7.14: Introduction to Connections to Other Metabolic Pathways - Biology

What you’ll learn to do: Discuss the connections between metabolic pathways

You have learned about the catabolism of glucose, which provides energy to living cells. Many of the products in a particular pathway are reactants in other pathways.

For your body to develop properly and stay healthy, many things must work together at many different levels - from organs to cells to genes.

From both inside and outside the body, cells are constantly receiving chemical cues prompted by such things as injury, infection, stress or even the presence or lack of food. To react and adjust to these cues, cells send and receive signals through biological pathways. The molecules that make up biological pathways interact with signals, as well as with each other, to carry out their designated tasks.

Biological pathways can act over short or long distances. For example, some cells send signals to nearby cells to repair localized damage, such as a scratch on a knee. Other cells produce substances, such as hormones, that travel through the blood to distant target cells.

These biological pathways control a person's response to the world. For example, some pathways subtly affect how the body processes drugs, while others play a major role in how a fertilized egg develops into a baby. Other pathways maintain balance while a person is walking, control how and when the pupil in the eye opens or closes in response to light, and affect the skin's reaction to changing temperature.

Biological pathways do not always work properly. When something goes wrong in a pathway, the result can be a disease such as cancer or diabetes.

For your body to develop properly and stay healthy, many things must work together at many different levels - from organs to cells to genes.

From both inside and outside the body, cells are constantly receiving chemical cues prompted by such things as injury, infection, stress or even the presence or lack of food. To react and adjust to these cues, cells send and receive signals through biological pathways. The molecules that make up biological pathways interact with signals, as well as with each other, to carry out their designated tasks.

Biological pathways can act over short or long distances. For example, some cells send signals to nearby cells to repair localized damage, such as a scratch on a knee. Other cells produce substances, such as hormones, that travel through the blood to distant target cells.

These biological pathways control a person's response to the world. For example, some pathways subtly affect how the body processes drugs, while others play a major role in how a fertilized egg develops into a baby. Other pathways maintain balance while a person is walking, control how and when the pupil in the eye opens or closes in response to light, and affect the skin's reaction to changing temperature.

Biological pathways do not always work properly. When something goes wrong in a pathway, the result can be a disease such as cancer or diabetes.


The Overall Equations of the Urea Cycle and Citric Acid Cycle

(1) (2)

Students are referred to Tables 1 and 2, to get a deeper insight into this topic.

1) mit + mit + 2ATP −4

→carbamoyl + 2ADP −3 + Pi −2 + 2H +

2) + carbamoyl

4) Citrullinecyt + ATP −4 +

+ AMP −2 + + 2 H +

Overall equation: + + 3ATP −4 + + H2O

→ureacyt + 2ADP −3 + AMP −2 + + 2 + + 5H +

  • Note. The synthesis of urea is the result of the cross talk between mitochondria and the cytosolic compartment: two mitochondrial reactions [reactions 1) and 2)], three cytosolic reactions [reactions 4), 5), and 6), and the transport of two intermediates from mithocondria to cytosol and viceversa (steps 3) and 7)].

4) α-Ketoglutarate 2– + HS-CoA + NAD 1+

5) Succinyl-CoA 1– + GDP 3– + Pi 2–

→ succinate 2– + GTP 4– + HS-CoA

Overall equation: Acetyl-S-CoAmit + FADmit + 3 + + Pi 2– mit + 4H2O → 2CO2 + FADH2mit + 6NADHmit + + 2H 1+ 2HS-CoAmit.

It has long been known that the urea cycle and the citric acid cycle are inextricably interwind. However, students should be aware that two reactions or two overall equations can be summarized only if at least one of the products of one of them becomes the substrate of the other. This is not the case for Eqs. 1) and 2. They show that there is no reaction product in the right side of Eq. 1 that is present in the left side of Eq. 2). Therefore, students are faced with the intriguing problem “how can urea cycle and citric acid cycle metabolically interact, if there is no reaction product in the right side of the urea cycle overall equation that is present also in the left side of overall equation of the citric acid cycle?

Metabolic Interrelationship between Urea Synthesis and Citric Acid Cycle Shunt

(3) (4)

(5) (6)

At first glance, this equation might appear too complicated to be discussed. Nevertheless, taking into accont the familiar formulas of urea, Glu and of OAA, it may be read as follows: during the interaction of ureagenesis and the citric acid cycle shunt, one the two nitrogen atoms of urea derives from , the other from the cytosolic (5C)Glu which generates a (4C)OAA and one CO2. The three ATP are hydrolyzed, as in the ureagenesis, to give two ADP, one AMP, one PPi and two Pi. GTP is synthesized by the succinyl-CoA synthetase. Finally, in aerobic conditions, the three NADH and the FADH2 will generate a total of 9 ATP via the oxidative phosphorylation.


