# Integrative genomic and transcriptomic analyses reveal marine Actinomycetota adaptations for hydrocarbon degradation

Bashar Haruna Gulumbe, Cristiana Cravo-Laureau, Robert Duran

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Bashar Haruna Gulumbe, Cristiana Cravo-Laureau, Robert Duran. Integrative genomic and transcriptomic analyses reveal marine Actinomycetota adaptations for hydrocarbon degradation. Environmental Technology and Innovation, 2025, 40, pp.104361. 10.1016/j.eti.2025.104361. hal-05162802

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Environmental Technology & Innovation

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# Integrative genomic and transcriptomic analyses reveal marine Actinomycetota adaptations for hydrocarbon degradation

Bashar Haruna Gulumbe<sup>a,b</sup>, Cristiana Cravo-Laureau<sup>a</sup>, Robert Duran<sup>a,*</sup>

## ARTICLE INFO

**Keywords:**

Bioremediation

Rhodococcus

Gordonia

Janibacter

Transcriptomics

Genomics

Oil pollution

Hydrocarbon

## ABSTRACT

Marine oil pollution poses critical ecological challenges, necessitating innovative and sustainable bioremediation strategies. This study provides a comprehensive multi-omics investigation into a synthetic coastal marine consortium comprising four strains belonging to Actinomycetota, including *Rhodococcus* sp. strain 1Y, *Gordonia* sp. strain BP1o, and two *Janibacter indicus* strains isolated from hydrocarbon-contaminated sediments. Genomic analysis identified multiple orthologous gene clusters associated with hydrocarbon degradation, encompassing pathways such as *alkB*, *cyp* and *nah*. *Rhodococcus* sp. strain 1Y exhibited the highest number of these hydrocarbon-degradation gene clusters, highlighting its robust genetic potential and versatility in metabolizing diverse hydrocarbons. Secondary metabolite analysis identified stress-related biosynthetic pathways, including ectoine and siderophore production, supporting hydrocarbon solubilization and enzymatic activity. In a medium containing a mixture of phenanthrene and hexadecane (50 mg/L each) as the sole source of carbon, the consortium demonstrated rapid hydrocarbon degradation, with nearly complete degradation of hexadecane (reduced to 2.76 %) and partial degradation of phenanthrene (reduced to 36.56 %) by Day 12. Transcriptomic profiling across time points revealed dynamic shifts in gene expression, with 502 differentially expressed genes between Day 3 and Day 12, of which 487 were downregulated, enriched in pathways associated with xenobiotics biodegradation, lipid metabolism, and membrane transport. Functional annotations highlighted the transcriptional activation of metabolic pathways enabling hydrocarbon uptake, breakdown, and stress adaptation. This integrated approach connects genomic potential with functional performance, emphasizing the metabolic versatility and ecological resilience of Actinomycetota for oil-spill bioremediation. These findings advance our understanding of microbial hydrocarbon degradation and offer actionable insights for developing eco-friendly remediation strategies.

## 1. Introduction

Oil pollution in marine environments exerts severe consequences on both ecosystem function and the health of aquatic organisms, necessitating eco-friendly remediation strategies (Genovese et al., 2014; Pribadi et al., 2023). Beyond acute toxicity, oil spills can cause long-term disruptions to marine food webs, alter microbial community structure, and lead to persistent contamination of sediments and coastal habitats (Gong et al., 2014; Vadillo Gonzalez et al., 2019). Identifying efficient hydrocarbon-degrading bacteria remains a

E-mail address: robert.duran@univ-pau.fr (R. Duran).

https://doi.org/10.1016/j.eti.2025.104361

Received 3 May 2025; Received in revised form 29 June 2025; Accepted 2 July 2025

Available online 5 July 2025

2352-1864/© 2025 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

<footnote><sup>a</sup> Université de Pau et des Pays de l'Adour, CNRS, IPREM, PAU, France</footnote>

<footnote><sup>b</sup> Department of Microbiology, Federal University Birnin-Kebbi, Nigeria</footnote>

<footnote>* Corresponding author.</footnote>
priority in this search for sustainable solutions (Bargiela et al., 2015; Omarova et al., 2019; Vila et al., 2015). Marine coastal sediments harbor diverse microbial communities that play pivotal roles in degrading environmental pollutants, particularly hydrocarbons resulting from oil spills and industrial discharges (Cravo-Laureau and Duran, 2014; Duran et al., 2015b). Among these microbes, Actinomycetota stand out due to their remarkable enzymatic pathways capable of degrading both aliphatic compounds and aromatic compounds (Fernandes et al., 2025), explaining their presence in marine polluted areas (Duran et al., 2015a). Equipped with high-GC genomes that confer resilience and metabolic versatility, these bacteria possess a specialized genetic repertoire enabling the metabolism of diverse hydrocarbon compounds, making them ideal candidates for bioremediation efforts (Fernandes et al., 2025). Microbial bioremediation offers distinct advantages, including self-sustaining degradation activity, adaptability to complex pollutant mixtures, and the potential for cost-effective application in situ (Lofrano et al., 2017).

Genomic and experimental studies have identified key genes and their associated enzymes, such as alkane monooxygenase (alkB) and flavin-binding monooxygenase (almA) for alkane metabolism, as well as catechol dioxygenase (catA) and naphthalene 1,2-dioxygenase (ndoABCR) for aromatic hydrocarbon degradation (Kumari et al., 2021; Muriel-Millán et al., 2019; Rojas-Vargas et al., 2024; Wang and Shao, 2014). These genes highlight the extensive metabolic adaptability of hydrocarbon-degrading organisms, reflecting the evolutionary and functional significance of their pathways. Studies on *Rhodococcus* strains have highlighted numerous gene clusters responsible for alkane hydroxylation and polycyclic aromatic hydrocarbon (PAH) ring cleavage (Martínková et al., 2009), while *Gordonia* strains have been reported as efficient degraders of long-chain alkanes (Ehiosun et al., 2022; Kim et al., 2018). Similarly, studies revealed *Janibacter* strains are capable of degrading a wide range of hydrocarbons including PAH (Yamazoe et al., 2004; Zhang et al., 2009). Despite extensive research on hydrocarbon-degrading bacteria, studies often focus on single genera under controlled or limited environmental conditions. This narrow focus overlooks the dynamic and interactive nature of microbial consortia in real-world settings. Specifically, how diverse genera such as *Rhodococcus*, *Gordonia*, and *Janibacter* collectively adapt to hydrocarbon-rich marine environments remains poorly explored. Moreover, the lack of integrative comparative genomic and transcriptomic analyses across these taxa restricts our ability to design robust, ecosystem-specific bioremediation strategies. Addressing this gap is essential to advance our understanding of microbial adaptation and optimize interventions for mitigating the environmental impact of oil pollution.

To address these limitations, we studied four hydrocarbon-degrading strains from contaminated marine sediments: one *Rhodococcus*, one *Gordonia*, and two *Janibacter* strains. These isolates were selected for their demonstrated capacity to degrade a broad spectrum of hydrocarbons, providing a well-defined system for investigating the genetic and functional adaptations that enable survival and metabolic activity in hydrocarbon-rich environments. To gain initial insights into their metabolic potential, we first performed whole-genome sequencing, which revealed a diverse repertoire of catabolic genes associated with hydrocarbon degradation. Building on this, we hypothesize that degradation occurs through the coordinated expression of key metabolic pathways. We anticipate the involvement of alkane hydroxylation, aromatic hydrocarbon breakdown, and oxidative stress responses as central mechanisms in this process. To test this hypothesis, we conducted a transcriptomic analysis to characterize the temporal regulation of these pathways, aiming to elucidate the shifts in gene expression that underlie hydrocarbon metabolism over time. This study represents one of the first integrative multi-omics investigations of a synthetic coastal marine consortium comprising *Rhodococcus* sp., *Gordonia* sp., and *Janibacter indicus* strains. This integrative approach, combining genomic data from DNA and transcriptomic data from RNA, reveals how marine Actinomycetota adapt at the molecular level to degrade hydrocarbons. It links specific genetic elements to their functional activity in a complex environmental context. The findings of this study will advance our understanding of the genetic and regulatory mechanisms underlying hydrocarbon degradation and pave the way for the development of tailored bioremediation strategies. Through pinpointing the genetic and physiological features that improve degradation efficiency, this research informs the development of more effective remediation strategies to combat hydrocarbon pollution in coastal marine areas.

## 2. Materials and methods

### 2.1. Strains and cultivation

Four bacterial strains, including *Rhodococcus* sp. strain 1Y, *Gordonia* sp. strain BP1o, *Janibacter indicus* strain 70, and *Janibacter indicus* strain 22, were selected from the IPREM laboratory collection based on their previously demonstrated potential for hydrocarbon degradation. These strains were originally isolated from sediments of the Etang de Berre coastal lagoon in southern France (43°29'05"N; 5°11'17"E), a site which salinity can vary from less than 8 to more than 20 g/L, and which has been impacted by intensive industrial activity and oil refinery waste for the past five decades (Hernandez-Raquet et al., 2006). During a preliminary evaluation, each strain exhibited measurable degradation activity but at different levels depending on the hydrocarbon compound used as carbon source, suggesting that better degradation can be obtained with a mixture of the strains. So, the consortium consisting by the mixture of the four strains exhibited higher degradation rates than the strains tested individually. Thus, we decided to explore the gene expression of the consortium by transcriptomics during hydrocarbon degradation. The strains were cultivated on Luria-Bertani (LB) agar plates, then pure cultures were transferred to LB broth and incubated at 28 °C with shaking at 150 rpm to achieve active growth.

