Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/362418

Decoupling of heterosis and inbreeding variances is evidenced in yeast’s life-history and proteomic traits

Petrizzelli, M.·de Vienne, D.·Dillmann, C.
View original source ↗Export metadata ↓

Abstract

Heterosis (hybrid vigor) and inbreeding depression, commonly considered as corollary phenomena, could nevertheless be decoupled under certain assumptions according to theoretical population genetics works. In order to explore this issue on real data, we analyzed the components of genetic variation in a population derived from a half-diallel cross between strains from Saccharomyces cerevisiae and S. uvarum, two related yeast species involved in alcoholic fermentation. A large number of phenotypic traits, either molecular (coming from quantitative proteomics) or related to fermentation and life-history, were measured during alcoholic fermentation. Because the parental strains were included in the design, we were able to distinguish between inbreeding effects, which measures phenotypic differences between inbred and hybrids, and heterosis, which measures phenotypic differences between a specific hybrid and the other hybrids sharing a common parent. The sources of phenotypic variation differed depending on the temperature, indicating the predominance of genotype by environment interactions. Decomposing the total genetic variance into variances of additive (intra- and inter-specific) effects, of inbreeding effects and of heterosis (intra- and inter-specific) effects, we showed that the distribution of variance components defined clear-cut groups of proteins and traits. Moreover, it was possible to cluster fermentation and life-history traits into most proteomic groups. Within groups, we observed positive, negative or null correlations between the variances of heterosis and inbreeding effects. To our knowledge, such a decoupling had never been experimentally demonstrated. This result suggests that, despite a common evolutionary history of individuals within a species, the different types of traits have been subject to different selective pressures.

Source connections

Explore related subjects

systems biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Petrizzelli, M., de Vienne, D., Dillmann, C.. 2018-07-04. Decoupling of heterosis and inbreeding variances is evidenced in yeast’s life-history and proteomic traits. https://doi.org/10.1101/362418

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Uncovering disease progression trajectories across multiple timescales with hybrid ODE-mixture models

Biological systems are capable of evolving across multiple timescales, yet most modeling approaches capture either short-term physiological responses or long-term disease progression. Additionally, methods that combine these time scales either struggle with inter-individual variation, or require large volumes of data to work. We present Latent Class Parameter Trajectory Analysis (LCPTA), a hybrid framework that combines mechanistic ordinary differential equation (ODE) modeling of short-term dynamics with latent class trajectory modeling of long-term parameter evolution. To support this approach, we developed TemporalMixtureModels.jl, a Julia package for univariate and multivariate temporal mixture modeling. We evaluated LCPTA in two synthetic longitudinal studies, covering a simulation of a heterogeneous population developing hypertension, as well as a synthetic longitudinal study of 75 virtual individuals followed over 30 years, representing muscle insulin resistance, liver insulin resistance, and healthy trajectories. We compared LCPTA with a conditional neural ODE and a conditional neural network for reconstruction of long-term parameter trajectories and subgroup separation. In both studies LCPTA recovered relevant subgroup structure and trajectory patterns. In the cardiovascular study, the neural approaches were able to distinguish inter-individual differences, but in the insulin sensitivity study, neural approaches failed to represent parameter trajectories faithfully, primarily separating individuals along a single dominant parameter and showing weaker multivariate trajectory reconstruction. In additional experiments with sparse and irregularly sampled unseen individuals, LCPTA maintained strong classification performance. We show that LCPTA is an efficient and interpretable strategy for multiscale modeling of heterogeneous metabolic disease progression, with potential utility for data-efficient digital twin applications.

systems biology↗
bioRxiv2026

The Global Phosphorylation Landscape of Acute Heat Stress in Reef-Building Corals

Warming oceans disrupt the coral-algal symbiosis that sustains reefs, yet the signaling mechanisms initiating bleaching remain poorly understood. Integrating global phosphoproteomics across three reef-building corals with protein structure modeling revealed conservation between coral and human kinases, enabling inference of coral kinase activities and uncovering a shared signaling program induced by acute heat stress. Heat rapidly activated ERK, JNK, and p38 MAP kinases, suppressed cell cycle signaling, and remodeled pathways controlling chromatin, RNA processing, and metabolism. Most strikingly, thermal stress rewired immune receptor signaling networks associated with non-self recognition, suggesting that heat shifts the coral host from tolerance to immune recognition of its algal symbionts. These findings define conserved signaling mechanisms that precede bleaching, revealing actionable molecular targets for enhancing coral resilience.

systems biology↗
bioRxiv2026

Estimating glioblastoma migration-to-proliferation ratio from single time-point MRI data via radial density distribution

Glioblastoma (GBM) tumors with a high migration-to-proliferation ratio (D/k) are associated with treatment resistance, motivating the estimation of D/k from routine pre-treatment magnetic resonance imaging (MRI) data and the investigation of mechanisms underlying its heterogeneity across patients. Unlike previous approaches based on nested spherical approximations of enhancing and non-enhancing tumor volumes, we incorporate the spatial arrangement of the enhancing and non-enhancing tumor regions by deriving a 1D tumor density distribution from their segmentations and fitting a 1D reaction-diffusion model to estimate D/k. We achieved an error of under 5% when comparing our method to synthetic density distributions and found excellent correlation with synthetic tumor segmentations (Pearson correlation coefficient (PCC) = 0.996). We then evaluated associations between patient-specific D/k values and their clinical, transcriptomic, and radiomic features. When applied to 102 pre-treatment MRI scans from the Brain Tumor Segmentation (BraTS) 2017 dataset, elevated D/k significantly correlated with reduced overall survival (log-rank p = 0.01), older age (p = 0.04), and a trend toward lower Karnofsky Performance Status (p = 0.059). In a subset of 91 patients with available microarray data, mesenchymal-like phenotypes consistently exhibited elevated D/k across three established molecular subtype classifications (p = 0.013, p = 0.055, and p = 0.127). Elastic Net regularization followed by overrepresentation analysis revealed an enrichment of glycosylation-related pathways among genes associated with D/k variability (p = 0.0123 and p = 0.0368). Furthermore, Multi-Omic Factor Analysis identified a radiomic factor correlated with D/k (r = 0.57, p = 4x10-9), with the highest-weighted features corresponding to smoothness of the non-enhancing tumor region on T2 and FLAIR.

systems biology↗