abstract: # Background
  .
  The reported health benefits of Korean red ginseng (KRG) include antioxidant, antitumor, antimutagenic, and immunomodulatory activities; however, the effects on oxidative stress have not yet been evaluated. Therefore, we assessed the effect of KRG on antioxidant enzymes and oxidative stress markers in humans.
  .
  # Methods
  .
  We conducted a randomized, double-blind, placebo-controlled study with three groups, including placebo, low-dose (3 g/day), and high-dose (6 g/day), which were randomly assigned to healthy subjects aged 20–65 years. Lymphocyte DNA damage, antioxidative enzyme activity, and lipid peroxidation were assessed before and after the 8-week supplementation.
  .
  # Results
  .
  Fifty-seven subjects completed the protocol. Plasma superoxide dismutase (SOD) activity after the 8-week KRG supplementation was significantly higher in the low-and high-dose groups compared to baseline. Plasma glutathione peroxidase (GPx) and catalase activities were also increased after the high-dose supplementation. Furthermore, the DNA tail length and tail moment were significantly reduced after the supplementation (low-dose and high-dose), and plasma oxidized low-density lipoprotein (LDL) levels were reduced in low-dose and high-dose groups, but increased in the placebo group. The net changes in oxidized LDL after the supplementation differed significantly between both KRG supplementation groups and the placebo group. Net changes in GPx, SOD and catalase activities, and DNA tail length and tail moment were significantly different between the high-dose group and the placebo group. Additionally, the net changes in urinary 8-epi-PGF~2α~ were significantly different between the KRG supplementation groups and the placebo group.
  .
  # Conclusions
  .
  KRG supplementation may attenuate lymphocyte DNA damage and LDL oxidation by upregulating antioxidant enzyme activity.
author: Ji Young Kim; Ju Yeon Park; Hee Jung Kang; Oh Yoen Kim; Jong Ho Lee
date: 2012
institute: 1Department of Culinary Nutrition, Woosong University, Daejeon, Korea; 2Division of Metabolic Diseases, Center for Biomedical Science, Korea National Institute of Health, Chungbuk, Korea; 3Department of Food and Nutrition, Research Laboratory for Clinical Nutrigenetics/Nutrigenomics, Yonsei University, Seoul, Korea; 4Department of Food Science and Nutrition, Dong-A University, Busan, Korea; 5Department of Food and Nutrition, Brain Korea 21 Project, College of Human Ecology, Yonsei University, Seoul, Korea; 6Department of Food and Nutrition, Yonsei University, 134 Shinchon-Dong, Sudaemun-Gu, Seoul, 120-749, Korea
references:
title: Beneficial effects of Korean red ginseng on lymphocyte DNA damage, antioxidant enzyme activity, and LDL oxidation in healthy participants: a randomized, double-blind, placebo-controlled trial

# Background

Oxidative stress describes a set of intracellular or extracellular conditions that lead to the chemical or metabolic generation of reactive oxygen/nitrogen species, including superoxide radicals, hydrogen peroxide (H~2~O~2~), hydroxyl radicals, singlet oxygen, lipid hydroperoxides, peroxynitrite, and related species \[1\]. Reactive oxygen/nitrogen species can cause oxidative damage to essential cellular constituents, such as membrane lipids, proteins, and DNA, which may ultimately result in cell death \[2\]). Aerobic organisms attempt to protect themselves against oxidative damage with exogenous antioxidants (e.g., vitamin E, ascorbic acid, and carotenoids) obtained through the diet as well as endogenous antioxidants (e.g., glutathione, glutathione peroxidase (GPx), catalase, and superoxide dismutase (SOD)). Furthermore, oxidative damage mediated by the reactive oxygen species listed above is known to contribute to the aging process and the pathogenesis of cancer, cardiovascular disease, and other degenerative diseases \[3\].

Ginseng (*Panax ginseng* C.A. Meyer) is a perennial herb belonging to the *Panax* family (Araliaceae). Its roots are used as a general tonic in traditional oriental medicine to increase health, longevity, and vitality, especially in the elderly \[4\]. White Ginseng is known to exert a number of effects, such as suppressing the growth and metastasis of melanoma (B16) \[5\] and activating GPx and SOD to protect against free radical damage \[6\]. Interest in Korean red ginseng (KRG) has grown recently, as it is one of the foods that can easily be consumed daily in Korea. Red ginseng is produced by steaming and drying fresh ginseng, a process that chemically transforms the components and alters their biological properties \[7\]. The non-saponin components of red ginseng are thought to improve learning and memory \[8\], while acid polysaccharides activate natural killer cells and stimulate interferon production \[9\]. Mochizuki et al. \[10\] reported that 20(R)- and 20(S)-ginsenoside-Rg3, the ginseng saponins, possess the ability to inhibit the lung metastasis of cancer by inhibiting the invasion and adhesion of tumor cells. According to Kubo and Tong \[11\], red ginseng extract influenced tumor cell lysosomes by promoting the uptake of mitomycin C, which has cytocidal effects. Kim et al. \[12\] also found that ginseng extracts scavenged 40% of hydroxyl radicals at 0.1 mg/ml and completely scavenged superoxide radicals at 2 mg/ml. In addition, KRG was also shown to affect immunological markers in individuals with human immunodeficiency virus \[13\]. Although a number of pre-clinical studies have reported medicinal benefits of KRG including antioxidant, antitumor, antimutagenic \[10-12\] and a clinical study has reported immunomodulatory actions \[13\], the effects of KRG on oxidative stress and lipid peroxidation have not been properly evaluated in healthy subjects. Thus, we conducted a randomized, double-blind, placebo-controlled clinical trial in healthy subjects to evaluate the effects of KRG supplementation on biomarkers of oxidative stress and lipid peroxidation, which included lymphocyte DNA damage, urinary 8-epi-prostaglandin F~2α~ (PGF~2α~), plasma oxidized low-density lipoprotein (LDL), and antioxidant enzymes.

# Methods

## Subjects

Sixty-nine healthy subjects (aged 20–65 years) were recruited during routine check-ups at a health promotion center at Yonsei University Hospital from May to November in 2009. Participants were asked to visit four times (1 week before and at 0, 4, and 8 weeks). Blood samples were taken at weeks 0 and 8, and diet and capsule intakes were assessed at weeks 0, 4, and 8. Healthy subjects who either used cigarettes or consumed alcohol (or both) were enrolled in this study at the Laboratory of Clinical Nutrigenetics/Nutrigenomics at Yonsei University. The exclusion criteria were the consumption of more than three servings of vegetables or fruit per day, antioxidant or vitamin/mineral supplementation, history of chronic disease (e.g., diabetes, heart disease, renal disease), or the use of medication (e.g., lipid-lowering or antihypertensive medications, anti-platelets, or disease-related medications). The participants were interviewed to determine their smoking and drinking habits. Smoking status was categorized as "current smoker" or "nonsmoker," and alcohol consumption was categorized as "current drinker" or "nondrinker." Nonsmokers or nondrinkers included both ex-consumers and those who had never consumed. Ex-smokers and ex-drinkers were defined as subjects who stopped smoking or drinking at least 1 year prior to participating in the study. Current smokers were asked the number of cigarettes smoked per day. Current drinkers were asked about the type and amount of alcohol consumed per day, and the amount of alcohol was then calculated as grams per day.

The study subjects number calculation assumed a two-tailed alpha = 0.05 and 1-β = 90% to detect a 10% difference with a standard error of 0.28 in the comet assay which is primary outcome and an attrition rate (20%). Biomarkers for testing antioxidant/oxidative effects and the number of subjects were determined by following the criteria recommended by the Korea Food and Drug Administration. Of the 69 study subjects, 12 subjects (placebo group, *n* = 4; low-dose group \[3 g KRG/day\], *n* = 4; high-dose group, \[6 g KRG/day\], *n* = 4) discontinued the study for personal reasons. No adverse reactions (e.g., fever, hot flush, nausea, vomiting, diarrhea) due to KRG supplementation were observed among the 57 subjects who completed the study.

## Test capsule and study design

Identical-looking capsules contained red ginseng (low dose, 300 mg; high dose, 600 mg) or placebo (300 mg of KRG-flavored capsule containing corn starch). Red ginseng and placebo capsules were provided by the Korea Ginseng Cooperation (KGC, Daejeon, Korea). The red ginseng contained 16.58 mg/g total ginsenosides, and the ratio of protopanaxadiol ginsenosides (Rb1, Rb2, Rc, Rd, and Rg3) to protopanaxatriol ginsenosides (Rg1, Re, and Rf) was 1.65. Analyses of common ginsenosides were performed in quadruplicate using standard HPLC-UV techniques \[14\] at the Korean Ginseng Research Institute in Daejeon, Korea.