### 2.2. DNA extraction and 16S rRNA phylogenetic analysis

Genomic DNA was extracted from overnight cultures using the Qiagen DNeasy Blood & Tissue Kit following the manufacturer's protocol to ensure high-purity DNA suitable for downstream applications. To confirm the taxonomic identity of each strain, the 16S ribosomal RNA gene region (16S rRNA) was PCR-amplified using universal primers 63 F (5'-AGAGTTTGATCCTGGCTCAG-3') and
1387 R (5'-GACGGGCGGTGTGTACAAG-3') (Marchesi et al., 1998). PCR products were purified using the Qiagen QIAquick PCR Purification Kit and sequenced by Sanger sequencing (Eurofins Genomics, Germany). Sequencing data were analyzed and compared against the NCBI 16S rRNA gene database for taxonomic identification.

Phylogenetic analysis was performed to elucidate the evolutionary relationships and genetic affiliations of the isolates. Thirty type strain sequences, representing *Rhodococcus*, *Gordonia*, and *Janibacter* (10 per genus), were retrieved from the EzBioCloud database (Yoon et al., 2017), prioritizing species with documented hydrocarbon degradation potential. These sequences, along with the isolates' 16S rRNA gene sequences, were aligned using the MUSCLE algorithm (Edgar, 2004). The phylogenetic tree was generated using the Neighbor-Joining (NJ) approach, with 1000 bootstrap replications. Phylogenetic tree visualization and annotation were performed using MEGA11 (Tamura et al., 2021), confirming the taxonomic affiliations of the isolates and revealing their evolutionary relationships with type strains.

## 2.3. Whole-genome sequencing, De Novo assembly and reference-guided scaffolding

Whole-genome sequencing of *Rhodococcus* sp. 1Y, *Gordonia* sp. BP10, *J. indicus* 70, and *J. indicus* 22 strains was performed using the Illumina NovaSeq 6000 platform (Novogene, UK). Libraries were prepared following standard protocols, including DNA fragmentation, end-repair, adapter ligation, and PCR amplification to generate high-quality sequencing libraries. Sequencing was conducted with paired-end reads of 150 bp in length, achieving a minimum coverage depth of 100 × to ensure comprehensive genome coverage.

The raw sequencing data were initially assessed for quality using FastQC (Andrews, 2011), enabling the identification of potential issues such as low-quality reads or adapter contamination. Following this, trimming and filtering were performed with Trimmomatic (Bolger et al., 2014) to remove low-quality bases and adapter sequences, resulting in a set of clean reads suitable for assembly. The cleaned reads were subjected to de novo genome assembly using SPAdes (Bankevich et al., 2012). Assemblies were performed under two conditions: first, using the default parameters, and second, utilizing the "careful" mode to minimize assembly errors and reduce the number of contigs. The "careful" mode is designed to decrease mismatches and short indels in the assembled contigs. To improve the accuracy of the assembly, the trimmed and filtered reads were aligned back to the assembled genome using Bowtie2 (Langmead and Salzberg, 2012). This alignment facilitated the identification of discrepancies between the reads and the assembly. The assembled genomes were then polished using Pilon (Walker et al., 2014), which corrected errors and enhanced the overall quality of the assembly based on the alignment data. To further improve assembly, reference-guided scaffolding of draft genomes was done using RagTag (Alonge et al., 2019) by selecting Scaffold: Homology-based Assembly Scaffolding. Assembly quality was comprehensively evaluated using QUAST (Gurevich et al., 2013), which provided metrics on contiguity, completeness, and overall assembly integrity. These metrics informed the selection of the most effective assembly strategy for subsequent genomic analyses.

## 2.4. Genome annotation

The draft genomes were generated using Prokka (Seemann, 2014), configured with settings optimized for high-GC content organisms to ensure comprehensive and accurate gene prediction. Specifically, a genus-specific BLAST database was employed to enhance annotation accuracy for each strain. The minimum contig length was set to 200 bp to comply with NCBI submission requirements and to exclude highly fragmented contigs from annotation. This was complemented with RASTtk (Brettin et al., 2015) for a robust result. Plasmid detection was performed using two tools: Plasmid (Carattoli et al., 2014) and MOB-recon (Robertson and Nash, 2018). PlasmidFinder identifies plasmid replicons through a replicon-based detection method, while MOB-recon reconstructs plasmid sequences from draft genome assemblies using clustered plasmid reference databases. To explore the potential for mobile genetic elements associated with hydrocarbon degradation, the tool mobileOG-db was employed (Brown et al., 2022). Additionally, secondary metabolite-related gene clusters were predicted using antiSMASH v7.0 (Blin et al., 2021), run on the assembled genomes in strict mode with default parameters. Identified clusters were annotated based on similarity to known biosynthetic pathways (e.g., ectoine, siderophores, biosurfactants) relevant to hydrocarbon degradation and associated stress responses. Genome maps were constructed using gcview (Grant and Stothard, 2008).

## 2.5. Phylogenomic analyses

Whole-genome sequences of closely related strains were identified via BLASTn analysis of the isolates' 16S rRNA gene sequences and subsequently retrieved from the NCBI database (Camacho et al., 2009). Representative genomes were selected based on high sequence similarity to the isolates, inclusion in the phylogenetic analyses, and relevance to hydrocarbon degradation.

Average Nucleotide Identity (ANI) analysis, performed using OrthoANI (Lee et al., 2016), validated species-level relationships among the selected genomes. To further investigate genetic diversity, orthologous gene clustering was performed using OrthoFinder (Emms and Kelly, 2015). The clustered orthogroups were then used to infer species-level relationships and evolutionary trajectories, providing a detailed resolution of genetic affiliations among the isolates.

## 2.6. Pan-genome analysis

The ROARY pipeline was employed for pan-genome analysis to identify core and accessory genes (Page et al., 2015). ROARY was configured with a minimum BLASTp percentage identity of 95 % to cluster homologous proteins and a 99 % threshold for core gene determination, ensuring the inclusion of nearly ubiquitous genes across the analyzed strains.
## 2.7. Orthologous gene clustering

Protein-coding sequences from Prokka annotations were analyzed using OrthoVenn3 and OrthoFinder to compare the four test strains and representative genomes (Emms and Kelly, 2015; Xu et al., 2019). Due to the 12-file limit in OrthoVenn3, the tool was used to cluster shared and unique genes among the four test strains only. OrthoFinder was then employed for a broader analysis, comparing the test strains with the representative genomes. This approach identified orthogroups, core, accessory, and unique gene clusters while revealing functional distinctions and conserved pathways.

## 2.8. Genomes synteny visualization

Complete genome sequences for *Rhodococcus* sp. strain 1Y, *Gordonia* sp. strain BP1o, *J. indicus* strain 70, and *J. indicus* strain 22 were obtained in FASTA format. Multiple sequence alignments were performed using the progressive Mauve algorithm in Mauve, which generated an XMFA alignment file to identify locally collinear blocks (Darling et al., 2010). These blocks represent conserved genomic regions shared across the isolates, providing insights into synteny and structural variations. Visualization of synteny was implemented in R (version 4.1.0) using the circlize package (v0.4.15) (Gu et al., 2014).

## 2.9. Functional analysis for hydrocarbon degradation

The annotation files in FASTA format were initially uploaded to eggNOG-mapper for broad functional annotation (Cantalapiedra et al., 2021). Using eggNOG-mapper, KEGG Orthology (KO) identifiers were assigned to the predicted proteins, which facilitated mapping these proteins onto KEGG metabolic pathways using the KEGG Mapper Reconstruct Pathway tool (Kanehisa and Sato, 2020). This mapping specifically targeted pathways associated with alkane and aromatic hydrocarbon degradation.

To further identify genes linked to hydrocarbon degradation, we employed Proteinortho (Lechner et al., 2011) with parameters (-identity = 50, -conn = 0.3) to compare orthologous groups against the HADEG database. This step pinpointed orthologs known to be involved in HC-degradation. To ensure a comprehensive catalog of HC-degradation genes, we complemented the HADEG-based predictions by integrating RAST-tk functional annotations. This strategy enabled a more complete description of the hydrocarbon degradation gene repertoire present within the genomes.

## 2.10. Experimental design

### 2.10.1. Seawater minimal medium (swMM) preparation

To simulate marine conditions, a seawater minimal medium (swMM) was utilized, comprising the following components: 0.75 g/L of KCl, 1.47 g/L of CaCl₂·2H₂O, 1.5 g/L of NH₄Cl, 6.64 g/L of MgSO₄·7H₂O, 20 g/L of NaCl, and 0.265 g/L of Na₂CO₃. Additionally, 1 mL of a trace elements solution was added, containing H₃BO₃ (300 mg/L), FeSO₄·7H₂O (1.1 g/L), CoCl₂·6H₂O (190 mg/L), MnCl₂·2H₂O (50 mg/L), ZnCl₂ (42 mg/L), NiCl₂·6H₂O (24 mg/L), and Na₂MoO₄·2H₂O (2 mg/L). A vitamin solution (1 mL) was also incorporated, which included biotin (2 mg/L), p-aminobenzoate (10 mg/L), thiamine (10 mg/L), pantothenate (5 mg/L), pyridoxamine (50 mg/L), vitamin B₁₂ (20 mg/L), and nicotinate (20 mg/L). Finally, 100 µL of a 50 mM phosphate buffer was added as previously described (Alvarez-Barragán et al., 2023). The pH was adjusted to 7.5 using HCl. All chemicals were purchased from Sigma Aldrich (Germany). This medium composition was designed to mimic the ionic strength and salinity of natural marine environments, providing conditions that closely reflect seawater and support the growth of marine bacteria.

