Metabolic Enzymes Function as Epigenetic Modulators: A Trojan Horse for Chromatin Regulation and Gene Expression

SummaryMany evolutionarily successful bacteria attain high growth rates across growth-permissive conditions. They express metabolic networks that synthesise all cellular components at a high rate. Metabolic reaction rates are bounded by the concentration of the catalysing enzymes and cells have finite resources available for enzyme synthesis. Therefore, bacteria that grow fast should express needed metabolic enzymes at precisely tuned concentrations. To maintain fast growth in a dynamic environment, cells should adjust gene expression of metabolic enzymes. The activity of many of the associated transcription factors is regulated by their binding to intracellular metabolites. We study optimal metabolite-mediated regulation of metabolic-gene expression that preserves maximisation of metabolic fluxes across varying conditions. We logically derive the underlying control logic of this type of optimal regulation, which we term ‘Specific Flux (q) Optimization by Robust Adaptive Control’ (qORAC), and illustrate it with several examples. We show that optimal metabolic flux can be maintained in the face of K changing parameters only if the number of transcription-factor-binding metabolites is at least equal to K. qORAC-regulation of metabolism can generally be achieved with basic biochemical interactions, indicating that metabolism can operate close to optimality. The theory that we present is directly applicable to synthetic biology, biotechnology and fundamental studies of the regulation of metabolism.

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EXPRESSION OF STEROID AND PEPTIDE HORMONE RECEPTORS, METABOLIC ENZYMES AND EMT-RELATED GENES IN PROSTATE TUMORS IN RELATION TO THE PRESENCE OF THE TMPRSS2/ERG FUSION

Experimental Oncology ◽

10.31768/2312-8852.2018.40(2):101-108 ◽

2018 ◽

Vol 40 (2) ◽

pp. 101-108 ◽

Cited By ~ 7

Author(s):

G V Gerashchenko ◽

L V Mevs ◽

L I Chashchina ◽

M V Pikul ◽

O P Gryzodub ◽

...

Keyword(s):

Gene Expression ◽

Prostate Cancer ◽

Expression Pattern ◽

Hormone Receptors ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Peptide Hormone ◽

Metabolic Enzymes ◽

Prostate Tumors ◽

Peptide Hormone Receptors

Aim: To analyze an expression pattern of the steroid and peptide hormone receptors, metabolic enzymes and EMT-related genes in prostate tumors in relation to the presence of the TMPRSS2/ERG fusion; and to examine a putative correlation between gene expression and clinical characteristics, to define the molecular subtypes of prostate cancer. Materials and Methods: The relative gene expression (RE) of 33 transcripts (27 genes) and the presence/absence of the TMPRSS2/ERG fusion were analyzed by a quantitative PCR. 37 prostate cancer tissues (T) paired with conventionally normal prostate tissue (CNT) and 21 samples of prostate adenomas were investigated. RE changes were calculated, using different protocols of statistics. Results: We demonstrated differences in RE of seven genes between tumors and CNT, as was calculated, using the 2−ΔCT model and the Wilcoxon matched paired test. Five genes (ESR1, KRT18, MKI67, MMP9, PCA3) showed altered expression in adenocarcinomas, in which the TMPRSS2/ERG fusion was detected. Two genes (INSR, isoform B and HOTAIR) expressed differently in tumors without fusion. Comparison of the gene expression pattern in adenomas, CNT and adenocarcinomas demonstrated that in adenocarcinomas, bearing the TMPRSS2/ ERG fusion, genes KRT18, PCA3, and SCHLAP1 expressed differently. At the same time, we detected differences in RE of AR (isoform 2), MMP9, PRLR and HOTAIR in adenocarcinomas without the TMPRSS2/ERG fusion. Two genes (ESR1 and SRD5A2) showed differences in RE in both adenocarcinoma groups. Fourteen genes, namely AR (isoforms 1 and 2), CDH1, OCLN, NKX3-1, XIAP, GCR (ins AG), INSR (isoform A), IGF1R, IGF1R tr, PRLR, PRL, VDR and SRD5A2 showed correlation between RE and tumor stage. RE of four genes (CDH2, ESR2, VDR and SRD5A2) correlated with differentiation status of tumors (Gleason score). Using the K-means clustering, we could cluster adenocarcinomas in three groups, according to gene expression profiles. A specific subtype of prostate tumors is characterized by the activated ERG signaling, due to the presence of TMPRSS2/ERG fusion, and also by high levels of the androgen receptor, prolactin, IGF, INSR and PCA3. Conclusions: We have found the specific differences in expression of the steroid and peptide hormone receptors, metabolic enzymes and EMT-related genes, depending on the presence/absence of the TMPRSS2/ERG fusion in prostate adenocarcinomas, CNT and adenomas. We showed three different gene expression profiles of prostate adenocarcinomas. One of them is characteristic for adenocarcinomas with the TMPRSS2/ERG fusion. Further experiments are needed to confirm these data in a larger cohort of patients.

