Divergent microbial community and functional dynamics in mangrove sediments along a polycyclic aromatic hydrocarbons (PAHs) gradient in China

Divergent microbial community and functional dynamics in mangrove sediments along a polycyclic aromatic hydrocarbons (PAHs) gradient in China | 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 Divergent microbial community and functional dynamics in mangrove sediments along a polycyclic aromatic hydrocarbons (PAHs) gradient in China Danyun Ou, Yue Ni, Weiyi He, Shuangshuang Lin, Weiwen Li, Lei Wang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7178259/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Mangrove ecosystems, despite their vital ecological and socioeconomic value, face escalating threats from anthropogenic disturbances, particularly the accumulation of persistent organic pollutants such as polycyclic aromatic hydrocarbons (PAHs). Understanding microbial responses to PAH contamination is crucial for developing effective bioremediation strategies. This study aimed to investigate PAH distribution, microbial community dynamics, and degradation mechanisms in mangrove sediments across Beihai and Zhangzhou, China, to inform targeted restoration efforts. Results: PAH concentrations in mangrove sediments varied significantly (395.49 vs 232.83 ng/g d. w.), with spatial heterogeneity driven by anthropogenic inputs, nutrient availability, and total organic carbon (TOC). High-PAH sediments (section M3) near pollution sources exhibited reduced microbial diversity but significant enrichment of hydrocarbon-degrading taxa, including Acinetobacter (7.84% in M3 vs 0.01% - 0.04% elsewhere), Mycobacterium , and Nocardioides , which collectively represented 46.5% of identified PAH degraders. Metagenomic profiling identified 565 KEGG orthologs (KOs) and 351 enzymes associated with PAH degradation, enabling the reconstruction of complete PAH degradation pathways from initial oxidation to mineralization, with ring-hydroxylating dioxygenases (RHDs) playing a pivotal role in initial oxidation. Notably, dominant PAH-degraders were also key producers of PAH-degrading enzymes and carbohydrate-active enzymes (CAZymes), particularly glycosyltransferases (GTs) and glycoside hydrolases (GHs), which facilitated co-metabolism and enhanced PAH degradation. Conclusions: This study elucidates the adaptive mechanisms of mangrove sediment microbiomes to PAH stress, highlighting the synergy between specialized degraders ( Acinetobacter , Mycobacterium , Nocardioides ), PAH-degrading enzymes, and CAZyme-mediated co-metabolism. These findings deepen our understanding of microbial adaptation to PAH stress and establish a framework for targeted bioremediation strategies, such as enzyme-enhanced solutions, to mitigate PAH pollution while preserving mangrove ecological functions. These insights are critical for balancing ecosystem health and anthropogenic pressures in coastal environments. Polycyclic aromatic hydrocarbon Mangrove sediment Microbial degradation Acinetobacter PAH-degrading enzymes CAZymes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Mangrove ecosystems represent the most biologically productive and ecologically vital coastal habitats, delivering indispensable ecosystem services and resources that sustain both marine diversity and human livelihoods [1, 2]. Despite their ecological significance, these ecosystems are increasingly imperiled by anthropogenic disturbances [3, 4], particularly through the accumulation of persistent organic pollutants such as polycyclic aromatic hydrocarbons (PAHs) [5-7]. PAHs, a class of non-polar organic compounds characterized by fused benzene rings which arranged in linear, angular or cluster configurations, originate predominantly from incomplete combustion of fossil-fuels and industrial emission [8]. Recognized for their environmental persistence, toxicity, mutagenicity, and carcinogenicity, 16 PAHs have been designated as priority pollutants by the U.S. Environmental Protection Agency (US EPA) [9, 10]. In mangrove sediments, PAHs exhibit strong adsorption to organic particulate matter, leading to their pronounced enrichment, while their spatial distribution is further modulated by hydrodynamic processes such as tidal flushing and porewater seepage [11, 12]. Microbial communities inhabiting mangrove sediments play a pivotal role in mediating biogeochemical cycles and exhibit extraordinary adaptability to environmental stressors [13]. While bacterial populations often demonstrate resilience to environmental stressors through compositional and functional plasticity, prolonged PAH exposure tends to reduce overall microbial diversity, favoring the specialized degraders such as Acinetobacter , Aeromonas , Aspergillus , Bacillus , Corynebacterium , Enterobacter , Microbulbifer , Micrococcus , Mycobacteria , Nocardioides , Paenibacillus , Pseudomonas , Pseudorhodoferax , Pseudoxanthomonas , Sphingomonas , Streptomyces , Xanthomonas , and so on [14-18]. Such community shifts may disrupt essential ecosystem functions, including organic matter decomposition and nutrient cycling, with cascading effects on mangrove ecosystem stability [19]. Although microbes-mediated PAH biodegradation has been extensively documented, the underlying metabolic pathways and enzymatic mechanisms—particularly those involving carbohydrate-active enzymes (CAZymes)—remain inadequately characterized, largely due to the structural complexity of PAHs and the inherent heterogeneity of microbial community [20-22]. Recent advances in culture-independent metagenomic approaches, including metagenome-assembled genomes (MAGs), has revolutionized our ability to decipher microbial degradation pathways by bridging taxonomic identity with functional potential [23-26]. High-resolution metabolic profiling has facilitated the identification of key catalytic enzymes (e.g., ring-hydroxylating oxygenases and hydrolases) critical for PAH breakdown [23, 27, 28]. Elucidating these adaptive mechanisms is paramount for developing targeted bioremediation strategies, as microbial responses are highly contingent on local contamination levels and sediment geochemistry [29-31]. This study investigates how divergent PAH contamination gradients shape microbial community structure and function in mangrove sediments across two geographically distinct sites in China. By leveraging high-throughput sequencing and metagenomic analysis, we (i) delineate PAH-induced shifts in microbial assemblages, (ii) identify putative PAH degrading taxa, and (iii) elucidate potential metabolic pathways underpinning PAH biodegradation. Our findings advance the understanding of microbial adaptation to hydrocarbon stress and provide actionable insights for mangrove bioremediation. 2. Material and Methods 2.1 Sample collection Sediment samples were collected from mangrove ecosystems across two regions in China representing varying degrees of anthropogenic impact. In Guangxi Province, samples were taken from sections M1 and M2 located in Beihai Coastal National Wetland Park (protected area) and section M3 near an urban municipal outfall in Beihai (impacted site). In Fujian Province, samples were collected from three sites (DY, CPT, and FG) in Zhangzhou Mangrove (Figure 1a). All sampling was conducted during low tide from August 26 th to September 1 st , 2022. Surface sediments (top 1 cm) were collected in quadruplicate using sterile stainless steel spatulas, immediately transferred to sterile sampling packs (Whirl-Pak, Nasco, USA). Subsequently, the samples were transported on ice to the laboratory where they were stored at –80 ℃ until analysis. 2.2 Sediment physicochemical analysis Nitrate (NO 3 - -N) and ammonia (NH 4 + -N) concentrations were determined following extraction of sediment samples using 2 M KCl ,followed by quantification using a continuous flow analyzer (Futura II, Alliance Instruments, France). Total organic carbon (TOC) and total nitrogen (TN) were measured with a TOC analyzer coupled with a nitrogen module (Vario TOC Cube, Elementar Analysen systeme, Germany) following carbonate removal through acidification with 5% HCl and drying at 40 °C. All data were normalized to dry weight after oven-drying at 75 °C [32]. Particle size distribution was analyzed using a laser diffraction system (Mastersizer 3000, Malvern Instrument Ltd., UK), with sediments classified as gravel (> 2mm), sand (0.063-2 mm), silt (0.004-0.063 mm), and clay (< 0.004 mm) [33]. 2.3 PAHs analysis Frozen sediment samples were lyophilized for 48 h in a vacuum lyophilizer (FC-1C-80, Biocool, China). PAHs were extracted using pressurized liquid extraction, desulfurized with activated copper power, and concentrated via solid phase extraction. Eighteen PAHs, including the 16 US EPA’s priority PAHs plus benzo[e]pyrene, benzo[j]fluoranthene, were quantified by gas chromatography-mass spectrometry (GC-MS, QP2010 TD30R, Shimadzu, Japan) equipped with a fused DB-5MS silica capillary column (60 m × 0.25 mm, Anpel, China) with stationary phase of 5%-phenyl-methylpolysiloxane [34]. Variation partition analysis (VPA) was performed to evaluate the relative contribution of environmental factors on PAH distribution patterns in the mangrove sediments using Vegan v2.6-4 package [35]. 2.4 DNA extraction Total genomic DNA was extracted from sediment samples using the DNeasy PowerSoil Pro kit (QIAGEN GmbH, Germay) following the manufacturer’s protocol. DNA concentration and purity were accessed using a NanoDrop2000 spectrophotometer (Thermo Fisher Scientific, USA), with DNA quality verified via by 1% agarose gel electrophoresis. The extracted DNA was subsequently used for 16S rRNA gene sequencing and metagenomic sequencing. 2.5 16S rRNA amplicon sequencing and data processing The hypervariable V3-V4 region of bacterial 16S rRNA genes was amplified using primers 338F (5’-ACTCCTACGGGAGGCAGCAG-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’) by PCR amplification performed using the PCR thermocycler (T100 TM Thermal Cycler, BIO-RAD, USA) [36]. PCR amplification cycling conditions were as follows: initial denaturation at 95 ℃ for 3 min, followed by 27 cycles of denaturing at 95 ℃ for 30 s, annealing at 55 ℃ for 30 s and extension at 72 ℃ for 45 s, final extension at 72 ℃ for 10 min, and end at 4 ℃. Amplicons were paired-end sequenced on an Illumina MiSeq PE300 platform (Illumina, San Diego, USA) by Majorbio Bio-pharm Technology Co., Ltd (Shanghai, China). The resulting data were analyzed using the Majorbio Cloud Platform, a free accessible online bioinformatics analysis system [37]. Raw reads were processed using fastp (v0.23.0) for quality-filtering [38] and FLASH (v1.2.11) for merging [39]. Amplicon sequence variants (ASVs) were generated using DADA2 [40] plugin in Qiime2 (v2020.2) [41] with taxonomic classification performed against the 16S rRNA database (Silva v138) using a Naive bayes consensus taxonomy classifier implemented in Qiime2 [42]. Based on the ASVs information, alpha diversity indices including Sobs, Chao1 richness, Shannon, Simpson indices, and Good’s coverage, were calculated using Mothur v1.30.2 [43]. Beta diversity was accessed via non-metric multidimensional scaling (NMDs) and principal coordinate analysis (PCoA) based on Bray-Curtis dissimilarity matrices generated by the Vegan v2.6-4 package [35]. Differential microbial abundance analysis were performed using the Kruskal-Wallis test and linear discriminant analysis (LDA) effect size (LEfSe) analysis (LDA score > 2, p < 0.05) [44], while environmental drivers of microbial communities were identified through distance-based redundancy analysis (db-RDA) and variance partitioning analysis (VPA) using Vegan v2.6-4 package [35]. 2.6 Metagenomic analysis 2.6.1 Metagenomic sequencing and sequences assembly DNA libraries was prepared by fragmenting genomic DNA to ~400 bp (Covaris M220, Gene Company Limited, China) and constructing paired-end libraries using the NEXTFLEX Rapid DNA-Seq kit (Bioo Scientific, USA). The resulting library was subjected to paired-end sequencing using the Illumina NovaSeq platform (Illumina Inc., CA, USA) at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China). Metagenomic data were analyzed on Majorbio Cloud Platform [37]. Raw reads were processed using fastp (v0.23.0) to remove adaptors and filter out low-quality reads (length<50 bp, quality score <20, or containing N bases) [38] . Metagenomic assembly was performed using MEGAHIT (v1.1.2) [45], retaining contigs ≥ 300 bp for downstream gene prediction and annotation. 2.6.2 Gene prediction, taxonomy, and functional annotation Open reading frames (ORFs) were predicted from assembled contigs using MetaGene with default parameters [46], retaining only ORFs ≥ 100 bp. These ORFs were translated into amino acid sequences following the standard NCBI genetic code (http://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/index.cgi?chapter=tgencodes#SG1). ORFs were clustered into a non-redundant gene catalog using CD-HIT (v4.6.1, http://www.bioinformatics.org/cd-hit/) with stringent clustering thresholds (90% sequence identity and 90% coverage) [47]. Gene abundance was quantified by mapping high-quality reads to the catalog using SOAP (v2.21) with 95% sequence identity requirement [48]. Representative sequences from the gene catalog were taxonomically annotated by alignment against the NR database using Diamond (v0.8.35) with an e -value cutoff of 1×e –5 . For functional characterization, representative sequences were annotated through metabolic pathway reconstruction via Kyoto Encyclopedia of Genes and Genomes (KEGG) database using Diamond, [49], and carbohydrate-Active Enzymes (CAZymes) identification through hmmscan against CAZy database [50], all employing consistent e -value cutoff (1×e –5 ) for significant hits. 2.6.3 Statistical analysis Microbial community analysis was conducted through an integrated bioinformatics pipeline. Hierarchical sample clustering based on taxonomic profiles was performed in Qiime using Bray-Curtis dissimilarity metric. Taxonomic or enzymatic biomarkers were identified through LEfSe analysis (LDA score > 2, p < 0.05) [44], while inter-group differences in bacterial genera or KEGG enzyme abundance were accessed using nonparametric Kruskal-Wallis test. To examine variance in bacterial communities, NMDs and PCoA were performed using the Vegan package (v2.6-4) in R [35]. The influence of environmental variables on the distribution patterns of microbial communities and functional loci in sediment samples was analyzed using db-RDA with the same R package [35]. For Metagenome Assembled Genomes (MAGs), quality-filtered contigs (≥1000 bp) were binned using MetaBAT 2 (v2.12.1) [51], with genome quality assessed by CheckM (v1.1.2-1; completeness > 50%, contamination < 10%) [52]. The taxonomy of the refined bins was assigned using GTDB-Tk (v2.3.0) [53], which infers classifications based on a set of 120 bacterial and 53 archaeal universal single-copy marker genes based on the Genome Taxonomy Database (GTDB, https://data.ace.uq.edu.au/public/gtdb/data/releases/release214/214.1/). Functional annotation was following established protocols for KEGG and CAZy databases as described in section 2.6.2. 