Mapping metabolic oscillations during cell cycle progression

Proteome-scale movements and compartment connectivity during the eukaryotic cell cycle

Cell cycle progression relies on coordinated changes in the composition and subcellular localization of the proteome. By applying two distinct convolutional neural networks on images of millions of live yeast cells, we resolved proteome-level dynamics in both concentration and localization during the cell cycle, with resolution of ∼20 subcellular localization classes. We show that a quarter of the proteome displays cell cycle periodicity, with proteins tending to be controlled either at the level of localization or concentration, but not both. Distinct levels of protein regulation are preferentially utilized for different aspects of the cell cycle, with changes in protein concentration being mostly involved in cell cycle control and changes in protein localization in the biophysical implementation of the cell cycle program. We present a resource for exploring global proteome dynamics during the cell cycle, which will aid in understanding a fundamental biological process at a systems level.

The bidirectional relationship between metabolism and cell cycle control

The relationship between metabolism and cell cycle progression is complex and bidirectional. Cells must rewire metabolism to meet changing biosynthetic demands across cell cycle phases. In turn, metabolism can influence cell cycle progression through direct regulation of cell cycle proteins, through nutrient-sensing signaling pathways, and through its impact on cell growth, which is linked to cell division. Furthermore, metabolism is a key player in mediating quiescence-proliferation transitions in physiologically important cell types, such as stem cells. How metabolism impacts cell cycle progression, exit, and re-entry, as well as how these processes impact metabolism, is not fully understood. Recent advances uncovering mechanistic links between cell cycle regulators and metabolic processes demonstrate a complex relationship between metabolism and cell cycle control, with many questions remaining.

Give and Take: The Reciprocal Control of Metabolism and Cell Cycle

Cell cycle is an ordered sequence of events that occur in a cell preparing for cell division . The cell cycle is a four-stage process in which the cell increases in size, copies its DNA , prepares to divide, and divides. All these stages require a coordination of signaling pathways as well as adequate levels of energy and building blocks. These specific signaling and metabolic switches are tightly orchestrated in order for the cell cycle to occur properly. In this book chapter, we will provide information on the basis of metabolism and cell cycle interplay, and we will finish by an unexhaustive list of metabolomics approaches available to study the reciprocal control of metabolism and cell cycle.

RAE1 promotes nitrosamine-induced malignant transformation of human esophageal epithelial cells through PPARα-mediated lipid metabolism

Esophageal cancer (EC) is the sixth cause of cancer-related deaths and still is a significant public health problem globally. Nitrosamines exposure represents a major health concern increasing EC risks. Exploring the mechanisms induced by nitrosamines may contribute to the prevention and early detection of EC. However, the mechanism of nitrosamine carcinogenesis remains unclear. Ribonucleic acid export 1 (RAE1), has an important role in mediating diverse cancer types, but, to date, there has been no study for any functional role of RAE1 in esophageal carcinogenesis. Here, we successfully verified the nitrosamine-induced malignant transformation cell (MNNG-M) by xenograft tumor model, based on which it was found that RAE1 was upregulation in the early stage of nitrosamine-induced esophageal carcinogenesis and EC tissues. RAE1 knockdown led to severe blockade in G2/M phase and significant inhibition of proliferation of MNNG-M cells, whereas RAE1 overexpression had the opposite effect. In addition, peroxisome proliferator-activated receptor-alpha (PPARα), was demonstrated as a downstream target gene of RAE1, and its down-regulation reduced lipid accumulation, resulting in causing cells accumulation in the G2/M phase. Mechanistically, we found that RAE1 regulates the lipid metabolism by maintaining the stability of PPARα mRNA. Taken together, our study reveals that RAE1 promotes malignant transformation of human esophageal epithelial cells (Het-1A) by regulating PPARα-mediated lipid metabolism to affect cell cycle progression, and offers a new explanation of the mechanisms underlying esophageal carcinogenesis.

