Metformin and phenformin deplete tricarboxylic acid cycle and glycolytic intermediates during cell transformation and NTPs in cancer stem cells

Metformin, a first-line diabetes drug linked to cancer prevention in retrospective clinical analyses, inhibits cellular transformation and selectively kills breast cancer stem cells (CSCs). Although a few metabolic effects of metformin and the related biguanide phenformin have been investigated in established cancer cell lines, the global metabolic impact of biguanides during the process of neoplastic transformation and in CSCs is unknown. Here, we use LC/MS/MS metabolomics (>200 metabolites) to assess metabolic changes induced by metformin and phenformin in an Src-inducible model of cellular transformation and in mammosphere-derived breast CSCs. Although phenformin is the more potent biguanide in both systems, the metabolic profiles of these drugs are remarkably similar, although not identical. During the process of cellular transformation, biguanide treatment prevents the boost in glycolytic intermediates at a specific stage of the pathway and coordinately decreases tricarboxylic acid (TCA) cycle intermediates. In contrast, in breast CSCs, biguanides have a modest effect on glycolytic and TCA cycle intermediates, but they strongly deplete nucleotide triphosphates and may impede nucleotide synthesis. These metabolic profiles are consistent with the idea that biguanides inhibit mitochondrial complex 1, but they indicate that their metabolic effects differ depending on the stage of cellular transformation.Altered metabolism is a hallmark of malignantly transformed cells. Cancer risk is linked to metabolic syndrome, a disease state that includes obesity, type 2 diabetes, high cholesterol, and atherosclerosis. Retrospective studies of type 2 diabetes patients treated with metformin, the most widely prescribed antidiabetic drug, show a strong correlation between drug intake and reduced tumor incidence or reduced cancer-related deaths (1–4).In the breast lineage, metformin inhibits growth of cancer cell lines (5–7), blocks transformation in a Src-inducible cell system (8, 9), and selectively inhibits the growth of cancer stem cells (CSCs) (8). As a consequence of its selective effects on CSCs, combinatorial therapy of metformin and standard chemotherapeutic drugs (doxorubicin, paclitaxel, and cisplatin) increases tumor regression and prolongs remission in mouse xenografts (8, 10). In addition, metformin can decrease the chemotherapeutic dose for prolonging tumor remission in xenografts involving multiple cancer types (10).Phenformin, a related biguanide and formerly used diabetes drug, acts as an anticancer agent in tumors including lung, lymphoma, and breast cancer with a greater potency than metformin. Phenformin mediates antineoplastic effects at a lower concentration than metformin in cell lines, a PTEN-deficient mouse model, breast cancer xenografts, and drug-induced mitochondrial impairment (11–14). The chemical similarities of these biguanides, as well as their similar effects in diabetes and cancer, have led to the untested assumption that phenformin is essentially a stronger version of metformin.In a Src-inducible model of cellular transformation and CSC formation, multiple lines of evidence suggest that metformin inhibits a signal transduction pathway that results in an inflammatory response (15). In the context of atherosclerosis, metformin inhibits NF-κB activation and the inflammatory response via a pathway involving AMP kinase (AMPK) and the tumor suppressor PTEN (16, 17). As metformin alters energy metabolism in diabetics, we speculated that metformin might block a metabolic stress response that stimulates the inflammatory pathway (15). However, very little is known about the metabolic changes that inhibit the inflammatory pathway.Previous studies on metformin-induced metabolic effects in cancer have focused on single metabolic alterations or pathways in already established cancer cell lines. Metformin leads to activation of AMPK, which plays a key role in insulin signaling and energy sensing (18). Metformin can reduce protein synthesis via mTOR inhibition (19). In addition, metformin may directly impair mitochondrial respiration through complex I inhibition and has been described to boost glycolysis as a compensation mechanism (14, 20). In this regard, lactic acidosis can be a side effect of metformin and phenformin treatment of diabetic patients, presumably because inhibition of complex I prevents NADH oxidation, thereby leading to a requirement for cytosolic NADH to be oxidized by the conversion of pyruvate to lactate. There is some knowledge about the metabolic effects of metformin (21, 22), but very little is known about the specific metabolic alterations linking biguanides to inhibition of neoplastic transformation.Here, we perform a metabolomic analysis on the effects of metformin and phenformin in a Src-inducible model of transformation and in CSCs. This inducible model permits an analysis of the transition from nontransformed to transformed cells in an isogenic cell system and hence differs from analyses of already established cancer cell lines. We studied CSCs to address why this population, which is resistant to standard chemotherapeutics and hypothesized to be a major reason for tumor recurrence, is selectively inhibited by metformin. Our results indicate the metabolic effects of metformin and phenformin are remarkably similar to each other, with only a few differences. Both biguanides dramatically decrease tricarboxylic acid (TCA) cycle intermediates in the early stages of transformation, and they inhibit the boost in select glycolytic intermediates that normally occurs with transformation along with increases in glycerol 3-phosphate and lactate, which are metabolites branching from glycolysis. Unexpectedly, in CSCs, biguanides have only marginal effects on glycolytic and TCA cycle metabolites, but they severely decrease nucleotide triphosphates. These detailed metabolic analyses provide independent support for the idea that metformin inhibits mitochondrial complex 1 (14, 20), and they indicate that the metabolic effects of biguanides depend on the stage of the cellular transformation.     