3. Results 3.1 PAHs variations among mangrove sections Surface sediments analysis from six mangrove sections in Beihai and Zhangzhou (10 sampling sites) detected 18 PAH compounds, including 16 USEPA priority PAHs (Figure 1b). Total PAH concentrations exhibited significant spatial heterogeneity (Kruskal-Wallis test, p < 0.05) among sections, ranging from 17.56 ng/g d.w. (CPT section) to 730.76 ng/g d.w. (M3 section), representing a 40-fold variation. Beihai mangroves showed significantly higher mean PAH concentration (395.49 ng/g d.w.) compared to Zhangzhou mangroves (232.83 ng/g d.w.) (Table S1). Significant spatial variations were observed not only in total PAH concentrations but also in PAH composition (Figure 1). Low-Molecular-weight (LMW) PAHs (2-3 rings) dominated in section CPT, accounting for 77.77% of total PAHs. In contrast, most other mangrove sediments contained predominantly high-molecular-weight (HMW) PAHs (4-6 rings), which contributed 53.52% to 83.61% of total PAHs (Table S1). The dominant PAH compounds in the surface sediments at M3 were chrysene, pyrene, Benzo[ghi]perylene, fluoranthene, and phenanthrene, whereas DY sediments—which exhibited the second-highest PAH concentration—were characterized by higher abundance of chrysene, Benzo[a]pyrene, pyrene, and fluoranthene (Figure 1b). Based on sediment physicochemical properties and PAHs distribution (Table S1), variation partition analysis (VPA) identified nitrogens (explaining independently 13.84% of PAH variation) as primary drivers of PAH distribution, followed by TOC (6.52%) and geographic location (4.72%), while sediment grain size having a negligible influence (0.90%) (Figure 1c). 3.2 characterization of microbes through 16S rRNA amplicon sequence analysis High-throughput sequencing of 51 mangrove sediment samples (3 replicates per sampling site) generated totally 3,542,630 high-quality reads (average length = 417 bp), which were clustered 98,498 ASVs across the six sections, ranging from 15,102 to 20,733 ASVs per section (Table S2). The exceptional sequencing depth was evidenced by Good's coverage values > 98.7% in all samples and clear asymptotes of the rarefaction curves (Figure S1), confirming comprehensive sampling of bacterial diversity in this study. Taxonomic classification revealed the presence of 214 bacterial classes, with Gammaproteobacteria (16.71%±4.89%), Alphaproteobacteria (12.74%±3.87%), Anaerolineae (10.23%±5.35%), Bacteroidia (8.67%±4.14%), Actinobacteria (6.28%±4.82%), Acidimicrobiia (5.66%±2.15%), and Clostridia (5.49%±4.22%) being most abundant (Figure S2a). At the order level, o__Rhizobiales, o__Flavobacteriales, o__Microtrichales, o__Rhodobacterales, o__SBR1031, o__Peptostreptococcales-Tissierellales, o__Anaerolineales, o__Steroidobacterales, o__Desulfobacterales, and o__unclassified_c__Gammaproteobacteria, collectively represented approximately 33.20% of the total sequences (Figure S2b). Among the 2,170 identified genera, the dominant taxa were: Anaerolineae-affiliated g_norank_f_Anaerolineaceae (2.60%±2.21%), SRB1031 (2.13%±1.55%), and g__norank_f__Caldilineaceae (1.83%±0.97%), Gammaproteobacteria-associated g__unclassified_c__Gammaproteobacteria (2.18%±0.95%), Acinetobacter (1.86%±4.35%), and Woeseia (1.54%±1.53%), Acidimicrobiia-clustered Ilumatobacter (2.32%±1.54%) and g__norank_o__Actinomarinales (1.64%±0.85%) , and Alphaproteobacteria-affiliated g__unclassified_f__Rhodobacteraceae (2.45%±1.12%) and KD4-96-associated g__norank_c__KD4-96 (1.51%±0.59%). These top 10 genera accounted for 20.06% of total sequences (Figure 2a). Section M3 exhibited a markedly distinct bacterial community profile to other sections. Most notably, the genus Acinetobacter was dramatically enriched in M3 sediment (7.84% relative abundance) compared to other sections (0.01% - 0.04%, Figure 2a). This unique microbial signature was further evidenced by reduced alpha diversity indices (Sobs, Pielou’s eveness, and Shannon indices) in M3 (Table S3) and clear separation in both NMDs and PCoA ordination analyses (Figure S2c, S2d). The pronounced dominance of Acinetobacter coupled with diminished diversity suggests strong environmental selection pressure in M3. The Kruskal-Wallis test identified 1,158 genera showing significant spatial variation across sampling sections ( p <0.05), with g__norank_f__Anaerolineaceae emerged as the most abundant genera (2.60%) (Figure 2b). LDA-LEfSe analysis revealed 598 discriminant genera, among which Acinetobacter , showed strongest association with M3 (LDA score = 4.59), along with g_norank_f_norank_o_PeM15 , and Exiguobacterium (Figure 2c, 2d). These results highlighted Acinetobacter as a key indicator taxa for the distinct microbial assemblage in M3 sediments. db-RDA based on Bray-Curtis dissimilarity confirmed the strong influence of environmental factors on microbial community ( p = 0.001), with total PAHs, TOC, and ammonium (NH 4 + ) concentrations showing significant correlations in M3 (Figure 2e). Meanwhile, VPA attributed 9.01% of microbial community variation to PAHs—a greater proportion than explained by nitrogens, TOC or soil components (Figure S3). These findings collectively demonstrated that PAHs contamination and nutrient enrichment act synergistically as primary environmental drivers shaping microbial community structure in these mangrove sediments, leading to the development of unique bacterial assemblages under polluted conditions. 3.3 Metagenomic Analysis 3.3.1 Taxonomic composition and enrichment of PAHs-degrading bacteria in mangrove section with PAHs contamination A total of 3,200,149,680 clean reads, accounting for ~98.36% of the raw reads, were generated from metagenomic sequencing of 36 sediment samples across six mangrove sections after quality filtering using fastp (v0.23.0) (Table S4). Taxonomic classification recovered 317 archaeal and bacterial classes, 4,655 genera, and 44,975 species (Table S5), with Alphaproteobacteria, Gammaproteobacteria, Actinomycetes, Anaerolineae, Acidimicrobiia, and Planctomycetia dominating the communities (collectively 53.94% of identified reads) (Table S5). The heavily contaminated M3 section exhibited a unique microbial signature characterized by striking enrichment of known hydrocarbon-degrading genera: Acinetobacter (2.233% of total reads vs 0.014%–0.042% in other sections), Nocardioides (1.612% vs 0.359%–0.680%), and Streptomyces (1.532% vs 0.318%–0.587%), with unexpected depletion of the typically considered PAHs-degrader Pseudomonas species (0.201%–0.304%) (Table S6). Non-parametric Kruskal-Wallis tests confirmed significant differences (p <0 0.05) in the distribution of Acinetobacter , Nocardioides , Streptomyces , Desulfuromonas and Robiginitales across sampling sections (Figure S4a), while LEfSe analysis further identified Acinetobacter as a key biomarker in M3 (Figure S4b). These findings showed strong concordance with 16S rRNA gene sequencing results (Figures 2a-2c), reinforcing the ecological specialization of these taxa with PAHs-impacted sediments. Remarkably, we detected all 51 recognized PAH-degrading genera [8], representing 4.10% of total microbial reads, dominated by Mycobacterium (18.57%), Nocardioides (17.90%), and Acinetobacter (10.08%), which together represented nearly half of all PAHs degrader reads (Figure 3a, Table S7). The PAH-degrading consortium in M3 was particularly robust (~7.73% of total microbial reads, averaging 1,712,982 reads) compared to the other sections (2.58%–4.49%, averaging 418,992–898,514 reads) (Table S7). Acinetobacter is exceptionally enriched in section M3 (496,820 reads and 28.90% of PAHs degraders vs 2,181–6,344 reads and 0.41%–1.27% elsewhere), accounting for nearly one-third of the site's degradation potential (Figure 3a). Both NMDS analysis and PCoA analysis confirmed distinct phylogenetic structure of PAH-degrading microbial community in M3 compared to other sections (Figure 3b&3c). 3.3.2 Functional variance of microbial communities in mangrove sediment sections Our metagenomic analysis revealed substantial functional diversity across sampling sites. The functional annotation identified 11,037–13,046 KEGG orthologs (KOs), 3,768–4,054 enzymes, and 464–470 metabolic pathways (Tables S8 & S9, Figure S5a). Section DY exhibited the most limited functional repertoire among all sampling sections. dbRDA of pre-filtered environmental parameters (VIF < 10) demonstrated that nitrate, ammonium, sediment sand content, TOC, and total PAH concentrations as key determinants of microbial functional profiles (Figure S5b&S5c). Notably, both enzymes composition (Figure S5b) and KO distribution (Figure S5c) showed strong spatial clustering by sampling section, indicating distinct location-specific functional signatures of microbial communities, while in section M3, they showed strong correlation with PAH, TOC, and ammonia. Statistical analyses revealed significant spatial heterogeneity in enzymatic profiles across sampling sections, with the Kruskal-Wallis test identifying 3,916 differentially abundant KEGG enzymes ( p 2) that were specifically enriched in section M3, predominantly comprising transferases, oxidoreductases, hydrolases, ligases, lyases, isomerases, and translocases (Table S11). Functional annotation demonstrated these enzymes biomarkers were principally functioned in metabolic pathways (15.38% of biomarkers-associated reads), biosynthesis of secondary metabolites (9.43%), microbial metabolism in diverse environments (7.24%), carbon metabolism (3.84%), and pyruvate metabolism (2.88%). Pathway-level analysis revealed M3’s microbial community exhibited marked enrichment in specialized metabolic processes, including biosynthesis of secondary metabolites (32.04%), biosynthesis of cofactors (10.18%), pyruvate metabolism (5.53%), glycolysis/gluconeogenesis (4.61%), butanoate metabolism (3.61%), alanine, aspartate and glutamate metabolism (3.49%), valine, leucine and isoleucine degradation (3.11%), reflecting adaptive metabolic reprogramming in response to environmental stressors. 3.3.3 Key metabolic genes and pathways related to PAHs degradation Recent studies have documented the upregulation of functional genes associated with PAH degradation in the PAHs contamination sediments [54, 55]. Our metagenomic analysis identified a comprehensive suite of totally 565 KOs associated with aromatic carbon degradation, predominantly encoding oxidoreductases and their subunits (301 KOs), transferases (81KOs), lyases (63 KOs), hydrolases (38KOs), isomerases (21 KOs) and so on (Table S12). Corresponding to these genetic elements, totally 351 enzymes were annotated, including 182 oxidoreductases, 59 transferases, 45 lyases, 31 hydrolases, 19 isomerases, 13 ligases, and 2 translocases (Table S13). Spatial analysis revealed significant biogeographic variation in degradation potential, with section M1 exhibiting the lowest abundance of degradation genes and enzymes (Table S12 & S13, Figure S6a, S6b). Multivariate statistics demonstrated strong correlation between PAH-degrading genes/enzymes and sediment characteristics (Mantel test: mantel_r = 0.372, p = 0.001), with PAH and ammonia concentration emerging as primary determinants of PAH-degrading elements distribution patterns in section M3 (db-RDA: r 2 = 0.292, p = 0.002; Figure S6c, S6d). Oxidoreductases emerged as the dominant PAH-degrading enzymes, and showed high correlation with PAH concentration (db-RDA: r 2 = 0.243, p = 0.015; Figure S6e). We calculated the relative contribution of the top 50 abundant bacterial genera to PAHs-degrading functions. Anaerolinea and Ilumatobacter were identified as primary contributors to the overall putative PAHs-degrading enzymes, while in the heavily contaminated section M3, Acinetobacter , Mycobacterium , and Nocardioiders species represented exceptional dominant sources of PAHs-degrading genes (Figure 4). This finding highlighted their crucial role in PAHs biodegradation under high-pollution conditions, corroborating their established reputation as versatile hydrocarbon degraders. Beyond canonical degradation pathways, emerging evidence indicates the involvement of CAZymes in PAH metabolism [20, 21]. The sediment microbiome harbored 33,678,720 CAZymes spanning seven functional classes, with glycoside hydrolases (GHs) and glycosyl transferases (GTs) constituting the most abundant classes (Table S14). Five dominant enzyme families (GT41, GT4, CE1, GT2_Glycos_transf_2, and GT83) collectively represented 30.70% of total CAZymes (Table S15). The contaminated M3 section exhibited distinct taxonomic contributors, with Anaerolinea , Caldilinea , Ilumatobacter , Litorilinea , Streptomyces , Mycobacterium , Nocardioides , and Acinetobacter serving as primary producers of CAZymes (Figure S7). Meanwhile, PAH concentration and nutrient level emerged as key environmental determinants of CAZymes distribution patterns, particularly in section M3 (Figure S8). These findings suggested a novel mechanistic linkage between PAH contamination and modified carbohydrate metabolism, potentially representing an adaptive strategy for hydrocarbon processing in polluted environments. 