Why lactate inhibits growth (or enhances death rate)?

Why lactate inhibits growth (or enhances death rate)?

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Extracellular lactate tends to inhibit cellular growth or enhance cell death. This happens in the vicinity of tumors and in cell cultures.

See for example this reference: Ozturk, Sadettin S., Mark R. Riley, and Bernhard O. Palsson. "Effects of ammonia and lactate on hybridoma growth, metabolism, and antibody production." Biotechnology and bioengineering 39.4 (1992): 418-431 (available here:

What is the mechanism behind this effect of lactate? Why lactate has these negative effects?

Lactate, a Neglected Factor for Diabetes and Cancer Interaction

Increasing body of evidence suggests that there exists a connection between diabetes and cancer. Nevertheless, to date, the potential reasons for this association are still poorly understood and currently there is no clinical evidence available to direct the proper management of patients presenting with these two diseases concomitantly. Both cancer and diabetes have been associated with abnormal lactate metabolism and high level of lactate production is the key biological property of these diseases. Conversely, high lactate contribute to a higher insulin resistant status and a more malignant phenotype of cancer cells, promoting diabetes and cancer development and progression. In view of associations between diabetes and cancers, the role of high lactate production in diabetes and cancer interaction should not be neglected. Here, we review the available evidence of lactate’s role in different biological characteristics of diabetes and cancer and interactive relationship between them. Understanding the molecular mechanisms behind metabolic remodeling of diabetes- and cancer-related signaling would endow novel preventive and therapeutic approaches for diabetes and cancer treatment.

1. Introduction

Globally, diabetes mellitus (DM) and cancer are two of the most predominant diseases, with cancer the 2nd and diabetes the 12th primary cause of death [1, 2]. The connection between these two diseases was first hypothesized over 75 years ago. More and more evidence proposes that DM is related to an augmented risk of cancer [3] and the higher mortality in cancer patients [4, 5]. Actually, recent studies have suggested that type 2 diabetes (T2DM) is an independent risk factor for the progress of various types of cancer [6]. Although these two diseases share a number of common risk factors, the biological link between them is still not well known [6, 7], which poses a challenge for clinical management. While a thorough picture is yet to emerge, several mechanisms have been suggested to explain this relationship, for example, hyperglycemia itself [3], oxidative stress [8–11], treatment for diabetes, hormonal disorders, insulin resistance with secondary hyperinsulinemia [3], metabolic alterations underlying the diseases [12], insulin-increased bioactivity of IGF-I [13, 14], insulin’s positive effect on estrogen bioavailability, the status of chronic inflammation, and obesity [7]. On the other hand, DM might also develop after tumor establishment in certain cancers that progress very rapidly, for example, pancreatic and liver cancers [15].

Lactate (2-hydroxypropanoic acid), formerly deemed a waste product of glycolysis, has drawn more and more attention as a crucial regulator of insulin resistance, DM, cancer development, maintenance, and metastasis. Over the last half century, substantial experiments revealed that lactate is both a powerful fuel and signaling molecule, and it is continuously being produced and circulated through the body [16]. Its presence in diabetes and cancer has been recognized, and recent studies suggest that suppressing it can be therapeutic, after 50 years of disavowal. Recently, cancer and DM have been associated with abnormal lactate metabolism. Lactate facilitates cancer cell intrinsic effects on metabolism and has extra noncancer cell autonomous effects which can induce tumorigenesis. In addition, lactate plays an important role in stimulating tumor inflammation and in promoting tumor angiogenesis by functioning as a signaling molecule [17]. Given that hyperlactacidemia is the most imperative biological feature of diabetes and cancer, it is reasonable to imagine that hyperlactacidemia might play an important role during diabetes and cancer interaction. Here, we review the available evidence of lactate’s role in different biological characteristics of diabetes and cancer and interactive relationship between them. It appears that hyperlactacidemia may function as an interaction hub between diabetes and cancer and contribute to a higher insulin resistant status and a more malignant phenotype of cancer cells.

2. Lactate Production and Metabolism

Lactate, a 3-carbon hydroxycarboxylic acid, is produced in the cytoplasm by the glycolysis pathway under anaerobic conditions, via the reduction of an intermediate metabolite pyruvate, with the simultaneous oxidation of NADH to NAD+. This reaction is catalyzed by lactate dehydrogenase (LDH) [18]. LDH is composed of four subunits of two distinct types (H and M), with each subunit type under distinct genetic control leading to five diverse isozymes including LDH-1 (H4), LDH-2 (H3M1), LDH-3 (H2M2), LDH-4 (H1M3), and LDH-5 (M4) [19]. Under aerobic conditions and in the presence of the enzyme pyruvate dehydrogenase (PDH), pyruvate is converted into acetyl CoA, subsequently entering the tricarboxylic acid (TCA) cycle or Kreb’s cycle.

The normal plasma concentration of lactate is 0.3–1.3 mM. In plasma, lactate is buffered by NaHCO3. Lactate may have two stereoisomers, namely, d-lactate and l-lactate. In humans, lactate exists predominantly in the levorotatory isoform. Most tissues in the human body produce lactate, but the majority of production is found in muscles [18]. Lactate is transported across the plasma membrane with the aid of the monocarboxylate transporters (MCTs), which facilitates the proton-linked transport of monocarboxylates, for example, L-lactate, pyruvate, and the ketone bodies [20, 21]. So far four isoforms, MCT1–4, have been functionally substantiated to implement this function in mammals, each with different substrate and inhibitor affinities [20, 21] (Figure 1).

Plasma concentrations of lactate represent an equilibrium between its production and metabolism. Lactate can be metabolized by various cells and tissues, for example, liver, germ cells, and neurons, converting to pyruvate via LDH and subsequently to glycogen or carbon dioxide [22]. Under normally physiological conditions, lactate is cleared by the livers and kidneys [23, 24]. At present, lactate is also considered as a regulator of energy homeostasis [16, 25, 26]. At a generalized level, lactate can be carried to the liver and reconverted into glucose through the Cori cycle, serving as an energy source [27].

3. Lactate Production Increases in Diabetes

Fasting plasma lactate level is increased in patients with DM including T1DM and T2DM versus nondiabetic persons [28–36]. Diabetic patients with obesity exhibit higher fasting plasma lactate levels than nondiabetic individuals with obesity [37, 38]. Barnett et al. proposed that diabetes-associated hyperlactatemia might be an early change in the time course of the disease [39]. Recently, Berhane et al. [40] demonstrated that lactate production progressively rises during hyperinsulinemic euglycemic clamp study, a condition of hyperinsulinemia similar to the early stages in the development of T2DM. Intriguingly, similar previous studies also report elevated lactate concentrations during the early stages of diabetes, prediabetes, and the hyperinsulinemia condition. In addition, Brouwers et al. [41] reported increased lactate levels in patients with poorly controlled T1DM and glycogenic hepatopathy, implying that enhanced plasma lactate concentrations are part of the clinical spectrum of these diseases. Furthermore, lactate has also been revealed to predict diabetes occurrence in the future [42, 43].

The mechanisms underlying diabetes-associated hyperlactatemia include serious changes in the intracellular glucose metabolism in insulin-sensitive tissues, for example, diminished glycogen synthesis, compromised glucose oxidative metabolism, and increased whole-body rate of nonoxidative glycolysis [28, 31, 44]. Importantly, when compared with controls, nonoxidative glycolysis rate retains higher in T2DM patients during hyperglycemic [31, 44, 45] and hyperinsulinemic [31, 44] status. In addition, the postprandially nonoxidative glycolysis is elevated in these patients relative to healthy controls and blood lactate level rises under this condition [36]. Insulin resistance plays a vital role in the pathogenesis of T2DM [46] and can be used as an early marker for the disease [40]. Under the insulin resistant condition, high levels of insulin promote glycolysis through activating two rate limiting enzymes, namely, phosphofructokinase and pyruvate dehydrogenase [47]. Thus, patients with insulin resistance/diabetes exhibit augmented activity of glycolysis [31, 48]. The elevated glycolysis results in enhanced formation of NADH and pyruvate and reduced NAD+ levels. Pyruvate is converted into lactate by LDH accompanied by NAD+ generation from NADH in a redox reaction. This reaction may be accentuated in insulin resistance since hyperinsulinemia induces enhanced glycolysis.

4. Contribution of Lactate to Insulin Resistance/Diabetes

As an imperative cellular metabolite in the glycolytic pathway, lactate might reflect the cellular metabolism status. Some studies suggest that augmented lactate levels in obesity, which might play a significant role in glucose transport and metabolism, profoundly influence insulin sensitivity [49]. Its high plasma level might be an early indication of the beginning of insulin resistance and can be utilized to identify a state of insulin resistance [40]. In addition, in HIV-infected patients treated with nucleoside reverse transcriptase inhibitors, both resting and postexercise levels of lactate are associated with insulin resistance in skeletal muscle [50]. Lactate alone or combined with other insulin secretagogues, for example, ketone bodies, stimulates insulin release in INS-1 cells and isolated pancreatic islets [51], indicating that increased plasma lactate promotes insulin secretion and pancreatic response to insulin secretagogues. Thus, these results suggest that lactate not only enhances insulin secretion from β-cells but also improves the responsiveness of these cells to insulin [51]. These data may explain that the transiently elevated lactate obtained during physical exercises and aerobic/anaerobic training improves DM symptoms. Instead, lactate concentrations are chronically increased in diabetic patients with obesity [52]. The chronical hyperlactatemia maintained by the enhanced lactate formation from adipocytes in obese individuals [53] is found preceding diabetes onset [52] and might participate in this pathologic process. Together, these data indicate that chronical hyperlactatemia might indicate the early stages of insulin resistance and contributes to the onset of diabetes. Actually, some epidemiologic studies suggest that high lactate levels might predict the occurrence of diabetes [42, 43]. Crawford et al. [43] in their cross-sectional study among white elderly people with severe carotid atheromatosis reveal a relationship between plasma lactate levels and prevailing T2DM nonetheless no association is detected among African Americans.

While the molecular mechanisms underlying lactate-induced insulin resistance/diabetes are yet uncertain, it has been proposed that inhibition of the ability to oxidize glucose, the repression of glucose transport, and insulin-stimulated glycolysis, as well as reduced insulin-induced glucose uptake is implicated in this phenomenon. Furthermore, it has been suggested that lactate-induced insulin resistance is related to compromised insulin signaling and reduced insulin-triggered glucose transport in skeletal muscle [54].

5. Lactate Production Increases in Cancer

A common feature of primary and metastatic cancers is increase in glycolysis rate, leading to augmented glucose uptake and lactate formation, even under normal oxygen conditions. This is also known as aerobic glycolysis or the “Warburg effect” [55], a metabolic hallmark of cancer. It was first described in the 1920s by Warburg and he hypothesized that cancer is caused by compromised mitochondrial metabolism. While this hypothesis has been proven wrong, the experimental observations of elevated glycolysis in cancers even under normoxic conditions have been repetitively substantiated [56]. Unlike anaerobic glycolysis that stimulates energy generation under hypoxia, the Warburg effect provides a proliferative advantage via converting carbohydrate fluxes from energy generation to biosynthetic processes. To meet cancer cell proliferation requirements, the glycolytic switch is related to increased glucose consumption and lactate accumulation [57]. It is shocking that the lactate levels determined in human cancers, for example, cervix cancer, can range from 4 mM to 40 mM [58], while the physiological levels of lactate in normal tissues are 1.8–2 mM [59].

The molecular mechanisms underlying upregulation of glycolysis in cancer are not well delineated. It is generally assumed that this phenomenon results from defective cellular respiration, oncogenic changes, and overexpression of metabolite transporters and glycolytic enzymes, for example, glucose transporters and hexokinases, which are the crucial regulatory molecules for glycolytic flux [60]. The oncogenes and tumor suppressor genes implicated in the metabolic alteration from oxidative phosphorylation to an increased glycolysis of cancer cells include hypoxia-inducible factor-1α (HIF-1α) [60, 61], epidermal growth factor (EGF), phosphoinositol 3-kinase (PI3-K), myc, nuclear Factor Kappa Beta, protein kinase B (PKB), insulin-like growth factor I, mTOR, Kirsten rat sarcoma viral oncogene homolog (KRAS), and 5′ adenosine monophosphate-activated protein kinase (AMPK). The majority of these oncogenes stimulate genes encoding proteins that regulate glycolysis and glutaminolysis [55].

Among the aforementioned oncogenes, the transcription factor HIF-1α is the most important controller of the glycolytic response and cellular adaptation [62]. Expression of HIF-1α-regulated genes results in an increased glycolytic flux in cancer cells in an oxygen-independent manner. The targets of HIF-1 include hexokinase II [63], angiogenic growth factors (e.g., VEGF), haematopoietic factors (e.g., erythropoietin and transferrin) [64], and membrane transporters including glucose transporter-1 (GLUT-1) and monocarboxylate transporter-4 (MCT-4). These membrane transporters contribute to both sufficient glucose transport into the cell and release of amassed lactate out of the cell. HIF-1α activates pyruvate dehydrogenase kinase 1 (PDK-1) and subsequently inactivates the pyruvate dehydrogenase complex (PDC), leading to reduced flux into oxidative phosphorylation [55]. In addition, the activated HIF-1α is related to constitutively high rate of glucose consumption. Furthermore, hypoxia-reoxygenation injury in cancers may stabilize HIF-1α [65], indicating that its constitutive upregulation may be caused by the cyclic oxic-hypoxic cycles which happen in premalignant cancers.

In addition to glycolysis, glutaminolysis is another primary pathway for energy generation and cause increased lactate formation in cancer cells. Moreover, glutaminolysis facilitates macromolecule synthesis in proliferating tumor cells [61]. The tumor-specific isoform of pyruvate kinase (PK) M2 (PKM2) offers an additional source of lactate by converting phosphoenolpyruvate (PEP) into pyruvate. Nevertheless, PEP may promote the production of pyruvate independent of PKM2 activity through serving as a phosphodonor for phosphoglycerate mutase 1 (PGAM1) [66].

6. Lactate Facilitates Cancer Development

High concentrations of lactate have been linked to unfavoured clinical outcome in some human cancers [57]. Augmented intratumoral lactate levels are related to elevated incidence of metastasis in cervical, breast, head, and neck cancers [58, 67, 68]. Due to lactate concentrations conversely correlated with overall and disease-free patient survival, tumor lactate generation, serum lactate, and LDH levels have long been recognized as prognostic biomarkers of patients with various types of epithelial cancers [55, 69–79]. Increased lactate alters microenvironment, fuels cancer cells, and results in acidosis, inflammation, angiogenesis, immunosuppression, and radio-resistance [80–83]. In the next paragraphs, we review these biological actions of increased lactate in cancer development and progress by describing the main evidences.

