Information

Regulation of the TCA cycle and glycolysis by adenine nucleotides

Regulation of the TCA cycle and glycolysis by adenine nucleotides


We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

Why is the tricarboxylic acid cycle regulated by the ADP/ATP ratio as stated in the following quote :

Isocitrate dehydrogenase is allosterically stimulated by ADP, which enhances the enzyme's affinity for substrates.1

while glycolysis is regulated by AMP/ATP ratio as in the following quote from the same book :

Why is AMP and not ADP the positive regulator of phosphofructokinase? When ATP is being utilized rapidly, the enzyme adenylate kinase (Section 9.4) can form ATP from ADP by the following reaction: 2

I want to check my hypothesis that it is because the TCA cycle needs acetyl-CoA ( which can come from glycolysis and other sources such as beta-oxidation of fatty acids) in order to operate, so it gets acetyl-CoA from many sources and isn't dependent on glycolysis alone, and that's why it is affected by ADP (which increases upon initial exercise) while AMP stimulates glycolysis (increases after more exercise) to give more acetyl-CoA and that's rate of the TCA cycle is faster than glycolysis?

References

1,2 J.M. Berg et al. (2002). Biochemistry, 5th ed. New York: A H Freeman.


Summary

Glycolysis and the tricarboxylic acid cycle (TCA cycle) are distinct processes which are not necessarily linked sequentially. It is therefore not surprising that their modes of regulation are not identical and, in fact, involve far more complex regulation than mentioned in the question. The use of AMP rather than ADP as sensor of energy deficit depends on the action of adenylate kinase, which may differ between cytoplasm and mitochondria.

Relationship Between glycolysis and the TCA cycle

The expectation evident in the question that glycolysis and the TCA cycle should be controlled in exactly the same way assumes that either the two processes are inextricably linked or at least that the functioning of the TCA cycle is dependent on that of glycolysis. This is not the case, although the student can be excused this misimpression from the (perhaps unavoidable) way the topics are generally treated sequentially in text books of biochemistry.

The primary function of glycolysis is to produce ATP directly from ADP. The end-product of the pathway, pyruvate, is in some circumstances converted to acetyl CoA for the TCA cycle, and the NADH generated used for production of ATP generation via the electron transport chain (etc) and oxidative phosphorylation. However pyruvate has a variety of other possible fates depending on the type of organism or - in higher organisms - the tissue, and their overall metabolic requirements. For example in anaerobic metabolism - including exercising muscle tissue mentioned by the poster - the pyruvate may be reduced by reactions that regenerate NAD+ from NADH, e.g. conversion to lactate. It can serve as a precursor of the amino acids, alanine, valine and leucine. It can also be converted in the reaction catalysed by pyruvic carboxylase to oxaloacetate, the key intermediate that allows acetyl CoA to enter the TCA cycle. The importance of direct synthesis of oxaloacetate in this way is that it allows continuation of the cycle when intermediates are used for synthetic purposes (discussed below), in which circumstances oxaloacetate becomes depleted.

As regards the TCA cycle, there are sources of acetyl CoA other than pyruvate produced by glycolysis, and it has functions other than energy generation. Acetyl CoA can arise from the breakdown of fatty acids and of certain amino acids. In multicellular organisms, pyruvate can also be produced from lactate taken up from the blood, although in liver cells it is likely to be used for gluconeogenesis. And certain bacteria - acetic acid bacteria such as Acetobacter - can utilize the ethanol produced by fermentation in the TCA cycle. In addition to its function in energy generation some of its intermediates (α-ketoglutarate, succinyl CoA and oxaloacetate) are precursors of biosynthetic pathways, and the production of citrate from acetyl CoA is a means of transferring the latter out of the mitochondrion, where it is reconverted to acetyl CoA by the citrate cleavage enzyme (ATP citrate lyase).

It is therefore not surprising that there will be differences in the regulation of glycolysis and the TCA cycle, and, indeed, that there will be differences between different organisms - something generally not mentioned in elementary texts.

Regulation of Glycolysis

The prime function of glycolysis is the direct generation of ATP (“at the substrate level”), and flux of glucose through it is controlled by the regulatory effects of both ATP (-ve) and AMP (+ve) on phosphofructokinase, which was established and rationalized by D. E. Atkinson (Biochemistry 1968, 7, 11, 4030-4034) in terms of a response to the overall energy charge of the system.

This arises from the ability of adenylate kinase to reconvert ADP to ATP:

2ADP → ATP + AMP

[from Atkinson and Walton (1967) J. Biol. Chem. 242 3239-3241]

It can be seen that AMP is a much better indicator of low energy charge than ADP in the circumstances represented by the graph above.

Citrate is also a negative effector of the enzyme, providing co-ordination between glycolysis and the TCA cycle. (Hormonal regulation via protein phosphorylation occurs in the tissues of higher eukaryotes.)

Regulation of the conversion of pyruvate to acetyl CoA

In order to enter the TCA cycle, pyruvate needs to be converted to acetyl CoA by the pyruvate dehydrogenase complex. The regulation of this enzyme determines the extent to which this occurs, and, hence, the extent to which pyruvate is metabolized by other processes. Negative regulators here are high concentrations of the reaction products, acetyl CoA and NADH, although in higher eukaryotes hormonally induced protein phosphorylation is important.

Regulation of the TCA cycle

Then first regulatory enzyme of the TCA cycle, isocitrate dehydrogenase, responds to the concentration of the key molecules involved oxidative phosphorylation: it is stimulated by ADP and +, and inhibited by ATP and NADH. It is worth reflecting on the consequences of inhibition. If a build-up of citrate occurs, this can feed back and inhibit phosphofructokinase in glycolysis. However the citrate can move to the cytoplasm of eukaryotes and be converted to acetyl CoA (for fatty acid synthesis) by the citrate cleavage enzyme, which is stimulated by ATP and inhibited by ADP.

A later regulatory stage is α-ketoglutarate dehydrogenase, which is inhibited by ATP, NADH and its product, succinyl CoA. Inhibition of this enzyme would allow a build-up of α-ketoglutarate, which is a precursor of several amino acids.

This the existence of several regulatory steps in pyruvate oxidation and the TCA cycle allows the use of the cycle for both energy generation and as a source of synthetic precursors. Hence it should be evident why its control is similar to that of glycolysis in some respects, but exhibits key differences.

[Berg et al. Biochemistry 5th ed, Figure 17.18]

But why isn't AMP a regulatory molecule in the TCA cycle?

Glycolysis and the TCA cycle do not need to march in step, although when they are both responding to the need for ATP their key enzymes are regulated by adenine nucleotides, among other molecules. The original question (or a modification of it) remains as to why is AMP a positive effector of phosphofructokinase, but not of isocitrate dehydrogenase, which responds to ADP. It is interesting that in the section of Berg et al. discussing the concept of energy charge, a rather lame rider is added without further explanation to the effect that the ATP/ADP ratio can also act as an index of energy status.

I don't know for certain, but suspect that the conditions under which Atkinson's equation for energy charge is valid do not hold in the mitochondrion. This is supported by Sobol et al. (1978) Eur J. Biochem. 87 377-390 who found concentrations of AMP in rat liver mitochondria to be much lower than would be expected from equilibration with ATP and ADP through the adenylate kinase reaction. They suggested additional reaction of AMP with GTP in the mitochondrion might be responsible and also cited reports that adenylate kinase was absent from the matrix of rat liver mitochondria. At least in rat liver, this would explain AMP would not be a useful regulatory molecule, whereas the ATP/ADP ratio would be indicative of the requirement for oxidative phosphorylation.


Frontiers in Physiology

The editor and reviewers' affiliations are the latest provided on their Loop research profiles and may not reflect their situation at the time of review.



SHARE ON

Abstract

Interest in the topic of tumour metabolism has waxed and waned over the past century of cancer research. The early observations of Warburg and his contemporaries established that there are fundamental differences in the central metabolic pathways operating in malignant tissue. However, the initial hypotheses that were based on these observations proved inadequate to explain tumorigenesis, and the oncogene revolution pushed tumour metabolism to the margins of cancer research. In recent years, interest has been renewed as it has become clear that many of the signalling pathways that are affected by genetic mutations and the tumour microenvironment have a profound effect on core metabolism, making this topic once again one of the most intense areas of research in cancer biology.


Krebs cycle: activators, inhibitors and their roles in the modulation of carcinogenesis

A fundamental metabolic feature of cancerous tissues is high glucose consumption. The rate of glucose consumption in a cancer cell can be 10–15 times higher than in normal cells. Isolation and cultivation of tumor cells in vitro highlight properties that are associated with intensive glucose utilization, the presence of minimal oxidative metabolism, an increase in lactate concentrations in the culture medium and a reduced rate of oxygen consumption. Although glycolysis is suggested as a general feature of malignant cells and recently identified as a possible contributing factor to tumor progression, several studies highlight distinct metabolic characteristics in some tumors, including a relative decrease in avidity compared to glucose and/or a glutamine dependency of lactate and even proliferative tumor cells. The aim of this review is to determine the particularities in the energy metabolism of cancer cells, focusing on the main nutritional substrates, such as glucose and glutamine, evaluating lactate dehydrogenase as a potential marker of malignancy and estimating activators and inhibitors in cancer treatment.

This is a preview of subscription content, access via your institution.


Oncogenes and tumor suppressors impinging on the TCA cycle

Genetic alterations and/or deregulations of tumor suppressors or oncogenes often drive metabolic reprograming in cancers, although this effect can differ based on specific alterations or deregulations, and is often context-dependent. Several oncogenes, including MYC, HIF, P53, and RAS, are known to regulate the metabolic phenotype of tumors and play a critical role in determining how the TCA cycle is utilized in these cancer cells.

The proto-oncogene MYC controls a wide range of cellular processes, including cell proliferation, metabolism, cellular differentiation and genomic instability, and is a dominant driver of tumor transformation and progression (Meyer and Penn, 2008). Aberrant MYC activity, resulting from chromosomal translocations, gene amplifications or increased mRNA/protein stability, is found in over half of all human cancers (Gabay et al., 2014). Importantly, MYC is a central regulator of cellular metabolism, and can promote a broad range of metabolic pathways, such as aerobic glycolysis, glutaminolysis, mitochondrial biogenesis, oxidative phosphorylation, and nucleotide and amino acid biosynthesis (Adhikary and Eilers, 2005 Gabay et al., 2014 Wahlstrom and Henriksson, 2015). As stated early in this review article, MYC transcriptionally activates key genes and enzymes regulating glutaminolysis, and serves as the principal driver of glutamine metabolism through the TCA cycle (i.e., glutamine anaplerosis). Specifically, to promote the import of glutamine into the cell, MYC transcriptionally upregulates glutamine transporters ASC amino acid transporter 2 (ASCT2) and system N transporter (SN2). Additionally, Gao et al. demonstrated that MYC controls the conversion of glutamine to glutamate by activating glutaminase 1 (GLS1) through transcriptional suppression of its negative regulator miR-23a/b (Wise et al., 2008 Gao et al., 2009). There are two independent pathways that control the conversion of glutamate to α-KG entering the TCA cycle: one controlled by GLUD and another by aminotransferases. MYC-dependent cancer cells can utilize either GLUD or aminotransferases to convert glutamine to α-KG for the TCA cycle (Wise et al., 2008 Wang et al., 2011). MYC may also play a role in directing fatty acid oxidation and directing its metabolites into the TCA cycle by way of acetyl-CoA. Specifically, MYC expression leads to the upregulation of fatty acid transporters (e.g., fatty acid-binding protein 4) and fatty acid oxidation genes such as hydroxyacyl-CoA dehydrogenase (Wang et al., 2011 Edmunds et al., 2015).