3.3.4 Metagenomic assembled genomes (MAGs) A total of 283 MAGs were reconstructed using the contigs derived from metagenomic data, with completeness values ranging from 64.79% to 100%. Taxonomic classification showed these MAGs were assigned to nineteen phyla, with Pseudomonadota (83 MAGs), Bacteroidota (47 MAGs), and Actinomycetota (46 MAGs) representing the most abundant lineages (Figure S9; Table S16). However, only 15 MAGs could be assigned at the species level, suggesting these sediments harbor substantial novel taxa with uncharacterized genomes (Figure S9; Table S16). A phylogenetic reconstruction of all the 283 MAGs using the marker gene set from GTDB-Tk demonstrated significant enrichment of MAGs affiliated with known hydrocarbon-degrading genera, namely Ilumatobacter , Mycobacterium , Lutimona s, and Anderseniella in the contaminated M3 section (Figure 5). KEGG annotation identified 405 aromatic degradation KOs across the MAGs, predominantly associated with lipid transport and metabolism, coenzyme transport and metabolism, and secondary metabolites biosynthesis, transport and catabolism (Table S17). Three Pseudomonadota MAGs (MAG270, MAG13, and MAG207) exhibited exceptional degradation potential, containing 109, 103, and 99 PAH-degrading KOs in their genomes (Table S18, Figure 5). Further analysis identified 30,369 CAZymes which were categoried into more than 450 families, and their largest share was contributed by Bacteroidota (6695 CAZymes) followed by Pseudomonadota (6099) and Chloroflexota (6000). Meanwhile, the top CAZyme-producing MAGs belonged to the Bacteroidetes and Planctomycetota phyla (Figure 5), suggesting these taxa may employ carbohydrate-active enzymes as an alternative strategy for PAH modification. The co-occurrence of canonical degradation pathways and diverse CAZymes indicates complex, multi-faceted microbial adaptation to hydrocarbon contamination in mangrove ecosystems. 4. Discussion 4.1 PAHs contamination in mangrove sediment PAHs were potent carcinogens and mutagens, posing significant ecological toxicity and potential human health risks due to their persistence and long-term accumulation in ecosystem [9, 56, 57]. Mangrove sediments, characterized by high sedimentation rates, organic matter (OM) richness, and biogeochemical reactivity, are particularly susceptible to PAH accumulation, serving as long-term sinks for these contaminants [11]. The unique biogeochemistry of mangrove sediments—marked by tidal flushing, sulfate reduction, and complex organic-mineral interactions—further influences PAH fate, modulating their persistence, bioavailability, and ecological impact [31, 58-60]. In this study, we detected 18 individual PAH compounds across 36 surface sediments samples collected from six mangrove sections in Beihai and Zhangzhou, China. Spatial analysis revealed marked heterogeneity in PAH distribution, with Beihai mangroves exhibiting significantly higher mean concentrations (395.49 ng/g d. w.) compared to Zhangzhou (232.83 ng/g d. w.) ( p < 0.05, Table S1). Notably, section M3, proximal to a local outfall, registered the highest PAH load (730.76 ng/g d.w.), Whereas sections M1 and M2, located within the protected Beihai Coastal National Wetland Park, showed lower PAH contamination levels (Fig. 1b). This gradient underscore the dominant role of localized anthropogenic inputs in driving PAHs accumulation in mangrove sediment, as corroborated by prior studies [11, 60]. The VPA results identified nutrients and TOC as key drivers of PAH distribution, explaining 13.84% and 6.52% of the observed variation, respectively (Fig. 1c). These findings align with two established mechanisms: (1) eutrophication-PAH synerby: Nutrient-rich environments (e.g., those impacted by agricultural or sewage runoff) often exhibit elevated PAH retention due to enhanced particle aggregation and sedimentation [61], and (2) organic matter-mediated sequestration: hydrophobic PAHs exhibit strong affinity for organic-rich matrices, leading to their long-term sequestration in sediments [62]. The high TOC content in mangroves thus acts as a “trap” for PAHs, reducing their mobility but prolonging their persistence [62, 63]. However, while organic matter enhances PAHs retention through these adsorption processes, it may simultaneously reduce their bioavailability and hinder microbial degradation by incorporating PAHs into recalcitrant humic complexes [64, 65]. 4.2 Revealing Microbial Community Shifts and Diversity PAH contamination exerts profound and selective pressures on microbial communities, driving structural and functional adaptations that reflect both toxicity-mediated supression and niche-based enrichment of specialized taxa [25, 54, 66, 67]. Leveraging 16S rRNA gene-based next-generation sequencing and metagenomic analysis, we documented systematic shifts in microbial diversity and composition across PAH contamination gradients. High-PAH sediments (M3) displayed significant reductions in microbial alpha diversity (invsimpson: 220.4 vs 322.8-806.8 in other sections; p < 0.05, Table S3), consistent with prior studies linking PAH toxicity to microbial diversity loss in contaminated ecosystems [30, 68-70]. This decline likely reflects the combined effects of direct toxicity to sensitive taxa and competitive exclusion by PAH-adapted specialists [66, 70]. Beta diversity analysis further highlighted distinct clustering of MC3 microbiomes (Figure 2e, S2d & 2e), dominanted by Acinetobacter (7.84% relative abundance; Figure 2a & 2b), a genus frequently associated with hydrocarbon degradation [71]. The prominence of Acinetobacter species in section M3 coincided their documented capacity to metabolize HMW PAHs (e.g., pyrene and Benzo[a]pyrene) [72-74] and even utilize acenaphthene and acenaphthylene as sole carbon sources [75]. The selective enrichment of PAH-degrading microorganisms in PAH contaminated environments has been well documented [76-78]. LDA-LEfSe analysis identified Acinetobacter as a key biomarker for PAH-enriched sediments (LDA score > 4.5; Figure 2d), corroborated by db-RDA and VPA results, which confirmed that nutrient availability and PAH contamination were the primary drivers of bacterial community variation in the sediments (Figure 2e & S3). These findings align with the ecological principle of “stress-induced selection”, wherein contamination favors taxa with specialized metabolic adaptations, while sensitive taxa may decline due to toxicity or competitive exclusion [17, 79]. Metagenomic profiling demonstrated significant enrichment of PAH-degrading taxa (7.73%) of the M3 community, nearly double their abundance in less contaminated sections (2.58–4.49% relative abundance; Figure 3a, Table S7). The dominance of Mycobacterium , Nocardioides , and Acinetobacter (collectively 46.5% of PAH degraders, Figure S4) reflects their specialized adaptive strategies in high-PAH niche, particularly through biosurfactant production to enhance PAH bioavailability and successive decomposition via ring-hydroxylating dioxygenases (RHDs) [8, 17, 80]. However, the precise mechanisms underlying PAH degradation by these genera remain insufficiently characterized, requiring further mechanistic and genomic investigations. MAGs phylogeny uncovered preferrence of additional hydrocarbon-degrading genera ( Ilumatobacter , Lutimona s, and Anderseniella ) in M3 (Figure 5), this discrepancy highlights the limitations of marker-gene surveys in capturing functional diversity and underscores the need for genome-resolved approaches to identify novel PAH degraders. 4.3 Identification of putative PAH-degrading genes and Enzymes Metagenomic analysis have revolutionized our understanding of PAH degradation by elucidating the complex network of enzymes, genes, and metabolic pathways involved in these processes, while simultaneously facilitating the discovery of novel biocatalysts with potential applications in bioremediation and industrial biotechnology [69, 81-83]. Our study identified 565 KOs and 351 enzymes associated with aromatic carbon degradation, predominantly belonging to oxidoreductases, transferases, lyases, and hydrolases (Table. S12 & S13). Oxidoreductases emerged as the most prominent enzyme classes, with their abundance strongly correlating with PAH contamination levels (Figure S6d). This aligns with previous studies linking oxidative enzyme activity to microbial detoxification potential in PAH-contaminated environments [26, 84]. Through systematic annotations of KEGG KOs and enzymes, we reconstructed a comprehensive PAH degradation pathway employed by indigeous microbial communities. (i) Initial Oxidation: RHDs catalyze the stereospecific incorporation of molecular O 2 into PAH structures, forming cis- dihydrodiol intermediates—a critical and often rate-limiting step [85]. (ii) Dehydrogenation: Dihydrodiol dehydrogenases convert these intermediates into catechols, priming them for ring cleavage [86]. (iii) Ring Cleavage: Catechol dioxygenase mediate either ortho-cleavage to generate cis -muconic acid derivatives, or meta_cleavage to form 2-hydroxymuconic semialdehyde derivatives, depending on the positioning of the cleaved C-C bond relative to the hydroxyl groups [87]. (iv) Mineralization: The β-ketoadipate pathway processes these products into tricarboxylic acid (TCA) cycle intermediates, ultimately leading to complete mineralization to CO 2 [88]. Despite this metabolic versatility, research has confirmed that five- or six-ring PAHs, such as dibenzo(a,h)anthracene and benzo(ghi)perylene, remain recalcitrant due to structural incompatibility with conventional RHD systems [89]. This underscores both the remarkable catalytic efficiency and inherent limitations of microbial degradation in natural environments with complex contaminants. RHDs represent pivotal biocatalysts in microbial PAH degradation, holding tremendous potential for enhancing bioremediation efficiency and building biotechnological solutions for PAH-contaminated environments [85, 90]. These multicomponent enzyme systems catalyze the initial oxygen incorporation into aromatic rings, forming cis-dihydrodiol—a step that dictates downstream degradation efficiency [91, 92]. Our research highlights the striking diversity of RHD substrate specificities, ranging from narrow-specific phenanthrene 3,4-dioxygenase (EC:1.13.11.-) that exclusively target phenanthrene [91], to broad-specificity catalysts such as naphthalene 1,2-dioxygenase (EC:1.14.12.12) capable of oxidizing multiple PAHs, including naphthalene, anthracene, pyrene, chrysene, fluorene, fluoranthene, phenanthrene, benzo[a]pyrene, and indeno[1,2,3-cd]pyrene [26, 89], and the versatile PAH dioxygenase (EC:1.14.12.-) that oxidized the rings of acenaphthylene, acenaphthene, anthracene, fluorene, fluoranthene, pyrene, chrysene, benz[a]anthracene, benzo[a]pyrene (Table S8) [25, 26]. Interestingly, despite varying PAH concentrations across mangrove sections, RHD distribution patterns remained constant, suggesting either methodological limitations in detecting active enzymes or microbial adaptations to mixed-PAH exposures, enabling functional redundancy [30, 93]. This Observation points to a critical gap in our understanding environmental PAH degradation. Current research is predominantly based on single-PAH degradation studies [8], neglecting mixed-PAH dynamics prevalent in natural environments where co-metabolic interactions such as synergistic degradation or competitive inhibition may significantly alter remediation outcomes but remain poorly understood [94]. Meanwhile, enzyme-substrate interactions in complex environmental metrices are insufficiently characterized, limiting predictive modeling of degradation efficiency. Future research must prioritize (1) the multi-PAH degradation studies to elucidate interference effects; (ii) metatranscriptomic/proteomic profiling to distinguish active vs. latent degradation pathways; (iii) structural enzymology to engineer RHDs for enhanced HMW-PAH degradation. By addressing these gaps, we can better harness microbial communities for targeted bioremediation strategies, optimizing their catalytic potential in natural contaminated ecosystems. 4.4 Exploring Co-metabolism Mechanisms Co-metabolism represents a fundamental microbial process wherein non-growth-supporting substrates are transformed in the presence of primary growth-supporting compounds [95, 96]. This mechanism plays a critical role in environmental remediation, particularly for persistent pollutants that resist conventional degradation pathways [97, 98]. Co-metabolism is especially pronounced in natural contexts, where PAHs invariably present as complex mixtures rather than isolated compounds [63]. In such scenarios, the degradation of one PAH compound may synergistically enhances the breakdown of others—especially HMW PAHs—through fortuitous action of broad-substrate-range enzymes such as oxygenases, hydroxylases, which are often induced by simpler carbon sources [96, 99-101]. A key component to co-metabolism processes is the diverse family of CAZymes, whose metabolic versatility extends beyond their primary function of polysaccharide modification to include transformation of recalcitrant compounds like PAHs through cross-adaptation mechanisms [102, 103]. CAZymes have been well-documented in the degradation of structurally complex biopolymers like chitin and lignin [104, 105], indicating their broader role in regulating organic matter cycling in terrestrial and aquatic ecosystems [106]. Notably, elevated CAZymes abundance is frequently associated with microbial communities processing strong organic matter decomposition capacities, which may translate to greater PAH degradation potential through shared oxidative enzymatic pathways [107, 108]. Metagenomic evidence strongly supports this functional overlap, demonstrating the co-occurrence of CAZyme genes and PAH degradation genes in sedimentary ecosystems [20], with certain CAZymes capable of directly oxidizing 3-4 ring PAH through both phenolic and nonphenolic aromatic compound oxidation pathways [109]. Our investigation of mangrove sediments revealed GTs and GHs which catalyze the synthesis and cleavage of glycosidic bonds, emerged as the dominant CAZyme classes, consistent with observations from diverse terrestrial and aquatic environments [110, 111]. Particularly compelling was the significant correlation between PAH concentrations and the CAZyme distribution patterns, most notably in section M3 which experiences higher PAH loads. This association strongly suggests CAZyme involvement in PAH co-metabolism under environmental stress. Supporting this hypothesis, our data demonstrated that the top three PAH-degrading genera— Mycobacterium , Nocardioides , and Acinetobacter —were also major CAZyme producers in contaminated sediments (Figure S7) . Among them, Acinetobacter stands out for its documented capacity to enhance bioremediation through carbohydrate-driven co-metabolic pathways, making it a promising candidate for large-scale bioremediation applications including wastewater treatment [99]. These findings collectively underscore the potential for co-metabolic interactions to synergistically improve PAH degradation efficiency in natural environments, where carbon source availability emerges as a critical regulator of degradation rates, microbial communities utilize shared enzymatic systems for both primary metabolism and xenobiotic breakdown, and strategic carbon input management may offer a promising approach to optimize bioremediation outcomes. Future research should focus on elucidating the precise molecular mechanisms underlying these co-metabolic processes, particularly the regulatory interplay between carbon metabolism and pollutant degradation pathways. Such understanding could revolutionize bioremediation strategies by enabling targeted approaches that harness native microbial communities' metabolic versatility for more effective and sustainable environmental remediation. 