Marques de Castro, G. | Search bioRxiv

bioRxiv2025

Sequencing and functional analysis of Sphingobium yanoikuyae A-TP genome reveals genes for utilization of limonene, α-pinene, and citronellol

Sphingobium yanoikuyae A-TP is a bacterial strain capable of utilizing and biotransforming commercially relevant monoterpenes. Genome mining and similarity-based analysis identified two gene clusters (24 and 30 genes) likely responsible for limonene, -pinene, and citronellol metabolism. Notably, genes associated with -pinene metabolism -- an oxide lyase and an aldehyde dehydrogenase -- were clustered with those for citronellol utilization, despite limonenes closer structural similarity to -pinene. Growth experiments confirmed citronellol as a carbon source, but linalool, geraniol and {beta}-myrcene were not substrates for this strain. To address the unresolved identity of limonene 8-hydratase, the enzyme catalyzing limonene conversion to -terpineol, we performed RNA-seq analysis on strains with contrasting phenotypes which identified highly upregulated candidate genes. Phylogenetic analysis supports the classification of this strain as a Sphingobium yanoikuyae species. This study advances the understanding of monoterpene catabolism in Sphingomonadaceae and provides a foundation for biotechnological production of monoterpenes and their derivatives. Impact statementMonoterpenes, such as limonene and -pinene, are often used as starting material to produce derivatives that are of interest to the aroma and fragrance industry. In this work we uncover the genes harbored in the Sphingobium yanoikuaye A-TP genome that are putatively involved in the metabolism of limonene, citronellol and -pinene. We also propose several candidates for the limonene 8-hydratase, an enzyme that converts limonene to -trepineol. The discovery of these genes can be explored for biotechnological production of industrially important compounds. Data summaryAll genomic and transcriptomic data from Illumina sequencing are available at the NCBI GenBank archive under the BioProject accession number PRJNA1196986. The Supplementary Material has been uploaded to Microbiology Society figshare: 10.6084/m9.figshare.28184306 Author NotesThe authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. Seven supplementary figures, three supplementary tables, and four supplementary files are available with the online version of this article.

genomics↗

bioRxiv2021

Cross-species prediction of essential genes in insects through machine learning and sequence-based attributes

Insects are organisms with a vast phenotypic diversity and key ecological roles. Several insect species also have medical, agricultural and veterinary importance as parasites and vectors of diseases. Therefore, strategies to identify potential essential genes in insects may reduce the resources needed to find molecular players in central processes of insect biology. Furthermore, the detection of essential genes that occur only in certain groups within insects, such as lineages containing insect pests and vectors, may provide a more rational approach to select essential genes for the development of insecticides with fewer off-target effects. However, most predictors of essential genes in multicellular eukaryotes using machine learning rely on expensive and laborious experimental data to be used as gene features, such as gene expression profiles or protein-protein interactions. This information is not available for the vast majority of insect species, which prevents this strategy to be effectively used to survey genomic data from non-model insect species for candidate essential genes. Here we present a general machine learning strategy to predict essential genes in insects using only sequence-based attributes (statistical and physicochemical data). We validate our strategy using genomic data for the two insect species where large-scale gene essentiality data is available: Drosophila melanogaster (fruit fly, Diptera) and Tribolium castaneum (red flour beetle, Coleoptera). We used publicly available databases plus a thorough literature review to obtain databases of essential and non-essential genes for D. melanogaster and T. castaneum, and proceeded by computing sequence-based attributes that were used to train statistical models (Random Forest and Gradient Boosting Trees) to predict essential genes for each species. Both models are capable of distinguishing essential from non-essential genes significantly better than zero-rule classifiers. Furthermore, models trained in one insect species are also capable of predicting essential genes in the other species significantly better than expected by chance. The Random Forest D. melanogaster model can also distinguish between essential and non-essential T. castaneum genes with no known homologs in the fly significantly better than a zero-rule model, demonstrating that it is possible to use our models to predict lineage-specific essential genes in a phylogenetically distant insect order. Here we report, to the best of our knowledge, the development and validation of the first general predictor of essential genes in insects using sequence-based attributes that can, in principle, be computed for any insect species where genomic information is available. The code and data used to predict essential genes in insects are freely available at https://github.com/g1o/GeneEssentiality/.

genomics↗