Hypoxia-inducible factors (HIFs) are transcription factors that respond to reduced oxygen availability. HIFs are heterodimers composed of an oxygen-dependent α-subunit and a constitutively expressed β-subunit. Under normoxia, the α-subunit is targeted for degradation upon hydroxylation by prolyl hydroxylases (PHD) and subsequent ubiquitination by von Hippel-Lindau (VHL) tumor suppressor. Tumors activate HIFα either in the face of hypoxia resulting from poor vascularization or due to genetic abrogation such as VHL loss (Gordan and Simon, 2007). HIF activation orchestrates a metabolic program that promotes the catabolism of glucose through aerobic glycolysis and thus shifts glucose away from the TCA cycle (Semenza, 2012). HIF promotes glycolysis and lactate production through transcriptional upregulation of glucose transporters (SLC2A1 and SLC2A3), glycolytic enzymes (e.g., hexokinase (HK) and pyruvate kinase (PK)), and lactate dehydrogenase A (LDHA) (Kim et al., 2006). Kim et al. demonstrated that HIF1 suppresses glucose metabolism through the TCA cycle (i.e., glucose anaplerosis) by directly activating pyruvate dehydrogenase kinase 1 (PDK-1), a negative regulator of cycle enzyme pyruvate dehydrogenase (PDH) (Kim et al., 2006). To compensate for the reduction of glucose feeding the TCA cycle, tumor cells with HIF activation often increase the usage of glutamine (Le et al., 2012). Under hypoxia conditions, glutamine largely fuels the TCA cycle in the form of α-KG to promote reductive carboxylation that produces citrate for lipogenesis (Wise et al., 2008 Metallo et al., 2011 Gameiro et al., 2013).

P53 is a transcription factor and known tumor suppressor that regulates many important cellular pathways, including cell survival, DNA repair, apoptosis, and senescence (Bensaad et al., 2009). Wild-type P53 plays an important role in metabolism by striking a balance between bioenergetics and biosynthesis. One of the ways it does so is by lowering rates of glycolysis and promoting oxidative phosphorylation. P53 acts to suppress glycolysis by directly downregulating glucose transporters (GLUT1 and GLUT4) and indirectly inhibiting the activity of glycolytic enzymes, phosphofructokinase 1 (PFK1) and phosphoglycerate mutase (Kondoh et al., 2005 Bensaad et al., 2006, 2009 Zhang et al., 2013). To promote oxidative phosphorylation, P53 ensures availability of anapleurotic substrates, glucose, and glutamine, to the TCA cycle. As an activator of PDH, P53 downregulates PDH’s negative regulator PDK2 and indirectly activates PDHA1 (PDH A1 subunit). Additionally, P53 promotes glutamine incorporation into the TCA through direct transcriptional upregulation of glutaminase 2 (GLS2) (Zhang et al., 2011 Contractor and Harris, 2012). In solid tumors, P53 is commonly mutated and somatic mutations of P53 occur in more than 50% of human malignancies (Kruiswijk et al., 2015). Subsequently, loss of wild-type P53 function has a significant impact on cellular metabolism, leading to enhanced glycolysis and repressed oxidative phosphorylation in these tumor cells.

The most frequently mutated RAS subfamily genes in cancer are KRAS, NRAS, and HRAS, which serve as intercellular signaling molecules to transduce extracellular signaling from receptor tyrosine kinase to downstream effectors (Pylayeva-Gupta et al., 2011 Stephen et al., 2014). RAS plays a critical role in activating scavenging pathways in certain types of tumors and promotes nutrient uptake through both the extracellular and intracellular sources (Pylayeva-Gupta et al., 2011 Stephen et al., 2014). For example, Kamphorst et al. demonstrated that KRAS-driven pancreatic cells scavenge proteins, such as glutamine, from the extracellular space and utilize them to fuel the TCA cycle (Kamphorst et al., 2015). Additionally, it has been shown that KRAS-driven non-small cell lung cancer cells utilize autophagy to access intracellular supplies of glutamine to promote TCA cycle function (Guo et al., 2011 Strohecker and White, 2014). Moreover, KRAS-driven cancer cells can scavenge branch chain amino acids (i.e., isoleucine, valine, and leucine) and convert them into acetyl-CoA to fuel the TCA cycle (Mayers et al., 2014). A recent study by Kerr et al. demonstrated that copy number gain of mutant KRAS associated with tumor progression can promote glucose anaplerosis to fuel the TCA cycle (Kerr et al., 2016).


References

Chandel, N. Navigating Metabolism (Cold Spring Harbor Laboratory, 2015).

Weissman, I. L. Stem cells: units of development, units of regeneration, and units in evolution. Cell 100, 157–168 (2000).

Shyh-Chang, N., Daley, G. Q. & Cantley, L. C. Stem cell metabolism in tissue development and aging. Development 140, 2535–4257 (2013).

Kohli, L. & Passegué, E. Surviving change: the metabolic journey of hematopoietic stem cells. Trends Cell Biol. 24, 479–487 (2014).

Folmes, C. D., Dzeja, P. P., Nelson, T. J & Terzic, A. Metabolic plasticity in stem cell homeostasis and differentiation. Cell Stem Cell 11, 596–606 (2012).

Zhang, J., Nuebel, E., Daley, G. Q., Koehler, C. M. & Teitell, M. A. Metabolic regulation in pluripotent stem cells during reprogramming and self-renewal. Cell Stem Cell 11, 589–595 (2012).

Xu, X. et al. Mitochondrial regulation in pluripotent stem cells. Cell Metab. 18, 325–332 (2013).

Teslaa, T. & Teitell, M. A. Pluripotent stem cell energy metabolism: an update. EMBO J. 34, 138–153 (2015).

Cho, Y. M. et al. Dynamic changes in mitochondrial biogenesis and antioxidant enzymes during the spontaneous differentiation of human embryonic stem cells. Biochem. Biophys. Res. Commun. 348, 1472–1478 (2006).

Hom, J. R. et al. The permeability transition pore controls cardiac mitochondrial maturation and myocyte differentiation. Dev. Cell 21, 469–478 (2011).

Tormos, K. V. et al. Mitochondrial complex III ROS regulate adipocyte differentiation. Cell Metab. 14, 537–544 (2011).

Folmes, C. D. et al. Somatic oxidative bioenergetics transitions into pluripotency-dependent glycolysis to facilitate nuclear reprogramming. Cell Metab. 14, 264–271 (2011).

Zhang, J. et al. UCP2 regulates energy metabolism and differentiation potential of human pluripotent stem cells. EMBO J. 30, 4860–4873 (2011).

Facucho-Oliveira, J. M., Alderson, J., Spikings, E. C., Egginton, S. & St John, J. C. Mitochondrial DNA replication during differentiation of murine embryonic stem cells. J. Cell Sci. 120, 4025–4034 (2007).

Todd, L. R. et al. Growth factor erv1-like modulates Drp1 to preserve mitochondrial dynamics and function in mouse embryonic stem cells. Mol. Biol. Cell 21, 1225–1236 (2010).

Vazquez-Martin, A. et al. Mitochondrial fusion by pharmacological manipulation impedes somatic cell reprogramming to pluripotency: new insight into the role of mitophagy in cell stemness. Aging 4, 393–401 (2012).

Son, M. J. et al. Mitofusins deficiency elicits mitochondrial metabolic reprogramming to pluripotency. Cell Death Differ. 22, 1957–1969 (2015).

Kaelin, W. G. Jr & McKnight, S. L. Influence of metabolism on epigenetics and disease. Cell 153, 56–69 (2013).

Wang, J. et al. Dependence of mouse embryonic stem cells on threonine catabolism. Science 325, 435–439 (2009).

Sperber, H. et al. The metabolome regulates the epigenetic landscape during naive-to-primed human embryonic stem cell transition. Nat. Cell Biol. 17, 1523–1535 (2015).

Cheung, T. H. & Rando, T. A. Molecular regulation of stem cell quiescence. Nat. Rev. Mol. Cell Biol. 14, 329–340 (2013).

Rando, T. A. Stem cells, ageing and the quest for immortality. Nature 441, 1080–1086 (2006).

Rossi, D. J., Jamieson, C. H. & Weissman, I. L. Stems cells and the pathways to aging and cancer. Cell 132, 681–696 (2008).

López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The hallmarks of aging. Cell 153, 1194–1217 (2013).

Adams, P. D., Jasper, H. & Rudolph, K. L. Aging-induced stem cell mutations as drivers for disease and cancer. Cell Stem Cell 16, 601–612 (2015).

Pietras, E. M., Warr, M. & Passegué, E. Cell cycle regulation in hematopoietic stem cells. J. Cell Biol. 195, 709–720 (2011).

Simsek, T. et al. The distinct metabolic profile of hematopoietic stem cells reflects their location in a hypoxic niche. Cell Stem Cell 7, 380–390 (2010).

Takubo, K. et al. Regulation of glycolysis by Pdk functions as a metabolic checkpoint for cell cycle quiescence in hematopoietic stem cells. Cell Stem Cell 12, 49–61 (2013).

Yu, W. M. et al. Metabolic regulation by the mitochondrial phosphatase PTPMT1 is required for hematopoietic stem cell differentiation. Cell Stem Cell 12, 62–74 (2013).

Maryanovich, M. et al. An MTCH2 pathway repressing mitochondria metabolism regulates haematopoietic stem cell fate. Nat. Commun. 6, 7901 (2015).

Takubo, K. et al. Regulation of the HIF-1alpha level is essential for hematopoietic stem cells. Cell Stem Cell 7, 391–402 (2010).

Maltepe, E. et al. Abnormal angiogenesis and responses to glucose and oxygen deprivation in mice lacking the protein ARNT. Nature 386, 403–407 (1997).

Adelman, D. M. et al. Placental cell fates are regulated in vivo by HIF-mediated hypoxia responses. Genes Dev. 14, 3191–3203 (2000).

Mazumdar J. et al. O2 regulates stem cells through Wnt/β-catenin signalling. Nat. Cell Biol. 12, 1007–1013 (2010).

Miharada, K. et al. Cripto regulates hematopoietic stem cells as a hypoxic-niche-related factor through cell surface receptor GRP78. Cell Stem Cell 9, 330–344 (2011).

