Regulation of the yeast metabolic cycle by transcription factors with periodic activities

Crucial aspects of metabolism and cell biology relating to industrial production and processing of Saccharomyces biomass

The multitude of applications to which Saccharomyces spp. are put makes these yeasts the most prolific of industrial microorganisms. This review considers biological aspects pertaining to the manufacture of industrial yeast biomass. It is proposed that the production of yeast biomass can be considered in two distinct but interdependent phases. Firstly, there is a cell replication phase that involves reproduction of cells by their transitions through multiple budding and metabolic cycles. Secondly, there needs to be a cell conditioning phase that enables the accrued biomass to withstand the physicochemical challenges associated with downstream processing and storage. The production of yeast biomass is not simply a case of providing sugar, nutrients, and other growth conditions to enable multiple budding cycles to occur. In the latter stages of culturing, it is important that all cells are induced to complete their current budding cycle and subsequently enter into a quiescent state engendering robustness. Both the cell replication and conditioning phases need to be optimized and considered in concert to ensure good biomass production economics, and optimum performance of industrial yeasts in food and fermentation applications. Key features of metabolism and cell biology affecting replication and conditioning of industrial Saccharomyces are presented. Alternatives for growth substrates are discussed, along with the challenges and prospects associated with defining the genetic bases of industrially important phenotypes, and the generation of new yeast strains."I must be cruel only to be kind: Thus bad begins, and worse remains behind." William Shakespeare: Hamlet, Act 3, Scene 4.

Tumor heterogeneity: preclinical models, emerging technologies, and future applications

Heterogeneity describes the differences among cancer cells within and between tumors. It refers to cancer cells describing variations in morphology, transcriptional profiles, metabolism, and metastatic potential. More recently, the field has included the characterization of the tumor immune microenvironment and the depiction of the dynamics underlying the cellular interactions promoting the tumor ecosystem evolution. Heterogeneity has been found in most tumors representing one of the most challenging behaviors in cancer ecosystems. As one of the critical factors impairing the long-term efficacy of solid tumor therapy, heterogeneity leads to tumor resistance, more aggressive metastasizing, and recurrence. We review the role of the main models and the emerging single-cell and spatial genomic technologies in our understanding of tumor heterogeneity, its contribution to lethal cancer outcomes, and the physiological challenges to consider in designing cancer therapies. We highlight how tumor cells dynamically evolve because of the interactions within the tumor immune microenvironment and how to leverage this to unleash immune recognition through immunotherapy. A multidisciplinary approach grounded in novel bioinformatic and computational tools will allow reaching the integrated, multilayered knowledge of tumor heterogeneity required to implement personalized, more efficient therapies urgently required for cancer patients.

Genome-Wide Analysis of Yeast Metabolic Cycle through Metabolic Network Models Reveals Superiority of Integrated ATAC-seq Data over RNA-seq Data

Saccharomyces cerevisiae undergoes robust oscillations to regulate its physiology for adaptation and survival under nutrient-limited conditions. Environmental cues can induce rhythmic metabolic alterations in order to facilitate the coordination of dynamic metabolic behaviors. Of such metabolic processes, the yeast metabolic cycle enables adaptation of the cells to varying nutritional status through oscillations in gene expression and metabolite production levels. In this process, yeast metabolism is altered between diverse cellular states based on changing oxygen consumption levels: quiescent (reductive charging [RC]), growth (oxidative [OX]), and proliferation (reductive building [RB]) phases. We characterized metabolic alterations during the yeast metabolic cycle using a variety of approaches. Gene expression levels are widely used for condition-specific metabolic simulations, whereas the use of epigenetic information in metabolic modeling is still limited despite the clear relationship between epigenetics and metabolism. This prompted us to investigate the contribution of epigenomic information to metabolic predictions for progression of the yeast metabolic cycle. In this regard, we determined altered pathways through the prediction of regulated reactions and corresponding model genes relying on differential chromatin accessibility levels. The predicted metabolic alterations were confirmed via data analysis and literature. We subsequently utilized RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) data sets in the contextualization of the yeast model. The use of ATAC-seq data considerably enhanced the predictive capability of the model. To the best of our knowledge, this is the first attempt to use genome-wide chromatin accessibility data in metabolic modeling. The preliminary results showed that epigenomic data sets can pave the way for more accurate metabolic simulations.

