Biology subjects

Clement-Ziza, M.

Publications and source records attributed to Clement-Ziza, M..

4 recordsLinked to original sources

bioRxiv2022

Large-scale changes of molecular network states explain complex traits

The complexity of many cellular and organismal traits results from poorly understood mechanisms integrating genetic and environmental factors via molecular networks. Here, we show when and how genetic perturbations lead to molecular changes that are confined to small parts of a network versus when they lead to large-scale adaptations of global network states. Integrating multi-omics profiling of genetically heterogeneous budding and fission yeast strains with an array of cellular traits identified a central state transition of the yeast molecular network that is related to PKA and TOR (PT) signaling. Genetic variants affecting this PT state globally shifted the molecular network along a single-dimensional axis, thereby modulating processes including energy- and amino acid metabolism, transcription, translation, cell cycle control and cellular stress response. We propose that genetic effects can propagate through large parts of molecular networks because of the functional requirement to centrally coordinate the activity of fundamental cellular processes. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=69 SRC="FIGDIR/small/519111v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@18d3323org.highwire.dtl.DTLVardef@1185111org.highwire.dtl.DTLVardef@17238c7org.highwire.dtl.DTLVardef@1edc6e6_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstract:C_FLOATNO Genetic variants directly or indirectly affecting the activity of PKA and/or TOR signaling cause global changes of transcriptomic and proteomic network states by modulating the activity of diverse cellular functions and network modules. Using marker genes acting downstream of PKA and TOR signaling we are able to quantify the activity status of combined PKA and TOR signaling (PT Score). This PT Score correlates with major transcriptomic and proteomic changes in response to genetic variability. Those large-scale molecular adaptations correlate with and explain phenotypic consequences for multiple cellular traits. Variants affecting the stoichiometry of proteins within a specific module have regional effects that remain confined to smaller parts of the molecular network. Variants affecting only one or very few proteins change molecular networks only locally. The global reorganization of network states caused by variants of the first type result in consequences for many cellular traits (i.e. pleiotropic effects), such as growth on different carbon sources, stress response, energy metabolism and replicative lifespan. (Created with BioRender.com) C_FIG

systems biology↗

bioRxiv2021

Epigenomic profiling of stem cells within the pilosebaceous unit identifies PRDM16 as a regulator of sebaceous gland homeostasis

The epidermis consists of different compartments such as the hair follicle (HF), sebaceous gland (SG) and interfollicular epidermis (IFE), each containing distinct stem cell (SC) populations. However, with the exception of the SCs residing within the HF bulge, other epidermal SC populations remain less well understood. Here we used an epigenomic strategy that combines H3K27me3 ChIP-seq and RNA-seq profiling to identify major regulators of pilosebaceous unit (PSU) SC located outside the bulge. When applied to the bulk of PSU SC isolated from mouse skin our approach identified both previously known and potentially novel non-bulge PSU SC regulators. Among the latter, we found that PRDM16 was predominantly enriched within the Junctional Zone (JZ), which harbors SC that contribute to renewal of the upper HF and the SG. To investigate PRDM16 function in the PSU SC, we generated an epidermal-specific Prdm16 Knock-out mouse model (K14-Cre-Prdm16fl/fl). Notably, SG homeostasis was disturbed upon loss of PRDM16 resulting in enlarged SGs, and excessive sebum production, resembling some of the features associated with human acne and sebaceous hyperplasia. Importantly, PRDM16 is essential to shut down proliferation in differentiating sebocytes. Overall, our study provides a list of putative novel regulators of PSU SC outside the bulge and identifies PRDM16 as a major regulator of SG homeostasis.

cell biology↗

bioRxiv2019

Integration of transcriptome, proteome and phosphoproteome data elucidates the genetic control of molecular networks

Genomic variation affects cellular networks by altering diverse molecular layers such as RNA levels, protein abundance, and post-translational protein modifications. However, it remains unclear how these different layers are affected by genetic polymorphisms and give rise to complex physiological phenotypes. To address these questions, we generated high-quality transcriptome, proteome, and phosphoproteome data for a panel of 112 genetically diverse yeast strains. While genetic effects on transcript abundances were generally transmitted to the protein level, we found a significant uncoupling of the transcript-protein relationship for certain protein classes, such as subunits of protein complexes. The additional phosphoproteomics data suggests that the same genetic locus often affects distinct sets of genes within each of these layers. In particular, QTLs tended to affect upstream regulatory proteins at the phosphorylation layer, whereas downstream pathway targets were typically affected at the transcript and protein abundance layers. Underscoring the importance of regulatory protein phosphorylation in linking genetic to phenotypic variation is the finding that the number of protein phosphosites associated with a given genetic locus was more predictive for its influence on cellular growth traits than the number of transcripts or proteins.\n\nThis study shows how multi-layered molecular networks mediate the effects of genomic variants to more complex physiological traits and highlights the important role of protein phosphorylation in mediating these effects.

systems biology↗

bioRxiv2018

Detection of epistatic interactions with Random Forest

In order to elucidate the influence of genetic factors on phenotype variation, non-additive genetic interactions (i.e., epistasis) have to be taken into account. However, there is a lack of methods that can reliably detect such interactions, especially for quantitative traits. Random Forest was previously recognized as a powerful tool to identify the genetic variants that regulate trait variation, mainly due to its ability to take epistasis into account. However, although it can account for interactions, it does not specifically detect them. Therefore, we propose three approaches that extract interactions from a Random Forest by testing for specific signatures that arise from interactions, which we termed paired selection frequency, split asymmetry, and selection asymmetry. Since they complement each other for different epistasis types, an ensemble method that combines the three approaches was also created. We evaluated our approaches on multiple simulated scenarios and two different real datasets from different Saccharomyces cerevisiae crosses. We compared them to the commonly used exhaustive pair-wise linear model approach, as well as several two-stage approaches, where loci are pre-selected prior to interaction testing. The Random Forest-based methods presented here generally outperformed the other methods at identifying meaningful genetic interactions both in simulated and real data. Further examination of the results for the simulated and real datasets established how interactions are extracted from the Random Forest, and explained the performance differences between the methods. Thus, the approaches presented here extend the applicability of Random Forest for the genetic mapping of biological traits.\n\nAuthor summaryThe genetic mechanisms underlying biological traits are often complex, involving the effects of multiple genetic variants. Interactions between these variants, also called epistasis, are also common. The machine learning algorithm Random Forest can be used to study genotype-phenotype relationships, by using genetic variants to predict the phenotype. One of Random Forests strengths is its ability to implicitly model interactions. However, Random Forest does not give any information about which predictors specifically interact, i.e. which variants are in epistasis.\n\nHere, we developed three approaches that identify interactions in a Random Forest. We demonstrated their ability to detect genetic interactions using simulations and real data from Saccharomyces cerevisiae. Our Random Forest-based methods generally outperformed several other commonly used approaches at detecting epistasis.\n\nThis study contributes to the long-standing problem of extracting information about the underlying model from a Random Forest. Since Random Forest has many applications outside of genetic association, this work represents a valuable contribution to not only genotype-phenotype mapping research, but also other scientific applications where interactions between predictors in a Random Forest might be of interest.

bioinformatics↗