### 2.10.2. Culture of synthetic bacterial consortium in swMM

To evaluate the hydrocarbon degradation capabilities of the selected strains, the experimental setup was established with the following setup: Each culture bottle in the Treatment Group (A) contained 60 mL of swMM medium supplemented with a mixture of phenanthrene and hexadecane, each added at 50 mg/L, serving as the sole carbon source. A 10 % inoculum (6 mL) of the synthetic coastal marine consortium, prepared by mixing equal volumes of four strains each adjusted to approximately to 6.84 × 10⁷ cells/mL was added to 54 mL of swMM medium in each bottle. In contrast, the Control Group (B) consisted of 60 mL of minimal medium with the same concentrations of phenanthrene and hexadecane but without the addition of bacterial strains. A total of 30 bottles were prepared, with three replicates for each time point across five sampling intervals. All culture bottles were incubated at 28 °C with continuous shaking at 150 rpm to ensure adequate aeration and mixing.

### 2.10.3. Monitoring and sampling procedures

Sampling was conducted on Days 3, 6, 9, and 12 to capture early, mid, and late transcriptional responses during hydrocarbon degradation, based on preliminary observations of growth dynamics and hydrocarbon removal patterns. Bacterial growth was monitored by microscopy using a Mallassez chamber to estimate cell densities at each sampling interval. At each sampling time point (days 3, 6, 9, and 12), 10 mL of culture from each bottle was harvested for RNA extraction. RNAprotec reagent (phenol:acetate buffer: ethanol, 1:1:19) was added at a 1:25 ratio to stabilize RNA. The samples were subsequently filtered using Microfunnel filters of 0.2 µm size (Pall Corporation, USA), and the filters were transferred to cryotubes and stored at -80 °C until RNA extraction.

The remaining culture volume was stored at -20 °C for subsequent hydrocarbon analysis. Hydrocarbons were extracted using 20 mL of dichloromethane (constituting a ratio of 1:2), spiked with heptamethylnonane as an internal standard at a concentration of 6.25 mg/L. Extraction was performed by shaking the samples at 220 rpm for 20 min. Extracted hydrocarbons were analyzed using Gas
Chromatography-Flame Ionization Detection (GC-FID) on an Agilent system, following the DEFKK_5.M method to quantify the degradation of phenanthrene and hexadecane.

### 2.10.4. RNA extraction and quantification

Total RNA was extracted from the harvested samples using the Qiagen RNeasy PowerSoil Total RNA Kit according to the manufacturer's instructions. Briefly, filters containing cells were lysed, and RNA was purified through a series of binding, washing, and elution steps to ensure high-quality RNA suitable for downstream gene expression analyses. Extracted RNA samples were quantified using a NanoDrop spectrophotometer and assessed for integrity via an Agilent Bioanalyzer. For each condition and time point, RNA was initially extracted from three biological replicates. Due to the technical challenges of obtaining sufficient high-quality RNA from

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Phylogenetic relationships of Actinomycetota isolates with reference type strains. The unrooted tree was constructed based on 16S rRNA gene sequences. The scale bar (0.02) represents 2% nucleotide substitutions per site. Bootstrap values, based on 1000 replications are indicated at the nodes. The four strains of this study are highlighted in bold. The sequence accession numbers are indicated in brackets.</figcaption></figure>
individual replicates in hydrocarbon-amended media, RNA from the three biological replicates was pooled prior to sequencing to obtain a composite sample taking into account the biological variability as proposed in similar studies (Bottos et al., 2020; Liu et al., 2017).

### 2.10.5. Transcriptomic analysis

Raw sequencing reads underwent stringent quality filtering to remove adapter sequences and low-quality bases, ensuring high-confidence data for downstream analysis. Clean reads were assembled de novo using the Trinity software suite, which constructs contigs through a modular process (Grabherr et al., 2011). To reduce redundancy and generate a non-redundant set of unigenes, assembled transcripts were clustered with CORSET (Davidson and Oshlack, 2014). The resulting unigene set was then functionally annotated by mapping against databases including KEGG, GO, CAZy, and eggNOG using DIAMOND (e-value ≤ 1e-5), with further filtering based on BLAST coverage ratios to ensure reliability (Cantalapiedra et al., 2021; Kanehisa and Sato, 2020).

Gene expression levels were quantified by aligning quality-filtered reads back to the annotated unigenes. Differential expression analysis was subsequently performed to identify genes significantly up- or down-regulated during hydrocarbon degradation (Li and Dewey, 2011; Love et al., 2014). This transcriptomic approach provided high-resolution insights into the dynamic regulation of hydrocarbon degradation pathways within the marine consortium, linking gene expression patterns to functional adaptations observed during the biodegradation process.

### 2.11. Statistical analysis

Residual hydrocarbon percentages (hexadecane and phenanthrene) were analyzed using t-tests in R (version 4.4.2). Pairwise comparisons of degradation rates between hexadecane and phenanthrene were conducted using independent t-tests. Results were considered statistically significant at p < 0.05.

## 3. Results

### 3.1. Phylogenomic and taxonomic confirmation of the strains

The 16S rRNA gene sequences of the studied strains exhibited high similarity to type strains associated with hydrocarbon degradation (Fig. 1). *Rhodococcus* sp. strain 1Y shared 95.87 % similarity with *Rhodococcus* sp. M1061 and clustered with the type strain *Rhodococcus ruber* in the phylogenetic tree, supporting its classification within the genus. *Gordonia* sp. strain BP1o showed 96.50 % similarity to *Gordonia alkanivorans* type strain, forming a distinct clade alongside with *Gordonia alkanivorans* and *Gordonia westfalica* type strains. *Janibacter* sp. 22 and *Janibacter* sp. 70 exhibited 98.75 % and 95.75 % similarity to *J. indicus*, respectively, clustering closely with this type strain, confirming their taxonomic affiliation.

ANI analysis further corroborated these classifications. *Rhodococcus* sp. strain 1Y showed ANI values of 97.50 %, 97.49 %, and 97.48 % with *Rhodococcus* sp. M8, M8_50, and M8_35, respectively, while an ANI of 81.33 % with *R. pyridinivorans* SB3094 indicated species-level distinction. For *Gordonia* sp. strain BP1o, ANI values of 99.02 % and 98.71 % with *Gordonia* sp. 135 and Swx-4 confirmed its taxonomic affiliation. Additionally, a similarity of 97.36 % with *G. alkanivorans* strain YC-RL2 and 93.09 % with *G. rubripertincta* strain CWB2 highlighted intra- and interspecies relationships. ANI analysis of *Janibacter* sp. 70 and *Janibacter* sp. 22 revealed 98.30 % and 98.32 % similarity to *J. indicus* strain YFY001 and 98.09 % and 98.03 % similarity to *J. indicus* strain TT2, respectively. Both strains also showed 97.07 % and 97.12 % similarity to *Janibacter* sp. YB324, further supporting their classification within the genus *Janibacter*.

### 3.2. Genomic characteristics of the Actinomycetota isolates

Genomes of the four bacterial strains, *Rhodococcus* sp. 1Y, *Gordonia* sp. strain BP1o, *J. indicus* 22, and *J. indicus* 70, were de novo

<table><caption>**Table 1**
Genomic Features of the Bacterial Isolates.</caption><thead><tr><th>Genomic information</th><th><i>Rhodococcus</i> sp. strain 1Y</th><th><i>Gordonia</i> sp. strain BP1o</th><th><i>Janibacter indicus</i> strain 70</th><th><i>Janibacter indicus</i> strain 22</th></tr></thead><tbody><tr><td>Total raw reads</td><td>15,992,264</td><td>12,782,964</td><td>10,708,096</td><td>11,762,864</td></tr><tr><td>GC content (%)</td><td>70.28</td><td>67.39</td><td>71.33</td><td>71.33</td></tr><tr><td>No. of contingts</td><td>17</td><td>47</td><td>18</td><td>19</td></tr><tr><td>Genome size (bp)</td><td>6195,909</td><td>5179,256</td><td>3473,073</td><td>3443,932</td></tr><tr><td>N50</td><td>376,015</td><td>147,450</td><td>254,151</td><td>264,036</td></tr><tr><td>Predicted genes (Prokka)</td><td>5719</td><td>4768</td><td>3366</td><td>3296</td></tr><tr><td>CDS (Prokka)</td><td>5642</td><td>4707</td><td>3310</td><td>3353</td></tr><tr><td>CDS (RASTtk)</td><td>5990</td><td>5019</td><td>3574</td><td>3477</td></tr><tr><td>No. of tRNA</td><td>73</td><td>57</td><td>51</td><td>52</td></tr><tr><td>No. of rRNA</td><td>3</td><td>3</td><td>4</td><td>4</td></tr><tr><td>No. of tmRNA</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>KEGG (KO terms)</td><td>2486</td><td>2068</td><td>1542</td><td>1542</td></tr></tbody></table>
assembled, yielding high-quality draft genomes (Table 1). The GC content of these assemblies ranged from 70.28 % in *Rhodococcus* to 67.39 % in *Gordonia* sp. BP1o, and to 71.33 % in the *Janibacter* strains. Assembly metrics demonstrated fairly robust contiguity. *Rhodococcus* sp. 1Y achieved an N50 of 376,015 bp with only 17 contigs, compared to 47 contigs for *Gordonia* sp. BP1o and similar counts for the *Janibacter* isolates. Gene prediction using Prokka identified between 3296 and 5719 genes across the strains. RASTtk annotations increased these counts slightly, revealing up to 5990 coding sequences for *Rhodococcus* sp. 1Y. Each genome contained a full complement of tRNA genes, multiple rRNA operons, and a tmRNA gene, indicating complete genetic toolkits.