Substantial studies have demonstrated that cancer cells can uptake lactate and use it for energetic production and amino acid formation. Accumulative evidence demonstrates that lactate is a fuel for the oxidative metabolism in oxygenated cancer cells [68, 84–87] and a signaling mediator in cancer and endothelial cells (ECs) [88–90]. Recently, Bonuccelli et al. [68] reveal that ketones and lactate fuel tumor growth and metastasis, which might illuminate why diabetic patients have an augmented cancer incidence and poor prognosis, because of elevated ketone/lactate production. In vitro studies suggest that cervical cancer SiHa cells and breast cancer MDA-MB-231 cells uptake lactate in a pH-dependent manner [84, 91]. Due to lack of sufficient oxygenation or an effective vascular network in the microenvironment, cancer uptake and exploitation of lactate is dependent on oxygen concentrations, lactate levels, amount of healthy mitochondria, and suitable MCT expression [92, 93]. Owing to the significant metastasis-promoting characteristics of lactate, one can reason that it is unwise to use lactate-containing intravenous injection solutions, for example, lactated Ringer’s or Hartmann’s solution in cancer patients [68].

The tumor microenvironment (TME) refers to a sophisticated network of extracellular matrix molecules, soluble factors, adipocytes, and stromal cells including tumor endothelial cells (TECs), tumor-associated fibroblasts (TAFs), and macrophages. Among the soluble factors in TME, large amounts of lactate are important due to its effects on tumor and stromal cells [18]. In addition, it decreases extracellular pH to 6.0–6.5 [94–96]. Actually, lactic acidosis frequently contributes to death in patients with some types of metastatic cancer, for example, metastatic breast cancer [97–113]. The acidic TME causes pain in cancer patients [114] and results in metastasis of some tumors [115]. Moreover, acidosis per se may be mutagenic [116], probably via suppression of DNA repair [95] and may result in spontaneous transformation of diploid fibroblasts [117]. Under some circumstances, low pH induces in vitro invasion [118] and in vivo metastasis [119], possibly via the metalloproteinases/cathepsins, which stimulate the degradation of the extracellular matrix and basement membranes [120, 121]. Lactic acidosis results in overexpression of matrix metalloproteinase-9 (MMP-9) [122], VEGF-A [123, 124], transforming growth factor-β2 (TGF-β2) [125] and IL-8 [126–128] in various cancer cells, rendering the TME even more complicated. Pavlides et al. [129] suggest that cancer cells stimulate aerobic glycolysis in CAFs. CAFs render tumor survival and a higher proliferative capacity by a number of factors including secreting lactate and pyruvate and alterations in cell metabolism. Accordingly, cancer cells may become accustomed to rapid alterations in the TME via reprograming stromal cells and via the metabolic interchange between oxidative and glycolytic cells [129, 130].

Within the tumor, TAFs exhibit a different lactate metabolic pathway than the cancer cells. TAFs mainly contain low levels of glucose importer GLUT1, lactate dehydrogenase-B and pyruvate dehydrogenase, while cancer cells contain high GLUT1, lactate dehydrogenase-A, pyruvate dehydrogenase kinase and hypoxia inducible factor-1α. Within cancer cells, the imported glucose is metabolized to pyruvate, while pyruvate dehydrogenase is inactive due to its phosphorylation by pyruvate dehydrogenase kinase phosphorylates. Therefore, LDH-5 (made of LDHA subunits) in an anaerobic manner converts pyruvate to lactate which is exported out of the cell. On the other hand, TAFs import the lactate and by their LDH-1 (containing LDHB subunits) activity convert it back to pyruvate which is funneled to aerobic pathways of mitochondria via the activity of pyruvate dehydrogenase. It seems that these two lactate metabolic pathways in cancer cells and TAFs work in a complementary manner as cancer cells generate high levels of lactate and acidify the microenvironment while TAF consume the lactate in an aerobic manner and decrease the acidity of the microenvironment [131, 132].

The angiogenesis process supports the new blood vessel development and plays an important role in restoring perfusion, oxygenation, and nutrient supply. Lactate is an imperative contributor to wound healing and angiogenesis [133–135]. Lactate itself induces cell migration [134], vascular morphogenesis [136], circulating vascular progenitor cell recruitment [137], and tube formation and promotes angiogenesis by activating the VEGF/VEGFR2 pathway [136, 138] and stimulating endothelial cells via MCT1, which induces the phosphorylation and degradation of IκBα, triggering the NF-kB/IL-8 (CXCL8) signaling pathway [90]. Lactate-stimulated angiogenesis depends on lactate oxidation by LDH-1, exploiting the enzymatic reaction products, for example, pyruvate and NADH, and lactate transporters [136, 137]. The enhancing production of pyruvate from lactate oxidation activates NF-κB and HIF-1, leading to overexpression of some growth factors required for angiogenesis, including VEGF, basic fibroblast growth factor (bFGF), and stromal cell-derived factor-1 (SDF-1) [139, 140]. In addition, Vegran et al. [90] demonstrate that lactate-stimulated NF-κB activation in ECs is associated with IL-8-mediated autocrine angiogenesis and that this pathway promotes EC migration and tube formation in vitro, as well as lactate-triggered tumor angiogenesis in vivo.

Endothelial cells of tumor vasculature import high levels of glucose (high GLUT1 levels). However, since they contain high LDH1 and low HIF-1α and lowLDH5, similar to TAFs, they show an aerobic metabolism. Meanwhile due to low expression of lactate transporters, endothelial cells perhaps do not import much of the lactate in the tumor. Hence, it seems the main role of endothelial cells is to respond to the tumor microenvironment by generating new vessels to support the cancer cells and other tumor associated cells. However, they may not participate in uptake and consumption of lactate within the tumor [132, 141].

One main reason for cancer development is that the immune system loses its ability to effectively eradicate aberrant cells. High levels of lactate have a harmful effect on the tumor infiltrating immune cells. Clinical evidence indicates that lactate restricts immune cell infiltration in renal cell carcinoma (RCC) and damages the metabolism and cytolytic functions of T cells in the TME [80, 142]. Lactate hinders proliferation and cytokine release of human cytotoxic T lymphocytes (CTLs) by 95% and their cytotoxic activity by 50%. Lactate released from melanoma cells impedes TAA-induced IFN-γ generation by specific CTLs in melanoma spheroid cocultures [143]. In addition, other studies substantiated that high levels of lactate suppresses TCR-stimulated cytokine release (IFN-γ, TNF-α, and IL-2) and prompts partial damage of lytic granules exocytosis in CTLs by selectively downregulating the MAPKs p38 and JNK/c-Jun signaling pathways [81]. Moreover, tumor-derived lactate enhances arginase-1 (ARG1) expression in tumor-associated macrophages (TAMs), hindering T-cell activity and proliferation [144], inhibiting antitumor immune responses and promoting tumor growth [145, 146]. Lately, Colegio et al. [145] demonstrated that, under normoxic conditions, lactate stabilizes HIF-1α, resulting in ARG1 and VEGF gene expression in macrophages. Furthermore, tumor-derived lactate changes monocytes’ function hinders their differentiation to DCs and inhibits the cytokine production from differentiated DCs and suppresses the activity of NK cells, thus contributing to immune suppression within tumors [82, 147, 148].

Some studies on experimental tumors, including about 1,000 xenografts of individual human head and neck squamous cell carcinoma, indicate that lactate levels are positively correlated with radio-resistance [149]. The mechanisms behind this correlation reside in, at least partially, the antioxidant characteristics of lactate [150]. Anticancer treatments, for example, ionizing radiation and a number of chemotherapeutic drugs, work through inducing overproduction of reactive oxygen species (ROS) in targeted cancer cells, which causes DNA/RNA damage, genomic instability, and lipid peroxidation. Hence, an accretion of lactate may promote resistance to radiation and lead to chemoresistance [151]. Wagner et al. reveal that lactate can modulate cellular DNA damage repair processes in the uterine cervix, leading to the resistance of cervical cancer cells to anticancer therapy [152]. Since animals receiving chemotherapy or radiotherapy exhibit a reduction in lactate [153], checking this metabolite in human cancers might be used to predict therapeutic responses. Accordingly, a recent study [154] proposes that lactate can be used as a quantitative biomarker of acute radiation response.

Finally, lactate is a mediator of inflammation [155, 156] and might be used as a biomarker of inflammatory processes [157]. Lactate and inflammation stimulate each other in a malicious cycle [83]. It promotes IL-4/IL-13 production [158] and stimulates the IL-23/IL17 pathway [18]. Lactate promotes IL-23p19 expression in tumor infiltrating immune cells by stimulating toll-like receptor. In addition, it stimulates splenocytes to secrete IL-17 in an IL-23-dependent manner. These effects stimulate local inflammatory responses, favoring the incidence and development of tumors [159]. In addition, lactate benefits the growth of inflammation-associated colorectal tumor by promoting PGE2 synthesis and gluconeogenesis in monocytes [160]. Together, these studies suggest that lactate plays a significant proinflammatory role in tumor development.

It is believed that in diabetic patients, the adipose tissue plays a major role in induction of metabolic syndrome. In these patients there is an underlying chronic inflammation in adipose tissue and a general increase in levels of cytokines such as TNF-α, IL-1, and IL-6 [161]. While these released factors play important roles in cancer biology, there is evidence that points to their possible reciprocal roles in the lactate level. For instance, TNFα can induce LDHA and lactate production in a short period of time [162], while lactate induces release of TNF-α and IL-6 in some cells [163]. In a study on rats, chronic infusion of IL-1α induced hyperlactacidemia [164] and in another study on rat ovaria cells, IL-1β enhanced glucose uptake and induced aerobic glycolysis [165]. Moreover, it has been shown that high levels of IL-6 correlated with high levels of lactate and can result in poor prognosis of patients with metastatic melanoma [166]. These findings indicate that the release cytokines may play roles in both cancer and metabolic syndrome and may be the connecting points between developments of both diseases.

7. Concluding Remarks and Future Perspectives

Accumulative evidence indicates a high incidence and mortality for a variety of malignancies in patients with diabetes. Diabetes and its risk factors are associated with cancer and they have an intricate and reciprocally reinforcing relationship. Nevertheless, the underlying mechanisms are poorly understood and currently there is no clinical evidence available to direct the proper management of patients presenting with these two diseases concomitantly. Diabetes and cancer interact with each other in a vicious cycle, where lactate plays a pivotal role in this mutual interaction. Insulin resistance/diabetes and cancer conditions produce high levels of lactate and conversely high lactate promotes diabetes and cancer development and progression (Figure 2).

Results and Discussion

Reduction of LDHA Induces Oxidative Stress and Cell Death.

We sought to understand the mechanisms of cell death following LDHA reduction by short interfering RNA (siLDHA), which has been shown to inhibit tumorigenesis. First, we determined the effect of reduced LDHA expression on oxygen consumption by human Panc (P) 493 B-lymphoid cells, because reduction of LDHA would favor the entry of pyruvate into mitochondria for oxidative phosphorylation, thereby enhancing oxygen consumption. Reduction of LDHA expression by siRNA in P493 cells resulted in an increase in oxygen consumption (Fig. 1 A and B). Oxygen consumption was similarly increased in a human pancreatic cancer line treated with siLDHA (Fig. S1 A and B).

Reduction of LDHA expression by siRNA leads to increased oxygen consumption and oxidative stress-induced cell death of P493 human lymphoma B cells. siRNAs targeting human LDHA (SMARTpool) were transfected via electroporation to knock down the LDHA expression transiently. (A) Immunoblotting was performed on whole-cell lysates, probed with rabbit monoclonal anti-LDHA, and reprobed with anti-α-tubulin as a loading control. (B) Oxygen consumption of P493 cells was determined by the use of a Clark-type oxygen electrode at 72 h posttransfection with siLDHA (slope = −1.7) or siControl (slope = −0.7). (C) Intracellular ROS production was detected with DCFDA fluorescence and monitored by flow cytometry at 72 h posttransfection with siLDHA or siControl in the presence or absence of NAC. (D) Cell death was determined by flow cytometry of annexin V- and 7-AAD-stained cells at 96 h posttransfection with siLDHA or siControl in the presence or absence of NAC (20 mM added 24 h after transfection). The number in each figure represents the average percentage (±SEM) of dead cells. The number of dead cells treated with siLDHA compared with the control group has a P value of 0.0002 using the Student’s t test those treated with siLDHA and NAC compared with the siLDHA group have a P value of 0.001. (E) Cell population growth of siControl cells compared with cells treated with siLDHA grown in the presence or absence 5 mM NAC added daily starting 24 h after transfection. Relative cell numbers at day 4 for siControl vs. siLDHA and siLDHA vs. siLDHA + NAC have a P value of 0.008 and 0.004, respectively. For siControl vs. siControl + NAC, the P value is 0.63.

Enhanced oxygen consumption through reduction of glycolysis by siLDHA was expected to increase the production of mitochondrial reactive oxygen species (ROS), particularly because glycolysis, which diverts pyruvate to lactate, diminishes cellular oxidative stress (20). We therefore determined the production of ROS by 5-(and-6)-carboxy-2′,7′-dichlorodihydrofluorescein diacetate (DCFDA) fluorescence as measured by flow cytometry (Fig. 1C). We found that treatment of cells with siLDHA induced significant ROS. Hence, we exposed cells to N-acetylcysteine (NAC), a well-known antioxidant, and observed a significant reduction in ROS from siLDHA-treated cells (Fig. 1C).

We then sought to determine whether cell death associated with increased ROS and reduced LDHA expression could be diminished by the antioxidant NAC. We found that reduction of LDHA expression with siRNA markedly increased necrosis or late cell death, which is characterized by enhanced labeling of both 7-amino-actinomycin (7-AAD) and annexin V (Fig. 1D). Treatment with NAC at 24 h after transfection partially reduced cell death, which was also accompanied by partial rescue of cell proliferation (Fig. 1E). We noted that daily addition of 5 mM NAC resulted in a better rescue of cell proliferation than 20 mM NAC added once after transfection, reflecting the short lifetime of active NAC (Fig. 1E). The daily addition of 10 or 20 mM NAC, however, was toxic to the siRNA-transfected and control cells.

Having observed that reduction of LDHA by siRNA could induce oxidative stress and cell death, we sought a small-molecule inhibitor of LDHA as a drug-like tool to study tumor metabolism. We evaluated a series of compounds generated by Vander Jagt and co-workers (21, 22), who were specifically interested in targeting malarial LDH (pLDH), and found among the analogues of gossypol, which itself is a toxic inhibitor of LDHA, two dihydroxynapthoates: 11f (FX11 Pubchem ID: 10498042) and 11e [E 2,3-dihydroxy-6-methyl-7-(methyl)-4-propylnaphthalene-1-carboxylic acid Pubchem ID: 10265351]. We selected FX11 as a candidate small molecule for inhibiting human LDHA because it preferentially inhibited LDHA as opposed to LDHB or pLDH (21, 22). Compound E was selected for comparison because it had much lower inhibitory activity than FX11.