- We summarize the research progress of epigenetic mechanisms in bone metabolism and osteoporosis.

- We summarize the role of DNA methylation in the osteogenic differentiation and osteoporosis.

- We summarize the role of histone modification in the osteogenic differentiation and osteoporosis, including histone methylation and histone acetylation.

- We summarize the role of non-coding RNA in the osteogenic differentiation and osteoporosis, including lncRNAs, miRNAs, and circRNAs.


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These studies identify PBRM1 as the first methyl reader for the α-TubK40me3 mark on microtubules. Recognition of the SETD2 α-TubK40me3 mark and recruitment of PBAF components to microtubules by PBRM1 reveals a coordinated functional relationship between an epigenetic writer and an epigenetic reader acting on the cytoskeleton. Although PBRM1 has both acetyl-binding BD and methyl-binding BAH domains, and microtubules can be acetylated or methylated at lysine-40, our data show that PBRM1 BAH domains specifically recognize the α-TubK40me3 SETD2 methyl mark. Thus, the chromatin and cytoskeletal functions of PBRM1 use distinct protein domains, allowing integration and coordination, rather than competition, for these activities in the cell (Fig. 7).

An intriguing aspect of microtubule biology is the luminal positioning of K40, target for both the α-TubK40me3 methyl mark made by SETD2 and the α-TubK40ac acetyl mark made by α-tubulin acetyltransferase (ATAT-1). Luminal positioning of acetyl and methyl marks raises the question as to how writers, such as ATAT-1 and SETD2, access these residues to modify α-tubulin, and how luminal acetyl and methyl marks direct the function of microtubules. Whether SETD2 methylates α-tubulin before or after assembly of α-/β-tubulin dimers into microtubules is unknown. However, ATAT-1 acetylates α-tubulin after assembly into microtubules and is thought to access luminal K40 to acetylate this residue via pores in the microtubule shaft (33, 34). These and other studies have led to the growing appreciation that lattice dynamics of even stable microtubules can provide access to luminal residues along the shaft of microtubule polymers (3537). Recently, a passive breathing mechanism for microtubules in which α-/β-tubulin dimers can be lost and replaced from the microtubule shaft was proposed (37), which is thought to be facilitated by the severing enzymes katanin and spastin (38). As SWI/SNF complexes are known to evict histones and other proteins from nucleosomes during chromatin remodeling (39), our current study suggests the intriguing hypothesis that PBAF could be playing a similar role in evicting tubulin and/or other microtubule-associated proteins during microtubule remodeling.

The BAH domain crystal structure has been defined for both chicken and human PBRM1 (40), but the function of this domain has not been clearly defined. Our data now confirm a role for PBRM1 BAH domains in recognition of the α-TubK40me3 mark. Furthermore, while the loss of PBRM1 is known to cause genomic instability (30, 32), we have identified a previously unknown functional role for PBRM1 in the maintenance of genomic stability via binding to spindle microtubules and recruitment of an ATPase-competent PBAF complex to the mitotic spindle. This activity requires the BAH domain(s), as BAH domain mutations that inhibit binding to methylated α-tubulin fail to rescue the genomic instability seen with PBRM1 loss despite their ability to form transcriptionally competent PBAF complexes. Recently, the BAH domain of the origin recognition complex (Orc1) in yeast was also shown to be important in maintenance of genomic stability, an activity distinct from its function in transcriptional silencing (41). This BAH domain provides an interface between Orc1 and the nucleosome, and BAH domain mutations that disrupt this interaction increased the frequency of double-strand breaks. Both BAH1 and BAH2 domains of PBRM1 appear to be required for maintenance of genomic stability, as mutations in either BAH1 or BAH2 decreased spindle localization and caused genomic instability. This suggests that there could be cooperativity between these domains for microtubule binding, perhaps due to sequential binding of the two domains to recognize microtubules and recruit PBAF, which contains two PBRM1 molecules for each BRG1 ATPase subunit (3).