Genome maps generated for each isolate provided comprehensive overviews of their genomic architecture (Fig. 2A-D). Circular representations highlighted coding sequences (CDS), tRNA, rRNA, tmRNA, and key functional regions involved in replication, recombination, and repair. GC content and GC skew were also visualized, reflecting genome stability and structural organization.

Comparative synteny analysis among the isolates revealed evolutionary conservation, with distinct patterns of homology and structural variation (Fig. 3A). The two *J. indicus* strains displayed extensive contiguous synteny, as indicated by yellow and green arcs, underlining their close evolutionary relationship and genomic stability. By contrast, *Gordonia* sp. BP1o and *Rhodococcus* sp. 1Y exhibited more fragmented synteny patterns, represented by mid-sized red and blue arcs. These segments suggest structural rearrangements such as inversions or translocations, contributing to differences in genome size and accessory content. This structural

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Circular genome maps of marine Actinomycetota isolates. (A) *Rhodococcus* sp. strain 1Y, (B) *Gordonia* sp. strain BP1o, (C) *Janibacter indicus* strain 70, (D) *Janibacter indicus* strain 22. The maps highlight genomic features, including coding sequences (CDS), tRNA, rRNA, tmRNA, and regions involved in replication, recombination, and repair. The GC content and GC skew are shown in the inner rings, while the outer ring annotates specific genes and regions of interest. Genome sizes range from 3.44 Mbp to 6.20 Mbp, consistent with assembly metrics.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Comparative genomic insights into hydrocarbon-degrading Actinomycetota. A) Circular synteny diagram illustrating genomic homology among the four strains. Colored arcs represent strain relationships: Blue (*Rhodococcus* sp. strain 1Y), Red (*Gordonia* sp. strain BP1o), Yellow (*Janibacter indicus* strain 70), and Green (*Janibacter indicus* strain 22). B) Venn diagram showing shared and unique orthologous gene clusters, including the core genome (1413 genes) and unique genes (299). C) Bar plot displaying the total number of orthologous gene clusters per isolate, reflecting accessory genome variations. D) Bar plot comparing total protein-coding genes (red) and genes assigned to orthogroups (blue), highlighting genomic divergence and strain-specific adaptations, particularly in *Rhodococcus* and *Gordonia* species. The four study strains are in bold, while the remaining represent representative genomes.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Hydrocarbon degradation pathways and gene distribution across isolates. Functional pathway distribution across isolates, showing the number of genes associated with various hydrocarbon degradation pathways. Bubble size represents gene abundance. B) The presence-absence matrix of hydrocarbon degradation genes, where blue indicates gene presence and white indicates absence. C) Generalized pathway for PAH degradation, highlighting key steps and genes identified in the strains. D) Generalized n-alkane degradation pathway. Color coding reflects gene distribution among the four isolates: Black: Present in all four strains. Blue: Present only in *Rhodococcus* sp. strain 1Y. Dark Blue: Present in both *Rhodococcus* sp. strain 1Y and *Gordonia* sp. strain BP1o. Red: Canonical pathway gene not detected in any of the genomes.</figcaption></figure>
analysis highlights the balance between shared genomic backbones and lineage-specific variations, supporting the metabolic flexibility of these hydrocarbon-degrading *Actinobacteria*.

### 3.3. Pangenome and comparative genomic insights

Pangenome analysis revealed a dynamic balance between conserved and variable genetic elements among the isolates. A total of 1413 core genes were shared across all strains, representing essential functions crucial for survival in hydrocarbon-contaminated marine environments. In addition, 365 genes were shared by three isolates, 2746 by two, and 299 genes were unique to individual strains, whereas the two *Janibacter* strains shared 1514 genes, indicating strain-specific adaptations (Fig. 3B). The *Janibacter indicus* strains exhibited the largest genomic repertoires (3292 clusters each), followed by *Rhodococcus* sp. strain 1Y (3056) and *Gordonia* sp. strain BP1o (2898), reflecting variations in accessory genome content that likely contribute to niche specialization and hydrocarbon degradation abilities (Fig. 3C).

Comparative genomic analysis revealed striking patterns of conservation and divergence among *Rhodococcus* sp. strain 1Y, *Gordonia* sp. strain BP1o, *J. indicus* strains 22, *J. indicus* 70, and their representative genomes (Fig. 3D). *Rhodococcus* sp. strain 1Y exhibited the largest genome and the highest number of unassigned genes (191), indicating its genomic innovation and potential for metabolic versatility, which aligns with its broader ecological roles. In contrast, *J. indicus* strains displayed compact genomes with exceptionally high orthogroup assignment rates (>99.6 %) and minimal unassigned genes, reflecting their highly conserved and specialized roles within the *Janibacter* genus. Interestingly, *Gordonia* sp. strain BP1o balanced genomic conservation with adaptation, sharing significant overlap with reference strains like *G. alkanivorans*, yet retaining 175 unassigned genes that may confer niche-specific metabolic functions. These findings reveal a dynamic interlinks between conserved core functions and species-specific adaptations, with *Rhodococcus* and *Gordonia* demonstrating broader ecological flexibility, while *Janibacter* strains highlight genomic streamlining for specialized hydrocarbon degradation. Such diversity across closely related taxa is indicative of the evolutionary strategies shaping functional roles in hydrocarbon-contaminated environments.

Analysis using mobileOG-db identified substantial genomic mobility across *Rhodococcus* strain 1Y (105 MGEs), *Gordonia* sp. strain BP1o (150 MGEs), *J. indicus* strain 70 (88 MGEs), and *J. indicus* strain 22 (89 MGEs), reflecting extensive genome plasticity. Integration/excision elements were most abundant in *Gordonia* sp. strain BP1o (72), followed by *J. indicus* strain 22 (41), strain 70 (39), and *Rhodococcus* strain 1Y (34). Similarly, the abundance of replication/recombination/repair genes was highest in *Gordonia* sp. strain BP1o (39), while *J. indicus* strains 70 and 22 each contained 23, and *Rhodococcus* strain 1Y harbored 37. Plasmid-associated elements, including replication (e.g., *dnaA*, *gyrA*, *polA*, *recA*, *ligA*), conjugation/transfer (e.g., *copR*, *comM*, *virD2*, *soj_1/2*, *oppC/oppF*), and stability modules (e.g., toxin-antitoxin systems, *hsdM*), were detected in all strains, with *Gordonia* sp. strain BP1o showing the highest representation. Similarly, insertion sequences and transposases were widespread, including IS901, *insI3*, *tnpA*, *tnpB*, and *istA*, supporting genome rearrangement. Integrase and site-specific recombinases (e.g., *int*, *xerC*, *ltr A*) suggest the presence of genomic islands and prophage integration sites, particularly in *Gordonia* sp. strain BP1o and *Janibacter*. Phage-related genes (e.g., *dnaK*, *clpB*, *groL*, *ftsH*, *lexA_2*, *nusA*, *qmcA*, *thyA*, *whiB*) were prevalent across all strains, with *Rhodococcus* strain 1Y (17), *Gordonia* sp. strain BP1o (17), *J. indicus* strain 70 (16), and *J. indicus* strain 22 (14) exhibiting comparable numbers. These findings reveal the fluid nature of these genomes, with a network of plasmids, transposable elements, genomic islands, and phage-related sequences that drive their adaptability. The abundance of mobile genetic elements suggests active mechanisms for acquiring and disseminating traits relevant to hydrocarbon degradation and environmental survival.

### 3.4. Functional insights into hydrocarbon degradation potential of the strains

Using the HADEG database alongside Proteinortho, multiple orthologous hydrocarbon degradation gene clusters were identified (Table S1). These included key genes such as *alkB*, *cyp*, *nahF*, *nahB*, *nahAB*, *nahAA*, *nahC*, *nahG*, and *nahE*, essential for aliphatic and aromatic hydrocarbon metabolism. Among the isolates, *Rhodococcus* sp. strain 1Y exhibited the highest number of orthologous genes, underlining its potential as a robust hydrocarbon degrader. *Gordonia* sp. strain BP1o and the two *J. indicus* strains demonstrated fewer orthologs, with notable strain-specific variations in gene presence.

