90%) of the sequences from most of the coral metagenome samples were associated with the domain \u003cem\u003eEukaryota\u003c/em\u003e (Suppl Fig.\u0026nbsp;4B). Thus, the development of new field-deployable host-depleting methods as well as high performance reagents to increase cell lysis are essential for advancing the study of coral-associated microbial communities using metagenomic approaches [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In this regard, MetaPolyzyme, a multi-lytic enzyme mixture targeting bacterial cell walls, may be used to enhance microbial cell lysis and increase the relative yield of prokaryotic-derived DNA [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Moreover, high proportions of unclassified sequences are another issue in microbiome analysis. In our study, a high percentage (e.g., ~\u0026thinsp;45% in mangrove sediments and seawater) of metagenomic-derived genes were designated as unclassified at the kingdom level (Suppl. Figure\u0026nbsp;4A), indicating that these sequences are lacking contextual sequences (i.e., references sequences with \u0026gt;\u0026thinsp;40% similarity) in established non-redundant databases (e.g., the GenBank-nr database) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This could be due to incomplete reference databases lacking representation of unique environmental microorganisms [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], high sequence divergence from known organisms, or limitations in taxonomic classification algorithms, hindering accurate classification in complex and understudied marine ecosystems like the Red Sea. It is also important to mention that ONT metagenome sequencing can produce high-quality \u0026ldquo;noise reads\u0026rdquo; that pass quality and length filters but appear to be artifacts from a subset of flow cell pores [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Moreover, the generation of gene and genome catalogs [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] from the Red Sea-derived microbial systems will be essential in improving future microbiome characterization using metagenomic methods.\u003c/p\u003e \u003cp\u003eMangrove ecosystems harbor immense and yet underexplored prokaryotic diversity [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In our study, this was confirmed by alpha diversity metrics (Fig.\u0026nbsp;2AB) and the high proportion of unclassified bacterial genes (~\u0026thinsp;30%) found in mangrove sediment metagenomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). These ecosystems receive a constant input of nutrients and marine microorganisms via seawater dispersal [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In particular, high salinity conditions on the Red Sea mangroves are ideal for halophilic prokaryotes, which have yet to be fully characterized. In our study, the family \u003cem\u003eBalneolaceae\u003c/em\u003e (belonging to the new phylum \u003cem\u003eBalneolota\u003c/em\u003e) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] was the most abundant family in mangrove sediments, showing similar relative abundance values across all three DNA extraction systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). This family of halophilic bacteria is composed of six genera, \u003cem\u003eAliifodinibius, Balneola\u003c/em\u003e, \u003cem\u003eFodinibius\u003c/em\u003e, \u003cem\u003eGracilimonas\u003c/em\u003e, \u003cem\u003eHalalkalibaculum\u003c/em\u003e, and \u003cem\u003eRhodohalobacter\u003c/em\u003e [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Elsewhere, these microorganisms have been found in the rhizosphere of plants growing in saline soils [\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] and in volcanic alkaline soils [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Currently, they are still poorly explored, and their ecological and functional roles in mangrove ecosystems remain unknown. In our study, genes derived from \u003cem\u003eGracilimonas\u003c/em\u003e, \u003cem\u003eAliifodinibius\u003c/em\u003e, and \u003cem\u003eHalalkalibaculum\u003c/em\u003e and from undetermined members of the family \u003cem\u003eBalneolaceae\u003c/em\u003e predominated mangrove sediments metagenomes, while \u003cem\u003eBalneola\u003c/em\u003e-derived genes were more abundant in seawater metagenomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Targeted and in-depth metagenomic analyses of \u003cem\u003eBalneolota\u003c/em\u003e phylum in mangrove microbiome surveys will generate more information about their lifestyles, their roles in nature, and their biotechnological potential.