The way biological systems respond to changes in parameter values causedby mutations is a key issue in evolution and quantitative genetics, as it affects fundamental aspects such as adaptation, selective neutrality, robustness, optimality, evolutionary equilibria, etc. We address this question using the enzyme-flux relationship as a model of the genotype-phenotype relationship. Applying an analogy between electrical circuits and metabolic networks, we show that a behaviour of diminishing returns, which is commonly observed at various phenotypic levels, is inevitable, irrespective of the complexity of the system.
@article{petrizzelli2023_mc,title={Formalising the law of diminishing returns in metabolic networks using an electrical analogy},author={Petrizzelli, Marianyela and Coton, Charlotte and De-Vienne, Dominique},journal={Research Square},pages={},year={2023},publisher={Research Square},}
2021
PLoS Comp Biol
Data integration uncovers the metabolic bases of phenotypic variation in yeast
The relationship between different levels of integration is a key feature for understanding the genotype-phenotype map. Here, we describe a novel method of integrated data analysis that incorporates protein abundance data into constraint-based modeling to elucidate the biological mechanisms underlying phenotypic variation. Specifically, we studied yeast genetic diversity at three levels of phenotypic complexity in a population of yeast obtained by pairwise crosses of eleven strains belonging to two species, Saccharomyces cerevisiae and S. uvarum. The data included protein abundances, integrated traits (life-history/fermentation) and computational estimates of metabolic fluxes. Results highlighted that the negative correlation between production traits such as population carrying capacity (K) and traits associated with growth and fermentation rates (Jmax) is explained by a differential usage of energy production pathways: a high K was associated with high TCA fluxes, while a high Jmax was associated with high glycolytic fluxes. Enrichment analysis of protein sets confirmed our results.
@article{petrizzelli2021data,title={Data integration uncovers the metabolic bases of phenotypic variation in yeast},author={Petrizzelli, Marianyela Sabina and de-Vienne, Dominique and Nidelet, Thibault and No{\^u}s, Camille and Dillmann, Christine},journal={PLoS Computational Biology},volume={17},number={7},pages={e1009157},year={2021},publisher={Public Library of Science San Francisco, CA USA},}
Methods Mol Biol
Systems biology analysis for Ewing sarcoma
Petrizzelli, Marianyela, Merlevede, Jane, and Zinovyev, Andrei
Ewing sarcoma (EwS) is a highly aggressive pediatric bone cancer that is defined by a somatic fusion between the EWSR1 gene and an ETS family member, most frequently the FLI1 gene, leading to expression of a chimeric transcription factor EWSR1-FLI1. Otherwise, EwS is one of the most genetically stable cancers. The situation when the major cancer driver is well known looks like a unique opportunity for applying the systems biology approach in order to understand the EwS mechanisms as well as to uncover some general mechanistic principles of carcinogenesis. A number of studies have been performed revealing the direct and indirect effects of EWSR1-FLI1 on multiple aspects of cellular life. Nevertheless, the emerging picture of the oncogene action appears to be highly complex and systemic, with multiple reciprocal influences between the immediate consequences of the driver mutation and intracellular and intercellular molecular mechanisms, including regulation of transcription, epigenome, and tumoral microenvironment. In this chapter, we present an overview of existing molecular profiling resources available for EwS tumors and cell lines and provide an online comprehensive catalogue of publicly available omics and other datasets. We further highlight the systems biology studies of EwS, involving mathematical modeling of networks and integration of molecular data. We conclude that despite the seeming simplicity, a lot has yet to be understood on the systems-wide mechanisms connecting the driver mutation and the major cellular phenotypes of this pediatric cancer. Overall, this chapter can serve as a guide for a systems biology researcher to start working on EwS.
@book{petrizzelli2021systems,title={Systems biology analysis for Ewing sarcoma},author={Petrizzelli, Marianyela and Merlevede, Jane and Zinovyev, Andrei},booktitle={Ewing Sarcoma - Methods and Protocols - Springer},year={2021},publisher={Cidre Aranaz, Florencia, Gr{\"u}newald, Thomas},}
iScience
The multilayer community structure of medulloblastoma
Núñez-Carpintero, Iker,
Petrizzelli, Marianyela, Zinovyev, Andrei, Cirillo, Davide, and Valencia, Alfonso
Multilayer networks allow interpreting the molecular basis of diseases, which is particularly challenging in rare diseases where the number of cases is small compared with the size of the associated multi-omics datasets. In this work, we develop a dimensionality reduction methodology to identify the minimal set of genes that characterize disease subgroups based on their persistent association in multilayer network communities. We use this approach to the study of medulloblastoma, a childhood brain tumor, using proteogenomic data. Our approach is able to recapitulate known medulloblastoma subgroups (accuracy >94%) and provide a clear characterization of gene associations, with the downstream implications for diagnosis and therapeutic interventions. We verified the general applicability of our method on an independent medulloblastoma dataset (accuracy >98%). This approach opens the door to a new generation of multilayer network-based methods able to overcome the specific dimensionality limitations of rare disease datasets.
