@article{fdi:010088893,
  title = {{G}enome mining of metabolic gene clusters in the {R}ubiaceae family},
  author = {de {L}emos, {S}. {M}. {C}. and {P}aschoal, {A}. {R}. and {G}uyot, {R}omain and {M}edema, {M}. and {D}omingues, {D}. {S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he {R}ubiaceae plant family, comprising 3 subfamilies and over 13,000 species, is known for producing significant bioactive compounds such as caffeine and monoterpene indole alkaloids. {D}espite an increase in available genomes from the {R}ubiaceae family over the past decade, a systematic analysis of the metabolic gene clusters ({MGC}s) encoded by these genomes has been lacking. {I}n this study, we aim to identify and analyze metabolic gene clusters within complete {R}ubiaceae genomes through a comparative analysis of eight species. {A}pplying two bioinformatics pipelines, we identified 2372 candidate {MGC}s, organized into 549 gene cluster families ({GCF}s). {T}o enhance the reliability of these findings, we developed coexpression networks and conducted orthology analyses. {U}sing genomic data from {S}olanum lycopersicum ({S}olanaceae) for comparative purposes, we provided a detailed view of predicted metabolic enzymes, pathways, and coexpression networks. {W}e bring some examples of {MGC}s and {GCF}s involved in biological pathways of terpenes, saccharides and alkaloids. {S}uch insights lay the groundwork for discovering new compounds and associated {MGC}s within the {R}ubiaceae family, with potential implications in developing more robust crop species and expanding the understanding of plant metabolism. {T}his large-scale exploration also provides a new perspective on the evolution and structure-function relationship of these clusters, offering opportunities for the highly efficient utilization of these unique metabolites. {T}he outcome of this study contributes to a broader comprehension of the biosynthetic pathways, elucidating multiple aspects of specialized metabolism and offering innovative avenues for biotechnological applications.},
  keywords = {{M}etabolic gene cluster ; {C}omparative genomics ; {R}ubiaceae},
  booktitle = {},
  journal = {{C}omputational and {S}tructural {B}iotechnology {J}ournal},
  volume = {23},
  numero = {},
  pages = {22--33},
  ISSN = {2001-0370},
  year = {2024},
  DOI = {10.1016/j.csbj.2023.11.034},
  URL = {https://www.documentation.ird.fr/hor/fdi:010088893},
}