Kocabas, F. et al. Meis1 regulates the metabolic phenotype and oxidant defense of hematopoietic stem cells. Blood 120, 4963–4972 (2012).

Mohrin, M. et al. Stem cell aging: a mitochondrial UPR-mediated metabolic checkpoint regulates hematopoietic stem cell aging. Science 347, 1374–1377 (2015).

Mantel, C. R. et al. Enhancing hematopoietic stem cell transplantation efficacy by mitigating oxygen shock. Cell 161, 1553–1565 (2015).

Nakada, D., Saunders, T. L. & Morrison, S. J. Lkb1 regulates cell cycle and energy metabolism in haematopoietic stem cells. Nature 468, 653–658 (2010).

Luchsinger, L. L. et al. Mitofusin 2 maintains haematopoietic stem cells with extensive lymphoid potential. Nature 529, 528–531 (2016).

Ito, K. et al. A PML–PPAR-δ pathway for fatty acid oxidation regulates hematopoietic stem cell maintenance. Nat. Med. 18, 1350–1358 (2012).

Katajisto, P. et al. Stem cells: asymmetric apportioning of aged mitochondria between daughter cells is required for stemness. Science 348, 340–343 (2015).

Biteau, B., Hochmuth, C. E. & Jasper, H. Maintaining tissue homeostasis: dynamic control of somatic stem cell activity. Cell Stem Cell 9, 402–411 (2011).

O'Brien, L. E., Soliman, S. S., Li, X. & Bilder, D. Altered modes of stem cell division drive adaptive intestinal growth. Cell 147, 603–614 (2011).

Amcheslavsky, A., Jiang, J. & Ip, Y. T. Tissue damage-induced intestinal stem cell division in Drosophila. Cell Stem Cell 4, 49–61 (2009).

Buchon, N., Broderick, N. A., Chakrabarti, S. & Lemaitre, B. Invasive and indigenous microbiota impact intestinal stem cell activity through multiple pathways in Drosophila. Genes Dev. 23, 2333–2344 (2009).

Biteau, B., Hochmuth, C. E. & Jasper, H. JNK activity in somatic stem cells causes loss of tissue homeostasis in the aging Drosophila gut. Cell Stem Cell 3, 442–455 (2008).

Biteau, B. et al. Lifespan extension by preserving proliferative homeostasis in Drosophila. PLoS Genet. 6, 1001159 (2010).

Rera, M., Clark, R. I. & Walker, D. W. Intestinal barrier dysfunction links metabolic and inflammatory markers of aging to death in Drosophila. Proc. Natl Acad. Sci. USA 109, 21528–21533 (2012).

Rera, M. et al. Modulation of longevity and tissue homeostasis by the Drosophila PGC-1 homolog. Cell Metab. 14, 623–634 (2011).

Hochmuth, C. E., Biteau, B., Bohmann, D. & Jasper, H. Redox regulation by Keap1 and Nrf2 controls intestinal stem cell proliferation in Drosophila. Cell Stem Cell 8, 1–12 (2011).

Hur, J. H. et al. Increased longevity mediated by yeast NDI1 expression in Drosophila intestinal stem and progenitor cells. Aging 5, 662–681 (2013).

Chen, C. T., Shih, Y. R., Kuo, T. K., Lee, O. K. & Wei, Y. H. Coordinated changes of mitochondrial biogenesis and antioxidant enzymes during osteogenic differentiation of human mesenchymal stem cells. Stem Cells 26, 960–968 (2008).

Fu, X., Zhu, M. J., Dodson, M. V. & Du, M. AMP-activated protein kinase stimulates Warburg-like glycolysis and activation of satellite cells during muscle regeneration. J. Biol. Chem. 290, 26445–26456 (2015).

Ryall, J. G. et al. The NAD + -dependent SIRT1 deacetylase translates a metabolic switch into regulatory epigenetics in skeletal muscle stem cells. Cell Stem Cell 16, 171–183 (2015).

Knobloch, M. et al. Metabolic control of adult neural stem cell activity by Fasn-dependent lipogenesis. Nature 493, 226–230 (2013).

Kenyon, C. J. The genetics of ageing. Nature 464, 504–512 (2010).

Harrison, D. E. et al. Rapamycin fed late in life extends lifespan in genetically heterogeneous mice. Nature 460, 392–395 (2009).

Laplante, M. & Sabatini, D. M. mTOR signaling in growth control and disease. Cell 149, 274–293 (2012).

Huang, J. & Manning, B. D. The TSC1–TSC2 complex: a molecular switchboard controlling cell growth. Biochem. J. 412, 179–190 (2008).

Kapahi, P. et al. With TOR, less is more: a key role for the conserved nutrient-sensing TOR pathway in aging. Cell Metab. 11, 453–465 (2010).

Wilkinson, J. E. et al. Rapamycin slows aging in mice. Aging Cell 11, 675–682 (2012).

Jasper, H. & Jones, D. L. Metabolic regulation of stem cell behavior and implications for aging. Cell Metab. 12, 561–565 (2010).

Amcheslavsky, A., Ito, N., Jiang, J. & Ip, Y. T. Tuberous sclerosis complex and Myc coordinate the growth and division of Drosophila intestinal stem cells. J. Cell Biol. 193, 695–710 (2011).

Kapuria, S., Karpac, J., Biteau, B., Hwangbo, D. & Jasper, H. Notch-mediated suppression of TSC2 expression regulates cell differentiation in the Drosophila intestinal stem cell lineage. PLoS Genet. 8, e1003045 (2012).

Quan, Z., Sun, P., Lin, G. & Xi, R. TSC1/2 regulates intestinal stem cell maintenance and lineage differentiation via Rheb–TorC1–S6K but independent of nutrition status or Notch activation. J. Cell Sci. 126, 3884–3892 (2013).

Yilmaz, O. H. et al. mTORC1 in the Paneth cell niche couples intestinal stem-cell function to calorie intake. Nature 486, 490–495 (2012).

Beyaz, S. et al. High-fat diet enhances stemness and tumorigenicity of intestinal progenitors. Nature 531, 53–58 (2016).

Sampson, L. L., Davis, A. K., Grogg, M. W. & Zheng, Y. mTOR disruption causes intestinal epithelial cell defects and intestinal atrophy postinjury in mice. FASEB J. 30, 1263–1275 (2016).

Magri, L. et al. Sustained activation of mTOR pathway in embryonic neural stem cells leads to development of Tuberous Sclerosis Complex-associated lesions. Cell Stem Cell 9, 447–462 (2011).

Easley, C. A. et al. mTOR-mediated activation of p70 S6K induces differentiation of pluripotent human embryonic stem cells. Cell Reprogram. 12, 263–273 (2010).

Chen, T. et al. Rapamycin and other longevity-promoting compounds enhance the generation of mouse induced pluripotent stem cells. Aging Cell 10, 908–911 (2011).

Rodgers, J. T. et al. mTORC1 controls the adaptive transition of quiescent stem cells from G0 to GAlert . Nature 510, 393–396 (2014).

Chen, C. et al. TSC–mTOR maintains quiescence and function of hematopoietic stem cells by repressing mitochondrial biogenesis and reactive oxygen species. J. Exp. Med. 205, 2397–2408 (2008).

Kasahara, A., Cipolat, S., Chen, Y., Dorn, G. W. II & Scorrano, L. Mitochondrial fusion directs cardiomyocyte differentiation via calcineurin and Notch signaling. Science 342, 734–737 (2013).

Yilmaz, O. H. et al. Pten dependence distinguishes haematopoietic stem cells from leukaemia-initiating cells. Nature 441, 475–482 (2006).

Kharas, M. G. et al. Constitutively active AKT depletes hematopoietic stem cells and induces leukemia in mice. Blood 115, 1406–1415 (2010).

Qian, P. et al. The Dlk1–Gtl2 locus preserves LT-HSC function by inhibiting the PI3K–mTOR pathway to restrict mitochondrial metabolism. Cell Stem Cell 18, 214–228 (2015).

Juntilla, M. M. et al. AKT1 and AKT2 maintain hematopoietic stem cell function by regulating reactive oxygen species. Blood 115, 4030–4038 (2010).

Wilson, A., Laurenti, E. & Trumpp, A. Balancing dormant and self-renewing hematopoietic stem cells. Curr. Opin. Genet. Dev. 19, 461–468 (2009).

Mortensen, M. et al. The autophagy protein Atg7 is essential for hematopoietic stem cell maintenance. J. Exp. Med. 208, 455–467 (2011).

Warr, M. R. et al. FOXO3A directs a protective autophagy program in haematopoietic stem cells. Nature 494, 323–327 (2013).

Malinska, D., Kudin, A. P., Bejtka, M. & Kunz, W. S. Changes in mitochondrial reactive oxygen species synthesis during differentiation of skeletal muscle cells. Mitochondrion 12, 144–148 (2012).

Biteau, B. & Jasper, H. EGF signaling regulates the proliferation of intestinal stem cells in Drosophila. Development 138, 1045–1055 (2011).

Hamanaka, R. B. et al. Mitochondrial reactive oxygen species promote epidermal differentiation and hair follicle development. Sci. Signal. 6, ra8 (2013).

Morimoto, H. et al. ROS are required for mouse spermatogonial stem cell self-renewal. Cell Stem Cell 12, 774–786 (2013).

Le Belle, J. E. et al. Proliferative neural stem cells have high endogenous ROS levels that regulate self-renewal and neurogenesis in a PI3K/AKT-dependant manner. Cell Stem Cell 8, 59–71 (2011).

Paul, M. K. et al. Dynamic changes in intracellular ROS levels regulate airway basal stem cell homeostasis through Nrf2-dependent notch signaling. Cell Stem Cell 15, 199–214 (2014).

Bakker, S. T. & Passegué, E. Resilient and resourceful: genome maintenance strategies in hematopoietic stem cells. Exp. Hematol. 41, 915–923 (2013).

Owusu-Ansah, E. & Banerjee, U. Reactive oxygen species prime Drosophila haematopoietic progenitors for differentiation. Nature 461, 537–541 (2009).

Ito, K. et al. Reactive oxygen species act through p38 MAPK to limit the lifespan of hematopoietic stem cells. Nat. Med. 12, 446–451 (2006).

Rimmelé, P. et al. Mitochondrial metabolism in hematopoietic stem cells requires functional FOXO3. EMBO Rep. 16, 1164–1176 (2015).

Lewandowski, D. et al. In vivo cellular imaging pinpoints the role of reactive oxygen species in the early steps of adult hematopoietic reconstitution. Blood 115, 443–452 (2010).

Ludin, A. et al. Reactive oxygen species regulate hematopoietic stem cell self-renewal, migration and development, as well as their bone marrow microenvironment. Antioxid. Redox Signal. 21, 1605–1619 (2014).

Jang, Y. Y. & Sharkis, S. J. A low level of reactive oxygen species selects for primitive hematopoietic stem cells that may reside in the low-oxygenic niche. Blood 110, 3056–3063 (2007).