Because of their colocalization on chromosome 3p, one allele each of PBRM1 and SETD2 are co-deleted as an early event in development of ccRCC (42), with “second hits” in these epigenetic proteins the second and third most common oncogenic events in this cancer. While PBRM1 mutations occur in excess of 30% of ccRCC, this is far from the only tumor type affected by PBRM1 mutation. Cholangiocarcinoma, for example, has more than 20% mutation rate for this gene. Other tumor types with more than 5% mutation rate include uterine, stomach, melanoma, mesothelioma, and bladder cancer (43, 44). The overall prevalence of PBRM1 is 4.9% (2% as loss of function) across the 11 most frequent tumor types in TCGA (45). These mutations are distributed across the gene, including mutations in the BAH domains (46). Aside from co-occurrence with VHL loss, observed in ccRCC (25), no other known co-occurring mutations have been discovered to date. Notably, mutational exclusivity is observed with other PBAF complex mutations common in cancer (ARID2, BRD7) (45). In a recent evolutionary model of ccRCC developed by the TRACERx study (47, 48), second hits resulting in loss of the reader PBRM1 precede loss of the writer SETD2, but in no case has loss of SETD2 (and its methyl mark) preceded PBRM1 loss. While the reason for this sequence has remained obscure, our results suggest that cancer cells with second hits that inactive SETD2 will lack the cytoskeletal methyl mark read by PBRM1, thereby abrogating PBRM1 cytoskeletal function, and may gain no further (cytoskeletal) advantage by loss of PBRM1. Conversely, while early loss of PBRM1 can contribute to tumor progression by eliminating an α-TubK40me3 reader, later loss of SETD2 will result in loss of both the cytoskeletal α-TubK40me3 and chromatin H3K36me3 marks. Notably, in the setting of loss of PBRM1, repeated subclonal selection for SETD2 mutations occurs (48), suggesting that complete loss of both cytoskeletal and chromatin methyl marks may be an evolutionary bottleneck in cancer progression. Thus, our study identifies dysregulation of microtubule methylation as novel nexus of convergence in explaining how mutations in an epigenetic writer (SETD2) and reader (PBRM1) can drive genomic instability and cancer progression via cytoskeletal defects. This opens new windows into understanding how defects in components of the epigenetic machine with chromatocytoskeletal activities can drive the development of cancer, and perhaps other diseases, via disruption of the cytoskeleton, potentially even independently of disruption of the epigenome.

Last, while epigenetic inheritance is well established, less is known regarding how natural selection acting on environmentally induced phenotypes could drive epimutations and evolution of the epigenome itself (49, 50). In this regard, the concept of an epigenetic machine that coordinates hereditary components of the epigenome with function of the cytoskeleton has interesting implications for evolutionary biology (51). Epigenetic readers, writers, and erasers are responsive to the environment. Examples include the impact of diet on 1-carbon metabolism and availability of methyl donors, regulation of the activity of demethylases by oxygen levels, and kinases that respond to environmental cues, which, when activated, can phosphorylate and modulate the activity of chromatin remodelers (52). Our data suggest that changes in the environment could produce coordinate changes in methylation on both the epigenome and the cytoskeleton, with functional consequences. Evolutionary theory indicates that environmentally induced phenotypes can supplement adaptation by enabling a population to survive in a new or changing environment until potentially adaptive genetic changes become fixed in the population. In the case of evolution of the epigenome, a changing environment could cause coordinated cytoskeletal changes (producing phenotypes upon which natural selection could act) and changes in the epigenome (which could be inherited generationally) (51). This would predict that over evolutionary time, epigenetic marks on chromatin and cytoskeleton could acquire coordinated functions. This is clearly the case for methyl marks made by SETD2, which function on both chromatin and the cytoskeleton to maintain genomic stability [the present report and (14, 15, 32)]. Although still quite speculative, as the dual chromatin and cytoskeletal activity of epigenetic readers, writers, and erasers more fully emerges, this hypothesis could provide useful ways to explore evolution of the epigenome and provide insights into how epigenetic variation and phenotypic variation, the actual target of selection, may be linked.

5. Lipid Metabolism

Aberrant lipid metabolism is one of the most pronounced metabolic alterations in cancer, and it greatly contributes to cancer cell growth and tumorigenesis [68]. Lipids, including sterols, mono/di/triglycerides, phospholipids, and glycolipids, are indispensable to cells. They serve as energy sources, as components of biological membranes, and as signaling molecules [69]. The many roles of lipids are a testament to the importance of processes that regulate their levels in cancer. Several aspects of lipid metabolism are reprogrammed in cancer, including the biosynthesis and oxidation of fatty acids (FAs), the uptake of FAs from the environment, and modification of FAs and release from other molecules ( Figure 6 ) [68], of which the mechanisms will be discussed.

