Biology subjects

Vignolle, G. A.

Publications and source records attributed to Vignolle, G. A..

4 recordsLinked to original sources

bioRxiv2025

Dark matter of an orchid: metagenome of the microbiome associated with the rhizosphere of Dactylorhiza traunsteineri

Plant microbiota forms complex co-associations with its host, promoting health in natural environments. Root-associated bacteria (RAB) colonize root compartments and can modulate plant functions by producing bioactive compounds as part of their secondary metabolism. We present an in-depth analysis of the rhizosphere-associated microbiome of the endangered marsh orchid Dactylorhiza traunsteineri. Using deep sequencing of 16S rRNA genes, we identified Proteobacteria, Actinobacteria, Myxococcota, Bacteroidota, and Acidobacteria as predominant phyla associated with the rhizosphere of D. traunsteineri. Using deep shotgun metagenomics and de novo assembly, we extracted high-quality metagenome-assembled genomes (MAGs), revealing significant metabolic and biosynthetic potentials of the D. traunsteineris RABs. Our study offers a comprehensive investigation into the microbial community of the D. traunsteineri rhizosphere, highlighting a potential novel source of critical bioactive substances. Our study offers novel insights and a robust platform for future investigations into D. traunsteineris rhizosphere, crucial for understanding plant-microbe interactions and aiding conservation efforts for endangered orchids.

microbiology↗

bioRxiv2025

Genome sequencing and physiological characterization of three Neoarthrinium moseri strains

Fungi play essential ecological roles and have been utilized by humans for diverse applications such as industrial enzyme production or as sources of bioactive compounds. Recent research has highlighted the Amphisphaeriales order (Ascomycota) as promising producers of secondary metabolites of pharmaceutical importance. Within this family, the recently established genus Neoarthrinium includes species such as N. brasiliensis, N. lithocarpicola, N. moseri, N. trachycarpi, and N. urticae. Existing literature has primarily focused on the taxonomy and phylogeny of Neoarthrinium, leaving its physiology, ecology, and metabolic potential unexplored. This study presents the first investigation of the metabolic and genomic potential of N. moseri. We describe the isolation of two South-Asian N. moseri strains and the genome sequencing of these strains alongside the Colombian ex-type strain for the species. Comparative genome analysis reveals an exceptionally high number of biosynthetic gene clusters (BGCs), surpassing those of many other fungi in the Amphisphaeriales order. Additionally, the genome of N. moseri contains a diverse repertoire of carbohydrate-active enzymes (CAZymes), supporting its hypothesized ecological role as a phyllosphere fungus (putatively an endophyte and/or saprotroph). Ecophysiological assays, including BIOLOG phenotyping, demonstrate its ability to utilize a broad range of carbon sources, emphasizing ecological versatility. This study highlights N. moseri as a promising candidate for secondary metabolite discovery, providing foundational insights into the metabolic and genomic potential of the Neoarthrinium genus and related fungi.

microbiology↗

bioRxiv2022

FunOrder 2.0 - a fully automated method for the identification of co-evolved genes

Coevolution is an important biological process that shapes interacting species or even proteins - may it be physically interacting proteins or consecutive enzymes in a metabolic pathway. The detection of co-evolved proteins will contribute to a better understanding of biological systems. Previously, we developed a semi-automated method, termed FunOrder, for the detection of co-evolved genes from an input gene or protein set. We demonstrated the usability and applicability of FunOrder by identifying essential genes in biosynthetic gene clusters from different ascomycetes. A major drawback of this original method was the need for a manual assessment, which may create a user bias and prevents a high-throughput application. Here we present a fully automated version of this method termed FunOrder 2.0. To fully automatize the method, we used several mathematical indices to determine the optimal number of clusters in the FunOrder output, and a subsequent k-means clustering based on the first three principal components of a principal component analysis of the FunOrder output. Further, we replaced the BLAST with the DIAMOND tool, which enhanced speed and allows the future integration of larger proteome databases. The introduced changes slightly decreased the sensitivity of this method, which is outweighed by enhanced overall speed and specificity. Additionally, the changes lay the foundation for future high-throughput applications of FunOrder 2.0 in different phyla to solve different biological problems. AUTHOR SUMMARYCoevolution is a process which arises between different species or even different proteins that interact with each other. Any change occurring in one partner must be met by a corresponding change in the other partner to maintain the interaction throughout evolution. These interactions may occur in symbiotic relationships or between rivaling species. Within an organism, consecutive enzymes of metabolic pathways are also subjected to coevolution. We developed a fully automated method, FunOrder 2.0, for the detection of co-evolved proteins, which will contribute to a better understanding of protein interactions within an organism. We demonstrate that this method can be used to identify essential genes of the secondary metabolism of fungi, but FunOrder 2.0 may also be used to detect pathogenicity factors or remains of horizontal gene transfer next to many other biological systems that were shaped by coevolution.

bioinformatics↗

bioRxiv2021

The Functional Order (FunOrder) tool - Identification of essential biosynthetic genes through computational molecular co-evolution

Secondary metabolites (SMs) are a vast group of compounds with different structures and properties. Humankind uses SMs as drugs, food additives, dyes, and as monomers for novel plastics. In many cases, the biosynthesis of SMs is catalysed by enzymes whose corresponding genes are co-localized in the genome in biosynthetic gene clusters (BGCs). Notably, BGCs may contain so-called gap genes, that are not involved in the biosynthesis of the SM. Current genome mining tools can identify BGCs but they have problems with distinguishing essential genes from gap genes and defining the borders of a BGC. This can and must be done by expensive, laborious, and time-consuming comparative genomic approaches or co-expression analyses. In this study, we developed a novel tool that allows automated identification of essential genes in a BGC based solely on genomic data. The Functional Order (FunOrder) tool - Identification of essential biosynthetic genes through computational molecular co-evolution - searches for co-evolutionary linked genes in the BGCs. In light of the growing number of genomic data available, this will contribute to the studies of BGCs in native hosts and facilitate heterologous expression in other organisms with the aim of the discovery of novel SMs, including antibiotics and other pharmaceuticals.

bioinformatics↗