Alternative carbon and energy metabolisms linked to hydrocarbon degradation are widely distributed across the different microbial communities from deep-sea sediments of the Gulf of Mexico

Alternative carbon and energy metabolisms linked to hydrocarbon degradation are widely distributed across the different microbial communities from deep-sea sediments of the Gulf of Mexico | 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 Alternative carbon and energy metabolisms linked to hydrocarbon degradation are widely distributed across the different microbial communities from deep-sea sediments of the Gulf of Mexico Mónica Torres-Beltrán, Mario Hernández-Guzman, Clara Barcelos, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6754051/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 Microorganisms are integral to Earth's ecosystems, driving biogeochemical cycles by transforming and recycling organic and inorganic molecules. Particularly in marine sediments, microorganisms play an essential role in biogeochemistry, sparking interest in understanding their metabolism for biotechnological applications such as bioremediation. Genomic techniques have enabled detailed exploration of microbial communities in the GoM, revealing rich diversity and functional potential, particularly in hydrocarbon degradation. Studies have shown depth, temperature, and dissolved oxygen gradients significantly influence microbial community composition and metabolic pathways. Research indicates microbial consortia, rather than individual species, are key in pollutant degradation, emphasizing the importance of community dynamics. Our study evaluated the prokaryotic microbial community in deep-sea GoM sediments, under a depth gradient, in Coatzacoalcos and Perdido regions, two areas influenced by crude-oil efflux and petroleum extraction. Findings showed associations between community composition, depth, and metabolic potential, showcasing microbial adaptation to deep-sea nutrient-limited conditions. Results suggest functional redundancy in amino acid and energy production metabolisms among microbial taxa like Alpha and Deltaproteobacteria. This underlines the importance of microbial community shifts in composition and structure in ensuring environmental resilience. This research contributes to advancing our understanding of alternative carbon and energy metabolisms linked to hydrocarbon degradation that are widely distributed across different microbial communities inhabiting deep-sea marine sediments. Gulf of Mexico marine sediments metagenomes microbial metabolism amino acids hydrocarbons Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Microorganisms catalyze most of the chemical reactions that shape Earth environments. Different species of bacteria and archaea intervene in processes that transform and recycle the organic and inorganic molecules needed to sustain the biosphere (Falkowski et al., 2008 ). These processes, collectively referred to as biogeochemical cycles, occur naturally in soils, aquatic environments, and sediments. However, environmental factors such as scarcity of nutrients and energy-yielding substrates, temperature, salinity, pH, oxygen availability, sediment porosity and permeability, may limit the deep-sea sedimentary microbial biosphere (Hoshino et al., 2020 ). Studies indicate these factors significantly contribute to the taxonomic composition of deep-sea microbial communities and thus their ecological function, suggesting inherent depth gradients may be correlated with community composition with possible implications on the ecosystem function (Gong et al., 2022 ; Walker et al., 2021 ; Graw et al., 2018 ). In some cases, pivotal metabolic functions mediating biogeochemical processes, relevant to the ecosystem function, are associated with specific taxonomic groups (Walker et al., 2021 ) resulting in compositional shifts shaped by different selection mechanisms i.e. consistent selection pressures in different locations or depths (Jamieson et al., 2013 ; Gong et al., 2022 ). Marine provinces, from continental margins to hadal regions, are subjected to different input and export fluxes for carbon and energy-yielding substrates that generally decreases rapidly with increasing water depth (Seiter et al., 2004 ), ending up in highly re-mineralized material that is still being slowly metabolized by microbial communities (Petro et al., 2017; La Rowe et al., 2020). Thus, gradients occurring along marine provinces throughout depth allow for understanding microbial communities’ mechanisms to cope with potential limiting factors in oligotrophic environments by conducting alternative metabolic strategies that may result in coupling biodegradation mechanisms of ‘emerging organic carbon’ sources (Joye et al., 2016 ), such as hydrocarbon seeps that contribute occasionally to the sedimentary carbon pool in the deep-sea (Feng et al., 2021 ). The understanding that microorganisms inhabiting marine sediments are metabolically active and play a relevant role in the deep-sea biogeochemistry, has ignited a growing interest in studying microbial communities composition, their metabolism and structural shifts associated with environmental gradients that could further advice on ecosystems’ function and resilience. The Gulf of Mexico encompasses different depositional environments, along the continental margins and slope provinces, that import sediments from the coast to deep waters, including terrigenous sediments via fluvial transport and direct precipitation of biogenic sediments i.e. calcium carbonate (Díaz-Asencio et al., 2023 ). Deep environments are primarily dominated by a combination of terrigenous and biogenic sediments whose rate of accumulation and deposited volume range widely in relation to coast distance, thus areas adjacent to rivers receive the greatest volume of sediment at the highest rates of delivery (Díaz-Asencio et al., 2023 ). As an example, among the Perdido (Northern Gulf of Mexico) and Coatzacoalcos (Southern Gulf of Mexico) regions sediments are delivered to their accumulation sites by different transportation processes so that accumulation rates in Perdido are considerably lower than in Coatzacoalcos, most likely because of the higher river freshwater fluxes in the south in comparison with those in the north (Díaz-Asencio et al., 2023 ). Particularly, the GoM deep-sea sediments have been reported to show low sedimentation rates (5–8 cm kyr − 1 ) and exceptionally low average organic carbon values (< 1%) compared to other deep slope and abyssal regions of the global ocean (Díaz-Asencio et al., 2020 ). Additionally, the underlying geology of the Gulf of Mexico supports a highly productive hydrocarbon province that allows for continuous offshore energy exploration resulting in a highly industrialized marine region (O`Reilly et al., 2022) as happens nowadays for the Perdido and Coatzacoalcos regions. The history of the oil industry in the GoM is marked by two major spill events, Ixtoc-I (1979) and Macondo (2010), that have seriously impacted the GoM ecosystem. Since then, attempts to assess the magnitude of the environmental damage caused have been conducted at different ecosystem levels (reviewed in Soto et al., 2014 ), including the evaluation on the bacterial response to oil inputs (Lizarraga-Partida et al. 1982,1991) and changes in the microbial communities using genomic tools (Hazen et al., 2010 ; Valentine et al., 2010 ; Kessler et al., 2011 ; Bik, 2012; Lamendella et al., 2014 ). Observations derived from these events rekindle new research questions regarding the microbial communities ecological role in the GoM biogeochemical processes that could be assessed by combined genomic approaches. Linkages between taxonomy and functional capabilities for substrates degradation by marine microorganisms are largely based on culture-dependent studies, however microbial metabolic processes are not commonly carried out by a single microbial species but rather by natural microbial consortia showing multifunctionality to efficiently utilize substrates to grow (Bala et al., 2022 ), leaving an open gap regarding the functional role of uncultured microbes and the community’s capacity to overcome limiting conditions to grow. Thus the use of combined genomic approaches provides information about microbial community profiling that results in a means to infer the potential for synergistic metabolic processes to occur by identifying metabolic pathways that shed light on the communal response of the microbial communities in a given time and growth conditions. Particularly, recent studies using 16S rRNA gene amplicons and metagenomic information support bacterial diversity in the GoM continental shelf and slope sediments harbors a rich microbial community that is significantly influenced by depth, temperature and dissolved oxygen gradients. In addition, surveys have indicated microbial communities on waters and sediments could respond rapidly to emerging carbon inputs, shifting the communities composition (Lizarraga-Partida et al., 1982, 1991) and suggesting microbial function is adapted to thrive under environmental gradients (Ramirez et al., 2020; Rodriguez-Salazar et al., 2021; Raggi et al., 2021; Godoy-Lozano et al., 2018 ) by different metabolic traits such as the potential coupling of functions (Torres-Beltran et al., 2022). As an example, in the southern GoM deep sediments, genomic observations have shown microbial communities' taxonomic and functional fingerprints are associated with the use of amino acids metabolism coupled with hydrocarbon degradation (Torres-Beltran et al., 2022). Recent studies using omic approaches have shown that for in situ bioremediation, the use of a co-substrate can accelerate biodegradation by secondary utilization as an electron donor during the degradation process (Harik et al., 2022 ). Additionally, hydrocarbon degradation has been stimulated and improved by the addition of readily biodegradable organic matter and by favoring microbial community interactions in relation to amino acid metabolism (Wang et al., 2022 ). Combined, evidence regarding bacterial communities composition and potential functional response to couple metabolic functions calls for studies to focus on understanding and evaluating potential shifts in microbial communities that take place along environmental gradients, that could better explain microbial metabolism in oligotrophic deep-sea sediments where carbon inputs from environmental sources are relevant as occur in the GoM sediments. In this study, we evaluated the prokaryotic microbial community in deep-sea sediments from two regions of the GoM Perdido and Coatzacoalcos, where oligotrophic conditions occur concomitant with natural crude-oil efflux, and associated extraction and prospection activities currently occur. This study encompasses 16S rRNA gene amplicon and metagenomic observations evaluating shifts in the community composition and structure in relation to depth and its functional potential in relation to alternative carbon and energy metabolism linked to hydrocarbon degradation. Our results sum into understanding how widespread the distribution of taxa and genes could be in deep-sea sediments microbial communities to provide insight into the alternative coupling of metabolisms that may contribute to efficiently utilize substrates in these limiting environments. Methods Sediments sampling and DNA sequencing For the molecular analysis, sediment samples were collected during the MMF-01 oceanographic campaign from February 25 to March 18 (2016), onboard the R/V Justo Sierra (UNAM) as previously described (Fernández-López et al., 2022 ). Briefly, eighteen sampling sites distributed equally from the Perdido and Coatzacoalcos regions (Supplementary Fig. 1; Supplementary Table I) were selected. Samples were taken at a seafloor depth ranging from 550 to 3205 m using a box corer, with subsampling in triplicate from the first 10 cm directly taken from the box core using sterile syringes. Each subsample was frozen and stored immediately in liquid nitrogen onboard and kept at − 80°C when they arrived at the laboratory, until nucleic acid extraction could be performed. Total DNA was extracted from three independent subsamples (250 mg wet sediment) using the Qiagen® PowerSoil® DNA Isolation kit following the manufacturer's instructions, with some modifications including the addition of Phenol: Chloroform: Isoamyl Alcohol (25: 24: 1) to improve cell lysis and the elution step was performed twice using 50 µL of elution buffer with the column incubated for 10 min at room temperature (Fernández-López et al., 2022 ). Extracted DNA was used to generate 16S rRNA gene amplicons and metagenomic data. The 16S-sequencing libraries were generated in one-PCR step following the dual-indexing strategy proposed by Kozich et al. ( 2013 ). In brief, the V4 region of the 16S rRNA gene was amplified using the 515F and 806R primers designed by Caporaso et al ( 2011 ). These primers also contain a linker, a sequencing oligo (or PAD), an index (which is different for each sample/library), and the Illumina adapter, and used as previously described (Covarrubias-Rodríguez, 2016 ). To facilitate the amplification process, DNA was diluted in DNase-free water to a final concentration of 3 ng/µL. Each reaction contained 4 µL of MyTaq™ reaction buffer (5x), 0.4 µM of each primer (10 µM), 0.1 U of Bioline MyTaq™ DNA polymerase, the volume equivalent to the concentration of desired DNA (20 ng) and DNase-free water to make up a volume of 20 µL. The paired-end sequencing was carried out in three different runs on the Illumina® MiSeqTM platform at the Ensenada Center for Scientific Research and Higher Education (CICESE). Metagenomic datasets were generated on five selected stations corresponding to one from the Perdido region (B7, 1210 m) and the rest from the Coatzacoalcos region (C10, 717 m; C13, 1887 m; C14, 3205 m; and D18, 1320 m). Stations were selected considering two factors after preliminary microbial composition analysis: 1) diversity profile derived from amplicon data and 2) their geographic location (depth). For instance, station C10 was located near the PEMEX hydrocarbon extraction pipelines and station D18 was located on or closer to a putative oil seep. Stations B7, C13 and C14 were chosen because its diversity profile was considered representative of samples within the 550 and 3203 m depth. Paired-end sequencing of the five metagenomes was performed at the MR DNA (Molecular Research LP, Shallowater, TX, USA) in a HiSeq 2500 instrument (2 × 150 bp). Amplicon and metagenomic data analysis Demultiplexed raw sequences from the V4-hypervariable region of 16S rRNA gene were analyzed using the QIIME2 (Quantitative insights into microbial ecology v2023.2) pipeline (Bolyen et al. 2018). Quality filtering, denoising, merging, and the inference of amplicon sequence variants (ASVs) were done with DADA2 algorithm (Callahan et al. 2016) using the following settings: --p-chimera-method pooling, --p-pooling-method pseudo . No trimming settings were used during DADA2 analyses. Taxonomic annotation of ASVs was done through the classify-sklearn command (qiime feature-classifier classify-sklearn) using a pre-trained Naive-Bayes classifier. Classifier training was done using a pre-formated SILVA 138 reference database which is available on QIIME2 documentation (Silva 138 SSURef NR99 515F/806R: docs.qiime2.org/2023.2/data-resources/#silva-16s-18s-rrna). Chloroplast and mitochondria annotated ASVs were discarded before downstream analyses. Downstream analyses were done using the R environment v4.2 (R Core Team, 2022 ). For instance, results from QIIME2 pipeline including ASVs’ frequency table, taxonomic information, representative sequences (ASVs) and the phylogenetic tree, were imported into R environment using the qiime2R package (Bisanz, 2018 ) for taxonomic filtering, visualization and alpha diversity analyses. Prokaryotic alpha diversity was determined using traditional diversity indexes (richness, Shannon, Chao1 and Simpson) using the estimate_richness function within the phyloseq package v1.4 (McMurdie and Holmes, 2013 ). To determine differences in prokaryotic beta diversity both Bray Curtis and weighted unifrac (Lozupone and Knight, 2005 ) distances were determined using QIIME2 pipeline and imported into R environment for principal coordinate analysis (PCoA) determination ( ape package v5.0, Paradis and Schliep ( 2019 )). PCoA analysis allowed for further discrete categorization of samples based on depth ranges. Statistical analysis of beta diversity was done using the permutational multivariate analysis of variance, i.e. PERMANOVA, with the adonis2 function using 9999 permutations ( vegan package v2.6-2, Oksanen et al. (2022)) using depth as the fixed factor. Datasets were rarefied down to the sample with the lowest sequencing depth (i.e., 7,970 seqs/sample) before alpha and beta diversity analyses. Metagenomes raw paired-end sequences were quality control checked using FastQC (Andrews, 2019 ), and adapters were removed using Trimmomatic (Bolger et al. 2014 ). High quality filtered and trimmed sequences were assembled using MEGAHIT using default settings (Li et al., 2015 ) (Supplementary Table II). Further, metagenomes contigs were analyzed using MetaPathways V2.5.1 (Konwar et al., 2015 ). Conceptually translated amino acid sequences of predicted open reading frames (ORFs) were BLAST compared to COG, MetaCyc, RefSeq and Uniprot reference databases. Sequence matches with higher than 70% identity were retrieved from the functional annotation table in the output directory and unified annotations were used to describe metagenomes’ taxonomic and functional content. Gene data in the functional annotation table was normalized by the total number of reads in each sample and used in downstream analyses. We used the functional annotation tool GraftM (Boyd et al., 2018; github.com/geronimp/graftM) and the mmf1 gene package file (Raggi et al 2020 ; github.com/garciafertson/mmf1_gpkgs.git) to search for marker genes involved in hydrocarbons metabolism. Metagenomic data visualization was carried out using the ggplot2 package (Wickham, 2016 ) in the R environment. Coupling environmental data To inform our genomic observations with an environmental framework, we used environmental data retrieved from oceanographic campaigns conducted in the Perdido and Coatzacoalcos regions, during October 2015 and May 2016 (Supplementary Table II). During each campaign, 27 soft bottom sediment samples were collected using a Hessler-Sandia box corer from perpendicular transects to the coastline extended in a bathymetric gradient (~ 44–3573 m depth) where physical and chemical parameters were taken in each station. We selected stations matching our MMF-01 grid based on the closest distance range, considering geographic coordinates and depth. Environmental data available included redox potential, organic matter content and grain size. Redox potential was measured directly on the sediment using the specific sensors (Extech pH 100 probe and Extech RE300 probe, respectively) from the box corer after the sediment was recovered. Organic matter (OM %) was determined in the laboratory from sediment subsamples (400 g) preserved at -4 ºC until analysis. Organic matter (OM %) was determined by oxidation with potassium dichromate (Hernández-Ávila et al., 2021 ). Data deposition Demultiplexed raw 16S rRNA gene datasets and metagenomes were deposited at the Sequence Read Archive (SRA) database. Datasets are publicly available through the NCBI BioProject accession numbers PRJNA1087004 and PRJNA1087009, respectively. These are the reviewer links for the datasets. 