Lipid Metabolic Reprogramming in Cancer. An overview of lipid metabolic pathways and how they are modified in cancer. (a). Tumor cells take up fatty acids (FAs) using multiple transporters, including CD36, FA binding proteins 1-6 (FABP1-6), and low-density lipoprotein receptor (LDLR) for low-density lipoproteins (LDL). These free FAs then become a part of the cellular FA pool where they can enter the citric acid (TCA) cycle and contribute to lipid formation. Upregulation of FA uptake in cancer occurs through hypoxia-inducible factor (HIF-1)-induced FABP1-6 overexpression. (b). Upregulation of lipogenesis and cholesterol biosynthesis is achieved through sterol regulatory element binding protein (SREBP) activation. SREBP1 activation induces expression of lipogenesis genes while SREBP2 activation induces expression of cholesterol biosynthesis genes. (c). Fatty acid oxidation (FAO) can be upregulated by cMyc, depending on the cancer type as a means to counteract oxidative stress. ACC1/2 acetyl-CoA carboxylase 1/2, ACLY ATP citrate lyase, ACS acyl-CoA synthetase, α-KG alpha-ketoglutarate, CoA coenzyme A, CPT1 carnitine palmitoyltransferase 1, FADS FA desaturases, FASN fatty acid synthase, FPP farnesyl-pyrophosphate, GLUT1 glucose transporter 1, HMG-CoA hydroxy-methylglutaryl-CoA, HMGCS hydroxy-methylglutaryl-CoA synthase, HMGCR hydroxy-methylglutaryl-CoA reductase, LD lipid droplets, MUFA monounsaturated fatty acids, PUFA polyunsaturated fatty acids, SCD1 stearoyl-CoA desaturase 1, SOAT sterol O-acyltransferase. Figure created with (accessed on 26 March 2021).

5.1. Lipid Acquisition: De Novo Lipogenesis and Lipid Uptake

Cells can acquire lipids in one of two ways, de novo synthesis or uptake [70]. Most lipids are derived from FAs, which are molecules containing long hydrocarbon chains. Adult cells normally obtain FAs from external sources, such as the diet or from lipids synthesized by the liver [68]. Cancer cells, however, reactivate de novo lipogenesis which removes their reliance on externally derived lipids and allows them to proliferate at a faster rate [69]. FA synthesis occurs using cytoplasmic acetyl-CoA that is generated from acetate, glucose, or glutamine. This acetyl-CoA is converted to malonyl-CoA and then 16-carbon saturated FA palmitate using the enzymes acetyl-CoA carboxylases (ACC1/2) and fatty acid synthase (FASN), respectively ( Figure 6 a). Palmitate can then be elongated to other FAs or desaturated using FA elongases and FA desaturases to form the cellular pool of non-essential FAs that are then further converted to form other important lipids, such as cholesterol, eicosanoids, and prostaglandins [71]. Increased FA de novo synthesis in cancer has been widely observed and this increase is essential for cancer cell growth [70,72,73,74,75,76].

Cancer cells activate de novo lipogenesis by upregulating several enzymes involved in the pathway, specifically acetyl-CoA carboxylase (ACC), fatty acid synthase (FASN), and stearoyl-CoA desaturase 1 (SCD1) [70]. These enzymes are upregulated through the activation of Sterol regulatory element-binding proteins (SREBPs), which are key transcription factors involved in lipid metabolism. SREBPs are initially translated as inactive precursors in the endoplasmic reticulum and associate with the chaperone SREBP cleavage activating protein (SCAP) [71]. Glucose uptake and low sterol concentration facilitates glucose-mediated N-glycosylation of SCAP, which allows it to transport SREBPs to the Golgi where they can become proteolytically activated and bind to the promoters of effector genes in the nucleus ( Figure 7 ) [77]. SREBP isoform SREBP-1 preferentially binds to genes involved in FA synthesis to promote their expression. SREBP activation is also regulated by upstream oncogenic signaling pathways, most predominantly by the PI3K/Akt/mTORC1 signaling axis. This axis increases the expression of enzymes needed for FA synthesis and activates ATP-citrate lyase (ACLY), which catalyzes acetyl-CoA production from citrate, which can enter into de novo lipogenesis. It also increases the production of NADPH via the activation of NRF2, which is used as a cofactor in FA synthesis reactions [68,69].

Activation of sterol regulatory element binding proteins (SREBPs) in cancer. SREBPs are the main transcription factors that regulate expression of genes involved in lipogenesis that are translated as inactive precursors in the endoplasmic reticulum associated with SREBP cleavage activating protein (SCAP) and insulin induced gene protein (INSIG). PI3K/AKT and glucose uptake results in the N-glycosylation of SREBPs, which separates the complex from INSIG and allows it to translocate to the Golgi and become proteolytically activated. Mature SREBPs bind to genes in the nucleus to induce their transcription. Mature SREBP1 preferentially binds genes involved in fatty acid (FA) synthesis while mature SREBP2 preferentially binds genes involved in cholesterol biosynthesis. The upregulation of these genes results in tumor growth. High concentrations of sterols inhibit SREBP activation. EGFR epidermal growth factor receptor, TCA cycle the citric acid cycle. Figure created with (accessed on 26 March 2021).

Regardless of the signaling molecules involved, increased de novo lipogenesis provides cancer cells with the ability to shunt into different biosynthetic pathways to create lipids with a wide variety of functions that allow them to adapt and respond to their surroundings and ensure continued proliferation. Specifically, increased FA synthesis reduces the number of polyunsaturated FAs (PUFAs) and increases the number of monounsaturated FAs (MUFAs). This helps provide protection from lipid peroxidation as PUFAs are subject to peroxidation in the presence of ROS. Increased FA synthesis in cancer cells also confers protection from ROS, contributes to pro-angiogenic signaling, and provides an escape from immune surveillance [78].