The metabolic control of DNA replication: mechanism and function

Metabolism and DNA replication are the two most fundamental biological functions in life. The catabolic branch of metabolism breaks down nutrients to produce energy and precursors used by the anabolic branch of metabolism to synthesize macromolecules. DNA replication consumes energy and precursors for faithfully copying genomes, propagating the genetic material from generation to generation. We have exquisite understanding of the mechanisms that underpin and regulate these two biological functions. However, the molecular mechanism coordinating replication to metabolism and its biological function remains mostly unknown. Understanding how and why living organisms respond to fluctuating nutritional stimuli through cell-cycle dynamic changes and reproducibly and distinctly temporalize DNA synthesis in a wide-range of growth conditions is important, with wider implications across all domains of life. After summarizing the seminal studies that founded the concept of the metabolic control of replication, we review data linking metabolism to replication from bacteria to humans. Molecular insights underpinning these links are then presented to propose that the metabolic control of replication uses signalling systems gearing metabolome homeostasis to orchestrate replication temporalization. The remarkable replication phenotypes found in mutants of this control highlight its importance in replication regulation and potentially genetic stability and tumorigenesis.

Glyoxalase 1 inhibitor BBGC suppresses the progression of chronic lymphocytic leukemia and promotes the efficacy of Palbociclib

Chronic lymphocytic leukemia (CLL) is a highly heterogeneous disease. Despite recent tremen-dous progress in managing CLL, the disease remains incurable with clinical therapies, and relapse is inevitable. To overcome this, new diagnostic and prognostic markers need to be investigated. We thus screened through the public database for genes with diagnostic, prognostic, and therapeutic implications in CLL. We further performed RT-qPCR and Western blot analysis to measure the candidate gene and protein expression levels, respectively, in peripheral blood mononuclear cells. Our results indicated that Glyoxalase 1 (GLO1) expression was significantly higher in patients with CLL than in healthy controls. Furthermore, cell proliferation, apoptosis, and cell cycle assay results together indicated that S-p-bromobenzylglutathione cyclopentyl diester (BBGC), an effective inhibitor of GLO1, suppresses the progression of CLL. Bioinformatics analysis revealed that GLO1 expression is closely associated with CDK4 expression in a wide variety of cancer types, and inhibition of CDK4 through silencing of genes or inhibitors can downregulate GLO1 expression. Subsequent validation experiments demonstrated that GLO1 protein levels were downregulated in MEC-1 and Jurkat cell lines after palbociclib exposure, and combination treatment of palbociclib with GLO1 inhibitor BBGC effectively delayed the growth of tumor cell lines.

Metabolic Rewiring and the Characterization of Oncometabolites

Simple Summary

Oncometabolites are produced by cancer cells and assist the cancer to proliferate and progress. Oncometabolites occur as a result of mutated enzymes in the tumor tissue or due to hypoxia. These processes result in either the abnormal buildup of a normal metabolite or the accumulation of an unusual metabolite. Definition of the metabolic changes that occur due to these processes has been accomplished using metabolomics, which mainly uses mass spectrometry platforms to define the content of small metabolites that occur in cells, tissues, organs and organisms. The four classical oncometabolites are fumarate, succinate, (2R)-hydroxyglutarate and (2S)-hydroxyglutarate, which operate by inhibiting 2-oxoglutarate-dependent enzyme reactions that principally regulate gene expression and response to hypoxia. Metabolomics has also revealed several putative oncometabolites that include lactate, kynurenine, methylglyoxal, sarcosine, glycine, hypotaurine and (2R,3S)-dihydroxybutanoate. Metabolomics will continue to be critical for understanding the metabolic rewiring involving oncometabolite production that underpins many cancer phenotypes.