Functional pathway analysis revealed a diverse repertoire of hydrocarbon degradation pathways across the four strains, suggesting both conserved and strain-specific adaptations (Fig. 4A). Among these, *Rhodococcus* sp. strain 1Y exhibited a broad metabolic capacity, with the highest number of genes linked to pathways such as benzoate degradation (41 genes), xylene degradation (17 genes), and aromatic compound degradation (55 genes). In contrast, the two *J. indicus* strains exhibited a narrower range, with strain 70 and strain 22 sharing similar pathway profiles, including degradation of benzoate (20 genes) and aromatic (18 genes) compounds. *Gordonia* sp. strain BP1o displayed a focus on aromatic and chlorinated compound degradation pathways, including benzoate degradation (21 genes) and chloroalkane and chloroalkene degradation (7 genes). Pathways linked to the metabolism of xenobiotics by cytochrome P450 were conserved across all isolates, though the gene counts varied, suggesting differential capacities for detoxification and adaptation to complex hydrocarbon mixtures.

The presence or absence of key hydrocarbon degradation genes across the four isolates is summarized in Fig. 4B. These genes are critical for breaking down aliphatic and aromatic hydrocarbons. *Rhodococcus* sp. strain 1Y exhibited the most comprehensive gene repertoire, with the presence of all key genes, including *alkB*, *antA*, and *bbsG*, which are integral to alkane and aromatic compound metabolism. In contrast, *Gordonia* sp. strain BP1o demonstrated a more limited gene set, sharing some key genes like *bphC* and *catD* with *Rhodococcus*, but lacking others such as *alkB* and *antA*. The *Janibacter* strains exhibited similar gene profiles, missing essential genes like *alkB*, *antA*, and *mhpA*, while retaining aromatic compound degradation genes, such as *paaJ* and *todE*.
Although several genes with possible direct and indirect roles in hydrocarbon degradation were identified, the key genes involved in PAH and alkane catabolism in the genomes of *Rhodococcus* sp. 1Y, *Gordonia* sp. BP1o, *J. indicus* 70, and *J. indicus* 22 were mapped (Fig. 4C and D) onto generalized canonical pathways. In Fig. 4C, the PAH ring-hydroxylation route (exemplified by *nahA/B*) yields cis-dihydrodiol, which is further dehydrogenated to catechol (via *nahB*). Notably, *nahABC* gene clusters were detected across the strains at the gene cluster level, although individual *nah* genes were not always annotated separately in single-strain analyses. Catechol is subsequently cleaved through either a meta-cleavage pathway (facilitated by *todF*, *xylE*, *bphC*, *mhpB*) or an ortho-cleavage pathway (facilitated by *catA*, *clcA*, *pcaG/H*). Downstream enzymes (*catD*, *mhpD/F*, *xylJ*, *paaG/J*, *hsaD*) support conversion of ring-fission products into central intermediates and their ultimate mineralization to CO₂. Similarly, Fig. 4D illustrates the progression of *n*-alkane oxidation, beginning with terminal hydroxylation of the alkane chain (via *alkB*, *almA*, *ladA*, *alkG*, *rubA*), yielding a primary alcohol. This is subsequently oxidized to an aldehyde (*adh*) and then to a carboxylic acid (*aldh*). Activation by *fadD3* produces an acyl-CoA, which is processed through β-oxidation (via *fadA*, *fadA5*, *fadB*, *echA8*) and ultimately enters the tricarboxylic acid cycle. Importantly, each strain was found to harbor at least one gene for most of the key activation steps in both pathways, particularly the initial oxidation and ring-cleavage steps critical for hydrocarbon breakdown. This underlines that all four isolates are genetically equipped to metabolize a wide range of hydrocarbons, from *n*-alkanes to more complex PAHs, although *Rhodococcus* sp. 1Y harbors the highest number of relevant genes. However, for certain intermediate steps where canonical pathway genes were not detected, degradation is likely mediated through less-characterized or functionally redundant enzymes encoded in the genomes.

Secondary metabolite analysis using antiSMASH revealed diverse pathways supporting hydrocarbon degradation across the isolates. *Rhodococcus* sp. strain 1Y demonstrated robust capabilities with ectoine biosynthesis (75 % similarity) for osmoprotection, alkylresorcinol synthesis (100 % similarity) for aliphatic hydrocarbon metabolism, and biosurfactants like mycosubtilin (30 % similarity) and siderophores such as heterobactin A/S2 (54 % similarity) to enhance solubilization and enzymatic activity. *Gordonia* sp. strain BP1o, on the other hand, highlighted stress adaptation through ectoine (75 % similarity) and antioxidant arylpolyene production, along with Type I polyketides and siderophores (amychelin, 18 % similarity) enabling complex hydrocarbon degradation. *J. indicus* strain 70 has ectoine (75 % similarity), carotenoid-like terpenes (66 % similarity), and siderophores (75 % similarity) for stress tolerance and enzymatic activity, while *J. indicus* strain 22 contributed through NRPS-like chejuenolide A/B (7 % similarity), carotenoids (66 % similarity), and siderophores (75 % similarity) for oxidative stress mitigation, iron acquisition, and detoxification.

### 3.5. Growth, biodegradation dynamics, and transcriptomic responses of a bacterial consortium during hydrocarbon degradation

Previously, we observed better hydrocarbon degradation when the four strains were mixed (data not shown). Thus, given the observed efficient degradation of hexadecane and phenanthrene by the bacterial consortium, we hypothesize that hydrocarbon degradation is achieved through the coordinated expression of key metabolic pathways involved in alkane hydroxylation, aromatic hydrocarbon breakdown, and oxidative stress responses. Transcriptomic analysis was conducted to uncover the temporal regulation of these pathways and identify shifts in gene expression that drive hydrocarbon metabolism over time.

The consortium was incubated in a medium containing phenanthrene and hexadecane as the sole carbon source in order to follow the gene expression during hydrocarbon degradation through transcriptomic analyses. The consortium exhibited rapid growth kinetics, with cell density increasing sharply from $6.84 \times 10^6$ cells/mL at Day 0 to a peak of $7.68 \times 10^7$ cells/mL at Day 6. Cell numbers slightly declined thereafter, reaching $5.64 \times 10^7$ cells/mL by Day 12, indicating possible nutrient depletion or metabolic shifts during hydrocarbon degradation. The observed growth demonstrates the consortium's ability to utilize hexadecane and phenanthrene as the sole carbon sources, as illustrated in Fig. 5A.

The bacterial consortium exhibited rapid degradation of both hexadecane and phenanthrene (Fig. 5B), with hexadecane reduced to 5.23 % by Day 3 and 2.76 % by Day 12, while phenanthrene degradation was slower, reaching 66.25 % by Day 6 and 36.56 % by Day 12 ($p < 0.001$). In contrast, uninoculated controls retained 89.25 % of hexadecane and 93.74 % of phenanthrene by Day 12, confirming that the observed degradation was predominantly driven by the biological activity of the strains. To further understand how the bacterial consortium orchestrates hydrocarbon degradation, we conducted a transcriptomic analysis to capture changes in gene expression across key metabolic pathways. Sequencing efforts yielded a total of 58,550,885 raw reads across all time points, which, following stringent quality control measures, resulted in 57,221,228 high-fidelity clean reads. The mean Q30 score of 93.05 % attests to the accuracy of the sequencing process. The GC content exhibited minimal variation (~69.4 %) across samples, suggesting a stable nucleotide composition characteristic of the microbial community under study. *De novo* transcriptome assembly using Trinity produced 5854 distinct transcripts and 2044 unigenes, with N50 values of 11,742 bp and 11,877 bp for transcripts and unigenes, respectively, indicating a well-assembled and structurally sound transcriptome (Table S2). A total of 1971 unigenes were annotated, revealing a rich repertoire of hydrocarbon degradation genes, including those involved in alkane hydroxylation (e.g., *alkB1_2*, *alkM*, *ladA*), aromatic hydrocarbon degradation (e.g., *catA*, *dmpC*), β-oxidation (e.g., *fadA*, *fadI*, *fadD36*), and oxidative stress response (e.g., *katG*, *ahpF*). These genes were mapped to both alkane and PAH degradation pathways (Fig. 5C and D).

Functional annotation of DEGs using KEGG pathway analysis unveiled key metabolic pathways activated during hydrocarbon degradation. "Xenobiotics biodegradation and metabolism" showed the highest enrichment, with over 200 annotated genes, emphasizing its central role (Fig. 5D). Other enriched pathways included "lipid metabolism" (~150 genes) and "membrane transport" (~140 genes), indicating robust adaptations for hydrocarbon uptake and breakdown. Additionally, pathways associated with cellular processes, such as "environmental information processing" (~100 genes) and "stress response," were prominent, showcasing the consortium's resilience under challenging conditions. These findings highlight the consortium's transcriptional and metabolic versatility in achieving efficient hydrocarbon degradation.