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study introduced a strategy that utilized innovative sample processing instruments, techniques (e.g., biopulverizer, handheld bead beater, concentrating pipette system and MetaPolyzyme treatment), and a portable system (i.e., xTitan) to perform on-site automated total DNA extraction for investigation of microbiomes. We have demonstrated the usability of such field-deployable technologies for microbial community characterization in Red Sea-derived ecosystems, including corals, mangrove sediments and seawater. In this study, the three DNA extraction systems (i.e., in-field xTitan, in-lab xTitan, and in-lab Qiagen) performed comparably, capturing consistent microbial diversity and taxonomic composition across distinct marine-associated samples. However, our results indicated that differences in DNA extraction systems, as well as short-term sample storage and transportation at cold temperatures, influenced microbial diversity, community structure, and functional profiles in a sample-specific manner, with pronounced effects in mangrove-associated samples. Given that xTitan system supports flexible chemistries for DNA and RNA extraction, future metatranscriptomic analyses incorporating on-site and in-lab RNA extractions can help determine what functions, metabolic activities, and active microbial taxa will be more affected by sample storage and transportation. Finally, this study highlights the effectiveness of novel sample processing methods, and on-site DNA extraction systems as reliable tools for microbial monitoring, particularly in remote, extreme, and marine ecosystems. These deployable technologies address challenges associated with sample storage and transportation, enabling in-field assessments that provide critical insights into the dynamics of microbial communities as they respond to real-time environmental conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent for publication have been obtained from co-authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe bacterial 16S rRNA gene amplicon and metagenome sequences of all samples characterized in this study were deposited in NCBI under BioProject no. PRJNA1171851. All the codes, figures, and data related to this project are available at https://github.com/Tahiraj/iMMC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.K. was employed by AI Biosciences, Inc., and S.W. is a co-founder of AI Biosciences, Inc., the company that developed the xTitan device used in this manuscript. AP was employed by InnovaPrep LLC and CTO of the company that developed the Innovaprep CP Select\u003csup\u003eTM\u003c/sup\u003e system used in this study. All other authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by KAUST Grant BAS/1/1096-01-0 (assigned to Prof. A. S. Rosado).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026apos;s contributions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiego J. Jim\u0026eacute;nez:\u0026nbsp;\u003c/strong\u003eSampling, DNA extraction, computational analyses, advising, figures design, and drafting of the first version of the manuscript. \u003cstrong\u003eTahira Jamil:\u003c/strong\u003e Computational analyses, figures design, and writing. \u003cstrong\u003eGeorgios Miliotis:\u003c/strong\u003e Sampling, experimental design, computational analyses, molecular procedures, advising, and writing. \u003cstrong\u003eJ\u0026uacute;nia Schultz:\u003c/strong\u003e Sampling, DNA extraction, and writing. \u003cstrong\u003eNiketan Patel:\u0026nbsp;\u003c/strong\u003eAdministration. \u003cstrong\u003eLila Aldakhee:\u003c/strong\u003e Sampling and DNA extraction. \u003cstrong\u003eNicholas Kontis:\u003c/strong\u003e Sampling and DNA extraction. \u003cstrong\u003eFrancisca C. Garc\u0026iacute;a:\u003c/strong\u003e DNA extraction, advising, and writing. \u003cstrong\u003eHelena Villela:\u003c/strong\u003e Sampling, advising, and writing. \u003cstrong\u003eGustavo A.S. Duarte:\u003c/strong\u003e Advising, DNA extraction, and writing. \u003cstrong\u003eAdam R. Barno:\u003c/strong\u003e Sampling and DNA extraction. \u003cstrong\u003eAyman Farran\u003c/strong\u003e: Sampling. \u003cstrong\u003eAhmed Alsagaaf:\u003c/strong\u003e Sampling. \u003cstrong\u003eErika Santoro:\u003c/strong\u003e Advising and sampling. \u003cstrong\u003eAnna Tumeo:\u003c/strong\u003e Bioinformatic analysis and writing. \u003cstrong\u003eAndy Page:\u003c/strong\u003e DNA extraction and writing. \u003cstrong\u003eSeason Wong:\u003c/strong\u003e Sampling, experimental design, DNA extraction and writing. \u003cstrong\u003eAdam Kabza:\u003c/strong\u003e Sampling and DNA extraction. \u003cstrong\u003eAlexander Putra:\u003c/strong\u003e DNA sequencing. \u003cstrong\u003ePark Changsook:\u003c/strong\u003e DNA sequencing. \u003cstrong\u003eAngel Angelov:\u003c/strong\u003e DNA sequencing and writing. \u003cstrong\u003ePatrick Driguez:\u003c/strong\u003e DNA sequencing and writing. \u003cstrong\u003eRaquel S. Peixoto:\u003c/strong\u003e Sampling, advising, resource acquisition, and writing. \u003cstrong\u003eStefan J. Green:\u003c/strong\u003e Sampling, experimental design, advising, and writing. \u003cstrong\u003eScott Tighe:\u003c/strong\u003e Sampling, experimental design, DNA extraction, advising, and writing. \u003cstrong\u003eAlexandre S. Rosado:\u003c/strong\u003e Sampling, experimental design, advising, resource acquisition, financial support and coordination. \u003cstrong\u003eKasthuri Venkateswaran:\u0026nbsp;\u003c/strong\u003eSampling, experimental design, advising, writing, and the coordination of all parties involved in the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Neus Garcias-Bonet for her comments on the first version of the methods. We thank Ping Xu for excellent administrative assistance and Morgan Bennett-Smith for photography. The fieldwork and sampling efforts were conducted in compliance with permits 23IBEC098 and 22IBEC003 and KAUST\u0026rsquo;s regulations, for coral reef and mangrove ecosystem, respectively.