This study design was the randomized double-blind, placebo-controlled intervention trial for 8 week and was approved by the Institutional Review Board of Yonsei University (#RA-2009-650). After written informed consent was obtained, subjects were randomly assigned to placebo (n = 23) or KRG (low-dose \[n = 24\] or high-dose \[n = 22\]) groups. All subjects were asked to take 10 capsules in total per day immediately after any main meals. For example, three capsules after breakfast, three capsules after lunch, and four capsules after dinner. Compliance of KRG consumption was assessed at the end of the study by counting capsules remaining and self-recording. If compliance was beneath 75%, the subject dropped out. All participants were encouraged to maintain their usual lifestyle and dietary habits.

## Assessment of dietary intake and level of physical activity

The subjects' usual diet information was obtained using both a 24-hour recall method and a semi-quantitative food frequency questionnaire (SQFFQ), which has been tested previously for validity \[15\]. We used the former to carry out analyses and the latter to check if the data collected by the 24-hour recall method was representative of the usual dietary pattern.

All the subjects were given written and verbal instructions by a registered dietitian for the completion of a 3-day (two weekdays and one weekend) dietary record every 4 weeks. On the sheet, the subjects were instructed to record the amount of food before ingestion and any remaining after ingestion by weighing the foods. One week before the start of the study, all the participants were advised to continue their usual diet for a week and were instructed to complete a 3-day dietary record as the baseline measurement, which was compared with the record obtained by 24-hour recall methods at a previous visit to check their records. The participants were also instructed to record their physical activity in a diary for 24 hours every 4 weeks. To check participants' compliance during the whole study period, the dietitian interviewed them biweekly by telephone. During the study period, all participants were also encouraged to maintain their usual lifestyle.

Dietary energy values and nutrient content from the 3-day food records were calculated using the Computer Aided Nutritional analysis program (CAN-pro 2.0, Korean Nutrition Society, Seoul, Korea). Total energy expenditure (kcal/day) was calculated from activity patterns, including basal metabolic rate, physical activity for 24 hours \[16\], and specific dynamic action of food. Basal metabolic rates for each subject were calculated with the Harris-Benedict equation \[17\].

## Anthropometric parameters, blood pressure, and blood and urine collection

Body weight and height were measured without clothes or shoes in the morning to determine the body mass index (BMI; kg/m^2^). Blood pressure was measured with an automatic blood pressure monitor (TM-2654, A&D, Tokyo, Japan) in triplicate using the left arm of a seated subject after a 20-min rest. After a 12 h fasting period, venous blood specimens were collected in EDTA-treated and plain tubes, centrifuged to produce plasma or serum, and stored at −70°C until analysis. Urine was collected in polyethylene bottles containing 1% butylated hydroxytoluene after 12 h of fasting. The tubes were immediately covered with aluminum foil and stored at −70°C until analysis.

## Serum lipid profile and fasting glucose

Fasting total cholesterol and triglycerides were measured using commercially available kits on a Hitachi 7150 Autoanalyzer (Hitachi Ltd., Tokyo, Japan). After precipitation of chylomicrons with dextran sulfate magnesium, concentrations of LDL and high-density lipoprotein (HDL) cholesterol in the supernatants were determined enzymatically. In participants with serum triglyceride concentrations below 400 mg/mL, LDL cholesterol was indirectly measured using the Friedewald formula. Fasting glucose levels were determined by the glucose oxidase method using a Beckman glucose analyzer (Beckman Instruments, Irvine, CA, USA).

## Safety parameters

Serum Blood Urea Nitrogen (BUN) and creatinine concentrations were measured by a colorimetric method using commercially available kits on a Hitachi 7180 Autoanalyzer (Hitachi Ltd., Tokyo, Japan). Serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) were measured by a kinetic UV method based on recommendations by the International Federation of Clinical Chemistry (IFCC) using commercially available kits on a Hitachi 7180 Autoanalyzer (Hitachi Ltd., Tokyo, Japan). A complete blood count (CBC) test was determined using the HOROBA ABX diagnostic (HORIBA ABX SAS, Parc Euromedicine, France).

## Alkaline comet assay for DNA damage

As a primary outcome measure, DNA damage was assessed using the comet method described by Green et al. \[18\]. Whole blood was mixed with phosphate buffered saline and poured gently over a lymphocyte separation solution (Histopaque-1077, Sigma Chemical Co., Korea). After centrifugation at 1,450 rpm for 4 min, lymphocytes were transferred to another tube, mixed with 0.7% low-melting agarose, and then added to slides pre-coated with 0.5% agarose. The slides were immersed in freshly prepared cold lysing solution and subjected to electrophoresis. The slides were then washed with neutralizing buffer and treated with ethanol. Tail length (μm) and tail moment (% of DNA in the tail × tail length) were evaluated using image analysis software (Komet 5.0, Kinetic Imaging, UK) and fluorescence microscopy (Leica, Germany) equipment with filters. Images from 50 cells from each slide were analyzed. All steps were performed in dim light, and the electrophoresis tank was covered with black paper to avoid additional light-induced DNA damage.

## Antioxidative enzyme activities

Plasma and erythrocyte antioxidative enzyme activities were measured as secondary outcomes. Plasma and erythrocyte SOD activity was determined based on the generation of superoxide radicals produced by xanthine and hypoxanthine. Superoxide radicals react with tetrazolium chloride salts to produce a red formazan dye. SOD activity was estimated by the inhibition of this colorimetric reaction using an assay kit (Cayman Chemical, Ann Arbor, MI, USA), and the absorbance at 450 nm was measured with a Wallac Victor^2^ multilabel counter (Perkin Elmer Life Sciences, Turka, Finland). The intra-assay and inter-assay coefficients of variance were 4.43% and 8.26%, respectively. Plasma GPx activity was indirectly measured using an assay kit that involved a coupled reaction with glutathione reductase (Cayman Chemical). Oxidized glutathione, generated by the reduction of hydroperoxide by GPx, was restored to its reduced state by glutathione reductase and nicotinamide adenine dinucleotide phosphate (NADPH). NADPH oxidation was monitored at a lower 340 nm absorbance, which was measured with the Wallac Victor^2^ multilabel counter. The intra-assay and inter-assay coefficients of variance were 6.01% and 7.68%, respectively. Plasma and erythrocyte catalase activity was measured based on the generation of formaldehyde from methanol in the presence of H~2~O~2~ using an assay kit (Cayman Chemical). Formaldehyde forms a bicyclic heterocycle with the chromogen Purpald, which changes from colorless to a purple color when oxidized. The absorbance was measured at 540 nm with the Wallac Victor^2^ multilabel counter. The intra-assay and inter-assay coefficients of variance were 5.52% and 6.78%, respectively.

## Plasma oxidized LDL concentration and Urinary 8-epi-PGF~2α~

As one of the secondary outcome measures, plasma oxidized LDL concentration and urinary 8-epi-PGF~2α~ excretion. Plasma oxidized LDL was determined with an enzyme immunoassay (Mercodia, Uppsala, Sweden), and the color reaction was measured at 450 nm with the Wallac Victor^2^ multilabel counter. The intra-assay and inter-assay coefficients of variance were 6.52% and 8.15%, respectively. Urinary 8-epi-PGF~2α~ was measured using a colorimetric enzyme immunoassay (Bioxytech urinary 8-epi-PGF~2α~ assay kit; OXIS International Inc., OR, USA). The resulting color was measured at 650 nm using the Wallac Victor^2^ multilabel counter. Urinary creatinine was determined using a Hitachi 7180 Autoanalyzer (Hitachi Ltd., Tokyo, Japan) by the alkaline picrated (Jeffe) reaction. Urinary 8-epi-PGF~2α~ concentrations were expressed as pmol/mmol of creatinine. The intra-assay and inter-assay coefficients of variance were 4.32% and 9.57%, respectively.