@article{nunez2021multilayer,title={The multilayer community structure of medulloblastoma},author={N{\'u}{\~n}ez-Carpintero, Iker and Petrizzelli, Marianyela and Zinovyev, Andrei and Cirillo, Davide and Valencia, Alfonso},journal={Iscience},volume={24},number={4},pages={102365},year={2021},publisher={Elsevier},}
2019
Front Genet
Probabilities of multilocus genotypes in SIB recombinant inbred lines
Jebreen, Kamel,
Petrizzelli, Marianyela, and Martin, Olivier C
Recombinant Inbred Lines (RILs) are obtained through successive generations of inbreeding. In 1931 Haldane and Waddington published a landmark paper where they provided the probabilities of achieving any combination of alleles in 2-way RILs for 2 and 3 loci. In the case of sibling RILs where sisters and brothers are crossed at each generation, there has been no progress in treating 4 or more loci, a limitation we overcome here without much increase in complexity. In the general situation of L loci, the task is to determine 2L probabilities, but we find that it is necessary to first calculate the 4L “identical by descent” (IBD) probabilities that a RIL inherits at each locus its DNA from one of the four originating chromosomes. We show that these 4L probabilities satisfy a system of linear equations that follow from self-consistency. In the absence of genetic interference—crossovers arising independently—the associated matrix can be written explicitly in terms of the recombination rates between the different loci. We provide the matrices for L up to 4 and also include a computer program to automatically generate the matrices for higher values of L. Furthermore, our framework can be generalized to recombination rates that are different in female and male meiosis which allows us to show that the Haldane and Waddington 2-locus formula is valid in that more subtle case if the meiotic recombination rate is taken as the average rate across female and male. Once the 4L IBD probabilities are determined, the 2L probabilities of RIL genotypes are obtained via summations of these quantities. In fine, our computer program allows to determine the probabilities of all the multilocus genotypes produced in such sibling-based RILs for L<=10, a huge leap beyond the L = 3 restriction of Haldane and Waddington.
@article{jebreen2019probabilities,title={Probabilities of multilocus genotypes in SIB recombinant inbred lines},author={Jebreen, Kamel and Petrizzelli, Marianyela and Martin, Olivier C},journal={Frontiers in genetics},pages={833},year={2019},publisher={Frontiers},}
Genetics
Decoupling the variances of heterosis and inbreeding effects is evidenced in yeast’s life-history and proteomic traits
Petrizzelli, Marianyela, de-Vienne, Dominique, and Dillmann, Christine
Heterosis (hybrid vigor) and inbreeding depression, commonly considered as corollary phenomena, could nevertheless be decoupled under certain assumptions according to theoretical population genetics works. 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 measure 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 interspecific) effects, of inbreeding effects, and of heterosis (intra- and interspecific) 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.
@article{petrizzelli2019decoupling,title={Decoupling the variances of heterosis and inbreeding effects is evidenced in yeast’s life-history and proteomic traits},author={Petrizzelli, Marianyela and de-Vienne, Dominique and Dillmann, Christine},journal={Genetics},volume={211},number={2},pages={741--756},year={2019},publisher={Oxford University Press},}
Thesis
Petrizzelli, M. (2019). Mathematical modelling and integration of complex biological data: analysis of the heterosis phenomenon in yeast. Université Paris Saclay.
The general framework of this thesis is the issue of the genotype-phenotype relationship, through the analysis of the heterosis phenomenon in yeast, in an approach combining biology, mathematics and statistics. Prior to this work, a very large set of heterogeneous data, corresponding to different levels of organization (proteomics, fermentation and life history traits), had been collected on a semi-diallel design involving 11 strains belonging to two species. This type of data is ideally suited for multi-scale modelling and for testing models for predicting the variation of integrated phenotypes from protein and metabolic (flux) traits, taking into account dependence patterns between variables and between observations. I first decomposed, for each trait, the total genetic variance into variances of additive, inbreeding and heterosis effects, and showed that the distribution of these components made it possible to define well-defined groups of proteins in which most of the characters of fermentation and life history traits took place. Within these groups, the correlations between the variances of heterosis and inbreeding effects could be positive, negative or null, which was the first experimental demonstration of a possible decoupling between the two phenomena. The second part of the thesis consisted of interfacing quantitative proteomic data with the yeast genome-scale metabolic model using a constraint-based modelling approach. Using a recent algorithm, I looked, in the space of possible solutions, for the one that minimized the distance between the flux vector and the vector of the observed abundances of proteins. I was able to predict unobserved fluxes, and to compare correlation patterns at different integration levels. Data allowed to distinguish between two major types of fermentation or life history traits whose biochemical interpretation is consistent in terms of trade-off, and which had not been highlighted from quantitative proteomic data alone. Altogether, my thesis work allows a better understanding of the evolution of the genotype-phenotype map.
@thesis{petrizzelli2019mathematical,title={Mathematical modelling and integration of complex biological data: analysis of the heterosis phenomenon in yeast},author={Petrizzelli, Marianyela},year={2019},school={Universit{\'e} Paris Saclay},}