16SrRNA: https://dataview.ncbi.nlm.nih.gov/object/PRJNA1087004?reviewer=u2tp59uhfnqne1r9bft5h503o7 Shotgun: https://dataview.ncbi.nlm.nih.gov/object/PRJNA1087009?reviewer=ge0d6gfg1ctrb7vqlns02cgcvf Results Environmental parameters in deep sea sediments from Perdido and Coatzacoalcos regions Environmental parameters were used to provide a physicochemical background for the genomic observations derived from deep sediments in the Perdido and Coatzacoalcos regions. Redox potential in Perdido deep sediments (500–2,000 m) ranged from 117–155 mV while for the Coatzacoalcos region we observed redox values from − 126.4 to 175.7 mV at 645–3,260 m depth. Total organic carbon ( %) and organic matter (%) values were higher in Perdido sediments than in Coatzacoalcos. Total organic carbon in Perdido ranged from 3.1–4.6% while in Coatzacoalcos ranged from 0.49–1.69%, similarly organic matter in Perdido was observed between 5.3 and 7.9%, and for Coatzaocalcos between 0.17 and 3.05%. In regards to grain size distribution, sand content (%) was greater at depths above the 1,000m in Perdido (73% max) than in Coatzacoalcos (46.15% max), while in depths below the 1,000m sand content was lower (24–26%) (Supplementary Table II). Microbial community structure in Perdido and Coatzacoalcos regions A total of 2,494,069 paired sequences were processed, resulting in 25,314 amplicon sequence variants (ASVs). To initially evaluate differences in the microbial community diversity throughout sampling locations, we used diversity measures on rarefied amplicon data. Rarefaction curves showed that sequencing depth was sufficient for identifying a representative number of microbial species in samples, such that ~ 7,970 ASVs sequences were sufficient for comparative analysis among samples (Supplementary Fig. 1). To further evaluate the effect of sampling location and depth on microbial community composition patterns we tested the significance of ASV diversity data among samples. Beta diversity results through PCoA analysis based on both Bray-Curtis and weighted UniFrac distances showed sample partition following a depth gradient i.e. shallow, transition and deep (Fig. 1 A). In addition, PERMANOVA results showed that depth was the main source of variation indicating a statistically significant effect on community structure ( p = 0.0001). However, we did not observe regions, Coatzacoalcos and Perdido, had a significant effect on microbial community composition. For instance, samples from stations located between 550 and 788 m depth (shallow stations) grouped together, while samples at depths greater than 1,000 m grouped together (deep stations) (Fig. 1 A), regardless of their corresponding region. Diversity indices (Chao1, Simpson and Shannon) allowed us to identify alpha diversity patterns related to depth. For instance, shallow ( 1,000 m), and diversity measures showed to be significantly different along the depth gradients (Simpson p = 0.004 and Shannon p < 0.001) (Supplementary Table III). Particularly, C10 showed the highest number of unique ASVs (717 m; 1,911 ASVs) followed by D16 (697 m; 1,422 ASVs) and D18 (1,320 m; 1,179 ASVs), while deep stations such as B8 (2,700 m) and C13 (1,887 m) showed the lowest number of unique ASVs (491 and 446 ASVs, respectively) (Fig. 1 B). In addition, shallow samples shared the greatest number of ASVs (n = 62) among them and deep samples shared the least number of ASVs (n = 34) (Fig. 1 B). Taxonomic patterns along depth To further evaluate shifts in the microbial community composition along the depth gradient, samples were separated into the three groups observed (Fig. 1 A): I) shallow ( 1,200 m). Microbial community from shallow sediments (between 550 and 788 m depth), was constituted mainly by Acidobacteriota, Desulfobacterota, Proteobacteria and Planctomycetota, and Crenarchaeota, Hydrothermachaeota and Nanoarchaeota, that contributed with more than 1% relative abundance. Overall, included the Brocadiales (8%), Steroidobacterales (4%), NB1-j (3%), MSBL9 (3%) and Syntrophobacterales (2%) as the most abundant bacterial orders at these depths. Orders affiliated with Nitrosopumilales (3%), Hydrothermarchaeales (2%) and Woesearchaeales (1%) were the archaeal order most abundant at these depths (Fig. 2 A). Similarly, the microbial community composition for the sediments transition depth was also constituted by Acidobacteriota, Nitrospinota, Methylomirabilota, Proteobacteria and Planctomycetota, and Crenarchaeota that contributed with more than 1% relative abundance. We observed among samples the most abundant bacterial orders were the Steroidobacterales (4%), Subgroup_21 affiliated with the Acidobacteriota (3%), Methylomirabilales (3%), NB1-j, MBMPE27 and AT-s2-59 affiliated with the Gammaproteobacteria (2%), Kiloniellales (2%), Defluviicoccales (1%) and Nitrosococcales (1%), while the archaeal order was mostly constituted by Nitrosopumilales (14%) (Fig. 2 A). The microbial community from deep sediments appeared to be more homogeneous among them with respect to ASVs abundance (Fig. 2 A), showing similar phyla distribution and an increase in abundance for Kiloniellales (4%), NB1-j (3%) and Defluviicoccales (2%), and the Nitrosopumilales reaching up to 20% (Fig. 2 A). Noteworthy, the community composition shifted at D18 (1,500 m) in which the most abundant phyla included Chloroflexota, Desulfobacterota, Nitrospinota and Planctomycetota, and Asgardarchaeota, Nanoarchaeota and Thermoplasmatota, constituted by Anaerolineales (10%), Syntrophobacterales (3%), MSBL9 affiliated with Planctomycetota (3%), Desulfobacterales (3%), Aminicenantales (2%), Desulfobulbales (1%), while the archaeal were primarily affiliated with Woesearchaeales (3%), Lokiarchaeia (2%) and Thermoplasmata (1%) (Fig. 2 A). Metagenomics taxonomic and functional annotation To identify microbial community gene composition, we analyzed genomic information consisting of 128×10 6 quality-filtered total reads from five metagenomes corresponding to samples collected at selected stations: B7, C10, C13, C14 and D18. These stations were selected based on their representative taxonomic signature for the shallow, transition and deep groups, as well as the hydrocarbon degradation potential for D18 defined in the methods section. A total of 22,208 genes were identified and their taxonomic affiliation was determined using the RefSeq annotation results. Genes’ affiliation was taxonomically distributed among metagenomic datasets mostly within Bacteria (89.5%) and Archaea (10.24%) domains, while 0.26% remained classified as Eukaryota, Virus and unclassified. The most abundant taxonomic affiliations (> 1% average relative abundance from the total number of genes) observed were Acidobacteriota (2%), Actinobacteriota (2%), Bacteroidota (1%), Bacillota (3%), Candidate divisions (7%), Chloroflexota (1%), Nitrospirota (1%), Planctomycetota (5%), Alpha- (14%), Delta- (19%) and Gammaproteobacteria (14%) among the Bacteria domain, and Bathyarchaeota (9%), Crenarchaeota (3%), Euryarchaeota (22%), Korarchaeota (1%), Nitrosopumilus (7%), Nitrosarchaeum (5%), Nitrosotenius (3%), Nitrosomarinus (2%), Nitrosotalea (2%), Nitrosocosmicus (1%), Nistrososphaerota (43%), Thorarchaeota (2%), Theionarchaea (2%) and Woesearchaeota (3%) among Archaea domain (Fig. 2 B). In addition, we observed a spatial distribution among microbial taxa, in which some abundance peaked in relation with depth. For instance, genes affiliated with Deltaproteobacteria were highly abundant (up to 63%) at stations C10 and D18 (717 and 1,320 m, respectively), while the Alphaproteobacteria were more abundant (up to 27%) at stations C13 and C14 (1,887–3,205 m). Furthermore, Candidate divisions (16%), Bacillota (6%) and Planctomycetota (23%), were more abundant at station C10 (717 m), Chloroflexota (3%) were more abundant at station D18 (1,300 m), Gammaproteobacteria (20%) were more abundant at station C13 (1,887 m), and Actinobacteriota (4%) and Acidobacteriota (3%) peaked in abundance at station C14 (3,205 m). In comparison, for archaeal genes, the Nitrososphaerota were the most abundant at deep stations C13 and C14, as well as in B7 (1,210 m) in the Perdido region (Fig. 2 B). To initially determine the functional distribution of identified genes, we used Clusters of Orthologous Groups (COG) categories. A total of 14,453 genes (65% of total number of identified genes) were classified within a COG category, and from the 7,755 remaining genes, approximately 51% were related to hypothetical proteins. From the total number of identified COGs, approximately 88% percent were affiliated with Bacterial genes, while ~ 12% were affiliated with Archaeal genes. Among the identified COG categories, the most abundant included amino acid transport and metabolism (13%), carbohydrate transport and metabolism (4%), cell wall/membrane/envelope biogenesis (4%), coenzyme transport and metabolism (6%), energy production and conversion (13%), function unknown (6%), general function prediction only (10%), inorganic ion transport and metabolism (3%), lipid transport and metabolism (4%), nucleotide transport and metabolism (3%), post translational modification, protein turnover, chaperones (5%), replication, recombination and repair (6%), signal transduction mechanisms (3%), transcription (4%) and translation, ribosomal structure and biogenesis (9%), as the most abundant categories (Supplementary Table IV). In addition, we observed a spatial distribution among COG categories, in which some peaked in abundance in relation to depth. For instance, energy production and conversion (16%), nucleotide transport and metabolism (5%) and posttranslational modification, protein turnover, chaperones (10%) were more abundant at station C10 (717 m), while amino acid transport and metabolism (12%), carbohydrate transport and metabolism (3%), coenzyme transport and metabolism (6%) and defense mechanisms (2%) peaked in abundance at station C13 (1,887 m) (Fig. 3 A). Potential distributed metabolism driven by a depth gradient Based on genes and taxa distribution, we explored the potential for distributed metabolism along a depth gradient for the most abundant COG categories and taxa. Initially, we evaluated the occurrence of COGs related to the “amino acid transport and metabolism” and “energy production and conversion” categories, which have resulted in the most abundant in all metagenomes. Overall, a total of 341 COGs (55% amino acid transport and metabolism and 45% energy production and conversion) were commonly distributed among metagenomes, of which only 5% (18 COGs) were shared throughout. These shared COGs showed a taxonomic distribution following a depth gradient and showing differences between de Coatzacoalcos and Perdido regions. For instance, COGs identified in marine stations from Coatzacoalcos region, i.e., C10 (717m) and D18 (1,320m), were mostly affiliated with the Chloroflexota (5–8%), Candidate divisions (8–30%) and Deltaproteobacteria (50–70%), while those from the Perdido region (B7, 1,210 m) were affiliated with the Alphaproteobacteria (30%) and Nitrososphaerota (60%). In comparison, deepest sediment samples, i.e. C13 and C14 at 1,887m and 3,205 m, respectively, were mostly affiliated with the Alphaproteobacteria (54–63%) and Nitrososphaerota (27 − 22%) (Fig. 3 B). Among the Alphaproteobacteria which dominated amino acid transport and metabolism COGs we observed for the Rhodospirillales, Rhizobiales and Rhodobacterales, while for the Deltaproteobacteria within this category we observed the Desulfobacterales, Desulfuromonadales, Desulfovibrionales and Syntrophobacterales. In comparison, the energy production and conversion was dominated by Deltaproteobacteria affiliated with Desulfobacterales, Synthrophobacterales, Desulfovibrionales and Desulfuromonadales (Supplementary Table V). These occurrence patterns for taxa related to COGs throughout the shallow to deep depth gradient were consistent among samples. For instance, amino acid transport and metabolism COGs related to threonine synthase, aspartate aminotransferase, glutamate synthase, amino acid transporter permease, cysteine desulfurase, methionine synthase, tryptophan synthase were distributed among distinct taxa where for the C10 and D18 COGs were mostly affiliated with the Candidate divisions and Deltaproteobacteria, and for the C14 were mostly affiliated with the Alphaproteobacteria and Nitrososphaeorota. Similarly, for energy production and conversion COGs related to ferredoxin, succinate-CoA ligase, formate dehydrogenase, fumarate reductase, and lactate dehydrogenase were mostly affiliated with the Chloroflexota, Alphaproteobacteria, and Nitrososphaerota (Supplementary Table VI). Potential for hydrocarbon metabolism Furthermore, to gain insight into the microbial community's potential to strive under a hydrocarbon rich environment, we searched for marker genes involved in aerobic and anaerobic hydrocarbon degradation, as well as those genes involved in the metabolism of contaminants derived from hydrocarbons i.e. aromatic cleavage, arsenite, benzoate, phenol and toluene degradation, C1 and methane metabolism and N-C degradation (Fig. 4 ). The taxonomic affiliation of genes involved in hydrocarbons metabolism was distributed primarily among the Acidobacteriota (2%), Actinobacteriota (7%), Candidate divisions (4%), Chloroflexota (2%), Bacillota (8%), Gemmatimonadota (2%), Nitrospirota (6%), Planctomycetota (3%), and Alpha-, Beta-, Delta- and Gammaproteobacteria (10%, 2%, 25% and 14%, respectively). However, we observed specific taxa dominated genes’ affiliation according to sample location, such as the Deltaproteobacterial genes were the most abundant at stations C10 and D18 (717 and 1,320 m) with a total relative abundance of 0.15–0.3%, respectively. In comparison, the Alphaproteobacterial genes were the most abundant at station C14 (3,205 m) with a total relative abundance of 0.15% respectively. For station B7, genes were uniquely affiliated with the Betaproteobacteria (Fig. 4 A; Supplementary Table VII). Within genes for hydrocarbon degradation we observed the CO dehydrogenase/acetyl-CoA synthase complex ( acsB ), haloalkane dehalogenase ( dha ), 50S ribosomal protein L3 ( rplC ) and 50S ribosomal protein L5 ( rplE ); nitroalkanes degradation the 2-nitropropane dioxygenase ( ncd ) gene; benzoate degradation genes such as 4-hydroxybenzoate 3-monooxygenase ( phbh ), 4-hydroxybenzoate polyprenyltransferase ( ubiA ) and acetyl-CoA C-acyltransferase ( acaB ); phenol degradation genes such as 2-octaprenyl-6-methoxyphenol hydroxylase ( ubiH ) and acetoin:2,6-dichlorophenolindophenol oxidoreductase ( acoA ); toluene and arsenite resistance including toluene tolerance protein ( ttg2D ), and arsenical-resistance protein ( ars ), arsenite methyltransferase ( As3MT ) and arsenite transporter ( acr ); C1-metabolism genes such as formylmethanofuran