Besides de novo lipogenesis, cancer cells also acquire a diverse pool of lipids by increasing lipid uptake [69]. Lipid uptake can occur via multiple routes, including the use of specialized transporters such as CD36 fatty acid translocase or the fatty acid transport proteins (FATPs of the SLC27 family of solute carriers), or receptor-mediated endocytosis of low-density lipoprotein (LDL) particles via the LDL receptor (LDLR), all of which are highly expressed in various types of cancer ( Figure 6 a) [68]. The uptake of exogenous FAs also promotes migration and metastasis. Through the remodeling of cellular FA composition, cancer cells can facilitate changes in membrane fluidity that promote cell migration and cancer progression [78,79]. Additionally, the uptake of lipids from the environment allows tumors to maintain their lipid pool, even in times of stress. For example, under hypoxic conditions, the conversion of saturated FAs into monounsaturated FAs is hindered, as the enzyme catalyzing the reaction, stearoyl-CoA desaturase-1 (SCD-1), requires oxygen. Hypoxic cells can compensate by taking up exogenous lysophospholipids to survive. Exogenous FA uptake is mediated by the master regulator, HIF-1α, and its control of overexpression of lipid-binding proteins, such as FA-binding protein 4 (FABP4) [69].

5.2. Lipid Storage and Export

One consequence of increased de novo lipid synthesis and uptake is that, with an excess of lipids, cancer cells must store them. Excess lipids are stored as lipid droplets, which are produced via conversion of cellular lipids to triglycerides and cholesteryl esters in the endoplasmic reticulum by sterol O-acyltransferase 1 (SOAT1), also known as acyl-CoA acyltransferase 1 (ACAT1) [70]. Cancer cells exhibit an increased number of lipid droplets compared to normal cells. These lipid droplets help maintain lipid homeostasis, prevent lipotoxicity, regulate autophagy, maintain ER and membrane homeostasis, and also provide a source of ATP and NADPH through their breakdown by lipophagy followed by β-oxidation in times of metabolic stress [69]. An accumulation of lipid droplets is found in several types of cancer, including breast, brain, liver, cervical, prostate, colon, skin, bile duct, clear-cell renal carcinoma, ovarian, and pancreatic cancer [80].

5.3. Lipolysis

Tumor cells also acquire FAs through the breakdown of lipid droplets through a process called lipolysis. Lipolysis refers to the breakdown of lipid droplets by lipoprotein lipase (LPL) to release free FAs [81]. These free FAs can then be taken up by CD36 and used to support increased growth. Increased expression of LPL occurs in breast cancer [82], non-small cell lung cancer [83], and chronic lymphocytic leukemia [84], with breast cancer also exhibiting increased CD36 expression. Increased lipolysis is associated with cachexia, a clinical manifestation of cancer referred to as �t wasting”. Cachexia is weight loss due to muscle and adipose tissue (AT) depletion that is found in multiple types of cancer and is associated with poorer prognosis. Although efforts in the past have mainly focused on muscle loss, recent studies focus on the role of lipolysis in this process, as the loss of AT is mainly due to increased induction of lipolysis [85]. Cytokines, such as TNF-α and IL-6, and lipid mobilizing factor, zinc-㬒-glycoprotein (ZAG), play a major role in the upregulation of lipolysis in cancer, although additional research is needed to understand the mechanism underlying this change [85].

5.4. Fatty Acid Oxidation

The process of lipolysis breaks down lipid droplets to free FAs and these free FAs can then be further broken down by fatty acid oxidation (FAO), referred to as β-oxidation. While the role of FA synthesis in cancer has been widely established, the role of β-oxidation has not been as well defined and is a newer area of study cancer metabolism. As a source of ATP and NADPH, β-oxidation provides the energy and reducing power for biosynthesis and a means to counteract oxidative stress. Most research, however, has focused on the generation of ATP through the Warburg Effect. NADPH can be produced via other metabolic pathways, such as the PPP, suggesting that FAO does not play a major role in the oncogenic landscape. Furthermore, malonyl-CoA, an intermediate of lipogenesis, coordinates the activity of both lipogenesis and FAO. Malonyl CoA acts as an inhibitor of the FAO rate-limiting enzyme carnitine palmitoyltransferase 1 (CPT1), supporting the idea that FA synthesis and FAO cannot occur at the same time. However, new evidence suggests that FAO may play a greater role in cancer growth and metastasis than previously thought [86].