The temporal progression of gene expression patterns further corroborates these findings (Fig. 5F). The most pronounced shift
<figure><img src="image_8.png" /><figcaption>B)</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>C)</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>D)</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>E)</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>F)</figcaption></figure>

<figure><img src="image_13.png" /></figure>

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**Fig. 5.** Growth dynamics, hydrocarbon degradation, and temporal regulation of hydrocarbon-degrading genes. A) Growth kinetics of the bacterial consortium over 12 days in the presence of hexadecane and phenanthrene as sole carbon sources. B) Residual hydrocarbon percentages over time, comparing degradation in inoculated cultures (solid lines) versus uninoculated controls (dashed lines). C) PAH degradation pathway mapped to detected genes at the metatranscriptomic level. In C-D, colors indicate transcriptional behavior relative to degradation phases: Green: Upregulated at early or mid time points (Days 3–9), corresponding to active degradation. Blue: Downregulated at late time point (Day 12), reflecting metabolic downscaling as substrates were depleted. Black: Detected but not differentially expressed. Red: Canonical pathway gene not detected in the transcriptome. D) Alkane degradation pathway. E) KEGG pathway annotation of DEGs, showing enrichment in xenobiotic degradation, lipid metabolism, and stress adaptation pathways. F) DEG counts across time points, illustrating transcriptional shifts.

occurred at Day 12, where 487 out of 502 DEGs were down-regulated compared to Day 3, and 450 out of 462 DEGs were suppressed relative to Day 6. This suggests a broad transcriptional shutdown, potentially driven by substrate depletion or a transition to dormancy. In stark contrast, Day 9 exhibited peak metabolic activity, with 156 genes up-regulated relative to Day 6, pointing to an intermediate phase of heightened metabolic function before the subsequent decline at Day 12. Further analysis of individual DEGs revealed distinct metabolic patterns across time points (Table S3). Early-stage comparisons (Day 3 vs. Day 6, Day 3 vs. Day 9) indicate moderate upregulation of genes involved in phenylacetate metabolism (*paaF*, *echA*, K01692), oxidative stress defense (*ethA*, K10215; *katG*, K03782), and lipid metabolism (*fadB*, *mmgb*, K00074), suggesting active hydrocarbon breakdown and adaptive responses to oxidative stress. By contrast, late-stage comparisons (Day 9 vs. Day 12, Day 6 vs. Day 12, Day 3 vs. Day 12) showed pervasive gene down-regulation, reinforcing the transcriptional shutdown observed at Day 12. Notably, *catA* (K03381), a key enzyme in catechol degradation, displayed recurrent differential expression, appearing in both upregulated and downregulated lists across multiple time points. These findings reveal the transcriptional and metabolic versatility of the microbial community, confirming its capacity to efficiently degrade hydrocarbons.

## 4. Discussion

Marine environments contaminated with hydrocarbons, such as PAHs and alkanes, represent significant ecological challenges due to their toxicity, persistence, and potential to disrupt marine ecosystems (Amran et al., 2022; Sheppard et al., 2014). These pollutants result from anthropogenic activities, including oil spills, industrial discharge, and natural seepage (Amran et al., 2022; Sheppard et al., 2014). Microbial degradation offers a sustainable and eco-friendly solution for mitigating hydrocarbon pollution, utilizing the metabolic capabilities of bacteria to break down complex hydrocarbons into less harmful compounds (Amran et al., 2022; Sheppard et al., 2014). Oil-contaminated sediments are dynamic ecosystems where microbial communities must navigate challenges such as limited nutrient availability, osmotic stress, and the toxicity of hydrocarbons (Duran et al., 2015b; Gan et al., 2020). These stressors drive microbial adaptation, selecting for specialized taxa capable of metabolizing hydrocarbons and surviving under harsh conditions. Actinomycetota, particularly members of the genera *Rhodococcus*, *Gordonia*, and *Janibacter*, are frequently associated with hydrocarbon degradation due to their broad metabolic capabilities, robust stress responses, and diverse ecological niches (Ehiosun et al., 2022; Kim et al., 2018; Martínková et al., 2009). This study integrates genomic and transcriptomic analyses to provide a comprehensive understanding of how these marine Actinomycetota adapt to hydrocarbon degradation.

### 4.1. Phylogenetic, taxonomic, and genomic insights into hydrocarbon-degrading Actinomycetota

Our findings shed light on the evolutionary relationships, genomic architecture, and functional potential of four Actinomycetota isolates, emphasizing their roles in hydrocarbon degradation within marine environments. These insights align with the growing recognition of *Rhodococcus*, *Gordonia*, and *Janibacter* as key contributors to the biodegradation of complex hydrocarbons, particularly in contaminated ecosystems (Ehiosun et al., 2022; Kim et al., 2018; Martínková et al., 2009).

The phylogenetic and ANI analyses firmly confirmed the taxonomic affiliations of the isolates. *Rhodococcus* sp. strain 1Y clustered closely with *R. ruber* but displayed significant divergence from other *Rhodococcus* species, as evidenced by its ANI values. This genomic separation underlines the adaptive radiation within the genus, which is often driven by exposure to hydrocarbon substrates. The high number of unassigned genes observed in *Rhodococcus* sp. strain 1Y points to its capacity for genomic plasticity, potentially linked to the acquisition of metabolic pathways for hydrocarbon utilization. Similar trends have been noted in *Rhodococcus* strains isolated from diverse environments (Martínková et al., 2009; Yang et al., 2014). For *Gordonia* sp. strain BP10, its taxonomic placement within the genus was corroborated by high ANI values and phylogenomic clustering. The substantial accessory genome content observed in this strain reinforces the versatility of *Gordonia* in degrading recalcitrant hydrocarbons, particularly aromatic compounds. These findings resonate with prior studies that highlight the metabolic breadth of *Gordonia* species in adapting to contaminated environments (Ehiosun et al., 2022; Kim et al., 2018). The two *J. indicus* strains, on the other hand, exhibited remarkable genomic conservation, with more than 99 % of their protein-coding genes assigned to orthogroups. While their genomes were smaller compared to *Rhodococcus* and *Gordonia*, the high degree of conservation suggests reliance on core metabolic pathways rather than extensive genomic innovation. This may reflect their ecological strategy of specialization within specific niches. Notably, the broader significance of *Janibacter* in hydrocarbon degradation has often been understated, with few studies exploring its genetic and metabolic potential. The identification of unique genes in *J. indicus* strains provides an opportunity to probe their contributions to microbial consortia in oil-contaminated environments.

The presence of mobile genetic elements (MGEs) enables the targeted acquisition of catabolic functions, potentially supplementing the core genome and driving rapid adaptation (Tokuda and Shintani, 2024). This genomic flexibility provides hydrocarbon-degrading
bacteria with an evolutionary and adaptive advantage, allowing them to optimize metabolic pathways in response to environmental pressures. In this regard, genome mobility has long been recognized as a fundamental mechanism in microbial adaptation to xenobiotic compounds, facilitating the lateral dissemination of catabolic operons through plasmids, transposases, and integrative conjugative elements (Top and Springael, 2003). In all strains, the presence of numerous insertion sequences, alongside recombination-associated genes, further indicates the likelihood of genome rearrangements that enable rapid phenotypic shifts in response to selective pressures such as those experienced in hydrocarbon contaminated environment.

Plasmid-associated elements, particularly those related to replication, conjugation, and stability, are well-represented across all four strains, yet their enrichment in *Gordonia* sp. strain BP1o is especially striking. Although plasmids themselves were not detected, the presence of these elements suggests that extrachromosomal DNA may still serve as a reservoir for adaptive traits, possibly linked to hydrocarbon metabolism. Given that hydrocarbon degradation often requires complex multi-gene pathways, the clustering of key metabolic genes within mobile elements would provide a mechanism for their efficient horizontal transfer among microbial populations (Heuer and Smalla, 2012; Top and Springael, 2003). The observed prevalence of phage-related genes further reinforces this notion, as bacteriophages are well-documented vectors for genetic exchange, capable of reshuffling metabolic modules between species (Pope et al., 2017).

A particularly compelling observation is the co-occurrence of MGEs with genes implicated in recombination and DNA repair (recA, *xerC*, *ltrA*). This suggests an ongoing process of genetic refinement (Benler and Koonin, 2022), wherein hydrocarbon degradation genes undergo selection, amplification, or loss. The striking disparity in integrative/excisive elements, most abundant in *Gordonia* sp. strain BP1o, yet comparatively lower in *Rhodococcus* strain 1Y, may reflect differences in ecological specialization, with the former exhibiting a higher degree of genome modularity, possibly to accommodate niche-specific hydrocarbon substrates. The abundance of MGEs, coupled with evidence of genomic islands and prophage integration, suggests that this consortium is not merely a static collection of hydrocarbon degraders but rather a highly interactive genetic system.

The pangenome analysis offered a detailed view of the genomic landscape of these isolates, with a core genome of 1413 genes representing shared metabolic pathways essential for hydrocarbon degradation and survival in extreme environments. The presence of 299 unique genes across the isolates, coupled with variation in accessory genome content, points to the balance between conservation and specialization. For instance, *J. indicus* strains exhibited unexpectedly large genomic repertoires, suggesting metabolic diversity within this genus. This complements earlier findings on the role of Actinomycetota in degrading both aliphatic and aromatic hydrocarbons, particularly under challenging environmental conditions as reported elsewhere (Ezzat and Ahmed, 2022; Khessairi et al., 2014; Zhang et al., 2021).