dehydrogenase ( fmd ); formylmethanofuran–tetrahydromethanopterin formyltransferase ( ftr ), methanol dehydrogenase ( mdh ), methenyltetrahydromethanopterin cyclohydrolase ( mch ), methylenetetrahydromethanopterin dehydrogenase ( mtd ) and tetrahydromethanopterin S-methyltransferase ( mtrA ); aromatic ring cleavage genes including nitronate monooxygenase ( nmo ; aromatic ring cleavage), quercetin 2,3-dioxygenase ( yhhW ); and within the Rieske super family the gene coding for Rieske 2Fe-2S domain-containing protein ( ambt ) (Fig. 4 B). Overall, hydrocarbon metabolism genes were not evenly distributed among locations and were observed in low abundances within samples (< 1% relative abundance from the total number of genes). Overall, genes related to aromatic cleavage, arsenite, benzoate, and toluene degradation, C1-methane and hydrocarbon metabolism were mostly identified in stations C10 and D18; in addition, N-C degradation, nitrogen, phenol degradation and Rieske metabolism were mostly observed in C14 (Fig. 4 B). As an example, mch was only observed at station C10, while dha , acr and nmo were only observed at station D18, and ambt , ubiH , ftr and phbh were uniquely observed at station C14. In comparison, yhhW was the only gene associated with hydrocarbon metabolism found at station B7 (Fig. 4 B). Regarding genes’ abundance, 91% of the total number of genes associated with hydrocarbons metabolism showed a relative abundance ranging from 0.01–0.08%, except for acsB that peaked in abundance (0.23%) at station D18, rplE that peaked in abundance (0.11%) at station C10, and yhhW that peaked in abundance (0.23%) at station B7 (Fig. 4 B). Discussion Here we analyzed amplicon and metagenomic information from deep-sea sediment samples taken in the GoM Mexican EZZ. Results allowed us to identify the main bacterial and archaeal taxa inhabiting the deep sediments of the Perdido and Coatzacoalcos regions, as well as differences in the gene composition and metabolic potential primarily in relation to alternative carbon, energy and hydrocarbon metabolisms. Environmental profiling of deep-sea sediments Commonly, the redox potential is considered as a readily descriptor of organic matter and bacterial activity as by definition it is the oxidizing or reducing capacity of a system based on the vertical distribution of electron acceptors, i.e. O 2 , NO 3 − , Mn, Fe, SO 4 − 2 . Thus, redox potential could also be used as a proxy for aerobic vs. anaerobic conditions, which may be relevant for microbial community composition and activity. Surveys in marine sediments suggest microbial communities have a differential response to environmental features which in turn could result in inhibiting or promoting their metabolic activity towards different substrates. For instance, redox potential combined with aliphatic hydrocarbon concentration showed to influence the taxonomic composition of the rare taxa (< 1% abundance) in deep-sea sediments in the GoM impacted by high PAHs contamination and heavy metals (Sánchez-Soto et al., 2018 , 2023 ). In this study, we observed that the largest number of unique ASVs corresponded to shallow locations where redox potential showed varying values according to depth. For instance, locations near to C10 (717 m) and D16 (697 m) with redox values from − 27.7 to -54.8mV, which would commonly correspond to anaerobic conditions, showed unique ASVs affiliated with ​​the classes Nanoarchaeia, Phycisphaerae and Anaerolineae. In comparison, locations near D18 (1320 m) showed higher redox values (178.8mV) which in addition showed the most atypical taxonomic profile with unique ASVs affiliated with the Desulfobacterales, Desulfobulbales, Dehalococcoidia, Thermodesulfovibrionia, and the Nanoarchaeia, Phycisphaerae and Anaerolinea as well. Similarly, the northern GoM shelf and slope locations (up to 1,200 m depth) showed lower redox conditions (-136 to 188 mV) that corresponded with high metals (Al and Pb, particularly) and PAH concentrations, where Desulfarculaceae , Desulfoeraceae , Syntrophobacteraceae , Nitrospiraceae , Anaerolinaceae , and Dehalococcoidia were significantly more abundant (Sánchez-Soto et al., 2023 ). Regarding organic matter and organic carbon content, values previously reported in the southern GoM sediments, 1.15–2.9% and 0.66–1.67% (Sánchez-Soto et al., 2023 , Godoy-Lozano et al., 2018 ) respectively, fall within the range concentrations we observed in the studied area (1.5% average organic matter and 0.85% average organic carbon). In the southern GoM sediments, low organic matter content combined with high PAHs availability and low redox potential define the geochemical landscape and have shown to drive microbial composition and potentially the community’s activity (Godoy-Lozano et al., 2018 ). For instance, recent observations in the southern GoM deep sediments environmental parameters such as organic matter, hydrocarbons and redox potential largely contributed to structuring microbial communities (Sánchez-Soto et al., 2023 , Godoy-Lozano et al., 2018 ), in which these environmental conditions may favor microbial groups such as those identified as unique taxa that are recently considered as a relevant role player in hydrocarbon degradation, aromatic compounds oxidation, to be resistant to metals and to use multiple electron acceptors. Microbial community composition Amplicon sequencing results allowed us to identify differences in the presence and abundance of a myriad of prokaryotic microorganisms in comparison to previous studies carried out in the Gulf of Mexico. Differences in the presence and abundance of microbial species have been commonly explained with respect to the water column depth gradient (with their respective variations in temperature and pressure) (Orcutt et al., 2010 ). In our study, species richness was higher at depths above 1,000 m, as suggested by Sánchez-Soto et al ( 2018 ) that microbial communities present in sediments at these depths are richer in species. In this sense, it is possible that shallower sediments could be influenced by the influx of organic matter and other compounds from the continental and oceanic surface so that microbial community composition is affected by diverse environmental factors in comparison to those from deeper sediments. In comparison, species richness values in deep stations (more than 1,000 m) are concurrent with what was expected according to Jochens and DiMarco ( 2008 ), where sediments in deep zones are less diverse than shallow ones. Overall our results support what was suggested by Covarrubias-Rodríguez ( 2016 ), that the microbial diversity in deeper sediments of the Gulf of Mexico is homogeneous in terms of microbial composition. Regarding the microbial community composition, we observed it was constituted by characteristic phyla including the Pseudomonadota and Thaumarchaeota (Covarrubias-Rodríguez, 2016 ). The Pseudomonadota phylum was consistently the most abundant (up to 30% average relative abundance) in all stations. Members of this phylum are considered of importance for deep sediments of the region as they are associated with nitrogen fixation using sulfur compounds as an oxidizing agent (Battistuzzi and Hedges, 2008 ), a metabolic feature relevant to redox gradients found in sediments. For instance, Deltaproteobacteria are closely related to biogeochemical cycles (Battistuzzi and Hedges, 2008 ). Members of the Desulfobacteraceae are usually involved in the pathways of sulfate reduction, nitrogen fixation, and methane oxidation; and like the Syntrophyobacteraceae, that could be associated with the degradation of hydrocarbons (Vigneron et al. 2018 , 2021 ). The second most abundant phylum was the Thaumarchaeota, which are nitrifying chemolithotrophic archaea commonly found in marine environments (Pester et al., 2011 ). We observed an increase in their relative abundance with depth, potentially because the Thaumarchaeota are better adapted to survive in environments with low energy fluxes and higher seafloor pressures (Valentine, 2010). Although the Proteobacteria and Thaumarchaeota were the most abundant taxa within domains, we observed differences in their distribution and abundance as a function of depth. At depths greater than 1,000 there is an increase in the abundance of the Rhodospirillales within the Alphaproteobacteria. Rhodospirillales could grow chemoheterotrophically in the dark or heterotrophically under aerobic or microaerobic conditions (Baldani et al., 2014 ). Furthermore, the presence of Rhodospirillales could be related to the anaerobic fermentation of lactate to acetate which is then used by Archaea species to produce methane (Madigan and Martinko, 2005 ). In addition, community members affiliated with Marine Group I within the Thaumarchaeota and members of the Euryarchaeota phylum abound in sediments collected at depths greater than 2,000 m. It has been reported that Thermoplasmatales within the Euryarchaeota inhabit deep sediments or hydrothermal vents as they have the potential for nitrate reduction, methanogenesis, and methane oxidation (Madigan et al., 2010). Worth noting, at station C13 we observed the highest abundance of this taxa. Covarrubias-Rodríguez ( 2016 ) suggested that these microorganisms could be methane oxidizers and that they are found in a mutualistic relationship (syntrophy) with other sulfate-reducing archaea, in this case, potentially those belonging to Marine Group I. Overall depth is the variable that could best explain the differences in the taxonomic profiles observed (Fig. 1 B; p = 0.0001), however the taxonomic profile at the D18 station does not seem to depend on this factor. For instance, stations A3, C12 and D18 were sampled at a depth range between 1320 and 2390m and are located along Perdido and Coatzacoalcos, of which stations A3 and C12 have a similar diversity and taxonomic profile contrasting with that of station D18, that resulted different from the rest of the stations (Fig. 2 A). These differences could be attributed to the station's specific environmental factors i.e. closeness to oil natural sources. Studies have shown that the distance to oil seeps i.e. within a 0–15 m distance could influence the occurrence and abundance of certain bacterial phyla changing the microbial community composition within those. For instance, at station D18, members of the Anaerolineales, Desulfobacterales and Syntrophobacterales were identified, which were previously reported as characteristic microorganisms of hydrocarbon emanation sites (methane and oil) in the northern Gulf of Mexico (Vigneron et al., 2017 ). Additionally, Vigneron et al, mention that the members of Anaerolineae could have genes related to the degradation of hydrocarbons (such as naphthalene) and to the reduction of sulfates. Although we cannot ensure that sediments were taken from an emanation site, the site's taxonomic fingerprint suggests community composition at this station could be driven by hydrocarbon sources availability. For instance, the presence of Aminicenantales in D18 could be another indicator of the proximity of a hydrocarbon source as these bacteria abound in sites contaminated by hydrocarbons or hydrothermal vents, in addition, they inhabit sites with low oxygen levels and participate in the degradation of carbon to methane in conjunction with Planctomycetota species including the Phycisphaerae such as MSBL9 (Farag et al., 2014 ; Robbins et al., 2016 ). Metabolic potential for microbial communities to couple Amino acids metabolism and Energy production with hydrocarbon degradation throughout a depth gradient Overall, evaluating the metabolic potential of microbial communities could underline a myriad of genes that are related to distinct metabolic pathways that could result in synergistic functions for the communities to function efficiently in nutrient-limited environments (Biggs et al., 2015 ). Here we evaluated metagenomic information that showed the microbial communities from the deep-sea sediments have the potential to couple different metabolic traits to sustain communities’ carbon and energy requirements throughout depth gradients. Results showed similarities with previous observations describing the microbial community functional core in the GoM deep sediments in relation to genes being distributed among different classes of metabolism that varied in abundance with depth (Torres-Beltrán, et al., 2022). In the present study, amino acid metabolism constituted up to 13% of total identified COGs, followed by the energy production and conversion (12.7%), showing its maximum abundance at deep and shallow depths, respectively. Particularly, here we observed among the amino acids metabolism the presence of the aspartate aminotransferase and cysteine desulfurase, which are particularly relevant for hydrocarbon and sulfur metabolism in the GoM sediments. For instance, the cysteine desulfurase allows the utilization of L-cysteine as a source of sulfur atoms and it has been associated with the formation of intracellular [Fe-S] clusters (Fontcave and Ollagnier-de-Chaudens, 2008), which are relevant for toluene and naphthalene degradation. Toluene and naphthalene dioxygenases are multicomponent enzymes with an active site containing a reductase and a ferredoxin; the latter with a Rieske-type [2Fe-2S] iron-sulfur redox center, where the iron ions have two cysteine ligands (Haddock, 2010 ). In addition, the aspartate aminotransferase is an ubiquitous enzyme that catalyzes the conversion of aspartate and α-ketoglutarate to oxaloacetate and glutamate, which can be connected to the TCA cycle as substrates that could facilitate microbial growth in deep marine sediments (reviewed in Torres-Beltran et al., 2022). Further, for the energy production and conversion COG categories we noted the presence of the succinate-CoA ligase, formate dehydrogenase, fumarate reductase, and lactate dehydrogenase, which are related to carbon cycling pathways. For instance, the succinate-CoA ligase is an enzyme that converts succinyl-CoA to succinate as an intermediate for the TCA cycle, while the formate dehydrogenase catalyzes the oxidation of formate to carbon dioxide (CO 2 ); the latter can be further fixated through major pathways including the Ribulose Monophosphate Pathway (RuMP) and the reductive acetyl-CoA pathway (Wood-Ljungdahl pathway) that are relevant for derived carbon fixation from C1 metabolism and occur in many oligotrophic environments (Zhou et al., 2019; Lazar et al., 2016). In fact, microbial community function may be influenced by geochemical variables in the sediments including organic matter content and carbon sources i.e. hydrocarbons. Godoy-Lozano, et al. ( 2018 ) suggested two geochemical variables with higher contributions to microbial community structure in sediment samples from the southern GoM were aromatic hydrocarbons and depth. In fact, natural leaks of oil are widely distributed and are suggested to have a basal hydrocarbon degrading microbial community (Godoy-Lozano, et al. 2018 ). Hydrocarbon degrading bacteria in the GoM has been characterized, based on 16S rRNA gene analyses, to fall within 16 predominant genera in the sediment that could be distributed along the northwestern and southwestern regions of the GoM (Ramirez et al., 2020; Rodriguez-Salazar et al., 2021; Raggi et al., 2021; Godoy-Lozano et al., 2018 ). In our study, we compared the potential for hydrocarbon metabolism in deep sediments among microbial groups following a strategy based on searching functional marker genes in curated databases that included gene package files for sulfur, nitrogen, methane and hydrocarbon metabolism, and that have been previously used in sediment samples from the Gulf of Mexico (Boyd et al., 2018; Raggi et al 2020 ). Previous observations focused on studying the hydrocarbon degradation for the Coatzacoalcos region reported for methane metabolism genes related to methanogenesis/AOM pathway ( fmd, ftr, mch, mtd, mer and mtr ), while for aerobic hydrocarbon metabolism, they observed the alkB gene and for anaerobic metabolism the bssA-like (Rieske family) gene, both primarily at D18 (Raggi et al., 2021). In our study, genes related with C1 and methane metabolism and hydrocarbon degradation were distributed along a depth gradient showing greater abundance at stations C10, D18 and C14, where D18 (1,320m) and C14 (3,205m) showed the highest diversity of searched genes. For instance, we observed genes related to hydrocarbon degradation ( rplC , rplE and acsB ), as well as those for benzoate and phenol degradation ( phbh, ubiA, acaB, ubiH, acoA ), toluene and arsenite resistance ( ttg2D, ars, As3MT, acr ), aromatic ring cleavage ( nmo, yhhW ) and Rieske family ( ambt ), that overall contrasted with what has been previously observed in sediments along this region of southern GoM (Raggi et al., 2021). Nonetheless, combined observations generated to date in the GoM contribute to understanding in greater detail the alternative mechanisms that allow carbon fixation in sediments in relation to hydrocarbon metabolism. In particular, our observations suggest beyond standard carbon fixation pathways i.e. Wood-Ljungdahl (WL) pathway, mediated by enzymes such as the carbon monoxide dehydrogenase (AcsB), formylmethanofuran dehydrogenase ( fmd) , formylmethanofuran tetrahydromethanopterin formyltransferase ( ftr) and methenyltetrahydromethanopterin cyclohydrolase ( mch) identified at D18, there is a potential link between amino acids and energy production and conversion metabolisms to be coupled with hydrocarbon degradation, which in turn may occur as a multifunctional metabolic strategy by microbial communities in the GoM deep sediments. Namely, the occurrence of genes related to sulfur sources i.e. cysteine desulfurase that favors the intracellular [Fe-S] clusters for Riske-family proteins, mainly affiliated with the Rhizobiales and Rhodobacterales within the Alphaproteobacteria. In addition, we observed the occurrence of succinate-CoA ligase that mediates the succinate conversion from succinyl Co-A. The effect of succinate as co-substrate for hydrocarbon degradation has been previously reported in biodegradation surveys (Wang et al., 2021), which suggested co-substrates improved the activity of inducible enzymes with higher affinity for PAHs. These biodegradation experiments amended with succinic acid and phthalic acid proven to enhance PAHs degradation, with concomitant increase in amino acid metabolism as well as potentially lead to mutualistic positive interactions among microbial community members (Wang et al., 2021). Furthermore, we observed shifts in the microbial taxa affiliated with COGs within these metabolisms, suggesting a potential succession in the microbial community composition due to a geochemical gradient throughout depth. In this study, shifts in abundance were observed for the Alpha and Deltaproteobacteria, Chloroflexota and Candidate division, and Euryarchaeota and Thaumarchaeota. Previous studies have suggested shifts in microbial community structure may be driven by PAH content and concentration, considering the depth variation, in the swGoM (Godoy-Lozano et al., 2018 ; Suarez-Moo et al ., 2020; Bacosa et al., 2012 ). In accordance with our observations, the Alpha and Gamma proteobacteria have been considered among the abundant classes in the swGoM sediments, which in addition to the Chloroflexota have shown to be metabolically versatile and harbored potential pathways for hydrocarbon degradation and respiratory processes (Hug et al., 2013 ; Dombrowski et al., 2017 ; Rahmeh et al., 2021 ). Particularly, the Alpha and Deltaproteobeacteria dominated the COG genes for these metabolisms, showing up to 60–70% abundance, where Deltaproteobacterial genes were more abundant at C10 and D18 progressively shifting to Alphaproteobacteria genes at C14, showing a similar distribution with hydrocarbon metabolism-related genes for these two classes. Studies showed their distribution in diverse environments is mainly controlled by carbon concentration where the Alphaproteobacteria have shown a greater potential to use more recalcitrant carbon compounds (Frexia et al., 2016; Sebastian et al., 2021). For instance, differences in amino acid and carbohydrates metabolism by Alphaproteobacteria have been observed in marine water column compartments where substrate uptake is greater at the bathypelagic zone, where amino acid-like material accounts for the largest component of organic matter (Sebastian et al., 2021). Moreover, regarding hydrocarbon metabolism, Zhang et al. ( 2019 ) observed the Alpha and Deltaproteobacteria became the dominant taxa when sediments were amended with PAHs sources i.e. pyrene at different stages of the incubation period. For instance, microbial succession showed the Deltaproteobacteria dominating the bacterial community at the early stage of incubation when pyrene concentration was higher, while the Alphaproteobacteria became more abundant at the late stage of the incubation period when alternative carbon sources may have resulted available because of the hydrocarbon degradation process (Zhang et al., 2019 ). In addition, Alpha and Deltaproteobacteria distribution has been previously examined in deep-sea sediments from the GoM to evaluate hydrocarbon aerobic and anaerobic metabolism. Kimes et al. ( 2013 ) observed that Alphaproteobacteria (predominantly the Rhizobiales and Rhodobacterales orders, also observed in the present study), peaked in abundance in samples furthest from the oil rig, while Deltaproteobacteria (Desulfobacterales, Desulfovibrionales and Desulfuromonadales) exhibited higher abundances in samples closest to the oil rig. Deltaproteobacteria abundance has also been associated with higher levels of PAHs, and detectable alkanes and alkenes that are commonly found in oil sources. In the GoM, Deltaproteobacteria could be considered as a fingerprint class for the presence of hydrocarbon sources (Kimes et al., 2013 ). Studies based on the use of gene annotation or predicted functional profiles suggest functional redundancy could occur for marine microbial communities globally, where metabolic pathways may be spread across taxa so that different microbial species conduct the same set of enzymatic reactions (Galand et al., 2018 ). Galand et al. ( 2018 ) suggest partial redundancy could be considered when organisms that share some specific function coexist but may nevertheless differ in other ecological requirements. In addition, partial functional redundancy has been suggested to be related to bacterial communities’ taxonomic and genomic richness, which in addition could be linked to environmental fluctuations. Based on our observations for the set of identified COGs we suggest functional redundancy for the Amino acid and Energy production metabolisms may occur in the GoM deep sediments where ecological niches could likely be distributed among microbial taxa such as the Alpha and Deltaproteobacteria. For instance, the fact that these genes could be identified in microbial communities from different sampling sites within an environment indicates the possible presence of a partial functional redundancy for amino acid and energy production metabolism while linked to hydrocarbon metabolism carried out by Alpha and Deltaproteobacteria, which occurrence and abundance could be related with environmental factors. In the GoM particular scenario, for natural and anthropogenic hydrocarbon sources, we could pinpoint how important is within a site microbial community composition and structure as a mean to ensure environment resilience, while at a physical gradient driven by temperature, pressure, and chemical characteristics define geographical regions that ultimately may allow for metabolism being distributed among microbial taxa that may conduct the same function to preserve their community´s metabolic network. Thus, further surveys including network analysis describing microbial communities' functional dynamics as well as genome amplified genomes could step forward into elucidating community-level metabolic models that could account for ecological traits in microbial communities summing up to the environment's function. Conclusion Combined amplicon sequence and metagenomic information suggest microbial community composition and potential function follow a gradient according to depth and that proximity to hydrocarbon sources may contribute to shaping them. In addition, our findings suggest that alternative carbon and energy metabolisms could potentially be coupled with hydrocarbon degradation. Here, we provide evidence suggesting metabolic niches in deep sediments could be shifted in relation to taxa succession along environmental gradients showing the metabolic capacity for coupling different metabolic functions by a natural microbial consortium showing multifunctionality. Our results sum into understanding how widespread the distribution of genes for hydrocarbon degradation could be in a shifting microbial community as well as the alternative coupling of metabolisms may contribute to efficiently utilize growth substrates in deep-sea marine sediments. Declarations Funding This research was supported by the Mexican National Council for Science and Technology (CONACyT)-Mexican Ministry of Energy (SENER)-Hydrocarbon Fund (project 201441). CONACYT awarded C. B. with a M. Sc. scholarship and M. H.-G. with a Postdoctoral scholarship. This is a contribution of the Gulf of Mexico Research Consortium (CIGoM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgements We would like to thank all the members of crew of the R/V Justo Sierra for their contribution to the MMF-01 successful oceanographic campaign. We acknowledge PEMEX’s specific request to the Hydrocarbon Fund to address the environmental effects of oil spills in the Gulf of Mexico. References Andrews S (2019) FastQC: A quality control tool for high throughput sequence data. Babraham Institute. 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ISBN 978-3-319-24277-4. ggplot2.tidyverse.org Zhang S, Hu Z, Wang H (2019) Metagenomic analysis exhibited the co-metabolism of polycyclic aromatic hydrocarbons by bacterial community from estuarine sediment. Environ Int 129:308–319. doi.org/10.1016/j.envint.2019.05.028 Zhichao Zhou Y, Liu W, Xu J, Pan Z-H, Luo M, Li (2019) Genome and community-level interaction insights on wide carbon utilizing and element cycling function of Hydrothermarchaeota from hydrothermal sediment. bioRxiv: 768564. doi.org/10.1101/768564 Supplementary Tables Supplementary Table 1 to 8 are not available with this version. Additional Declarations The authors declare no competing interests. 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-6754051","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":462167067,"identity":"dadf9b77-bd33-4df8-8651-eb1f04097e9a","order_by":0,"name":"Mónica Torres-Beltrán","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mónica","middleName":"","lastName":"Torres-Beltrán","suffix":""},{"id":462168499,"identity":"9e67ecff-d0fc-48a1-9d35-354f9c8e4234","order_by":1,"name":"Mario Hernández-Guzman","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mario","middleName":"","lastName":"Hernández-Guzman","suffix":""},{"id":462168500,"identity":"87b3a611-0633-46d8-a91b-dc357b4c48ad","order_by":2,"name":"Clara Barcelos","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Clara","middleName":"","lastName":"Barcelos","suffix":""},{"id":462168501,"identity":"352c9507-590c-426c-9b57-02a49c0e30bb","order_by":3,"name":"Jennyfers Chong-Robles","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jennyfers","middleName":"","lastName":"Chong-Robles","suffix":""},{"id":462168502,"identity":"47d5fd1f-ab1c-48db-a40d-b19efee86586","order_by":4,"name":"Karla Sidón-Ceseña","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Karla","middleName":"","lastName":"Sidón-Ceseña","suffix":""},{"id":462168503,"identity":"43ebc6fc-562c-47aa-9fda-57ee65308677","order_by":5,"name":"José Q. 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A) Principal Coordinate Analysis (PCoA) based on both Bray-Curtis (left) and weighted UniFrac (right) distances. Samples are shown as dots colored based on depth gradient (shallow = light blue, transition = yellow and deep = dark blue). B) Unique and shared ASVs observed throughout samples. UpsetR graph shows the total number of ASVs per station as histograms (left blue bars) as well as the number of unique ASVs per station and those shared among samples (top black bars). Samples distribution is shown as a dot panel on the bottom, samples showing shared ASVs are connected\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6754051/v1/a1764b965b8df12757d27bf3.png"},{"id":83560436,"identity":"80a090be-b9b4-4058-997b-56828de47ab7","added_by":"auto","created_at":"2025-05-28 13:17:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":176830,"visible":true,"origin":"","legend":"\u003cp\u003eAmplicons and metagenomes taxonomic profiles. A) Taxonomic composition for abundant taxa (1 % relative abundance -RA) in 16S rRNA gene amplicon samples. Abundant taxonomic groups are shown in stack barplots for each sampling station and colored as indicated in key, bars include a rare taxa (\u0026lt; 1 % RA) category named as Others. Sampling stations are ordered according to depth groups (shallow \u0026lt; 1000m, transition ~1000m and deep \u0026gt;1000m). B) Taxonomic composition for abundant taxa (1 % relative abundance -RA) in metagenomic samples. Abundant taxonomic groups are shown in stack barplots for each sampling station and colored as indicated in key, bars include a rare taxa (\u0026lt; 1 % RA) category named as Others.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6754051/v1/d4f939ab8de1617c9d58c294.png"},{"id":83560435,"identity":"79ae421c-c1aa-4042-967d-de3f0caa043a","added_by":"auto","created_at":"2025-05-28 13:17:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":139112,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional annotation and distribution of genes. A) Functional annotation of genes into clusters of orthologous groups (COGs) colored according to categories indicated in key. Percentage of relative abundance of total annotated genes is shown as pie charts divided by those affiliated with Bacteria and Archaea throughout stations (C10, D18, C13, C14 and B7). B) Summary of taxonomic distribution of genes annotated with COGs within the amino acid transport and metabolism and energy production and conversion. The relative abundance of COGs annotated within these categories throughout stations is shown as bars for each given taxonomic affiliation.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6754051/v1/02727613d3ad78001b0648c4.png"},{"id":83560437,"identity":"efaf292b-6272-40d6-a649-84e07efb10cd","added_by":"auto","created_at":"2025-05-28 13:17:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":141395,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of genes related to hydrocarbon pollution metabolism. A) Taxonomic affiliation of genes related to aromatic cleavage, arsenite, benzoate, phenol and toluene degradation, C1 and methane metabolism and N-C degradation and hydrocarbon metabolism. Total number of genes observed within a metabolic category and taxonomic affiliation are shown in stack barplot for each station (C10, D18, C13, C14 and B7). Taxa is colored coded as indicated in key. B) Genes distribution is shown as bars for the percentage of abundance among samples calculated out of the total number of genes. Observed genes for hydrocarbon degradation include the CO dehydrogenase/acetyl-CoA synthase complex (\u003cem\u003eacsB\u003c/em\u003e), haloalkane dehalogenase (\u003cem\u003edha\u003c/em\u003e), 50S ribosomal protein L3 (\u003cem\u003erplC\u003c/em\u003e) and 50S ribosomal protein L5 (\u003cem\u003erplE\u003c/em\u003e); nitroalkanes degradation the 2-nitropropane dioxygenase (\u003cem\u003encd\u003c/em\u003e) gene; benzoate degradation genes such as 4-hydroxybenzoate 3-monooxygenase (\u003cem\u003ephbh\u003c/em\u003e), 4-hydroxybenzoate polyprenyltransferase (\u003cem\u003eubiA\u003c/em\u003e) and acetyl-CoA C-acyltransferase (\u003cem\u003eacaB\u003c/em\u003e); phenol degradation genes such as 2-octaprenyl-6-methoxyphenol hydroxylase (\u003cem\u003eubiH\u003c/em\u003e) and acetoin:2,6-dichlorophenolindophenol oxidoreductase (\u003cem\u003eacoA\u003c/em\u003e); toluene and arsenite resistance including toluene tolerance protein (\u003cem\u003ettg2D\u003c/em\u003e), and arsenical-resistance protein (\u003cem\u003ears\u003c/em\u003e), arsenite methyltransferase (\u003cem\u003eAs3MT\u003c/em\u003e) and arsenite transporter (\u003cem\u003eacr\u003c/em\u003e); C1-metabolism genes such as formylmethanofuran dehydrogenase (\u003cem\u003efmd\u003c/em\u003e); formylmethanofuran--tetrahydromethanopterin formyltransferase (\u003cem\u003eftr\u003c/em\u003e), methanol dehydrogenase (\u003cem\u003emdh\u003c/em\u003e), methenyltetrahydromethanopterin cyclohydrolase (\u003cem\u003emch\u003c/em\u003e), methylenetetrahydromethanopterin dehydrogenase (\u003cem\u003emtd\u003c/em\u003e) and tetrahydromethanopterin S-methyltransferase (\u003cem\u003emtrA\u003c/em\u003e); aromatic ring cleavage genes including nitronate monooxygenase (\u003cem\u003enmo\u003c/em\u003e; aromatic ring cleavage), quercetin 2,3-dioxygenase (\u003cem\u003eyhhW\u003c/em\u003e); and within the Rieske super family the gene coding for Rieske 2Fe-2S domain-containing protein (\u003cem\u003eambt\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6754051/v1/809b47766a571fb23a25bad2.png"},{"id":83561076,"identity":"3ebad972-e438-4d93-91aa-7ab8300e5192","added_by":"auto","created_at":"2025-05-28 13:25:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1378481,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6754051/v1/2d68462b-34ae-4eb9-8f98-2f46004a666f.