Itkin, T. et al. FGF-2 expands murine hematopoietic stem and progenitor cells via proliferation of stromal cells, c-Kit activation, and CXCL12 down-regulation. Blood 120, 1843–1855 (2012).

Ludin, A. et al. Monocytes-macrophages that express alpha-smooth muscle actin preserve primitive hematopoietic cells in the bone marrow. Nat. Immunol. 13, 1072–1082 (2012).

Golan, K. et al. S1P promotes murine progenitor cell egress and mobilization via S1P1-mediated ROS signaling and SDF-1 release. Blood 119, 2478–2488 (2012).

Ishikawa, E. T. et al. Connexin-43 prevents hematopoietic stem cell senescence through transfer of reactive oxygen species to bone marrow stromal cells. Proc. Natl Acad. Sci. USA 109, 9071–9076 (2012).

Spencer, J. A. et al. Direct measurement of local oxygen concentration in the bone marrow of live animals. Nature 508, 269–273 (2014).

Itkin, T. et al. Distinct bone marrow blood vessels differentially regulate haematopoiesis. Nature 532, 323–328 (2016).

Matsui, K. et al. NAD-dependent histone deacetylase, SIRT1, plays essential roles in the maintenance of hematopoietic stem cells. Biochem. Biophys. Res. Commun. 418, 811–817 (2012).

Rimmelé, P. et al. Aging-like phenotype and defective lineage specification in SIRT1-deleted hematopoietic stem and progenitor cells. Stem Cell Rep. 3, 44–59 (2014).

Brown, K. et al. SIRT3 reverses aging-associated degeneration. Cell Rep. 3, 319–327 (2013).

Miyamoto, K. et al. FoxO3a regulates hematopoietic homeostasis through a negative feedback pathway in conditions of stress or aging. Blood 112, 4485–4493 (2008).

Mehta, A. et al. The microRNA-132 and microRNA-212 cluster regulates hematopoietic stem cell maintenance and survival with age by buffering FOXO3 expression. Immunity 42, 1021–1032 (2015).

Tothova, Z. et al. FoxOs are critical mediators of hematopoietic stem cell resistance to physiologic oxidative stress. Cell 128, 325–339 (2007).

Miyamoto, K. et al. Foxo3a is essential for maintenance of the hematopoietic stem cell pool. Cell Stem Cell 1, 101–112 (2007).

Murakami, S. & Motohashi, H. Roles of Nrf2 in cell proliferation and differentiation. Free Radic. Biol. Med. 88, 168–178 (2015).

Sykiotis, G. P. & Bohmann, D. Keap1/Nrf2 signaling regulates oxidative stress tolerance and lifespan in Drosophila. Dev. Cell. 14, 76–85 (2008).

Harman, D. The free radical theory of aging. Antioxid. Redox Signal. 5, 557–561 (2003).

Park, C. B. & Larsson, N. G. Mitochondrial DNA mutations in disease and aging. J. Cell Biol. 193, 809–818 (2011).

Trifunovic, A. et al. Premature ageing in mice expressing defective mitochondrial DNA polymerase. Nature 429, 417–423 (2004).

Kujoth, G. C. et al. Mitochondrial DNA mutations, oxidative stress, and apoptosis in mammalian aging. Science 309, 481–484 (2005).

Chen, M. L. et al. Erythroid dysplasia, megaloblastic anemia, and impaired lymphopoiesis arising from mitochondrial dysfunction. Blood 114, 4045–4053 (2009).

Ahlqvist, K. J. et al. Somatic progenitor cell vulnerability to mitochondrial DNA mutagenesis underlies progeroid phenotypes in Polg mutator mice. Cell Metab. 15, 100–109 (2011).

Hämäläinen, R. H. et al. mtDNA mutagenesis disrupts pluripotent stem cell function by altering redox signaling. Cell Rep. 11, 1614–1624 (2015).

Norddahl, G. L. et al. Accumulating mitochondrial DNA mutations drive premature hematopoietic aging phenotypes distinct from physiological stem cell aging. Cell Stem Cell 8, 499–510 (2011).

Cosentino, C. & Mostoslavsky, R. Metabolism, longevity and epigenetics. Cell Mol. Life Sci. 70, 1525–1541 (2013).

Lu, C. & Thompson, C. B. Metabolic regulation of epigenetics. Cell Metab. 16, 9–17 (2012).

Keating, S. T. & El-Osta, A. Epigenetics and metabolism. Circ. Res. 116, 715–736 (2015).

Greer, E. L. et al. Transgenerational epigenetic inheritance of longevity in Caenorhabditis elegans. Nature 479, 365–371 (2011).

Beerman, I. & Rossi, D. J. Epigenetic control of stem cell potential during homeostasis, aging, and disease. Cell Stem Cell 16, 613–625 (2015).

Chinopoulos, C. Which way does the citric acid cycle turn during hypoxia? The critical role of α-ketoglutarate dehydrogenase complex. J. Neurosci Res. 91, 1030–1043 (2013).

Koivunen, P. et al. Inhibition of hypoxia-inducible factor (HIF) hydroxylases by citric acid cycle intermediates: possible links between cell metabolism and stabilization of HIF. J. Biol. Chem. 282, 4524–4532 (2007).

Beerman, I. et al. Proliferation-dependent alterations of the DNA methylation landscape underlie hematopoietic stem cell aging. Cell Stem Cell 12, 413–425 (2013).

Salminen, A., Kauppinen, A., Hiltunen, M. & Kaarniranta, K. Krebs cycle intermediates regulate DNA and histone methylation: epigenetic impact on the aging process. Ageing Res Rev. 16, 45–65 (2014).

Chambers, S. M. et al. Aging hematopoietic stem cells decline in function and exhibit epigenetic dysregulation. PLoS Biol. 5, 201 (2007).

Sun, D. et al. Epigenomic profiling of young and aged HSCs reveals concerted changes during aging that reinforce self-renewal. Cell Stem Cell 14, 673–688 (2014).

Challen, G. A. et al. Dnmt3a is essential for hematopoietic stem cell differentiation. Nat. Genet. 44, 23–31 (2011).

Imai, S. & Guarente, L. NAD + and sirtuins in aging and disease. Trends Cell Biol. 24, 464–471 (2014).

Braidy, N. et al. Age related changes in NAD + metabolism oxidative stress and Sirt1 activity in wistar rats. PLoS One 6, 19194 (2011).

Florian, M. C. et al. Cdc42 activity regulates hematopoietic stem cell aging and rejuvenation. Cell Stem Cell 10, 520–530 (2012).

Zhang, X. P. et al. Oxidized low-density lipoprotein induces hematopoietic stem cell senescence. Cell Biol Int. 37, 940–948 (2013).

Tie, G., Messina, K. E., Yan, J., Messina, J.A. & Messina, L. M. Hypercholesterolemia induces oxidant stress that accelerates the ageing of hematopoietic stem cells. J. Am. Heart Assoc. 3, 000241 (2014).

Chen, J., Astle, C. M. & Harrison, D. E. Hematopoietic senescence is postponed and hematopoietic stem cell function is enhanced by dietary restriction. Exp. Hematol. 31, 1097–1103 (2003).

Cheng, C. W. et al. Prolonged fasting reduces IGF-1/PKA to promote hematopoietic-stem-cell-based regeneration and reverse immunosuppression. Cell Stem Cell 14, 810–823 (2014).

Passegué, E, Wagers, A. J., Giuriato, S., Anderson, W. C. & Weissman, I. L. Global analysis of proliferation and cell cycle gene expression in the regulation of hematopoietic stem and progenitor cell fates. J. Exp. Med. 202, 1599–1611 (2005).

Tang, D. et al. Dietary restriction improves repopulation but impairs lymphoid differentiation capacity of hematopoietic stem cells in early aging. J. Exp. Med. 213, 535–553 (2016).

Martin-Montalvo, A. & de Cabo, R. Mitochondrial metabolic reprogramming induced by calorie restriction. Antioxid. Redox Signal. 19, 310–320 (2013).

López-Lluch, G. et al. Calorie restriction induces mitochondrial biogenesis and bioenergetic efficiency. Proc. Natl Acad. Sci. USA 103, 1768–1773 (2006).

Cerletti, M., Jang, Y. C., Finley, L. W., Haigis, M. C. & Wagers, A. J. Short-term calorie restriction enhances skeletal muscle stem cell function. Cell Stem Cell 10, 515–519 (2012).

Hekimi, S., Lapointe, J. & Wen, Y. Taking a “good” look at free radicals in the aging process. Trends Cell Biol. 21, 569–576 (2011).

Tanaka, Y. et al. JmjC enzyme KDM2A is a regulator of rRNA transcription in response to starvation. EMBO J. 29, 1510–1522 (2010).

Shimazu, T. et al. Suppression of oxidative stress by β-hydroxybutyrate, an endogenous histone deacetylase inhibitor. Science 339, 211–214 (2013).

Boroughs, L. K. & DeBerardinis, R. J. Metabolic pathways promoting cancer cell survival and growth. Nat. Cell Biol. 17, 351–359 (2015).

Locasale, J. W. Serine, glycine and one-carbon units: cancer metabolism in full circle. Nat. Rev. Cancer 13, 572–583 (2013).


STRATEGIC PLANNING

The programs for both the HPLC with the Hyperclone C18 (ODS) column and plate reader need to be prepared for the measurement before starting.

  • Column for HPLC: Hyperclone C18 (ODS) column (Phenomenex)
  • Gradient flowrate 0.8 ml/min (50 min/sample)
    • ○ 0-0.2 min equilibration: 95% Buffer A: 5% Buffer B
    • ○ 0.2-53 min (linear gradient): up to 50% Buffer B
    • ○ Recalibration: 7 min with 5% Buffer B

    2. Microplate reader (e.g., Biotek EL 808 with 570-nm filter)

    1: PREPARATION OF PLANT MATERIAL FOR THE MEASUREMENT

    Both adenylate and pyrimidine nucleotide act as “currency” within living cells and play an important role in regulating plant growth and development (Gakière, Fernie, & Pétriacq, 2018 Geigenberger, Riewe, & Fernie, 2010 ). Although several publications use different approaches for measurement of adenylate and pyrimidine nucleotide, there is no detailed protocol for the measurement of these compounds in plants. Here, we present detailed protocols for sampling and extraction of whole seedlings, leaves, or roots. Because of the extremely high turnover rates of adenylate and pyrimidine nucleotides (down to milliseconds), plant material needs to be harvested as rapidly as possible, then instant-frozen, ideally by using the freeze-clamping technique (Ap Rees, Fuller, & Wright, 1977 ). When working with leaves, it is important to quench the samples directly in light, and not to alter the incident irradiance via shading. Given the instability of these metabolites, the plant materials should be frozen at –80°C for no more than 1 year. Here, we use Arabidopsis leaves as an example.

