Urmia Lake: A promising pool of petroleum hydrocarbon biodegrades

Urmia Lake: A promising pool of petroleum hydrocarbon biodegrades | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Urmia Lake: A promising pool of petroleum hydrocarbon biodegrades Roohollah kheiri, Mohammad Ali Amoozegar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7699662/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 In this study, halotolerant microorganisms capable of degrading petroleum hydrocarbons were isolated and analyzed from Urmia Lake. The research focused on identifying microbial consortia resistant to salinity and capable of growing in high concentrations of crude oil. Biodegradation of crude oil at salinity levels ranging from 0% to 27% and concentrations from 1000 to 5000 ppm was evaluated using BOD measurement and GC analysis. The results demonstrated the highest removal efficiency at 3000 ppm crude oil and salinity levels of 0%, 5%, and 10% within 18 days. Metataxonomic analysis of the petroleum hydrocarbon-degrading consortium identified 308,642 sequences, which were clustered into 31,609 OTUs with a 97% similarity threshold. Relative abundance analysis of these sequences revealed the dominance of Salinicoccaceae (57%), Dietziaceae (28.3%), Micrococcaceae (4.2%), and Bacillaceae (3.1%). In metagenomic analysis, whole-genome sequencing data were used for MAG reconstruction and functional gene screening. To isolate pure strains, the crude oil-enriched consortium was cultured on a growth medium, and colonies were isolated after incubation. Among six purified strains, three with higher efficiency were selected for further analysis. 16S rRNA gene sequencing identified these strains as Salinicoccus roseus, Nesterenkonia muleiensis, and Cytobacillus firmus . GC analysis confirmed that Salinicoccus roseus was the most effective hydrocarbon degrader. Although Salinicoccus roseus outperformed the consortium in degrading most petroleum hydrocarbons, the degradation of C26 hydrocarbons required the collective activity of consortium members. BOD Hydrocarbon biodegradation Metagenomics Metataxonomy Nesterenkonia muleiensis Salinicoccus roseus Urmia Lake. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Highlights Urmia Lake is a promising pool of hydrocarbon-degrading microorganisms. There may be yet-to-be-identified microorganisms in the Urmia Lake. Salinicoccus roseus proved to be an efficient biodegrader of hydrocarbons. 3. Novel metabolic pathways and biosurfactants may play a critical role in hydrocarbon degradation. Introduction: Environmental pollution is one of the main concerns in today's world. Petroleum compounds, known as hydrocarbons (Vijayanand, et al., 2023) are among the most persistent and harmful pollutants due to their toxicity, genotoxicity, mutagenicity, and carcinogenic properties, which adversely impact living organisms and ecosystems (Kheiri & Akhtari, 2017). These pollutants arise from various stages of petroleum exploration, production, transportation, and storage, with saline and hypersaline environments being particularly vulnerable due to their association with oil-related activities (Wu, et al., 2022). Bioremediation has emerged as a promising strategy for addressing petroleum pollution, offering an eco-friendly and cost-effective alternative to physical and chemical remediation approaches. This biological process relies on the metabolic capabilities of microorganisms to degrade hydrocarbons and can be implemented through biostimulation or bioaugmentation. While biostimulation optimizes the activity of native microbial communities, bioaugmentation involves introducing external microorganisms to enhance degradation in environments with insufficient indigenous degraders (S. Nwankwegu, et al., 2022). Among microorganisms, bacteria have demonstrated the most significant hydrocarbon degradation potential, with genera such as Pseudomonas and Bacillus playing critical roles (Elayaperumal, et al., 2025). In addition to bacteria, fungal genera including Graphium , Talaromyces , Neosartorya , and Amorphoteca have been proven to be efficient members for removing hydrocarbon compounds (Habes Almutairi, 2024). Although algae and protozoa are important parts of microorganism communities both onshore and offshore, there is little information about their hydrocarbon degradation capabilities (R & F, 2001). As a biological process, bioremediation strategies are highly dependent on both the metabolic capability of microorganisms and environmental factors such as pH, temperature, available water, amount of nutrients, hydrocarbon structure, and salinity (Ellis, et al., 2022). Although salinity is one of the limiting factors that correlate with failure of bioremediation due to its negative impact on several factors, including activating water, hydrocarbon bioavailability, and activation of critical enzymes, it would be promising to find a halotolerant or halophile bacterium capable of biodegrading petroleum in a large range of salinity (S. Ayilara & O. Babalola, 2023). Regarding the value of halotolerant biodegrading bacteria, hypersaline environments, such as Urmia Lake, can be a potential reservoir of such bacteria. Urmia Lake, located in northwest Iran, was the greatest lake in the Middle East and the sixth-largest hypersaline on Earth; however, because of loss of input and high rate of evaporation, it has reached a fifth of its original area. It is noteworthy that, based on high ionic levels of Cl − , SO 4 2− , Na + , Mg 2+ , K + , and Ca 2+ , it is classified as a thalassohaline lake, and paleogeographic studies suggest it has originated from the Paratethys sea (Kheiri, et al., 2023). Recent advances in metagenomics have revolutionized the study of microbial diversity and functional potential in various environments, including hypersaline ecosystems. Metagenomics enables the direct analysis of genetic material from environmental samples, bypassing the limitations of traditional culture-based methods (Elena Pérez-Cobas, et al., 2020). This approach has proven invaluable for uncovering the vast microbial diversity (Ranjbar, et al., 2019)and the metabolic potential of microorganisms, particularly in extreme environments where many microbes are unculturable. In the context of petroleum hydrocarbon biodegradation, metagenomics has been instrumental in identifying genes and pathways involved in the degradation of complex hydrocarbons and has revealed the roles of various enzymatic systems ( Kong, et al., 2024). For hypersaline environments, metagenomics holds the promise of discovering novel halotolerant or halophilic microorganisms with unique enzymatic systems tailored to extreme salinity. Since we previously analyzed microbial diversity, genetic variation, and metabolic capabilities of microorganisms populated in Urmia Lake (Kheiri, et al., 2023), we conducted this survey to explore microorganisms capable of degrading hydrocarbon compounds. Methods and Materials Water sampling and physicochemical analysis Located between the provinces of East Azerbaijan and West Azerbaijan in Iran and west of the southern portion of the Caspian Sea, Urmia Lake, at its most expansive, was the largest lake in the Middle East and the sixth-largest hypersaline lake on Earth. However, due to the sharp reduction of input, the complete disappearance of the lake is imminent (Kheiri, et al., 2023). To conduct our study, sampling was performed on October 20th, 2020, for physicochemical, microbiological, and metagenomic analysis. The water temperature, pH, and Conductivity were measured in situ using a Hach HQ40D portable multimeter (HACH company, USA). Samples were transported at 4°C to Tehran University, the Extremophile laboratory, for further analysis. For physico-chemical analysis, standard methods based on the American Public Health Association (APHA) were used (E. W, et al., 2017). Evaluation of the salinity impact on the hydrocarbon-degrading of the consortium Optimizing factors to reach the maximum consortium efficiency of total petroleum hydrocarbon (TPH) degradation was a crucial part of our study. Incubation time, salinity, and TPH level were three factors that we focused on to find the optimum range for consortium metabolism. To evaluate and optimize these factors, the Biochemical Oxygen Demand (BOD) parameter, which simply unveils the amount of oxygen used by microorganisms in liquid environments, was used. Based on the fact that the amount of consumed oxygen for aerobic microorganisms’ degradations correlates with aerobic metabolism, BOD can be a good indicator of hydrocarbon biodegradation to evaluate and optimize incubation time, TPH level, and salinity. In other words, the higher BOD would imply a higher microbial metabolic activity. For this purpose, a recently calibrated WTW OxiTop®-i IS 12 respirometric BOD measuring system was used. To determine the optimum and maximum tolerable salinity concentration of Urmia Lake microorganisms for degrading hydrocarbons, considering the 27% w/v salinity of Urmia Lake water, different salinities, namely 5%, 10%, 15%, 20%, and 27% were prepared using Bushnell-Haas medium as the diluent (Li, et al., 2022), a primary concentration of 2000 ppm of crude oil (0.2% v/v), separated Urmia Lake microorganisms (via centrifugation and filtration), as well as nBOD inhibitor buffer (based on WTW OxiTop®-i manual) were added into BOD bottles. OxiTop barometers were screwed and closed tightly. Incubation started at 20°C and was prolonged up to 20 days, while daily oxygen consumption was recorded. To guarantee accuracy and lower any faults, the experiments were conducted in duplicate. GC analysis of the consortium-degraded hydrocarbons Daily measurement of BOD confirmed the biodegradation of TPH. To determine specific hydrocarbon biodegradation, GC was used. For this purpose, using the EPA 3510C: Separatory Funnel Liquid-Liquid Extraction method, the extracted samples were analyzed by an Agilent 7890A GC system with Helium (99.999%) as the carrier gas at a constant flow rate of 1 ml/min. The oven temperature was programmed as follows: initial temperature of 40°C (held for 1 min), and an increased rate of 3°C/min to 270°C (EPA, 1996). Details are presented in Supplementary Table S1 . Optimization of biodegradation: optimal TPH concentration and Incubation time Following the salinity evaluation, we assessed the TPH level impact on the microbial consortium’s activity to conclude optimum and toxic concentrations. To do so, as mentioned above, optimized pre-defined salinity, separated microorganisms, nBOD inhibitor buffer, and different concentrations of crude oil, including 1000, 2000, 3000, 4000, and 5000 ppm, were added into standard sterile BOD bottles. Based on the WTW OxiTop®-i manual, measuring started at 20°C and was prolonged up to 18 days, while daily oxygen consumption was recorded. The