We recharacterized FX11 and E using purified human liver LDHA and found Kis of 8 and >90 μM, respectively (Fig. S2 A and B). FX11 is a competitive inhibitor of LDHA with respect to NADH in the conversion of pyruvate to lactate by LDHA, whereby NADH is converted to NAD + . To document the selective binding of FX11 vs. E to LDHA further, we performed affinity chromatography with P493 cell lysate using FX11 or E immobilized on sepharose beads. Equal amounts of cell lysate were loaded onto FX11 or E affinity beads and extensively washed, and the bound LDHA was eluted with 1 mM NADH. The FX11 affinity beads yielded 4-fold more LDHA activity than the beads with immobilized E (Fig. S2C). Collectively, these results indicate that FX11 can bind and inhibit human LDHA enzyme activity. Because GAPDH is another pivotal glycolytic enzyme that converts NAD + to NADH, we also sought to determine whether FX11 can inhibit its NAD + -dependent conversion of glyceraldehyde-3-phosphate to bis-phosphoglycerate. We found that even at 74 μM FX11, GAPDH activity was not inhibited. Through formal Michaelis–Menten kinetics, the estimated Ki is >>300 μM for GAPDH, indicating that FX11 was selective for LDHA among glycolytic enzymes that use the cofactor NAD.

As observed with siRNA-mediated reduction of LDHA, inhibition of LDHA by FX11 also resulted in increased oxygen consumption, ROS production, and cell death (Fig. 2 A–C). We found that the NAMPT inhibitor of NAD + synthesis, FK866, also increased ROS production (Fig. 2B). Because the increase in ROS levels might result from lowered NADPH production attributable to the possible inhibition of glucose transport (and hence diminished hexose monophosphate shunt activity, which produces NADPH) by an indirect effect of FX11 and FK866, we measured and found that glucose uptake, as measured by NBD-glucose [2-(N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl)amino)-2-deoxyglucose], was not significantly diminished and that NADPH levels were virtually unaltered by treatment with FX11 or FK866 (Fig. S3). NAC could partially rescue the diminished proliferation of P493 cells treated with either FX11 or FK866 (Fig. 2 D and E), indicating that oxidative stress contributed partially to the inhibition of cell proliferation. Given the significant effect of FX11 on the proliferation of P493 cells, which are dependent on Myc, we ruled out the trivial possibility that FX11 could inhibit Myc expression itself (Fig. S1C). In aggregate, these studies document that reduction of LDHA levels or activity triggers oxidative stress and cell death.

Inhibition of LDHA by FX11 resulted in increased oxygen consumption, ROS production, and cell death. (A) Oxygen consumption of P493 cells was determined by a Clark-type oxygen electrode in the presence (slope = −2.4) and absence (slope = −1.7) of FX11. Data are representative of duplicate experiments. (B) ROS levels were determined by DCFDA fluorescence in P493 cells treated with FX11 or FK866. Data are representative of triplicate samples of two separate experiments. (C) Cell death was determined by flow cytometry of Annexin V- and 7-AAD-stained cells after 24 h of FX11 treatment as compared with control. The number in each figure represents the average percentage of dead cells. The FX11-treated cells compared with the control group have a P value of 8.92e-06. (D and E) Cell population growth of control cells compared with cells treated with FX11 or FK866 in the presence or absence of 20 mM NAC. All cells were grown at 1 × 10 5 cells/mL. Cell counts were performed in triplicate and shown as mean ± SD, and the entire experiment was replicated, with similar results.

FX11 Inhibits Glycolysis and Alters Cellular Energy Metabolism.

In addition to oxidative stress induced by inhibition of LDHA, we sought to determine how FX11 affects cellular bioenergetics. First, we observed that both FX11 and FK866 decreased mitochondrial membrane potential and that the combination accentuated the abnormality (Fig. 3A). In this regard, the combination of FX11 and FK866 was more toxic to P493 cells than either one alone, causing a more profound inhibition of cell proliferation (Fig. 3B). A dose–response study using a combination of FX11 and FK866 doses revealed a combination index (CI-50) of 0.78, suggesting a slightly synergistic effect of the combination on cell proliferation (Fig. S4A). After 20 h of exposure to FX11 or FK866, a decrease in ATP levels was accompanied by activation of AMP kinase and phosphorylation of its substrate acetyl-CoA carboxylase (Fig. 3 C and D and Fig. S1D), suggesting that, in addition to induction of oxidative stress, these agents depleted cellular energy levels. Decreased ATP levels, despite an increase in cellular oxygen consumption, suggests that FX11 treatment increased nonproductive mitochondrial respiration as reported with shRNA-mediated knock-down of LDHA (16). Because inhibition of LDHA decreases NAD + recycling, treatment of P493 cells with FX11 was associated with increases in the NADH/NAD + ratio (Fig. 3E) and cellular autofluorescence, reflective of elevated cellular NADH levels (Fig. S4 B and C). In contrast to FK866, FX11 significantly diminished but did not completely inhibit cellular production of lactate (Fig. 3F). These observations suggest that FX11 targets LDHA, inhibits glycolysis, and shunts pyruvate into the mitochondrion.

(A) FK866 enhances FX11-induced loss of mitochondrial membrane potential. P493 cells treated with control vehicle, FK866, FX11, or both inhibitors were stained with JC-1 and subjected to flow cytometric analysis, with FL2 representing red fluorescence intensity and FL1 representing green fluorescence intensity, which is reflective of cells with decreased mitochondrial membrane. The average percentage (±SEM) of cells with decreased membrane potential is indicated in each panel. The P values were 0.03, 0.002, and 0.0008 when comparing FX11-treated cells, FK866-treated cells, or cells treated with both, respectively, with the control group. (B) Effect of FK866 and FX11 on P493-6 cell proliferation. Live cells were counted using trypan blue dye exclusion. Data are shown as the mean ± SD of triplicate samples. (C) Effect of FX11 or FK866 on ATP levels. P493 cells were treated with 9 μM FX11 or 0.5 nM FK866 for 20 h and counted. ATP levels (mean ± SEM, n = 5 experiments) were determined by luciferin–luciferase-based assay on aliquots containing an equal number of live cells. *P = 0.008 **P = 0.003. (D) Immunoblot of phosphor-AMP kinase (PAMPK) in lysates of cell treated with FX11 or FK866. Tubulin serves as a loading control. AICAR, an AMP analogue that activates AMPK, was used to treat the cells as a positive control. (E) FX11 increases the NADH/NAD + ratio. NADH/NAD + ratio in P493 cells treated with 9 μM FX11 for 24 h as compared with vehicle control. *P = 0.028. (F) FX11 inhibits lactate production. Lactate levels in the media of P493 human B cells treated with 9 μM FX11 or 0.5 nM FK866 for 24 h as compared with control. Control RPMI contained 10.7 mmol/L glucose and no detectable lactate. *P = 6.9E-06.

FX11 Inhibits Cells that Are Dependent on Glycolysis.

It stands to reason that if FX11 targets LDHA, cells that depend on LDHA for glycolysis would be more susceptible to FX11 inhibition than those that primarily use oxidative phosphorylation. In this regard, we sought to determine whether metabolic phenotypes could affect the sensitivity of cancer cells to FX11. We used the human RCC4 renal cell carcinoma cell line and the RCC4 cell line reconstituted with VHL (RCC4-VHL). Loss of VHL in RCC4 rendered these cells constitutively glycolytic because of the stabilization and expression of HIF-1 and HIF-2. Reconstitution with VHL resulted in degradation of HIF-1α and HIF-2α and increased mitochondrial biogenesis and oxygen consumption (23). Given the metabolic differences in these isogenic cell lines, we expect a more significant influence of FX11 on the RCC4 cells as compared with the RCC4-VHL cells. Indeed, a dose–response study revealed that RCC4 is more sensitive to FX11 compared with RCC4-VHL (Fig. S5 A and B).

To corroborate these findings further, we studied the glycolytic MCF-7 and the oxidative MDA-MB-453 breast carcinoma cell lines (24). We confirmed that MCF-7 was more dependent on glucose, whereas MDA-MB-453 was more dependent on glutamine oxidation (Fig. S6 A and B), such that deprivation of glucose has a more profound growth inhibitory effect on MCF-7. A dose-dependent study further revealed that MCF-7 is more sensitive to FX11 (Fig. S5 C and D). Although there are many other differences between these cell lines, the correlation of FX11 sensitivity and glucose dependency of MCF-7 supports the notion that glycolysis predisposes cancer cells to growth inhibition by FX11.

We further tested whether the inhibition of human P493 B cells by FX11 depended on glucose or on LDHA. The growth of P493 was inhibited by about 60% when depleted of glucose as compared with growth in normal medium (Fig. S5E). Addition of FX11 could not inhibit P493 cells further in the absence of glucose, suggesting that the effect of FX11 on cell proliferation was glucose-dependent. Furthermore, knock-down of LDHA expression by two sequential electroporations with siRNA caused a markedly diminished proliferative rate that was not further slowed by FX11 (Fig. S5F). Although we noted that siControl cells had a modestly diminished proliferation rate as compared with untreated cells, the siLDHA treatment remarkably disabled cell proliferation contemporaneous with reduced LDHA expression (Fig. S6 C and D). These observations collectively indicate that the growth inhibitory effect of FX11 is consistent with its ability to inhibit LDHA.

We surmised that the human P493 B-lymphoma cells would be sensitive to FX11 because they express LDHA (25) but that the sensitivity would be heightened under hypoxia when glycolysis is favored. This is particularly important, because P493 cells depend on both glucose and glutamine metabolism when cultured at 20% (vol/vol) O2 (26). When P493 cells were subjected to a dose–response study with FX11, we found that growth inhibition by 9 μM FX11 was increased when the cells were cultured at 1% O2 (Fig. S7 A and B). Hypoxia also sensitized the human P198 pancreatic cancer cell line to inhibition by FX11 (Fig. S7 C and D), suggesting that reliance on LDHA for hypoxic metabolism caused cancer cells to be susceptible to the growth-inhibitory effects of LDHA inhibition by FX11.

FX11 Inhibits Tumorigenesis in Vivo.

Although the renewed interest in the Warburg effect is accompanied by a greater understanding of its molecular underpinnings, targeting it for therapeutical purposes remains a major challenge (27). By characterizing a small-molecule inhibitor of LDHA, we found that it is effective in inhibiting cellular growth and triggering cell death by both inducing ROS production and depleting ATP. We observed that hypoxia further sensitized human P493 lymphoma cells to LDHA inhibition by FX11. In this regard, the pervasive hypoxic tumor microenvironment, as compared with normal tissues, ought to force a further dependency of these lymphoma cells on glycolysis, and particularly on LDHA (Fig. 4A and Fig. S8A). Of note, primary human lymphomas have been documented to have elevated LDHA expression particularly in hypoxic regions (28). Hence, we sought to determine whether in vivo efficacy could be demonstrated with FX11 as an inhibitor of LDHA. We calculated a desired FX11 dose of 42 μg for daily i.p. injection. We expected an initial serum level of ∼100 μM, assuming a uniform and immediate distribution in the vascular system without accounting for the drug half-life or drug metabolism. It should be noted that solubility was a significant dose-limiting factor, because we could only double the dose further before reaching the limited solubility of FX11 in aqueous solution.

In vivo efficacy of FX11 as an antitumor agent. (A) Immunohistochemical staining to detect hypoxic regions (dark brown) of spleen, liver, and P493 lymphoma by pimonidazole labeling. (B) Effect of FX11 on growth of palpable human P493 B-cell xenografts. Control animals were treated with daily i.p. injection of vehicle (2% (vol/vol) DMSO), and doxycycline (0.8 mg/day) was used as a positive control because it inhibits Myc expression and tumorigenesis in P493 cells. (C) Effect of FX11 and/or FK866 daily treatment as compared with control or compound E (a weak LDHA inhibitor) on established human lymphoma xenografts. (Inset) Photographs of representative animals treated with control vehicle or FX11. (D) FX11 inhibited P198 human pancreatic cancer xenografts as compared with compound E. For experiments in all panels, 2.0 × 10 7 P493 cells or 5 × 10 6 P198 cells were injected s.c. into SCID mice or athymic nude mice, respectively. When the tumor volume reached 200 mm 3 , 42 μg of FX11 and/or 100 μg of FK866 was injected i.p. daily and observed for 10–14 days. The tumor volumes were measured using digital calipers every 4 days and calculated using the following formula: [length (mm) × width (mm) × width (mm) × 0.52]. The results represent the average ± SEM.

First, we sought to determine whether FX11 could inhibit P493 tumor initiation after a palpable tumor developed. As controls, we injected animals with vehicle (2% (vol/vol) DMSO) or with 0.8 mg of doxycycline to inhibit MYC expression in these transformed human B cells. As expected, doxycycline profoundly inhibited palpable tumor xenograft growth as compared with vehicle-injected control animals (Fig. 4B). Intriguingly, daily i.p. injection of 42 μg of FX11 also resulted in a remarkable inhibition of tumor growth. It is notable that we have ruled out the trivial possibility that FX11 might directly inhibit Myc expression to mediate this profound effect (Fig. S1C). These observations indicate that LDHA is necessary for tumor initiation.

To test whether LDHA is required for tumor maintenance or progression, we challenged the ability of FX11 to inhibit tumor xenograft growth by treating P493 lymphomas or human P198 pancreatic tumors that reached the size of 200 mm 3 before treatment commenced. For comparison, we treated animals with vehicle control or a compound related to FX11, called E, that lacks the benzyl group and has a Ki for LDHA of >90 μM, or more than 10-fold higher than that of FX11. We found that E had no antitumor activity as compared with vehicle. At this solubility-limiting dose, FX11 displayed a static but significant effect over 10 days (Fig. 4C). Intriguingly, we observed that the hypoxic regions associated with untreated control tumors were relatively diminished in the FX11-treated tumors (Fig. S8B). We speculated that hypoxic tumor cells are more dependent on glycolysis, and hence are diminished by FX11 relative to the nonhypoxic tumor cells. We also observed a significant response of human P198 tumor xenografts to FX11 as compared with E (Fig. 4D). Although a more aggressive human pancreatic tumor xenograft LZ10.7 grew significantly faster than P198, it was also sensitive to FX11 as a single agent (29) (Fig. S8C). The structure and activity relation of FX11 and E in vivo correlated with the inhibition and binding of LDHA by these compounds in vitro, further supporting the notion that FX11 targets LDHA. These results collectively indicate that LDHA plays a role in tumor progression and maintenance.

On the basis of our studies of cultured cells (Fig. 3B), we hypothesized that FX11 might accentuate the effect of FK866 in the treatment of the P493 human lymphomas. Hence, we selected a dose of 100 μg of FK866, which gave a static outcome, and, indeed, we found remarkable tumor regression when animals were treated with both FX11 and FK866 (Fig. 4C). These findings underscore the fact that targeting cancer metabolism is feasible and that LDHA is a significant candidate target for further development.

Given the significant effects of FX11 as an LDHA inhibitor in vivo, we studied a group of treated animals to begin to understand the potential side effects of FX11. It is notable that humans lacking LDHA develop normally but have been shown to display exertional myopathy (30). In this regard, although we did not formally exercise the animals to examine exertional tolerance, we did not note lethargy or the inability to eat and drink. In fact, animals treated with FX11 did not lose weight. In initial studies of the hematology and blood chemistry, we did not see cytopenia in animals treated with FX11 alone however, two (of five studied) animals treated with FK866 did show mild thrombocytopenia (Fig. S9). The average leukocyte count in the control group was skewed upward by two animals that had leukocytosis with >15 K/μL (normal range: 1.8–to 10.7 K/μL). The blood chemistries did not reveal any evidence of kidney [blood urea nitrogen (BUN) or creatinine] or liver (aspartate aminotransferase, alanine aminotransferase, and alkaline phosphatase) toxicity in animals treated with FX11 or FK866 alone at doses that affected tumor growth in vivo (Fig. S9). However, the combination of FX11 and FK866, as compared with control, increased BUN.