    Materials

    • Plant material
    • Liquid nitrogen
    • 2-ml microcentrifuge tubes
    • 1.5 ml Safe-Lock tubes
    • Ball mill (Retsch)
    • Balance

    1. Harvest around 100 mg of Arabidopsis or other plant leaf material in a 2.0-ml tube and snap-freeze it in liquid nitrogen.

    2. Homogenize the plant material in a ball mill by adding a clean and pre-cooled metal ball to the leaf material, and grind for 30-45 s at medium speed.

    3. Transfer aliquots of approximately 25 mg (±2 mg) to new 1.5-ml tubes and record the exact weight.

    4. If assays cannot be performed immediately, store samples at –80°C until measurement.

    2: MEASUREMENT OF ATP, ADP, AND AMP VIA HPLC

    As the “molecular unit of currency” of intracellular energy transfer, ATP is a metabolite that provides energy to multiple processes in the living cell, including protein synthesis, chemical synthesis, metabolism, cell division, and transport processes. It can be converted to either ADP or AMP when consumed in living cells. Measurement of ATP, ADP, and AMP have been described in several articles (Liang et al., 2016 Pham et al., 2015 Zhang et al., 2018 Zhu et al., 2019 ), but detailed protocols are unavailable. Here, we describe in detail the process of ATP, ADP, and AMP measurement in plant samples (Fig. 1).

    Materials

    • ATP disodium salt trihydrate (Sigma-Aldrich 10519987001)
    • ADP (Sigma-Aldrich 01905)
    • AMP disodium salt (Sigma-Aldrich 01930)
    • Sample: Arabidopsis leaf material prepared as in Basic Protocol 1
    • 0.1 M HCl
    • CP buffer (see Table 1)
    • Chloroacetaldehyde (Sigma-Aldrich 317276-5 ml HIGHLY TOXIC!)
    • Adenosine (Sigma-Aldrich A9251)
    • Running buffer A for HPLC (see Table 2)
    • Running buffer B for HPLC (see Table 3)
    • Refrigerated centrifuge
    • Heated shaker
    • Glass vials for HPLC
    • Hyperclone C18 (ODS) column (Phenomenex)
    • HPLC system (Dionex)

    Adjust the pH to 4 with NaOH.

    Reagent Source Final conc. For 10 ml For 25 ml
    Citric acid monohydrate, 1 M Commonly available 62 mM 0.62 ml 1.55 ml
    (Na)2HPO4·2H2O, 1 M Commonly available 76 mM 0.76 ml 1.90 ml
    Water 8.62 ml 21.55 ml

    Adjust the pH to 4 with NaOH.

    Adjust the pH to 5.8 with KOH.

    Reagent Source Final conc. For 2 L For 5 L
    Tetrabutylammonium hydrogen sulfate (TBAS) Sigma 155837 5.7 mM 3.87 g 9.68 g
    KH2PO4 Roth 3904.2 30.5 mM 8.30 g 20.76 g

    Adjust the pH to 5.8 with KOH.

    Reagent Source Final conc. For 2 L For 5 L
    Acetonitrile, HPLC-grade Commonly available 67% 1340 ml 3350 ml
    Running buffer A See Table 2 33% 660 ml 1650 ml

    Preparation of adenylate standards for the measurement (always prepare fresh immediately before measurement)

    1. Separately weigh defined amounts of AMP, ADP, and ATP powder to make 100 mM solutions. Dilute these solutions to 1 mM and then to 100 µM using 0.1 M HCl.

    2. Take an equal amount of each of the 100 µM solutions of the three compounds to obtain 25, 12.5, 6.25, and 3.125 µM mixed solutions of the three compounds together.

    3. Also prepare separate 25, 12.5, 6.25, and 3.125 µM solutions of each of the three individual compounds.

    4. Take 15 µl of each of the different concentrations of mixed solution and each individual standard at different concentrations in a new 1.5-ml tube and keep on ice for the HPLC measurement.

    ATP, ADP, and AMP measurement via HPLC

    5. Add 200 µl of 0.1 M HCl to a 25-mg aliquot of the Arabidopsis leaf material in a 1.5-ml tube, prepared as in Basic Protocol 1, step 3, and vortex for 15 s prior to putting on ice for 30 s. Repeat vortexing for 15s, then centrifuge 10 min at 12,000 × g, 4°C.

    6. Take 15 µl of the supernatant for HPLC measurement.

    7. Dilute the 15 µl of plant extract from step 6 (sample) and each of the 15-μl aliquots of standard mixtures/single standards at different concentrations (step 4) with 77 µl CP buffer and keep on ice.

    8. Add 8 µl 45% chloroacetaldehyde (HIGHLY TOXIC!) to each sample (to a volume of 100 µl).

    9. Incubate at 80°C for 10 min.

    10. Centrifuge 30 min at 12,000 × g, 20°C.

    11. Transfer 90 µl of the supernatant to glass HPLC vials.

    12. Run all the samples and standards on HPLC.

    HPLC program (also see Strategic Planning and Basic Protocol 5 )

    The analysis of adenosines is performed by reversed-phase HPLC on a Hyperclone C18 (ODS) column connected to an HPLC system (Dionex). The HPLC analysis is carried out as described previously (Estavillo et al., 2011 ). The gradient for separation of adenosine derivatives is optimized as follows: equilibration of column for 0.2 min with 95% (v/v) of running buffer A (Table 2) and 5% (v/v) running buffer B (Table 3), linear gradient for 53 min up to 50% (v/v) of running buffer B, and re-equilibration for 7 min with 5% (v/v) running buffer B. The flowrate is set to 0.8 ml/min.

    See Basic Protocol 5 for data interpretation.

    As the cofactor of the central metabolism, nicotinamide adenine dinucleotide (NAD) carries electrons from one reaction to another and is deeply involved in the regulation of redox. Oxidized nicotinamide adenine dinucleotide (NAD + ) accepts electrons from other molecules and converts to the reduced form (NADH), especially in glycolysis and the mitochondrial TCA cycle (Zhang & Fernie, 2018 Zhang et al., 2017 Youjun Zhang et al., 2018 ). Nicotinamide adenine dinucleotide phosphate (NADP) is a cofactor in anabolic reactions, such as lipid and nucleic acid biosynthesis and the Calvin cycle, which use NADPH as a reducing agent (Kern et al., 2014 ). NAD + can be converted to NADP + by NAD + kinase via the addition of a further phosphate group at the 2′ position of the ribose ring that carries the adenine moiety.

    In this protocol, the reduced forms, i.e., NADH or NADPH, are first selectively destroyed at low pH and high temperature then, NAD + and NADP + are measured using highly sensitive cycling assays. Alcohol dehydrogenase catalyzes the oxidation of ethanol, which involves the reduction of NAD + (Fig. 2), and glucose-6-phosphate dehydrogenase catalyzes the oxidation of glucose 6-phosphate, which involves the reduction of NADP + (Fig. 2B). Phenazine ethosulfate then catalyzes the reduction of MTT into deep-purple formazan, the absorbance of which can be measured at 570 nm. Concomitantly, the re-oxidation of NADH or NADPH makes it possible to re-commence the oxidation of ethanol or glucose-6-phosphate. This cycle, which obeys a zero-order kinetic rate law, is maintained at a constant rate as long as its substrates, i.e., ethanol or glucose-6-phosphate, and MTT, are saturating.

    Materials

    • β-nicotinamide adenine dinucleotide hydrate (NAD + , Roche 10127990001)
    • β-nicotinamide adenine dinucleotide phosphate disodium salt (NADP + , Roche 10128031001)
    • 0.1 M HCl
    • Liquid nitrogen
    • 0.1 M perchloric acid (HClO4)
    • Sample: Arabidopsis leaf material prepared as in Basic Protocol 1
    • 0.1 M KOH in 0.2 M Tris⋅HCl, pH 8.4
    • Double-distilled water
    • Detection mix for NAD + /NADH (Table 4)
    • Detection mix for NADP + /NADPH (Table 5)
    • Refrigerated centrifuge with rotor for microcentrifuge tubes, capable of speed of 20,000 × g
    • 1.5-ml microcentrifuge tubes
    • Heat block
    • 96-well microplates (Sarstedt maximum well volume, 300 µl transparent flat bottom)
    • Microplate reader (e.g., Biotek EL 808 with 570-nm filter)
    Reagent with final conc. Stock conc. Source Amt. stock for 1 reaction Amt. stock for 100 reactions
    0.3 M Tricine/KOH, pH 9.0 1 M Carl Roth 6977.3 15 µl 1500 µl
    12 mM disodium EDTA, pH 8.0 0.5 M Commonly available 1.2 µl 120 µl
    0.3 mM PESa a

    Ethyldibenzopyrazine ethyl sulfate salt.

    Thiazolyl Blue tetrazolium bromide.

    Ethyldibenzopyrazine ethyl sulfate salt.

    Thiazolyl Blue tetrazolium bromide.

    Reagent with final conc. Stock conc. Source Amt. stock for 1 reaction Amt. stock for 100 reactions
    0.3 M Tricine/KOH, pH 9.0 1 M Carl Roth 6977.3 15 µl 1500 µl
    12 mM disodium EDTA, pH 8.0 100 mM Commonly available 1.2 µl 120 µl
    0.3 mM PMSa a

    Methylphenazinium methyl sulfate.

    Thiazolyl Blue tetrazolium bromide.

    Methylphenazinium methyl sulfate.

    Thiazolyl Blue tetrazolium bromide.

    Preparation of NAD + /NADP + standards for the measurement

    1. Prepare 1 ml each of 20 mM NAD + and NADP + in 0.1 M HCl.

    2. Divide the NAD + and NADP + solutions into 50-µl aliquots, freeze in liquid nitrogen, and store in a –80°C freezer.

    Method for NAD + /NADP + measurement

    3. Add 200 µl of 0.1 M HClO4 to a 25-mg aliquot of Arabidopsis leaf material in a 1.5-ml tube, prepared as in Basic Protocol 1.

    4. Immediately mix tube by tapping, vortex the tube for 15 s (keeping on ice), and repeat vortexing until the leaf powder has become homogenized.

    5. Incubate tube 10 min on ice, then centrifuge 10 min at 12,000 × g, 4°C.

    6. Transfer 200 µl of supernatant into a new 1.5-ml tube.

    7. Heat tube to 95°C for 2 min, then cool down on ice.

    8. Add 200 µl of 0.1 M KOH in 0.2 M Tris⋅HCl, pH 8.4, to neutralize the extract. Vortex.

    The pH of the extract should be between 8 and 8.5. Check pH of one or two samples with pH test paper.

    9. Incubate tubes on ice for a minimum of 15 min.

    10. Heat up the 20 mM NAD + and NADP + standards (each 50-µl aliquot prepared in step 2) to 95°C for 10 min, then cool down on ice.

    11. Dilute the 20 mM standards to 1 µM with double-distilled water. Do this shortly before you start pipetting, because the 1 µM standard solution is very unstable and can only be used for maximum 4 hr.