5. Conclusion This study presents a multi-dimensional characterization of PAH contamination and its biodegradation mechanisms in mangrove ecosystems, integrating geochemical profiling with advanced bioinformatic analyses. Our findings reveals that spatial heterogeneity in PAH distribution is predominantly driven by anthropogenic inputs, and further modulated by nutrient availability and TOC content. Notably, high-PAH sediments exhibited significant enrichment of Acinetobacter species, underscoring their ecological role in hydrocarbon degradation. Through microbial functions analysis and enzyme annotation, we identified key PAH degrading taxa ( Mycobacterium , Nocardioides , Acinetobacter ). A PAH degradation framework was established based on the identification and annotation of 565 KOs and 351 enzymes involved in PAH degradation, with RHDs playing a pivotal role in the initial oxidation step. Furthermore, we uncovered compelling evidence for CAZyme-mediated co-metabolism, particularly through GT/GH activities associated with dominant degraders. The discovery of shared enzymatic machinery between central carbon metabolism and xenobiotic degradation provides new perspectives for bioremediation strategies. These insights not only deepen our understanding of PAH remediation but also pave the way for practical remediation application—such as enzyme-enhanced solutions targeting recalcitrant HMW PAHs—while ensuring ecological sustainability. Declarations Ethics approval and consent to participate This study did not involve human participants, animal experiments, or clinical data requiring ethical approval. Consent for publication The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Funding This work was funded by Scientific Research Foundation of the Third Institute of Oceanography, MNR (2020017), Natural Science Foundation of Fujian (2023J011374). Data availability The 16S rRNA gene sequences and metagenomic data are reported in this paper have been deposited in the Genome Sequence Archive [112] in National Genomics Data Center [113], China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA028312 and CRA028284, respectively) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa. Competing interests The authors declare that they have no competing interests. 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Nucleic Acids Res. 2025;53,(D1):D30-D44. Additional Declarations No competing interests reported. Supplementary Files TableS1PAHs.xlsx TableS2ASVs.xlsx TableS4SequencesSummary.xlsx TableS3alphadiversityindex.xlsx TableS5NRannotationresults.xlsx TableS7PAHdegradingmicrobes.xlsx TableS8KEGGEnzymeprofile.xlsx TableS6Microbialdistributionacrosssections.xlsx TableS11LEfSeM5enzymebiomarker.xlsx TableS10KruskalwallistestKEGGenzyme.xlsx TableS9KEGGKOprofile.xlsx TableS14CAZymesclassprofile.xlsx TableS13PAHdegradingenzymesprofile.xlsx TableS15CAZymesfamilyprofile.xlsx TableS12PAHdegradingKOsprofile.xlsx TableS18MAGsPAHdegradingKOsnumber.xlsx TableS16MAGsrelativeabundanceprofile.xlsx TableS17MAGsPAHsdegradingKOs.xlsx SupplementaryMaterial.docx 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7178259","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515684648,"identity":"7576fb63-af33-49ab-9185-954b2a6144cf","order_by":0,"name":"Danyun Ou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYHACAzDJz8DYQKIWyTaStRgcI1Y9v0Tyxs+FbYfzjO83Nz5gqLhn18B+9gBeLZIz0oqlZ7YdLjY7xthswHCmOLmBJy8Bv6tu5BhI87YdTtx2jLFNgrEtIZlBgseAkBbj3yAtm9tI0GIGtmUDG0SLHUEtkj3Pyqx5zqUnzjiW2GyQcCYhgY0nB78Wfvbkzbd5yqwT+5uPP3zwoSLBnp/9DH4tYMDIBmUkMDAkthFWDwJ/EEx74nSMglEwCkbBSAIAg+lBZIhb2gIAAAAASUVORK5CYII=","orcid":"","institution":"Ministry of Natural Resources","correspondingAuthor":true,"prefix":"","firstName":"Danyun","middleName":"","lastName":"Ou","suffix":""},{"id":515684649,"identity":"e9136bbe-7d16-4da6-b9f4-99193a8a086f","order_by":1,"name":"Yue Ni","email":"","orcid":"","institution":"Ministry of Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Ni","suffix":""},{"id":515684650,"identity":"e7c348d7-0dd7-40dc-b64a-211ce1464fa6","order_by":2,"name":"Weiyi He","email":"","orcid":"","institution":"Ministry of Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Weiyi","middleName":"","lastName":"He","suffix":""},{"id":515684651,"identity":"1492a448-6bc9-4235-b757-bb3d00aee49e","order_by":3,"name":"Shuangshuang Lin","email":"","orcid":"","institution":"Ministry of Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Shuangshuang","middleName":"","lastName":"Lin","suffix":""},{"id":515684652,"identity":"3af8d7ba-23c5-46ac-b7c3-9a9a61045bfe","order_by":4,"name":"Weiwen Li","email":"","orcid":"","institution":"Ministry of Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Weiwen","middleName":"","lastName":"Li","suffix":""},{"id":515684653,"identity":"4eb5344b-8e3b-4f51-a283-b6dd61c43c2a","order_by":5,"name":"Lei Wang","email":"","orcid":"","institution":"Ministry of Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Wang","suffix":""},{"id":515684654,"identity":"08c17962-a237-41bf-bc96-42e998f9c362","order_by":6,"name":"Hao Huang","email":"","orcid":"","institution":"Ministry of Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Huang","suffix":""},{"id":515684655,"identity":"62da807a-f846-4aa0-bf3b-88abb9809907","order_by":7,"name":"Shangwei Wang","email":"","orcid":"","institution":"Ministry of Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Shangwei","middleName":"","lastName":"Wang","suffix":""},{"id":515684656,"identity":"5c2ecb2c-25de-4dec-810d-117a5a352ef9","order_by":8,"name":"Shunyang Chen","email":"","orcid":"","institution":"Ministry of Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Shunyang","middleName":"","lastName":"Chen","suffix":""},{"id":515684657,"identity":"8bf41765-c266-4914-9a41-44a769d0d7c9","order_by":9,"name":"Jiahui Chen","email":"","orcid":"","institution":"Ministry of Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Jiahui","middleName":"","lastName":"Chen","suffix":""},{"id":515684658,"identity":"1b827bb4-4c85-4c42-8ebf-6fefc633a821","order_by":10,"name":"Guangcheng Chen","email":"","orcid":"","institution":"Ministry of Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Guangcheng","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-07-21 13:54:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7178259/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7178259/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91513890,"identity":"e2156ed5-1850-47ac-a947-6a35b1cd75bc","added_by":"auto","created_at":"2025-09-17 09:07:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":585707,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution and variance of PAHs in mangrove sediments across study sections. (a), Sampling location in mangrove ecosystems of Beihai and Zhangzhou, China. Six sampling sections were analyzed: CPT, FG, DY, M1, M2, and M3. (b), Concentration profiles of 18 individual PAH compounds detected in mangrove sediments. (c), Variance partitioning analysis (VPA) of PAH component influenced by nitrogen, TOC, geographic location, and soil component.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7178259/v1/2b4e4376ef82b097efff0465.png"},{"id":91513894,"identity":"85b5a1ae-7e97-4e33-aa2b-9789e9229b31","added_by":"auto","created_at":"2025-09-17 09:07:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1192189,"visible":true,"origin":"","legend":"\u003cp\u003eDistinct bacterial community structures in mangrove sediments across sites with varying PAH contamination levels, as revealed by 16S rRNA sequencing. (a), Percentage of bacterial community abundance at the genus level across sampling sections. Grey bars represent the relative abundance of \u003cem\u003eAcinetobacter\u003c/em\u003e; (b), Differential abundance of dominant bacterial genera among sampling sites revealed by Kruskal-Wallis test, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). (c), LEfSe analysis results highlighting taxa with significant differences (Wilcoxon rank-sum test, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 LDA \u0026gt; 3). Concentric circles represent phylogenetic hierarchy (phylum to genus), with circle size proportional to taxon abundance. Colored nodes indicate taxa significantly enriched in specific sampling sections, while taxa with no significant differences are uniformly colored in yellow. (d) Indicator bacterial taxa (LDA \u0026gt; 3) across sampling sections. Taxonomic levels: p: phylum, o: order, c: class, f: family, g: genus. (e), Distance-based Redundancy Analysis (db-RDA) plot illustrating the correlation between prefiltered environmental factors (VIF \u0026lt; 10, vectors) and microbial community composition (circles) across different sections\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7178259/v1/9a75733f86bb3a5ea03e4fa6.png"},{"id":91513892,"identity":"28c05e1c-5a4e-4c9b-99e7-ae7bea0cebaa","added_by":"auto","created_at":"2025-09-17 09:07:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1110974,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution patterns of PAH-degrading microbial communities in mangrove sediments across six sampling sections. (a), Circos plot illustrating the relative abundance and distribution of PAH-degrading archaeal and bacterial genera, highlighting the enrichment of \u003cem\u003eAcinetobacter \u003c/em\u003ein section M3. (b), Non-metric multidimensional scaling (NMDs) ordination of PAH-degrading microbial communities, demonstrating distinct clustering among sampling sections. (c), Principal Coordinate Analysis (PCoA) based on Bray-curtis dissimilarity revealing a divergent PAH-degrading microbial community in M3 compared to other sections.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7178259/v1/98eeec449c2ce03f096e4d66.png"},{"id":91516749,"identity":"7a4be792-d826-45f7-8449-9097cc850803","added_by":"auto","created_at":"2025-09-17 09:23:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":480598,"visible":true,"origin":"","legend":"\u003cp\u003eThe relative contribution of dominant bacterial genus (top 50) of to putative PAH-degrading enzymes across six mangrove sections. The heat map displays standardized genes abundance values (log-transformed) representing putative PAH-degrading enzymes, based on simultaneous annotation against both NR and KEGG-enzyme databases.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7178259/v1/c244379c958807290ea12bdb.png"},{"id":91513899,"identity":"4b2e6ef4-eab4-407f-ae43-1713b8f89144","added_by":"auto","created_at":"2025-09-17 09:07:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1599135,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic diversity and functional potential of MAGs recovered from mangrove sediments. Phylogenetic tree was constructed by comparing the MAGs with archaeal and bacterial conserved marker genes in GTDB database (v214.1), with branch colors indicating taxonomic affiliation. Inner sectors display the relative abundance of each MAG across the six sampling sections. Number of genes encoding CAZymes and number of PAH-degrading KOs per MAG was illustrated in bars.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7178259/v1/a987fe080c81873cba184972.png"},{"id":95027631,"identity":"0f82bbed-b855-4446-ac6a-32949efca33e","added_by":"auto","created_at":"2025-11-03 13:38:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5153962,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7178259/v1/53c8bfda-f322-44f0-a612-48bc145715db.pdf"},{"id":91513891,"identity":"063ba298-01f1-4da1-a05f-6ec7027b2770","added_by":"auto","created_at":"2025-09-17 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09:15:59","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1817792,"visible":true,"origin":"","legend":"","description":"","filename":"TableS8KEGGEnzymeprofile.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7178259/v1/5d0bfdcef12bad3f8cac48da.xlsx"},{"id":91513904,"identity":"3b573ecd-dc99-4214-a9d0-518e9b1b4a00","added_by":"auto","created_at":"2025-09-17 09:07:59","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":2197115,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6Microbialdistributionacrosssections.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7178259/v1/f2119313ffe2c219f6a7e792.xlsx"},{"id":91513902,"identity":"acfd11ab-617a-43cc-a54b-7f5e5fbe6e03","added_by":"auto","created_at":"2025-09-17 09:07:59","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":37629,"visible":true,"origin":"","legend":"","description":"","filename":"TableS11LEfSeM5enzymebiomarker.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7178259/v1/5170be54d0cf67a1365cfb3e.xlsx"},{"id":91516748,"identity":"1cb1e2da-74a4-4e47-bd6b-0f6fa6b1b30f","added_by":"auto","created_at":"2025-09-17 09:23:59","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":673721,"visible":true,"origin":"","legend":"","description":"","filename":"TableS10KruskalwallistestKEGGenzyme.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7178259/v1/4c9f884035ec191c5470e419.xlsx"},{"id":91513905,"identity":"0714a936-fada-424a-a99e-37e87e9653a9","added_by":"auto","created_at":"2025-09-17 09:07:59","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":4130776,"visible":true,"origin":"","legend":"","description":"","filename":"TableS9KEGGKOprofile.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7178259/v1/f812ddec954aa5e3c98287df.xlsx"},{"id":91513898,"identity":"ebf4731f-baeb-4661-93c2-21f33fe1b4bd","added_by":"auto","created_at":"2025-09-17 09:07:59","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":13038,"visible":true,"origin":"","legend":"","description":"","filename":"TableS14CAZymesclassprofile.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7178259/v1/5c269769c74b11a5e83a8f70.xlsx"},{"id":91515788,"identity":"a246f9a3-0ebb-4050-8c29-2e754ca3b25a","added_by":"auto","created_at":"2025-09-17 09:15:59","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":121990,"visible":true,"origin":"","legend":"","description":"","filename":"TableS13PAHdegradingenzymesprofile.