Cell-cell metabolite exchange creates a pro-survival metabolic environment that extends lifespan.

Metabolism is deeply intertwined with aging. Effects of metabolic interventions on aging have been explained with intracellular metabolism, growth control, and signaling. Studying chronological aging in yeast, we reveal a so far overlooked metabolic property that influences aging via the exchange of metabolites. We observed that metabolites exported by young cells are re-imported by chronologically aging cells, resulting in cross-generational metabolic interactions. Then, we used self-establishing metabolically cooperating communities (SeMeCo) as a tool to increase metabolite exchange and observed significant lifespan extensions. The longevity of the SeMeCo was attributable to metabolic reconfigurations in methionine consumer cells. These obtained a more glycolytic metabolism and increased the export of protective metabolites that in turn extended the lifespan of cells that supplied them with methionine. Our results establish metabolite exchange interactions as a determinant of cellular aging and show that metabolically cooperating cells can shape the metabolic environment to extend their lifespan.

Natural proteome diversity links aneuploidy tolerance to protein turnover.

Accessing the natural genetic diversity of species unveils hidden genetic traits, clarifies gene functions and allows the generalizability of laboratory findings to be assessed. One notable discovery made in natural isolates of Saccharomyces cerevisiae is that aneuploidy-an imbalance in chromosome copy numbers-is frequent1,2 (around 20%), which seems to contradict the substantial fitness costs and transient nature of aneuploidy when it is engineered in the laboratory3-5. Here we generate a proteomic resource and merge it with genomic1 and transcriptomic6 data for 796 euploid and aneuploid natural isolates. We find that natural and lab-generated aneuploids differ specifically at the proteome. In lab-generated aneuploids, some proteins-especially subunits of protein complexes-show reduced expression, but the overall protein levels correspond to the aneuploid gene dosage. By contrast, in natural isolates, more than 70% of proteins encoded on aneuploid chromosomes are dosage compensated, and average protein levels are shifted towards the euploid state chromosome-wide. At the molecular level, we detect an induction of structural components of the proteasome, increased levels of ubiquitination, and reveal an interdependency of protein turnover rates and attenuation. Our study thus highlights the role of protein turnover in mediating aneuploidy tolerance, and shows the utility of exploiting the natural diversity of species to attain generalizable molecular insights into complex biological processes.

Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast.

Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein coexpression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.

Engineering the architecture of erythritol-inducible promoters for regulated and enhanced gene expression in Yarrowia lipolytica.

The non-conventional model yeast Yarrowia lipolytica is of increasing interest as a cell factory for producing recombinant proteins or biomolecules with biotechnological or pharmaceutical applications. To further develop the yeast's efficiency and construct inducible promoters, it is crucial to better understand and engineer promoter architecture. Four conserved cis-regulatory modules (CRMs) were identified via phylogenetic footprinting within the promoter regions of EYD1 and EYK1, two genes that have recently been shown to be involved in erythritol catabolism. Using CRM mutagenesis and hybrid promoter construction, we identified four upstream activation sequences (UASs) that are involved in promoter induction by erythritol. Using RedStarII fluorescence as a reporter, the strength of the promoters and the degree of erythritol-based inducibility were determined in two genetic backgrounds: the EYK1 wild type and the eyk1Δ mutant. We successfully developed inducible promoters with variable strengths, which ranged from 0.1 SFU/h to 457.5 SFU/h. Erythritol-based induction increased 2.2 to 32.3 fold in the EYK1 + wild type and 2.9 to 896.1 fold in the eyk1Δ mutant. This set of erythritol-inducible hybrid promoters could allow the modulation and fine-tuning of gene expression levels. These promoters have direct applications in protein production, metabolic engineering and synthetic biology.

Transgenic Medaka Identify Embryonic Periods Sensitive to Disruption of Sex Determination.

Gonadal development in medaka (Oryzias latipes) is dependent on the synergy between estrogens and androgens. Disruption of steroid hormone levels can lead to ovo-testis. To determine the sensitive windows for hormonally induced sex reversal in medaka, we developed a novel 42sp50-GFP_ChgH-GFP transgenic medaka line, allowing the identification of female gonadal tissue by fluorescence present in developing oocytes. Germinal transgenesis resulted in a stable line exhibiting a strong green fluorescent protein signal constitutively in the ovaries and in the liver in response to estrogens. The sensitivity of this line to disruption of sex determination following 16-d chronic exposures was in the nanograms per liter range. To identify the developmental period sensitive to exogenous agents, fry were exposed to 24-h pulses of high concentrations of 17ß-estradiol (E2) or 5α-dihydrotestosterone (DHT) at various time points between days postfertilization (dpf) 0 and 12. Evaluation of phenotype followed by genotyping at 16 dpf revealed sensitivity to E2 between 1 and 8 dpf as well as 2 periods of susceptibility to DHT between 0 and 1 dpf and 4 and 8 dpf. No phenotypic sex reversal was detected after exposure to DHT or E2 on 11 or 12 dpf. The observed effects persisted to at least 24 dpf. The identified sensitive embryonic time periods for disruption of sex determination will aid future research on sex determination and the development of screening assays using early embryonic life stages. Environ Toxicol Chem 2020;39:842-851. © 2020 SETAC.

Integrating transcriptional activity in genome-scale models of metabolism.

BACKGROUND: Genome-scale metabolic models provide an opportunity for rational approaches to studies of the different reactions taking place inside the cell. The integration of these models with gene regulatory networks is a hot topic in systems biology. The methods developed to date focus mostly on resolving the metabolic elements and use fairly straightforward approaches to assess the impact of genome expression on the metabolic phenotype. RESULTS: We present here a method for integrating the reverse engineering of gene regulatory networks into these metabolic models. We applied our method to a high-dimensional gene expression data set to infer a background gene regulatory network. We then compared the resulting phenotype simulations with those obtained by other relevant methods. CONCLUSIONS: Our method outperformed the other approaches tested and was more robust to noise. We also illustrate the utility of this method for studies of a complex biological phenomenon, the diauxic shift in yeast.

Inference and interrogation of a coregulatory network in the context of lipid accumulation in Yarrowia lipolytica.

Complex phenotypes, such as lipid accumulation, result from cooperativity between regulators and the integration of multiscale information. However, the elucidation of such regulatory programs by experimental approaches may be challenging, particularly in context-specific conditions. In particular, we know very little about the regulators of lipid accumulation in the oleaginous yeast of industrial interest Yarrowia lipolytica. This lack of knowledge limits the development of this yeast as an industrial platform, due to the time-consuming and costly laboratory efforts required to design strains with the desired phenotypes. In this study, we aimed to identify context-specific regulators and mechanisms, to guide explorations of the regulation of lipid accumulation in Y. lipolytica. Using gene regulatory network inference, and considering the expression of 6539 genes over 26 time points from GSE35447 for biolipid production and a list of 151 transcription factors, we reconstructed a gene regulatory network comprising 111 transcription factors, 4451 target genes and 17048 regulatory interactions (YL-GRN-1) supported by evidence of protein-protein interactions. This study, based on network interrogation and wet laboratory validation (a) highlights the relevance of our proposed measure, the transcription factors influence, for identifying phases corresponding to changes in physiological state without prior knowledge (b) suggests new potential regulators and drivers of lipid accumulation and