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAlternative carbon and energy metabolisms linked to hydrocarbon degradation are widely distributed across the different microbial communities from deep-sea sediments of the Gulf of Mexico\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMicroorganisms catalyze most of the chemical reactions that shape Earth environments. Different species of bacteria and archaea intervene in processes that transform and recycle the organic and inorganic molecules needed to sustain the biosphere (Falkowski et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These processes, collectively referred to as biogeochemical cycles, occur naturally in soils, aquatic environments, and sediments. However, environmental factors such as scarcity of nutrients and energy-yielding substrates, temperature, salinity, pH, oxygen availability, sediment porosity and permeability, may limit the deep-sea sedimentary microbial biosphere (Hoshino et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Studies indicate these factors significantly contribute to the taxonomic composition of deep-sea microbial communities and thus their ecological function, suggesting inherent depth gradients may be correlated with community composition with possible implications on the ecosystem function (Gong et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Walker et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Graw et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In some cases, pivotal metabolic functions mediating biogeochemical processes, relevant to the ecosystem function, are associated with specific taxonomic groups (Walker et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) resulting in compositional shifts shaped by different selection mechanisms i.e. consistent selection pressures in different locations or depths (Jamieson et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gong et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Marine provinces, from continental margins to hadal regions, are subjected to different input and export fluxes for carbon and energy-yielding substrates that generally decreases rapidly with increasing water depth (Seiter et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), ending up in highly re-mineralized material that is still being slowly metabolized by microbial communities (Petro et al., 2017; La Rowe et al., 2020). Thus, gradients occurring along marine provinces throughout depth allow for understanding microbial communities\u0026rsquo; mechanisms to cope with potential limiting factors in oligotrophic environments by conducting alternative metabolic strategies that may result in coupling biodegradation mechanisms of \u0026lsquo;emerging organic carbon\u0026rsquo; sources (Joye et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), such as hydrocarbon seeps that contribute occasionally to the sedimentary carbon pool in the deep-sea (Feng et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The understanding that microorganisms inhabiting marine sediments are metabolically active and play a relevant role in the deep-sea biogeochemistry, has ignited a growing interest in studying microbial communities composition, their metabolism and structural shifts associated with environmental gradients that could further advice on ecosystems\u0026rsquo; function and resilience.\u003c/p\u003e \u003cp\u003eThe Gulf of Mexico encompasses different depositional environments, along the continental margins and slope provinces, that import sediments from the coast to deep waters, including terrigenous sediments via fluvial transport and direct precipitation of biogenic sediments i.e. calcium carbonate (D\u0026iacute;az-Asencio et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Deep environments are primarily dominated by a combination of terrigenous and biogenic sediments whose rate of accumulation and deposited volume range widely in relation to coast distance, thus areas adjacent to rivers receive the greatest volume of sediment at the highest rates of delivery (D\u0026iacute;az-Asencio et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As an example, among the Perdido (Northern Gulf of Mexico) and Coatzacoalcos (Southern Gulf of Mexico) regions sediments are delivered to their accumulation sites by different transportation processes so that accumulation rates in Perdido are considerably lower than in Coatzacoalcos, most likely because of the higher river freshwater fluxes in the south in comparison with those in the north (D\u0026iacute;az-Asencio et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Particularly, the GoM deep-sea sediments have been reported to show low sedimentation rates (5\u0026ndash;8 cm kyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and exceptionally low average organic carbon values (\u0026lt;\u0026thinsp;1%) compared to other deep slope and abyssal regions of the global ocean (D\u0026iacute;az-Asencio et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, the underlying geology of the Gulf of Mexico supports a highly productive hydrocarbon province that allows for continuous offshore energy exploration resulting in a highly industrialized marine region (O`Reilly et al., 2022) as happens nowadays for the Perdido and Coatzacoalcos regions. The history of the oil industry in the GoM is marked by two major spill events, Ixtoc-I (1979) and Macondo (2010), that have seriously impacted the GoM ecosystem. Since then, attempts to assess the magnitude of the environmental damage caused have been conducted at different ecosystem levels (reviewed in Soto et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), including the evaluation on the bacterial response to oil inputs (Lizarraga-Partida et al. 1982,1991) and changes in the microbial communities using genomic tools (Hazen et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Valentine et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kessler et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bik, 2012; Lamendella et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Observations derived from these events rekindle new research questions regarding the microbial communities ecological role in the GoM biogeochemical processes that could be assessed by combined genomic approaches.\u003c/p\u003e \u003cp\u003eLinkages between taxonomy and functional capabilities for substrates degradation by marine microorganisms are largely based on culture-dependent studies, however microbial metabolic processes are not commonly carried out by a single microbial species but rather by natural microbial consortia showing multifunctionality to efficiently utilize substrates to grow (Bala et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), leaving an open gap regarding the functional role of uncultured microbes and the community\u0026rsquo;s capacity to overcome limiting conditions to grow. Thus the use of combined genomic approaches provides information about microbial community profiling that results in a means to infer the potential for synergistic metabolic processes to occur by identifying metabolic pathways that shed light on the communal response of the microbial communities in a given time and growth conditions. Particularly, recent studies using 16S rRNA gene amplicons and metagenomic information support bacterial diversity in the GoM continental shelf and slope sediments harbors a rich microbial community that is significantly influenced by depth, temperature and dissolved oxygen gradients. In addition, surveys have indicated microbial communities on waters and sediments could respond rapidly to emerging carbon inputs, shifting the communities composition (Lizarraga-Partida et al., 1982, 1991) and suggesting microbial function is adapted to thrive under environmental gradients (Ramirez et al., 2020; Rodriguez-Salazar et al., 2021; Raggi et al., 2021; Godoy-Lozano et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e ) by different metabolic traits such as the potential coupling of functions (Torres-Beltran et al., 2022). As an example, in the southern GoM deep sediments, genomic observations have shown microbial communities' taxonomic and functional fingerprints are associated with the use of amino acids metabolism coupled with hydrocarbon degradation (Torres-Beltran et al., 2022). Recent studies using omic approaches have shown that for \u003cem\u003ein situ\u003c/em\u003e bioremediation, the use of a co-substrate can accelerate biodegradation by secondary utilization as an electron donor during the degradation process (Harik et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, hydrocarbon degradation has been stimulated and improved by the addition of readily biodegradable organic matter and by favoring microbial community interactions in relation to amino acid metabolism (Wang et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Combined, evidence regarding bacterial communities composition and potential functional response to couple metabolic functions calls for studies to focus on understanding and evaluating potential shifts in microbial communities that take place along environmental gradients, that could better explain microbial metabolism in oligotrophic deep-sea sediments where carbon inputs from environmental sources are relevant as occur in the GoM sediments.\u003c/p\u003e \u003cp\u003eIn this study, we evaluated the prokaryotic microbial community in deep-sea sediments from two regions of the GoM Perdido and Coatzacoalcos, where oligotrophic conditions occur concomitant with natural crude-oil efflux, and associated extraction and prospection activities currently occur. This study encompasses 16S rRNA gene amplicon and metagenomic observations evaluating shifts in the community composition and structure in relation to depth and its functional potential in relation to alternative carbon and energy metabolism linked to hydrocarbon degradation. Our results sum into understanding how widespread the distribution of taxa and genes could be in deep-sea sediments microbial communities to provide insight into the alternative coupling of metabolisms that may contribute to efficiently utilize substrates in these limiting environments.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSediments sampling and DNA sequencing\u003c/h2\u003e \u003cp\u003eFor the molecular analysis, sediment samples were collected during the MMF-01 oceanographic campaign from February 25 to March 18 (2016), onboard the R/V Justo Sierra (UNAM) as previously described (Fern\u0026aacute;ndez-L\u0026oacute;pez et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Briefly, eighteen sampling sites distributed equally from the Perdido and Coatzacoalcos regions (Supplementary Fig.\u0026nbsp;1; Supplementary Table I) were selected. Samples were taken at a seafloor depth ranging from 550 to 3205 m using a box corer, with subsampling in triplicate from the first 10 cm directly taken from the box core using sterile syringes. Each subsample was frozen and stored immediately in liquid nitrogen onboard and kept at \u0026minus;\u0026thinsp;80\u0026deg;C when they arrived at the laboratory, until nucleic acid extraction could be performed.\u003c/p\u003e \u003cp\u003eTotal DNA was extracted from three independent subsamples (250 mg wet sediment) using the Qiagen\u0026reg; PowerSoil\u0026reg; DNA Isolation kit following the manufacturer's instructions, with some modifications including the addition of Phenol: Chloroform: Isoamyl Alcohol (25: 24: 1) to improve cell lysis and the elution step was performed twice using 50 \u0026micro;L of elution buffer with the column incubated for 10 min at room temperature (Fern\u0026aacute;ndez-L\u0026oacute;pez et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Extracted DNA was used to generate 16S rRNA gene amplicons and metagenomic data. The 16S-sequencing libraries were generated in one-PCR step following the dual-indexing strategy proposed by Kozich et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In brief, the V4 region of the 16S rRNA gene was amplified using the 515F and 806R primers designed by Caporaso et al (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These primers also contain a linker, a sequencing oligo (or PAD), an index (which is different for each sample/library), and the Illumina adapter, and used as previously described (Covarrubias-Rodr\u0026iacute;guez, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). To facilitate the amplification process, DNA was diluted in DNase-free water to a final concentration of 3 ng/\u0026micro;L. Each reaction contained 4 \u0026micro;L of MyTaq\u0026trade; reaction buffer (5x), 0.4 \u0026micro;M of each primer (10 \u0026micro;M), 0.1 U of Bioline MyTaq\u0026trade; DNA polymerase, the volume equivalent to the concentration of desired DNA (20 ng) and DNase-free water to make up a volume of 20 \u0026micro;L. The paired-end sequencing was carried out in three different runs on the Illumina\u0026reg; MiSeqTM platform at the Ensenada Center for Scientific Research and Higher Education (CICESE).\u003c/p\u003e \u003cp\u003eMetagenomic datasets were generated on five selected stations corresponding to one from the Perdido region (B7, 1210 m) and the rest from the Coatzacoalcos region (C10, 717 m; C13, 1887 m; C14, 3205 m; and D18, 1320 m). Stations were selected considering two factors after preliminary microbial composition analysis: 1) diversity profile derived from amplicon data and 2) their geographic location (depth). For instance, station C10 was located near the PEMEX hydrocarbon extraction pipelines and station D18 was located on or closer to a putative oil seep. Stations B7, C13 and C14 were chosen because its diversity profile was considered representative of samples within the 550 and 3203 m depth. Paired-end sequencing of the five metagenomes was performed at the MR DNA (Molecular Research LP, Shallowater, TX, USA) in a HiSeq 2500 instrument (2 \u0026times; 150 bp).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAmplicon and metagenomic data analysis\u003c/h3\u003e\n\u003cp\u003eDemultiplexed raw sequences from the V4-hypervariable region of 16S rRNA gene were analyzed using the QIIME2 (Quantitative insights into microbial ecology v2023.2) pipeline (Bolyen et al. 2018). Quality filtering, denoising, merging, and the inference of amplicon sequence variants (ASVs) were done with DADA2 algorithm (Callahan et al. 2016) using the following settings: \u003cem\u003e--p-chimera-method pooling, --p-pooling-method pseudo\u003c/em\u003e. No trimming settings were used during DADA2 analyses. Taxonomic annotation of ASVs was done through the classify-sklearn command (qiime feature-classifier classify-sklearn) using a pre-trained Naive-Bayes classifier. Classifier training was done using a pre-formated SILVA 138 reference database which is available on QIIME2 documentation (Silva 138 SSURef NR99 515F/806R: docs.qiime2.org/2023.2/data-resources/#silva-16s-18s-rrna). Chloroplast and mitochondria annotated ASVs were discarded before downstream analyses.\u003c/p\u003e \u003cp\u003eDownstream analyses were done using the R environment v4.2 (R Core Team, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For instance, results from QIIME2 pipeline including ASVs\u0026rsquo; frequency table, taxonomic information, representative sequences (ASVs) and the phylogenetic tree, were imported into R environment using the \u003cem\u003eqiime2R\u003c/em\u003e package (Bisanz, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) for taxonomic filtering, visualization and alpha diversity analyses. Prokaryotic alpha diversity was determined using traditional diversity indexes (richness, Shannon, Chao1 and Simpson) using the \u003cem\u003eestimate_richness\u003c/em\u003e function within the phyloseq package v1.4 (McMurdie and Holmes, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). To determine differences in prokaryotic beta diversity both Bray Curtis and weighted unifrac (Lozupone and Knight, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) distances were determined using QIIME2 pipeline and imported into R environment for principal coordinate analysis (PCoA) determination (\u003cem\u003eape\u003c/em\u003e package v5.0, Paradis and Schliep (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)). PCoA analysis allowed for further discrete categorization of samples based on depth ranges. Statistical analysis of beta diversity was done using the permutational multivariate analysis of variance, i.e. PERMANOVA, with the \u003cem\u003eadonis2\u003c/em\u003e function using 9999 permutations (\u003cem\u003evegan\u003c/em\u003e package v2.6-2, Oksanen et al. (2022)) using depth as the fixed factor. Datasets were rarefied down to the sample with the lowest sequencing depth (i.e., 7,970 seqs/sample) before alpha and beta diversity analyses.\u003c/p\u003e \u003cp\u003eMetagenomes raw paired-end sequences were quality control checked using FastQC (Andrews, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and adapters were removed using Trimmomatic (Bolger et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). High quality filtered and trimmed sequences were assembled using MEGAHIT using default settings (Li et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (Supplementary Table II). Further, metagenomes contigs were analyzed using MetaPathways V2.5.1 (Konwar et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Conceptually translated amino acid sequences of predicted open reading frames (ORFs) were BLAST compared to COG, MetaCyc, RefSeq and Uniprot reference databases. Sequence matches with higher than 70% identity were retrieved from the functional annotation table\u0026thinsp;\u0026lt;\u0026thinsp;ORF_annotation_table.txt\u0026thinsp;\u0026gt;\u0026thinsp;in the \u0026lt;\u0026thinsp;results/annotation table\u0026thinsp;\u0026gt;\u0026thinsp;output directory and unified annotations were used to describe metagenomes\u0026rsquo; taxonomic and functional content. Gene data in the functional annotation table was normalized by the total number of reads in each sample and used in downstream analyses. We used the functional annotation tool GraftM (Boyd et al., 2018; github.com/geronimp/graftM) and the \u003cem\u003emmf1\u003c/em\u003e gene package file (Raggi et al \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; github.com/garciafertson/mmf1_gpkgs.git) to search for marker genes involved in hydrocarbons metabolism. Metagenomic data visualization was carried out using the \u003cem\u003eggplot2\u003c/em\u003e package (Wickham, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) in the R environment.