experiments were conducted in duplicate (Vijayanand, et al., 2023). Metataxonomic analysis of TPH-Degrading Consortium For the Metataxonomic analysis of the consortium capable of degrading TPH, DNA of 5% salinity consortium (the highest performance of degradation) was extracted using QIAprep® Miniprep (Qiagen) DNA extraction Kit, according to the manufacturer’s instructions. The quantity and quality of the extracted DNA were analyzed by a NanoDrop™ One Microvolume UV–Vis Spectrophotometer and agarose gel electrophoresis. V3-V4 16S rRNA gene metabarcoding was performed by the Illumina MiSeq platform at Novogene Co., Ltd (China). Microbiome 16S rRNA gene diversity was assessed with QIAGEN CLC Genomics Workbench (version 22) (QIAGEN, 2022). Briefly, raw reads were quality-checked and trimmed, singletons and adaptors were removed, and chimeric sequences were filtered. Taxonomic classification of the reads and Operational Taxonomic Unit affiliation were based on the genome taxonomy database (GTDB) 16S rRNA sequences (Release 09-RS220)(H Parks, et al., 2022) for a minimum similarity of 97%. The rarefaction curves were constructed based on the number of OTUs per sample. Screening TPH-biodegrading strains An agar-solidified 5% salinity water, covered by 1,2-dichloroethane-solved crude oil, was used to isolate bacteria of the consortium. The biodegrading consortium was streaked on agar medium and incubated at room temperature (approximately 25°C). Isolates capable of consuming petroleum hydrocarbons as the sole carbon and energy source appeared in colonies on the surface of the agar medium. To ensure pure isolates, three subcultures were performed (S Al-Wasify & R Hamed, 2014). Evaluation of the TPH-biodegrading efficiency of isolated bacteria Since isolates may have different efficiencies in biodegrading hydrocarbons, to evaluate their performance, they were inoculated in nutrient broth (Merck) and incubated overnight for McFarland turbidity standards preparation. 1 ml containing approximately a cell density of 1.5 x 10 8 per ml of the cultures was inoculated in 200 ml of 5% salinity-diluted Bushnell Hass medium supplemented with 0.3%w/v carbon source, and the cultures were incubated at room temperature, in a shaking incubator (150 rpm) for 20 days (S Al-Wasify & R Hamed, 2014). Following the finishing measurement period (20 days), TPH was extracted for GC evaluation to find out hydrocarbons degraded by isolates (EPA, 1996). Evaluation of biosurfactant production of isolated bacteria To evaluate the biosurfactant production of the strains, various approaches, including oil spreading, drop collapse, Emulsification Index (%E24), and surface tension, were used. For the oil displacement approach, pure isolates were inoculated in Bushnell-Haas medium supplemented with 0.1% crude oil. The cultures were incubated at 30°C, in a shaking incubator, agitated at 120 rpm for 7 days. To continue the procedure, 50 ml of distilled water was poured into a Petri dish (150 mm), 100 µl of crude oil was added to the water surface, and finally, 10 µl of the cultured strains was added. The presence of biosurfactants was evaluated by creating a clear zone on the surface of the oil. Drop collapse assay was performed according to Youssef et al. (Youssef, et al., 2004). The rationale behind this assay relies on surfactants' destabilization of oil-coated, solid surfaces. Scoring was performed by setting sterile deionized water as a negative control (-) and a 10 − 4 dilution of the “S-200 oil-gone” commercial BS solution as a positive (+++) control and comparing the diameter of droplets from the examined cultures. The emulsification index (%E24) was analyzed, as reported earlier by Alka Kumari et al (Kumari, et al., 2021). Briefly, an Equal volume of centrifuged-cell-free supernatant of the culture (2 ml) was added to crude oil and vortexed at maximum speed for 3 min and allowed to stand for 24 hours at room temperature, followed by calculating %E24. As amphiphilic compounds, biosurfactants are surface-active molecules. To analyze the surface tension, the capillary rise method was used. Capillarity is the combined effect of cohesive and adhesive forces that causes cell-free supernatant to rise in thin tubes. Phylogenetic analysis of TPH-biodegrading strains Colonies with the highest degradation capability were selected to be phylogenetically analyzed for the 16S rRNA gene. For this purpose, genomic DNA was extracted, and the 16S rRNA region of the bacterial gene was targeted by the universal bacterial primers PCR 27f (5’ AGAGTTTGATCMTGGCTCAG 3’) and 1492r (5’ TACCTTGTTACGACTT 3’) as previously described by Jeremy Frank et al. (A. Frank, et al., 2008), followed by Sanger sequencing by Microsynth Seqlab (Microsynth AG, Switzerland). Using QIAGEN CLC Genomics Workbench (version 22) (QIAGEN, 2022), the 16S rRNA sequences were checked against the genome taxonomy database (GTDB) 16S rRNA sequences (Release 09-RS220) (H Parks, et al., 2022), as well as the online EzBioCloud database ( https://www.ezbiocloud.net/ ) ( Yoon, et al., 2017). Phylogenetic trees for 16S rRNA of isolated strains, as well as reference strains retrieved from NCBI GenBank, were subjected to multiple alignments using Clustal W, and a phylogram was built by the maximum likelihood method using Ngphylogeny.fr (Lemoine, et al., 2019) followed by iTOL v6 (Letunic & Bork, 2021 ). DNA extraction and quality control Among the key enzymatic systems involved in hydrocarbon biodegradation are alkane hydroxylases and various monooxygenases, facilitating their subsequent breakdown into less toxic and more bioavailable metabolites. We performed a comprehensive metagenomic analysis to identify these genes in our whole-genome shotgun sequencing dataset obtained from Lake Urmia. To do so, we sampled Urmia Lake water, collected biomass, extracted DNA, and checked the DNA quality, as previously reported (Kheiri, et al., 2023). Briefly, pre-filtering samples through 3-µm filters (cellulose-nitrate, Millipore) removed eukaryotes such as Dunaliella salina . For microbial collection, centrifugation at 3260× g for 60 min, and 0.22-µm membrane filtration was used, followed by QIAprepR Miniprep (Qiagen) DNA extraction. The extracted DNA was analyzed for quantity and quality using a NanoDrop™ One C Microvolume UV–Vis Spectrophotometer and agarose gel electrophoresis (Supplementary Table S2). Sequencing, Assembly, Gene Prediction, and Target Gene Identification For sequencing, we utilized the Illumina NovaSeq 6000 platform (Novogene Co., Ltd., China) with a paired-end (PE150) library. Raw metagenomic reads were assessed for quality, trimmed to remove low-quality bases, and filtered to eliminate sequencing artifacts. Quality control was conducted using FastQC (v0.11.9) ( Wingett & Andrews, 2019), followed by read trimming with Trimmomatic (v0.39) (M. Bolger, et al., 2014). The trimmed reads were then assembled into longer contiguous sequences (contigs) using MEGAHIT (v1.2.9) (Li, et al., 2016), employing a k-mer range of 21–149. The resulting contig.fa file contained the assembled contigs, which were subsequently used for gene prediction and open reading frame (ORF) identification. Protein-coding sequences were predicted from the assembled contigs using Prodigal (v2.6.3) (Hyatt, et al., 2010) in metagenomic mode. The translated amino acid sequences were stored in the predicted_proteins.faa file and utilized for functional gene screening. To identify putative cytochrome P450 (CYP450) and other monooxygenase genes, we applied HMMER (v3.3.2) (D Finn, et al., 2011) with Hidden Markov Model (HMM) profiles sourced from the Pfam (Mistry, et al., 2020) and TIGRFAM (Haft, et al., 2001) databases. This was followed by HMM searches against the predicted protein sequences to pinpoint the target genes. Results Physico-chemical features of Urmia Lake. Sampling was performed during the period of lowest rainfall and input volume in the year when the lake water reached the highest salt concentration. As shown in Fig. 1 , water samples were collected from six locations (Supplementary Table S3), with approximately 20 L of water taken at a depth of 20 cm in sterile containers. In the previous study, we identified six major ions, including Cl − (180,000 mg/l), SO 4 2− (25,260 mg/l), Na + (92,500 mg/l), Mg 2+ (17,750 mg/l), K + (4,000 mg/l), and Ca 2+ (512 mg/l) are the main ions of the water (Kheiri, et al., 2023) (Supplementary Table S4). Incubation time and salinity BOD was measured for 20 days. Daily recording measurements showed a rise in BOD level up to the 18th day of incubation, while prolonged incubation time did not increase BOD. This implied the appropriate incubation time for the Urmia Lake consortium for TPH bio-degradation was up to 18 days (Fig. 2 ). For salinity evaluation, BOD measurements proved the optimum salinity of TPH bio-degradation was 5%, followed by 0% and 10%. This means that not only 5% salinity did not inhibit metabolic activities, but also such a level of salinity (ions) may have a promoting role in microbial activities. The GC spectrum also confirms the high efficiency of the microbial consortium in biodegrading TPH in 5% salt. As can be seen in the GC peak area analysis table, the lowest peak areas correspond to 5% total salt concentration, followed by 0%, 10%, 15%, and 20%. While increasing the salinity to 27%, a severe drop in BOD level is observed, which indicates inhibition of microbial consortium metabolism (Fig. 3 ). Details are presented in Supplementary Table S5. Optimizing TPH concentration A gradient of crude oil concentration, including 1000 ppm, 2000 ppm, 3000 ppm, 4000 ppm, and 5000 ppm, containing pre-optimized (5%) salinity used for the BOD assay, confirmed 3000 ppm crude oil to be the maximum tolerable concentration. As shown in Fig. 4 , a correlation exists between crude oil concentration and BOD levels up to a concentration of 3000 ppm. However, at concentrations of 4000 and 5000 ppm, a sharp drop in BOD level is observed, which can be inferred as a toxic level. Taxonomy assignments of the consortium Metataxonomy analysis of the Urmia Lake TPH-degrading consortium resulted in a total of 308642 sequences. Followed by filtering and merging, 96.12% of the amplicons ranged from 450–460 bp in length. Low-quality and chimeric sequences were removed, and high-quality reads were clustered into 31609 OTUs, with a 97% similarity cutoff. The relative abundance of the families assigned to these sequences showed the dominance of Salinicoccaceae and Dietziaceae , with relative abundances of 57% and 28.3%, respectively (Fig. 5 ). Other families with a representative abundance in the consortium were Micrococcaceae and Bacillaceae , with relative abundances of 4.2% and 3.1%, respectively. Isolation and identification of the candidate strains A total of 6 isolates nominated as Urmia Lake strain1 (LUst1), LUst2, LUst3, LUst4, LUst5, and LUst6 of distinct morphology capable of using crude oil as the sole carbon and energy source were carefully picked and purified by 3 successive subculturing on the same medium and conditions as potential candidates of biodegradation. However, 3 of which, namely LUst2, LUst5, and LUst6, demonstrated the highest efficiency in biodegradation and the lowest peak. Non-inoculated (blank) and strain-inoculated 3000 ppm crude oil, including 5% salinity-diluted Bushnell Hass GC results, are shown in Fig. 6 . Demonstrating the highest efficiency of biodegradation, LUst2, LUst5, and LUst6 were selected for taxonomic identification and biosurfactant assays. Identification and characterization of bacterial isolates Due to their hydrocarbon degradation capability, LUst2, LUst5, and LUst6 were selected for further evaluation. For taxonomic identification, the 16S rRNA gene was amplified, sequenced, and checked against two databases. As shown in Table 2 , LUst2, LUst5, and LUst6 are affiliated with Nesterenkonia muleiensis , Cytobacillus firmus , and Salinicoccus roseus , respectively. The sequences were deposited in GenBank with the accession numbers listed in Table 2 . The phylogram in Fig. 7 illustrates the phylogenetic position of the LUst2. With approximately 97% similarity to Nesterenkonia muleiensis , it appears to represent a new genus that warrants further investigation. Table 2 Taxonomy affiliation of biodegrading strains Ezbiocloud GTDB Accession number Top Hit Taxon % Similarity Top Hit Taxon % Similarity LUst2 Nesterenkonia 97.9 Nesterenkonia muleiensis 96.7 OQ651249.1 LUst5 Cytobacillus 99.9 Cytobacillus firmus_B 98.3 OQ651247.1 LUst6 Salinicoccus 99.5 Salinicoccus roseus 99.4 OQ651248.1 Biosurfactant assays of the strains The selected strains’ drop collapse test results are shown in Table 3 . As shown, followed by LUst2, and LUst6, LUst5 showed the highest efficiency.To evaluate the halotolerance ability of microorganisms, which plays an essential role in a successful bioremediation process in hypersaline soils, the maximum tolerable concentration of NaCl of isolated bacteria was determined for 24 h of incubation. Our results showed all isolates were able to grow in a wide range of salinity from 0 to 15%, however, salinity higher than 5% suppressed and reduced the hydrocarbon degradation efficiency. Even though there is no significant difference between 0% and 5%, in concentrations higher than 5%, including 10%, 15%, 20%, and 25%, our microbial consortium activity reduced and correlated with salinity concentration. For the oil spread test, the experiments were conducted in triplicate, and biosurfactant activity was determined as the diameter of the clear zone on the oil surface in cm. Results shown in Table 3 confirm the equal potency of the LUst2 and LUst5 in the oil spread test. Finally, LUst5 achieved the highest score in E24 and surface tension assays to be selected as the best biosurfactant producer strain. Table 3 Results of biosurfactant assay Strain/ Assay E24 Drop collapse Oil spread (cm) Surface tension (cm) Test 1 Test 2 Test 3 LUst2 40% +++ 3 3.5 3.5 2 LUst5 52% ++++ 4 3.5 3 2 LUst6 20% + 2 2.5 2.5 1 Specific genes survey in metagenomic reads We reported a comprehensive metagenomic analysis of Urmia Lake previously (Kheiri, et al., 2023), therefore, in this study, we focused on screening the dataset for specific genes involved in hydrocarbon biodegradation, rather than reconstructing metagenome-assembled genomes (MAGs) or performing taxonomic affiliation. In summary, the dataset comprised 204 million high-quality paired-end reads (150 bp) with a Phred quality score greater than 30, ensuring sufficient sequencing depth. Despite the high genomic variation observed, 22 MAGs were reconstructed, with completeness levels ranging from 44.88% to 97.15%. To investigate genes potentially involved in hydrocarbon biodegradation, HMMER analyzed 1,733,777 sequences (224,154,182 residues) and produced the results summarized in Table 4 . However, no significant matches were identified in any of the searches, suggesting the absence of functional activity for these genes in the analyzed metagenomic dataset. Table 4 HMMER scanning results of the target genes in metagenomic contigs ID (Gene) Description number of hits TIGR03860 NtaA/DmoA family FMN-dependent monooxygenase 500 PF00743 Flavin-binding monooxygenase-like 291 PIRSF036487 Alkane 1-monooxygenase 43 PF00067 Cytochrome P450 monooxygenases 592 PF00848 Ring hydroxylating alpha subunit (catalytic domain) 41 Discussion The widespread contamination of aquatic environments with petroleum hydrocarbons, particularly in saline and hypersaline ecosystems, presents a global environmental challenge (Kheiri & Akhtari, 2017). Such environments, often linked to oil exploration and extraction activities, experience extreme salinity levels that inhibit the activity of conventional hydrocarbon-degrading microorganisms. This study aimed to address these challenges by exploring the microbial community of Urmia Lake, a hypersaline lake with salinity levels reaching up to 27% w/v, and evaluating its potential for hydrocarbon biodegradation (Abou Khalil, et al., 2021). Our findings revealed that the microbial consortium demonstrated optimal biodegradation efficiency at 5% salinity, as evidenced by the peak oxygen consumption recorded through BOD measurements. This salinity likely represents a balance between enhancing ionic strength for solubilizing hydrocarbons and avoiding the osmotic stress that limits microbial activity. Beyond 10% salinity, a sharp decline in biodegradation was observed, reflecting the inhibitory effects of high ionic concentrations, consistent with findings by Ventosa et al. (Ventosa, et al., 1998) and Das et al. (Das & Chandran, 2011). These studies have shown that moderate salinity conditions improve the bioavailability of hydrophobic hydrocarbons, facilitating microbial access and degradation. GC analysis further validated these findings, showing significant reductions in hydrocarbon peaks in consortium-inoculated samples (Figs. 3 and 6 ). The consortium demonstrated higher efficiency compared to individual strains, highlighting the synergistic interactions among its members. The ability to degrade a broad range of hydrocarbons was particularly evident in the activity of Salinicoccus roseus (LUst6), which achieved the highest hydrocarbon degradation efficiency (51.97%). This strain’s enzymatic versatility in targeting aromatic hydrocarbons corroborates previous studies, such as those by Al-Wahaibi et al. (Al-Wahaibi, et al., 2014). Several studies have examined microbial consortia and isolated colonies for the biodegradation of pollutants, yielding different outcomes. Zhang et al. argue that microbial consortia can enhance synergistic degradation, reduce the accumulation of intermediate products, and generate crude enzymes, making them more efficient for soil bioremediation (Zhang & Zhang, 2022). In contrast, Obianuju et al. demonstrated that single strains of Serratia marcescens , Providencia vermicola W8, and Pseudomonas aeruginosa W15 exhibited higher hydrocarbon degradation rates than when these strains were used in a consortium (Obiajulu Nnabuife, et al., 2022). Our current study reflects both of these findings. As shown in Supplementary Table S6, LUst 6 was the most efficient strain for biodegrading various hydrocarbons. However, C26 biodegradation requires cooperation between strains, suggesting that the production of one strain served as the substrate for another, highlighting the importance of inter-strain interaction in some cases. Metataxonomic profiling provided a comprehensive overview of the microbial diversity within the consortium, identifying Salinicoccaceae (57%), Dietziaceae (28.3%), and Bacillaceae (3.1%) as dominant families. The predominance of these taxa reflects their evolutionary adaptation to hypersaline conditions and their metabolic versatility. The role of Dietziaceae , for instance, in metabolizing alkanes (C6–C36) has been well-documented by Banat et al. (M Banat, et al., 2010) and is evident in the consortium's performance in degrading crude oil at 3000 ppm. The rarefaction curves (Fig. 5 A) confirm adequate sequencing depth, ensuring that the observed diversity accurately represents the microbial community's functional potential. The biosurfactant production assays revealed another critical mechanism enhancing hydrocarbon degradation. LUst5 ( Cytobacillus firmus ) exhibited the highest emulsification index (52%) and reduced surface tension by 35%, demonstrating its ability to improve hydrocarbon bioavailability. Biosurfactants, as amphiphilic compounds, increase the accessibility of hydrophobic hydrocarbons to microbial enzymes. Similar findings were reported by Fariq et al. (Fariq & Yasmin, 2020), who identified biosurfactant-producing halotolerant bacteria as pivotal in bioremediation. Biosurfactant production emerged as a key mechanism driving hydrocarbon degradation. Cytobacillus firmus (LUst5) demonstrated the highest biosurfactant activity, achieving an E24 emulsification index of 52% and reducing surface tension by 35%. Biosurfactants are amphiphilic molecules that facilitate the dispersion and solubilization of hydrophobic hydrocarbons, enabling microbial access. Compared to synthetic surfactants, biosurfactants are biodegradable, non-toxic, and functional under extreme conditions, making them ideal for saline environments. Similar findings were reported by Youssef et al. (Youssef, et al., 2004), who demonstrated the effectiveness of biosurfactants in enhancing microbial hydrocarbon degradation in contaminated environments. The role of LUst5 in producing biosurfactants under hypersaline conditions highlights its potential for industrial applications, including enhanced oil recovery (EOR) and wastewater treatment. Overall, Salinicoccus roseus was the most successful isolated strain in the degradation of hydrocarbon compounds, according to chromatography graphs (51.97% efficiency). This Gram-positive coccus produces extracellular enzymes, including lipase, esterase, gelatinase, and protease. Numerous studies have highlighted its use in wastewater treatment, pigment production, and its potential anti-cancer effects through the inhibition of the p388 factor in leukemia (Mohamed Ali, et al., 2024). Cytobacillus firmus has also been isolated and identified as a strain with considerable potential for the degradation of hydrocarbon compounds. The efficiency of this strain had been measured by Al-Wahaibi et al. (Al-Wahaibi, et al., 2014). Within 97% similarity to Nesterenkonia muleinsis , our third highly hydrocarbon-degraded strain was identified. The isolation of a strain with such 16S rRNA similarity suggests the discovery of a potentially novel species. Taxonomic thresholds established by Stackebrandt