Cellular transformation involves the deregulated control of cell proliferation, resistance to cell death, immune evasion and circumvention of growth suppressor activities, which finally allow cancer establishment (1). Additionally, it has been observed that tumor cells have the remarkable ability to adjust their energetic metabolism as part of their mechanisms for tumor survival, this feature is now recognized as a hallmark of cancer (2). The increased metabolic rate in several neoplasms, was first studied by Otto Warburg in 1926 demonstrating that tumor cells uptake high amounts of glucose as a primary energy source, producing excessive amounts of lactate, even in the presence of oxygen (3). In 1972, Efraim Racker named such effect as the “Warburg Effect,” also known as �robic glycolysis” (4). Initially, it was proposed that the driving event of the enhanced glycolysis in tumor cells was caused by an irreversible damage of the mitochondrial function. Although defects in mitochondria function have been shown in some types of cancer (5), this process alone cannot explain the metabolic preference of tumor cells.

The Warburg phenotype is present in several neoplasms including breast, colon, cervical and liver cancer (6𠄹). The increased glucose uptake and metabolism by neoplastic cells represents the basis for tumor detection using positron emission tomography (PET) PET imaging uses a radioisotope-labeled glucose tracer, 18 F-fluorodeoxyglucose ( 18 F-FDG), to identify areas of high glucose uptake/metabolism in the body. After 18 F-FDG distribution, the radionuclide is transported into the cells by glucose transporters, and consequently phosphorylated by the hexokinase to produce 18 F-FDG-6-phosphate ( 18 F-FDG-6-p). Once inside the cell, the 18 F-FDG-6-p accumulates in the cytoplasm since this molecule cannot be further metabolized through the glycolytic pathway because it lacks the necessary 2'hydroxyl group (10). Additionally, due to its highly polar nature the 18 F-FDG-6-p is trapped inside the cell, thus the accumulated amounts of 18 F-FDG-6-p are used to identify the presence of solid tumors as well as the effectiveness of treatments (10).

The Warburg effect involves the alteration of metabolic enzymes, including hexokinase 2 (HK2), pyruvate kinase type M2 (PKM2), glucose transporter 1 (GLUT1), lactate dehydrogenase (LDH) and lactate transporters (monocarboxilate transporters [MCTs]) (11�). Importantly, the Warburg phenotype has been associated, not only with an increased obtention of energy but also with the activation of numerous transcription factors, such as c-Myc, NF-㮫, and Hypoxia-Inducible Factor 1-α (HIF 1-α) (15�). These transcription factors can regulate the expression of metabolic enzymes resulting in the deregulated conversion of glucose to lactate (18) then promoting a “tumor lactagenesis” state (19).

Glycolysis is by far less efficient than oxidative phosphorylation for ATP production, and for this reason cancer cells increase their glucose uptake and glycolytic rate. The high utilization of glucose by cancer cells results in the accumulation of extracellular lactate affecting a number of cell types within the tumor microenvironment (TME), composed by a variety of different cell types such as endothelial cells, cancer-associated fibroblasts (CAFs), immune cells and non-cancer stroma cells (20).

For a long time, lactate was only recognized as a “metabolic waste product” derived of aerobic glycolysis, however, it has now been firmly demonstrated that lactate can be incorporated into the tricarboxylic acid (TCA) cycle and be a source of energy, and even act as an oncometabolite with signaling properties. In this review we describe the role of lactate in tumor progression, highlighting its ability to promote invasion and metastasis. We also show the role of lactate as a metabolic fuel for tumor cells, as well as its participation in drug resistance (Figure 1). The importance of the suppressive acidic tumor microenvironment induced by lactate is also presented. Finally, we discuss the possible targeting of lactate production as a novel therapeutic approach.

Figure 1. Role of lactate in cancer. Excessive production of lactate by both, tumor and stromal cells, is associated with increased aggressiveness due to the extracellular acidification that also induces invasion and metastasis, inhibition of the antitumor immune response and resistance to therapy. Moreover, this lactate can be used as an alternative source of fuel by tumor cells.


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Lactate promotes resistance to glucose starvation via upregulation of Bcl-2 mediated by mTOR activation

The essential characteristic of tumors is rapid growth therefore, tumors need to generate a large number of new blood vessels to provide nutrients. Nonetheless, compared with normal tissue, tumor angiogenesis is disarranged and functions poorly (1). The rapid tumor growth and the physiological characteristics of the tumor vasculature mean that the rate of formation of blood vessels providing the energy substances is unable to catch up with the rate of increase in tumor volume, such that many tumors exist in a low glucose environment. The concentration of glucose in colon and stomach tumor tissue is only 0.12 and 0.4 mM, respectively (2). Tumors can survive in harsh environments, such as poor glucose and oxygen depletion (3). In the case of glucose deficiency, tumors in vivo adjust their own state to adapt to the environment and obtain more nutrition. By contrast, lack of glucose in cultured tumor cells in vitro does not support the survival of tumor cells. Therefore, to explore how tumors survive glucose depletion in vivo is important.

Cancer cells preferentially use the glycolysis pathways for energy generation, even in the presence of oxygen, the so called ‘aerobic glycolysis’, as first proposed by Warburg (4). Glycolysis is far less efficient than oxidative phosphorylation in terms of ATP generation therefore, cancer cells exhibit abnormally high glycolytic rates to maintain energy homeostasis (5). Such dysregulated metabolism in cancer cells also leads to the accumulation of the metabolic product of glycolysis, lactic acid, in solid tumors. Many measurements have been made to determine the level of tumor lactate and significant variations have been found, with the average ranging from 7 to 10 mM/g and a maximum of up to 25.9 mM/g (6,7). In contrast to tumor hypoxia, tumor glycolysis and lactate biology have received little scientific attention for many years. However, findings concerning the overexpression of glycolysis-related genes in 70% of all human cancers worldwide and the exploitation of increased glucose uptake of cancer cells for tumor diagnostics by positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG), have contributed to the topic experiencing a renaissance. This may lead to improvement of cancer diagnosis and therapeutic follow-up in a clinical setting.

As an important part of the tumor microenvironment, studies of lactate were justified for the following reasons. Extracellular lactate inhibits the differentiation of monocytes to dendritic cells (DCs) and inactivates cytokine release from DCs (8) and cytotoxic T cells (9), the key players in antitumoral response. The addition of exogenous lactate led to a concentration-dependent increase in random migration of various cancer cell lines (10). Lactate concentrations are positively correlated with radioresistance (11). This reprogramming is necessary for the growth and survival of tumors in stress conditions (12).

In the present study, we found that lactate could rescue cancer cells from glucose starvation-induced death. Furthermore, we explored the mechanism of the role of lactic acid in the process of tumor adaptation to glucose deficiency. We found that lactate rescues cancer cells from glucose starvation-induced cell death by regulating the Akt/mammalian target of rapamycin (mTOR)/B-cell lymphoma 2 (Bcl-2) signaling pathway. These data suggest that lactate is an important determinant of the sensitivity of tumors to glucose starvation, and reducing lactate or inhibiting the Akt/mTOR/Bcl-2 signaling pathway may influence the response of cancers to glucose starvation.

Materials and methods

Cell lines and cell culture

A549, H1299, PC3, DU145 and U87-MG cell lines were purchased from the Cell Bank of the Type Culture Collection of the Chinese Academy of Sciences. A549 and U87-MG were cultured in complete Dulbecco’s modified Eagle’s medium (DMEM, cat. no. 12430-054), with 10% fetal bovine serum (cat. no. 10100-147) (both from Gibco, USA), 100 U/ml penicillin and 100 μg/ml streptomycin. DU145 and H1299 were cultured in RPMI-1640 medium (cat. no. 11875-093 Gibco) supplemented as above. PC3 cells were cultured in F-12 medium (cat. no. 21700-075 Gibco) supplemented as above. The glucose concentration in the medium was 25 mM, unless otherwise stated. For the glucose-starvation experiment, we mixed no-glucose DMEM (cat. no. 11966-025) or RPMI-1640 (cat. no. 11879-020) (both from Gibco) with the complete medium mentioned above in a certain proportion to make the glucose concentration 5 mM. We added sodium L-lactate (cat. no. 71718) or L-lactic acid (cat. no. L1750) (both from Sigma-Aldrich, USA) or HCl into the medium to create different culture environments with different lactate concentrations and pHs.

Cell viability assay

Cells (10 4 /well) were seeded in 24-well plates in complete medium with 25 mM glucose, 24 h before the experiment. The following day we changed the medium to one containing no glucose. Meanwhile, we added a different concentration of lactate into the medium at the start of the experiment. From day 2 to 12, we counted the live cells every 2 days 3 wells/day for each culture environment.


A small interfering RNA (siRNA) targeting Bcl-2 (cat. no. GS596 Qiagen, Germany) was transfected into cells to block its function. We used Lipofectamine 2000 (cat. no. 11668019 Invitrogen, USA) as the transfection reagent and a negative control siRNA (cat. no. SI03650318 Qiagen). Forty-eight hours after transfection, we collected the mRNA and protein of the transfected cells. To assess knockdown efficiency, we analyzed the mRNA level using real-time PCR (LightCycler 480 Roche, Switzerland) and the level of protein by western blotting (Mini-PROTEAN ® 165–8004 Bio-Rad, USA). Both values were normalized to the expression of β-actin. Experiments were performed between 24 and 72 h after transfection.

Analysis of cell metabolism using a Seahorse Bioscience XF24 instrument

The oxygen consumption rate (OCR) measurements of cells were performed using a Seahorse Bioscience XF24 instrument (Seahorse Bioscience, Billerica, MD, USA). Before running the experiment, the growth medium was removed and the cells were washed with PBS containing Ca 2+ /Mg 2+ (pH 7.4), which was then aspirated and replaced with 700 ml of reduced serum (RS) buffer [CaCl 2 (1.8 mM), MgCl 2 (0.6 mM), KH 2 PO 4 (0.5 mM), KCl (5.33 mM), Na 2 HPO 4 (0.5 mM), NaCl (130 mM), glucose (5.6 mM)], glutamax, minimum essential medium (MEM) amino acid solution, MEM nonessential amino acids, MEM vitamin solution, penicillin/streptomycin, 1% bovine serum albumin (BSA, factor V fatty acid free), 1% FBS and insulin (100 nM). All components, except FBS and insulin, were combined before filter sterilization. Following the addition of FBS and insulin (usually 24–48 h pre-experiment), the RS buffer was warmed to 37°C and the pH adjusted to 7.4. The template for testing was set up as follows. Measurements were performed every 5 min, repeating metabolic measurements 3–4 times per condition for statistical analyses. The 5-min cycle included a 2-min mix period, a 1-min wait and a 2-min measuring time. After measurements of baseline activity, oligomycin (an ATP coupler) was injected, rates were measured, carbonylcyanide p -trifluoromethoxyphenylhydrazone (FCCP) (an electron transport chain accelerator) was injected, and finally rotenone and antimycin (mitochondrial inhibitors) were injected, followed by a final rate measurement.

Measurement of extracellular lactate levels

Cells were seeded onto plates and allowed to grow in medium with 25 mM glucose for attachment overnight. The next day, we replaced the medium with fresh medium with 0 mM glucose at time zero. From time zero, every 24 h, we collected the medium samples and counted the number of cells in the same plate. The concentrations of lactate in the medium samples were measured with lactate reagent (cat. no. P0000024 CMA Microdialysis, Sweden). The results were normalized to the number of the cells in each sample.

Measurement of glucose starvation-induced apoptosis

Cells were placed in 6-well plates and incubated for 24 h before the experiment. Cells in the control group were cultured in complete medium containing 25 mM glucose. Cells in the treated group were cultured in complete medium containing 0 mM glucose with different lactate contents. After 48 h of treatment, cells were trypsinized, centrifuged and resuspended in binding buffer with Annexin V-FITC and propidium iodide (PI) from the Dead Cell Apoptosis Kit #2 (cat. no. V13241 Life Technologies, USA). Stained cells were incubated for 15 min at room temperature in the dark. Flow cytometry was used to analyze the stained cells, measuring the fluorescence emission at 530 and 575 nm using 488 nm excitation.

Western blotting and antibodies

Treated cells were lysed using RIPA lysis buffer with 1% phenylmethanesulfonyl fluoride (PMSF). Cell lysates were separated through a 10% SDS gel and blotted onto nitrocellulose membranes. The membranes were blocked with 5% non-fat dry milk in PBST at room temperature for 1 h and incubated separately with primary antibodies against Akt (cat. no. 4685), phospho-Akt (Thr308, cat. no. 13038) (both from Cell Signaling Technology, USA), phospho-Akt (Ser473, cat. no. ab66138), PTEN (cat. no. ab32199), Mcl-1 (cat. no. ab114016), Bcl-2 (cat. no. ab117115), Bcl-xL (cat. no. ab32370), phospho-mTOR (Ser2448, cat. no. ab84400) (all from Abcam, UK) or β-actin (cat. no. A1978 Sigma-Aldrich) overnight at 4°C. The next day, the membranes were washed 3 times with PBST and incubated with secondary antibodies for 1 h at room temperature. After washing the membranes 3 times with PBST, the membranes were scanned using an Odyssey Infrared Imaging System (Li-COR Biosciences, USA).


LY294002, perifosine and rapamycin were purchased from Selleck Chemicals, USA (cat. nos. S1105, S1037 and S1039). Insulin and resveratrol were purchased from Sigma-Aldrich (cat. no. R5010). Insulin-like growth factor-1 (IGF-1) was purchased from Invitrogen (cat. no. PHG0071).

Statistical analysis

Statistical analysis was performed using GraphPad Prism 5.0 and Instat 3.1 packages (GraphPad Software, Inc., San Diego, CA, USA). The results are expressed as the means ± standard error (SE). Statistical differences between the groups were compared using t-tests. P-values <0.05 were considered to indicate statistically significant results.


Lactate rescues cancer cells from glucose starvation-induced cell death

To determine the effect of lactate on glucose starvation, we exposed A549 cells to different culture conditions: complete DMEM containing different sodium lactate concentrations and without glucose. We found that in glucose-free conditions, adding sodium lactate into the medium effectively prolonged the survival times of the A549 cell line (Fig. 1A). At low doses, cell proliferation and survival time increased with the increment of sodium lactate concentration. The survival time of A549 cells in the presence of 20 mM sodium lactate was longer than that of any other concentrations of sodium lactate. Indeed, A549 cells grown with sodium lactate in glucose-free conditions survived for more than 2 weeks even 1 month later there were still viable cells. At sodium lactate concentrations >20 mM, cell proliferation and survival time decreased. Without sodium lactate, in A549 cells onset of cell death started in 1–2 days and all the cells died after 4 days. However, high concentrations of sodium lactate (such as 80 mM) caused immediate death of A549 cells (Fig. 1B).