\u003c/p\u003e\n\u003ch3\u003eCoupling environmental data\u003c/h3\u003e\n\u003cp\u003eTo inform our genomic observations with an environmental framework, we used environmental data retrieved from oceanographic campaigns conducted in the Perdido and Coatzacoalcos regions, during October 2015 and May 2016 (Supplementary Table II). During each campaign, 27 soft bottom sediment samples were collected using a Hessler-Sandia box corer from perpendicular transects to the coastline extended in a bathymetric gradient (~\u0026thinsp;44\u0026ndash;3573 m depth) where physical and chemical parameters were taken in each station. We selected stations matching our MMF-01 grid based on the closest distance range, considering geographic coordinates and depth. Environmental data available included redox potential, organic matter content and grain size. Redox potential was measured directly on the sediment using the specific sensors (Extech pH 100 probe and Extech RE300 probe, respectively) from the box corer after the sediment was recovered. Organic matter (OM %) was determined in the laboratory from sediment subsamples (400 g) preserved at -4 \u0026ordm;C until analysis. Organic matter (OM %) was determined by oxidation with potassium dichromate (Hern\u0026aacute;ndez-\u0026Aacute;vila et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData deposition\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDemultiplexed raw 16S rRNA gene datasets and metagenomes were deposited at the Sequence Read Archive (SRA) database. Datasets are publicly available through the NCBI BioProject accession numbers PRJNA1087004 and PRJNA1087009, respectively. These are the\u0026nbsp;reviewer links for the datasets.\u003c/p\u003e\n\u003cp\u003e16SrRNA:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;https://dataview.ncbi.nlm.nih.gov/object/PRJNA1087004?reviewer=u2tp59uhfnqne1r9bft5h503o7\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eShotgun:\u003c/p\u003e\n\u003cp\u003ehttps://dataview.ncbi.nlm.nih.gov/object/PRJNA1087009?reviewer=ge0d6gfg1ctrb7vqlns02cgcvf\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental parameters in deep sea sediments from Perdido and Coatzacoalcos regions\u003c/h2\u003e \u003cp\u003eEnvironmental parameters were used to provide a physicochemical background for the genomic observations derived from deep sediments in the Perdido and Coatzacoalcos regions. Redox potential in Perdido deep sediments (500\u0026ndash;2,000 m) ranged from 117\u0026ndash;155 mV while for the Coatzacoalcos region we observed redox values from \u0026minus;\u0026thinsp;126.4 to 175.7 mV at 645\u0026ndash;3,260 m depth. Total organic carbon ( %) and organic matter (%) values were higher in Perdido sediments than in Coatzacoalcos. Total organic carbon in Perdido ranged from 3.1\u0026ndash;4.6% while in Coatzacoalcos ranged from 0.49\u0026ndash;1.69%, similarly organic matter in Perdido was observed between 5.3 and 7.9%, and for Coatzaocalcos between 0.17 and 3.05%. In regards to grain size distribution, sand content (%) was greater at depths above the 1,000m in Perdido (73% max) than in Coatzacoalcos (46.15% max), while in depths below the 1,000m sand content was lower (24\u0026ndash;26%) (Supplementary Table II).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMicrobial community structure in Perdido and Coatzacoalcos regions\u003c/h2\u003e \u003cp\u003eA total of 2,494,069 paired sequences were processed, resulting in 25,314 amplicon sequence variants (ASVs). To initially evaluate differences in the microbial community diversity throughout sampling locations, we used diversity measures on rarefied amplicon data. Rarefaction curves showed that sequencing depth was sufficient for identifying a representative number of microbial species in samples, such that ~\u0026thinsp;7,970 ASVs sequences were sufficient for comparative analysis among samples (Supplementary Fig.\u0026nbsp;1). To further evaluate the effect of sampling location and depth on microbial community composition patterns we tested the significance of ASV diversity data among samples. Beta diversity results through PCoA analysis based on both Bray-Curtis and weighted UniFrac distances showed sample partition following a depth gradient \u003cem\u003ei.e.\u003c/em\u003e shallow, transition and deep (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). In addition, PERMANOVA results showed that depth was the main source of variation indicating a statistically significant effect on community structure (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001). However, we did not observe regions, Coatzacoalcos and Perdido, had a significant effect on microbial community composition. For instance, samples from stations located between 550 and 788 m depth (shallow stations) grouped together, while samples at depths greater than 1,000 m grouped together (deep stations) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), regardless of their corresponding region.\u003c/p\u003e \u003cp\u003eDiversity indices (Chao1, Simpson and Shannon) allowed us to identify alpha diversity patterns related to depth. For instance, shallow (\u0026lt;\u0026thinsp;1,000 m) to transition (~\u0026thinsp;1,000 m) stations showed significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.020) higher richness index than deep stations (\u0026gt;\u0026thinsp;1,000 m), and diversity measures showed to be significantly different along the depth gradients (Simpson \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004 and Shannon \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Supplementary Table III). Particularly, C10 showed the highest number of unique ASVs (717 m; 1,911 ASVs) followed by D16 (697 m; 1,422 ASVs) and D18 (1,320 m; 1,179 ASVs), while deep stations such as B8 (2,700 m) and C13 (1,887 m) showed the lowest number of unique ASVs (491 and 446 ASVs, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In addition, shallow samples shared the greatest number of ASVs (n\u0026thinsp;=\u0026thinsp;62) among them and deep samples shared the least number of ASVs (n\u0026thinsp;=\u0026thinsp;34) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTaxonomic patterns along depth\u003c/h2\u003e \u003cp\u003eTo further evaluate shifts in the microbial community composition along the depth gradient, samples were separated into the three groups observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA): I) shallow (\u0026lt;\u0026thinsp;1000 m), II) transition (1,000-1200 m) and III) deep (\u0026gt;\u0026thinsp;1,200 m). Microbial community from shallow sediments (between 550 and 788 m depth), was constituted mainly by Acidobacteriota, Desulfobacterota, Proteobacteria and Planctomycetota, and Crenarchaeota, Hydrothermachaeota and Nanoarchaeota, that contributed with more than 1% relative abundance. Overall, included the Brocadiales (8%), Steroidobacterales (4%), NB1-j (3%), MSBL9 (3%) and Syntrophobacterales (2%) as the most abundant bacterial orders at these depths. Orders affiliated with Nitrosopumilales (3%), Hydrothermarchaeales (2%) and Woesearchaeales (1%) were the archaeal order most abundant at these depths (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilarly, the microbial community composition for the sediments transition depth was also constituted by Acidobacteriota, Nitrospinota, Methylomirabilota, Proteobacteria and Planctomycetota, and Crenarchaeota that contributed with more than 1% relative abundance. We observed among samples the most abundant bacterial orders were the Steroidobacterales (4%), Subgroup_21 affiliated with the Acidobacteriota (3%), Methylomirabilales (3%), NB1-j, MBMPE27 and AT-s2-59 affiliated with the Gammaproteobacteria (2%), Kiloniellales (2%), Defluviicoccales (1%) and Nitrosococcales (1%), while the archaeal order was mostly constituted by Nitrosopumilales (14%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eThe microbial community from deep sediments appeared to be more homogeneous among them with respect to ASVs abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), showing similar phyla distribution and an increase in abundance for Kiloniellales (4%), NB1-j (3%) and Defluviicoccales (2%), and the Nitrosopumilales reaching up to 20% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Noteworthy, the community composition shifted at D18 (1,500 m) in which the most abundant phyla included Chloroflexota, Desulfobacterota, Nitrospinota and Planctomycetota, and Asgardarchaeota, Nanoarchaeota and Thermoplasmatota, constituted by Anaerolineales (10%), Syntrophobacterales (3%), MSBL9 affiliated with Planctomycetota (3%), Desulfobacterales (3%), Aminicenantales (2%), Desulfobulbales (1%), while the archaeal were primarily affiliated with Woesearchaeales (3%), Lokiarchaeia (2%) and Thermoplasmata (1%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMetagenomics taxonomic and functional annotation\u003c/h2\u003e \u003cp\u003eTo identify microbial community gene composition, we analyzed genomic information consisting of 128\u0026times;10\u003csup\u003e6\u003c/sup\u003e quality-filtered total reads from five metagenomes corresponding to samples collected at selected stations: B7, C10, C13, C14 and D18. These stations were selected based on their representative taxonomic signature for the shallow, transition and deep groups, as well as the hydrocarbon degradation potential for D18 defined in the methods section.\u003c/p\u003e \u003cp\u003eA total of 22,208 genes were identified and their taxonomic affiliation was determined using the RefSeq annotation results. Genes\u0026rsquo; affiliation was taxonomically distributed among metagenomic datasets mostly within Bacteria (89.5%) and Archaea (10.24%) domains, while 0.26% remained classified as Eukaryota, Virus and unclassified. The most abundant taxonomic affiliations (\u0026gt;\u0026thinsp;1% average relative abundance from the total number of genes) observed were Acidobacteriota (2%), Actinobacteriota (2%), Bacteroidota (1%), Bacillota (3%), Candidate divisions (7%), Chloroflexota (1%), Nitrospirota (1%), Planctomycetota (5%), Alpha- (14%), Delta- (19%) and Gammaproteobacteria (14%) among the Bacteria domain, and Bathyarchaeota (9%), Crenarchaeota (3%), Euryarchaeota (22%), Korarchaeota (1%), Nitrosopumilus (7%), Nitrosarchaeum (5%), Nitrosotenius (3%), Nitrosomarinus (2%), Nitrosotalea (2%), Nitrosocosmicus (1%), Nistrososphaerota (43%), Thorarchaeota (2%), Theionarchaea (2%) and Woesearchaeota (3%) among Archaea domain (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In addition, we observed a spatial distribution among microbial taxa, in which some abundance peaked in relation with depth. For instance, genes affiliated with Deltaproteobacteria were highly abundant (up to 63%) at stations C10 and D18 (717 and 1,320 m, respectively), while the Alphaproteobacteria were more abundant (up to 27%) at stations C13 and C14 (1,887\u0026ndash;3,205 m). Furthermore, Candidate divisions (16%), Bacillota (6%) and Planctomycetota (23%), were more abundant at station C10 (717 m), Chloroflexota (3%) were more abundant at station D18 (1,300 m), Gammaproteobacteria (20%) were more abundant at station C13 (1,887 m), and Actinobacteriota (4%) and Acidobacteriota (3%) peaked in abundance at station C14 (3,205 m). In comparison, for archaeal genes, the Nitrososphaerota were the most abundant at deep stations C13 and C14, as well as in B7 (1,210 m) in the Perdido region (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eTo initially determine the functional distribution of identified genes, we used Clusters of Orthologous Groups (COG) categories. A total of 14,453 genes (65% of total number of identified genes) were classified within a COG category, and from the 7,755 remaining genes, approximately 51% were related to hypothetical proteins. From the total number of identified COGs, approximately 88% percent were affiliated with Bacterial genes, while\u0026thinsp;~\u0026thinsp;12% were affiliated with Archaeal genes. Among the identified COG categories, the most abundant included amino acid transport and metabolism (13%), carbohydrate transport and metabolism (4%), cell wall/membrane/envelope biogenesis (4%), coenzyme transport and metabolism (6%), energy production and conversion (13%), function unknown (6%), general function prediction only (10%), inorganic ion transport and metabolism (3%), lipid transport and metabolism (4%), nucleotide transport and metabolism (3%), post translational modification, protein turnover, chaperones (5%), replication, recombination and repair (6%), signal transduction mechanisms (3%), transcription (4%) and translation, ribosomal structure and biogenesis (9%), as the most abundant categories (Supplementary Table IV). In addition, we observed a spatial distribution among COG categories, in which some peaked in abundance in relation to depth. For instance, energy production and conversion (16%), nucleotide transport and metabolism (5%) and posttranslational modification, protein turnover, chaperones (10%) were more abundant at station C10 (717 m), while amino acid transport and metabolism (12%), carbohydrate transport and metabolism (3%), coenzyme transport and metabolism (6%) and defense mechanisms (2%) peaked in abundance at station C13 (1,887 m) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePotential distributed metabolism driven by a depth gradient\u003c/h2\u003e \u003cp\u003eBased on genes and taxa distribution, we explored the potential for distributed metabolism along a depth gradient for the most abundant COG categories and taxa. Initially, we evaluated the occurrence of COGs related to the \u0026ldquo;amino acid transport and metabolism\u0026rdquo; and \u0026ldquo;energy production and conversion\u0026rdquo; categories, which have resulted in the most abundant in all metagenomes. Overall, a total of 341 COGs (55% amino acid transport and metabolism and 45% energy production and conversion) were commonly distributed among metagenomes, of which only 5% (18 COGs) were shared throughout. These shared COGs showed a taxonomic distribution following a depth gradient and showing differences between de Coatzacoalcos and Perdido regions. For instance, COGs identified in marine stations from Coatzacoalcos region, i.e., C10 (717m) and D18 (1,320m), were mostly affiliated with the Chloroflexota (5\u0026ndash;8%), Candidate divisions (8\u0026ndash;30%) and Deltaproteobacteria (50\u0026ndash;70%), while those from the Perdido region (B7, 1,210 m) were affiliated with the Alphaproteobacteria (30%) and Nitrososphaerota (60%). In comparison, deepest sediment samples, i.e. C13 and C14 at 1,887m and 3,205 m, respectively, were mostly affiliated with the Alphaproteobacteria (54\u0026ndash;63%) and Nitrososphaerota (27\u0026thinsp;\u0026minus;\u0026thinsp;22%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Among the Alphaproteobacteria which dominated amino acid transport and metabolism COGs we observed for the Rhodospirillales, Rhizobiales and Rhodobacterales, while for the Deltaproteobacteria within this category we observed the Desulfobacterales, Desulfuromonadales, Desulfovibrionales and Syntrophobacterales. In comparison, the energy production and conversion was dominated by Deltaproteobacteria affiliated with Desulfobacterales, Synthrophobacterales, Desulfovibrionales and Desulfuromonadales (Supplementary Table V).\u003c/p\u003e \u003cp\u003eThese occurrence patterns for taxa related to COGs throughout the shallow to deep depth gradient were consistent among samples. For instance, amino acid transport and metabolism COGs related to threonine synthase, aspartate aminotransferase, glutamate synthase, amino acid transporter permease, cysteine desulfurase, methionine synthase, tryptophan synthase were distributed among distinct taxa where for the C10 and D18 COGs were mostly affiliated with the Candidate divisions and Deltaproteobacteria, and for the C14 were mostly affiliated with the Alphaproteobacteria and Nitrososphaeorota. Similarly, for energy production and conversion COGs related to ferredoxin, succinate-CoA ligase, formate dehydrogenase, fumarate reductase, and lactate dehydrogenase were mostly affiliated with the Chloroflexota, Alphaproteobacteria, and Nitrososphaerota (Supplementary Table VI).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePotential for hydrocarbon metabolism\u003c/h2\u003e \u003cp\u003eFurthermore, to gain insight into the microbial community's potential to strive under a hydrocarbon rich environment, we searched for marker genes involved in aerobic and anaerobic hydrocarbon degradation, as well as those genes involved in the metabolism of contaminants derived from hydrocarbons \u003cem\u003ei.e.