## Statistical analysis

Statistical analyses were performed with SPSS version 12.0 for Windows (Statistical Package for the Social Sciences (SPSS) Inc., Chicago, IL, USA). We used the Wilcoxon signed-rank test to determine within-group differences of biochemical parameters before (baseline) and after the intervention. The nonparametric Kruskal-Wallis test and analysis of covariance (ANCOVA) using the general linear model (GLM) with adjustments for age, sex, BMI, smoking, drinking, systolic BP, diastolic BP, and baseline values were used to compare the three groups. Spearman's correlation coefficients were used to determine relationships between variables. After determining whether data was normally distributed by the Kolmogorov-Smirnov and Shapiro-Wilk W tests, a logarithmic transformation was applied to non-normal data before statistical analysis. For descriptive purposes, the results are expressed as untransformed and unadjusted mean values. Continuous variables are expressed as mean ± SEM, and categorical variables are expressed as absolute numbers and percentages. A two-tailed value of *P* less than 0.05 was considered significant.

# Results

## Baseline clinical characteristics and dietary intake

Table 1 shows the baseline clinical characteristics of the study subjects. Anthropometric and biochemical parameters before treatment were not significantly different among the groups. In addition, KRG supplementation had no significant effect on BMI and blood pressure. After treatment, serum lipid profiles (e.g., total cholesterol, triglyceride, HDL- and LDL-cholesterol) and safety parameters (e.g., liver and kidney function tests, complete blood count) did not differ among groups (data not shown). In addition, no significant differences were observed for daily nutrient intake before and after the intervention in all the groups (Table 2).

Anthropometric parameters and blood pressure before and after intervention

|   | **Placebo (n = 19)** | **Low-dose (n = 19)** | **High-dose (n = 19)** | ***P***^***a)***^ |   |   |   |
|:---|----|----|----|----|----|----|----|
| Age (yr) | 37.0 ± 2.24 | 36.4 ± 1.73 | 35.3 ± 1.68 | 0.910 |   |   |   |
| Male / Female (n) | 7/12 | 7/12 | 9/10 | 0.747 |   |   |   |
| Body mass index (BMI; kg/m^2^) |   |   |   |   |   |   |   |
|  Pre-treatment | 23.4 ± 0.60 | 24.0±0.93 | 23.5±0.70 |   |   |   |   |
|  Post-treatment | 23.6 ± 0.67 | 24.3±0.93 | 23.6±0.65 |   |   |   |   |
|  Change | 0.27±0.16 | 0.26±0.15 | 0.11±0.14 | 0.850 |   |   |   |
| Cigarette smoker, n (%) | 8 (42.1) | 6 (31.6) | 8 (42.1) | 0.948 |   |   |   |
| Alcohol drinker, n (%) | 15 (78.9) | 13 (68.4) | 13 (68.4) | 0.706 |   |   |   |
| Blood pressure (mmHg) |   |   |   |   |   |   |   |
| Systolic blood pressure (mmHg) |  |  |  |  |   |   |   |
|  Pre-treatment | 115.3±2.92 | 118.8±3.22 | 117.5±2.32 |   |   |   |   |
|  Post-treatment | 114.7±2.08 | 118.8±3.10 | 118.2±1.94 |   |   |   |   |
|  Change | −0.55±1.97 | −0.03±0.19 | 0.74±1.65 | 0.740 |   |   |   |
| Diastolic blood pressure (mmHg) |  |  |  |  |   |   |   |
|  Pre-treatment | 76.6±1.83 | 80.6±1.98 | 78.4±1.81 |   |   |   |   |
|  Post-treatment | 75.6±1.93 | 77.3±2.14^\*^ | 76.2±1.71 |   |   |   |   |
|  Change | −0.92±1.88 | −3.29±1.28 | −2.16±1.75 | 0.599 |   |   |   |

Results are expressed as mean ± standard error of the mean (SEM) or n (%).

Baseline values for the placebo and test groups were not significantly different (nonparametric Kruskal-Wallis test).

^\*^*P* \< 0.05 compared with the baseline in each group (Wilcoxon signed-rank test).

^a)^ Evaluated by nonparametric Kruskal-Wallis test.

Daily energy expenditure and nutrient intake before and after intervention

|   | **Placebo (n = 19)** | **Low-dose (n = 19)** | **High-dose (n = 19)** | ***P***^***a)***^ |
|:---|----|----|----|----|
| Total energy expenditure (kcal) |   |   |   |   |
|  Pre-treatment | 2329.3±78.9 | 2331.8±90.5 | 2299.5±57.6 |   |
|  Post-treatment | 2268.3±76.7 | 2273.2±98.1 | 2300.0±68.1 |   |
|  Change | −61.0±36.9 | −58.6±33.6 | 0.50±32.9 | 0.317 |
| Estimates of dietary nutrient intake |   |   |   |   |
| Total calorie intake (kcal) |   |   |   |   |
|  Pre-treatment | 1789.2±79.6 | 1726.4±104.4 | 1847.9±114.2 |   |
|  Post-treatment | 1892.9±72.9 | 1820.1±81.9 | 1966.3±97.8 |   |
|  Change | 103.7±79.0 | 93.7±69.1 | 118.4±75.1 | 0.991 |
| Carbohydrate (%) |   |   |   |   |
|  Pre-treatment | 60.0±1.57 | 60.8±1.09 | 62.4±0.92 |   |
|  Post-treatment | 61.2±1.66 | 59.9±1.72 | 61.1±1.34 |   |
|  Change | 1.16±1.78 | −0.94±1.78 | −1.28±1.44 | 0.767 |
| Protein (%) |   |   |   |   |
|  Pre-treatment | 16.0±0.61 | 16.3±0.62 | 15.8±0.42 |   |
|  Post-treatment | 16.5±0.75 | 16.0±0.61 | 15.4±0.53 |   |
|  Change | 0.56±0.86 | −0.36±0.75 | −0.38±0.67 | 0.280 |
| Fat (%) |   |   |   |   |
|  Pre-treatment | 24.4±1.47 | 23.9±1.05 | 22.4±1.07 |   |
|  Post-treatment | 22.7±1.79 | 24.3±1.55 | 22.9±1.14 |   |
|  Change | −1.67±1.65 | 0.43±1.22 | 0.50±1.22 | 0.733 |
| TEE/TCI |   |   |   |   |
|  Pre-treatment | 1.37±0.11 | 1.43±0.09 | 1.34±0.09 |   |
|  Post-treatment | 1.23±0.06 | 1.29±0.08 | 1.23±0.07 |   |
|  Change | −0.14±0.10 | −0.14±0.07 | −0.12±0.07 | 0.990 |

Results are expressed as standard error of the mean (SEM).

No significant differences in the week 0 value between the placebo and test groups were found by nonparametric Kruskal-Wallis test.

^a)^ Evaluated by nonparametric Kruskal-Wallis test.

## Antioxidant enzyme activities

Plasma SOD activity after the 8-week KRG supplementation was significantly higher than baseline activity (low-dose; *P* = 0.038, high-dose; *P* = 0.016), and the net changes were significantly different between the high-dose group and the placebo group (*P* = 0.021) after adjusting for age, sex, BMI, smoking, drinking, systolic BP, diastolic BP, and baseline values (Table 3). The plasma GPx and catalase activities after the 8-week high-dose supplementation were higher than baseline (*P* = 0.006 and 0.016, respectively), and the net changes also were significantly different between the high-dose group and the placebo group (*P* = 0.032 and 0.019, respectively) after adjustment. Erythrocyte SOD and catalase activities were also measured. No significant differences were observed in the baseline activities of both enzymes among the three groups (SOD: placebo, 44.1 ± 3.14; low-dose, 46.0 ± 2.93; high-dose; 42.3 ± 2.99; Catalase: placebo, 231.1 ± 1.91; low-dose, 224.3 ± 3.48; high-dose; 223.7 ± 2.78). After the eight weeks, the activities of both enzymes were significantly increased in the high-dose group (p \< 0.05). In particular, the net changed values for erythrocyte SOD activities were significantly different among the three groups after the adjustment (SOD: placebo, -3.58 ± 2.39; low-dose, -0.07 ± 1.81; high-dose; 3.50 ± 1.95, p = 0.030; Catalase: placebo, -6.74 ± 2.94; low-dose, 1.21 ± 3.45; high-dose; 4.46 ± 2.55, p = 0.120).