Comparative genomic analysis further revealed striking differences in unassigned gene counts among the isolates. *Rhodococcus* sp. strain 1Y and *Gordonia* sp. strain BP1o contained a higher proportion of unassigned genes, likely reflecting evolutionary pressures that drive metabolic diversification, whereas *Janibacter* strains maintained a more streamlined genomic profile, suggesting a more niche-adapted metabolic strategy. *Gordonia* strains demonstrating broader metabolic flexibility compared to the more focused adaptations of *Janibacter*. Such variation among genera aligns with previous studies documenting the complementary roles of Gram-positive Actinomycetota in hydrocarbon degradation (Gao et al., 2023; Garrido-Sanz et al., 2020; Parab et al., 2022).

## 4.2. Genomic adaptations for hydrocarbon metabolism

The genomic analysis of the Actinomycetota isolates revealed diverse adaptations facilitating hydrocarbon degradation, with key distinctions observed across strains. *Rhodococcus* sp. strain 1Y emerged as the most versatile degrader, harboring a comprehensive repertoire of hydrocarbon-degradation genes, including *alkB*, *nahA* and *antA*. These genes are integral to the breakdown of both aliphatic and aromatic hydrocarbons (Muriel-Millán et al., 2019; Wang and Shao, 2014), reinforcing *Rhodococcus* sp. strain 1Y as a potential candidate for bioremediation of such compounds. Its extensive gene set is consistent with previous reports of *Rhodococcus* species as dominant hydrocarbon degraders in various environments (Garrido-Sanz et al., 2020; Martínková et al., 2009). In comparison, *Gordonia* sp. strain BP1o displayed a more specialized genomic profile, with a focus on aromatic compound degradation pathways, evidenced by the presence of *bphC*, *catD*, and chloroalkane degradation genes. The absence of *alkB* and *antA* suggests a narrower substrate range, or alternative mechanism of degradation. *J. indicus* strain 22 and *J. indicus* strain 70, on the one hand, exhibited similar genomic patterns, lacking key genes such as *alkB* but retaining aromatic hydrocarbon degradation genes, including *paaJ* and *todE*. This suggests the presence of alternative or less-characterized pathways enabling the degradation of both aliphatic and aromatic hydrocarbons. While the genomic data emphasize aromatic compound metabolism, the ability to utilize aliphatic hydrocarbons points to a more versatile metabolic strategy that may rely on unconventional or auxiliary mechanisms not captured by canonical gene annotations. This points to the adaptive potential of these strains in hydrocarbon-impacted environments, contributing to their ecological resilience and functional diversity.

Functional pathway analysis further highlighted both conserved and unique metabolic traits. *Rhodococcus* sp. strain 1Y demonstrated the broadest metabolic capacity, with pathways enabling the breakdown of diverse aromatic compounds, including benzoate and xylene, highlighting its versatility in hydrocarbon degradation. In contrast, *Gordonia* sp. strain BP1o exhibited a more specialized metabolic profile, with pathways predominantly linked to chloroalkane degradation, suggesting a narrower but more targeted functional role. The *Janibacter* strains, on the other hand displayed an intermediate metabolic strategy, maintaining a balance between pathway diversity and specificity, which likely supports their adaptation to specific aromatic hydrocarbons. These variations in metabolic potential reinforce the distinct ecological roles of each strain, where some act as broad-spectrum degraders, while others are more specialized contributors within microbial consortia.

Secondary metabolite pathway analyses further revealed the ecological resilience of these marine strains. The presence of
biosynthetic clusters encoding ectoine across all isolates suggests robust osmoprotection strategies, enabling them to withstand the osmotic stress characteristic of hypersaline environments. This adaptation is unsurprising, given their evolutionary history in marine ecosystems, where fluctuating salinity levels demand such survival mechanisms. These findings further explain the ecological versatility of these strains in thriving under challenging environmental conditions. Additionally, *Rhodococcus* sp. strain 1Y exhibited biosurfactant-producing metabolites like mycosubtilin, which enhance hydrocarbon solubilization and bioavailability (Mnif and Ghribi, 2015). Siderophores such as heterobactin A/S2 in *Rhodococcus* and amychelin in *Gordonia* were linked to iron acquisition (Bosello et al., 2012), a critical factor in supporting enzymatic degradation processes. Similar adaptations in oxidative stress mitigation were observed across the strains, with *J. indicus* strains exhibiting carotenoid-like terpene biosynthesis and arylpolyene production, aligning with their roles in oxidative environments.

These findings collectively point to a balance between core metabolic capabilities and alternative pathways enabling niche-specific adaptations across the isolates. The comprehensive gene repertoire of *Rhodococcus* sp. strain 1Y positions it as a potential keystone species for hydrocarbon bioremediation, capitalizing on its versatility in both aliphatic and aromatic hydrocarbon degradation. Meanwhile, the specialized genomic traits of *Gordonia* sp. strain BP10 and the *Janibacter* strains, including auxiliary or alternate metabolic pathways, suggest complementary roles within microbial consortia. These interactions likely enhance overall biodegradation efficiency, demonstrating the resilience and functional diversity of these strains in hydrocarbon-impacted marine environments. Such insights have significant implications for designing effective and sustainable bioremediation strategies.

### 4.3. Transcriptional regulation of hydrocarbon degradation and adaptive metabolic responses

The observed growth dynamics of the bacterial consortium suggest a strong adaptive response to hydrocarbon exposure. This trend likely reflects nutrient depletion or metabolic shifts associated with hydrocarbon degradation, corroborating the consortium's efficient utilization of a mixture of hydrocarbons as sole carbon sources (Bacosa et al., 2011; Poddar et al., 2019). The pronounced cell density increase aligns with hexadecane degradation kinetics, where residual hexadecane levels dropped rapidly. In comparison, phenanthrene degradation proceeded at a slower pace, with levels significantly (p < 0.001). The slower degradation rate of phenanthrene may reflect the complexity of aromatic hydrocarbon metabolism compared to aliphatic hydrocarbons (Liu et al., 2022). Abiotic controls retained 89.25 % and 93.74 % of hexadecane and phenanthrene, respectively, by Day 12, underlining the critical role of the consortium in enzymatically driven hydrocarbon breakdown.

Further information about the bacterial consortium's genetic capacity for hydrocarbon degradation is provided by its transcriptional profiling. The presence of genes associated with alkane and PAH metabolism, including *alkB1_2*, *alkM*, *ladA* for alkane hydroxylation, *catA*, *dmpC* for aromatic hydrocarbon degradation, *fadA*, *fadI*, *fadD*36 for β-oxidation, and *katG*, *ahpF* for oxidative stress response, attests to a microbial system well-equipped to metabolize petroleum-derived contaminants. However, these key genes were not differentially expressed, likely owing to the time of sampling. This is likely so particularly in the case of alkane degradation, wherein nearly complete substrate depletion by Day 3, suggesting that active oxygenation had largely subsided by the time of sampling. Similar findings of *alkB* detection and their expression detected only within 2 days of a microcosm experiment was reported elsewhere, although alkane degradation was observed throughout the 14 days of incubation (Paisse et al., 2011). Similarly, the identification of genes encoding β-oxidation enzymes, such as *fadA*, *fadI* (Iram and Cronan, 2006), indicates that the consortium possesses the necessary machinery to process hydrocarbon-derived fatty acids into central metabolic intermediates. The β-oxidation pathway serves as a crucial link between alkane oxidation and cellular energy metabolism, enabling the sequential removal of acetyl-CoA units for integration into the tricarboxylic acid (TCA) cycle (Alvarez, 2003).

The detection of *catA* and *dmpC* related genes, both of which play critical roles in catechol and methylcatechol oxidation, respectively (Kruyer et al., 2020; Pérez-Pantoja et al., 2008), suggests that the consortium is also well-equipped to metabolize aromatic hydrocarbons via the β-ketoadipate pathway. Aside primary hydrocarbon metabolism, the identification of oxidative stress response genes such as *katG* and *ahpF* (Ochsner et al., 2000) highlights the physiological burden of hydrocarbon oxidation. However, while the presence of these genes is indicative of metabolic potentials, their role in hydrocarbon degradation can only be determined through an analysis of differential gene expression. The initial upregulation of genes associated with phenylacetate metabolism (*paaF*, *echA*), oxidative stress defense (*ethA*, *katG*), and lipid metabolism (*fadB*, *mmgB*) in early time points (Day 3 to Day 6) aligns with the expected metabolic response to hostile environments (Nambi et al., 2015) such as xenobiotic compounds like hydrocarbons (Lemire et al., 2017). This phase likely reflects the active degradation of available hydrocarbons, with oxidation pathways operating at full capacity to convert phenanthrene into intermediary metabolites. This is consistent with previous study with *Croceicoccus naphthovorans* strain PQ-2, wherein an activation of downstream catabolic pathways essential for hydrocarbon degradation was induced by phenanthrene exposure (Meng et al., 2023). In the present study, by Day 9, the upregulation of 156 genes relative to Day 6 suggests that the consortium had reached an optimal metabolic state, efficiently processed hydrocarbons while simultaneously activating stress response mechanisms to counteract ROS generated during oxidation. The simultaneous upregulation of *katG* affirms this interpretation, as ROS accumulation is a well-documented consequence of xenobiotic compound metabolism (Lemire et al., 2017).