Figure 1

Lactate promotes cancer cell resistance to glucose starvation. (A) Cell growth curves of A549 cells, which were cultured in glucose-free medium with different concentrations of lactate. (B) The numbers of A549 cells were counted 3 days after culture in glucose-free medium with different concentrations of lactate. (C) The numbers of H1299, DU145, A549, PC3 and U87-MG cells counted 3 days after culture in glucose-free medium with different concentrations of lactate. (D) Survival of H1299, DU145, A549, PC3 and U87-MG cells after culture in glucose-free medium with different concentrations of lactate. (E) The oxygen consumption rate (OCR) of A549 cells with or without lactate was measured at baseline and continuously throughout the experimental period and in the presence of the indicated drugs: oligomycin (1 μg/ml), carbonylcyanide p-trifluoromethoxyphenylhydrazone (FCCP) (1 μM), rotenone (1 μM) plus antimycin A (1 μM). (F) The lactate concentration in the medium was detected under the same experimental conditions as in A. (G) The numbers of A549 cells were counted 3 days after culture in different environments: no glucose, no glucose + 20 mM sodium lactate (pH 7.4), no glucose + 5 mM lactic acid (pH 6.8), and no glucose + 5 mM hydrochloric acid (pH 6.8). Data are mean ± standart error (SE), n=3.

We also confirmed the above phenomena in other types of tumor cell lines. We cultured four human cancer cell lines derived from the brain (U87-MG), prostate (PC3 and DU145), and lung (H1299) in no-glucose cultivation conditions with various lactate concentrations. The addition of sodium lactate (20 mM) effectively prolonged the survival time of all 5 types of tumor cells. We then compared the proliferation and survival time between all 5 types of cancer cells in the presence of sodium lactate. We found that DU145, A549 and H1299 cell proliferation activity was stronger than PC3 and U87-MG cells (Fig. 1C), and their survival times were longer than those of the PC3 and U87-MG cell lines (Fig. 1D).

Some studies have reported that tumor cells have the ability to take up lactate and utilize it as an energy source via oxidative phosphorylation. Determination of dissolved oxygen in the culture of cells monitored in the Seahorse Bioscience XF24 instrument showed that, upon addition of lactate, oxidative phosphorylation levels did not change significantly (Fig. 1E). In addition, we examined the lactate concentrations in the cell culture the extracellular lactate concentrations did not change significantly over the course of the experiment (Fig. 1F). These results suggest that under deprivation of glucose, lactate was not used as a substrate for energy metabolism.

Cell secretion of lactic acid would affect the value of pH in the extracellular environment. To confirm whether the acidic environment plays a role in tumor cell survival in a glucose-free environment, we cultured A549 cells separately in the following 4 types of environment: no glucose, no glucose + 20 mM sodium lactate (pH 7.4), no glucose + 20 mM lactic acid (pH 6.8), no glucose + 20 mM hydrochloric acid (pH 6.8). After 72 h of culture, we found that adding hydrochloric acid to the tumor cells in glucose-free environment had no significant effect on survival, adding lactic acid had some effect, and adding lactate had the most significant effect (Fig. 1G). We concluded that tumor cell survival in a glucose-free environment was mainly a function of lactate itself, and had little to do with the acidic environment.

Effect of activation of Akt signaling on the role of lactate in tumor cell survival in glucose-free conditions

In the above experiments, we found that sodium lactate had different effects on the survival of the different cell lines in glucose-free conditions. Accordingly, we divided the five cancer cell lines into 2 groups: a lactate-sensitive group, including DU145, A549 and H1299 and a lactate-insensitive group, including PC3 and U87-MG. To explore why lactate had different effects on the survival of the 2 groups of cells in glucose-free conditions, we compared the genetic background of the 2 groups of cells. We found that the 2 lactate insensitive cell lines, PC3 and U87-MG, had lost PTEN activity (phosphatase and tensin homolog deleted on chromosome 10) (Fig. 2A). PTEN is a tumor-suppressor gene with phosphatase activity, which is involved in the negative regulation of the Akt signaling pathway, where it blocks the activation of Akt and its downstream effector molecules (13). The Akt signaling pathway plays an important role in cell proliferation and survival (14). Therefore, to confirm the existence of activation of the Akt signaling pathway in PC3 and U87-MG cell lines with PTEN deletion, we examined the phosphorylation status of Akt in the 2 cell lines (Fig. 2B). The results showed that, compared with the lactate-sensitive group, Akt phosphorylation in the lactate-insensitive group increased, which would result in Akt signaling pathway activation. We also compared the reactions of the 2 groups of cells to insulin, an activator of the Akt signaling pathway (Fig. 2C). Insulin caused a dose-dependent increase in cell numbers in the cells of the lactate-sensitive group. By contrast, the growth of the cells in the lactate-insensitive group was not affected by insulin. The above results suggest that the state of the Akt signaling pathway was a key factor that distinguished the role of lactate in cancer cell proliferation and survival time in glucose-free conditions.

Figure 2

Activation of Akt signaling correlates with the role of lactate in glucose starvation. (A) Protein expression of PTEN in five cell lines (H1299, DU145, A549, PC3 and U87-MG) was examined by western blotting. (B) Protein expression of phospho-S473 Akt and total Akt in five cell lines (H1299, DU145, A549, PC3 and U87-MG) was examined by western blotting. β-actin was used as the normalization control. (C) Proliferation curves of cancer cells from five cell lines cultured in the presence of increasing concentrations of insulin. Data are mean ± standard error (SE), n=3.

Lactate induces Akt phosphorylation through PI3K

To analyze Akt signaling induced by lactate, A549 cells were cultured overnight in DMEM without serum, and the cells were stimulated with sodium lactate for 30 min. Lysates of these cells were analyzed by western blotting, and Akt activation was measured by detection of Thr308 and Ser473 phosphorylated forms of Akt. We used insulin to stimulate the serum-deprived cells as a positive control for the activation of Akt. Under this environment, we observed that Akt Thr308 and Ser473 phosphorylation increased in the presence of lactate to a similar extent to that obtained with insulin (Fig. 3A and C). We then used perifosine (an inhibitor of Akt) to suppress the activation of Akt. We found that Akt Thr308 and Ser473 phosphorylation by lactate or insulin were suppressed by perifosine (Fig. 3C). We obtained the same result when we used different cell lines, such as H1299 and DU145, indicating that the differences in Akt phosphorylation were not specific to the A549 cell line (Fig. 3B). Time course experiments showed that Akt phosphorylation at Thr308 and Ser473 phosphorylation sites were both rapid (1–5 min after incubation with lactate), with maximum levels being achieved between 10 and 30 min (Fig. 3D). To estimate whether lactate induced Akt phosphorylation downstream of PI3K, we used LY294002 (an inhibitor of PI3K). Akt Thr308 and Ser473 phosphorylation in the presence of lactate were substantially reduced in the presence of LY294002 (Fig. 3E). These results suggest that lactate induced the activation of Akt significantly and rapidly through Thr308 and Ser473 phosphorylation mediated by PI3K.

Figure 3

Akt is phosphorylated at Thr308 and Ser473 in response to lactate, mediated by PI3K. (A) Protein expression of phospho-Thr308 Akt, phospho-Ser473 Akt and total Akt in the A549 cell line with different concentrations of lactate was examined by western blotting. (B) Protein expression of phospho-Thr308 Akt, phospho-Ser473 Akt in the H1299 and DU145 cell lines with lactate (20 mM) or insulin (100 nM) was examined by western blotting. (C) Protein expression of phospho-Thr308 Akt, phospho-Ser473 Akt in the A549 cell line treated with lactate (20 mM) or insulin (100 nM) or perifosine (1 μM) was examined by western blotting. (D) Protein expression of phospho-Thr308 Akt in the A549 cell line stimulated with lactate (20 mM) for 30 min was examined by western blotting. (E) Protein expression of phospho-Thr308 Akt, phospho-Ser473 Akt in the A549 cell line treated with lactate (20 mM) or LY294002 (5 μM) was examined by western blotting. β-actin was used as the normalization control.

Lactate activates the Akt/mTOR/Bcl-2 signaling pathway to modulate the apoptotic response of cancer cells to glucose starvation

Akt plays an important role in cell apoptosis and survival in response to extracellular stimuli, such as insulin or growth factors. Our previous experiments showed that the state of the PI3K/Akt signaling pathway was a key factor to distinguish the effect of lactate on cancer cell proliferation and survival in glucose-free conditions, where lactate induces the activation of Akt by Thr308 and Ser473 phosphorylation. We hypothesized that lactate helps tumor cells to survive by activating Akt in glucose-free conditions. To test the hypothesis, we cultured A549 cells with perifosine, an Akt inhibitor. We found that treatment with perifosine inhibited the cell survival induced by lactate in glucose-free conditions. In addition, treatment with an Akt activator, IGF-1, increased A549 cell survival in glucose-free conditions, but less effectively compared with lactate (Fig. 4A). These results showed that the lactate increased tumor cell survival in glucose free conditions through Akt activation.

Figure 4

Reduced apoptosis by lactate during glucose starvation is mediated through the activation of the PI3K/Akt/mammalian target of rapamycin (mTOR)/B-cell lymphoma (Bcl-2) signaling pathway. (A) The numbers of A549 cells were counted 3 days after culture in glucose-free medium treated with lactate (20 mM) or lactate (20 mM) + LY294002 (1 nM) or lactate (20 mM) + LY294002 (5 nM). (B) A549 cells were cultured in 25 mM glucose medium or no glucose medium with lactate 48 h before staining with Annexin V-FITC and propidium iodide (PI). Flow cytometry to measure the fluorescence intensity at 530 and 575 nm, using 488 nm excitation, allowed us to calculate the early and late apoptosis rates of the cells. (C) The early and late apoptosis rates of A549 cells were calculated after they were cultured in 25 mM glucose medium, no glucose medium with lactate (20 mM), lactate (20 mM) + perifosine (1 μM) or IGF-1 (100 nM) for 48 h. (D) Protein expression of Mcl-1, Bcl-2 and Bcl-xL in the A549 cell line treated with or without lactate (20 mM) in glucose-free medium was examined by western blotting. (E) The early and late apoptosis rates of A549 cells were calculated after they were cultured in 25 mM glucose medium, no glucose medium with lactate (20 mM) + control small interfering RNA (siRNA), lactate (20 mM) + siBcl-2 or resveratrol (100 μM) for 48 h. (F) Protein expression of phospho-Thr308 Akt, Bcl-2 and phospho-Ser2448 mTOR in the A549 cell line treated with or without lactate (20 mM) in glucose-free medium was examined by western blotting. (G) Protein expression of phospho-Ser2448 mTOR and Bcl-2 in the A549 cell line treated with lactate (20 mM), lactate (20 mM) + perifosine (1 μM), or IGF-1 (100 nM) was examined by western blotting. (H) Protein expression of phospho-Ser2448 mTOR and Bcl-2 in the A549 cell line treated with rapamycin was examined by western blotting. β-actin was used as the normalization control.

Many studies have demonstrated that under the metabolic stress of no glucose, tumor cells undergo significant apoptosis. Thus, we investigated metabolic stress resistance to apoptosis in tumor cells incubated with lactate (Fig. 4B). We cultured A549 cells in high glucose (25 mM glucose), glucose-free and glucose-free with added lactate conditions. Twenty-four hours later, cells were trypsinized, centrifuged and resuspended in binding buffer with Annexin V-FITC and PI. The stained cells were analyzed by flow cytometry. We found that compared with high glucose conditions, there were many apoptotic cells in the glucose-free environment. There was an

10.1- fold (P<0.001) increase in early apoptosis and an

12-fold (P<0.001) increase in late apoptosis in the population of cells without glucose compared to the cells with high glucose. The apoptosis rate in the culture with added lactate culture was greatly reduced, and was close to that under the high glucose cultivation. The above results showed that under glucose-free conditions, a large number of A549 underwent apoptosis, and the addition of lactate could prevent this apoptosis, the mechanism of which will be further explored below.

We checked the apoptosis rate of A549 cells and then added perifosine or IGF-1. We found that treatment with perifosine prevented the decrease in apoptosis induced by lactate in glucose-free conditions, and that IGF-1 reduced the cell apoptosis caused by no glucose (Fig. 4C). We concluded that lactate prevented apoptosis in glucose-free conditions through Akt activation.

Several studies have pointed out that the anti-apoptotic Bcl-2 family is pivotal for cell survival under metabolic stress. We found lactate can reduce tumor cell apoptosis caused by the lack of glucose. We, therefore, examined whether lactate affected the expression levels of anti-apoptotic Bcl-2 family proteins such as Mcl-1, Bcl-2 and Bcl-xL. A549 cells were cultured with added lactate in glucose-free conditions. After 48 h, lysates of these cells were analyzed by western blotting. Treatment with lactate increased Bcl-2 levels, while Mcl-1 or Bcl-xL levels were not affected by treatment with lactate (Fig. 4D). To confirm whether Bcl-2 plays a major role in cell survival, we used a Bcl-2 targeting siRNA to lower its protein expression. The results showed that treatment with siBcl-2 prevented resistance to apoptosis induced by lactate in glucose-free conditions. Furthermore, we observed that resveratrol, a Bcl-2 activator, reduced the apoptosis of A549 cells in glucose-free conditions, similarly to lactate (Fig. 4E). These findings revealed that the upregulation of Bcl-2 by lactate is important for tumor cell resistance to apoptosis in glucose-free conditions.

Akt plays a core role in promoting cell survival, through activation of anti-apoptotic substances. Therefore, we aimed to confirm whether Bcl-2 upregulation is mediated via activation of the Akt/mTOR signaling pathway upon treatment with lactate in glucose-free conditions. First, we found that in A549 cells treated with lactate, Akt was activated and the expression of the Bcl-2 protein was increased, accompanied by mTOR activation (Fig. 4F). To demonstrate that the increase in the expression levels of Bcl-2 and mTOR were dependent on the activation of Akt, we added perifosine and found that the expression levels of Bcl-2 and mTOR were restored to the original levels. Furthermore, the expression levels of Bcl-2 and mTOR were upregulated in the presence of IGF-1 (Fig. 4G). Addition of rapamycin, an mTOR inhibitor, blocked the increased expression of Bcl-2 (Fig. 4H). These results demonstrated that Akt activation and mTOR upregulation by lactate in glucose-free conditions led to the upregulation of Bcl-2.

Taking all the above results into consideration, we conclude that lactate, through activation of Akt by phosphorylation mediated by PI3K, activates mTOR and further increases the expression of anti-apoptotic protein Bcl-2, to help tumor cells resist apoptosis caused by glucose starvation.