Antioxidant enzyme activity and lipid peroxidation before and after intervention

|   | **Placebo (n = 19)** | **Low-dose (n = 19)** | **High-dose (n = 19)** |   |   |   |
|:---|:---|:---|:---|:---|:---|:---|
| SOD activity (U/ml) |   |   |   |   |   |   |
|  Pre-treatment | 12.6±0.97 | 10.1±0.68 | 11.9±0.98 |   |   |  |
|  Post-treatment | 12.2±0.96 | 12.1±1.03^\*^ | 14.2±1.15^\*^ |   |   |   |
|  Change | −0.46±0.57 | 1.95±0.80 | 2.24±0.86^†^ |   |   |   |
| GPx activity (nmol/min/ml) |   |   |   |   |   |   |
|  Pre-treatment | 41.1±3.71 | 34.3±1.88 | 34.9±2.53 |   |   |   |
|  Post-treatment | 39.1±3.72 | 37.4±2.27 | 43.5±2.53^\*\*^ |   |   |   |
|  Change | −2.00±2.08 | 3.05±2.15 | 8.66±2.63^†^ |   |   |   |
| Catalase (nmol/min/ml) |   |   |   |   |   |   |
|  Pre-treatment | 103.2±11.5 | 94.5±8.56 | 86.9±11.7 |   |   |   |
|  Post-treatment | 99.0±13.2 | 118.3±11.8 | 135.5±12.3^\*\*^ |   |   |   |
|  Change | −4.17±12.4 | 23.8±12.4 | 48.5±12.3^†^ |   |   |   |
| 8-epi-PGF~2α~ (pg/mg creatinine)^a)^ |  |   |   |   |   |   |
|  Pre-treatment | 1450.3±103.7 | 1519.0±222.3 | 1516.3±105.9 |   |   |   |
|  Post-treatment | 1641.6±136.5 | 1323.4±106.6 | 1383.5±120.2 |   |   |   |
|  Change | 191.3±107.7 | −195.6±147.6^†^ | −132.8±127.9^†^ |   |   |   |

SOD, superoxide dismutase; GPx, glutathione peroxidase; 8-epi-PGF~2α~, 8-epi-prostaglandin F~2α~.

^a)^Tested after logarithmic transformation.

Results are expressed as standard error of the mean (SEM).

Tested by Wilcoxon signed-rank test or analysis of covariance (ANCOVA) using a general linear model (GLM) with adjustments for age, sex, BMI, smoking, drinking, systolic BP, diastolic BP, and baseline values.

^\*^*P* \<0.05,^\*\*^*P* \<0.01 compared with the baseline in each group; ^†^*P* \<0.05, ^††^*P* \<0.01 compared with the placebo group.

## Lymphocyte DNA damage

The DNA tail length significantly decreased after KRG supplementation compared to baseline (low-dose; *P* = 0.004, high-dose; *P* = 0.003; Figure 1). The tail moment was also significantly reduced by KRG supplementation (low-dose; *P* = 0.012, high-dose; *P* = 0.001). The net changes in tail length and tail moment differed significantly between the high-dose group and the placebo group (*P* = 0.032 and *P* = 0.019, respectively) after adjustment. The images showing lymphocyte DNA damage, as performed by comet assay before and after the treatment, between the placebo and high-dose groups are presented in Figure 1.

## Oxidative stress biomarkers, oxidized LDL, and urinary 8-epi-PGF~2α~

After 8 weeks, the plasma oxidized LDL levels decreased in the low-dose (*P* = 0.003) and high-dose (*P* = 0.001) groups, but increased in the placebo group (*P* = 0.035). The net changes differed significantly between the KRG supplementation groups and the placebo group (vs. low-dose, *P* = 0.001; vs. high-dose, *P \<*0.001) after adjustment (Figure 1). We also found significant differences in the net changes of urinary excretion of 8-epi-PGF~2α~ between the KRG supplementation groups and the placebo group (vs. low-dose, *P* = 0.033; vs. high-dose, *P =* 0.049) after adjustment (Table 3).

## Relationship between changes in oxidative stress markers and catalase activity

Among all subjects, changes in oxidized LDL from baseline correlated positively with changes in urinary 8-epi-PGF~2α~ (*r* = 0.305, *P* = 0.030), negatively with changes in catalase activity (*r* = −0.419, *P* = 0.002), and appeared to correlate positively with changes in DNA tail moment (*r* = 0.231, *P* = 0.096); however, this correlation was not significant. After adjusting for sex and age, changes in oxidized LDL correlated positively with both changes in urinary 8-epi-PGF~2α~ (*r* = 0.332, *P* = 0.026) and DNA tail moment (*r* = 0.295, *P* = 0.049) and correlated negatively with changes in catalase activity (*r* = −0.401, *P* = 0.006).

# Discussion

It has been reported that about 200 substances, such as ginsenosides, polysaccharides, polyacetylenes, peptides, and amino acids etc, have been isolated from Korean ginseng, and more than 100 substances have also been isolated from American ginseng and Notoginseng \[19,20\]. Among the substances isolated from ginseng, ginsenosides are the primary active components of ginseng \[21\]. According the recent report, more than 60 ginsenosides have been found in the American ginseng \[22\]. These ginsenosides have been discovered continuously in the Korean ginseng, and it has been assumed that Korean ginseng contains over 60 ginsenosides \[20\]. Furthermore, Korean ginseng contains more primary non-saponin compounds, phenol compounds, acid polysaccharides, and polyethylene compounds than American ginseng or Sanchi ginseng \[23\]. KRG is produced by a special process of steaming and drying fresh Korean ginseng. The steaming process hydrolyzes and converts ginsenosides into other types of ginsenosides, such as red ginseng-specific compounds with anticancer properties (ginsenoside-Rh2, -Rh4, -Rs3, -Rs4, -Rg5) and a compound with anti-metastasis and vasodilation properties (ginsenoside-Rg3). The anti-oxidative and anti-cancer activities of KRG appear to be superior to those of white ginseng \[21,24\].

The reduction in oxidative DNA damage after KRG supplementation observed in the present study is consistent with findings from a previous study using a rat model. Park et al. \[25\] reported that red ginseng extract inhibited the breaking of *Escherichia coli* ColE1 plasmid DNA as well as the nuclear DNA of rat hepatocytes damaged by oxidative stress. In the present study, 8 weeks of KRG supplementation (6 g/day) significantly decreased DNA tail length (μm) and tail moment, indicators of lymphocyte DNA damage, compared to placebo. Zhang et al. \[26\] also reported in mice that a 2-day oral administration of 20(S)-ginsenoside Rg3 (20 mg/kg per day) significantly inhibited cyclophosphamide-induced DNA damage in bone marrow cells and peripheral lymphocytes.

Oxidative stress results from an imbalance between antioxidants and reactive oxygen species, such as superoxide anions, H~2~O~2~, and hydroxyl radicals \[27\]. High levels of reactive oxygen species damage biomolecules (nucleic acids, proteins, and lipids) and generate oxidized LDL, which upregulates lectin-like oxidized low-density lipoprotein receptor-1 in endothelial cells, thereby inducing the generation of more reactive oxygen species through an unclear intracellular feedback mechanism \[28\]. The antioxidant enzyme SOD eliminates superoxide anions by producing H~2~O~2~ and is the first line of defense against reactive oxygen species toxicity. Furthermore, the enzymes catalase and GPx convert harmful H~2~O~2~ to harmless H~2~O \[3,29\]. Thus, boosting the activity of these three main antioxidant enzymes by KRG supplementation may decrease oxidative stress.

In the present study, plasma SOD, catalase, GPx, and erythrocyte SOD activities were significantly higher in subjects who received KRG supplementation than in subjects who received the placebo. In the past, the antioxidative effects of KRG have primarily been demonstrated in animal models or *in vitro* studies. Kim et al. \[30\] reported that, in mice subjected to paraquat-mediated oxidative stress, SOD, catalase, and GPx levels in liver tissue were significantly increased by red ginseng saponin supplementation. In a separate publication, Kim et al. \[31\] demonstrated that ginsenoside Rh2, a characteristic ginsenoside of red ginseng, is a particularly important active compound that significantly boosts catalase activity. Similarly, Chang et al. \[32\] reported that the Rb2 subfraction of panaxadiol ginsenosides appeared to strongly induce SOD1 and catalase, which maintain cell viability by lowering the level of oxygen radicals generated by the intracellular metabolism.

The limitations of our study include a small sample size, which renders us unable to generalize our study findings to other populations. We were also limited by the short study duration, and the mechanisms of action were not addressed in the study design. The important findings presented in this study will require further investigation in larger trials that will be carefully designed to include the optimal dose and form of KRG intervention, the study duration, and subject characteristics. In addition, enzyme mass and gene and protein expression levels should be measured to determine whether the increased enzyme activity is due to an increase in protein mass or through the elevation of the specific activities of the enzyme.