Abstract

The study of low-molecular-weight metabolites that exist in cells and organisms is known as metabolomics and is often conducted using mass spectrometry laboratory platforms. Definition of oncometabolites in the context of the metabolic phenotype of cancer cells has been accomplished through metabolomics. Oncometabolites result from mutations in cancer cell genes or from hypoxia-driven enzyme promiscuity. As a result, normal metabolites accumulate in cancer cells to unusually high concentrations or, alternatively, unusual metabolites are produced. The typical oncometabolites fumarate, succinate, (2R)-hydroxyglutarate and (2S)-hydroxyglutarate inhibit 2-oxoglutarate-dependent dioxygenases, such as histone demethylases and HIF prolyl-4-hydroxylases, together with DNA cytosine demethylases. As a result of the cancer cell acquiring this new metabolic phenotype, major changes in gene transcription occur and the modification of the epigenetic landscape of the cell promotes proliferation and progression of cancers. Stabilization of HIF1α through inhibition of HIF prolyl-4-hydroxylases by oncometabolites such as fumarate and succinate leads to a pseudohypoxic state that promotes inflammation, angiogenesis and metastasis. Metabolomics has additionally been employed to define the metabolic phenotype of cancer cells and patient biofluids in the search for cancer biomarkers. These efforts have led to the uncovering of the putative oncometabolites sarcosine, glycine, lactate, kynurenine, methylglyoxal, hypotaurine and (2R,3S)-dihydroxybutanoate, for which further research is required.

Evolution of opposing regulatory interactions underlies the emergence of eukaryotic cell cycle checkpoints

In eukaryotes the entry into mitosis is initiated by activation of cyclin-dependent kinases (CDKs), which in turn activate a large number of protein kinases to induce all mitotic processes. The general view is that kinases are active in mitosis and phosphatases turn them off in interphase. Kinases activate each other by cross- and self-phosphorylation, while phosphatases remove these phosphate groups to inactivate kinases. Crucial exceptions to this general rule are the interphase kinase Wee1 and the mitotic phosphatase Cdc25. Together they directly control CDK in an opposite way of the general rule of mitotic phosphorylation and interphase dephosphorylation. Here we investigate why this opposite system emerged and got fixed in almost all eukaryotes. Our results show that this reversed action of a kinase-phosphatase pair, Wee1 and Cdc25, on CDK is particularly suited to establish a stable G2 phase and to add checkpoints to the cell cycle. We show that all these regulators appeared together in LECA (Last Eukaryote Common Ancestor) and co-evolved in eukaryotes, suggesting that this twist in kinase-phosphatase regulation was a crucial step happening at the emergence of eukaryotes.

Effect of Baicalin on Transcriptome Changes in Piglet Vascular Endothelial Cells Induced by a Combination of Glaesserella parasuis and Lipopolysaccharide

Glaesserella parasuis causes porcine Glässer's disease and lipopolysaccharide (LPS) induces acute inflammation and pathological damage. Baicalin has antioxidant, antimicrobial, and anti-inflammatory functions. Long noncoding RNAs (lncRNAs) play key regulatory functions during bacterial infection. However, the role of lncRNAs in the vascular dysfunction induced by a combination of G. parasuis and LPS during systemic inflammation and the effect of baicalin on lncRNA expression induced in porcine aortic vascular endothelial cells (PAVECs) by a combination of G. parasuis and LPS have not been investigated. In this study, we investigated the changes in lncRNA and mRNA expression induced in PAVECs by G. parasuis, LPS, or a combination of G. parasuis and LPS, and the action of baicalin on lncRNA expression induced in PAVECs by the combination of G. parasuis and LPS. Our results showed 133 lncRNAs and 602 genes were differentially expressed when PAVECs were stimulated with the combination of G. parasuis and LPS, whereas 107 lncRNAs and 936 genes were differentially expressed when PAVECs were stimulated with the combination of G. parasuis and LPS after pretreatment with baicalin. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed the dominant signaling pathways triggered by the combination of G. parasuis and LPS were the tumor necrosis factor signaling pathway, phosphatidylinositol signaling system, and inositol phosphate metabolism. Protein-protein interaction network analysis showed the differentially expressed target genes of the differentially expressed lncRNAs (DELs) were related to each other. A coexpression analysis indicated the expression levels of the DELs were co-regulated with those of their differentially expressed target genes. This is the first study to systematically compare the changes in lncRNAs and mRNAs in PAVECs stimulated with a combination of G. parasuis and LPS. Our data clarified the mechanisms underlying the vascular inflammation and damage triggered by G. parasuis and LPS, and it may provide novel targets for the treatment of LPS-induced systemic inflammation.