Reprogramming energy metabolism is a hallmark of cancer (5). Warburg (4) first observed that even in cases with an adequate oxygen supply, tumors still preferred to utilize glucose via glycolysis. Compared with oxidative phosphorylation, glycolysis is a low efficiency method of energy production therefore, compared with normal tissue, tumors often require more glucose. The clinical diagnosis of cancer by PET with a radiolabeled analog of glucose (FDG) as a reporter, a widely used method, is possible as tumor cells have increased glucose uptake (15). These changes in tumor metabolic patterns, increased glucose uptake and glycolysis as the main production method, eventually lead to the accumulation of lactate, the end product of glycolysis, in tumors. Several studies have reported increased lactate levels within tumors, which reflects the high metastasis rate and poor prognosis in human cervical cancers (16), human head and neck cancers (17), human rectal adenocarcinomas (18), human hepatocellular carcinoma (19) and non-small cell lung cancers (20). Some studies have suggested that lactate could be used as an energy source by oxidative phosphorylation to generate ATP (21,22). Lactate can also be used as a signaling molecule in tumor cells (23). Lactate can produce the promotion of VEGF in wound healing (24), and lactate is also sufficient to instigate signals for angiogenesis (25).

In solid tumors, along with the rapid growth of the tumor, the development of blood vessels within is incomplete, which leads to certain areas of the tumor to suffer glucose deficit. In blood-rich regions, aerobic glycolysis consumes a large amount of glucose to produce lactate, whereas the lactate in the blood-poor regions of tumor cells plays an important role. In the present study, in the absence of glucose, added lactate in culture significantly prolonged the survival of A549 cells in a concentration-dependent manner. This result was consistent with those of the Wu et al (26). Subsequent experiments in different cell lines confirmed the role of lactate. Some studies reported that lactate could be used as an energy substrate to produce ATP by tumor cells through oxidative phosphorylation we determined whether under no glucose conditions, lactate could be used as an energy substrate. The results showed no significant changes in oxygen consumption and or any lactate consumption. Thus, under glucose-free conditions, lactate is not an energy substrate. We also ruled out an acidic environment in glucose-free conditions as having any influence on maintaining tumor cell growth.

Notably, lactate had different effects on different cell lines, allowing cell lines to be divided into sensitive and insensitive lactate groups. We compared the genetic backgrounds of the five cell lines and found that the cell lines insensitive to lactate lacked PTEN function. Insulin-mediated stimulation of growth confirmed the activation of the Akt signaling pathway in the insensitive cell lines. Therefore, to further explore the mechanism of lactate, we focused on the Akt signaling pathway. Akt signaling pathway is an important signaling pathway in tumor cell survival and development. It is activated by upstream signaling molecules, such as growth factors, and is then further regulated by downstream molecules to participate in the occurrence and development of tumors (14). The activation of the PI3K/Akt pathway could help tumor resistance under dietary deficient conditions (27). Western blotting showed that lactate activated Akt via the rapid phosphorylation at Thr308 and Ser473 mediated by PI3K. Lactate can be used as a signaling molecule in the regulation of certain signaling pathways. For example, lactate was found to upregulate the transcription of 673 genes in L6 cells and was further involved in mitochondrial biogenesis (28). In tumors, lactate, in the presence of oxygen, stimulated the expression of HIF-1α and upregulated various hypoxia-inducible dependent genes (29). Lactate could increase TLR4 signaling and NF-κB pathway-mediated gene transcription in macrophages (30). Lactate increased the level of TGF-β2 in glioma (31). Interestingly, further experiments demonstrated that lactate helped tumor cell survival in glucose-free conditions through activation of Akt by phosphorylation.

Apoptosis tends to occur when cells are under metabolic stress because of lack of glucose. We found significant apoptosis of A549 cells under glucose-free environment conditions, and the addition of lactate prevented this apoptosis. The Bcl-2 family of anti-apoptotic proteins are key regulators of cell apoptosis (32). We found that lactate increased the expression of Bcl-2, and downregulation of Bcl-2 protein expression using an siRNA reduced the resistance to apoptosis induced by lactate in glucose-free conditions.

The PI3K/Akt signaling pathway plays a key role in inhibiting apoptosis, thereby promoting cell proliferation. The activation of Akt can act directly on apoptosis-related proteins to regulate apoptosis. Activation of Akt induces phosphorylation of caspase-9 at the Ser196 site, which inhibits apoptosis (33). The PI3K/Akt signaling pathway can directly or indirectly affect the functions of transcription factors to regulate cell survival. Akt can inhibit the enzyme IκBα that phosphorylates NF-κB, whereas unphosphorylated NF-κB in the nucleus regulates anti-apoptotic gene transcription (34). Akt can prevent the mitochondrial release of cytochrome c and apoptosis-inducing factors, contributing to apoptosis resistance (35). In the present study, we confirmed that lactate acts via the PI3K/Akt pathway to regulate Bcl-2, inhibiting apoptosis caused by the lack of glucose.

In conclusion, lactate helps tumor cells to resist apoptosis caused by glucose starvation. Lactate, through PI3K, activates Akt by phosphorylation, which activates mTOR and further increases the expression of anti-apoptotic protein Bcl-2. This study indicates that treatments targeting lactate could more effectively inhibit the survival of tumors.


The present study was supported by research grants from the National Basic Research Program of China (973 Program, 2012CB932600), Significant New Drug Creation Five-Year Plan Special Science and Technology Major (2012ZX09506001-005), the National Natural Science Foundation of China (nos. 30830038, 81071180 and 30970842), the Key Project of Science and Technology Commission of Shanghai Municipality (no. 10JC1410000), and the Shanghai Leading Academic Discipline Project (project no. S30203).


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Steady-state concentrations of metabolites can mirror the metabolic status of live tissues. Unlike most healthy organs, malignant tumors are extremely heterogeneous with regard to the spatial arrangement of vasculature, various cellular subpopulations, and localized concentrations of metabolites (Aly et al. 2015, Jeng et al. 2015). There is evidence that this characteristic tumor heterogeneity is one major cause of therapeutic failure in medical oncology (Walther et al. 2015). Consequently, imaging metabolites in cancerous tissue in a biologically and clinically significant manner requires the quantitative detection of metabolic substances within microscopic dimensions in association with the histological tissue structure.

Metabolic analyses based on tumor biopsies are routinely performed in the clinic for pathohistological diagnosis. Spare material from this procedure is often available for scientific purposes under ethical considerations and patient consent. We have shown that appropriate removal and rapid liquid nitrogen freezing of such biopsies are possible in the clinical setting, and that these specimens enable the analysis of a tissue's momentary metabolic status (metabolic snapshot) (Walenta et al. 2000, 2016). We have demonstrated that in most cases, one tumor biopsy from the pathological routine can be representative of metabolic features of an entire tumor when compared with measurements from two or three biopsies from the same cancer (Walenta et al. 2016). Furthermore, we found that tissue concentrations of lactate do not change when biopsies were kept in liquid nitrogen for ten years (Walenta et al. 2016), which provides potential for long-term storage of metabolic tissue banks.

An increasing number of signaling pathways have been closely linked to cancer metabolism. As a result, the metabolic deregulation in tumors may be recognized as a complex network of interrelated pathways that is unpredictable in its functionality in individual tumors (Carroll et al. 2015). In contrast, there is a common readout of cancer cell metabolism integrating over its various signaling activities, i.e., the cellular efflux of lactate into the tumor microenvironment (Dhup et al. 2012, Hirschhaeuser et al. 2011, Luc et al. 2015). The clinical significance and implications of the extremely variable lactate concentrations in solid tumors were first identified by our group in 2000 (Walenta et al. 2000) using induced metabolic bioluminescence imaging (imBI). The imBI technique allows for the quantification of various metabolites, such as glucose, lactate, pyruvate, ATP, glucose-6-phophate, or D2-hydroxygluturate, and for the assessment of the regional distribution of these metabolites within tissues of interest. The method has been developed in our laboratory on the basis of biochemical precursor studies, mainly on brain metabolism (Kim et al. 1993, Kricka 2000, Paschen 1985, Paschen et al. 1981). The current status of imBI and its advantages and limitations have been reviewed previously (see Walenta et al. 2014 for further methodological details).

The use of imBI has generated a huge amount of data in a wide range of experimental and clinical tumors. In all tumors studied, tissue concentrations of lactate showed the largest variability compared to all other metabolites investigated. Lactate concentrations ranged across tumors from 0 to 50 μmol/g of tissue, which corresponds to approximately 0–50 mM in a liquid phase. Considering that the physiological range of lactate in human blood is 0–2 mM, cancer cells survive in the face of exorbitant lactate concentrations combined with a severe metabolic acidosis (as discussed elsewhere). Even considering that blood lactate concentrations reach transient values of 10–15 mM during exhausting physical work, this metabolite can be cleared from blood within 30 minutes post-exercise. In contrast, cancer cells are chronically exposed to elevated lactate concentrations, acidic pHe, and carbonic dioxide tensions up to 80 mmHg, which can be considered chronically pathophysiological conditions (summarized by Walenta & Mueller-Klieser 2004 and Walenta et al. 2000).

5.1. Clinical Relevance of Lactate Accumulation in the Tumor Microenvironment

During quantitative evaluation of tumor lactate concentrations and their clinical relevance, it appeared advantageous to classify tumors into high- and low-lactate cancers by separating the data values using the median lactate concentration as a limit between the two classes. Since the difference between the two lactate classes is most likely generated by different glycolytic activities of the tumor tissue, the terms “high-” and “low-glycolytic tumors” were eventually used in the literature as synonyms for “high-” and “low-lactate tumors,” respectively. Interestingly, the separating limit between high- and low-lactate tumors was invariantly in a range of 10 ± 2 mM in all tumors investigated, i.e., in different independent studies, experimental and clinical settings, and tumor entities.

In most cancers investigated, high-lactate tumors were associated with reduced long-term survival or disease-free survival compared to their low-lactate counterparts (Walenta & Mueller-Klieser 2004 Walenta et al. 2000, 2004). In some tumor entities, such as head and neck cancer, the statistical probability of tumor recurrence was dramatically higher in high- versus low-lactate tumors (Brizel et al. 2001). In line with high-lactate tumors, the expression of the hypoxia-inducible H + /lactate symporter MCT4 demonstrated the strongest deleterious impact on survival in two separate cohorts of 770 node-negative breast tumors (Doyen et al. 2014).

The emergence of metastasis is a primary clinical factor that limits patient survival. Incidence of early distant metastasis at first tumor diagnosis was significantly higher in high-lactate primary cancers compared to low-lactate primary cancers (summarized by Walenta & Mueller-Klieser 2004). It has been shown that lactate per se stimulates angiogenesis through activation of the VEGF/VEGFR2 pathway, which may support the metastatic process (Dhup et al. 2012, Porporato et al. 2012). Another pathophysiological mechanism enhancing the formation of metastasis is the stimulation of tumor cell motility by lactate (Baumann et al. 2009, Goetze et al. 2011). At present, several G protein–coupled receptors (GPCRs), GPR4, GPR65, GPR68, GPR81, and GPR132, have been identified as putative lactate or proton sensors (Justus et al. 2013). While GPR81 was initially detected and classified as an orphan receptor in adipocytes (Cai et al. 2008), its function as not only a cell surface l -lactate receptor but also a hydroxycarboxylic receptor has been investigated in several cell types including malignant cells (see Romero-Garcia et al. 2016). However, in the context of the tumor acidic microenvironment, the proton-sensing GPR4, GPR65, GPR68, and GPR132 have received the greatest attention (Weiss et al. 2017). They are activated via the protonation of several histidine residues in response to an extracellular pH drop. Tumor acidity, generated from lactic and carbonic acids (Newell et al. 1993), transmits intracellular signals through G proteins coupled to either adenylate cyclase (GPR4, GPR65), phospholipase C (GPR68), or a presently unidentified effector (GPR132) (Justus et al. 2013, Seuwen et al. 2006).

Of great interest, two acidic-sensitive GPCRs, GPR132 and GPR65, both expressed in tumor-associated macrophages (TAMs), have now been recognized to exhibit a reciprocal interaction between cancer cells and macrophages for breast cancer (Chen et al. 2017) and melanoma (Bohn et al. 2018). Although the nature of acidic-activated GPR132 signaling is lacking, GPR132 activates the M2-like macrophage phenotype, which facilitates cancer cell migration, invasion, and metastasis (Chen et al. 2017). In contrast, acidic-activated GPR65 induces via cyclic AMP the transcriptional repressor ICER (inducible cyclic AMP early repressor) in tumor-associated macrophages, which leads to their functional polarization toward a noninflammatory phenotype and promotes tumor growth (Bohn et al. 2018).

In 2010, we published a collaborative study with Michael Baumann's group on radioresistance in a large cohort of human head and neck cancer xenografts (Sattler et al. 2010), following standard clinical protocols for irradiation dose and fractionation scheme. Unlike many metabolic parameters investigated, lactate concentration was a dominant modulator of tumor response to irradiation, with the highest-lactate tumors being most resistant to treatment. Among other factors, this may be explained by the generation of a reductive milieu by high-glycolytic turnover rates under these conditions, pyruvate can act as an antioxidant by nonenzymatic formation of acetate and concomitant scavenging of hydrogen peroxide (Salahudeen et al. 1991). Furthermore, it has been shown that the addition of exogenous lactate to endothelial cell cultures leads to an increase of the NAD(P)H:NAD(P) ratio and to the transcriptional control of several genes mediated by the redox-regulated transcription factor complex AP-1 (Hoffmann et al. 2001). In analogy to radioresistance caused by reductive milieu conditions, chemoresistance may occur with those drugs that are inactivated under these conditions, such as doxorubicin (Velaei et al. 2016).

Facing the significance of the tumor redox status for cancer therapy, we used imBI technology for structure-related quantitative redox imaging (Sattler et al. 2007). This is illustrated in Figure 4, which shows the histology of a human head and neck squamous cell carcinoma next to striated muscle (Figure 4a) and a color-coded map of lactate-to-pyruvate ratios (Figure 4b). The coded colors clearly mirror the intensively reduced redox state of the malignant versus normal tissue.

5.2. Noninvasive Detection of Lactate and Related Metabolites

Although invasive, the imBI technology has a unique combination of properties, including spatial resolution on a microscopic level, quantitative measurements of metabolites in absolute units (micromoles per gram of tissue), biochemical versatility with regard to a broad spectrum of possibly detectable metabolites, the direct colocalization of metabolites and histological structure, and clinical applicability. Presently, these traits of imBI cannot be met by any of the up-to-date metabolic imaging techniques currently used experimentally or routinely in the clinic. Nevertheless, imaging techniques are urgently required and need to be advanced to improve our knowledge of human malignant disease, of comprehensive diagnosis, and of versatile, customized therapies. Numerous efforts and advances in noninvasive metabolic imaging have been reported in the recent literature, but only a few select examples of lactate imaging–related studies can be briefly mentioned here. Unlike glucose, tissue lactate fluxes have yet been detected by positron emission tomography (PET). However, a recent report showed that [ 18 F]-3-fluoro-2-hydroxypropionate can serve as an analog of lactate, which enables monitoring of cellular uptake of lactate by MCT1 in PET studies (Van Hee et al. 2017). Using a combination of modified PET and magnetic resonance spectroscopy (MRS) techniques, both glucose and lactate were identified as TCA cycle carbon sources in patient lung tumors (Faubert et al. 2017). Recently, in an H1-MRS study in patients with neuroepithelial tumors, Nakamura et al. (2018) were able to quantify tumor lactate content in relative terms and demonstrate that this quantification supported tumor grading. Lactate profiling of tumors in the clinic therefore appears to be an essential parameter in the progression toward improved anticancer therapies.


