Biology subjects

Greve, M.

Publications and source records attributed to Greve, M..

3 recordsLinked to original sources

bioRxiv2022

Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data

Understanding the mechanisms underlining forage production and its biomass nutritive quality at the omics level is crucial for boosting the output of high-quality dry matter per unit of land. Despite the advent of multiple omics integration for the study of biological systems in major crops, investigations on forage species are still scarce. Therefore, this study aimed to combine multi-omics from grass hybrids by prioritizing omic features based on the reconstruction of interacting networks and assessing their relevance in explaining economically important phenotypes. Transcriptomic and NMR-based metabolomic data were used for sparse estimation via the fused graphical lasso, followed by modularity-based gene expression and metabolite-metabolite network reconstruction, node hub identification, omic-phenotype association via pairwise fitting of a multivariate genomic model, and machine learning-based prediction study. Analyses were jointly performed across two data sets composed of family pools of hybrid ryegrass (Lolium perenne x L. multiflorum) and Festulolium loliaceum (L. perenne x Festuca pratensis), whose phenotypes were recorded for eight traits in field trials across two European countries in 2020/21. Our results suggest substantial changes in gene co-expression and metabolite-metabolite network topologies as a result of genetic perturbation by hybridizing L. perenne with another species within the genus relative to across genera. However, conserved hub genes and hub metabolomic features were detected between pedigree classes, some of which were highly heritable and displayed one or more significant edges with agronomic traits in a weighted omics-phenotype network. In spite of tagging relevant biological molecules as, for example, the light-induced rice 1 (LIR1), hub features were not necessarily better explanatory variables for omics-assisted prediction than features stochastically sampled. The use of the graphical lasso method for network reconstruction and identification of biological targets is discussed with an emphasis on forage grass breeding.

genetics↗

bioRxiv2022

Leveraging spatio-temporal genomic breeding value estimates of dry matter yield and herbage quality in ryegrass via random regression models

Joint modeling of correlated multi-environment and multi-harvest data of perennial crop species may offer advantages in prediction schemes and a better understanding of the underlying dynamics in space and time. The goal of the present study was to investigate the relevance of incorporating the longitudinal dimension of within-season multiple harvests of biomass yield and nutritive quality traits of forage perennial ryegrass (Lolium perenne L.) in a reaction norm model setup that additionally accounts for genotype-environment interactions. Genetic parameters and accuracy of genomic breeding value predictions were investigated by fitting three random regression (random coefficients) linear mixed models (gRRM) using Legendre polynomial functions to the data. All models accounted for heterogeneous residual variance and moving average-based spatial adjustments within environments. The plant material consisted of 381 bi-parental family pools and four check varieties of diploid perennial ryegrass evaluated in eight environments for biomass yield and nutritive quality traits. The longitudinal dimension of the data arose from multiple harvests performed four times annually. The specified design generated a total of 16,384 phenotypic data points for each trait. Genomic DNA sequencing was performed using DNA nanoball-based technology (DNBseq) and yielded 56,645 single nucleotide polymorphisms (SNPs) which were used to calculate the allele frequency-based genomic relationship matrix used in all genomic random regression models. Biomass yields estimated additive genetic variance and heritability values were higher in later harvests. The additive genetic correlations were moderate to low in early measurements and peaked at intermediates, with fairly stable values across the environmental gradient, except for the initial harvest data collection. This led to the conclusion that complex genotype-by-environment interaction (GxE) arises from spatial and temporal dimensions in the early season, with lower re-ranking trends thereafter. In general, modeling the temporal dimension with a second-order orthogonal polynomial in the reaction norm mixed model framework improved the accuracy of genomic estimated breeding value prediction for nutritive quality traits, but no gain in prediction accuracy was detected for dry matter yield. This study leverages the flexibility and usefulness of gRRM models for perennial ryegrass research and breeding and can be readily extended to other multi-harvest crops.

genetics↗

bioRxiv2022

Globally invariant metabolism but density-diversity mismatch in springtails

Soil life supports the functioning and biodiversity of terrestrial ecosystems1, 2. Springtails (Collembola) are among the most abundant soil animals regulating soil fertility and flow of energy through above- and belowground food webs3-5. However, the global distribution of springtail diversity and density, and how these relate to energy fluxes remains unknown. Here, using a global dataset collected from 2,470 sites, we estimate total soil springtail biomass at 29 Mt carbon (threefold higher than wild terrestrial vertebrates6) and record peak densities up to 2 million individuals per m{superscript 2} in the Arctic. Despite a 20-fold biomass difference between tundra and the tropics, springtail energy use (community metabolism) remains similar across the latitudinal gradient, owing to the increase in temperature. Neither springtail density nor community metabolism were predicted by local species richness, which was highest in the tropics, but comparably high in some temperate forests and even tundra. Changes in springtail activity may emerge from latitudinal gradients in temperature, predation7, 8, and resource limitation7, 9, 10 in soil communities. Contrasting temperature responses of biomass, diversity and activity of springtail communities suggest that climate warming will alter fundamental soil biodiversity metrics in different directions, potentially restructuring terrestrial food webs and affecting major soil functions.

ecology↗