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7178259/v1/7ec8aa8f771ca12cd0c9820e.xlsx"},{"id":91513907,"identity":"34e341d4-e082-4614-89f9-082fa57e7c75","added_by":"auto","created_at":"2025-09-17 09:07:59","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":257435,"visible":true,"origin":"","legend":"","description":"","filename":"TableS15CAZymesfamilyprofile.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7178259/v1/d6163cf89793c4c0084ae281.xlsx"},{"id":91515792,"identity":"936db75e-6fbe-4dda-b624-4135e58b2bc4","added_by":"auto","created_at":"2025-09-17 09:15:59","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":184468,"visible":true,"origin":"","legend":"","description":"","filename":"TableS12PAHdegradingKOsprofile.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7178259/v1/ba2d67782eefc1e8349e2136.xlsx"},{"id":91513909,"identity":"45404ce8-fa75-4b0a-b5b5-0bc5d72660e4","added_by":"auto","created_at":"2025-09-17 09:07:59","extension":"xlsx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":27207,"visible":true,"origin":"","legend":"","description":"","filename":"TableS18MAGsPAHdegradingKOsnumber.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7178259/v1/da59062e28aa8971489fd91f.xlsx"},{"id":91513908,"identity":"4154603b-c144-437d-8b4d-843244382b49","added_by":"auto","created_at":"2025-09-17 09:07:59","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":109202,"visible":true,"origin":"","legend":"","description":"","filename":"TableS16MAGsrelativeabundanceprofile.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7178259/v1/7e45b156eebc702b1f20b3cb.xlsx"},{"id":91513912,"identity":"7a602564-756d-496e-817a-d7dc0ec84256","added_by":"auto","created_at":"2025-09-17 09:07:59","extension":"xlsx","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":2103304,"visible":true,"origin":"","legend":"","description":"","filename":"TableS17MAGsPAHsdegradingKOs.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7178259/v1/1b9b18384d3e7e3b6699a44e.xlsx"},{"id":91513911,"identity":"b6952ae8-8c31-40fa-bb3a-df9371a68d27","added_by":"auto","created_at":"2025-09-17 09:07:59","extension":"docx","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":5010161,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7178259/v1/020f4f41a2fb0ac6c5f484ca.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Divergent microbial community and functional dynamics in mangrove sediments along a polycyclic aromatic hydrocarbons (PAHs) gradient in China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMangrove ecosystems represent the most biologically productive and ecologically vital coastal habitats, delivering indispensable ecosystem services and resources that sustain both marine diversity and human livelihoods [1, 2]. Despite their ecological significance, these ecosystems are increasingly imperiled by anthropogenic disturbances [3, 4], particularly through the accumulation of persistent organic pollutants such as polycyclic aromatic hydrocarbons (PAHs) [5-7]. PAHs, a class of non-polar organic compounds characterized by fused benzene rings which arranged in linear, angular or cluster configurations, originate predominantly from incomplete combustion of fossil-fuels and industrial emission [8]. Recognized for their environmental persistence, toxicity, mutagenicity, and carcinogenicity, 16 PAHs have been designated as priority pollutants by the U.S. Environmental Protection Agency (US EPA) [9, 10]. In mangrove sediments,\u0026nbsp;PAHs exhibit strong adsorption to organic particulate matter, leading to their pronounced\u0026nbsp;enrichment, while their spatial distribution is further modulated by hydrodynamic processes such as tidal flushing and porewater seepage\u0026nbsp;[11, 12].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMicrobial communities inhabiting mangrove sediments play a pivotal role in mediating biogeochemical cycles and exhibit extraordinary adaptability to environmental stressors [13]. While bacterial populations often demonstrate resilience to environmental stressors through compositional and functional plasticity, prolonged PAH exposure tends to reduce overall microbial diversity, favoring the specialized degraders such as \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003eAeromonas\u003c/em\u003e, \u003cem\u003eAspergillus\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eCorynebacterium\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, \u003cem\u003eMicrobulbifer\u003c/em\u003e, \u003cem\u003eMicrococcus\u003c/em\u003e, \u003cem\u003eMycobacteria\u003c/em\u003e,\u0026nbsp;\u003cem\u003eNocardioides\u003c/em\u003e,\u0026nbsp;\u003cem\u003ePaenibacillus\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003ePseudorhodoferax\u003c/em\u003e, \u003cem\u003ePseudoxanthomonas\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e,\u0026nbsp;\u003cem\u003eStreptomyces\u003c/em\u003e,\u0026nbsp;\u003cem\u003eXanthomonas\u003c/em\u003e, and so on\u0026nbsp;[14-18]. Such community shifts may disrupt essential ecosystem functions, including organic matter decomposition and nutrient cycling, with cascading effects on mangrove ecosystem stability\u0026nbsp;[19]. Although microbes-mediated PAH biodegradation has been extensively documented, the underlying metabolic pathways and enzymatic mechanisms\u0026mdash;particularly those involving carbohydrate-active enzymes (CAZymes)\u0026mdash;remain inadequately characterized, largely due to the structural complexity of PAHs and the inherent heterogeneity of microbial community\u0026nbsp;[20-22].\u003c/p\u003e\n\u003cp\u003eRecent advances in culture-independent metagenomic approaches, including metagenome-assembled genomes (MAGs), has revolutionized our ability to decipher microbial degradation pathways by bridging taxonomic identity with functional potential [23-26]. High-resolution metabolic profiling has facilitated the identification of key catalytic enzymes (e.g., ring-hydroxylating oxygenases and hydrolases) critical for PAH breakdown [23, 27, 28].\u0026nbsp;Elucidating these adaptive mechanisms is paramount for developing targeted bioremediation strategies, as microbial responses are highly contingent on local contamination levels and sediment geochemistry\u0026nbsp;[29-31].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study investigates how divergent PAH contamination gradients shape microbial community structure and function in mangrove sediments across two geographically distinct sites in China. By leveraging high-throughput sequencing and metagenomic analysis, we (i) delineate PAH-induced shifts in microbial assemblages, (ii) identify putative PAH degrading taxa, and (iii) elucidate potential metabolic pathways underpinning PAH biodegradation. Our findings advance the understanding of microbial adaptation to hydrocarbon stress and provide actionable insights for mangrove bioremediation.\u0026nbsp;\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003ch2\u003e\u003cem\u003e2.1 Sample collection\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eSediment samples were collected from mangrove ecosystems across two regions in China representing varying degrees of anthropogenic impact. In Guangxi Province, samples were taken from sections M1 and M2 located in\u0026nbsp;Beihai Coastal National Wetland Park (protected area) and section M3 near an urban municipal outfall in Beihai (impacted site). In Fujian Province, samples were collected from three sites (DY, CPT, and FG) in Zhangzhou Mangrove (Figure 1a). All sampling was conducted during low tide from August 26\u003csup\u003eth\u003c/sup\u003e to September 1\u003csup\u003est\u003c/sup\u003e, 2022. Surface sediments (top 1 cm) were collected in quadruplicate using sterile stainless steel spatulas, immediately transferred to sterile sampling packs (Whirl-Pak, Nasco, USA). Subsequently, the samples were transported on ice to the laboratory where they were stored at \u0026ndash;80 ℃ until analysis.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.2 Sediment physicochemical analysis\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eNitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N) and ammonia (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N) concentrations were determined following extraction of sediment samples using 2 M KCl ,followed by quantification using a continuous flow analyzer (Futura II, Alliance Instruments, France). Total organic carbon (TOC) and total nitrogen (TN) were measured with a TOC analyzer coupled with a nitrogen module (Vario TOC Cube, Elementar Analysen systeme, Germany) following carbonate removal through acidification with 5% HCl and drying at 40 \u0026deg;C. All data were normalized to dry weight after oven-drying at 75 \u0026deg;C [32]. Particle size distribution was analyzed using a laser diffraction system (Mastersizer 3000, Malvern Instrument Ltd., UK), with sediments classified as gravel (\u0026gt; 2mm), sand (0.063-2 mm), silt (0.004-0.063 mm), and clay (\u0026lt; 0.004 mm)\u0026nbsp;[33].\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.3 PAHs analysis\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eFrozen sediment samples were lyophilized for 48 h in a vacuum lyophilizer (FC-1C-80, Biocool, China). PAHs were extracted using pressurized liquid extraction, desulfurized with activated copper power, and concentrated via solid phase extraction. Eighteen PAHs, including the 16 US EPA\u0026rsquo;s priority PAHs plus benzo[e]pyrene, benzo[j]fluoranthene, were quantified by gas chromatography-mass spectrometry (GC-MS, QP2010 TD30R, Shimadzu, Japan) equipped with a fused DB-5MS silica capillary column (60 m \u0026times; 0.25 mm, Anpel, China) with stationary phase of 5%-phenyl-methylpolysiloxane [34]. Variation partition analysis (VPA) was performed to evaluate the relative contribution of environmental factors on PAH distribution patterns in the mangrove sediments using Vegan v2.6-4 package [35].\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.4 DNA extraction\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eTotal genomic DNA was extracted from sediment samples using the DNeasy PowerSoil Pro kit (QIAGEN GmbH, Germay) following the manufacturer\u0026rsquo;s protocol. DNA concentration and purity were accessed using a NanoDrop2000 spectrophotometer (Thermo Fisher Scientific, USA), with DNA quality verified via by 1% agarose gel electrophoresis. The extracted DNA was subsequently used for 16S rRNA gene sequencing and metagenomic sequencing.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.5 16S rRNA amplicon sequencing and data processing\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe hypervariable V3-V4 region of bacterial 16S rRNA genes was amplified using primers 338F (5\u0026rsquo;-ACTCCTACGGGAGGCAGCAG-3\u0026rsquo;) and 806R (5\u0026rsquo;-GGACTACHVGGGTWTCTAAT-3\u0026rsquo;) by PCR amplification performed using the PCR thermocycler (T100\u003csup\u003eTM\u003c/sup\u003e Thermal Cycler, BIO-RAD, USA) [36]. PCR amplification cycling conditions were as follows: initial denaturation at 95 ℃ for 3 min, followed by 27 cycles of denaturing at 95 ℃ for 30 s, annealing at 55 ℃ for 30 s and extension at 72 ℃ for 45 s, final extension at 72 ℃ for 10 min, and end at 4 ℃. Amplicons were paired-end sequenced on an Illumina MiSeq PE300 platform (Illumina, San Diego, USA) by Majorbio Bio-pharm Technology Co., Ltd (Shanghai, China). The resulting data were analyzed using the Majorbio Cloud Platform, a free accessible online bioinformatics analysis system [37]. Raw reads were processed using fastp (v0.23.0) for quality-filtering [38] and FLASH (v1.2.11) for merging [39]. Amplicon sequence variants (ASVs) were generated using DADA2 [40] plugin in Qiime2 (v2020.2) [41] with taxonomic classification \u0026nbsp;performed against the 16S rRNA database (Silva v138) using a Naive bayes consensus taxonomy classifier implemented in Qiime2 [42].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the ASVs information, alpha diversity indices including Sobs, Chao1 richness, Shannon, Simpson indices, and Good\u0026rsquo;s coverage, were calculated using Mothur v1.30.2 [43]. Beta diversity was accessed via non-metric multidimensional scaling (NMDs) and principal coordinate analysis (PCoA)\u0026nbsp;based on Bray-Curtis dissimilarity matrices generated by the Vegan v2.6-4 package\u0026nbsp;[35].\u0026nbsp;Differential microbial abundance analysis were performed using the Kruskal-Wallis test\u0026nbsp;and\u0026nbsp;linear discriminant analysis (LDA) effect size (LEfSe) analysis\u0026nbsp;(LDA score \u0026gt; 2, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05)\u0026nbsp;[44], while environmental drivers of microbial communities were identified through distance-based redundancy analysis (db-RDA) and variance partitioning analysis (VPA) using Vegan v2.6-4 package\u0026nbsp;[35].\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.6 Metagenomic analysis\u0026nbsp;\u003c/em\u003e\u003c/h2\u003e\n\u003ch3\u003e\u003cem\u003e2.6.1 Metagenomic sequencing and sequences assembly\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eDNA libraries was prepared by fragmenting genomic DNA to ~400 bp (Covaris M220, Gene Company Limited, China) and constructing paired-end libraries using the NEXTFLEX\u003cimg width=\"9\" height=\"9\" src=\"data:image/png;base64,R0lGODlhCQAJAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAAAJAAkAhAAAAAAAAAICAgwMDA8PDwcHBxAQEBkZGRMTEwEBAREREQgICAUFBQMDAwoKCgYGBhUVFSIiIiYmJkRERFVVVWZmZgECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwUnICCOJCAMRCGM6wEMQCAWsSLKAAM0ALHfIhhgAYQ9ABBSYTFwlUohADs=\" alt=\"IMG_256\"\u003e Rapid DNA-Seq kit (Bioo Scientific, USA). The resulting library was subjected to paired-end sequencing using the Illumina NovaSeq platform (Illumina Inc., CA, USA) at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China). Metagenomic data were analyzed on Majorbio Cloud Platform\u0026nbsp;[37]. Raw reads were processed using fastp (v0.23.0) to remove adaptors and filter out low-quality reads (length\u0026lt;50 bp, quality score \u0026lt;20, or containing N bases) [38]\u003csup\u003e\u0026nbsp;\u003c/sup\u003e.\u0026nbsp;Metagenomic assembly was performed using MEGAHIT (v1.1.2) [45],\u0026nbsp;retaining contigs \u0026ge; 300 bp for downstream gene prediction and annotation.