Cell culture and preparation of bitter melon extract (BME)

HNSCC cell line Cal27 was purchased from the ATCC. JHU022 cell line was procured from the Johns Hopkins University. Cal27 and JHU022 cells were maintained in RPMI1640 media supplemented with 10% FBS and 1% penicillin/streptomycin in a humidified CO2 incubator. The cell lines are routinely tested in our laboratory to rule out mycoplasma contamination using commercial Lonza MycoAlert™ Mycoplasma Detection kit. Bitter melon extract (BME) was prepared from the Chinese variety of young bitter melons (raw and green) as discussed previously [13]. Briefly, BME was extracted from whole fruit without seeds using a household juicer at room temperature and centrifuged at 15000x g at 4 °C for 30 min. BME was stored at − 80 °C for further analysis. Cal27 cells were treated with 2% BME and JHU022 cells were treated with 3% BME and different analysis were performed. All the experiments were done at least in triplicate.

RNA isolation and expression analysis

Cal27 and JHU022 cells were treated with/without BME for 30 h. Total RNA was extracted by TRIzol reagent followed by cDNA synthesis with SuperScript III Reverse Transcriptase (Life technology, USA). Real-time PCR was performed for quantitation of gene expression using specific primers (Table 1) by SYBR green based detection system. 18 s rRNA was used as an endogenous control. The relative gene expression was analysed by 2 -∆∆CT method. Each sample was loaded in triplicate.

Protein isolation and western blot analysis

Control or BME treated cell lysates were prepared, and western blot analysis was performed using specific antibodies to GLUT-1, PFKP, LDHA, PDK3, ACLY, FASN, and Flot-1 (Santa Cruz Biotechnology), ACC1 and CHOP (Cell Signaling Technology). Anti-mouse or anti-rabbit secondary antibodies were purchased from BIO-RAD. The blot was reprobed with actin-HRP antibody to compare protein load in each lane. Densitometry analysis was done using Image J software (NIH).

Determination of lactate and pyruvate level by GC/MS

Cal27 and JHU022 cells were treated with or without BME for 30 h. Cells were washed in ice-cold PBS and rapidly quenched with 80% methanol. [ 13 C3] lactate and [ 13 C3] pyruvate (Cambridge Isotope Labs, Tewksbury, MA) were spiked into the samples as internal standards for quantitative analysis. Extracts were then sonicated and centrifuged at 14,000×g for 15 min at 4 °C. The clear supernatant was dried under a stream of nitrogen gas to complete dryness to clear precipitate and gas chromatography/mass spectrometry (GC/MS) experiment was performed, as described previously [20].

Determination of extracellular acidification and glycolytic rate

The Cal27 and JHU022 cells (2 × 10 4 cells/ well) were seeded into a 96 well-plate (Seahorse XF96 Cell Culture Microplates, Agilent) and treated with or without BME for 24–36 h. Cells were assessed for extracellular acidification rate (ECAR) and oxygen consumption rate (OCR) to understand glycolysis rate (ECAR/ OCR) using the Seahorse XF analyser (Agilent) as described previously [21].


Control or BME treated Cal27 and JHU022 cell suspensions were subsequently subjected to modified Bligh-Dyer extraction in the presence of lipid class internal standards, including heptadecanoic acid, 1,2-ditetradecanoyl-sn-glycero-3-phosphoethanolamine, heptadecanoyl cholesteryl ester, N-heptadecanoyl ceramide, and 1,2-dieicosanoyl-sn-glycero-3-phosphocholine [22]. For phospholipids, lipid extracts were diluted in methanol/chloroform (4/1, v/v), and molecular species were quantified by electrospray ionization-tandem mass spectrometry (ESI-MS/MS) on a triple quadrupole instrument (Thermo Fisher Quantum Ultra) using shotgun lipidomics. Phosphatidylcholine molecular species were quantified as sodiated adducts in the positive-ion mode using neutral loss scanning for 59.1 amu (collision energy = − 28 eV). Cholesteryl ester molecular species were quantified as sodiated adducts in the positive-ion mode using neutral loss scanning for 368.5 amu (collision energy = − 25 eV). Ceramide molecular species were quantified in the negative-ion mode using neutral loss scanning for 256.2 amu (collision energy = 32 eV). Phosphatidylethanolamine and plasmenylethanolamine molecular species were first derivatized to their fMOC species and then analysed by neutral loss scanning for 222.2 amu (collision energy = 30 eV) in negative ion mode (HAN). Individual molecular species were quantified by comparing the ion intensities of the individual molecular species to that of the lipid class internal standard, with additional corrections for type I and type II [13C] isotope effects.

Calcium-independent phospholipase A2 (iPLA2) activity assay

Control or BME treated Cal27 and JHU022 cell was subjected to iPLA2 activity was measured as described previously [23]. Briefly, cells were washed with ice-cold PBS followed by PLA2 assay buffer, and PLA2 activity in the supernatant was measured at 37 °C for 5 mins, using 100 μM (16:0, [ 3 H] 18:1) plasmenylcholine as substrate.

Reactive oxygen species (ROS) analysis

Cal27 and JHU022 cells were treated with/without BME for 30 h. For mitochondrial ROS measurement cells were stained with MitoSOX (Molecular Probes, Invitrogen) at 5 μM for 40 min at 37 °C and flow cytometry analysis was performed as described previously [24].

Immunofluorescence analysis

Control and BME treated cells were fixed with chilled methanol for 5 min at -20 °C. After blocking with 5% BSA, primary antibody to Flotillin was added for overnight at 4 °C, and anti-mouse immunoglobulin conjugated to Alexa Fluor 647 (Molecular Probes) for 1 h at room temperature. Cells were counter stained with DAPI (4′,6′-diamidino-2-phenylindole) for nuclear staining. Two-channel optical images (red and blue) were collected using the sequential scanning mode of the Olympus FV1000 confocal system. The images were merged digitally to monitor co-localization in which two different colors produce a distinct color, whereas physically separate signals retain their individual colors.

Statistical analysis

The results are expressed as means ± standard errors of the means. Student’s t test was used for comparisons of two groups (control and BME-treated). P-values less than 0.05 was considered statistically significant. All experiments were repeated at least three times, and representative data are shown.

Regulation of PKM2

Regulation of PKM2 expression

The expression of PKM2 is regulated at multiple levels through regulation of DNA methylation, transcription factors, pre‐mRNA splicing of PKM, PKM2‐specific microRNAs (miRNAs) and post‐translational modifications of the PKM2 protein (see poster).

Analyses of The Cancer Genome Atlas DNA methylation data have revealed that elevated PKM2 expression correlates well with a hypomethylation status of intron 1 of the PKM gene in multiple cancer types, suggesting that epigenetic regulation by DNA methylation is an important mechanism in controlling PKM transcription in tumors (Desai et al., 2014).

Several transcriptional factors have been reported to regulate the activity of the PKM promoter, which contains five putative binding sites for SP1 and SP3. Both SP1 and SP3 interact with the nuclear factor (NF)‐YA transcriptional factor (see poster). Indeed, overexpression of SP1 or SP3 and NF‐YA synergistically stimulates the distal promoter activity of the PKM gene (Discher et al., 1998 Yamada et al., 2000). Phosphoinositide 3‐kinase (PI3K) and mammalian target of rapamycin (mTOR) activation, which can be induced by insulin stimulation, has also been shown to increase PKM2 expression through hypoxia‐inducible factor 1α (HIF1α)‐regulated transcription of the PKM gene (Iqbal et al., 2013 Sun et al., 2011). Peroxisome proliferator‐activated receptor γ (PPARγ), a nuclear hormone receptor, can also specifically and transcriptionally regulate PKM2 expression. Activation of AKT in PTEN‐deficient fatty livers results in the binding of PPARγ to PPAR response elements (PPRE) in the promoter region of both PKM and the hexokinase‐2 (HK2) gene, and contributes to liver steatosis, hypertrophy and hyperplasia (Panasyuk et al., 2012). These results suggest that activation of the PI3K–AKT–mTOR pathway, coupled with other activated signaling modulators, regulates PKM2 expression in a manner that depends on the signaling context and is tissue specific. In response to epidermal growth factor receptor (EGFR) activation, which occurs in many types of human cancers, PKM transcription is upregulated by a signaling cascade that includes EGFR, phospholipase C γ1 (PLCγ1), protein kinase C ε (PKCε), and NF‐κB. Activation of EGFR results in the activation of PLCγ1 and the subsequent production of diacylglycerol this in turn activates PKCε, which is then monoubiquitylated by the E3 ligase RINCK1 (also known as TRIM41) at K321, allowing it to interact with a ubiquitin‐binding motif located in the zinc finger region of NF‐κB essential modulator (NEMO also known as IKKγ). This interaction recruits the cytosolic IκB kinase (IKK) complex, which is composed of NEMO, IKKα and IKKβ, to the plasma membrane, where PKCε phosphorylates IKKβ at S177 and activates IKKβ. Activated IKKβ phosphorylates inhibitor of nuclear factor κB (IκB) and abrogates its repressive effect on RelA (the p65 subunit of NF‐κB), thereby allowing it to translocate to the nucleus where it directly binds to the PKM promoter and enhances PKM2 expression, resulting in the Warburg effect and tumorigenesis (Yang et al., 2012a) (see poster).

PKM2 expression can also be regulated at the level of transcribed PKM pre‐mRNA by splicing factors. Heterogeneous nuclear ribonucleoproteins (hnRNPs), including PTB (also known as hnRNP1), hnRNPA1 and hnRNPA2, are upregulated by the oncogenic transcription factor c‐Myc and subsequently bind to splicing signals that flank PKM exon 9, repressing the inclusion of exon 9 and thus promoting an enhanced expression of the PKM2 isoform (David et al., 2010 Sun et al., 2011).

miRNAs, noncoding RNAs that bind to specific target mRNAs and promote their degradation and/or hinder their translation, provide another means for regulating PKM2 mRNA. Both miR‐133a and miR‐133b target the PKM transcript, and these miRNAs have been found to be significantly reduced in tongue squamous cell carcinoma cells, resulting in PKM2 overexpression (Wong et al., 2008). miR‐122, which is highly expressed in normal liver tissue, is reduced in hepatocellular carcinoma and directly targets PKM2 mRNA (Liu et al., 2014), whereas miR‐326 regulates PKM2 expression in human glioma (Kefas et al., 2010), suggesting that different miRNAs are involved in the tissue‐specific regulation of PKM2 expression. In addition, the mRNAs encoding the splicing factors PTB, hnRNPA1 and hnRNPA2 are targeted by miR‐124, miR‐137 and miR‐340. Consequently, these miRNAs mediate a switch in expression of the PKM gene from the PKM2 isoform to PKM1, which results in a reduced glycolysis rate and promotes the glucose flux into oxidative phosphorylation, consequently leading to impaired cancer cell growth (Sun et al., 2012).

Finally, PKM2 protein levels are also regulated at the level of post‐translational modifications. For instance, acetylation of PKM2 at K305 promotes its degradation under high‐glucose concentrations. Acetylated PKM2 interacts with heat shock protein HSC70 (also known as HSPA8), which leads to lysosomal‐dependent degradation of PKM2 by chaperone‐mediated autophagy (see poster). Accordingly, expression of an acetylation‐mimetic PKM2 K305Q mutant, which undergoes degradation at high concentrations of glucose, results in the accumulation of glycolytic intermediates upstream of PKM2 for biosynthesis, which promotes cell proliferation and tumorigenesis (Lv et al., 2011).

Regulation of the enzymatic activity and metabolic functions of PKM2

PKM2 catalyzes the last step of glycolysis, and its activity can be regulated by glycolytic intermediates. Fructose 1,6‐bisphosphate (FBP), an allosteric activator of PKM2, binds to PKM2 and promotes its tetramerization. Tetrameric PKM2 is more active than dimeric PKM2, and the conversion between these two forms is dynamically regulated (Dombrauckas et al., 2005) (see poster). Binding of PKM2 to phosphorylated tyrosine releases FBP and disrupts tetrameric PKM2 into the PKM2 dimer (Christofk et al., 2008b). Human PKM2 mutants with heterozygous missense mutations H391Y and K422R, which have been identified in cells from Bloom syndrome patients, heterooligomerize with the wild‐type PKM2 and reduce the overall activity of PKM2, resulting in an increased accumulation of glycolytic intermediates and NADPH, cell proliferation, polyploidy and tumor growth (Gupta et al., 2010 Iqbal et al., 2014a Iqbal et al., 2014b). JMJD5, a dioxygenase containing a Jumonji C domain, interacts with the region of PKM2 at the intersubunit interface, which impedes PKM2 tetramerization and blocks pyruvate kinase activity (Wang et al., 2014a). Decreased PKM2 pyruvate kinase activity has been shown to result in PEP‐dependent histidine phosphorylation and activation of phosphoglycerate mutase (PGAM1), as well as in reduced levels of PEP‐dependent ATP production this metabolic switch might provide an alternate glycolytic pathway that decouples ATP from PEP‐mediated phosphotransfer, thereby allowing for the high rate of glycolysis to support the anabolic metabolism (Vander Heiden et al., 2010b). Furthermore, in primary mouse embryonic fibroblasts in which PKM2 was deleted, PKM1 expression is upregulated and impairs nucleotide production and the ability of cells to synthesize DNA and progress the cell cycle, suggesting that an appropriate level of PKM2 expression is important for normal cell proliferation (Lunt et al., 2015).

Serine is another allosteric activator of PKM2 (Chaneton et al., 2012) (see poster). It binds to and activates PKM2 in a manner similar to but independent of FBP. When serine is abundant, PKM2 is fully active, enabling maximal use of glucose through glycolysis. When serine is limited, however, PKM2 activity is immediately curtailed, resulting in rapid diversion of glucose‐derived carbon to serine biosynthesis and thus compensating for the serine shortfall (Chaneton et al., 2012). Succinyl‐5‐aminoimidazole‐4‐carboxamide‐1‐ribose‐5′‐phosphate (SAICAR), an intermediate of the de novo purine nucleotide synthesis pathway, also regulates PKM2 activity allosterically and independently of FBP. Cellular SAICAR concentration increases upon glucose starvation, which stimulates PKM2 activity to enhance glucose and glutamine consumption rates, and promotes cancer cell survival (Keller et al., 2012). Thus, allosteric regulation of PKM2 might allow cancer cells to coordinate different metabolic pathways to support cancer cell growth in the often nutrient‐limited tumor microenvironment.