A common X-linked inborn error of carnitine biosynthesis may be a risk factor for nondysmorphic autism

We recently reported a deletion of exon 2 of the trimethyllysine hydroxylase epsilon (TMLHE) gene in a proband with autism. TMLHE maps to the X chromosome and encodes the first enzyme in carnitine biosynthesis, 6-N-trimethyllysine dioxygenase. Deletion of exon 2 of TMLHE causes enzyme deficiency, resulting in increased substrate concentration (6-N-trimethyllysine) and decreased product levels (3-hydroxy-6-N-trimethyllysine and γ-butyrobetaine) in plasma and urine. TMLHE deficiency is common in control males (24 in 8,787 or 1 in 366) and was not significantly increased in frequency in probands from simplex autism families (9 in 2,904 or 1 in 323). However, it was 2.82-fold more frequent in probands from male-male multiplex autism families compared with controls (7 in 909 or 1 in 130; P = 0.023). Additionally, six of seven autistic male siblings of probands in male-male multiplex families had the deletion, suggesting that TMLHE deficiency is a risk factor for autism (metaanalysis Z-score = 2.90 and P = 0.0037), although with low penetrance (2-4%). These data suggest that dysregulation of carnitine metabolism may be important in nondysmorphic autism; that abnormalities of carnitine intake, loss, transport, or synthesis may be important in a larger fraction of nondysmorphic autism cases; and that the carnitine pathway may provide a novel target for therapy or prevention of autism.     

Observational Study Mortality in Treated Primary Aldosteronism: The German Conn's Registry

In comparison with essential hypertension, primary aldosteronism (PA) is associated with an increased risk of cardiovascular morbidity. To date, no data on mortality have been published. We assessed mortality of patients treated for PA within the German Conn's registry and identified risk factors for adverse outcome in a case-control study. Patients with confirmed PA treated in 3 university centers in Germany since 1994 were included in the analysis. All of the patients were contacted in 2009 and 2010 to verify life status. Subjects from the population-based F3 survey of the Cooperative Health Research in the Region of Augsburg served as controls. Final analyses were based on 600 normotensive controls, 600 hypertensive controls, and 300 patients with PA. Kaplan-Meyer survival curves were calculated for both cohorts. Ten-year overall survival was 95% in normotensive controls, 90% in hypertensive controls, and 90% in patients with PA (P value not significant). In multivariate analysis, age (hazard ratio, 1.09 per year [95% CI, 1.03-1.14]), angina pectoris (hazard ratio, 3.6 [95% CI, 1.04-12.04]), and diabetes mellitus (hazard ratio, 2.55 [95% CI, 1.07-6.09]) were associated with an increase in all-cause mortality, whereas hypokalemia (hazard ratio, 0.41 per mmol/L [95% CI, 0.17-0.99]) was associated with reduced mortality. Cardiovascular mortality was the main cause of death in PA (50% versus 34% in hypertensive controls; P<0.05). These data indicate that cardiovascular mortality is increased in patients treated for PA, whereas all-cause mortality is not different from matched hypertensive controls.     