IMPORTANCE Dynamic chromatin organization mediates the emergence of condition-specific phenotypes in eukaryotic organisms. Saccharomyces cerevisiae can alter its metabolic profile via regulation of genome accessibility and robust transcriptional oscillations under nutrient-limited conditions. Thus, both epigenetic information and transcriptomic information are crucial in the understanding of condition-specific metabolic behavior in this organism. Based on genome-wide alterations in chromatin accessibility and transcription, we investigated the yeast metabolic cycle, which is a remarkable example of coordinated and dynamic yeast behavior. In this regard, we assessed the use of ATAC-seq and RNA-seq data sets in condition-specific metabolic modeling. To our knowledge, this is the first attempt to use chromatin accessibility data in the reconstruction of context-specific metabolic models, despite the extensive use of transcriptomic data. As a result of comparative analyses, we propose that the incorporation of epigenetic information is a promising approach in the accurate prediction of metabolic dynamics.

Obtaining a series of native gradient promoter-5'-UTR sequences in Corynebacterium glutamicum ATCC 13032

BACKGROUND

Corynebacterium glutamicum is an important industrial microorganism used for the production of many valuable compounds, especially amino acids and their derivatives. For fine-tuning of metabolic pathways, synthetic biological tools are largely based on the rational application of promoters. However, the limited number of promoters make it difficult.

RESULTS

In this study, according to the analysis of RNA-Seq data, 90 DNA fragments with lengths of 200-500 bp that may contain promoter-5'-UTR (PUTR) sequences were amplified and linked to a fluorescent protein gene. When compared with the common strong PUTR P_sodUTR, 17 strong PUTRs were obtained, which maintained stable expression strengths from the early to post stationary phase. Among them, P_NCgl1676UTR was the strongest and its fluorescent protein expression level was more than five times higher than that of P_sodUTR. Furthermore, nine typical chemicals related to the biosynthesis of sulfur-containing amino acids (such as L-methionine, L-cysteine) were selected as stress substances to preliminarily explore the stress on these PUTRs. The results showed that the expression of P_brnFUTR was activated by L-methionine, while that of P_NCgl1202UTR was severely inhibited by L-lysine.

CONCLUSIONS

These findings demonstrated that the selected PUTRs can stably express different genes, such as the red fluorescence protein gene, and can be useful for fine-tuning regulation of metabolic networks in C. glutamicum or for establishing high-throughput screening strategies through biosensor for the production of useful compounds.

Recognition of Histone Crotonylation by Taf14 Links Metabolic State to Gene Expression

Metabolic signaling to chromatin often underlies how adaptive transcriptional responses are controlled. While intermediary metabolites serve as co-factors for histone-modifying enzymes during metabolic flux, how these modifications contribute to transcriptional responses is poorly understood. Here, we utilize the highly synchronized yeast metabolic cycle (YMC) and find that fatty acid β-oxidation genes are periodically expressed coincident with the β-oxidation byproduct histone crotonylation. Specifically, we found that H3K9 crotonylation peaks when H3K9 acetylation declines and energy resources become limited. During this metabolic state, pro-growth gene expression is dampened; however, mutation of the Taf14 YEATS domain, a H3K9 crotonylation reader, results in de-repression of these genes. Conversely, exogenous addition of crotonic acid results in increased histone crotonylation, constitutive repression of pro-growth genes, and disrupted YMC oscillations. Together, our findings expose an unexpected link between metabolic flux and transcription and demonstrate that histone crotonylation and Taf14 participate in the repression of energy-demanding gene expression.