et al. suggest that a similarity below 97% typically represents a new species [28]. Supporting this, Yarza et al. [29] demonstrated that strains with 16S rRNA similarities between 95–97% were subsequently confirmed as novel species through genome-based analyses. The Urmia Lake microbial consortium also shows a bell-shaped diagram for the impact of TPH concentration on consortium activity, as indicated by BOD. BOD measurements showed consortium activity at 1000 ppm and 5000 ppm is slightly similar to each other and is the lowest compared to other concentrations of TPH. In addition, BOD measurements also found almost similar activity within consortia that contained 2000 ppm and 4000 ppm of TPH concentration. TPH concentration at 3000 ppm showed maximum microbial activity according to the measurement of BOD, which was considerably more than any other consortium. The BOD factor was utilized by our research to measure microbial consortium activity while they were exposed to hydrocarbon compounds. The length of incubation, optimum salinity concentration, and optimum TPH concentration have all been measured using BOD. BOD-dependent tests are a novel approach for TPH measurements in saline and hypersaline environments. Our study suggested utilizing the BOD factor as an alternative approach for studying microbial consortia in similar studies. The findings of our study also show that Urmia Lake's microbial community has the potential for hydrocarbon degradation at high concentrations of salinity. Metagenomic analysis revealed substantial genomic diversity (Kheiri, et al., 2023), while no significant matches were identified for canonical hydrocarbon-degrading genes. This finding suggests that microbial communities in hypersaline environments may rely on unconventional or novel enzymatic systems or synergistic microbial interactions in which microbial consortia cooperatively degrade hydrocarbons. This means the product of one cell is the substrate of the other cell. A likely explanation for the absence of these genes is the dominance of Haloquadratum walsbyi , Halonotius , and Salinibacter ruber genes in the contigs, as these microorganisms lack any genes associated with hydrocarbon biodegradation. In contrast, hydrocarbon-degrading microorganisms—primarily bacteria—are present in extremely low numbers at 27% salinity, and they can demonstrate their activity only under optimal degradation conditions within a consortium. Conclusion These findings not only reshape our understanding of microbial life in extreme habitats but also present a promising, eco-friendly approach to bioremediation in oil-contaminated saline environments, as well as a probable pool of yet-to-be-identified microorganisms, while suggesting the presence of alternative enzymatic pathways and highlighting the ecological and biotechnological significance of hypersaline environments. The identification of biosurfactant-producing strains further underscores their industrial relevance in enhancing oil recovery and wastewater treatment. Future studies should focus on functional and transcriptomic analyses to fully elucidate the metabolic potential and pathways of these microbial communities. Declarations Conflict of interests: The authors declare no competing interests. Author Contribution R.K.: Applied the empirical test, wrote the draft, and performed the bioinformatics.MA.A: Revised the manuscript and conducted the whole project. Data availability As reported before, the Urmia Lake metagenome in this study can be accessed under the BioProject accession PRJNA825141 and the following accession link: https://www.ncbi.nlm.nih.gov/bioproject/ PRJNA 825141. The 16S rRNA sequences of the strains were deposited in NCBI through the accession numbers listed in Table 2. References Kong, L. et al., 2024. Metagenomic analysis of petroleum biodegradation coupled to specific N-cycling process in oil-contaminated soil. Applied Soil Ecology , Volume 193. Wingett, S. W. & Andrews, S., 2019. FastQ Screen: A tool for multi-genome mapping and quality control. F1000Research. Yoon, S.-H.et al., 2017. 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Additional Declarations No competing interests reported. Supplementary Files TPHSupplementaryfile.xlsx 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. 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1","display":"","copyAsset":false,"role":"figure","size":9860,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUrmia Lake is located in northwest Iran. Image taken using ArcGIS Desktop 10.8 \u003c/strong\u003e(ESRI, 2019)\u003cstrong\u003e. Numbers show the locations of sampling sites.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7699662/v1/d8ce79d770864fd37682dc74.jpg"},{"id":92137810,"identity":"5e07149d-16b7-4b6d-ba61-c1351deeaee2","added_by":"auto","created_at":"2025-09-25 05:04:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":139171,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBOD level to determine the optimal salinity and incubation time. This scattered chart confirms that 5% salinity within 18 days of incubation is the optimum condition for the Urmia Lake consortium to biodegrade the TPH.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7699662/v1/fa38ffffd27735037629e539.png"},{"id":92137809,"identity":"c7fbe418-fa7a-4f88-9968-8c03488f1f12","added_by":"auto","created_at":"2025-09-25 05:04:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":153232,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A: left) GC spectrum of the 2000 ppm crude oil, non-inoculated Blank sample. Peaks indicate the components of crude oil. (B: right) GC spectrum of\u003c/strong\u003e \u003cstrong\u003ethe 2000 ppm crude oil from the Urmia Lake consortium, inoculated with a 5% salt concentration. The removal of peaks indicates the success of the biodegradation of crude oil hydrocarbons.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7699662/v1/41ea1d6547eda8f305091808.png"},{"id":92139128,"identity":"cb9e179e-5467-47fc-8a09-f23b025b8f40","added_by":"auto","created_at":"2025-09-25 05:12:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":120885,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBOD level in different concentrations of TPH. This scattered chart confirms that 3000 ppm crude oil is the highest tolerable concentration of TPH to be degraded by the consortium, This means 4000 ppm and 5000 ppm are toxic.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7699662/v1/4fde8bef86c1021e51df6538.png"},{"id":92139126,"identity":"b00d86cf-6ba0-4af3-bf47-5813dd040429","added_by":"auto","created_at":"2025-09-25 05:12:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":85550,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBar chart of diversity and taxonomic composition of the TPH-degrading consortium growing on crude oil at the family level. (a) Rarefaction curves of OTUs over the number of sequences.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7699662/v1/77320fe88a06c3e7a2d4bc24.png"},{"id":92188870,"identity":"1271ec25-2e99-429a-808d-6c6356f9f976","added_by":"auto","created_at":"2025-09-25 14:54:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":162987,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGC spectrum of 3000 ppm crude oil biodegradation. (A) Non-inoculated (Blank) sample. (B) ST2-inoculated sample. (C) ST5-inoculated sample. (D) ST6-inoculated sample.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7699662/v1/bac654094a9ea3709d652721.png"},{"id":92137816,"identity":"61da9dc9-68af-4fd8-8e90-7d0005b3eaa2","added_by":"auto","created_at":"2025-09-25 05:04:45","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":195688,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogram based on 16S-rRNA sequences of the Urmia Lake atrains. The tree was rooted with \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eHalomonas alkaliphila\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7699662/v1/ea3f18b6a7b103e7425dc67b.png"},{"id":92620663,"identity":"369f7dda-bee6-4dea-966b-163d89119cb3","added_by":"auto","created_at":"2025-10-01 19:16:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2309508,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7699662/v1/27a46d7e-e369-4af9-9867-030e62f67bdb.pdf"},{"id":92137812,"identity":"c3ce9225-4274-41c4-ac5e-3e15204538dd","added_by":"auto","created_at":"2025-09-25 05:04:45","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":484372,"visible":true,"origin":"","legend":"","description":"","filename":"TPHSupplementaryfile.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7699662/v1/3893b147e97ef934f7345b1e.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Urmia Lake: A promising pool of petroleum hydrocarbon biodegrades","fulltext":[{"header":"Highlights","content":"\u003col class=\"decimal_type\"\u003e\n \u003cli\u003eUrmia Lake is a promising pool of hydrocarbon-degrading microorganisms.\u003c/li\u003e\n \u003cli\u003eThere may be yet-to-be-identified microorganisms in the \u0026nbsp;Urmia Lake.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eSalinicoccus roseus\u003c/em\u003e proved to be an efficient biodegrader of hydrocarbons.\u003c/li\u003e\n \u003cli\u003e3. Novel metabolic pathways and biosurfactants may play a critical role in hydrocarbon degradation. \u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Introduction:","content":"\u003cp\u003eEnvironmental pollution is one of the main concerns in today's world. Petroleum compounds, known as hydrocarbons (Vijayanand, et al., 2023) are among the most persistent and harmful pollutants due to their toxicity, genotoxicity, mutagenicity, and carcinogenic properties, which adversely impact living organisms and ecosystems (Kheiri \u0026amp; Akhtari, 2017). These pollutants arise from various stages of petroleum exploration, production, transportation, and storage, with saline and hypersaline environments being particularly vulnerable due to their association with oil-related activities (Wu, et al., 2022). Bioremediation has emerged as a promising strategy for addressing petroleum pollution, offering an eco-friendly and cost-effective alternative to physical and chemical remediation approaches. This biological process relies on the metabolic capabilities of microorganisms to degrade hydrocarbons and can be implemented through biostimulation or bioaugmentation. While biostimulation optimizes the activity of native microbial communities, bioaugmentation involves introducing external microorganisms to enhance degradation in environments with insufficient indigenous degraders (S. Nwankwegu, et al., 2022).