Recent studies have demonstrated that there is increased expression of several FAO enzymes in cancer, including CD-36, CPT1 isoforms A, B, and C, carnitine transporter CT2 [42], and Acyl-CoA synthetase long chain 3 [82,86]. Consistent with this observation, several types of cancer exhibit increased FAO, such as triple negative breast cancer (TNBC) [87], gastric cancer [88], glioma [89], and prostate cancer [90]. These types of cancer rely on FAO as a main source of ATP for rapid growth and even prefer to metastasize to tissues rich in adipocytes [86]. Non-glycolytic tumors, such as those in prostate cancer, employ FAO as their main bioenergetic pathway [90]. Increased expression of FAO enzymes and upregulation is achieved by overexpression of oncogenic c-Myc ( Figure 6 c) [86]. As a generator of NADPH, FAO also helps cancer cells respond to oxidative stress and avoid cell death [91]. Additionally, FAO has been implicated in metastasis through its potential role in the reprogramming of cancer stem cells [92]. Taken together, the data suggest that FAO plays an important role in cancer metabolism.

5.5. Mevalonate Pathway

The generation of important lipids, such as cholesterol, vitamin D, and lipoproteins, through reprogramming of the mevalonate pathway (MVA) in cancer has been extensively studied, with a focus placed on cholesterol biosynthesis. The MVA uses acetyl-CoA derived from glycolysis to generate its products, with mevalonate production catalyzed by 3-hydroxy-3-methylglutaryl-CoA reductase (scdCR) being the rate-limiting step of the entire pathway. Mevalonate is then converted to isopentenyl pyrophosphate (IPP) and later, farnesyl pyrophosphate (FPP). FPP is critical for production of squalene, the precursor to cholesterol. Cholesterol itself is an important component of cell membranes and is the precursor to hormones, bile acids, and lipid rafts [93].

Many enzymes of the MVA are often overexpressed in cancer, including HMGCR, farnesyl diphosphate synthase (FDPS), geranylgeranyl pyrophosphate synthase (GGPPS), squalene synthase, and squalene epoxidase [93]. The transcription of these enzymes is controlled by SREBPs, in manner similar to de novo lipogenesis, with isoform SREBP2 showing a preference for the promoters of MVA and cholesterol biosynthesis genes ( Figure 6 b) [68]. Again, like de novo lipogenesis, SREBP2 is mediated by the PI3K/Akt/mTORC1 signaling axis. This results in increased HMGCR expression, and thus, increased flux through the MVA. SREBP2 can also interact with mutant p53 to drive the post-translational modification of oncogenes, such as the farnesylation of Ras, and regulates mediators of epigenetic changes, such as histone deacetylases (HDACs) and DNA methyltransferases (DNMTs) [69,94]. Increased expression of HMGCR in cancer leads to increased production of cholesterol, which provides a continuous resource for membrane synthesis in dividing cells and of estrogen and androgens to support tumorigenesis [93]. As a result, inhibition of cholesterol biosynthesis with statins greatly impairs cancer growth [95,96].

In addition to cholesterol, other products of the MVA pathway play roles in cancer cell growth. One such product is ubiquinone, a key electron transfer molecule in respiration. Oxidative phosphorylation is an active metabolic pathway in many tumors, and hence, ubiquinone is an important product of the MVA for continued cell proliferation. Ubiquinone is also a regulator of ROS, and more recently, it was reported that ubiquinone supports pyrimidine biosynthesis in colorectal and pancreatic cancer [69]. Thus, the MVA pathway contribute a number of molecules needed for cancer cell survival.

Prospects, frontiers and applications

Where do we go from here? Armed with a broader view of metabolism’s influence, it should become increasingly feasible to reverse disease states caused by genetically-determined metabolic dysfunction. One area that should benefit from this new information is the treatment of children with IEMs. Although dietary modifications and other advancements have greatly reduced morbidity and mortality in some of these diseases, the treatments are often invasive and do not fully protect patients from occasional states of severe metabolic decompensation, which can result in permanent disability. It is possible that agents developed to manipulate metabolism in cancer will find applications in other diseases that share some of the same metabolic features, like dysregulated production of lactic acid or altered TCA cycle function. For example, recent work demonstrates that tumor cells with mutations in the TCA cycle or ETC (Mullen et al., 2011), or with hypoxia-induced suppression of oxidative metabolism (Metallo et al., 2011 Scott et al., 2011 Wise et al., 2011) use reductive carboxylation of α-KG to produce citrate and other precursors during growth. In this reaction, NADPH-dependent isoforms of IDH act in reverse with respect to their conventional role as oxidative decarboxylases ( Fig. 3B ). It is possible that a similar pathway contributes to disease in patients with fixed defects in mitochondrial metabolism, or with perinatal asphyxia, stroke, cardiac ischemia and other conditions involving pathological hypoxia.

