Contreras Moreira, B. | Search bioRxiv

bioRxiv2021

Evolution and functional dynamics of dehydrins in model Brachypodium grasses

Dehydration proteins (dehydrins, DHNs) confer tolerance to water-stress deficit to plants, thus playing a fundamental role in plant response and adaptation to water-deprivation stressful environments. We have performed a comparative genomics and evolutionary study of DHN genes in four model Brachypodium grass species, and a drought-induced functional analysis in 32 ecotypes of the flagship species B. distachyon, to gain insight into the origins and dynamics of these proteins and the correlated drought-mediated phenotypic responses in ecotypes showing different hydric requirements. Genomic sequence analysis detected 10 types of dehydrin genes (Bdhn) across the Brachypodium species, totalling 47 genes. Domain and conserved motif contents of peptides encoded by Bdhn genes revealed eight protein architectures, YS{phi}K2 being the most common architecture. Bdhn genes were spread across several chromosomes and more frequent in syntenic chromosomes 3 and 4 of B. distachyon, 4 and 5 of B. stacei and 4 of B. sylvaticum. Tandem and segmental duplication events were detected for four Bdhn genes. Selection analysis indicated that all the Bdhn genes were constrained by purifying selection. Three upstream cis-regulatory motifs (BES1, MYB124, ZAT) were consistently detected in several Bdhn genes. Functional analysis in 32 natural accessions of B. distachyon demonstrated that only four Bdhn genes (Bdhn1, Bdhn2, Bdhn3, Bdhn7) were expressed in mature leaves and that all of them were significantly more highly expressed in plants under drought conditions. These genes corresponded to wheat orthologs that were also significantly more expressed under drought stress. Brachypodium dehydrin expression was significantly correlated with drought-response phenotypic traits (plant biomass, leaf carbon and proline contents and WUE increases, leaf water and nitrogen content changes) being more pronounced in drought-tolerant ecotypes. Bdhn expression, associated phenotypic trait changes and climate niche variation did not show significant phylogenetic signal when tested in the B. distachyon genealogical-species tree. By contrast, some of them showed low or marginal significant phylogenetic signal when tested in the B. distachyon Bdhn tree, suggesting that Bdhn gene evolution is partially related to adaptation to drought in this species. Our results demonstrate that dehydrin composition and regulation is a key factor determining the acquisition of water-stress tolerance in grasses.

genetics↗

bioRxiv2017

MEBS, a software platform to evaluate large (meta)genomic collections according to their metabolic machinery: unraveling the sulfur cycle

BACKGROUNDThe increasing number of metagenomic and genomic sequences has dramatically improved our understanding of microbial diversity, yet our ability to infer metabolic capabilities in such datasets remains challenging.\n\nFINDINGSWe describe the Multigenomic Entropy Based Score pipeline (MEBS), a software platform designed to evaluate, compare and infer complex metabolic pathways in large omic datasets, including entire biogeochemical cycles. MEBS is open source and available through https://github.com/eead-csic-compbio/metagenome_Pfam_score. To demonstrate its use we modeled the sulfur cycle by exhaustively curating the molecular and ecological elements involved (compounds, genes, metabolic pathways and microbial taxa). This information was reduced to a collection of 112 characteristic Pfam protein domains and a list of complete-sequenced sulfur genomes. Using the mathematical framework of relative entropy (H), we quantitatively measured the enrichment of these domains among sulfur genomes. The entropy of each domain was used to both: build up a final score that indicates whether a (meta)genomic sample contains the metabolic machinery of interest and to propose marker domains in metagenomic sequences such as DsrC (PF04358). MEBS was benchmarked with a dataset of 2,107 non-redundant microbial genomes from RefSeq and 935 metagenomes from MG-RAST. Its performance, reproducibility, and robustness were evaluated using several approaches, including random sampling, linear regression models, Receiver Operator Characteristic plots and the Area Under the Curve metric (AUC). Our results support the broad applicability of this algorithm to accurately classify (AUC=0.985) hard to culture genomes (e.g., Candidatus Desulforudis audaxviator), previously characterized ones and metagenomic environments such as hydrothermal vents, or deep-sea sediment.\n\nCONCLUSIONSOur benchmark indicates that an entropy-based score can capture the metabolic machinery of interest and be used to efficiently classify large genomic and metagenomic datasets, including uncultivated/unexplored taxa

bioinformatics↗