INO80 Chromatin Remodeling Coordinates Metabolic Homeostasis with Cell Division

Adaptive survival requires the coordination of nutrient availability with expenditure of cellular resources. For example, in nutrient-limited environments, 50% of all S. cerevisiae genes synchronize and exhibit periodic bursts of expression in coordination with respiration and cell division in the yeast metabolic cycle (YMC). Despite the importance of metabolic and proliferative synchrony, the majority of YMC regulators are currently unknown. Here, we demonstrate that the INO80 chromatin-remodeling complex is required to coordinate respiration and cell division with periodic gene expression. Specifically, INO80 mutants have severe defects in oxygen consumption and promiscuous cell division that is no longer coupled with metabolic status. In mutant cells, chromatin accessibility of periodic genes, including TORC1-responsive genes, is relatively static, concomitant with severely attenuated gene expression. Collectively, these results reveal that the INO80 complex mediates metabolic signaling to chromatin to restrict proliferation to metabolically optimal states.

Metabolic rhythms: A framework for coordinating cellular function

Circadian clocks are widespread among eukaryotes and generally involve feedback loops coupled with metabolic processes and redox balance. The organising power of these oscillations has not only allowed organisms to anticipate day-night cycles, but also acts to temporally compartmentalise otherwise incompatible processes, enhance metabolic efficiency, make the system more robust to noise and propagate signals among cells. While daily rhythms and the function of the circadian transcription-translation loop have been the subject of extensive research over the past four decades, cycles of shorter period and respiratory oscillations, with which they are intertwined, have received less attention. Here, we describe features of yeast respiratory oscillations, which share many features with daily and 12 hr cellular oscillations in animal cells. This relatively simple system enables the analysis of dynamic rhythmic changes in metabolism, independent of cellular oscillations that are a product of the circadian transcription-translation feedback loop. Knowledge gained from studying ultradian oscillations in yeast will lead to a better understanding of the basic mechanistic principles and evolutionary origins of oscillatory behaviour among eukaryotes.

Elucidating the Role of Chromatin State and Transcription Factors on the Regulation of the Yeast Metabolic Cycle: A Multi-Omic Integrative Approach

The Yeast Metabolic Cycle (YMC) is a model system in which levels of around 60% of the yeast transcripts cycle over time. The spatial and temporal resolution provided by the YMC has revealed that changes in the yeast metabolic landscape and chromatin status can be related to cycling gene expression. However, the interplay between histone modifications and transcription factor activity during the YMC is still poorly understood. Here we apply an innovative statistical approach to integrate chromatin state (ChIP-seq) and gene expression (RNA-seq) data to investigate the transcriptional control during the YMC. By using the multivariate regression models N-PLS (Partial Least Squares) and MORE (Multi-Omics REgulation) methodologies, we assessed the contribution of histone marks and transcription factors to the regulation of gene expression in the YMC. We found that H3K18ac and H3K9ac were the most important histone modifications, whereas Sfp1, Hfi1, Pip2, Mig2, and Yhp1 emerged as the most relevant transcription factors. A significant association in the co-regulation of gene expression was found between H3K18ac and the transcription factors Pip2 (involved in fatty acids metabolism), Xbp1 (cyclin implicated in the regulation of carbohydrate and amino acid metabolism), and Hfi1 (involved in the formation of the SAGA complex). These results evidence the crucial role of histone lysine acetylation levels in the regulation of gene expression in the YMC through the coordinated action of transcription factors and lysine acetyltransferases.

The molecular basis of metabolic cycles and their relationship to circadian rhythms

Metabolic cycles result from the partitioning of oxidative and reductive metabolism into rhythmic phases of gene expression and oscillating post-translational protein modifications. Relatively little is known about how these switches in gene expression are controlled, although recent studies have suggested that transcription itself may play a central role. This review explores the molecular basis of the metabolic and gene-expression oscillations in the yeast Saccharomyces cerevisiae, as well as how they relate to other biological time-keeping mechanisms, such as circadian rhythms.