Furthermore, our understanding of the pathophysiology of IEMs still draws heavily from the traditional view of intermediary metabolism that focuses primarily on catabolism. The metabolic regulation of macromolecular synthesis, chromatin dynamics and cell renewal is just beginning to be considered, and the interplay between metabolite accumulation, cell signaling and other processes highly relevant to these diseases should be investigated further. For example, the biology of IDH1/2-mutant tumors may provide insight into the effects of global 2-HG accumulation in patients with (L)- or (D)-2-hydroxyglutaric aciduria. This point was emphasized by the recent observation that up to half of patients with (D)-2-hydroxyglutaric aciduria contain heterozygous active-site mutations in IDH2 (Kranendijk et al., 2010). It is unknown whether these patients or patients with other genetic forms of 2-HG aciduria have any of the epigenetic effects observed in IDH1/2-mutant tumors.

Conversely, it should be possible to bring nearly 100 years of clinical experience with IEMs to bear on cancer and other diseases. There is significant interest in developing agents to inhibit the Warburg effect in tumors, but because glycolysis is nearly ubiquitous among human tissues, there is concern about the toxicity of such agents. The extremely large number of well-characterized IEMs in almost every known metabolic pathway may provide a guide as to which types of toxicity to anticipate for some of these targets. SLC2A1 is a HIF-1 transcriptional target and encodes the predominant glucose transporter in cancer cells, GLUT1. GLUT1 is also the major transporter at the blood-brain barrier and is responsible for supplying glucose to the brain. GLUT1 deficiency has a wide phenotypic spectrum but its most common form involves severe central nervous system manifestations including seizures, mental retardation and poor growth of the postnatal brain. Glucose levels in the cerebrospinal fluid may be less than half of normal. It is unknown whether the increased glucose dependence of tumors relative to differentiated tissue will support an acceptable therapeutic window in cancer treatment, but clearly extreme caution would be indicated in attempts to inhibit GLUT1. Other glycolytic IEMs have less severe manifestations. LDHA is a transcriptional target of HIF-1 and c-Myc. It encodes the isoform of lactate dehydrogenase observed in most tumors and is responsible for robust lactate synthesis in cancer cell lines. Severe LDHA deficiency is also a well-characterized human disease, Glycogen Storage Disease Type XI. This disorder causes exercise intolerance, cramps and occasional myoglobinuria in adolescents and adults, but is not associated with severe dysfunction of major homeostatic organs. Perhaps inhibition of LDHA would be tolerated during intermittent cancer therapy. Ongoing population-based studies in metabolomics and genomics should uncover the full breadth of metabolic diversity in healthy humans, and this information may help predict how to target metabolic pathways safely in cancer.

Altered metabolic states in disease also present a tremendous opportunity to develop novel methods in diagnostic imaging. Several platforms for metabolic imaging are already in clinical practice, particularly FDG-PET. But with an ever-expanding knowledge of the interplay between metabolism and disease, it will become more feasible to expand the repertoire of non-invasive techniques to detect diseased tissue and monitor its response to therapy. A number of efforts in this regard are already underway and have a good chance for translation to human patients in the near future. The importance of additional nutrients besides glucose to tumor metabolism has prompted the development of several new PET agents for cancer, including glutamate and glutamine (Koglin et al., 2011 Qu et al., 2011). These agents would add another level of molecular detail to help assess individual tumors, and may improve the detection of tumors invisible on FDG-PET. Magnetic resonance spectroscopy (MRS) at 1.5-Tesla has been used for decades to produce semi-quantitative, non-invasive measurements of abundant metabolite pools in cancer (Glunde and Bhujwalla, 2011). Recently, higher-field human MRI systems have drastically improved the resolution of 1 H spectra and made it possible to detect many additional metabolites in human tumors. Successful quantification of significant metabolites like glycine and 2-HG has already been reported for human gliomas (Choi, 2011a Choi et al., 2011b), and additional metabolites will be added to the list as their relevance in cancer, IEMs, or other diseases is more firmly established. It is also now possible to image metabolites labeled with 13 C and to monitor specific enzyme activities in vivo by tracking the transfer of the label from substrate to product. This method requires that polarization of the 13 C nuclear spin state be enhanced by transferring to it the high spin polarization of an unpaired electron from a free radical, a process known as hyperpolarization of the 13 C nucleus. This produces a transient gain in sensitivity of 13 C detection by 10,000-fold or more. Hyperpolarization has been used successfully to quantify oncogene-driven metabolic activities in experimental tumors (Hu et al., 2011). Although still an investigational method, metabolic imaging using hyperpolarization of 13 C and other stable isotopes has considerable translational potential in human disease because of its high sensitivity, integration with MRI, lack of the need for radioactive tracers, and in particular its ability to probe the flux of specific enzymatic reactions in vivo (Kurhanewicz et al., 2011).