# Conclusions

In this randomized, double-blind, placebo-controlled study, KRG supplementation improved biomarkers of oxidative stress*,* as evidenced by decreased plasma oxidized LDL, attenuated lymphocyte DNA damage and increased plasma antioxidant enzyme activity in healthy participants (20–65 years old). In addition, changes in plasma oxidized LDL correlated positively with the decreases in oxidative DNA damage and urinary 8-epi-PGF~2α~ and negatively with changes in catalase activity. Larger prospective studies with a longer intervention period (3 or 6 months) will be necessary to further characterize the beneficial antioxidative activity of KRG and its mechanism of action.

# Abbreviations

GPx, Glutathione peroxidase; SOD, Superoxide dismutase; KRG, Korean red ginseng; PGF2α, 8-epi-prostaglandin F2α; LDL, Low-density lipoprotein; HDL, High-density lipoprotein; AST, Serum aspartate aminotransferase; ALT, Alanine aminotransferase; BUN, Blood urea nitrogen; CBC, Complete blood count; NADPH, Nicotinamide adenine dinucleotide phosphate.

# Competing interests

The authors declare that they have no competing interests.

# Authors' contributions

JYK and JHL conceptualized this study, and JHL was the Principal Investigator. JYP and HJK also participated in the design of the study and performed the analyses. JYK and OYK contributed to the writing of the manuscript. All authors have read and approved the final manuscript.

## Acknowledgements

We sincerely thank the research subjects who participated in the studies described in this report. This work was supported by a 2009 grant from the Korean Society of Ginseng funded by the Korea Ginseng Cooperation and National Research Foundation, Ministry of Education, Science and Technology (Mid-Career Researcher Program: 2010–0015017), Seoul, Korea.


abstract: A^−^β^+^ ketosis-prone diabetes (KPD) is an emerging syndrome of obesity, unprovoked ketoacidosis, reversible β-cell dysfunction, and near-normoglycemic remission. We combined metabolomics with targeted kinetic measurements to investigate its pathophysiology. Fasting plasma fatty acids, acylcarnitines, and amino acids were quantified in 20 KPD patients compared with 19 nondiabetic control subjects. Unique signatures in KPD—higher glutamate but lower glutamine and citrulline concentrations, increased β-hydroxybutyryl-carnitine, decreased isovaleryl-carnitine (a leucine catabolite), and decreased tricarboxylic acid (TCA) cycle intermediates—generated hypotheses that were tested through stable isotope/mass spectrometry protocols in nine new-onset, stable KPD patients compared with seven nondiabetic control subjects. Free fatty acid flux and acetyl CoA flux and oxidation were similar, but KPD had slower acetyl CoA conversion to β-hydroxybutyrate; higher fasting β-hydroxybutyrate concentration; slower β-hydroxybutyrate oxidation; faster leucine oxidative decarboxylation; accelerated glutamine conversion to glutamate without increase in glutamate carbon oxidation; and slower citrulline flux, with diminished glutamine amide–nitrogen transfer to citrulline. The confluence of metabolomic and kinetic data indicate a distinctive pathogenic sequence: impaired ketone oxidation and fatty acid utilization for energy, leading to accelerated leucine catabolism and transamination of α-ketoglutarate to glutamate, with impaired TCA anaplerosis of glutamate carbon. They highlight a novel process of defective energy production and ketosis in A^−^β^+^ KPD.
author: Sanjeet G. Patel; Jean W. Hsu; Farook Jahoor; Ivonne Coraza; James R. Bain; Robert D. Stevens; Dinakar Iyer; Ramaswami Nalini; Kerem Ozer; Christiane S. Hampe; Christopher B. Newgard; Ashok BalasubramanyamCorresponding author: Ashok Balasubramanyam, <email@example.com>.[^1]
date: 2013-03
references:
title: Pathogenesis of A^−^β^+^ Ketosis-Prone Diabetes

Ketosis-prone diabetes (KPD) is characterized by presentation with diabetic ketoacidosis (DKA) in persons who do not fit traditional categories of types 1 or 2 diabetes (1–5). We have defined four subgroups of KPD based on presence or absence of β-cell autoantibodies (A^+^ or A^−^), and recovery or lack of recovery of β-cell functional reserve following the index episode of DKA (β^+^ or β^−^) (1,6,7).

The A^−^β^+^ KPD subgroup represents a novel syndrome of severe but reversible β-cell dysfunction (1,3,5,8,9). Approximately 50% of these patients develop DKA without a precipitating factor at diagnosis of diabetes. These new-onset, unprovoked A^−^β^+^ KPD patients display male predominance (10) and low frequencies of human leukocyte antigen class II susceptibility alleles for autoimmune diabetes (11). β-Cell function increases markedly within 1–3 months after the index DKA, with sustained glycemic improvement and insulin independence (1,9,11,12). The cause of the unprovoked ketoacidosis is unknown. Over 5–10 years, patients may relapse to unprovoked ketosis (3,5).

This syndrome provides a model to identify novel mechanisms of obesity, ketosis, and reversible β-cell dysfunction. We used a metabolomics approach to identify unique alterations in A^−^β^+^ KPD patients, with a kinetics approach to specify the pathophysiology.

# RESEARCH DESIGN AND METHODS

## Metabolomic analysis

### Human subjects.

Protocols were approved by the Human Studies Institutional Review Boards of Baylor College of Medicine and Duke University (13). Written informed consent was obtained. New-onset, unprovoked male A^−^β^+^ KPD patients (*n* = 20) were identified by absence of GAD65/67, IA-2, or ZnT8 autoantibodies and presence of β-cell functional reserve 4–8 weeks after the index DKA (1,6). Men (*n* = 19) from the Study of the Effects of Diet on Metabolism and Nutrition study (13) were nondiabetic obese control subjects. KPD patients were selected from a longitudinal database using the MatchIT program (15) to minimize intragroup differences in initial HbA~1c~ and intergroup differences in age, ethnicity, BMI, waist circumference, fasting C-peptide, glucose, and lipids. At the time of blood sampling, all patients were clinically stable, taking twice-daily neutral protamine Hagedorn insulin (± short-acting insulin); three patients were also taking metformin (1 g daily). Five additional, newly diagnosed, unprovoked A^−^β^+^ KPD patients were subsequently recruited for measurements of glutamine, glutamate, isoleucine, and leucine.

### Plasma collection.

The KPD samples were collected in acid citrate dextrose (#364606, BD Biosciences) after an overnight fast before the morning dose of insulin, 20 days (median) after the index DKA (5). Blood was kept on ice for 2–4 h, centrifuged at 4°C, and the plasma stored at −20°C. Control subjects' samples were collected after an overnight fast in serum separator tubes (#366510, BD Biosciences), centrifuged, and the serum stored at −80°C. Blood samples were also taken from five fresh A^−^β^+^ KPD patients 1 month after their index DKA, collected in serum separator tubes, processed immediately, and stored.

### Metabolite analysis.

To measure 15 amino acids and 45 acylcarnitines, samples were deproteinized by methanol precipitation, esterified with hot, acidic methanol or *n*-butanol, and then analyzed by tandem mass spectrometry (MS/MS) with a Quattro Micro instrument (Waters Corporation, Milford, MA) (13). Leucine and isoleucine are reported as a single analyte (Leu/Ile) because they were not resolved by this method. These conditions partially hydrolyze glutamine to glutamate and asparagine to aspartate, so "glx" (Glu/Gln) or "asx" (Asp/Asn) signify glutamate or aspartate with contributions from hydrolysis reactions of glutamine and asparagine. Final concentrations of isoleucine, leucine, glutamine, and glutamate were measured by reverse-phase high-performance liquid chromatography (HPLC; Hewlett-Packard 1090; Hewlett-Packard, Avondale, PA); frozen samples were thawed on ice prior to HPLC and glutamine standards prepared fresh in ice-cold water.

To measure seven nonesterified free fatty acids (FFAs), samples were methylated and purified by solid-phase extraction (13). Derivatized fatty acids were analyzed by gas chromatography/mass spectrometry (GC/MS; Trace DSQ; Thermo Electron Corporation, Austin, TX).

All MS analyses used stable isotope dilution to quantify metabolites (13). Because acid citrate dextrose tubes were used for the original KPD patients, a dilution correction factor (1.31) was used for analytes measured in those samples (16).