\u003c/p\u003e\u003cp\u003eAmong microorganisms, bacteria have demonstrated the most significant hydrocarbon degradation potential, with genera such as \u003cem\u003ePseudomonas\u003c/em\u003e and \u003cem\u003eBacillus\u003c/em\u003e playing critical roles (Elayaperumal, et al., 2025). In addition to bacteria, fungal genera including \u003cem\u003eGraphium\u003c/em\u003e, \u003cem\u003eTalaromyces\u003c/em\u003e, \u003cem\u003eNeosartorya\u003c/em\u003e, and \u003cem\u003eAmorphoteca\u003c/em\u003e have been proven to be efficient members for removing hydrocarbon compounds (Habes Almutairi, 2024). Although algae and protozoa are important parts of microorganism communities both onshore and offshore, there is little information about their hydrocarbon degradation capabilities (R \u0026amp; F, 2001). As a biological process, bioremediation strategies are highly dependent on both the metabolic capability of microorganisms and environmental factors such as pH, temperature, available water, amount of nutrients, hydrocarbon structure, and salinity (Ellis, et al., 2022). Although salinity is one of the limiting factors that correlate with failure of bioremediation due to its negative impact on several factors, including activating water, hydrocarbon bioavailability, and activation of critical enzymes, it would be promising to find a halotolerant or halophile bacterium capable of biodegrading petroleum in a large range of salinity (S. Ayilara \u0026amp; O. Babalola, 2023).\u003c/p\u003e\u003cp\u003eRegarding the value of halotolerant biodegrading bacteria, hypersaline environments, such as Urmia Lake, can be a potential reservoir of such bacteria. Urmia Lake, located in northwest Iran, was the greatest lake in the Middle East and the sixth-largest hypersaline on Earth; however, because of loss of input and high rate of evaporation, it has reached a fifth of its original area. It is noteworthy that, based on high ionic levels of Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e, and Ca\u003csup\u003e2+\u003c/sup\u003e, it is classified as a thalassohaline lake, and paleogeographic studies suggest it has originated from the Paratethys sea (Kheiri, et al., 2023).\u003c/p\u003e\u003cp\u003eRecent advances in metagenomics have revolutionized the study of microbial diversity and functional potential in various environments, including hypersaline ecosystems. Metagenomics enables the direct analysis of genetic material from environmental samples, bypassing the limitations of traditional culture-based methods (Elena P\u0026eacute;rez-Cobas, et al., 2020). This approach has proven invaluable for uncovering the vast microbial diversity (Ranjbar, et al., 2019)and the metabolic potential of microorganisms, particularly in extreme environments where many microbes are unculturable. In the context of petroleum hydrocarbon biodegradation, metagenomics has been instrumental in identifying genes and pathways involved in the degradation of complex hydrocarbons and has revealed the roles of various enzymatic systems ( Kong, et al., 2024). For hypersaline environments, metagenomics holds the promise of discovering novel halotolerant or halophilic microorganisms with unique enzymatic systems tailored to extreme salinity.\u003c/p\u003e\u003cp\u003eSince we previously analyzed microbial diversity, genetic variation, and metabolic capabilities of microorganisms populated in Urmia Lake (Kheiri, et al., 2023), we conducted this survey to explore microorganisms capable of degrading hydrocarbon compounds.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eWater sampling and physicochemical analysis\u003c/h2\u003e\u003cp\u003eLocated between the provinces of East Azerbaijan and West Azerbaijan in Iran and west of the southern portion of the Caspian Sea, Urmia Lake, at its most expansive, was the largest lake in the Middle East and the sixth-largest hypersaline lake on Earth. However, due to the sharp reduction of input, the complete disappearance of the lake is imminent (Kheiri, et al., 2023). To conduct our study, sampling was performed on October 20th, 2020, for physicochemical, microbiological, and metagenomic analysis. The water temperature, pH, and Conductivity were measured in situ using a Hach HQ40D portable multimeter (HACH company, USA). Samples were transported at 4\u0026deg;C to Tehran University, the Extremophile laboratory, for further analysis. For physico-chemical analysis, standard methods based on the American Public Health Association (APHA) were used (E. W, et al., 2017).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEvaluation of the salinity impact on the hydrocarbon-degrading of the consortium\u003c/h3\u003e\n\u003cp\u003eOptimizing factors to reach the maximum consortium efficiency of total petroleum hydrocarbon (TPH) degradation was a crucial part of our study. Incubation time, salinity, and TPH level were three factors that we focused on to find the optimum range for consortium metabolism. To evaluate and optimize these factors, the Biochemical Oxygen Demand (BOD) parameter, which simply unveils the amount of oxygen used by microorganisms in liquid environments, was used. Based on the fact that the amount of consumed oxygen for aerobic microorganisms\u0026rsquo; degradations correlates with aerobic metabolism, BOD can be a good indicator of hydrocarbon biodegradation to evaluate and optimize incubation time, TPH level, and salinity. In other words, the higher BOD would imply a higher microbial metabolic activity. For this purpose, a recently calibrated WTW OxiTop\u0026reg;-i IS 12 respirometric BOD measuring system was used.\u003c/p\u003e\u003cp\u003eTo determine the optimum and maximum tolerable salinity concentration of Urmia Lake microorganisms for degrading hydrocarbons, considering the 27% w/v salinity of Urmia Lake water, different salinities, namely 5%, 10%, 15%, 20%, and 27% were prepared using Bushnell-Haas medium as the diluent (Li, et al., 2022), a primary concentration of 2000 ppm of crude oil (0.2% v/v), separated Urmia Lake microorganisms (via centrifugation and filtration), as well as nBOD inhibitor buffer (based on WTW OxiTop\u0026reg;-i manual) were added into BOD bottles. OxiTop barometers were screwed and closed tightly. Incubation started at 20\u0026deg;C and was prolonged up to 20 days, while daily oxygen consumption was recorded. To guarantee accuracy and lower any faults, the experiments were conducted in duplicate.\u003c/p\u003e\n\u003ch3\u003eGC analysis of the consortium-degraded hydrocarbons\u003c/h3\u003e\n\u003cp\u003eDaily measurement of BOD confirmed the biodegradation of TPH. To determine specific hydrocarbon biodegradation, GC was used. For this purpose, using the EPA 3510C: Separatory Funnel Liquid-Liquid Extraction method, the extracted samples were analyzed by an Agilent 7890A GC system with Helium (99.999%) as the carrier gas at a constant flow rate of 1 ml/min. The oven temperature was programmed as follows: initial temperature of 40\u0026deg;C (held for 1 min), and an increased rate of 3\u0026deg;C/min to 270\u0026deg;C (EPA, 1996). Details are presented in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eOptimization of biodegradation: optimal TPH concentration and Incubation time\u003c/h3\u003e\n\u003cp\u003eFollowing the salinity evaluation, we assessed the TPH level impact on the microbial consortium\u0026rsquo;s activity to conclude optimum and toxic concentrations. To do so, as mentioned above, optimized pre-defined salinity, separated microorganisms, nBOD inhibitor buffer, and different concentrations of crude oil, including 1000, 2000, 3000, 4000, and 5000 ppm, were added into standard sterile BOD bottles. Based on the WTW OxiTop\u0026reg;-i manual, measuring started at 20\u0026deg;C and was prolonged up to 18 days, while daily oxygen consumption was recorded. The experiments were conducted in duplicate (Vijayanand, et al., 2023).\u003c/p\u003e\n\u003ch3\u003eMetataxonomic analysis of TPH-Degrading Consortium\u003c/h3\u003e\n\u003cp\u003eFor the Metataxonomic analysis of the consortium capable of degrading TPH, DNA of 5% salinity consortium (the highest performance of degradation) was extracted using QIAprep\u0026reg; Miniprep (Qiagen) DNA extraction Kit, according to the manufacturer\u0026rsquo;s instructions. The quantity and quality of the extracted DNA were analyzed by a NanoDrop\u0026trade; One Microvolume UV\u0026ndash;Vis Spectrophotometer and agarose gel electrophoresis. V3-V4 16S rRNA gene metabarcoding was performed by the Illumina MiSeq platform at Novogene Co., Ltd (China). Microbiome 16S rRNA gene diversity was assessed with QIAGEN CLC Genomics Workbench (version 22) (QIAGEN, 2022). Briefly, raw reads were quality-checked and trimmed, singletons and adaptors were removed, and chimeric sequences were filtered. Taxonomic classification of the reads and Operational Taxonomic Unit affiliation were based on the genome taxonomy database (GTDB) 16S rRNA sequences (Release 09-RS220)(H Parks, et al., 2022) for a minimum similarity of 97%. The rarefaction curves were constructed based on the number of OTUs per sample.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eScreening TPH-biodegrading strains\u003c/h2\u003e\u003cp\u003eAn agar-solidified 5% salinity water, covered by 1,2-dichloroethane-solved crude oil, was used to isolate bacteria of the consortium. The biodegrading consortium was streaked on agar medium and incubated at room temperature (approximately 25\u0026deg;C). Isolates capable of consuming petroleum hydrocarbons as the sole carbon and energy source appeared in colonies on the surface of the agar medium. To ensure pure isolates, three subcultures were performed (S Al-Wasify \u0026amp; R Hamed, 2014).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEvaluation of the TPH-biodegrading efficiency of isolated bacteria\u003c/h3\u003e\n\u003cp\u003eSince isolates may have different efficiencies in biodegrading hydrocarbons, to evaluate their performance, they were inoculated in nutrient broth (Merck) and incubated overnight for McFarland turbidity standards preparation. 1 ml containing approximately a cell density of 1.5 x 10\u003csup\u003e8\u003c/sup\u003e per ml of the cultures was inoculated in 200 ml of 5% salinity-diluted Bushnell Hass medium supplemented with 0.3%w/v carbon source, and the cultures were incubated at room temperature, in a shaking incubator (150 rpm) for 20 days (S Al-Wasify \u0026amp; R Hamed, 2014). Following the finishing measurement period (20 days), TPH was extracted for GC evaluation to find out hydrocarbons degraded by isolates (EPA, 1996).\u003c/p\u003e\n\u003ch3\u003eEvaluation of biosurfactant production of isolated bacteria\u003c/h3\u003e\n\u003cp\u003eTo evaluate the biosurfactant production of the strains, various approaches, including oil spreading, drop collapse, Emulsification Index (%E24), and surface tension, were used. For the oil displacement approach, pure isolates were inoculated in Bushnell-Haas medium supplemented with 0.1% crude oil. The cultures were incubated at 30\u0026deg;C, in a shaking incubator, agitated at 120 rpm for 7 days. To continue the procedure, 50 ml of distilled water was poured into a Petri dish (150 mm), 100 \u0026micro;l of crude oil was added to the water surface, and finally, 10 \u0026micro;l of the cultured strains was added. The presence of biosurfactants was evaluated by creating a clear zone on the surface of the oil.