    12. Pipette samples (from step 9) and standards in a 96-well microplate with a final volume of 100 µl per well. Standard final concentration should be in the 0-25 pmol range (see Table 6). Do not put the plate on ice. Normally, we use the first two rows for the standards and the third row for the leaf samples.

    Final conc. in pmol per well 0 5 10 15 20 25
    X µl 1 µM standard 0 5 10 15 20 25
    X µl water 100 95 90 85 80 75

    13. First, make a test plate with two samples and the standards to check if the amount of NAD + and NADP + being produced is in the range of the standards.

    14. For the test plate, pipette 2, 5, 10, and 25 µl of the two samples and fill the well to 100 µl with double-distilled water.

    15. Prepare detection mix as in Table 4 for NAD + and as in Table 5 for NADP + , adding PES for NAD + and PMS for NADP + shortly before adding the mix to the samples and standards. Add 50 µl of detection mix per well of the test plate under dim light.

    Acquire data and calculate results

    16. Basic Protocol 5 describes data analysis.

    17. Read absorbance at 570 nm with the microplate reader for 45 min at 30°C.

    18. Use Vmean to calculate the amount of NAD(P) + .

    19. Check the amount of NAD + and NADP + in the samples and whether the values are in the range of the standards—if not, dilute or concentrate the samples accordingly. Repeat the measurement with all samples. Here, we suggest processing three technical replicates for standards and samples. If you have six to eight biological replicates, you can decrease the number of technical replicates to two.

    Oxidized forms, i.e., NAD + or NADP + , are first selectively destroyed at high pH and high temperature then NADH and NADPH are measured using the same assays as in Basic Protocol 3 (Fig. 2B).

    Materials

    • β-Nicotinamide adenine dinucleotide, reduced disodium salt hydrate (NADH, Roche 10107735001)
    • β-Nicotinamide adenine dinucleotide 2′-phosphate reduced tetrasodium salt hydrate (NADPH, Roche 10621692001)
    • 0.1 M KOH
    • Liquid nitrogen
    • Sample: Arabidopsis leaf material prepared as in Basic Protocol 1
    • 0.1 M HClO4 in 0.2 M Tris pH 8.4 (mix 10 µl of 1 M HClO4 and 200 µl of 1 M Tris base, add 60 µl of ddH2O, adjust pH to 8.4, and make up volume to 100 µl with ddH2O)
    • Double-distilled water
    • Detection mix for NAD + /NADH (Table 4)
    • Detection mix for NADP + /NADPH (Table 5)
    • Refrigerated centrifuge with rotor for microcentrifuge tubes, capable of speed of 20,000 × g
    • 1.5-ml microcentrifuge tubes
    • Heat block
    • 96-well microplates (Sarstedt maximum well volume, 300 µl transparent flat bottom)
    • Microplate reader

    Preparation of NADH/NADPH standards for the measurement

    1. Prepare 1 ml each of 20 mM NADH and NADPH in 0.1 M KOH.

    2. Divide the NADH and NADPH solutions into 50-µl aliquots, freeze them in liquid nitrogen, and store in a –80°C freezer.

    Method for NADH/NADPH measurement

    3. Add 0.1 M KOH to a 25-mg aliquot of Arabidopsis leaf powder material in a 1.5-ml tube, prepared as in Basic Protocol 1.

    4. Immediately mix tube by tapping, vortex the tube for 15 s (keeping on ice), and repeat vortexing until the leaf powder has become homogenized.

    5. Incubate tube 10 min on ice, then centrifuge 10 min at 12,000 × g, 4°C.

    6. Transfer 200 µl of the supernatant into a new 1.5-ml tube.

    7. Heat tube to 95°C for 2 min, then cool down on ice.

    8. Add 200 µl of 0.1 M HClO4 in 0.2 M Tris·HCl, pH 8.4, to neutralize the extract. Vortex.

    The pH of the extract should be between 8 and 8.5. Check pH of one or two samples with pH test paper.

    9. Incubate tubes on ice for a minimum of 15 min.

    10. Heat up the 20 mM NADH and NADPH standards (each 50-µl aliquot prepared in step 2) to 95°C for 10 min, then cool down on ice.

    11. Dilute the 20 mM standards to 1 µM with double-distilled water. Do this shortly before you start pipetting, because the 1 µM standard solution is very unstable and can only be used for a maximum of 4 hr.

    12. Pipette samples and standards in a 96-well microplate for a final volume of 100 µl per well. Standard final concentration should be in the 0-25 pmol range (see Table 6). Do not put the plate on ice. Normally, we use the first two rows for the standards and the third row for the leaf samples.

    13. First make a test plate with two samples and the standards to check if the amount of NADH and NADPH being produced is in the range of the standards.

    14. For the test plate, pipette 2, 5, 10, 25 µl of two samples and fill up to 100 µl with double-distilled water

    15. Prepare detection mix as in Table 4 for NADH and as in Table 5 for NADPH, adding PES for NADH and PMS for NADPH shortly before adding the mix to the samples and standards. Add 50 µl of detection mix per well of the test plate under dim light.

    Acquire data and calculate results

    16. Basic Protocol 5 describes data analysis.

    17. Read absorbance at 570 nm at 30°C with the microplate reader until the rate stabilizes.

    18. Use Vmean (calculated by the software of the machine) to calculate the amount of NAD(P)H.

    19. Check the amount of NADH and NADPH in the samples and whether the values are in the range of the standards—if not, dilute or concentrate the samples accordingly. Repeat the measurement with all samples. For standards and samples you should have three technical replicates. If you have three to four biological replicates, you can decrease the number of technical replicates to two.

    5: DATA ANALYSIS AND QUALITY CONTROL

    After completing the HPLC and plate reading, the raw data can be used for the analysis of the final levels of the metabolites. The normalized values are processed with at least six biological replicates in our laboratory and used to calculate the concentration of the adenine nucleotides and nicotinamide adenine dinucleotide. It is very important to note that the values of samples should not be above or below the range of standards used for the standard curves. Here, we use the wild-type Arabidopsis (Col-0 accession) seedling as example to analyze the concentration of adenine nucleotides and nicotinamide adenine dinucleotide.

    Data analysis

    1. In the ATP/ADP/AMP measurement (Basic Protocol 2), we use 200 µl extraction buffer and 15 µl reaction buffer. 10-µl samples are injected into the column. Here we use ATP as the example and the analysis ADP or AMP methods are same as for ADP (Fig. 3A).

    2. First, we analyze the ATP standards from the HPLC and get the values of slope and blank (Fig. 3B).

    3. After we get the slope, we can calculate concentrations within samples as shown in Fig. 3C. Briefly, the reaction concentration is calculated as (value − blank)/slope and normalized by dilution factor and fresh weight (FW). Normally, the final level is normalized to provide a value with the units µmol/gFW (Fig. 3C), where gFW refers to the fresh weight of the plant material in grams.

    4. Similarly to the ATP analysis, the concentrations of pyrimidine nucleotide are also normalized from the standard. Here, we use NAD + as an example (Fig. 4A).

    5. We first use the raw data of the standard to get the slope and Vblank (Fig. 4B).

    6. After we get the slope, we can go back to the concentration of samples as shown in Figure 4C. Briefly, the concentration is calculated as (value + blank)/slope and normalized by dilution factor, sample volume, and fresh weight. Usually, the final level is normalized to provide a value with the units nmol/gFW, where gFW refers to the fresh weight of the plant material in grams.


    Changes in the contents of adenine nucleotides and intermediates of glycolysis and citric acid cycle in flight muscle of the locust upon flight and their relationship to the control of the cycle

    Andrew N. Rowan, Eric A. Newsholme Changes in the contents of adenine nucleotides and intermediates of glycolysis and citric acid cycle in flight muscle of the locust upon flight and their relationship to the control of the cycle. Biochem J 15 January 1979 178 (1): 209–216. doi: https://doi.org/10.1042/bj1780209

    1. The contents of some intermediates of glycolysis, the citric acid cycle and adenine nucleotides have been measured in the freeze-clamped locust flight muscle at rest and after 10s and 3min flight. The contents of glucose 6-phosphate, pyruvate, alanine and especially fructose bisphosphate and triose phosphates increased markedly upon flight. The content of acetyl-CoA is decreased after 3min flight whereas that of acetylcarnitine is decreased markedly after 10s flight, but returns towards the resting value after 3min flight. The content of citrate is markedly decreased after both 10s and 3min flight, whereas that of isocitrate is changed very little after 10s and is increased by 50% after 3min. The content of oxaloacetate is very low in insect flight muscle and hence it was measured by a sensitive radiochemical assay. The content of oxaloacetate increased about 2-fold after 3min flight. A similar change was observed in the content of malate. The content of ATP decreased about 15%, whereas those of ADP and AMP increased about 2-fold after 3min flight. 2. Calculations based on O2 uptake of the intact insect indicate that the rate of the citric acid cycle must be increased >100-fold during flight. Consequently, if citrate synthase catalyses a non-equilibrium reaction, the activity of the enzyme must increase >100-fold during flight. However, changes in the concentrations of possible regulators of citrate synthase, oxaloacetate, acetyl-CoA and citrate (which is an allosteric inhibitor), are not sufficient to account for this change in activity. It is concluded that there may be much larger changes in the free concentration of oxaloacetate than are indicated by the changes in the total content of this metabolite or that other unknown factors must play an additional role in the regulation of citrate synthase activity. 3. The increased content of oxaloacetate could be produced via pyruvate carboxylase, which may be stimulated during the early stages of flight by the increased concentration of pyruvate. 4. The decreases in the concentrations of citrate and α-oxoglutarate indicate that isocitrate dehydrogenase and oxoglutarate dehydrogenase may be stimulated by factors other than their pathway substrates during the early stages of flight. 5. Calculated mitochondrial and cytosolic NAD + /NADH ratios are both increased upon flight. The change in the mitochondrial ratio indicates the importance of the intramitochondrial ATP/ADP concentration ratio in the regulation of the rate of electron transfer in this muscle.


    Synopsis

    In natural environment, nutrient availability may vary ‘from feast to famine’ and vice versa very suddenly and frequently causing excessive nutritional stresses. The robustness of an organism pictures its ability to cope with these transitions. Among the essential nutrient for growth, the more investigated of all is still the carbon source with a preference for glucose. The sudden increase in glucose concentration causes fast changes in the concentration of metabolites acting as secondary messengers, for example, cAMP. However, with the exception of adenosine nucleotides ( Theobald et al, 1997 ) and central carbon metabolism intermediates ( Visser et al, 2004 ), little is known about the fast changes at metabolome level occurring within the first minutes following the addition of excess glucose. By integrating metabolome and transcriptome data collected within the first minutes following the sudden relief of glucose limitation, this work provides the first comprehensive study, clearly picturing how yeast cells adapt to new environmental conditions.