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003e2.6.2 Gene prediction, taxonomy, and functional annotation\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eOpen reading frames (ORFs) were predicted from assembled contigs using MetaGene with default parameters [46], retaining only ORFs \u0026ge; 100 bp. These ORFs were translated into amino acid sequences following the standard NCBI genetic code (http://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/index.cgi?chapter=tgencodes#SG1). ORFs were clustered into a non-redundant gene catalog using CD-HIT (v4.6.1, http://www.bioinformatics.org/cd-hit/) with stringent clustering thresholds (90% sequence identity and 90% coverage) [47]. Gene abundance was quantified by mapping high-quality reads to the catalog using SOAP (v2.21) with 95% sequence identity requirement [48]. Representative sequences from the gene catalog were taxonomically annotated by alignment against the NR database using Diamond\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(v0.8.35) with an \u003cem\u003ee\u003c/em\u003e-value cutoff of 1\u0026times;e\u003csup\u003e\u0026ndash;5\u003c/sup\u003e. For functional characterization, representative sequences were annotated through metabolic pathway reconstruction via Kyoto Encyclopedia of Genes and Genomes (KEGG) database using Diamond,\u0026nbsp;[49], and carbohydrate-Active Enzymes (CAZymes) identification through hmmscan against CAZy database\u0026nbsp;[50], all employing consistent \u003cem\u003ee\u003c/em\u003e-value cutoff (1\u0026times;e\u003csup\u003e\u0026ndash;5\u003c/sup\u003e) for significant hits.\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003e2.6.3 Statistical analysis\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eMicrobial community analysis was conducted through an integrated bioinformatics pipeline. Hierarchical sample clustering based on taxonomic profiles was performed in Qiime using Bray-Curtis dissimilarity metric. Taxonomic or enzymatic biomarkers were identified through LEfSe analysis (LDA score \u0026gt; 2, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) [44], while inter-group differences in bacterial genera or KEGG enzyme abundance were accessed using nonparametric Kruskal-Wallis test. To examine variance in bacterial communities, NMDs and PCoA were performed using the Vegan package (v2.6-4) in R [35]. The influence of environmental variables on the distribution patterns of microbial communities and functional loci in sediment samples was analyzed using db-RDA with the same R package [35].\u003c/p\u003e\n\u003cp\u003eFor Metagenome Assembled Genomes (MAGs), quality-filtered contigs (\u0026ge;1000 bp) were binned using MetaBAT 2 (v2.12.1) [51], with genome quality assessed by CheckM (v1.1.2-1; completeness \u0026gt; 50%, contamination \u0026lt; 10%) [52]. The taxonomy of the refined bins was assigned using GTDB-Tk (v2.3.0) [53], which infers classifications based on a set of 120 bacterial and 53 archaeal universal single-copy marker genes based on the Genome Taxonomy Database (GTDB, https://data.ace.uq.edu.au/public/gtdb/data/releases/release214/214.1/). Functional annotation was following established protocols for KEGG and CAZy databases as described in section 2.6.2.\u003c/p\u003e"},{"header":"3. Results","content":"\u003ch2\u003e\u003cem\u003e3.1 PAHs variations among mangrove sections\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eSurface sediments analysis from six mangrove sections in Beihai and Zhangzhou (10 sampling sites) detected 18 PAH compounds, including 16 USEPA priority PAHs (Figure 1b). Total PAH concentrations exhibited significant spatial heterogeneity (Kruskal-Wallis test, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) among sections, ranging from 17.56 ng/g d.w. (CPT section) to 730.76 ng/g d.w. (M3 section), representing a 40-fold variation. Beihai mangroves showed significantly higher mean PAH concentration (395.49 ng/g d.w.) compared to Zhangzhou mangroves (232.83 ng/g d.w.) (Table S1). Significant spatial variations were observed not only in total PAH concentrations but also in PAH composition (Figure 1). Low-Molecular-weight (LMW) PAHs (2-3 rings) dominated in section CPT, accounting for 77.77% of total PAHs. In contrast, most other mangrove sediments contained predominantly high-molecular-weight (HMW) PAHs (4-6 rings), which contributed 53.52% to 83.61% of total PAHs (Table S1). The dominant PAH compounds in the surface sediments at M3 were chrysene, pyrene, Benzo[ghi]perylene, fluoranthene, and phenanthrene, whereas DY sediments\u0026mdash;which exhibited the second-highest PAH concentration\u0026mdash;were characterized by higher abundance of chrysene, Benzo[a]pyrene, pyrene, and fluoranthene (Figure 1b). Based on sediment physicochemical properties and PAHs distribution (Table S1), variation partition analysis (VPA) identified nitrogens (explaining independently 13.84% of PAH variation) as primary drivers of PAH distribution, followed by TOC (6.52%) and geographic location (4.72%), while sediment grain size having a negligible influence (0.90%) (Figure 1c).\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e3.2 characterization of microbes through 16S rRNA amplicon sequence analysis\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eHigh-throughput sequencing of 51 mangrove sediment samples (3 replicates per sampling site) generated totally 3,542,630 high-quality reads (average length = 417 bp), which were clustered 98,498 ASVs across the six sections, ranging from 15,102 to 20,733 ASVs per section (Table S2). The exceptional sequencing depth was evidenced by Good\u0026apos;s coverage values \u0026gt; 98.7% in all samples and clear asymptotes of the rarefaction curves (Figure S1), confirming comprehensive sampling of bacterial diversity in this study. Taxonomic classification revealed the presence of 214 bacterial classes, with Gammaproteobacteria (16.71%\u0026plusmn;4.89%), Alphaproteobacteria (12.74%\u0026plusmn;3.87%), Anaerolineae (10.23%\u0026plusmn;5.35%), Bacteroidia (8.67%\u0026plusmn;4.14%), Actinobacteria (6.28%\u0026plusmn;4.82%), Acidimicrobiia (5.66%\u0026plusmn;2.15%), and Clostridia (5.49%\u0026plusmn;4.22%) being most abundant (Figure S2a). At the order level, o__Rhizobiales, o__Flavobacteriales, o__Microtrichales, o__Rhodobacterales, o__SBR1031, o__Peptostreptococcales-Tissierellales, o__Anaerolineales, o__Steroidobacterales, o__Desulfobacterales, and o__unclassified_c__Gammaproteobacteria, collectively represented approximately 33.20% of the total sequences (Figure S2b).\u003c/p\u003e\n\u003cp\u003eAmong the 2,170 identified genera, the dominant taxa were: Anaerolineae-affiliated \u003cem\u003eg_norank_f_Anaerolineaceae\u003c/em\u003e (2.60%\u0026plusmn;2.21%), \u003cem\u003eSRB1031\u003c/em\u003e (2.13%\u0026plusmn;1.55%), and \u003cem\u003eg__norank_f__Caldilineaceae\u003c/em\u003e (1.83%\u0026plusmn;0.97%), Gammaproteobacteria-associated \u003cem\u003eg__unclassified_c__Gammaproteobacteria\u003c/em\u003e (2.18%\u0026plusmn;0.95%), \u003cem\u003eAcinetobacter\u003c/em\u003e (1.86%\u0026plusmn;4.35%), and \u003cem\u003eWoeseia\u0026nbsp;\u003c/em\u003e(1.54%\u0026plusmn;1.53%), Acidimicrobiia-clustered \u003cem\u003eIlumatobacter\u003c/em\u003e (2.32%\u0026plusmn;1.54%) and \u003cem\u003eg__norank_o__Actinomarinales\u003c/em\u003e (1.64%\u0026plusmn;0.85%) , and Alphaproteobacteria-affiliated \u003cem\u003eg__unclassified_f__Rhodobacteraceae\u0026nbsp;\u003c/em\u003e(2.45%\u0026plusmn;1.12%) and KD4-96-associated \u003cem\u003eg__norank_c__KD4-96\u0026nbsp;\u003c/em\u003e(1.51%\u0026plusmn;0.59%). These top 10 genera accounted for 20.06% of total sequences (Figure 2a).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSection M3 exhibited a markedly distinct bacterial community profile to other sections. Most notably, the genus \u003cem\u003eAcinetobacter\u003c/em\u003e was dramatically enriched in M3 sediment (7.84% relative abundance) compared to other sections (0.01% - 0.04%, Figure 2a). This unique microbial signature was further evidenced by reduced alpha diversity indices (Sobs, Pielou\u0026rsquo;s eveness, and Shannon indices) in M3 (Table S3) and clear separation in both NMDs and PCoA ordination analyses (Figure S2c, S2d). The pronounced dominance of \u003cem\u003eAcinetobacter\u0026nbsp;\u003c/em\u003ecoupled with diminished diversity suggests strong environmental selection pressure in M3.\u003c/p\u003e\n\u003cp\u003eThe Kruskal-Wallis test identified 1,158 genera showing significant spatial variation across sampling sections (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05), with \u003cem\u003eg__norank_f__Anaerolineaceae\u003c/em\u003e emerged as the most abundant genera (2.60%) (Figure 2b). LDA-LEfSe analysis revealed 598 discriminant genera, among which \u003cem\u003eAcinetobacter\u003c/em\u003e, showed strongest association with M3 (LDA score = 4.59), along with \u003cem\u003eg_norank_f_norank_o_PeM15\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003eand \u003cem\u003eExiguobacterium\u003c/em\u003e (Figure 2c, 2d). These results highlighted \u003cem\u003eAcinetobacter\u003c/em\u003e as a key indicator taxa for the distinct microbial assemblage in M3 sediments.\u003c/p\u003e\n\u003cp\u003edb-RDA based on Bray-Curtis dissimilarity confirmed the strong influence of environmental factors on microbial community (\u003cem\u003ep\u003c/em\u003e = 0.001), with total PAHs, TOC, and ammonium (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e) concentrations showing significant correlations in M3 (Figure 2e). Meanwhile, VPA attributed 9.01% of microbial community variation to PAHs\u0026mdash;a greater proportion than explained by nitrogens, TOC or soil components (Figure S3). These findings collectively demonstrated that PAHs contamination and nutrient enrichment act synergistically as primary environmental drivers shaping microbial community structure in these mangrove sediments, leading to the development of unique bacterial assemblages under polluted conditions. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e3.3 Metagenomic Analysis\u003c/em\u003e\u003c/h2\u003e\n\u003ch3\u003e\u003cem\u003e3.3.1 Taxonomic composition and enrichment of PAHs-degrading bacteria in mangrove section with PAHs contamination\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eA total of 3,200,149,680 clean reads, accounting for ~98.36% of the raw reads, were generated from metagenomic sequencing of 36 sediment samples across six mangrove sections after quality filtering using fastp (v0.23.0) (Table S4). Taxonomic classification recovered 317 archaeal and bacterial classes, 4,655 genera, and 44,975 \u0026nbsp;species (Table S5), with Alphaproteobacteria, Gammaproteobacteria, Actinomycetes, Anaerolineae, Acidimicrobiia, and Planctomycetia dominating the communities (collectively 53.94% of identified reads) (Table S5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe heavily contaminated M3 section exhibited a unique microbial signature characterized by striking enrichment of known hydrocarbon-degrading genera: \u003cem\u003eAcinetobacter\u003c/em\u003e (2.233% of total reads vs 0.014%\u0026ndash;0.042% in other sections), \u003cem\u003eNocardioides\u003c/em\u003e (1.612% vs 0.359%\u0026ndash;0.680%), and \u003cem\u003eStreptomyces\u003c/em\u003e (1.532% vs 0.318%\u0026ndash;0.587%), with unexpected depletion of the typically considered PAHs-degrader \u003cem\u003ePseudomonas\u003c/em\u003e species (0.201%\u0026ndash;0.304%) (Table S6). Non-parametric Kruskal-Wallis tests confirmed significant differences (p \u0026lt;0 0.05) in the distribution\u003cem\u003e\u0026nbsp;\u003c/em\u003eof \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003eNocardioides\u003c/em\u003e, \u003cem\u003eStreptomyces\u003c/em\u003e, \u003cem\u003eDesulfuromonas\u0026nbsp;\u003c/em\u003eand \u003cem\u003eRobiginitales\u003c/em\u003e across sampling sections (Figure S4a), while LEfSe analysis further identified \u003cem\u003eAcinetobacter\u003c/em\u003e as a key biomarker in M3 (Figure S4b). These findings showed strong concordance with 16S rRNA gene sequencing results (Figures 2a-2c), reinforcing the ecological specialization of these taxa with PAHs-impacted sediments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRemarkably, we detected all 51 recognized PAH-degrading genera [8], representing 4.10% of total microbial reads, dominated by\u003cem\u003e\u0026nbsp;Mycobacterium\u0026nbsp;\u003c/em\u003e(18.57%), \u003cem\u003eNocardioides\u0026nbsp;\u003c/em\u003e(17.90%), and \u003cem\u003eAcinetobacter\u0026nbsp;\u003c/em\u003e(10.08%),\u003cem\u003e\u0026nbsp;\u003c/em\u003ewhich together represented nearly half of all PAHs degrader reads (Figure 3a, Table S7). The PAH-degrading consortium in M3 was particularly robust (~7.73% of total microbial reads, averaging 1,712,982 reads) compared to the other sections (2.58%\u0026ndash;4.49%, averaging 418,992\u0026ndash;898,514 reads) (Table S7). \u003cem\u003eAcinetobacter\u003c/em\u003e is exceptionally enriched in section M3 (496,820 reads and 28.90% of PAHs degraders vs 2,181\u0026ndash;6,344 reads and 0.41%\u0026ndash;1.27% elsewhere), accounting for nearly one-third of the site\u0026apos;s degradation potential (Figure 3a). Both NMDS analysis and PCoA analysis confirmed distinct phylogenetic structure of PAH-degrading microbial community in M3 compared to other sections (Figure 3b\u0026amp;3c).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003e3.3.2 Functional variance of microbial communities in mangrove sediment sections\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eOur metagenomic analysis revealed substantial functional diversity across sampling sites. The functional annotation identified 11,037\u0026ndash;13,046 KEGG orthologs (KOs), 3,768\u0026ndash;4,054 enzymes, and 464\u0026ndash;470 metabolic pathways (Tables S8 \u0026amp; S9, Figure S5a). Section DY exhibited the most limited functional repertoire among all sampling sections. dbRDA of pre-filtered environmental parameters (VIF \u0026lt; 10) demonstrated that nitrate, ammonium, sediment sand content, TOC, and total PAH concentrations as key determinants of microbial functional profiles (Figure S5b\u0026amp;S5c). Notably, both enzymes composition (Figure S5b) and KO distribution (Figure S5c) showed strong spatial clustering by sampling section, indicating distinct location-specific functional signatures of microbial communities, while in section M3, they showed strong correlation with PAH, TOC, and ammonia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analyses revealed significant spatial heterogeneity in enzymatic profiles across sampling sections, with the Kruskal-Wallis test identifying 3,916 differentially abundant KEGG enzymes (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, Table S10). LEfSe analysis further delineated 144 enzyme biomarkers (LDA \u0026gt; 2) that were specifically enriched in section M3, predominantly comprising transferases, oxidoreductases, hydrolases, ligases, lyases, isomerases, and translocases (Table S11). Functional annotation demonstrated these enzymes biomarkers were principally functioned in metabolic pathways (15.38% of biomarkers-associated reads), biosynthesis of secondary metabolites (9.43%), microbial metabolism in diverse environments (7.24%), carbon metabolism (3.84%), and pyruvate metabolism (2.88%). Pathway-level analysis revealed M3\u0026rsquo;s microbial community exhibited marked enrichment in specialized metabolic processes, including biosynthesis of secondary metabolites (32.04%), biosynthesis of cofactors (10.18%), pyruvate metabolism (5.53%), glycolysis/gluconeogenesis (4.61%), butanoate metabolism (3.61%), alanine, aspartate and glutamate metabolism (3.49%), valine, leucine and isoleucine degradation (3.11%), reflecting adaptive metabolic reprogramming in response to environmental stressors.