Using bearing area parameters to quantify early erosive tooth surface changes in enamel: A pilot study

Objectives

Most in vitro studies investigate the erosive process using relatively simple roughness parameters such as roughness average (R_a). In isolation, R_a may misrepresent the surface features. Further, few studies report baseline surface characteristics after sample preparation. This study aimed to test the hypothesis that measuring the bearing area parameters in addition to R_a may be useful when qualifying the surface of enamel at baseline and after an erosive challenge. The null hypothesis for this study was that the bearing area parameters provide no more useful information than R_a alone, when qualifying the surface of enamel at baseline and after an erosive challenge.

Methods

Enamel slabs (n = 20) were prepared from human (n = 2) and bovine (n = 4) incisor teeth and polished with 0.05 μm paste. Roughness average (R_a) and bearing parameters (MR1, MR2, R_pk, R_k, R_vk) were used to record baseline characteristics. Specimens were subjected to erosion with 1% citric acid solution for 1 min. Profilometric characteristics were recorded post-erosion, along with the maximum height changes within the profile. T-tests were carried out in order to compare baseline surface characteristics between tissue types. Post-erosion, analysis of variance (ANOVA) was used to test the effects of tissue type (bovine or human).

Results

There was no significant difference in R_a between human and bovine incisor enamel at baseline (human 0.11 μm, bovine 0.12 μm P > 0.05), and no significant difference was observed post-erosion (human 0.23 μm, bovine 0.20 μm P > 0.05). There were significant differences in bearing parameters at baseline and post-erosion that were not identified by the R_a measurement alone.

Conclusions

The results suggest that if R_a alone is measured, important differences in surface characteristics may be missed. The null hypothesis is rejected, and the recommendation is made that bearing parameters are included within profile measurements in order to further triangulate the results of surface analysis studies.

Clinical relevance

In isolation, R_a may misrepresent the surface features of a profile. These results have shown that the bearing parameters are an important and informative set of measurements. The recommendation is made that bearing parameters are included within profile measurements at baseline and post-erosion, in order to further triangulate the results of surface analysis studies.     

Treatment with Thalidomide and Cyclophosphamide (TCID) is Superior to Vincristine (VID) and to Vinorelbine (VRID) Regimens in Patients with Refractory or Recurrent Multiple Myeloma

Treatment of relapsed or refractory multiple myeloma remains a challenge and novel treatment regimen are required. Here, a matched pair analysis was performed comparing TCID (thalidomide, cyclophosphamide, idarubicin, dexamethasone) treatment to the treatment of patients with VID (vincristine, idarubicin, dexamethasone) or with VRID (vinorelbine, idarubicin, dexamethasone) for relapsed or refractory multiple myeloma. In total, 197 patients were enrolled in multicenter trials. After matching for important prognostic variables 46 matched-pairs (total of 138 patients) could be analysed with regard to survival, toxicity and efficacy. Interestingly, a significant improvement of overall response rate (ORR) for TCID treatment compared to VID and VRID was found. In addition, TCID treatment also led to a significantly higher overall survival (OS) as well as progression-free survival (PFS) compared to VID and VRID. In conclusion, TCID treatment appears to be superior to VRID and VID treatment in patients with progressive or refractory myeloma.