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Growth-Rate Dependent And Nutrient-Specific Gene Expression Resource Allocation In Fission Yeast

10.1101/2021.03.16.435638 ◽

2021 ◽

Author(s):

Istvan T. Kleijn ◽

Amalia Martínez-Segura ◽

François Bertaux ◽

Malika Saint ◽

Holger Kramer ◽

...

Keyword(s):

Gene Expression ◽

Growth Rate ◽

Resource Allocation ◽

Fission Yeast ◽

Metabolic Enzymes ◽

Specific Gene ◽

Specific Gene Expression ◽

Rate Dependent ◽

Specific Regulation ◽

Yeast Genes

Cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate. Quantitative studies using various growth conditions have singled out growth rate as a major physiological variable explaining relative protein abundances. Here, we used the simple eukaryote Schizosaccharomyces pombe to determine the importance of growth rate in explaining relative changes in protein and mRNA levels during growth on a series of non-limiting nitrogen sources. Although half of fission yeast genes were significantly correlated with the growth rate, this came alongside wide-spread nutrient-specific regulation. Proteome and transcriptome often showed coordinated regulation but with notable exceptions, such as metabolic enzymes. Genes positively correlated with growth rate participated in every level of protein production with the notable exception of RNA polymerase II, whereas those negatively correlated mainly belonged to the environmental stress response programme. Critically, metabolic enzymes, which represent ~55-70% of the proteome by mass, showed mainly condition-specific regulation. Specifically, many enzymes involved in glycolysis and NAD-dependent metabolism as well as the fermentative and respiratory pathways were condition-dependent and not consistently correlated with growth. In summary, we provide a rich account of resource allocation to gene expression in a simple eukaryote, advancing our basic understanding of the interplay between growth-rate dependent and nutrient-specific gene expression.

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Interplay Among Metabolism, Epigenetic Modifications, and Gene Expression in Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.793428 ◽

2021 ◽

Vol 9 ◽

Author(s):

Miaomiao Huo ◽

Jingyao Zhang ◽

Wei Huang ◽

Yan Wang

Keyword(s):

Gene Expression ◽

Cancer Metabolism ◽

Metabolic Enzymes ◽

Epigenetic Modifications ◽

Metabolic Reprogramming ◽

Post Translational Modifications ◽

Dna And Rna ◽

Mechanisms Of Carcinogenesis ◽

Non Coding Rnas ◽

Host Metabolism

Epigenetic modifications and metabolism are two fundamental biological processes. During tumorigenesis and cancer development both epigenetic and metabolic alterations occur and are often intertwined together. Epigenetic modifications contribute to metabolic reprogramming by modifying the transcriptional regulation of metabolic enzymes, which is crucial for glucose metabolism, lipid metabolism, and amino acid metabolism. Metabolites provide substrates for epigenetic modifications, including histone modification (methylation, acetylation, and phosphorylation), DNA and RNA methylation and non-coding RNAs. Simultaneously, some metabolites can also serve as substrates for nonhistone post-translational modifications that have an impact on the development of tumors. And metabolic enzymes also regulate epigenetic modifications independent of their metabolites. In addition, metabolites produced by gut microbiota influence host metabolism. Understanding the crosstalk among metabolism, epigenetic modifications, and gene expression in cancer may help researchers explore the mechanisms of carcinogenesis and progression to metastasis, thereby provide strategies for the prevention and therapy of cancer. In this review, we summarize the progress in the understanding of the interactions between cancer metabolism and epigenetics.

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