Plasma insulin, C-peptide, glucagon, cortisol, and free metanephrines were measured by radioimmunoassay (Linco, St. Louis, MO), and β-hydroxybutyrate (BOHB) by spectrophotometry (Wako, Richmond, VA).

### Statistical analysis.

Data were inspected for normality using the Shapiro-Wilk tests. The *t* tests were used to compare levels of hormones, baseline clinical parameters, and metabolites. BOHB values were not normally distributed, so median levels were compared using the Mann-Whitney U test. The Fisher exact test was used to assess deviations from the order of metabolites as an indicator of substrate preference due to altered consumption or production. Significance was defined at *P* \< 0.05.

## Kinetic analyses

### Human subjects.

The study was approved by the Baylor institutional review board and written informed consent obtained. Nine new-onset, unprovoked A^−^β^+^ KPD patients (six men, three women), all clinically stable, were recruited 6–8 weeks after the index DKA episode. Seven nondiabetic control subjects (six men, one woman) were matched for BMI, age, and ethnicity, with the six men and one woman among the KPD patients. Exclusions were: serum creatinine \>1.2 mg/day, retinopathy, neuropathy, or heart failure; history of alcohol abuse; alanine aminotransferase/aspartate aminotransferase (AST)/alkaline phosphatase \>2× upper limit of normal; medications likely to affect fat, glucose, or protein metabolism; and other chronic illnesses. Fasting plasma C-peptide and glucose, peak and area under the curve of C-peptide response to glucagon, fasting lipids, and liver and thyroid functions were measured. Details of the glucagon stimulation test and cutoffs to classify β-cell reserve have been reported (1).

Subjects underwent four stable isotope protocols in the General Clinical Research Center. For 2 days prior to each study, they consumed an isocaloric, standardized diet (30% calories from fat, 65% from carbohydrates, 1 g/kg protein) prepared by the General Clinical Research Center kitchen. KPD patients were treated with low doses of neutral protamine Hagedorn insulin for 8 weeks prior to the studies. The last dose was administered at 8–10 p.m., and each study commenced the following morning after a 10–14-h fast, with no insulin administered until study completion. One venous catheter was placed for infusions, with another in the opposite hand, heated for arterialized blood sampling.

### Protocol 1.

After baseline blood and breath sampling, an oral 50 mg/kg bolus of ^2^H~2~O was administered. After an intravenous priming dose of NaH^13^CO~3~ (1.2 µmol/kg), ^2^H~5~-glycerol (prime 4.5 µmol/kg, constant 9.0 µmol/kg/h), ^2^H~2~-potassium palmitate (prime 3 µmol/kg, constant 4 µmol/kg/h), and ^13^C~1~-acetate (prime 62.5 µmol/kg, constant 150 µmol/kg/h) were infused for 4 h. Blood was collected hourly for the first 3 h and every 15 min during the fourth hour. Indirect calorimetry was performed during the last 30 min of the fourth hour. At hour 4, glycerol and palmitate infusions ceased, but the acetate infusion continued for another 2 h. Blood and breath samples were drawn every 15 min between hours 5 and 6.

Tracer-to-tracee ratios of plasma free palmitate, acetate, and BOHB were determined by negative chemical ionization GC/MS of its pentafluorobenzyl derivative (17). Plasma palmitate and BOHB concentrations were measured by in vitro isotope dilution with U-^13^C-palmitate or ^13^C~2~-BOHB (Cambridge Isotope Laboratories, Andover, MA) as internal standards, using negative chemical ionization-GC/MS (17).

Tracer-to-tracee ratio of plasma glycerol was determined by electron impact ionization GC/MS of its tripropionate derivative (18). Breath ^13^CO~2~ content was determined by GC/gas isotope ratio MS. Zinc was used to reduce water in 10-µl plasma in quartz vessels and ^2^H~2~ abundance of the resulting hydrogen determined by GC/gas isotope ratio MS.

### Protocol 2.

After baseline blood sampling, ^2^H~2~-citrulline (prime 1.0 µmol/kg, constant 1.0 µmol/kg/h), ^15^N~1~-amide-glutamine (prime 13.6 µmol/kg, constant 13.6 µmol/kg/h), ^2^H~8~-valine (prime 1 µmol/kg, constant 1 µmol/kg/h), and NaH^13^CO~3~ (prime 4 µmol/kg, constant 4 µmol/kg/h) were infused. A 0.5-µmol/kg prime of ^15^N~1~-citrulline was also given. Citrulline, glutamine, and valine were infused for 6 h. NaH^13^CO~3~ infusion was infused for 2 h; at the second hour, ^13^C~1~-leucine (prime 4 µmol/kg, constant 4 µmol/kg/h) was started and infused until hour 6. Blood was collected hourly during the first 4 h and every 15 min during the fifth hour. Breath was collected at baseline and every 15 min between hours 1 and 2 and 5 and 6.

Tracer-to-tracee ratio of plasma α-ketoisocaproic acid (α-KICA) from leucine was measured by negative chemical ionization-GC/MS of its pentafluorobenzyl derivative (17).

Plasma citrulline, glutamine, and valine isotopic enrichment (IE) were measured after conversion to their 5-(dimethylamino)-1-napthalene sulfonamide derivatives by liquid chromatography-MS/MS (Synergi MAX-RP column; Phenomenex, Torrance, CA) (19).

### Protocol 3.

After baseline blood and breath sampling, ^13^C~1~-glutamine (prime 13.6 µmol/kg, constant 13.6 µmol/kg/h) was infused for 6 h. A priming dose of NaH^13^CO~3~ (4 µmol/kg) was given at hour 0. Indirect calorimetry was performed during the last 30 min of the fourth hour. Blood and breath were collected hourly for 5 h and then every 15 min between hours 5 and 6.

Plasma glutamine and glutamate IE were measured by liquid chromatography-MS/MS as in protocol 2.

### Protocol 4.

After baseline blood and breath sampling, a priming dose of NaH^13^CO~3~ (4 µmol/kg) was given, and then 2,4-^13^C-β-hydroxybutyrate (prime 20 µmol/kg, constant 12 µmol/kg/h) was infused for 3 h. Blood and breath were collected hourly for the first 2 h and every 15 min during the third hour. Indirect calorimetry was performed during the last 30 min of the second hour. Blood was collected in K-EDTA tubes and spun immediately and one aliquot deproteinized with perchloric acid and frozen in liquid nitrogen.

IE of BOHB was measured as in protocol 1.

Plasma glucose was measured with a glucose analyzer (YSI, Yellow Springs, OH), insulin by radioimmunoassay (Linco) and FFAs by spectrophotometry (Wako). Amino acids were measured by HPLC.

### Calculations.

Ra (palmitate, glycerol, acetate, or BOHB) (in µmol/kg/h):

where IE~inf~ is the IE (mole %) in the infusate, IE~p~ is IE (mole %) in the plasma at steady state, and *i* is the tracer infusion rate.

Rate of appearance of FFAs (µmol · kg^−1^ · h^−1^):

where Ra palmitate is the rate of appearance of palmitate (Eq. 1), \[palmitate\]~p~ is the concentration of plasma palmitate, and \[FFA\]~p~ is the concentration of plasma FFA.

Whole-body fatty acid oxidation is calculated from RQ, VO~2~, and (20).

Rate of acetate oxidation (µmol · kg^−1^ · h^−1^) in the tricarboxylic acid (TCA) cycle:

where is the CO~2~ excretion rate measured by indirect calorimetry, IE~CO2~ is the IE of CO~2~ in the breath, IE~acetate~ is the IE of plasma acetate, and 0.56 is the acetate retention factor (21).

Acetate oxidized in the TCA cycle (%):

BOHB from acetate (µmol/kg/h):

where Ra~BOHB~ is the value from Eq. 1, IE~BOHB~ is the M+1 isotopomer enrichment of BOHB derived from the ^13^C~1~-acetate infusate, and IE~acetate~ is the IE of acetate.

Total body water (TBW) (from oral deuterated water):

where is the enrichment of deuterated water consumed, is the enrichment of plasma H~2~O at steady state, and the constant 1.04 corrects for deuterium's volume of distribution. Fat-free mass (FFM) is TBW/0.72, where 0.72 is the hydration constant of fat-free tissue. Fat mass (kg) = body weight (kg) − FFM.

Ra (citrulline or glutamine amide-N) (μmol/kg/h) was calculated as in Eq. (1).