\u003c/p\u003e\u003cp\u003eDrop collapse assay was performed according to Youssef \u003cem\u003eet al.\u003c/em\u003e (Youssef, et al., 2004). The rationale behind this assay relies on surfactants' destabilization of oil-coated, solid surfaces. Scoring was performed by setting sterile deionized water as a negative control (-) and a 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e dilution of the \u0026ldquo;S-200 oil-gone\u0026rdquo; commercial BS solution as a positive (+++) control and comparing the diameter of droplets from the examined cultures.\u003c/p\u003e\u003cp\u003eThe emulsification index (%E24) was analyzed, as reported earlier by Alka Kumari \u003cem\u003eet al\u003c/em\u003e (Kumari, et al., 2021). Briefly, an Equal volume of centrifuged-cell-free supernatant of the culture (2 ml) was added to crude oil and vortexed at maximum speed for 3 min and allowed to stand for 24 hours at room temperature, followed by calculating %E24.\u003c/p\u003e\u003cp\u003eAs amphiphilic compounds, biosurfactants are surface-active molecules. To analyze the surface tension, the capillary rise method was used. Capillarity is the combined effect of cohesive and adhesive forces that causes cell-free supernatant to rise in thin tubes.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePhylogenetic analysis of TPH-biodegrading strains\u003c/h2\u003e\u003cp\u003eColonies with the highest degradation capability were selected to be phylogenetically analyzed for the 16S rRNA gene. For this purpose, genomic DNA was extracted, and the 16S rRNA region of the bacterial gene was targeted by the universal bacterial primers PCR 27f (5\u0026rsquo; AGAGTTTGATCMTGGCTCAG 3\u0026rsquo;) and 1492r (5\u0026rsquo; TACCTTGTTACGACTT 3\u0026rsquo;) as previously described by Jeremy Frank \u003cem\u003eet al.\u003c/em\u003e (A. Frank, et al., 2008), followed by Sanger sequencing by Microsynth Seqlab (Microsynth AG, Switzerland). Using QIAGEN CLC Genomics Workbench (version 22) (QIAGEN, 2022), the 16S rRNA sequences were checked against the genome taxonomy database (GTDB) 16S rRNA sequences (Release 09-RS220) (H Parks, et al., 2022), as well as the online EzBioCloud database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ezbiocloud.net/\u003c/span\u003e\u003cspan address=\"https://www.ezbiocloud.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ) ( Yoon, et al., 2017). Phylogenetic trees for 16S rRNA of isolated strains, as well as reference strains retrieved from NCBI GenBank, were subjected to multiple alignments using Clustal W, and a phylogram was built by the maximum likelihood method using Ngphylogeny.fr (Lemoine, et al., 2019) followed by iTOL v6 (Letunic \u0026amp; Bork, 2021 ).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDNA extraction and quality control\u003c/h2\u003e\u003cp\u003eAmong the key enzymatic systems involved in hydrocarbon biodegradation are alkane hydroxylases and various monooxygenases, facilitating their subsequent breakdown into less toxic and more bioavailable metabolites. We performed a comprehensive metagenomic analysis to identify these genes in our whole-genome shotgun sequencing dataset obtained from Lake Urmia. To do so, we sampled Urmia Lake water, collected biomass, extracted DNA, and checked the DNA quality, as previously reported (Kheiri, et al., 2023). Briefly, pre-filtering samples through 3-\u0026micro;m filters (cellulose-nitrate, Millipore) removed eukaryotes such as \u003cem\u003eDunaliella salina\u003c/em\u003e. For microbial collection, centrifugation at 3260\u0026times;\u003cem\u003eg\u003c/em\u003e for 60 min, and 0.22-\u0026micro;m membrane filtration was used, followed by QIAprepR Miniprep (Qiagen) DNA extraction. The extracted DNA was analyzed for quantity and quality using a NanoDrop\u0026trade; One C Microvolume UV\u0026ndash;Vis Spectrophotometer and agarose gel electrophoresis (Supplementary Table S2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSequencing, Assembly, Gene Prediction, and Target Gene Identification\u003c/h2\u003e\u003cp\u003eFor sequencing, we utilized the Illumina NovaSeq 6000 platform (Novogene Co., Ltd., China) with a paired-end (PE150) library. Raw metagenomic reads were assessed for quality, trimmed to remove low-quality bases, and filtered to eliminate sequencing artifacts. Quality control was conducted using FastQC (v0.11.9) ( Wingett \u0026amp; Andrews, 2019), followed by read trimming with Trimmomatic (v0.39) (M. Bolger, et al., 2014).\u003c/p\u003e\u003cp\u003eThe trimmed reads were then assembled into longer contiguous sequences (contigs) using MEGAHIT (v1.2.9) (Li, et al., 2016), employing a k-mer range of 21\u0026ndash;149. The resulting contig.fa file contained the assembled contigs, which were subsequently used for gene prediction and open reading frame (ORF) identification. Protein-coding sequences were predicted from the assembled contigs using Prodigal (v2.6.3) (Hyatt, et al., 2010) in metagenomic mode. The translated amino acid sequences were stored in the predicted_proteins.faa file and utilized for functional gene screening. To identify putative cytochrome P450 (CYP450) and other monooxygenase genes, we applied HMMER (v3.3.2) (D Finn, et al., 2011) with Hidden Markov Model (HMM) profiles sourced from the Pfam (Mistry, et al., 2020) and TIGRFAM (Haft, et al., 2001) databases. This was followed by HMM searches against the predicted protein sequences to pinpoint the target genes.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003ePhysico-chemical features of Urmia Lake.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSampling was performed during the period of lowest rainfall and input volume in the year when the lake water reached the highest salt concentration. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, water samples were collected from six locations (Supplementary Table S3), with approximately 20 L of water taken at a depth of 20 cm in sterile containers. In the previous study, we identified six major ions, including Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e (180,000 mg/l), SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e (25,260 mg/l), Na\u003csup\u003e+\u003c/sup\u003e (92,500 mg/l), Mg\u003csup\u003e2+\u003c/sup\u003e (17,750 mg/l), K\u003csup\u003e+\u003c/sup\u003e (4,000 mg/l), and Ca\u003csup\u003e2+\u003c/sup\u003e (512 mg/l) are the main ions of the water (Kheiri, et al., 2023) (Supplementary Table S4).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eIncubation time and salinity\u003c/h2\u003e\u003cp\u003eBOD was measured for 20 days. Daily recording measurements showed a rise in BOD level up to the 18th day of incubation, while prolonged incubation time did not increase BOD. This implied the appropriate incubation time for the Urmia Lake consortium for TPH bio-degradation was up to 18 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For salinity evaluation, BOD measurements proved the optimum salinity of TPH bio-degradation was 5%, followed by 0% and 10%. This means that not only 5% salinity did not inhibit metabolic activities, but also such a level of salinity (ions) may have a promoting role in microbial activities.\u003c/p\u003e\u003cp\u003eThe GC spectrum also confirms the high efficiency of the microbial consortium in biodegrading TPH in 5% salt. As can be seen in the GC peak area analysis table, the lowest peak areas correspond to 5% total salt concentration, followed by 0%, 10%, 15%, and 20%. While increasing the salinity to 27%, a severe drop in BOD level is observed, which indicates inhibition of microbial consortium metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Details are presented in Supplementary Table S5.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eOptimizing TPH concentration\u003c/h2\u003e\u003cp\u003eA gradient of crude oil concentration, including 1000 ppm, 2000 ppm, 3000 ppm, 4000 ppm, and 5000 ppm, containing pre-optimized (5%) salinity used for the BOD assay, confirmed 3000 ppm crude oil to be the maximum tolerable concentration.\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, a correlation exists between crude oil concentration and BOD levels up to a concentration of 3000 ppm. However, at concentrations of 4000 and 5000 ppm, a sharp drop in BOD level is observed, which can be inferred as a toxic level.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eTaxonomy assignments of the consortium\u003c/h2\u003e\u003cp\u003eMetataxonomy analysis of the Urmia Lake TPH-degrading consortium resulted in a total of 308642 sequences. Followed by filtering and merging, 96.12% of the amplicons ranged from 450\u0026ndash;460 bp in length. Low-quality and chimeric sequences were removed, and high-quality reads were clustered into 31609 OTUs, with a 97% similarity cutoff. The relative abundance of the families assigned to these sequences showed the dominance of \u003cem\u003eSalinicoccaceae\u003c/em\u003e and \u003cem\u003eDietziaceae\u003c/em\u003e, with relative abundances of 57% and 28.3%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Other families with a representative abundance in the consortium were \u003cem\u003eMicrococcaceae\u003c/em\u003e and \u003cem\u003eBacillaceae\u003c/em\u003e, with relative abundances of 4.2% and 3.1%, respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eIsolation and identification of the candidate strains\u003c/h2\u003e\u003cp\u003eA total of 6 isolates nominated as Urmia Lake strain1 (LUst1), LUst2, LUst3, LUst4, LUst5, and LUst6 of distinct morphology capable of using crude oil as the sole carbon and energy source were carefully picked and purified by 3 successive subculturing on the same medium and conditions as potential candidates of biodegradation. However, 3 of which, namely LUst2, LUst5, and LUst6, demonstrated the highest efficiency in biodegradation and the lowest peak. Non-inoculated (blank) and strain-inoculated 3000 ppm crude oil, including 5% salinity-diluted Bushnell Hass GC results, are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eDemonstrating the highest efficiency of biodegradation, LUst2, LUst5, and LUst6 were selected for taxonomic identification and biosurfactant assays.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eIdentification and characterization of bacterial isolates\u003c/h2\u003e\u003cp\u003eDue to their hydrocarbon degradation capability, LUst2, LUst5, and LUst6 were selected for further evaluation. For taxonomic identification, the 16S rRNA gene was amplified, sequenced, and checked against two databases. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, LUst2, LUst5, and LUst6 are affiliated with \u003cem\u003eNesterenkonia muleiensis\u003c/em\u003e, \u003cem\u003eCytobacillus firmus\u003c/em\u003e, and \u003cem\u003eSalinicoccus roseus\u003c/em\u003e, respectively. The sequences were deposited in GenBank with the accession numbers listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe phylogram in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates the phylogenetic position of the LUst2. With approximately 97% similarity to \u003cem\u003eNesterenkonia muleiensis\u003c/em\u003e, it appears to represent a new genus that warrants further investigation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTaxonomy affiliation of biodegrading strains\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eEzbiocloud\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eGTDB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAccession number\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTop Hit Taxon\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e% Similarity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTop Hit Taxon\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e% Similarity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLUst2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eNesterenkonia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eNesterenkonia muleiensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e96.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOQ651249.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLUst5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCytobacillus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eCytobacillus firmus_B\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e98.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOQ651247.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLUst6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalinicoccus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eSalinicoccus roseus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e99.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOQ651248.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eBiosurfactant assays of the strains\u003c/h2\u003e\u003cp\u003eThe selected strains\u0026rsquo; drop collapse test results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. As shown, followed by LUst2, and LUst6, LUst5 showed the highest efficiency.To evaluate the halotolerance ability of microorganisms, which plays an essential role in a successful bioremediation process in hypersaline soils, the maximum tolerable concentration of NaCl of isolated bacteria was determined for 24 h of incubation. Our results showed all isolates were able to grow in a wide range of salinity from 0 to 15%, however, salinity higher than 5% suppressed and reduced the hydrocarbon degradation efficiency. Even though there is no significant difference between 0% and 5%, in concentrations higher than 5%, including 10%, 15%, 20%, and 25%, our microbial consortium activity reduced and correlated with salinity concentration.\u003c/p\u003e\u003cp\u003eFor the oil spread test, the experiments were conducted in triplicate, and biosurfactant activity was determined as the diameter of the clear zone on the oil surface in cm. Results shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e confirm the equal potency of the LUst2 and LUst5 in the oil spread test. Finally, LUst5 achieved the highest score in E24 and surface tension assays to be selected as the best biosurfactant producer strain.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of biosurfactant assay\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eStrain/ Assay\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eE24\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDrop collapse\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eOil spread (cm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSurface tension (cm)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTest 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTest 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTest 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLUst2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLUst5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e++++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLUst6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eSpecific genes survey in metagenomic reads\u003c/h2\u003e\u003cp\u003eWe reported a comprehensive metagenomic analysis of Urmia Lake previously (Kheiri, et al., 2023), therefore, in this study, we focused on screening the dataset for specific genes involved in hydrocarbon biodegradation, rather than reconstructing metagenome-assembled genomes (MAGs) or performing taxonomic affiliation. In summary, the dataset comprised 204\u0026nbsp;million high-quality paired-end reads (150 bp) with a Phred quality score greater than 30, ensuring sufficient sequencing depth. Despite the high genomic variation observed, 22 MAGs were reconstructed, with completeness levels ranging from 44.88% to 97.15%.\u003c/p\u003e\u003cp\u003eTo investigate genes potentially involved in hydrocarbon biodegradation, HMMER analyzed 1,733,777 sequences (224,154,182 residues) and produced the results summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. However, no significant matches were identified in any of the searches, suggesting the absence of functional activity for these genes in the analyzed metagenomic dataset.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHMMER scanning results of the target genes in metagenomic contigs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID (Gene)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003enumber of hits\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTIGR03860\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNtaA/DmoA family FMN-dependent monooxygenase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePF00743\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFlavin-binding monooxygenase-like\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e291\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePIRSF036487\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlkane 1-monooxygenase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePF00067\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCytochrome P450 monooxygenases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e592\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePF00848\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRing hydroxylating alpha subunit (catalytic domain)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe widespread contamination of aquatic environments with petroleum hydrocarbons, particularly in saline and hypersaline ecosystems, presents a global environmental challenge (Kheiri \u0026amp; Akhtari, 2017). Such environments, often linked to oil exploration and extraction activities, experience extreme salinity levels that inhibit the activity of conventional hydrocarbon-degrading microorganisms. This study aimed to address these challenges by exploring the microbial community of Urmia Lake, a hypersaline lake with salinity levels reaching up to 27% w/v, and evaluating its potential for hydrocarbon biodegradation (Abou Khalil, et al., 2021).\u003c/p\u003e\u003cp\u003eOur findings revealed that the microbial consortium demonstrated optimal biodegradation efficiency at 5% salinity, as evidenced by the peak oxygen consumption recorded through BOD measurements. This salinity likely represents a balance between enhancing ionic strength for solubilizing hydrocarbons and avoiding the osmotic stress that limits microbial activity. Beyond 10% salinity, a sharp decline in biodegradation was observed, reflecting the inhibitory effects of high ionic concentrations, consistent with findings by Ventosa \u003cem\u003eet al.\u003c/em\u003e (Ventosa, et al., 1998) and Das \u003cem\u003eet al.\u003c/em\u003e (Das \u0026amp; Chandran, 2011). These studies have shown that moderate salinity conditions improve the bioavailability of hydrophobic hydrocarbons, facilitating microbial access and degradation. GC analysis further validated these findings, showing significant reductions in hydrocarbon peaks in consortium-inoculated samples (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The consortium demonstrated higher efficiency compared to individual strains, highlighting the synergistic interactions among its members. The ability to degrade a broad range of hydrocarbons was particularly evident in the activity of \u003cem\u003eSalinicoccus roseus\u003c/em\u003e (LUst6), which achieved the highest hydrocarbon degradation efficiency (51.97%). This strain\u0026rsquo;s enzymatic versatility in targeting aromatic hydrocarbons corroborates previous studies, such as those by Al-Wahaibi \u003cem\u003eet al.\u003c/em\u003e (Al-Wahaibi, et al., 2014).\u003c/p\u003e\u003cp\u003eSeveral studies have examined microbial consortia and isolated colonies for the biodegradation of pollutants, yielding different outcomes. Zhang \u003cem\u003eet al.\u003c/em\u003e argue that microbial consortia can enhance synergistic degradation, reduce the accumulation of intermediate products, and generate crude enzymes, making them more efficient for soil bioremediation (Zhang \u0026amp; Zhang, 2022). In contrast, Obianuju et al. demonstrated that single strains of \u003cem\u003eSerratia marcescens\u003c/em\u003e, \u003cem\u003eProvidencia\u003c/em\u003e vermicola W8, and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e W15 exhibited higher hydrocarbon degradation rates than when these strains were used in a consortium (Obiajulu Nnabuife, et al., 2022). Our current study reflects both of these findings. As shown in Supplementary Table S6, LUst 6 was the most efficient strain for biodegrading various hydrocarbons. However, C26 biodegradation requires cooperation between strains, suggesting that the production of one strain served as the substrate for another, highlighting the importance of inter-strain interaction in some cases.\u003c/p\u003e\u003cp\u003eMetataxonomic profiling provided a comprehensive overview of the microbial diversity within the consortium, identifying \u003cem\u003eSalinicoccaceae\u003c/em\u003e (57%), \u003cem\u003eDietziaceae\u003c/em\u003e (28.3%), and \u003cem\u003eBacillaceae\u003c/em\u003e (3.1%) as dominant families. The predominance of these taxa reflects their evolutionary adaptation to hypersaline conditions and their metabolic versatility. The role of \u003cem\u003eDietziaceae\u003c/em\u003e, for instance, in metabolizing alkanes (C6\u0026ndash;C36) has been well-documented by Banat \u003cem\u003eet al.