\u003c/em\u003e aromatic cleavage, arsenite, benzoate, phenol and toluene degradation, C1 and methane metabolism and N-C degradation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The taxonomic affiliation of genes involved in hydrocarbons metabolism was distributed primarily among the Acidobacteriota (2%), Actinobacteriota (7%), Candidate divisions (4%), Chloroflexota (2%), Bacillota (8%), Gemmatimonadota (2%), Nitrospirota (6%), Planctomycetota (3%), and Alpha-, Beta-, Delta- and Gammaproteobacteria (10%, 2%, 25% and 14%, respectively). However, we observed specific taxa dominated genes\u0026rsquo; affiliation according to sample location, such as the Deltaproteobacterial genes were the most abundant at stations C10 and D18 (717 and 1,320 m) with a total relative abundance of 0.15\u0026ndash;0.3%, respectively. In comparison, the Alphaproteobacterial genes were the most abundant at station C14 (3,205 m) with a total relative abundance of 0.15% respectively. For station B7, genes were uniquely affiliated with the Betaproteobacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA; Supplementary Table VII).\u003c/p\u003e \u003cp\u003eWithin genes for hydrocarbon degradation we observed the CO dehydrogenase/acetyl-CoA synthase complex (\u003cem\u003eacsB\u003c/em\u003e), haloalkane dehalogenase (\u003cem\u003edha\u003c/em\u003e), 50S ribosomal protein L3 (\u003cem\u003erplC\u003c/em\u003e) and 50S ribosomal protein L5 (\u003cem\u003erplE\u003c/em\u003e); nitroalkanes degradation the 2-nitropropane dioxygenase (\u003cem\u003encd\u003c/em\u003e) gene; benzoate degradation genes such as 4-hydroxybenzoate 3-monooxygenase (\u003cem\u003ephbh\u003c/em\u003e), 4-hydroxybenzoate polyprenyltransferase (\u003cem\u003eubiA\u003c/em\u003e) and acetyl-CoA C-acyltransferase (\u003cem\u003eacaB\u003c/em\u003e); phenol degradation genes such as 2-octaprenyl-6-methoxyphenol hydroxylase (\u003cem\u003eubiH\u003c/em\u003e) and acetoin:2,6-dichlorophenolindophenol oxidoreductase (\u003cem\u003eacoA\u003c/em\u003e); toluene and arsenite resistance including toluene tolerance protein (\u003cem\u003ettg2D\u003c/em\u003e), and arsenical-resistance protein (\u003cem\u003ears\u003c/em\u003e), arsenite methyltransferase (\u003cem\u003eAs3MT\u003c/em\u003e) and arsenite transporter (\u003cem\u003eacr\u003c/em\u003e); C1-metabolism genes such as formylmethanofuran dehydrogenase (\u003cem\u003efmd\u003c/em\u003e); formylmethanofuran\u0026ndash;tetrahydromethanopterin formyltransferase (\u003cem\u003eftr\u003c/em\u003e), methanol dehydrogenase (\u003cem\u003emdh\u003c/em\u003e), methenyltetrahydromethanopterin cyclohydrolase (\u003cem\u003emch\u003c/em\u003e), methylenetetrahydromethanopterin dehydrogenase (\u003cem\u003emtd\u003c/em\u003e) and tetrahydromethanopterin S-methyltransferase (\u003cem\u003emtrA\u003c/em\u003e); aromatic ring cleavage genes including nitronate monooxygenase (\u003cem\u003enmo\u003c/em\u003e; aromatic ring cleavage), quercetin 2,3-dioxygenase (\u003cem\u003eyhhW\u003c/em\u003e); and within the Rieske super family the gene coding for Rieske 2Fe-2S domain-containing protein (\u003cem\u003eambt\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eOverall, hydrocarbon metabolism genes were not evenly distributed among locations and were observed in low abundances within samples (\u0026lt;\u0026thinsp;1% relative abundance from the total number of genes). Overall, genes related to aromatic cleavage, arsenite, benzoate, and toluene degradation, C1-methane and hydrocarbon metabolism were mostly identified in stations C10 and D18; in addition, N-C degradation, nitrogen, phenol degradation and Rieske metabolism were mostly observed in C14 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). As an example, \u003cem\u003emch\u003c/em\u003e was only observed at station C10, while \u003cem\u003edha\u003c/em\u003e, \u003cem\u003eacr\u003c/em\u003e and \u003cem\u003enmo\u003c/em\u003e were only observed at station D18, and \u003cem\u003eambt\u003c/em\u003e, \u003cem\u003eubiH\u003c/em\u003e, \u003cem\u003eftr\u003c/em\u003e and \u003cem\u003ephbh\u003c/em\u003e were uniquely observed at station C14. In comparison, \u003cem\u003eyhhW\u003c/em\u003e was the only gene associated with hydrocarbon metabolism found at station B7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Regarding genes\u0026rsquo; abundance, 91% of the total number of genes associated with hydrocarbons metabolism showed a relative abundance ranging from 0.01\u0026ndash;0.08%, except for \u003cem\u003eacsB\u003c/em\u003e that peaked in abundance (0.23%) at station D18, \u003cem\u003erplE\u003c/em\u003e that peaked in abundance (0.11%) at station C10, and \u003cem\u003eyhhW\u003c/em\u003e that peaked in abundance (0.23%) at station B7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHere we analyzed amplicon and metagenomic information from deep-sea sediment samples taken in the GoM Mexican EZZ. Results allowed us to identify the main bacterial and archaeal taxa inhabiting the deep sediments of the Perdido and Coatzacoalcos regions, as well as differences in the gene composition and metabolic potential primarily in relation to alternative carbon, energy and hydrocarbon metabolisms.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental profiling of deep-sea sediments\u003c/h2\u003e \u003cp\u003eCommonly, the redox potential is considered as a readily descriptor of organic matter and bacterial activity as by definition it is the oxidizing or reducing capacity of a system based on the vertical distribution of electron acceptors, i.e. O\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, Mn, Fe, SO\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e. Thus, redox potential could also be used as a proxy for aerobic \u003cem\u003evs.\u003c/em\u003e anaerobic conditions, which may be relevant for microbial community composition and activity. Surveys in marine sediments suggest microbial communities have a differential response to environmental features which in turn could result in inhibiting or promoting their metabolic activity towards different substrates. For instance, redox potential combined with aliphatic hydrocarbon concentration showed to influence the taxonomic composition of the rare taxa (\u0026lt;\u0026thinsp;1% abundance) in deep-sea sediments in the GoM impacted by high PAHs contamination and heavy metals (S\u0026aacute;nchez-Soto et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, we observed that the largest number of unique ASVs corresponded to shallow locations where redox potential showed varying values according to depth. For instance, locations near to C10 (717 m) and D16 (697 m) with redox values from \u0026minus;\u0026thinsp;27.7 to -54.8mV, which would commonly correspond to anaerobic conditions, showed unique ASVs affiliated with ​​the classes Nanoarchaeia, Phycisphaerae and Anaerolineae. In comparison, locations near D18 (1320 m) showed higher redox values (178.8mV) which in addition showed the most atypical taxonomic profile with unique ASVs affiliated with the Desulfobacterales, Desulfobulbales, Dehalococcoidia, Thermodesulfovibrionia, and the Nanoarchaeia, Phycisphaerae and Anaerolinea as well. Similarly, the northern GoM shelf and slope locations (up to 1,200 m depth) showed lower redox conditions (-136 to 188 mV) that corresponded with high metals (Al and Pb, particularly) and PAH concentrations, where \u003cem\u003eDesulfarculaceae\u003c/em\u003e, \u003cem\u003eDesulfoeraceae\u003c/em\u003e, \u003cem\u003eSyntrophobacteraceae\u003c/em\u003e, \u003cem\u003eNitrospiraceae\u003c/em\u003e, \u003cem\u003eAnaerolinaceae\u003c/em\u003e, and \u003cem\u003eDehalococcoidia\u003c/em\u003e were significantly more abundant (S\u0026aacute;nchez-Soto et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding organic matter and organic carbon content, values previously reported in the southern GoM sediments, 1.15\u0026ndash;2.9% and 0.66\u0026ndash;1.67% (S\u0026aacute;nchez-Soto et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Godoy-Lozano et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) respectively, fall within the range concentrations we observed in the studied area (1.5% average organic matter and 0.85% average organic carbon). In the southern GoM sediments, low organic matter content combined with high PAHs availability and low redox potential define the geochemical landscape and have shown to drive microbial composition and potentially the community\u0026rsquo;s activity (Godoy-Lozano et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For instance, recent observations in the southern GoM deep sediments environmental parameters such as organic matter, hydrocarbons and redox potential largely contributed to structuring microbial communities (S\u0026aacute;nchez-Soto et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Godoy-Lozano et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), in which these environmental conditions may favor microbial groups such as those identified as unique taxa that are recently considered as a relevant role player in hydrocarbon degradation, aromatic compounds oxidation, to be resistant to metals and to use multiple electron acceptors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMicrobial community composition\u003c/h2\u003e \u003cp\u003eAmplicon sequencing results allowed us to identify differences in the presence and abundance of a myriad of prokaryotic microorganisms in comparison to previous studies carried out in the Gulf of Mexico. Differences in the presence and abundance of microbial species have been commonly explained with respect to the water column depth gradient (with their respective variations in temperature and pressure) (Orcutt et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In our study, species richness was higher at depths above 1,000 m, as suggested by S\u0026aacute;nchez-Soto et al (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) that microbial communities present in sediments at these depths are richer in species. In this sense, it is possible that shallower sediments could be influenced by the influx of organic matter and other compounds from the continental and oceanic surface so that microbial community composition is affected by diverse environmental factors in comparison to those from deeper sediments. In comparison, species richness values in deep stations (more than 1,000 m) are concurrent with what was expected according to Jochens and DiMarco (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), where sediments in deep zones are less diverse than shallow ones. Overall our results support what was suggested by Covarrubias-Rodr\u0026iacute;guez (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), that the microbial diversity in deeper sediments of the Gulf of Mexico is homogeneous in terms of microbial composition.\u003c/p\u003e \u003cp\u003eRegarding the microbial community composition, we observed it was constituted by characteristic phyla including the Pseudomonadota and Thaumarchaeota (Covarrubias-Rodr\u0026iacute;guez, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The Pseudomonadota phylum was consistently the most abundant (up to 30% average relative abundance) in all stations. Members of this phylum are considered of importance for deep sediments of the region as they are associated with nitrogen fixation using sulfur compounds as an oxidizing agent (Battistuzzi and Hedges, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), a metabolic feature relevant to redox gradients found in sediments. For instance, Deltaproteobacteria are closely related to biogeochemical cycles (Battistuzzi and Hedges, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Members of the Desulfobacteraceae are usually involved in the pathways of sulfate reduction, nitrogen fixation, and methane oxidation; and like the Syntrophyobacteraceae, that could be associated with the degradation of hydrocarbons (Vigneron et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The second most abundant phylum was the Thaumarchaeota, which are nitrifying chemolithotrophic archaea commonly found in marine environments (Pester et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). We observed an increase in their relative abundance with depth, potentially because the Thaumarchaeota are better adapted to survive in environments with low energy fluxes and higher seafloor pressures (Valentine, 2010). Although the Proteobacteria and Thaumarchaeota were the most abundant taxa within domains, we observed differences in their distribution and abundance as a function of depth. At depths greater than 1,000 there is an increase in the abundance of the Rhodospirillales within the Alphaproteobacteria. Rhodospirillales could grow chemoheterotrophically in the dark or heterotrophically under aerobic or microaerobic conditions (Baldani et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Furthermore, the presence of Rhodospirillales could be related to the anaerobic fermentation of lactate to acetate which is then used by Archaea species to produce methane (Madigan and Martinko, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In addition, community members affiliated with Marine Group I within the Thaumarchaeota and members of the Euryarchaeota phylum abound in sediments collected at depths greater than 2,000 m. It has been reported that Thermoplasmatales within the Euryarchaeota inhabit deep sediments or hydrothermal vents as they have the potential for nitrate reduction, methanogenesis, and methane oxidation (Madigan et al., 2010). Worth noting, at station C13 we observed the highest abundance of this taxa. Covarrubias-Rodr\u0026iacute;guez (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) suggested that these microorganisms could be methane oxidizers and that they are found in a mutualistic relationship (syntrophy) with other sulfate-reducing archaea, in this case, potentially those belonging to Marine Group I.\u003c/p\u003e \u003cp\u003eOverall depth is the variable that could best explain the differences in the taxonomic profiles observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001), however the taxonomic profile at the D18 station does not seem to depend on this factor. For instance, stations A3, C12 and D18 were sampled at a depth range between 1320 and 2390m and are located along Perdido and Coatzacoalcos, of which stations A3 and C12 have a similar diversity and taxonomic profile contrasting with that of station D18, that resulted different from the rest of the stations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). These differences could be attributed to the station's specific environmental factors i.e. closeness to oil natural sources. Studies have shown that the distance to oil seeps i.e. within a 0\u0026ndash;15 m distance could influence the occurrence and abundance of certain bacterial phyla changing the microbial community composition within those. For instance, at station D18, members of the Anaerolineales, Desulfobacterales and Syntrophobacterales were identified, which were previously reported as characteristic microorganisms of hydrocarbon emanation sites (methane and oil) in the northern Gulf of Mexico (Vigneron et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, Vigneron et al, mention that the members of Anaerolineae could have genes related to the degradation of hydrocarbons (such as naphthalene) and to the reduction of sulfates. Although we cannot ensure that sediments were taken from an emanation site, the site's taxonomic fingerprint suggests community composition at this station could be driven by hydrocarbon sources availability. For instance, the presence of Aminicenantales in D18 could be another indicator of the proximity of a hydrocarbon source as these bacteria abound in sites contaminated by hydrocarbons or hydrothermal vents, in addition, they inhabit sites with low oxygen levels and participate in the degradation of carbon to methane in conjunction with Planctomycetota species including the Phycisphaerae such as MSBL9 (Farag et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Robbins et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eMetabolic potential for microbial communities to couple Amino acids metabolism and Energy production with hydrocarbon degradation throughout a depth gradient\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOverall, evaluating the metabolic potential of microbial communities could underline a myriad of genes that are related to distinct metabolic pathways that could result in synergistic functions for the communities to function efficiently in nutrient-limited environments (Biggs et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Here we evaluated metagenomic information that showed the microbial communities from the deep-sea sediments have the potential to couple different metabolic traits to sustain communities\u0026rsquo; carbon and energy requirements throughout depth gradients.