Rate of transfer of glutamine amide ^15^N to citrulline:

where Ra citrulline is the value from Eq. 7, IE^15^~N-citrulline~ is the isotopic (M+1) enrichment of citrulline from ^15^N-glutamine, and IE^15^~N-glutamine~ is the isotopic (M+1) enrichment of glutamine.

Flux of glutamine carbon is calculated as in Eq. (7).

^13^C-glutamine reaches steady state quickly, but ^13^C-glutamate formed from the glutamine does not, because of the large whole-body free glutamate pool. Hence, the fraction of the glutamate pool derived from glutamine (fractional synthesis rate \[FSR\] of glutamate from glutamine) is calculated using the precursor-product equation:

where IEglu*~t6~* − IEglu*~t3~* is the increase in glutamate IE from 3–6 h of the infusion, and IEgln*~pl~* is the plateau IE of glutamine.

Absolute synthesis rate (ASR) of glutamate from glutamine:

The rate of formation of ^13^CO~2~ from ^13^C~1~-glutamine over the 6-h infusion, an index of glutamate dehydrogenase activity, is calculated as follows:

^13^C~1~-glutamine oxidation (mmol/6 h) = area under the curve of breath ^13^CO~2~ = AUC~0–6\ h~ of ^13^CO~2~:

where V~CO2~/0.78 is the production rate of CO~2~, assuming 22% of CO~2~ is retained in the bicarbonate pool (22), and IE~CO2~ is the IE of CO~2~ (atom percent excess).

a relative measure of glutamate entry in the TCA cycle at α-ketoglutarate and an estimate of glutaminase plus glutamate dehydrogenase activity.

# RESULTS

## Metabolomics

### Clinical and biochemical parameters.

BMI and waist circumference were similar in KPD and control subjects (Table 1). KPD subjects had elevated initial HbA~1c~ (13.5 ± 0.5 %) and fasting plasma glucose levels 4–12 weeks after the index DKA episode. Characteristically, HbA~1c~ was markedly improved 3–6 months after the index DKA (7.2 ± 0.5), when the patients were on low doses of insulin (*N* = 13) or off insulin (*N* = 7). Fasting C-peptide was higher in control subjects. The C-peptide/glucose ratio was one-third of the control level in the KPD group. Fasting insulin levels were not different, and quantitative insulin sensitivity check index (QUICKI), an index of insulin sensitivity, demonstrated similar levels of insulin resistance in the two groups. Triglycerides showed a trend to be higher, and HDL-cholesterol (HDL-C) was ∼30% lower in KPD. The mean BOHB level was twice as high in KPD as in control subjects, but the difference did not attain statistical significance due to individual variability. KPD patients had normal fasting metanephrines and cortisol and normal thyroid, renal, and liver functions. The KPD group included 1 white, 7 African American, and 11 Hispanic subjects, whereas the control subjects included 14 white, 4 African American, and no Hispanic subjects; ethnicity was not correlated with any inter- or intragroup differences in metabolite or hormone levels.

Metabolomic survey: demographic and biochemical data and fasting plasma concentrations of amino acids, acylcarnitines, and FFAs

![](912tbl1)

### Amino acids and related acylcarnitines.

"Asx" was 2.3-fold (*P* \< 0.01) and "glx" 4.8-fold (*P* \< 0.0001) higher in the KPD group (Table 1). Smaller elevations were observed in KPD for glycine (1.4-fold; *P* \< 0.001), serine (1.4-fold; *P* \< 0.001), alanine (1.2-fold; *P* = 0.03), proline (1.2-fold; *P* = 0.02), and histidine (1.2-fold; *P* \< 0.001). Methionine (0.85-fold; *P* = 0.02) and citrulline (0.75-fold; *P* = 0.02) were lower in KPD.

The unusually high levels of glx in KPD suggested partial ex vivo conversion of glutamine to glutamate or pyroglutamine (23–25) in those samples. HPLC also demonstrated very low glutamine and very high glutamate levels in the KPD samples. To avoid alterations of glutamine and glutamate due to collection methods or storage, five additional, newly diagnosed A^−^β^+^ KPD patients were recruited and their plasma collected using stringent collection and processing methods (26). The mean glutamine concentration in these new KPD samples was 207.20 ± 10.29 μmol/L (0.56-fold less than control subjects; *P* = 0.0001) and the mean glutamate concentration 192.03 ± 7.98 μmol/L (1.75-fold more than control subjects; *P* = 0.02).

Concentrations of total branched chain amino acids (BCAA) were similar in KPD and control subjects, with no significant differences in valine or isoleucine/leucine. Because MS/MS cannot distinguish isoleucine from leucine, HPLC was used to separate them in samples from 11 KPD patients and 16 control subjects. Leucine and isoleucine concentrations were not different between KPD and control subjects (128.8 ± 11.6 vs. 137.3 ± 7.7 and 59.6 ± 7.0 vs. 62.2 ± 5.3 μmol/L, respectively; *P* = NS for both). This was surprising because fasting plasma concentrations of leucine and isoleucine are usually elevated in diabetic patients with imperfect glycemic control (27–30). Hence, we tracked the catabolic pathways of BCAA through acylcarnitine esters of their distinct ketoacid metabolites (Table 1). Isobutyryl-carnitine (C4-AC), from decarboxylation of the valine ketoacid, was 1.5-fold higher in KPD (*P* \< 0.01). In contrast, C5-AC, representing acylcarnitine esters of both isovaleryl-CoA and α-isomethylbutyryl-CoA (from decarboxylation of the leucine and isoleucine ketoacids, respectively), was 1.3-fold lower in KPD (*P* \< 0.01), but tiglyl-carnitine (C5:1-AC), an isoleucine-specific catabolite, was 1.2-fold higher in KPD (*P* = 0.1). In sum, these data indicated that the leucine ketoacid metabolite was decreased in KPD patients.

BCAA are deaminated by branch chain aminotransferase (BCAT) to produce KICA from leucine, α-keto-β-methyvaleric acid (KMVA) from isoleucine and α-ketoisovaleric acid (KIVA) from valine, and the NH~2~ group is transferred to α-ketoglutarate to generate glutamate. The ketoacids are processed by branch chain ketoacid dehydrogenase (BCKDH) and subsequently follow distinct pathways to either generate ketone bodies (KICA, KMVA) or enter the TCA cycle for oxidation (KIVA, KMVA). Glutamate is exported from mitochondria with aspartate and the remaining ketoacids. Glutamate, aspartate, KMVA, and KIVA are precursors for gluconeogenesis; in contrast, KICA is a primary amino acid source of ketones. The metabolomic data suggested acceleration of the initial steps of BCAA catabolism in muscle as an explanation for key metabolic signatures in KPD patients: lack of elevated BCAA, low C5-AC, elevated glutamate, and elevated ketones.

### TCA/anaplerotic metabolites.

Acetylcarnitine (C2, acylcarnitine ester of acetyl CoA) was lower in KPD (1.3-fold; *P* = 0.001), suggesting either decreased production of acetyl CoA or increased shunting of acetyl CoA into oxidative or synthetic pathways (Table 1). β-hydroxybutyryl-carnitine (C4-OH), representing intracellular BOHB, was threefold higher in KPD (*P* = 0.01), suggesting increased ketone synthesis or diminished ketone oxidation. Other short-chain acylcarnitines (C4-DC, C5-DC), which include metabolites arising distal to α-ketoglutarate in the TCA cycle, such as methylmalonyl/succinyl-carnitine and glutaryl-carnitine, were diminished in KPD.

### Fatty acids and ketones.

The molar sum of the seven most abundant fatty acids was similar in KPD and control subjects (Table 1). Persons consuming American diets have the following rank order of fasting fatty acid concentrations: oleate \> palmitate ≥ linoleate (31); this order was preserved in both groups, and levels of palmitate and oleate were similar, making it unlikely that there were group differences in habitual dietary fat composition. These data suggested that the proclivity for ketosis in KPD patients is not due to excessive fatty acid supply.

## Kinetics.

In the kinetic study cohort, age, weight, and BMI were similar in the two groups (Table 2). HbA~1c~ was very high in the KPD patients at the time of the index DKA; 3 to 4 months later, mean HbA~1c~ and fasting glucose were markedly improved, but still higher than in the control subjects. At that time, fasting and glucagon-stimulated peak C-peptide and C-peptide/glucose ratio were similar in KPD and control subjects. Fasting insulin levels were higher in KPD; however, they were very similar to those of the KPD patients in the metabolomics study (Table 1), and QUICKI demonstrated a significantly higher degree of insulin resistance in KPD. Thyroid-stimulating hormone, blood urea nitrogen, creatinine, and AST were normal and similar in both groups. Triglycerides (163 ± 9 mg/dL) were elevated, whereas HDL-C (40.6 ± 3.9 mg/dL) was low, and LDL-cholesterol level was 85 ± 9 mg/dL in the KPD group.