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003e3.3.3 Key metabolic genes and pathways related to PAHs degradation\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eRecent studies have documented the upregulation of functional genes associated with PAH degradation in the PAHs contamination sediments [54, 55]. Our metagenomic analysis identified a comprehensive suite of totally 565 KOs associated with aromatic carbon degradation, predominantly encoding oxidoreductases and their subunits (301 KOs), transferases (81KOs), lyases (63 KOs), hydrolases (38KOs), isomerases (21 KOs) and so on (Table S12). Corresponding to these genetic elements, totally 351 enzymes were annotated, including 182 oxidoreductases, 59 transferases, 45 lyases, 31 hydrolases, 19 isomerases, 13 ligases, and 2 translocases (Table S13). Spatial analysis revealed significant biogeographic variation in degradation potential, with section M1 exhibiting the lowest abundance of degradation genes and enzymes (Table S12 \u0026amp; S13, Figure S6a, S6b). Multivariate statistics demonstrated strong correlation between PAH-degrading genes/enzymes and sediment characteristics (Mantel test: mantel_r = 0.372, \u003cem\u003ep\u003c/em\u003e = 0.001), with PAH and ammonia concentration emerging as primary determinants of PAH-degrading elements distribution patterns in section M3 (db-RDA: r\u003csup\u003e2\u003c/sup\u003e = 0.292, \u003cem\u003ep\u003c/em\u003e = 0.002; Figure S6c, S6d). Oxidoreductases emerged as the dominant PAH-degrading enzymes, and showed high correlation with PAH concentration (db-RDA: r\u003csup\u003e2\u003c/sup\u003e = 0.243, \u003cem\u003ep\u003c/em\u003e = 0.015; Figure S6e).\u003c/p\u003e\n\u003cp\u003eWe calculated the relative contribution of the top 50 abundant bacterial genera to PAHs-degrading functions. \u003cem\u003eAnaerolinea\u003c/em\u003e and \u003cem\u003eIlumatobacter\u003c/em\u003e were identified as primary contributors to the overall putative PAHs-degrading enzymes, while in the heavily contaminated section M3, \u003cem\u003eAcinetobacter\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Mycobacterium\u003c/em\u003e, and\u003cem\u003e\u0026nbsp;Nocardioiders\u0026nbsp;\u003c/em\u003especies represented exceptional dominant sources of PAHs-degrading genes (Figure 4). This finding highlighted their crucial role\u003cem\u003e\u0026nbsp;\u003c/em\u003ein PAHs biodegradation under high-pollution conditions, corroborating their established reputation as versatile hydrocarbon degraders.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBeyond canonical degradation pathways, emerging evidence indicates the involvement of CAZymes in PAH metabolism [20, 21]. The sediment microbiome harbored 33,678,720 CAZymes spanning seven functional classes, with glycoside hydrolases (GHs) and glycosyl transferases (GTs) constituting the most abundant classes (Table S14). Five dominant enzyme families (GT41, GT4, CE1, GT2_Glycos_transf_2, and GT83) collectively represented 30.70% of total CAZymes (Table S15). The contaminated M3 section exhibited distinct taxonomic contributors, with \u003cem\u003eAnaerolinea\u003c/em\u003e, \u003cem\u003eCaldilinea\u003c/em\u003e, \u003cem\u003eIlumatobacter\u003c/em\u003e, \u003cem\u003eLitorilinea\u003c/em\u003e, \u003cem\u003eStreptomyces\u003c/em\u003e, \u003cem\u003eMycobacterium\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Nocardioides\u003c/em\u003e, and \u003cem\u003eAcinetobacter\u003c/em\u003e serving as primary producers of CAZymes (Figure S7). Meanwhile, PAH concentration and nutrient level emerged as key environmental determinants of CAZymes distribution patterns, particularly in section M3 (Figure S8). These findings suggested a novel mechanistic linkage between PAH contamination and modified carbohydrate metabolism, potentially representing an adaptive strategy for hydrocarbon processing in polluted environments.\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003e3.3.4 Metagenomic assembled genomes (MAGs)\u0026nbsp;\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eA total of 283 MAGs were reconstructed using the contigs derived from metagenomic data, with completeness values ranging from 64.79% to 100%. Taxonomic classification showed these MAGs were assigned to nineteen phyla, with Pseudomonadota (83 MAGs), Bacteroidota (47 MAGs), and Actinomycetota (46 MAGs) representing the most abundant lineages (Figure S9; Table S16). However, only 15 MAGs could be assigned at the species level, suggesting these sediments harbor substantial novel taxa with uncharacterized genomes (Figure S9; Table S16). A phylogenetic reconstruction of all the 283 MAGs using the marker gene set from GTDB-Tk demonstrated significant enrichment of MAGs affiliated with known hydrocarbon-degrading genera, namely \u003cem\u003eIlumatobacter\u003c/em\u003e, \u003cem\u003eMycobacterium\u003c/em\u003e, \u003cem\u003eLutimona\u003c/em\u003es, and \u003cem\u003eAnderseniella\u003c/em\u003e in the contaminated M3 section (Figure 5).\u003c/p\u003e\n\u003cp\u003eKEGG annotation identified 405 aromatic degradation KOs across the MAGs, predominantly associated with lipid transport and metabolism, coenzyme transport and metabolism, and secondary metabolites biosynthesis, transport and catabolism (Table S17). Three Pseudomonadota MAGs (MAG270, MAG13, and MAG207) exhibited exceptional degradation potential, containing 109, 103, and 99 PAH-degrading KOs in their genomes (Table S18, Figure 5). Further analysis identified 30,369 CAZymes which were categoried into more than 450 families, and their largest share was contributed by Bacteroidota (6695 CAZymes) followed by Pseudomonadota (6099) and Chloroflexota (6000). Meanwhile, the top CAZyme-producing MAGs belonged to the Bacteroidetes and Planctomycetota phyla (Figure 5), suggesting these taxa may employ carbohydrate-active enzymes as an alternative strategy for PAH modification. The co-occurrence of canonical degradation pathways and diverse CAZymes indicates complex, multi-faceted microbial adaptation to hydrocarbon contamination in mangrove ecosystems.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003ch2\u003e\u003cem\u003e4.1 PAHs contamination in mangrove sediment\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003ePAHs were potent carcinogens and mutagens, posing significant ecological toxicity and potential human health risks due to their persistence and long-term accumulation in ecosystem [9, 56, 57]. Mangrove sediments, characterized by high sedimentation rates, organic matter (OM) richness, and biogeochemical reactivity, are particularly susceptible to PAH accumulation, serving as long-term sinks for these contaminants [11]. The unique biogeochemistry of mangrove sediments\u0026mdash;marked by tidal flushing, sulfate reduction, and complex organic-mineral interactions\u0026mdash;further influences PAH fate, modulating their persistence, bioavailability, and ecological impact\u0026nbsp;[31, 58-60].\u003c/p\u003e\n\u003cp\u003eIn this study, we detected 18 individual PAH compounds across 36 surface sediments samples collected from six mangrove sections in Beihai and Zhangzhou, China. Spatial analysis revealed marked heterogeneity in PAH distribution, with Beihai mangroves exhibiting significantly higher mean concentrations (395.49 ng/g d. w.) compared to Zhangzhou (232.83 ng/g d. w.) (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, Table S1). Notably, section M3, proximal to a local outfall, registered the highest PAH load (730.76 ng/g d.w.), Whereas sections M1 and M2, located within the protected Beihai Coastal National Wetland Park, showed lower PAH contamination levels (Fig. 1b). This gradient underscore the dominant role of localized anthropogenic inputs in driving PAHs accumulation in mangrove sediment, as corroborated by prior studies [11, 60].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe VPA results identified nutrients and TOC as key drivers of PAH distribution, explaining 13.84% and 6.52% of the observed variation, respectively (Fig. 1c). These findings align with two established mechanisms: (1) eutrophication-PAH synerby: Nutrient-rich environments (e.g., those impacted by agricultural or sewage runoff) often exhibit elevated PAH retention due to enhanced particle aggregation and sedimentation [61], and (2) organic matter-mediated sequestration: hydrophobic PAHs exhibit strong affinity for organic-rich matrices, leading to their long-term sequestration in sediments [62]. The high TOC content in mangroves thus acts as a \u0026ldquo;trap\u0026rdquo; for PAHs, reducing their mobility but prolonging their persistence [62, 63]. However, while organic matter enhances PAHs retention through these adsorption processes, it may simultaneously reduce their bioavailability and hinder microbial degradation by incorporating PAHs into recalcitrant humic complexes [64, 65].\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e4.2 Revealing Microbial Community Shifts and Diversity\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003ePAH contamination exerts profound and selective pressures on microbial communities, driving structural and functional adaptations that reflect both toxicity-mediated supression and niche-based enrichment of specialized taxa [25, 54, 66, 67]. Leveraging 16S rRNA gene-based next-generation sequencing and metagenomic analysis, we documented systematic shifts in microbial diversity and composition across PAH contamination gradients. High-PAH sediments (M3) displayed significant reductions in microbial alpha diversity (invsimpson: 220.4 vs 322.8-806.8 in other sections; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, Table S3), consistent with prior studies linking PAH toxicity to microbial diversity loss in contaminated ecosystems [30, 68-70]. This decline likely reflects the combined effects of direct toxicity to sensitive taxa and competitive exclusion by PAH-adapted specialists [66, 70]. Beta diversity analysis further highlighted distinct clustering of MC3 microbiomes (Figure 2e, S2d \u0026amp; 2e), dominanted by \u003cem\u003eAcinetobacter\u003c/em\u003e (7.84% relative abundance; Figure 2a \u0026amp; 2b), a genus frequently associated with hydrocarbon degradation [71]. The prominence of \u003cem\u003eAcinetobacter\u003c/em\u003e species in section M3 coincided their documented capacity to metabolize HMW PAHs (e.g., pyrene and Benzo[a]pyrene) [72-74] and even utilize acenaphthene and acenaphthylene as sole carbon sources [75].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe selective enrichment of PAH-degrading microorganisms in PAH contaminated environments has been well documented [76-78]. LDA-LEfSe analysis identified \u003cem\u003eAcinetobacter\u003c/em\u003e as a key biomarker for PAH-enriched sediments (LDA score \u0026gt; 4.5; Figure 2d), corroborated by db-RDA and VPA results, which confirmed that nutrient availability and PAH contamination were the primary drivers of bacterial community variation in the sediments (Figure 2e \u0026amp; S3). These findings align with the ecological principle of \u0026ldquo;stress-induced selection\u0026rdquo;, wherein contamination favors taxa with specialized metabolic adaptations, while sensitive taxa may decline due to toxicity or competitive exclusion [17, 79]. Metagenomic profiling demonstrated significant enrichment of PAH-degrading taxa (7.73%) of the M3 community, nearly double their abundance in less contaminated sections (2.58\u0026ndash;4.49% relative abundance; Figure 3a, Table S7). The dominance of \u003cem\u003eMycobacterium\u003c/em\u003e, \u003cem\u003eNocardioides\u003c/em\u003e, and \u003cem\u003eAcinetobacter\u003c/em\u003e (collectively 46.5% of PAH degraders, Figure S4) reflects their specialized adaptive strategies in high-PAH niche, particularly through biosurfactant production to enhance PAH bioavailability and successive decomposition via ring-hydroxylating dioxygenases (RHDs) [8, 17, 80]. However, the precise mechanisms underlying PAH degradation by these genera remain insufficiently characterized, requiring further mechanistic and genomic investigations. MAGs phylogeny uncovered preferrence of additional hydrocarbon-degrading genera (\u003cem\u003eIlumatobacter\u003c/em\u003e, \u003cem\u003eLutimona\u003c/em\u003es, and \u003cem\u003eAnderseniella\u003c/em\u003e) in M3 (Figure 5), this discrepancy highlights the limitations of marker-gene surveys in capturing functional diversity and underscores the need for genome-resolved approaches to identify novel PAH degraders.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e4.3 Identification of putative PAH-degrading genes and Enzymes\u0026nbsp;\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eMetagenomic analysis have revolutionized our understanding of PAH degradation by elucidating the complex network of enzymes, genes, and metabolic pathways involved in these processes, while simultaneously facilitating the discovery of novel biocatalysts with potential applications in bioremediation and industrial biotechnology [69, 81-83]. Our study identified 565 KOs and 351 enzymes associated with aromatic carbon degradation, predominantly belonging to oxidoreductases, transferases, lyases, and hydrolases (Table. S12 \u0026amp; S13). Oxidoreductases emerged as the most prominent enzyme classes, with their abundance strongly correlating with PAH contamination levels (Figure S6d). This aligns with previous studies linking oxidative enzyme activity to microbial detoxification potential in PAH-contaminated environments [26, 84].