PKM2 activity can also be regulated by post‐translational modifications (see poster). Fibroblast growth factor receptor type 1 (FGFR1) phosphorylates PKM2 at Y105, which disrupts the binding of FBP to PKM2 (Hitosugi et al., 2009) (see poster). This phosphorylation, which promotes the tetramer‐to‐dimer conversion of PKM2, inhibits its pyruvate kinase activity and enhances its protein kinase activity towards STAT3 phosphorylation (Gao et al., 2013). Expression of the PKM2 Y105F mutant impairs cell proliferation and tumorigenesis (Hitosugi et al., 2009). In addition, acute increase in intracellular concentrations of reactive oxygen species (ROS) induced by H2O2, diamide (a thiol‐oxidizing compound) or hypoxia inhibits PKM2 activity through oxidation of C358 of PKM2, leading to diversion of glucose flux into the pentose phosphate pathway to generate a reducing potential for the detoxification of ROS and tumor growth (Anastasiou et al., 2011). Similarly, reduction of PKM2 activity by ROS‐induced PKM2 oxidation in response to insulin stimulation has also been reported (Iqbal et al., 2013).

Regulation of the subcellular localization of PKM2

As a glycolytic enzyme, PKM2 predominantly localizes in the cytosol. However, PKM2 translocates into the nucleus to promote cell proliferation (see poster). Our group has demonstrated a crucial mechanism underlying the nuclear translocation of PKM2. Upon EGFR activation, activated extracellular signal‐regulated kinase 1 and 2 (ERK1/2) binds to the region that is encoded by exon 10 of PKM2 through a docking groove in ERK1/2, resulting in phosphorylation of S37 of PKM2, but not PKM1. Phosphorylated PKM2 then recruits peptidyl‐prolyl cis‐trans isomerase NIMA‐interacting 1 (PIN1), which specifically catalyzes the cis‐trans isomerization of peptide bonds between phosphorylated serine or threonine residues and proline residues (Lu and Hunter, 2014). This isomerization of PKM2 exposes its nuclear localization sequence (NLS) and promotes its binding to importin α5, which facilitates its nuclear translocation (Yang et al., 2012c). In addition, sumoylation of PKM2 mediated by the SUMO‐E3 ligase PIAS3, and acetylation of PKM2 at K433 mediated by the p300 acetyltransferase (also known as EP300) prevent the binding of FBP to PKM2, thereby enhancing its nuclear translocation (Lv et al., 2013 Spoden et al., 2009).


In nature, microorganisms grow mainly in biofilms, complex and dynamic ecosystems that form on a variety of environmental surfaces, from industrial conduits and water treatment pipelines to rocks in river beds. Biofilms are not restricted to solid surface substrates, however. Almost any surface in a liquid environment containing some minimal nutrients will eventually develop a biofilm. Microbial mats that float on water, for example, are biofilms that contain large populations of photosynthetic microorganisms. Biofilms found in the human mouth may contain hundreds of bacterial species. Regardless of the environment where they occur, biofilms are not random collections of microorganisms rather, they are highly structured communities that provide a selective advantage to their constituent microorganisms.

Biofilm Structure

Observations using confocal microscopy have shown that environmental conditions influence the overall structure of biofilms. Filamentous biofilms called streamers form in rapidly flowing water, such as freshwater streams, eddies, and specially designed laboratory flow cells that replicate growth conditions in fast-moving fluids. The streamers are anchored to the substrate by a “head” and the “tail” floats downstream in the current. In still or slow-moving water, biofilms mainly assume a mushroom-like shape. The structure of biofilms may also change with other environmental conditions such as nutrient availability.

Detailed observations of biofilms under confocal laser and scanning electron microscopes reveal clusters of microorganisms embedded in a matrix interspersed with open water channels. The extracellular matrix consists of extracellular polymeric substances (EPS) secreted by the organisms in the biofilm. The extracellular matrix represents a large fraction of the biofilm, accounting for 50%–90% of the total dry mass. The properties of the EPS vary according to the resident organisms and environmental conditions.

EPS is a hydrated gel composed primarily of polysaccharides and containing other macromolecules such as proteins, nucleic acids, and lipids. It plays a key role in maintaining the integrity and function of the biofilm. Channels in the EPS allow movement of nutrients, waste, and gases throughout the biofilm. This keeps the cells hydrated, preventing desiccation. EPS also shelters organisms in the biofilm from predation by other microbes or cells (e.g., protozoans, white blood cells in the human body).

Biofilm Formation

Free-floating microbial cells that live in an aquatic environment are called planktonic cells. The formation of a biofilm essentially involves the attachment of planktonic cells to a substrate, where they become sessile (attached to a surface). This occurs in stages, as depicted in Figure 16. The first stage involves the attachment of planktonic cells to a surface coated with a conditioning film of organic material. At this point, attachment to the substrate is reversible, but as cells express new phenotypes that facilitate the formation of EPS, they transition from a planktonic to a sessile lifestyle. The biofilm develops characteristic structures, including an extensive matrix and water channels. Appendages such as fimbriae, pili, and flagella interact with the EPS, and microscopy and genetic analysis suggest that such structures are required for the establishment of a mature biofilm. In the last stage of the biofilm life cycle, cells on the periphery of the biofilm revert to a planktonic lifestyle, sloughing off the mature biofilm to colonize new sites. This stage is referred to as dispersal.

Figure 16. Stages in the formation and life cycle of a biofilm. (credit: modification of work by Public Library of Science and American Society for Microbiology)

Within a biofilm, different species of microorganisms establish metabolic collaborations in which the waste product of one organism becomes the nutrient for another. For example, aerobic microorganisms consume oxygen, creating anaerobic regions that promote the growth of anaerobes. This occurs in many polymicrobial infections that involve both aerobic and anaerobic pathogens.

The mechanism by which cells in a biofilm coordinate their activities in response to environmental stimuli is called quorum sensing. Quorum sensing—which can occur between cells of different species within a biofilm—enables microorganisms to detect their cell density through the release and binding of small, diffusible molecules called autoinducers. When the cell population reaches a critical threshold (a quorum), these autoinducers initiate a cascade of reactions that activate genes associated with cellular functions that are beneficial only when the population reaches a critical density. For example, in some pathogens, synthesis of virulence factors only begins when enough cells are present to overwhelm the immune defenses of the host. Although mostly studied in bacterial populations, quorum sensing takes place between bacteria and eukaryotes and between eukaryotic cells such as the fungus Candida albicans, a common member of the human microbiota that can cause infections in immunocompromised individuals.

Figure 17. Short peptides in gram-positive bacteria and N-acetylated homoserine lactones in gram-negative bacteria act as autoinducers in quorum sensing and mediate the coordinated response of bacterial cells. The R side chain of the N-acetylated homoserine lactone is specific for the species of gram-negative bacteria. Some secreted homoserine lactones are recognized by more than one species.

The signaling molecules in quorum sensing belong to two major classes. Gram-negative bacteria communicate mainly using N-acylated homoserine lactones, whereas gram-positive bacteria mostly use small peptides (Figure 17). In all cases, the first step in quorum sensing consists of the binding of the autoinducer to its specific receptor only when a threshold concentration of signaling molecules is reached. Once binding to the receptor takes place, a cascade of signaling events leads to changes in gene expression. The result is the activation of biological responses linked to quorum sensing, notably an increase in the production of signaling molecules themselves, hence the term autoinducer.

Biofilms and Human Health

The human body harbors many types of biofilms, some beneficial and some harmful. For example, the layers of normal microbiota lining the intestinal and respiratory mucosa play a role in warding off infections by pathogens. However, other biofilms in the body can have a detrimental effect on health. For example, the plaque that forms on teeth is a biofilm that can contribute to dental and periodontal disease. Biofilms can also form in wounds, sometimes causing serious infections that can spread. The bacterium Pseudomonas aeruginosa often colonizes biofilms in the airways of patients with cystic fibrosis, causing chronic and sometimes fatal infections of the lungs. Biofilms can also form on medical devices used in or on the body, causing infections in patients with in-dwelling catheters, artificial joints, or contact lenses.

Pathogens embedded within biofilms exhibit a higher resistance to antibiotics than their free-floating counterparts. Several hypotheses have been proposed to explain why. Cells in the deep layers of a biofilm are metabolically inactive and may be less susceptible to the action of antibiotics that disrupt metabolic activities. The EPS may also slow the diffusion of antibiotics and antiseptics, preventing them from reaching cells in the deeper layers of the biofilm. Phenotypic changes may also contribute to the increased resistance exhibited by bacterial cells in biofilms. For example, the increased production of efflux pumps, membrane-embedded proteins that actively extrude antibiotics out of bacterial cells, have been shown to be an important mechanism of antibiotic resistance among biofilm-associated bacteria. Finally, biofilms provide an ideal environment for the exchange of extrachromosomal DNA, which often includes genes that confer antibiotic resistance.

Think about It

  • What is the matrix of a biofilm composed of?
  • What is the role of quorum sensing in a biofilm?

Key Concepts and Summary

  • Most bacterial cells divide by binary fission. Generation time in bacterial growth is defined as the doubling time of the population.
  • Cells in a closed system follow a pattern of growth with four phases: lag, logarithmic (exponential), stationary, and death.
  • Cells can be counted by direct viable cell count. The pour plate and spread plate methods are used to plate serial dilutions into or onto, respectively, agar to allow counting of viable cells that give rise to colony-forming units. Membrane filtration is used to count live cells in dilute solutions. The most probable cell number (MPN) method allows estimation of cell numbers in cultures without using solid media.
  • Indirect methods can be used to estimate culture density by measuring turbidity of a culture or live cell density by measuring metabolic activity.
  • Other patterns of cell division include multiple nucleoid formation in cells asymmetric division, as in budding and the formation of hyphae and terminal spores.
  • Biofilms are communities of microorganisms enmeshed in a matrix of extracellular polymeric substance. The formation of a biofilm occurs when planktonic cells attach to a substrate and become sessile. Cells in biofilms coordinate their activity by communicating through quorum sensing.
  • Biofilms are commonly found on surfaces in nature and in the human body, where they may be beneficial or cause severe infections. Pathogens associated with biofilms are often more resistant to antibiotics and disinfectants.

Multiple Choice

Which of the following methods would be used to measure the concentration of bacterial contamination in processed peanut butter?

  1. turbidity measurement
  2. total plate count
  3. dry weight measurement
  4. direct counting of bacteria on a calibrated slide under the microscope

In which phase would you expect to observe the most endospores in a Bacillus cell culture?

  1. death phase
  2. lag phase
  3. log phase
  4. log, lag, and death phases would all have roughly the same number of endospores.

During which phase would penicillin, an antibiotic that inhibits cell-wall synthesis, be most effective?

Which of the following is the best definition of generation time in a bacterium?

  1. the length of time it takes to reach the log phase
  2. the length of time it takes for a population of cells to double
  3. the time it takes to reach stationary phase
  4. the length of time of the exponential phase

What is the function of the Z ring in binary fission?

  1. It controls the replication of DNA.
  2. It forms a contractile ring at the septum.
  3. It separates the newly synthesized DNA molecules.
  4. It mediates the addition of new peptidoglycan subunits.

If a culture starts with 50 cells, how many cells will be present after five generations with no cell death?

Filamentous cyanobacteria often divide by which of the following?

Which is a reason for antimicrobial resistance being higher in a biofilm than in free-floating bacterial cells?

  1. The EPS allows faster diffusion of chemicals in the biofilm.
  2. Cells are more metabolically active at the base of a biofilm.
  3. Cells are metabolically inactive at the base of a biofilm.
  4. The structure of a biofilm favors the survival of antibiotic resistant cells.

Quorum sensing is used by bacterial cells to determine which of the following?

  1. the size of the population
  2. the availability of nutrients
  3. the speed of water flow
  4. the density of the population

Which of the following statements about autoinducers is incorrect?

  1. They bind directly to DNA to activate transcription.
  2. They can activate the cell that secreted them.
  3. N-acylated homoserine lactones are autoinducers in gram-negative cells.
  4. Autoinducers may stimulate the production of virulence factors.

Fill in the Blank

Direct count of total cells can be performed using a ________ or a ________.

The ________ method allows direct count of total cells growing on solid medium.

A statistical estimate of the number of live cells in a liquid is usually done by ________.

For this indirect method of estimating the growth of a culture, you measure ________ using a spectrophotometer.

Active growth of a culture may be estimated indirectly by measuring the following products of cell metabolism: ________ or ________.


Match the definition with the name of the growth phase in the growth curve.

___Number of dying cells is higher than the number of cells dividing A. Lag phase
___Number of new cells equal to number of dying cells B. Log phase
___New enzymes to use available nutrients are induced C. Stationary phase
___Binary fission is occurring at maximum rate D. Death phase

  1. In death phase, the number of dying cells is higher than the number of cells dividing. (D)
  2. In stationary phase, the number of new cells equal to number of dying cells. (C)
  3. In lag phase, new enzymes to use available nutrients are induced. (A)
  4. In log phase, binary fission is occurring at maximum rate. (B)

Think about It

  1. Why is it important to measure the transmission of light through a control tube with only broth in it when making turbidity measures of bacterial cultures?
  2. In terms of counting cells, what does a plating method accomplish that an electronic cell counting method does not?
  3. Order the following stages of the development of a biofilm from the earliest to the last step.
    1. secretion of EPS
    2. reversible attachment
    3. dispersal
    4. formation of water channels
    5. irreversible attachment

    Critical Thinking

    A patient in the hospital has an intravenous catheter inserted to allow for the delivery of medications, fluids, and electrolytes. Four days after the catheter is inserted, the patient develops a fever and an infection in the skin around the catheter. Blood cultures reveal that the patient has a blood-borne infection. Tests in the clinical laboratory identify the blood-borne pathogen as Staphylococcus epidermidis, and antibiotic susceptibility tests are performed to provide doctors with essential information for selecting the best drug for treatment of the infection. Antibacterial chemotherapy is initiated and delivered through the intravenous catheter that was originally inserted into the patient. Within 7 days, the skin infection is gone, blood cultures are negative for S. epidermidis, and the antibacterial chemotherapy is discontinued. However, 2 days after discontinuing the antibacterial chemotherapy, the patient develops another fever and skin infection and the blood cultures are positive for the same strain of S. epidermidis that had been isolated the previous week. This time, doctors remove the intravenous catheter and administer oral antibiotics, which successfully treat both the skin and blood-borne infection caused by S. epidermidis. Furthermore, the infection does not return after discontinuing the oral antibacterial chemotherapy. What are some possible reasons why intravenous chemotherapy failed to completely cure the patient despite laboratory tests showing the bacterial strain was susceptible to the prescribed antibiotic? Why might the second round of antibiotic therapy have been more successful? Justify your answers.

    Between Death and Survival: Retinoic Acid in Regulation of Apoptosis

    The vitamin A metabolite all-trans-retinoic acid (RA) regulates multiple biological processes by virtue of its ability to regulate gene expression. It thus plays critical roles in embryonic development and is involved in regulating growth, remodeling, and metabolic responses in adult tissues. RA can also suppress carcinoma cell growth and is currently used in treatment of some cancers. Growth inhibition by RA may be exerted by induction of differentiation, cell cycle arrest, or apoptosis, or by a combination of these activities. Paradoxically, in the context of some cells, RA not only fails to inhibit growth but, instead, enhances proliferation and survival. This review focuses on the involvement of RA in regulating apoptotic responses. It includes brief overviews of transcriptional signaling by RA and of apoptotic pathways, and then addresses available information on the mechanisms by which RA induces apoptosis or, conversely, inhibits cell death and enhances survival.