بازسازي شبكه متابوليك اگزوفيالا اسپنيفرا براي بهبود فرآيند گوگردزدايي زيستي از دي بنزوتيوفن

Abstract The fungal strain Exophiala spinifera FM, a black yeast isolated from oil-contaminated soil, possesses the ability to utilize polycyclic aromatic sulfur compounds such as dibenzothiophene (DBT) as its sole sulfur source, making it a promising can‎didate for biodesulfurization applications. Despite the advantages of biological methods in removing complex sulfur compounds, the metabolic mechanisms an‎d molecular pathways involved in this process remain poorly understood in this strain. The primary objective of this study was to investigate the metabolic capabilities of Exophiala spinifera strain FM through genome sequencing an‎d analysis, followed by reconstruction of a genome-scale metabolic model to comprehensively understan‎d its metabolism an‎d desulfurization pathways. In this study, Illumina technology was used to sequence the complete genome of Exophiala spinifera strain FM. Data quality was assessed using FastQC, adapter removal an‎d trimming were performed with Trimmomatic, an‎d the genome was assembled using SPAdes. Gene prediction an‎d eva‎luation were conducted using Augustus an‎d BUSCO. Annotation was enhanced using BLAST an‎d databases such as KEGG, UniProt, an‎d NCBI. Biosynthetic gene clusters were identified using AntiSMASH. Metabolic model reconstruction was performed using the RAVEN toolbox in MATLAB, an‎d metabolic gaps in the draft model were filled using the FastGapFill function from the COBRA toolbox. Manual refinements an‎d model optimization were carried out using the Python version of COBRA. Model validation included flux balance analysis, shadow price analysis, an‎d gene essentiality assessment. The final model, named iEsp1694, represents the first genome-scale metabolic model for Exophiala spinifera. It comprises 4,438 reactions, 1,694 genes, an‎d 3,019 metabolites distributed across 14 intracellular compartments. Among these reactions, 1,088 are transport reactions, 297 are exchange reactions, an‎d 3,052 are metabolic reactions. In this model, 95% of metabolic reactions are associated with at least one gene. Of the 1,694 genes, 48.8% are monofunctional, while the remaining (768) participate in multiple reactions. In terms of model connectivity, 1,321 metabolites are involved in two metabolic reactions, 395 in three reactions, an‎d 840 in more than three reactions. The model encompasses a wide range of metabolic reactions in Exophiala spinifera, including key pathways such as central metabolism, amino acid biosynthesis, nucleotide metabolism, sulfur metabolism, an‎d even xenobiotic degradation pathways for compounds like PET an‎d atrazine. The model successfully predicted an‎d explained prior experimental observations, contributing to a better understan‎ding of biodesulfurization processes. Shadow price analysis revealed that metabolites such as 3ʹ-phospho-5ʹ-adenylyl sulfate, 5ʹ-adenylyl sulfate, an‎d choline sulfate are costly for the cell when dibenzothiophene is used as the sulfur source. iEsp1694 provides a comprehensive framework for understan‎ding the metabolic capabilities of this valuable fungal strain an‎d serves as a key resource for future studies aimed at better utilizing Exophiala spinifera for industrial applications. It also enables model-driven hypothesis generation an‎d lays the foundation for metabolic engineering an‎d optimization of biodesulfurization processes in the future. Keywords: Exophiala spinifera , Biodesulfurization, Dibenzothiophene, System biology, Genome-scale metabolic model.

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