\u003c/em\u003e (M Banat, et al., 2010) and is evident in the consortium's performance in degrading crude oil at 3000 ppm. The rarefaction curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) confirm adequate sequencing depth, ensuring that the observed diversity accurately represents the microbial community's functional potential.\u003c/p\u003e\u003cp\u003eThe biosurfactant production assays revealed another critical mechanism enhancing hydrocarbon degradation. LUst5 (\u003cem\u003eCytobacillus firmus\u003c/em\u003e) exhibited the highest emulsification index (52%) and reduced surface tension by 35%, demonstrating its ability to improve hydrocarbon bioavailability. Biosurfactants, as amphiphilic compounds, increase the accessibility of hydrophobic hydrocarbons to microbial enzymes. Similar findings were reported by Fariq \u003cem\u003eet al.\u003c/em\u003e (Fariq \u0026amp; Yasmin, 2020), who identified biosurfactant-producing halotolerant bacteria as pivotal in bioremediation. Biosurfactant production emerged as a key mechanism driving hydrocarbon degradation. \u003cem\u003eCytobacillus firmus\u003c/em\u003e (LUst5) demonstrated the highest biosurfactant activity, achieving an E24 emulsification index of 52% and reducing surface tension by 35%. Biosurfactants are amphiphilic molecules that facilitate the dispersion and solubilization of hydrophobic hydrocarbons, enabling microbial access. Compared to synthetic surfactants, biosurfactants are biodegradable, non-toxic, and functional under extreme conditions, making them ideal for saline environments. Similar findings were reported by Youssef \u003cem\u003eet al.\u003c/em\u003e (Youssef, et al., 2004), who demonstrated the effectiveness of biosurfactants in enhancing microbial hydrocarbon degradation in contaminated environments. The role of LUst5 in producing biosurfactants under hypersaline conditions highlights its potential for industrial applications, including enhanced oil recovery (EOR) and wastewater treatment.\u003c/p\u003e\u003cp\u003eOverall, \u003cem\u003eSalinicoccus roseus\u003c/em\u003e was the most successful isolated strain in the degradation of hydrocarbon compounds, according to chromatography graphs (51.97% efficiency). This Gram-positive coccus produces extracellular enzymes, including lipase, esterase, gelatinase, and protease. Numerous studies have highlighted its use in wastewater treatment, pigment production, and its potential anti-cancer effects through the inhibition of the p388 factor in leukemia (Mohamed Ali, et al., 2024). \u003cem\u003eCytobacillus firmus\u003c/em\u003e has also been isolated and identified as a strain with considerable potential for the degradation of hydrocarbon compounds. The efficiency of this strain had been measured by Al-Wahaibi \u003cem\u003eet al.\u003c/em\u003e (Al-Wahaibi, et al., 2014). Within 97% similarity to \u003cem\u003eNesterenkonia muleinsis\u003c/em\u003e, our third highly hydrocarbon-degraded strain was identified. The isolation of a strain with such 16S rRNA similarity suggests the discovery of a potentially novel species. Taxonomic thresholds established by Stackebrandt et al. suggest that a similarity below 97% typically represents a new species [28]. Supporting this, Yarza et al. [29] demonstrated that strains with 16S rRNA similarities between 95\u0026ndash;97% were subsequently confirmed as novel species through genome-based analyses.\u003c/p\u003e\u003cp\u003eThe Urmia Lake microbial consortium also shows a bell-shaped diagram for the impact of TPH concentration on consortium activity, as indicated by BOD. BOD measurements showed consortium activity at 1000 ppm and 5000 ppm is slightly similar to each other and is the lowest compared to other concentrations of TPH. In addition, BOD measurements also found almost similar activity within consortia that contained 2000 ppm and 4000 ppm of TPH concentration. TPH concentration at 3000 ppm showed maximum microbial activity according to the measurement of BOD, which was considerably more than any other consortium. The BOD factor was utilized by our research to measure microbial consortium activity while they were exposed to hydrocarbon compounds. The length of incubation, optimum salinity concentration, and optimum TPH concentration have all been measured using BOD. BOD-dependent tests are a novel approach for TPH measurements in saline and hypersaline environments. Our study suggested utilizing the BOD factor as an alternative approach for studying microbial consortia in similar studies. The findings of our study also show that Urmia Lake's microbial community has the potential for hydrocarbon degradation at high concentrations of salinity.\u003c/p\u003e\u003cp\u003eMetagenomic analysis revealed substantial genomic diversity (Kheiri, et al., 2023), while no significant matches were identified for canonical hydrocarbon-degrading genes. This finding suggests that microbial communities in hypersaline environments may rely on unconventional or novel enzymatic systems or synergistic microbial interactions in which microbial consortia cooperatively degrade hydrocarbons. This means the product of one cell is the substrate of the other cell. A likely explanation for the absence of these genes is the dominance of \u003cem\u003eHaloquadratum walsbyi\u003c/em\u003e, \u003cem\u003eHalonotius\u003c/em\u003e, and \u003cem\u003eSalinibacter ruber\u003c/em\u003e genes in the contigs, as these microorganisms lack any genes associated with hydrocarbon biodegradation. In contrast, hydrocarbon-degrading microorganisms\u0026mdash;primarily bacteria\u0026mdash;are present in extremely low numbers at 27% salinity, and they can demonstrate their activity only under optimal degradation conditions within a consortium.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThese findings not only reshape our understanding of microbial life in extreme habitats but also present a promising, eco-friendly approach to bioremediation in oil-contaminated saline environments, as well as a probable pool of yet-to-be-identified microorganisms, while suggesting the presence of alternative enzymatic pathways and highlighting the ecological and biotechnological significance of hypersaline environments. The identification of biosurfactant-producing strains further underscores their industrial relevance in enhancing oil recovery and wastewater treatment. Future studies should focus on functional and transcriptomic analyses to fully elucidate the metabolic potential and pathways of these microbial communities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of interests:\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.K.: Applied the empirical test, wrote the draft, and performed the bioinformatics.MA.A: Revised the manuscript and conducted the whole project.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eAs reported before, the Urmia Lake metagenome in this study can be accessed under the BioProject accession PRJNA825141 and the following accession link: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/bioproject/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/bioproject/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PRJNA 825141. The 16S rRNA sequences of the strains were deposited in NCBI through the accession numbers listed in Table\u0026nbsp;2.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKong, L. et al., 2024. Metagenomic analysis of petroleum biodegradation coupled to specific N-cycling process in oil-contaminated soil. \u003cem\u003eApplied Soil Ecology\u003c/em\u003e, Volume 193.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWingett, S. W. \u0026amp; Andrews, S., 2019. FastQ Screen: A tool for multi-genome mapping and quality control. \u003cem\u003eF1000Research.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoon, S.-H.et al., 2017. Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies. \u003cem\u003eInternational Journal of Systematic and Evolutionary Microbiology.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eA. 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Microbial Consortia Are Needed to Degrade Soil Pollutants. \u003cem\u003eMicroorganisms.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"BOD, Hydrocarbon biodegradation, Metagenomics, Metataxonomy, Nesterenkonia muleiensis, Salinicoccus roseus, Urmia Lake.","lastPublishedDoi":"10.21203/rs.3.rs-7699662/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7699662/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, halotolerant microorganisms capable of degrading petroleum hydrocarbons were isolated and analyzed from Urmia Lake. The research focused on identifying microbial consortia resistant to salinity and capable of growing in high concentrations of crude oil. Biodegradation of crude oil at salinity levels ranging from 0% to 27% and concentrations from 1000 to 5000 ppm was evaluated using BOD measurement and GC analysis. The results demonstrated the highest removal efficiency at 3000 ppm crude oil and salinity levels of 0%, 5%, and 10% within 18 days.\u003c/p\u003e\u003cp\u003eMetataxonomic analysis of the petroleum hydrocarbon-degrading consortium identified 308,642 sequences, which were clustered into 31,609 OTUs with a 97% similarity threshold. Relative abundance analysis of these sequences revealed the dominance of \u003cem\u003eSalinicoccaceae\u003c/em\u003e (57%), \u003cem\u003eDietziaceae\u003c/em\u003e (28.3%), \u003cem\u003eMicrococcaceae\u003c/em\u003e (4.2%), and \u003cem\u003eBacillaceae\u003c/em\u003e (3.1%). In metagenomic analysis, whole-genome sequencing data were used for MAG reconstruction and functional gene screening.\u003c/p\u003e\u003cp\u003eTo isolate pure strains, the crude oil-enriched consortium was cultured on a growth medium, and colonies were isolated after incubation. Among six purified strains, three with higher efficiency were selected for further analysis. 16S rRNA gene sequencing identified these strains as \u003cem\u003eSalinicoccus roseus, Nesterenkonia muleiensis, and Cytobacillus firmus\u003c/em\u003e. GC analysis confirmed that \u003cem\u003eSalinicoccus roseus\u003c/em\u003e was the most effective hydrocarbon degrader. Although \u003cem\u003eSalinicoccus roseus\u003c/em\u003e outperformed the consortium in degrading most petroleum hydrocarbons, the degradation of C26 hydrocarbons required the collective activity of consortium members.\u003c/p\u003e","manuscriptTitle":"Urmia Lake: A promising pool of petroleum hydrocarbon biodegrades","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 05:04:40","doi":"10.21203/rs.3.rs-7699662/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":"2292a813-a2d7-461a-ab43-80a5daac0485","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-01T19:08:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-25 05:04:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7699662","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7699662","identity":"rs-7699662","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}