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThrough systematic annotations of KEGG KOs and enzymes, we reconstructed a comprehensive PAH degradation pathway employed by indigeous microbial communities. (i) Initial Oxidation: RHDs catalyze the stereospecific incorporation of molecular O\u003csub\u003e2\u003c/sub\u003e into PAH structures, forming \u003cem\u003ecis-\u003c/em\u003edihydrodiol intermediates\u0026mdash;a critical and often rate-limiting step\u0026nbsp;[85]. (ii) Dehydrogenation: Dihydrodiol dehydrogenases convert these intermediates into catechols, priming them for ring cleavage\u0026nbsp;[86]. (iii) Ring Cleavage: Catechol dioxygenase mediate either ortho-cleavage to generate \u003cem\u003ecis\u003c/em\u003e-muconic acid derivatives, or meta_cleavage to form 2-hydroxymuconic semialdehyde derivatives, depending on the positioning of the cleaved C-C bond relative to the hydroxyl groups\u0026nbsp;[87]. (iv) Mineralization: The \u0026beta;-ketoadipate pathway processes these products into tricarboxylic acid (TCA) cycle intermediates, ultimately leading to complete mineralization to CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003e[88]. Despite this metabolic versatility, research has confirmed that five- or six-ring PAHs, such as dibenzo(a,h)anthracene and benzo(ghi)perylene, remain recalcitrant due to structural incompatibility with conventional RHD systems\u0026nbsp;[89]. This underscores both the remarkable catalytic efficiency and inherent limitations of microbial degradation in natural environments with complex contaminants.\u003c/p\u003e\n\u003cp\u003eRHDs represent pivotal biocatalysts in microbial PAH degradation, holding tremendous potential for enhancing bioremediation efficiency and building biotechnological solutions for PAH-contaminated environments [85, 90]. These multicomponent enzyme systems catalyze the initial oxygen incorporation into aromatic rings, forming cis-dihydrodiol\u0026mdash;a step that dictates downstream degradation efficiency [91, 92]. Our research highlights the striking diversity of RHD substrate specificities, ranging from narrow-specific phenanthrene 3,4-dioxygenase (EC:1.13.11.-) that exclusively target phenanthrene [91], to broad-specificity catalysts such as naphthalene 1,2-dioxygenase (EC:1.14.12.12) capable of oxidizing multiple PAHs, including naphthalene, anthracene, pyrene, chrysene, fluorene, fluoranthene, phenanthrene, benzo[a]pyrene, and indeno[1,2,3-cd]pyrene [26, 89], and the versatile PAH dioxygenase (EC:1.14.12.-) that oxidized the rings of acenaphthylene, acenaphthene, anthracene, fluorene, fluoranthene, pyrene, chrysene, benz[a]anthracene, benzo[a]pyrene (Table S8) [25, 26]. Interestingly, despite varying PAH concentrations across mangrove sections, RHD distribution patterns remained constant, suggesting either methodological limitations in detecting active enzymes or microbial adaptations to mixed-PAH exposures, enabling functional redundancy [30, 93].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis Observation points to a critical gap in our understanding environmental PAH degradation. Current research is predominantly based on single-PAH degradation studies [8], neglecting mixed-PAH dynamics prevalent in natural environments \u0026nbsp;where co-metabolic interactions such as synergistic degradation or competitive inhibition may significantly alter remediation outcomes but remain poorly understood [94]. Meanwhile, enzyme-substrate interactions in complex environmental metrices are insufficiently characterized, limiting predictive modeling of degradation efficiency. Future research must prioritize (1) the multi-PAH degradation studies to elucidate interference effects; (ii) metatranscriptomic/proteomic profiling to distinguish active vs. latent degradation pathways; (iii) structural enzymology to engineer RHDs for enhanced HMW-PAH degradation. By addressing these gaps, we can better harness microbial communities for targeted bioremediation strategies, optimizing their catalytic potential in natural contaminated ecosystems.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e4.4 Exploring Co-metabolism Mechanisms\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eCo-metabolism represents a fundamental microbial process wherein \u0026nbsp;non-growth-supporting substrates are transformed in the presence of primary growth-supporting compounds [95, 96]. This mechanism plays a critical role in environmental remediation, particularly for persistent pollutants that resist conventional degradation pathways [97, 98]. Co-metabolism is especially pronounced in natural contexts, where PAHs invariably present as complex mixtures rather than isolated compounds [63]. In such scenarios, the degradation of one PAH compound may synergistically enhances the breakdown of others\u0026mdash;especially HMW PAHs\u0026mdash;through fortuitous action of broad-substrate-range enzymes such as oxygenases, hydroxylases, which are often induced by simpler carbon sources\u0026nbsp;[96, 99-101].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA key component to co-metabolism processes is the diverse family of CAZymes, whose metabolic versatility extends beyond their primary function of polysaccharide modification to include transformation of recalcitrant compounds like PAHs through cross-adaptation mechanisms [102, 103]. CAZymes have been well-documented in the degradation of structurally complex biopolymers like chitin and lignin [104, 105], indicating their broader role in regulating organic matter cycling in terrestrial and aquatic ecosystems [106]. Notably, elevated CAZymes abundance is frequently associated with microbial communities processing strong organic matter decomposition capacities, which may translate to greater PAH degradation potential through shared oxidative enzymatic pathways [107, 108]. Metagenomic evidence strongly supports this functional overlap, demonstrating the co-occurrence of CAZyme genes and PAH degradation genes in sedimentary ecosystems [20], with certain CAZymes capable of directly oxidizing 3-4 ring PAH through both phenolic and nonphenolic aromatic compound oxidation pathways [109].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur investigation of mangrove sediments revealed GTs and GHs which catalyze the synthesis and cleavage of glycosidic bonds, emerged as the dominant CAZyme classes, consistent with observations from diverse terrestrial and aquatic environments [110, 111]. Particularly compelling was the significant correlation between PAH concentrations and the CAZyme distribution patterns, most notably in section M3 which experiences higher PAH loads. This association strongly suggests CAZyme involvement in PAH co-metabolism under environmental stress. Supporting this hypothesis, our data demonstrated that the top three PAH-degrading genera\u0026mdash;\u003cem\u003eMycobacterium\u003c/em\u003e, \u003cem\u003eNocardioides\u003c/em\u003e, and \u003cem\u003eAcinetobacter\u003c/em\u003e\u0026mdash;were also major CAZyme producers in contaminated sediments (Figure S7) . Among them, \u003cem\u003eAcinetobacter\u003c/em\u003e stands out for its documented capacity to enhance bioremediation through carbohydrate-driven co-metabolic pathways, making it a promising candidate for large-scale bioremediation applications including wastewater treatment\u0026nbsp;[99].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese findings collectively underscore the potential for co-metabolic interactions to synergistically improve PAH degradation efficiency in natural environments, where carbon source availability emerges as a critical regulator of degradation rates, microbial communities utilize shared enzymatic systems for both primary metabolism and xenobiotic breakdown, and strategic carbon input management may offer a promising approach to optimize bioremediation outcomes. Future research should focus on elucidating the precise molecular mechanisms underlying these co-metabolic processes, particularly the regulatory interplay between carbon metabolism and pollutant degradation pathways. Such understanding could revolutionize bioremediation strategies by enabling targeted approaches that harness native microbial communities\u0026apos; metabolic versatility for more effective and sustainable environmental remediation.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study presents a multi-dimensional characterization of PAH contamination and its biodegradation mechanisms in mangrove ecosystems, integrating geochemical profiling with advanced bioinformatic analyses. Our findings reveals that spatial heterogeneity in PAH distribution is predominantly driven by anthropogenic inputs, and further modulated by nutrient availability and TOC content. Notably,\u0026nbsp;high-PAH sediments exhibited\u0026nbsp;significant enrichment of \u003cem\u003eAcinetobacter\u003c/em\u003e species, underscoring their ecological role in hydrocarbon degradation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThrough microbial functions analysis and enzyme annotation, we identified key\u0026nbsp;PAH degrading taxa (\u003cem\u003eMycobacterium\u003c/em\u003e, \u003cem\u003eNocardioides\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e).\u0026nbsp;A PAH degradation framework was established\u0026nbsp;based on the identification and annotation of 565 KOs and 351 enzymes involved in PAH degradation, with RHDs playing a pivotal role in the initial oxidation step. Furthermore, we uncovered compelling evidence for CAZyme-mediated co-metabolism, particularly through GT/GH activities associated with dominant degraders.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe discovery of shared enzymatic machinery between central carbon metabolism and xenobiotic degradation provides new perspectives for bioremediation strategies. These insights not only deepen our understanding of PAH remediation but also pave the way for practical remediation application\u0026mdash;such as enzyme-enhanced solutions targeting recalcitrant HMW PAHs\u0026mdash;while ensuring ecological sustainability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study did not involve human participants, animal experiments, or clinical data requiring ethical approval.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eThe manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was funded by Scientific Research Foundation of the Third Institute of Oceanography, MNR (2020017), Natural Science Foundation of Fujian (2023J011374).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;The 16S rRNA gene sequences and metagenomic data are reported in this paper have been deposited in the Genome Sequence Archive [112] in National Genomics Data Center [113], China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA028312 and CRA028284, respectively) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe authors would like to thank Xihuang Lin and Cifu Lin for the assistance in PAHs analysis\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDas Sudhir Chandra, Das Shreya, Tah Jagatpati, Mangrove ecosystems and their services. 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Nucleic Acids Res. 2025;53,(D1):D30-D44.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Polycyclic aromatic hydrocarbon, Mangrove sediment, Microbial degradation, Acinetobacter, PAH-degrading enzymes, CAZymes","lastPublishedDoi":"10.21203/rs.3.rs-7178259/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7178259/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eMangrove ecosystems, despite their vital ecological and socioeconomic value, face escalating threats from anthropogenic disturbances, particularly the accumulation of persistent organic pollutants such as polycyclic aromatic hydrocarbons (PAHs). Understanding microbial responses to PAH contamination is crucial for developing effective bioremediation strategies. This study aimed to investigate PAH distribution, microbial community dynamics, and degradation mechanisms in mangrove sediments across Beihai and Zhangzhou, China, to inform targeted restoration efforts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003ePAH concentrations in mangrove sediments varied significantly (395.49 vs 232.83 ng/g d. w.), with spatial heterogeneity driven by anthropogenic inputs, nutrient availability, and total organic carbon (TOC). High-PAH sediments (section M3) near pollution sources exhibited reduced microbial diversity but significant enrichment of hydrocarbon-degrading taxa, including \u003cem\u003eAcinetobacter\u003c/em\u003e(7.84% in M3 vs 0.01% - 0.04% elsewhere), \u003cem\u003eMycobacterium\u003c/em\u003e, and \u003cem\u003eNocardioides\u003c/em\u003e, which collectively represented 46.5% of identified PAH degraders. Metagenomic profiling identified 565 KEGG orthologs (KOs) and 351 enzymes associated with PAH degradation, enabling the reconstruction of complete PAH degradation pathways from initial oxidation to mineralization, with ring-hydroxylating dioxygenases (RHDs) playing a pivotal role in initial oxidation. Notably, dominant PAH-degraders were also key producers of PAH-degrading enzymes and carbohydrate-active enzymes (CAZymes), particularly glycosyltransferases (GTs) and glycoside hydrolases (GHs), which facilitated co-metabolism and enhanced PAH degradation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003eThis study elucidates the adaptive mechanisms of mangrove sediment microbiomes to PAH stress, highlighting the synergy between specialized degraders (\u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003eMycobacterium\u003c/em\u003e, \u003cem\u003eNocardioides\u003c/em\u003e), PAH-degrading enzymes, and CAZyme-mediated co-metabolism. These findings deepen our understanding of microbial adaptation to PAH stress and establish a framework for targeted bioremediation strategies, such as enzyme-enhanced solutions, to mitigate PAH pollution while preserving mangrove ecological functions. These insights are critical for balancing ecosystem health and anthropogenic pressures in coastal environments.\u003c/p\u003e","manuscriptTitle":"Divergent microbial community and functional dynamics in mangrove sediments along a polycyclic aromatic hydrocarbons (PAHs) gradient in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 09:07:54","doi":"10.21203/rs.3.rs-7178259/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":"59feecdf-8577-46db-867c-4efd729dc7a6","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T13:38:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-17 09:07:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7178259","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7178259","identity":"rs-7178259","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}