The pronounced transcriptional decline observed at Day 12, with 487 out of 502 DEGs down-regulated compared to Day 3, marks a striking metabolic shift. A similar pattern was observed in comparisons between Day 12 and Day 6, where 450 out of 462 DEGs were suppressed. This widespread gene repression reveals a global downregulation of metabolic activity, likely driven by hydrocarbon depletion. As substrates become scarce, bacteria often enter a state of metabolic quiescence, reducing energy-intensive processes and shifting towards maintenance metabolism (Valcourt et al., 2012). An alternative explanation for this decline could be physiological stress, potentially linked to nutrient imbalances or toxic byproducts accumulating over time. In hydrocarbon-degrading consortia, prolonged exposure to recalcitrant compounds can lead to metabolic bottlenecks, where the depletion of primary substrates (e.g.,
hexadecane) forces a shift to less energetically favorable pathways, ultimately leading to growth limitation (Howland et al., 2025; Morris et al., 2013).

Among the differentially expressed genes, *catA* displayed an intriguing pattern, appearing in both upregulated and downregulated gene sets across multiple time points. This suggests a complex regulatory interplay rather than a simple on/off transcriptional response. The plausible explanations are either isoform- or strain-specific regulation, where different *catA* variants are expressed under varying conditions by a strain or different strains, respectively. For instance, *Burkholderia* sp. strain TH2 contains two distinct catechol 1,2-dioxygenases that are differentially expressed depending on the substrate present (Suzuki et al., 2002). This differential expression allows the organism to adapt to various aromatic compounds by selectively utilizing specific isoforms. Another possibility is a metabolic shift, where catechol degradation is initially active but later suppressed due to feedback inhibition or depletion of upstream precursors (Collier et al., 1998). Thus, recurrent differential expression of *catA* across time points affirms its centrality in aromatic hydrocarbon metabolism and indicates that catechol degradation is dynamically modulated in response to changing metabolic demands. Importantly, the transcriptional responses of the bacterial consortium showed clear temporal coordination (Fig. 5F) that reflected the observed hydrocarbon degradation patterns (Fig. 5B). For alkanes, initial oxidation genes such as *alkB* (K00496) and *ladA* (K20938) were detected in the transcriptome but were not differentially expressed across time points. This aligns with the rapid depletion of hexadecane by day 3, suggesting that alkane hydroxylation occurred swiftly and predominantly before our first transcriptionomic time point, resulting in little further induction. In contrast, downstream β-oxidation genes such as *fadD* (K01897), *fadB/fadE homologs* (K01692), and *thiolase* (K00626) showed clear, time-resolved regulation: these genes were upregulated between days 3 and 9, consistent with ongoing fatty acid metabolism following initial oxidation, and were subsequently downregulated by day 12 as the substrate pool was depleted. For PAH degradation, initial ring-hydroxylating dioxygenase genes such as *bphA* (K05712) and *tdoA* (K00446), though detected, were not differentially expressed. This is likely due to their known low catalytic efficiency toward phenanthrene, consistent with the slower and more gradual phenanthrene degradation observed. Their basal expression may be sufficient to support limited initial oxidation. Meanwhile, downstream pathway genes exhibited dynamic responses: *catA* (K03381) showed fluctuating expression, suggesting responsive flux through the ortho-cleavage pathway in response to intermediate accumulation, while *K00500* (meta-cleavage) was downregulated between days 6 and 12, indicating reduced pathway activity as phenanthrene levels declined. The final ring-cleavage gene *K02554* remained constitutively expressed. This pattern mirrors the overall DEG trends observed during the phenanthrene degradation phase, with an activation phase dominated by upregulation (95 upregulated genes in 3 vs. 9; 156 upregulated in 6 vs. 9), as the consortium metabolized phenanthrene and its intermediates, followed by a marked transcriptional downscaling at day 12 (487 downregulated genes in 3 vs. 12), corresponding to substrate depletion and reduced metabolic demand. These dynamics indicate a coordinated gene expression program that paralleled the temporal progression of phenanthrene degradation, fully supporting the observed functional activity of the consortium.

The observed transcriptional patterns corroborate the idea that hydrocarbon degradation in bacterial consortia is not a static process but a highly regulated, temporally structured adaptation to environmental conditions (Cao et al., 2022; Potts et al., 2018). These findings contribute to our understanding of the microbial dynamics underlying biodegradation and highlight the importance of temporal transcriptional regulation in optimizing hydrocarbon removal. The metatranscriptomic analysis on the four strains consortium presented here provides strong evidence of transcriptional activity, identifying key genes involved in hydrocarbon degradation. However, we acknowledge that targeted qPCR validation of key genes would be required to further strengthen the conclusions. It should also be noted that the metatranscriptomic analysis was performed on composite samples from biological triplicates because of difficulties in obtaining sufficient high-quality RNA from individual replicates in hydrocarbon-amended media as reported in similar studies (Bottos et al., 2020; Bradford et al., 2018; Liu et al., 2017; Zampolli et al., 2021). Nevertheless, although further studies are still needed to confirm the transcriptional dynamics observed during hydrocarbon degradation by the consortium, our study provides useful information to focus future studies on key metabolic pathways.

## 5. Conclusion and future directions

This study provides valuable insights into the genomic and functional adaptations of marine Actinomycetota, emphasizing their pivotal role in hydrocarbon degradation. The findings reveal a balance between core metabolic capabilities and alternative pathways enabling niche-specific adaptations, highlighting the ecological and functional diversity of these strains. The comprehensive gene repertoire of *Rhodococcus* sp. strain 1Y positions it as a potential keystone species for hydrocarbon bioremediation, utilizing its versatility in degrading both aliphatic and aromatic hydrocarbons. Meanwhile, the specialized genomic traits of *Gordonia* sp. strain BP1o and the *Janibacter* strains, including auxiliary or alternate metabolic pathways, suggest distinct roles within microbial synthetic coastal marine consortium. Their metabolic capabilities suggest potential contributions to hydrocarbon degradation, reflecting the adaptability of these strains in hydrocarbon-impacted marine environments. This study, through linking genomic potential with functional outcomes, reinforces the value of *Actinomycetota* as a key resource for addressing environmental pollution. The integrative genomic and transcriptomic approach employed here offers a comprehensive understanding of microbial hydrocarbon degradation processes, providing a robust framework for advancing microbial bioremediation strategies. Such insights hold immense potential for application in bioremediation technologies, particularly in mitigating the environmental impact of oil spills and hydrocarbon-contaminated sediments. Harnessing the capabilities of these microbial consortia could pave the way for sustainable and eco-friendly solutions to hydrocarbon pollution in marine ecosystems. The findings of this study directly inform the design of bioremediation approaches suitable for marine environments. The strains were not only isolated from hydrocarbon-contaminated marine sediments but were also tested under conditions that closely mimic the natural marine environment, using swMM formulated to match coastal seawater salinity and composition. The consortium demonstrated the ability to degrade both aliphatic and aromatic
hydrocarbons in this medium, highlighting their ecological fitness in saline, hydrocarbon-impacted habitats. Furthermore, the dynamic regulation of hydrocarbon degradation pathways, osmoprotective ectoine synthesis, biosurfactant production to enhance hydrocarbon bioavailability, and oxidative stress mitigation collectively underscore the adaptive strategies that enable these strains to function effectively under marine conditions. These insights support the practical relevance of deploying such consortia for in situ bioremediation of oil-contaminated marine sediments and contribute to advancing microbial solutions for mitigating the environmental impact of marine oil pollution.

Future research should expand metatranscriptomic studies to explore broader microbial communities, uncovering additional functional adaptations and interactions critical for hydrocarbon degradation. In addition, future studies should aim to further clarify strain-specific contributions within synthetic coastal marine consortium, enabling a more detailed understanding of their respective functional roles in hydrocarbon degradation. *In-situ* applications should also be prioritized to validate these findings and scale bioremediation strategies in real-world scenarios. Furthermore, investigating microbial consortia in diverse environmental contexts will deepen our understanding of their adaptive mechanisms, enabling tailored interventions to address complex pollution challenges. No doubt, these efforts will further advance the design of effective and sustainable bioremediation strategies, contributing to the restoration of marine ecosystems impacted by hydrocarbon pollution.

### CRediT authorship contribution statement

**Robert Duran:** Writing – review & editing, Writing – original draft, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization. **Cristiana Cravo-Laureau:** Writing – review & editing, Writing – original draft, Validation, Supervision, Methodology, Investigation, Formal analysis, Conceptualization. **Bashar Haruna Gulumbe:** Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Conceptualization.

### Ethics Statement

This study did not involve human or animal subjects and therefore did not require ethical approval. Microbial strains were obtained from environmental samples, and all procedures adhered to institutional and national biosafety regulations.

### Funding

We extend our gratitude for the financial support provided to the MAEWA project under the H2020-EU-PRIMA program (PRIMA No. 1946), with funding administered through the French National Research Agency (ANR-23-P012-0004-01). Additionally, BHG acknowledges the support of a PTDF grant (PTDF/ED/OSS/PHD/BHG/1798/2020PHD106), which contributed to the success of this work.

### Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

We acknowledge Dr. Maria Lorena Falco for her technical expertise and assistance throughout this study.

### Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.eti.2025.104361.

### Data availability

Data will be made available on request.

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