    For the quantitative systems analysis of the dynamic response to glucose availability, it is essential that experimental conditions are tightly controlled. To achieve such analysis, the yeast Saccharomyces cerevisiae was grown under glucose limitation at a growth rate of 0.05/h in chemostat culture. At steady state, the glucose concentration was instantaneously increased from 0.15 to 5 mM. The culture was sampled after 30, 60, 90, 120, 210, 300 and 330 s for metabolite and transcript analysis.

    As the metabolites varied within seconds after the addition of glucose to the steady-state culture, the transcriptome response only changed between 120 and 210 s. Despite this time shift, a very high correlation in the nature of these responses was observed. Among the fast metabolite fluctuations, an important drop in AXP pool was measured. This drop was accompanied by a time-delayed upregulation of the genes encoding the complete purine biosynthetic pathway. Interestingly, the transcript data revealed that besides the purine biosynthesis genes, purine salvage, one carbon (C1), sulfur assimilation pathways were all coordinately upregulated, strengthening the metabolome data pointing out a requirement of purines (Figures 3 and 4). The coordinated elicitation of purine synthetic and salvage, one carbon (C1) and sulfur assimilation pathways suggests that methylation reactions mediated via S-adenosylmethionine might play a crucial role in the growth acceleration process. Further analysis of the transcriptome response by incorporating overrepresentation of functional categories and location analysis data from more than 100 transcription factors allowed mapping of the regulatory circuit taking place in this metabolic transition. It pictured that the cells were gearing up to accelerate growth as shown by the reprogramming of the transcription and translation machinery and were trying to recover from severe redox stress.

    Very early on (after 120 s), a large part of the genes involved in translation of ribosomal DNA (subunits of the RNA pol I) and ribosome processing were significantly upregulated. In conjunction, fine-tuning of the translation seems to set up, as many elements of the translation initiation machinery were upregulated as well.

    In addition to the ‘energetic stress’, as shown by a loss of the cellular energy charge, the cells faced redox stress. Based on the data presented here, we hypothesized that the imbalance in intermediates of the top (high) and the bottom (low) of the glycolysis was a direct consequence of the inhibition of glyceraldehyde-3-phosphate dehydrogenase by the increased NADH/NAD ratio. To restore redox homeostasis, the cell used both metabolic and transcriptional regulations. Increase of the intracellular trehalose-6-phosphate concentration was in line with its inhibitory role of the hexokinases preventing the cell to die from the so-called ‘glucose accelerated death’. The modulation of the expression of the genes encoding tricarboxylic acid enzymes was also essential in the restoration of the redox cellular status.

    More surprising was the rapidity of the transcript turnover. Measurement of mRNA half-lives undoubtedly showed that a much faster decay of transcript was occurring upon the relief of glucose limitation. The average half-life of mRNA displaying significant downregulation was nine-fold shorter than reported earlier (Wang et al, 2001), suggesting that mRNA degradation participates actively in the regulation of translation. Consequently, one could consider that this accelerated mRNA decay represents a widespread regulatory level of glucose-triggered catabolite repression.

    Conversely to that one could think, the nutritional transition from limitation to excess glucose is not only limited to growth acceleration but it represents a more balanced process where the cell has to overcome glucose-induced stress while the cellular processes required for gearing up growth are building up.

    Experiments that, in addition to transcriptome and metabolome data, include information at other relevant information levels (e.g. proteome, phosphoproteome and fluxome, references) will be essential to meet the longstanding challenge of cellular physiology/systems biology: to come to an integral understanding of the responses of living cells to their physical and chemical environment.


    6 Contribution of substrate metabolism to contractile dysfunction in the diabetic heart

    It has been recognized for many years that diabetic patients have a significantly greater incidence and severity of angina, acute myocardial infarction (AMI), congestive heart failure, and other manifestations of atherosclerosis compared to the nondiabetic population [75–79]. More recently, it has been determined that ventricular performance can be impaired (diabetic cardiomyopathies) even in the absence of ischemic heart disease [80–88]. Although an increased incidence of atherosclerosis in diabetics contributes to these complications, population-based studies have shown that noncoronary factors are also important contributing factors [2]. For instance, the incidence and severity of complications associated with AMI are greater in the diabetic population even though the size of the infarct is not significantly different, and may even be smaller, compared to the nondiabetic population [89, 90]. Diabetes-induced changes within the heart appear to be important contributing factors to injury during and following an AMI [1, 91, 92]. Both heart failure following an AMI and diabetic cardiomyopathies have been correlated with the degree of glycemic control in the patient [93–95]. Furthermore, cardiomyopathies in the absence of ischemic heart disease can be improved by correction of hyperglycemia [95]. Accumulating evidence has implicated changes in myocardial energy substrate use as contributing to diabetic cardiomyopathies [96–102].

    Several lines of evidence, both clinical and experimental, suggest that high plasma levels of free fatty acids and high rates of fatty acid oxidation in the myocardium result in impaired contractile function and more arrhythmias during and after ischemia in both the normal and diabetic heart [32, 103]. Accumulation of fatty acids and their toxic intermediates have been associated with mechanical dysfunction and cell damage in diabetic hearts subjected to ischemia [104]and to depressed sarcoplasmic reticulum Ca 2+ pump and myofibrillar ATPase activities and myosin isozymes [105]. The best evidence for a causative link between high fatty acid oxidation and impaired cardiac function comes from studies in isolated rat hearts where either fatty acid oxidation was inhibited (with CPT-I inhibitors) or PDH activity stimulated (with DCA), and contractile recovery from ischemia improved. However, to date, results of clinical trials aimed at suppressing fatty acid oxidation during AMI in diabetics have not been reported. Several pharmacological approaches are available that suppress fatty acid oxidation and increase flux through PDH, such as suppression of peripheral lipolytic rate and plasma free fatty acid levels with insulin, direct inhibition of β-oxidation [106, 107], inhibition of CPT-I [108], or decreasing intramitochondrial acetyl CoA levels with carnitine [109, 110]. As a result, there is a clear rationale for using this approach to improve contractile function and decrease irreversible damage following AMI.


    DISCUSSION

    We have performed transcription profiling using DNA microarrays on a collection of mutants defective in each of the 15 genes that encode subunits of TCA cycle proteins. This analysis revealed >400 genes that were highly responsive to TCA cycle defects, suggesting that nuclear gene signaling is responsive to TCA cycle function. In this report, we have concentrated on two sets of genes that appear to be responding to distinct metabolic signals resulting from TCA cycle dysfunction. The first signaling pathway appears to monitor the general state of the TCA cycle, because defects throughout the cycle elicit a response in nuclear gene expression. The second pathway represents signaling resulting from a single enzyme defect in the α-ketoglutarate dehydrogenase complex and appears to be the result of aberrant heme-dependent signaling. To the best of our knowledge, these responses to TCA cycle dysfunction have not been reported previously.

    Four of the eight TCA cycle enzymes that we inactivated are encoded from a single gene, whereas the other four enzymes are encoded by multiple (2–4) genes. By comparing expression profiles in response to defects in genes encoding different subunits of hetero-oligomeric TCA cycle proteins, it was possible to analyze how the cell responds to different defects in the same enzyme. Although isocitrate dehydrogenase and succinyl-CoA ligase require both subunits for activity, subcomplexes composed of only some subunits of α-ketoglutarate dehydrogenase complex and succinate dehydrogenase can be detected (Repetto and Tzagoloff, 1991 Scheffler, 1998). However, cluster analysis suggested that the responses to defects in genes encoding different subunits of the hetero-oligomeric TCA cycle enzymes were, for the most part, very similar (Figure 3). This was most apparent for theSDH1–4 mutant arrays, which were clustered into the same branch. Some differences among arrays of genes encoding subunits of the same enzyme could also be explained. For instance, althoughKGD1 and KGD2 arrays were observed in the same branch, the LPD1 array was clustered in a close outgroup. Because the lipoamide dehydrogenase encoded by LPD1 is also a subunit in three other proteins, the slightly different response to an LPD1 defect probably reflects an aggregate response to loss of all four enzyme complexes. Because the responses to different mutations encoding distinct subunits of the same enzyme produced similar expression patterns, it is possible that one mutation per enzyme is sufficient to establish a reliable expression profile.

    A specific response to defects in the KGD1–2 andLPD1 genes was the elevated expression of hypoxic genes and a diminished expression of oxidative genes. This appeared to be a specific response to defects in the genes encoding the α-ketoglutarate dehydrogenase complex and was not apparent with other TCA cycle defects (Figure 9), although some hypoxic genes were elevated in aconitase-deficient cells. The most likely rationale for the observed changes in oxidative and hypoxic gene expression is that heme levels are diminished in the α-ketoglutarate dehydrogenase mutants. Succinyl-CoA is produced by the α-ketoglutarate dehydrogenase complex, and this metabolite is used directly for heme biosynthesis by δ-aminolevulinate synthase (Figure 1). An α-ketoglutarate dehydrogenase defect should result in lowered succinyl-CoA and therefore diminished cellular heme. The heme deficiency would result in the diminished expression of oxidative genes that are regulated through the Hap1p, Hap2/3/4/5, and HDS complexes. Diminished heme levels also result in lower levels of active Rox1p and other factors that repress hypoxic genes, leading to the elevated expression of hypoxic genes, such as CYC7, ANB1, and DAN1. The magnitude of the expression changes in the α-ketoglutarate dehydrogenase mutants appears to be close to but less than the maximal changes in expression reported in heme auxotrophs or by mutations inHAP1 or ROX1 (Kwast et al., 2002 Ter Linde and Steensma, 2002). Because the α-ketoglutarate dehydrogenase mutants are not heme auxotrophs, there must be other routes for the synthesis of succinyl-CoA. One such enzyme might be succinyl-CoA ligase, which is a reversible enzyme that can synthesize succinyl-CoA from succinate (Przybyla et al., 1998). However, on the basis of the magnitude of the expression changes, α-ketoglutarate dehydrogenase may be a significant source of succinyl-CoA under these conditions.

    It is not entirely clear why the changes in heme-dependent genes are also observed with aconitase deficiency (Figure 9). Aconitase deficiency should result in the accumulation of citrate and aconitate and a deficiency of isocitrate and α-ketoglutarate. Sources of glutamate should be able to compensate for the α-ketoglutarate deficiency however, under the culture conditions used in these experiments, this may not be occurring. The seripauperin family genes were the most responsive to the aconitase defect, suggesting that the response by these genes may not be entirely because of cellular heme levels (Cohen et al., 2001). Mutants in ACO1display the most severe growth defects on both fermentable and nonfermentable carbon sources. They are glutamate auxotrophs and are unable to grow on some fermentable carbon sources that do not repress oxidative gene expression, such as raffinose, suggesting that oxidative functions may be severely compromised. Many aconitase-deficient cells are also petites (Figure 2B), and such mtDNA mutations can have profound effects on nuclear gene expression (Epstein et al., 2001b Traven et al., 2001). Hence, the expression profile displayed by ACO1 defects is expected to be complex and to result from many different factors, such as altered heme levels, mtDNA defects, and a slower growth rate.