In fact one of the primary challenges now in human metabolism research – and one of the next frontiers in systems biology – is to analyze the impact of disease on metabolic flux in vivo that is, to use sensitive and efficient methods to measure the transfer of carbon, nitrogen, etc. along metabolic pathways in live subjects, with and without disease. While methods to catalog and quantify small molecules in biological fluids (metabolomics) are highly informative, they do not present a complete view of metabolism and in some cases can produce misleading results. Newborn screening programs provide a valuable example of this issue. An elevated level of a metabolite in these screening tests almost always reflects decreased rather than increased flux of the pathway, similar to the effect of a traffic light on the accumulation of automobiles ( Figure 4A ). Furthermore, because metabolism involves so many intersecting pathways, it may be impossible to infer which pathway(s) are affected when abnormal levels of a metabolite are observed. For example, it is unknown which extracellular nutrients are metabolized to 2-HG in IDH1/2-mutant tumors in vivo. One way to address these issues is to combine metabolic pathway analysis with metabolomic studies. Such studies would involve introducing isotopically-labeled nutrients into an animal model or a human patient, and then harvesting metabolites of interest from the blood, urine, breath, or tissue samples ( Figure 4B ). The total abundance of these metabolites would be measured using a metabolomics platform, then a subset of the most informative metabolites would be studied further by mass spectrometry and/or NMR spectroscopy to determine the abundance and position of isotopic label within each molecule. Similar approaches focusing on a handful of metabolites have already been used successfully in humans to measure gluconeogenesis and the urea cycle in vivo, and to compare metabolism between lung tumors and surrounding tissue (Fan et al., 2009 Landau et al., 1996 Yudkoff et al., 2010). Combined metabolomics-metabolic flux studies, despite their technical challenges, have tremendous value because they could produce a quantitative and comprehensive readout of the variation in metabolic pathway activity, leading to a deeper understanding of metabolic individuality and the biological basis of disease in humans.

(A) Analysis of metabolism is similar in principle to the analysis of traffic patterns, with many of the same uncertainties. The high 𠇏lux” on a four-lane highway leads to a low density of cars, all of which travel unimpeded southward. Upon exiting the highway, drivers experience reduced flux because of the traffic light, akin to mutation or under-expression of a metabolic enzyme. This causes an increased density of cars north of the light. Flux downstream of the block is unimpeded. Note that red cars on the two-lane road also merge with black cars, leading to a reduced fraction of red cars downstream of the intersection. The sum effect of these factors on overall flux is demonstrated by counting the cars that pass the checkered flags. On the highway, 1000 cars, all red, pass in one hour. On the two-lane road, only 200 cars pass, and only half are red.

(B) Simple schematic of metabolic flux analysis. Glucose labeled with 13 C at positions 1 and 6 (red asterisks) is given via injection or oral administration to a subject, which metabolizes it. After a period of time, tissue or body fluids are sampled to determine the abundance of various metabolites, the fraction of the metabolite that contains 13 C, and the position(s) of 13 C within the molecule. Data are acquired using mass spectrometry or NMR spectroscopy. Mathematical models are then applied to translate the data into metabolic flux. In this example, labeling of lactate and acetyl-CoA are examined. The pathways producing these two metabolites diverge at pyruvate. LDH, a highly active enzyme, rapidly converts pyruvate to lactate, resulting in a very high enrichment in the lactate pool in a short time. Meanwhile, two factors conspire to reduce enrichment in acetyl-CoA. First, this pathway involves PDH, a highly regulated and less active enzyme. Second, entry of carbon from unlabeled nutrients contributes to the acetyl-CoA pool, reducing the fraction of acetyl-CoA molecules containing 13 C from glucose.


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Keywords : EMT, cancer, reprogramming, energy metabolism, glycolysis, OXPHOS

Citation: Lai X, Li Q, Wu F, Lin J, Chen J, Zheng H and Guo L (2020) Epithelial-Mesenchymal Transition and Metabolic Switching in Cancer: Lessons From Somatic Cell Reprogramming. Front. Cell Dev. Biol. 8:760. doi: 10.3389/fcell.2020.00760

Received: 27 May 2020 Accepted: 20 July 2020
Published: 06 August 2020.

Marco Fiorillo, University of Salford, United Kingdom

Anup Kumar Singh, Beckman Research Institute, City of Hope, United States
Monica Montopoli, University of Padua, Italy

Copyright © 2020 Lai, Li, Wu, Lin, Chen, Zheng and Guo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Campbell Biology / Lisa A. Urry, Mills College, Oakland, California, Michael L. Cain, Bowdoin College, Brunswick, Maine, Steven A. Wasserman, University of California, San Diego, Peter V. Minorsky, Mercy College, Dobbs Ferry, New York, Jane B. Reece, Berkeley, California.

This edition was published in 2017 by Pearson Education, Inc. in New York, NY .

Edition Description

1 volume (various pagings) : illustrations (chiefly color), color maps 29 cm

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