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCruaud P, Vigneron A, Lucchetti-Miganeh C, Ciron PE, Godfroy A, Cambon-Bonavita MA. Influence of DNA extraction method, 16S rRNA targeted hypervariable regions, and sample origin on microbial diversity detected by 454 pyrosequencing in marine chemosynthetic ecosystems. Appl Environ Microbiol. 2014;80(15):4626\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllaband C, Lingaraju A, Flores Ramos S, Kumar T, Javaheri H, et al. Time of sample collection is critical for the replicability of microbiome analyses. 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Astrobiology. 2024;24(8):824\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Balneolaceae, Corals, Endozoicomonadaceae, Halophiles, Mangroves, Marine microbiomes, Samples storage","lastPublishedDoi":"10.21203/rs.3.rs-5928577/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5928577/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this study, xTitan, a field-deployable, automated, and versatile nucleic acid extraction system was employed to characterize microbial communities in Red Sea-derived samples, including coral colonies, mangrove sediments, and seawater. The use of the xTitan in the field was intended to minimize sample transport bias, obtaining data that may be closer to \u0026ldquo;ground truth\u0026rdquo; for microbial diversity. The observed microbial communities from DNA extracted in the field using the xTitan system were compared to DNA extractions performed in a laboratory setting using both xTitan and a commercial Qiagen kit after approximately 24 h of samples transfer and storage.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMicrobial community analyses conducted on DNA extracted using the xTitan system and the Qiagen kit yielded similar alpha diversity metric values, with a trend toward higher diversity observed in most samples extracted with the xTitan. The microbial community structure in samples from a \u003cem\u003ePocillopora verrucosa\u003c/em\u003e colony, mangrove sediments, and seawater was affected by the DNA extraction system. In the \u003cem\u003eP. verrucosa\u003c/em\u003e colony, an amplicon sequence variant (ASV) identified as \u003cem\u003eEndozoicomonas acroporae\u003c/em\u003e was preferentially abundant when DNA was extracted in the field with the xTitan system rather than in the lab. In mangrove sediments, significant differences (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in beta diversity and functional gene profiles were observed when comparing in-field to in-lab xTitan DNA extracts. In seawater, a pronounced decrease in the relative abundance of cyanobacterial populations was observed when DNA was extracted with both methods after samples were transported to the lab on ice. In addition, hundreds of ASVs from mangrove-associated samples were differentially abundant when DNA was extracted on-site with xTitan system compared to in-lab extractions. \u003cem\u003eBalneolaceae\u003c/em\u003e was one of the most abundant taxa in mangrove sediments and several genera from this family were detected in all replicates across all DNA extraction systems.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe usability of different field-deployable instruments for microbial community characterization in marine-derived samples was demonstrated. Moreover, differences in beta diversity were observed when DNA was extracted in-field versus in-lab using the xTitan system, particularly for mangrove-associated samples. These results highlight the value of on-site nucleic acid extraction for enhancing the detection of microbial taxa that can be sensitive to cold storage. This study enabled the testing of the xTitan on Red Sea-derived samples, generating comprehensive information on the effects of DNA extraction systems and transportation of samples on coral and mangrove-associated microbiomes.\u003c/p\u003e","manuscriptTitle":"Microbial community characterization in Red Sea-derived samples using a field-deployable DNA extraction system and nanopore sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-17 18:32:42","doi":"10.21203/rs.3.rs-5928577/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4cacdcf3-452f-47a0-be36-fbc516f37eb5","owner":[],"postedDate":"March 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-09T11:39:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-17 18:32:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5928577","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5928577","identity":"rs-5928577","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}