    Many genes responded in a similar manner to generalized defects within the TCA cycle. Whereas some genes did not appear to be properly induced (Figure 5A), other genes were hyperinduced (Figure 5B), perhaps in an attempt to overcome the absence of this critical metabolic pathway. Of particular interest was a set of genes that responded in a similar manner to TCA cycle dysfunction but whose response pattern varied with the enzyme defect (Figure 6). Expression was elevated in response to aconitase and isocitrate dehydrogenase deficiencies, diminished in response to α-ketoglutarate dehydrogenase complex and succinyl-CoA ligase deficiencies, elevated again in response to succinate dehydrogenase and fumarase deficiencies, and diminished again in response to malate dehydrogenase and citrate synthase deficiencies. Although it is not immediately apparent why this alternating pattern of expression occurs in response to defects in contiguous pairs of TCA cycle enzymes, it is presumably a reflection of changes in metabolic signals. As an approach to understanding this pattern, we have reduced the TCA cycle into four steps by combining the adjacent enzymatic reactions that yielded similar expression patterns (Figure10). From this framework, we looked for similarities and differences among these four enzyme sets. Each of these enzyme pairs contains one reaction that generates reduced nucleotides (NADH or FADH2) during TCA cycle function, suggesting that changes in redox state are not primarily responsible for generating this response. The net ΔG values for each pair are negative, indicating an overall exergonic reaction (Matthewset al. 2000). In addition, each set contains one reaction that is highly reversible and one reaction that is essentially irreversible when assayed individually (except for the succinate dehydrogenase–fumarase pair, in which both reactions are reversible). Four carboxylic acids separate these enzyme pairs: citrate, α-ketoglutarate, succinate, and malate. It is possible that changes in one or more of these metabolites may be critical in establishing this expression pattern as a result of TCA cycle dysfunction. For instance, an increase in succinate caused by a succinate dehydrogenase or fumarase defect may signal for increased transcription of these genes, whereas a decrease in succinate formation caused by a succinyl-CoA ligase or an α-ketoglutarate dehydrogenase defect might signal for a decrease in gene expression. Although changes in these TCA cycle metabolites within the mitochondrial matrix may initiate a signaling pathway that results in altered nuclear gene expression, it is not certain whether they serve directly as the signaling molecules. For instance, glutamate and glutamine are derived from α-ketoglutarate through successive transamination reactions, and both amino acids have been demonstrated to signal changes in the metabolic state of the cell that regulate nuclear gene expression (Butow, 2002Crespo et al., 2002).

    Fig. 10. A sectored model of the TCA cycle to explain the alternating gene expression pattern in response to TCA cycle dysfunction. TCA cycle enzymes are paired on the basis of the alternating gene expression pattern in response to enzyme dysfunction. Enzyme defects in white resulted in decreased expression, and enzyme defects in gray resulted in elevated gene expression. ΔG values (kJ/mol) for each pair are calculated as the sum of the individual enzymes (Matthews et al., 2000). Potential signaling metabolites at the inflection points between these pairs are shown in black boxes. CS, citrate synthase ACO, aconitase IDH, isocitrate dehydrogenase KGD, α-ketoglutarate dehydrogenase SCL, succinyl-CoA ligase SDH, succinate dehydrogenase FUM, fumarase MDH, malate dehydrogenase.

    One of the more interesting aspects of the alternating gene expression pattern is its correlation with a previously identified set of TCA cycle gene defects that were identified as growth-enhancing mutations of isocitrate dehydrogenase–dysfunctional cells. Mutations in theCIT1, KGD1–2, LPD1,LSC1–2, and MDH1 genes can serve as growth enhancers of isocitrate dehydrogenase dysfunction, and these same defects result in the diminished expression response (Figure 6). This suggests a link between glycerol growth enhancement of isocitrate dehydrogenase dysfunction and the oscillating gene expression pattern reported here.

    An extensive analysis of the glycerol suppressor accumulation phenotype associated with isocitrate dehydrogenase dysfunction has been reported (Przybyla-Zawislak et al., 1999). A collection of mutations in each of the 15 genes encoding TCA cycle polypeptides was screened for this phenotype. In addition, two complementary approaches were taken to determine the identities of the suppressor mutations. First, on the basis of the previous characterization of defects inCIT1 as the most abundant class of suppressors (Gadde and McCammon, 1997), mutations in genes encoding TCA cycle proteins were tested for their ability to enhance growth of isocitrate dehydrogenase–dysfunctional cells on glycerol. Second, a collection of spontaneous suppressor mutations was characterized. Several conclusions were drawn from these studies. First, the glycerol suppressor accumulation phenotype is a unique phenotype associated with the loss of isocitrate dehydrogenase polypeptides. Second, defects in genes (CIT1, KGD1–2, LPD1,LSC1–2, and MDH1) encoding half of the TCA cycle enzymes could function as growth enhancers partial function alleles ofKGD1–2 and LPD1 that can grow on glycerol were capable of growth enhancement, whereas deletion mutations could not. Third, neither deletion mutations nor partial function glycerol + alleles of ACO1,IDH1, SDH1–4, and FUM1 could function as growth enhancers. Fourth, only defects in MDH andCIT genes encoding the TCA cycle isozymes were capable of suppression. Finally, eight other genes involved in oxidative metabolism were identified as growth enhancers, indicating that not all defects in oxidative metabolism were capable of suppression. These results divided the TCA cycle genes between suppressing genes and nonsuppressing genes and established limits to the number and types of genes that are capable of growth enhancement. However, the physiological basis for the growth enhancement remained undefined.

    To investigate the relationship between the alternating gene expression pattern and the growth enhancing mutations, microarray analysis was used to determine the effects of an Δidh2 Δcit1double mutation. Inactivation of either CIT1 orIDH2 resulted in diminished (CIT1) or elevated (IDH2) patterns of gene expression. With defects in both genes, the expression pattern was largely corrected and was very similar to wild-type levels (Table 1). These results led to the hypothesis that overexpression of one or several of these responsive genes is deleterious to growth and that the suppressor mutations function to correct this altered expression. We have begun to test this idea by assaying the effect of inactivation of genes showing the alternating pattern of expression on glycerol growth. To date, we have found that two of six genes tested can serve as growth enhancing mutations in isocitrate dehydrogenase–deficient strains,CIT1 and YGR067C (Figure 8). CIT1 is the only TCA cycle suppressor gene that also displays the alternating gene expression pattern in response to TCA cycle dysfunction.YGR067C appears to encode a transcription factor and may therefore regulate the expression of a number of other genes.

    Eight other suppressor genes were also identified that do not encode TCA cycle proteins (Przybyla-Zawislak et al., 1999). While the identities of these genes have not been determined, the mutations display growth phenotypes on nonfermentable carbon sources, suggesting that the encoded proteins are involved in oxidative metabolism. It will be interesting to determine how these defects affect the alternating gene expression pattern of the genes reported here. For instance, do these and other suppressor mutations affect the alternating genes in a similar manner? In addition, is it possible the suppressor mutations enhance growth by decreasing the expression of one or more of the genes displaying an alternating expression pattern that may be deleterious when overexpressed? There are several reasons why the suppressor mutations may be specifically detected with isocitrate dehydrogenase defects and not with other TCA cycle mutations that result in the same pattern of alternating gene expression. First, strains in whichIDH1 or IDH2 are inactivated are able to grow on certain nonfermentable carbon sources whereas the other related TCA cycle gene defects (i.e., in ACO1, SDH1–4,FUM1) cannot (Przybyla-Zawislak et al., 1999). Second, many of the responsive genes display their highest expression defect in strains deleted for IDH1 and IDH2(Figure 6), and, therefore, the levels of the potentially deleterious proteins may be highest in the isocitrate dehydrogenase dysfunctional strains. Finally, these potentially deleterious genes may be particularly sensitive to metabolic signals resulting from isocitrate dehydrogenase dysfunction.

    Isocitrate dehydrogenase dysfunction results in two types of DNA instability. As described above, second site nuclear mutations arise that enhance growth of cells lacking isocitrate dehydrogenase on glycerol. Second, isocitrate dehydrogenase dysfunction results in mtDNA instability, and strains lacking this enzyme have a high frequency of petite [ρ − ] mutations with large deletions in mtDNA (Elzinga et al., 1993 Lin et al., 2001). mtDNA instability is a phenotype associated with other TCA cycle defects and with a number of genes encoding proteins in mitochondrial oxidative phosphorylation and biogenesis (Contamine and Picard, 2000). Many of these latter proteins are bifunctional and appear to play additional roles in the translation and/or assembly of mitochondrially encoded proteins. Isocitrate dehydrogenase is a bifunctional protein since it binds to mitochondrially encoded mRNA and appears to regulate translation of these transcripts (Elzinga et al., 1993 de Jong et al., 2000). However, it is not clear whether the mtDNA instability associated with isocitrate dehydrogenase dysfunction results from the loss of catalytic activity or from the aberrant expression and turnover of mitochondrial respiratory complexes (de Jonget al., 2000 Lin et al., 2001). These functions may not be distinct, because mRNA binding by isocitrate dehydrogenase inhibits its catalytic activity (Anderson and McAlister-Henn, 2000). These observations suggest a mechanism whereby TCA cycle metabolic flux and the synthesis of respiratory complexes are coordinately regulated (Anderson and McAlister-Henn, 2000). Mutations in CIT1 also suppress the mtDNA instability of isocitrate dehydrogenase dysfunctional cells (our unpublished results), indicating that these two properties are functionally linked.

    Recent studies have revealed that fumarase and succinate dehydrogenase genes act as tumor suppressors in humans. Fumarase defects were associated with dominantly inherited uterine fibroids, skin leiomyomata, and renal cell cancer (Tomlinson et al., 2002), whereas two types of brain tumors were found to be caused by mutations in genes encoding succinate dehydrogenase subunits (Baysal et al., 2000 Niemann and Muller, 2000 Astuti et al., 2001). Our studies in yeast have revealed that defects in either succinate dehydrogenase or fumarase produce similar responses in gene expression, and aconitase and isocitrate dehydrogenase defects produce similar response patterns. The correlation of this pattern with genetic suppressor defects of isocitrate dehydrogenase dysfunction suggests that genetic instability may be a consequence of TCA cycle dysfunction. Thus, the genetic instability caused by isocitrate dehydrogenase dysfunction may provide clues to aberrant growth properties of tumor cells, and this yeast model may prove useful in understanding how metabolic signaling and changes in TCA cycle function affect cell function and genomic stability. Given the ubiquity of the TCA cycle, especially in eukaryotes, it is predicted that similar signaling pathways between the TCA cycle and the nucleus are operative in higher organisms.


    Watch the video: Биохимия. Биополимеры: белки, нуклеиновые кислоты, полисахариды. Основные понятия. (September 2022).


Comments:

  1. Kathy

    It is very welcome.

  2. Kebar

    the phrase Excellent and is timely

  3. Shaine

    Sounds tempting

  4. Crevan

    Well done, this sentence was just about



Write a message