\u003c/p\u003e \u003cp\u003eResults showed similarities with previous observations describing the microbial community functional core in the GoM deep sediments in relation to genes being distributed among different classes of metabolism that varied in abundance with depth (Torres-Beltr\u0026aacute;n, et al., 2022). In the present study, amino acid metabolism constituted up to 13% of total identified COGs, followed by the energy production and conversion (12.7%), showing its maximum abundance at deep and shallow depths, respectively. Particularly, here we observed among the amino acids metabolism the presence of the aspartate aminotransferase and cysteine desulfurase, which are particularly relevant for hydrocarbon and sulfur metabolism in the GoM sediments. For instance, the cysteine desulfurase allows the utilization of L-cysteine as a source of sulfur atoms and it has been associated with the formation of intracellular [Fe-S] clusters (Fontcave and Ollagnier-de-Chaudens, 2008), which are relevant for toluene and naphthalene degradation. Toluene and naphthalene dioxygenases are multicomponent enzymes with an active site containing a reductase and a ferredoxin; the latter with a Rieske-type [2Fe-2S] iron-sulfur redox center, where the iron ions have two cysteine ligands (Haddock, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In addition, the aspartate aminotransferase is an ubiquitous enzyme that catalyzes the conversion of aspartate and α-ketoglutarate to oxaloacetate and glutamate, which can be connected to the TCA cycle as substrates that could facilitate microbial growth in deep marine sediments (reviewed in Torres-Beltran et al., 2022). Further, for the energy production and conversion COG categories we noted the presence of the succinate-CoA ligase, formate dehydrogenase, fumarate reductase, and lactate dehydrogenase, which are related to carbon cycling pathways. For instance, the succinate-CoA ligase is an enzyme that converts succinyl-CoA to succinate as an intermediate for the TCA cycle, while the formate dehydrogenase catalyzes the oxidation of formate to carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e); the latter can be further fixated through major pathways including the Ribulose Monophosphate Pathway (RuMP) and the reductive acetyl-CoA pathway (Wood-Ljungdahl pathway) that are relevant for derived carbon fixation from C1 metabolism and occur in many oligotrophic environments (Zhou et al., 2019; Lazar et al., 2016).\u003c/p\u003e \u003cp\u003eIn fact, microbial community function may be influenced by geochemical variables in the sediments including organic matter content and carbon sources i.e. hydrocarbons. Godoy-Lozano, et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) suggested two geochemical variables with higher contributions to microbial community structure in sediment samples from the southern GoM were aromatic hydrocarbons and depth. In fact, natural leaks of oil are widely distributed and are suggested to have a basal hydrocarbon degrading microbial community (Godoy-Lozano, et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Hydrocarbon degrading bacteria in the GoM has been characterized, based on 16S rRNA gene analyses, to fall within 16 predominant genera in the sediment that could be distributed along the northwestern and southwestern regions of the GoM (Ramirez et al., 2020; Rodriguez-Salazar et al., 2021; Raggi et al., 2021; Godoy-Lozano et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e ). In our study, we compared the potential for hydrocarbon metabolism in deep sediments among microbial groups following a strategy based on searching functional marker genes in curated databases that included gene package files for sulfur, nitrogen, methane and hydrocarbon metabolism, and that have been previously used in sediment samples from the Gulf of Mexico (Boyd et al., 2018; Raggi et al \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious observations focused on studying the hydrocarbon degradation for the Coatzacoalcos region reported for methane metabolism genes related to methanogenesis/AOM pathway (\u003cem\u003efmd, ftr, mch, mtd, mer and mtr\u003c/em\u003e), while for aerobic hydrocarbon metabolism, they observed the alkB gene and for anaerobic metabolism the bssA-like (Rieske family) gene, both primarily at D18 (Raggi et al., 2021). In our study, genes related with C1 and methane metabolism and hydrocarbon degradation were distributed along a depth gradient showing greater abundance at stations C10, D18 and C14, where D18 (1,320m) and C14 (3,205m) showed the highest diversity of searched genes. For instance, we observed genes related to hydrocarbon degradation (\u003cem\u003erplC\u003c/em\u003e, \u003cem\u003erplE\u003c/em\u003e and \u003cem\u003eacsB\u003c/em\u003e), as well as those for benzoate and phenol degradation (\u003cem\u003ephbh, ubiA, acaB, ubiH, acoA\u003c/em\u003e), toluene and arsenite resistance (\u003cem\u003ettg2D, ars, As3MT, acr\u003c/em\u003e), aromatic ring cleavage (\u003cem\u003enmo, yhhW\u003c/em\u003e) and Rieske family (\u003cem\u003eambt\u003c/em\u003e), that overall contrasted with what has been previously observed in sediments along this region of southern GoM (Raggi et al., 2021). Nonetheless, combined observations generated to date in the GoM contribute to understanding in greater detail the alternative mechanisms that allow carbon fixation in sediments in relation to hydrocarbon metabolism.\u003c/p\u003e \u003cp\u003eIn particular, our observations suggest beyond standard carbon fixation pathways i.e. Wood-Ljungdahl (WL) pathway, mediated by enzymes such as the carbon monoxide dehydrogenase (AcsB), formylmethanofuran dehydrogenase (\u003cem\u003efmd)\u003c/em\u003e, formylmethanofuran tetrahydromethanopterin formyltransferase (\u003cem\u003eftr)\u003c/em\u003e and methenyltetrahydromethanopterin cyclohydrolase (\u003cem\u003emch)\u003c/em\u003e identified at D18, there is a potential link between amino acids and energy production and conversion metabolisms to be coupled with hydrocarbon degradation, which in turn may occur as a multifunctional metabolic strategy by microbial communities in the GoM deep sediments. Namely, the occurrence of genes related to sulfur sources i.e. cysteine desulfurase that favors the intracellular [Fe-S] clusters for Riske-family proteins, mainly affiliated with the Rhizobiales and Rhodobacterales within the Alphaproteobacteria. In addition, we observed the occurrence of succinate-CoA ligase that mediates the succinate conversion from succinyl Co-A. The effect of succinate as co-substrate for hydrocarbon degradation has been previously reported in biodegradation surveys (Wang et al., 2021), which suggested co-substrates improved the activity of inducible enzymes with higher affinity for PAHs. These biodegradation experiments amended with succinic acid and phthalic acid proven to enhance PAHs degradation, with concomitant increase in amino acid metabolism as well as potentially lead to mutualistic positive interactions among microbial community members (Wang et al., 2021).\u003c/p\u003e \u003cp\u003eFurthermore, we observed shifts in the microbial taxa affiliated with COGs within these metabolisms, suggesting a potential succession in the microbial community composition due to a geochemical gradient throughout depth. In this study, shifts in abundance were observed for the Alpha and Deltaproteobacteria, Chloroflexota and Candidate division, and Euryarchaeota and Thaumarchaeota. Previous studies have suggested shifts in microbial community structure may be driven by PAH content and concentration, considering the depth variation, in the swGoM (Godoy-Lozano et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Suarez-Moo et al ., 2020; Bacosa et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In accordance with our observations, the Alpha and Gamma proteobacteria have been considered among the abundant classes in the swGoM sediments, which in addition to the Chloroflexota have shown to be metabolically versatile and harbored potential pathways for hydrocarbon degradation and respiratory processes (Hug et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dombrowski et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rahmeh et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Particularly, the Alpha and Deltaproteobeacteria dominated the COG genes for these metabolisms, showing up to 60\u0026ndash;70% abundance, where Deltaproteobacterial genes were more abundant at C10 and D18 progressively shifting to Alphaproteobacteria genes at C14, showing a similar distribution with hydrocarbon metabolism-related genes for these two classes. Studies showed their distribution in diverse environments is mainly controlled by carbon concentration where the Alphaproteobacteria have shown a greater potential to use more recalcitrant carbon compounds (Frexia et al., 2016; Sebastian et al., 2021). For instance, differences in amino acid and carbohydrates metabolism by Alphaproteobacteria have been observed in marine water column compartments where substrate uptake is greater at the bathypelagic zone, where amino acid-like material accounts for the largest component of organic matter (Sebastian et al., 2021).\u003c/p\u003e \u003cp\u003eMoreover, regarding hydrocarbon metabolism, Zhang et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) observed the Alpha and Deltaproteobacteria became the dominant taxa when sediments were amended with PAHs sources i.e. pyrene at different stages of the incubation period. For instance, microbial succession showed the Deltaproteobacteria dominating the bacterial community at the early stage of incubation when pyrene concentration was higher, while the Alphaproteobacteria became more abundant at the late stage of the incubation period when alternative carbon sources may have resulted available because of the hydrocarbon degradation process (Zhang et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, Alpha and Deltaproteobacteria distribution has been previously examined in deep-sea sediments from the GoM to evaluate hydrocarbon aerobic and anaerobic metabolism. Kimes et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) observed that Alphaproteobacteria (predominantly the Rhizobiales and Rhodobacterales orders, also observed in the present study), peaked in abundance in samples furthest from the oil rig, while Deltaproteobacteria (Desulfobacterales, Desulfovibrionales and Desulfuromonadales) exhibited higher abundances in samples closest to the oil rig. Deltaproteobacteria abundance has also been associated with higher levels of PAHs, and detectable alkanes and alkenes that are commonly found in oil sources. In the GoM, Deltaproteobacteria could be considered as a fingerprint class for the presence of hydrocarbon sources (Kimes et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudies based on the use of gene annotation or predicted functional profiles suggest functional redundancy could occur for marine microbial communities globally, where metabolic pathways may be spread across taxa so that different microbial species conduct the same set of enzymatic reactions (Galand et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Galand et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) suggest partial redundancy could be considered when organisms that share some specific function coexist but may nevertheless differ in other ecological requirements. In addition, partial functional redundancy has been suggested to be related to bacterial communities\u0026rsquo; taxonomic and genomic richness, which in addition could be linked to environmental fluctuations. Based on our observations for the set of identified COGs we suggest functional redundancy for the Amino acid and Energy production metabolisms may occur in the GoM deep sediments where ecological niches could likely be distributed among microbial taxa such as the Alpha and Deltaproteobacteria. For instance, the fact that these genes could be identified in microbial communities from different sampling sites within an environment indicates the possible presence of a partial functional redundancy for amino acid and energy production metabolism while linked to hydrocarbon metabolism carried out by Alpha and Deltaproteobacteria, which occurrence and abundance could be related with environmental factors. In the GoM particular scenario, for natural and anthropogenic hydrocarbon sources, we could pinpoint how important is within a site microbial community composition and structure as a mean to ensure environment resilience, while at a physical gradient driven by temperature, pressure, and chemical characteristics define geographical regions that ultimately may allow for metabolism being distributed among microbial taxa that may conduct the same function to preserve their community\u0026acute;s metabolic network. Thus, further surveys including network analysis describing microbial communities' functional dynamics as well as genome amplified genomes could step forward into elucidating community-level metabolic models that could account for ecological traits in microbial communities summing up to the environment's function.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eCombined amplicon sequence and metagenomic information suggest microbial community composition and potential function follow a gradient according to depth and that proximity to hydrocarbon sources may contribute to shaping them. In addition, our findings suggest that alternative carbon and energy metabolisms could potentially be coupled with hydrocarbon degradation. Here, we provide evidence suggesting metabolic niches in deep sediments could be shifted in relation to taxa succession along environmental gradients showing the metabolic capacity for coupling different metabolic functions by a natural microbial consortium showing multifunctionality. Our results sum into understanding how widespread the distribution of genes for hydrocarbon degradation could be in a shifting microbial community as well as the alternative coupling of metabolisms may contribute to efficiently utilize growth substrates in deep-sea marine sediments.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was supported by the Mexican National Council for Science and Technology (CONACyT)-Mexican Ministry of Energy (SENER)-Hydrocarbon Fund (project 201441). CONACYT awarded C. B. with a M. Sc. scholarship and M. H.-G. with a Postdoctoral scholarship. This is a contribution of the Gulf of Mexico Research Consortium (CIGoM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe would like to thank all the members of crew of the R/V Justo Sierra for their contribution to the MMF-01 successful oceanographic campaign. We acknowledge PEMEX\u0026rsquo;s specific request to the Hydrocarbon Fund to address the environmental effects of oil spills in the Gulf of Mexico.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndrews S (2019) FastQC: A quality control tool for high throughput sequence data. Babraham Institute. 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Particularly in marine sediments, microorganisms play an essential role in biogeochemistry, sparking interest in understanding their metabolism for biotechnological applications such as bioremediation. Genomic techniques have enabled detailed exploration of microbial communities in the GoM, revealing rich diversity and functional potential, particularly in hydrocarbon degradation. Studies have shown depth, temperature, and dissolved oxygen gradients significantly influence microbial community composition and metabolic pathways. Research indicates microbial consortia, rather than individual species, are key in pollutant degradation, emphasizing the importance of community dynamics. Our study evaluated the prokaryotic microbial community in deep-sea GoM sediments, under a depth gradient, in Coatzacoalcos and Perdido regions, two areas influenced by crude-oil efflux and petroleum extraction. Findings showed associations between community composition, depth, and metabolic potential, showcasing microbial adaptation to deep-sea nutrient-limited conditions. Results suggest functional redundancy in amino acid and energy production metabolisms among microbial taxa like Alpha and Deltaproteobacteria. This underlines the importance of microbial community shifts in composition and structure in ensuring environmental resilience. This research contributes to advancing our understanding of alternative carbon and energy metabolisms linked to hydrocarbon degradation that are widely distributed across different microbial communities inhabiting deep-sea marine sediments.\u003c/p\u003e","manuscriptTitle":"Alternative carbon and energy metabolisms linked to hydrocarbon degradation are widely distributed across the different microbial communities from deep-sea sediments of the Gulf of Mexico","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-28 13:17:19","doi":"10.21203/rs.3.rs-6754051/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":"313ea159-a1aa-41e9-a5c3-0e02d93248af","owner":[],"postedDate":"May 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-28T13:17:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-28 13:17:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6754051","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6754051","identity":"rs-6754051","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}