Kinetic study: demographic, hormonal, and biochemical data

![](912tbl2)

1\. *Fasting rates of FFA release and acetyl CoA flux are similar in KPD and control subjects. BOHB oxidation and fatty acid disposal are slowed, leading to elevated plasma concentrations of BOHB and FFA*.

Ra FFA (total FFA flux) was not different, whereas Ra glycerol (index of triglyceride hydrolysis) was significantly lower, and Ra palmitate (palmitate flux) trended lower in KPD (Fig. 1). Furthermore, Ra acetate (index of whole-body acetyl CoA flux) was similar in KPD and control subjects (1,344 ± 260 vs. 1,570 ± 141 µmol/kgFM/h; *P* = 0.50), and acetate oxidation (breath ^13^CO~2~ liberated by isocitrate dehydrogenase activity, reflecting acetyl CoA flux through the TCA cycle) was also similar (861 ± 175 vs. 1,007 ± 87 µmol/kgFM/h; *P* = 0.49). Despite the similar FFA flux, plasma concentration of FFA was higher in KPD (587 ± 51 vs. 412 ± 8 µmol/L; *P* = 0.04) (Fig. 1), suggesting slower FFA disposal (corroborated by the slower palmitate clearance). Plasma concentration of BOHB was also higher in the KPD patients (97 ± 17 vs. 43 ± 10 μmol/L; *P* = 0.02) in the face of slower conversion of acetyl CoA to BOHB (Fig. 2). Collectively, these findings indicated defective BOHB and fatty acid disposal, which, after a 10–14-h fast, is predominantly oxidative. To test BOHB oxidation directly, protocol 4 was performed in three control subjects and seven KPD patients. KPD had slower BOHB flux (330 ± 80 vs. 696 ± 103 µmol/kgFM/h; *P* = 0.03) but also slower oxidative BOHB disposal (191 ± 47 vs. 427 ± 84 μmol/kgFM/h; *P* = 0.03), a combination that would increase BOHB concentration. (Of note, plasma BOHB enrichment was used in this study as a proxy for intracellular acetate or acetyl CoA enrichment; in reality, it is acetyl CoA that is oxidized. The IE of the acetyl CoA would be lower than the plasma BOHB enrichment, because there are additional sources of unlabeled acetyl CoA in the intracellular pool. Because the more highly enriched BOHB is the denominator used in the calculations, the actual oxidation rate of BOHB \[which must first be converted to acetyl CoA to be oxidized via intermediates in the TCA cycle\] is probably underestimated in all subjects.)

2\. *Oxidative decarboxylation of leucine is accelerated in KPD.*

There was no difference between KPD and control subjects in leucine flux and a trend toward higher valine flux in KPD (Fig. 3). The rate of oxidative decarboxylation, or removal of the \#1 carbon of leucine by BCKDH, showed a strong trend to be faster in KPD (60.0 ± 2.8 vs. 51.7 ± 2.9 μmol/kgFFM/h; *P* = 0.06). Increased BCKDH activity requires increased flux through the earlier BCAT-catalyzed step that drives transfer of NH~2~ from leucine to α-ketoglutarate to generate glutamate (32). Unless matched by acceleration of the opposing reaction that oxidatively deaminates glutamate to α-ketoglutarate, this would promote accumulation of glutamate.

3\. *Conversion of glutamine to glutamate is accelerated in KPD, without a parallel increase in glutamate carbon oxidation.*

There was no group difference in glutamine flux, but plasma glutamine concentration trended lower in KPD (557 ± 29 vs. 655 ± 36 μmol/L; *P* = 0.06) (Fig. 4A). The rate of glutamine carbon transfer to glutamate was faster in KPD by several measures: slope of change, absolute synthesis rate of glutamate from glutamine (Fig. 4A), and fractional synthesis rate of glutamate from glutamine (Fig. 4B). However, the rate of oxidation of the same glutamate carbon in the TCA cycle was not different between the groups (Fig. 4B). Thus, increased glutamate production from glutamine in KPD is not matched by a parallel increase in glutamate carbon anaplerosis into the TCA cycle, as predicted from the results of the leucine infusion study.

4\. *Transfer of amide nitrogen from glutamine to citrulline is slower in KPD.*

Citrulline flux was slower in KPD than in control subjects (Fig. 5), with a trend toward slower transfer of glutamine amide nitrogen to ornithine (7.8 ± 0.4 vs. 8.6 ± 0.3 μmol/kgFFM/h; *P* = 0.1) for citrulline synthesis.

# DISCUSSION

In this study, the pathophysiology of A^−^β^+^ KPD was investigated comprehensively using a two-step discovery method. First, fasting plasma metabolomics compared KPD patients to nondiabetic obese subjects. Hypotheses generated from key differences were tested by kinetic tracer studies targeting the suspected metabolic pathways in a fresh cohort of patients and control subjects.

The metabolomic analysis revealed signals of altered BCAA catabolism in KPD associated with changes in glutamine/glutamate. We hypothesized that the lack of elevated leucine in KPD was due to accelerated catabolism of this amino acid, indicated by decreased C5-AC and elevated glutamate (33,34). Evidence that accelerated leucine catabolism extended beyond isovaleryl CoA to β-methylcrotonyl CoA and ketogenesis was provided by the lower level of 3-hydroxyisovaleryl-carnitine (C5-OH). Concomitantly, glutamine was 50% lower, whereas glutamate was 175% higher, in KPD. Glutamine and glutamate are nitrogen sinks for catabolism of other amino acids, including BCAA, in the fasted state. Elevated glutamate and decreased glutamine levels could reflect absence of upregulation of glutamate oxidation rates in KPD subjects in the face of faster conversion of glutamine to glutamate and accelerated leucine deamidation.

Changes in small-chain acylcarnitines reflecting TCA cycle intermediates and anaplerosis broadened the pathophysiologic hypothesis. Acetylcarnitine was significantly lower in KPD, suggesting decreased production or increased consumption of acetyl CoA. Acylcarnitines that include glutaryl CoA and succinyl CoA were lower and β-hydroxybutyryl-carnitine was threefold higher in KPD, suggesting diversion of acetyl CoA disposal toward ketogenesis. Finally, fasting FFA levels and composition were similar in KPD and control subjects, suggesting that excessive fatty acid flux from lipolysis is not the driver of ketogenesis in KPD.

In sum, the metabolomic data suggested that patients with unprovoked A^−^β^+^ KPD have accelerated leucine catabolism toward ketogenesis. This would expand the nitrogen sinks of transamination reactions, resulting in elevated levels of glutamate and aspartate. The data also indicated that KPD patients have defects in transferring carbon from glutamine/glutamate to the TCA cycle and nitrogen from glutamine/glutamate to the urea cycle.

The results of the kinetic studies confirmed the hypothesis and revealed also that KPD patients have altered ketone oxidation. Clinically stable KPD patients with excellent glycemic control have elevated plasma BOHB. The cause is not excessive fatty acid–derived ketone production—the conversion of acetyl CoA to BOHB is slower, and rates of acetyl CoA flux and isocitrate decarboxylation are unaltered. However, the rate of ketone oxidation is significantly slowed in KPD, leading to elevated concentrations of BOHB and its intracellular metabolites, and this likely plays a prominent role in the most unique characteristic of the phenotype, proclivity for ketosis. Fatty acid disposal is also impaired.

Unable to maximize rates of oxidation of fat-derived sources for energy, KPD patients after an overnight fast draw upon amino acids for energy. There is increased flux through the leucine catabolic pathway toward ketone production. Acceleration of leucine catabolism also leads, through reductive deamination of α-ketoglutarate, to glutamate accumulation (exacerbated by increased conversion from glutamine) and blunted glucose carbon anaplerosis in the TCA cycle as manifested by an unchanged rate of glutamate carbon oxidation. A similar cascade occurs in McArdle syndrome, in which accelerated BCAA catabolism eventually reduces flux into the TCA cycle through increased conversion of α-ketoglutarate to glutamate (35).

Peripheral oxidation of ketone bodies is a TCA cycle-dependent process; in this regard, an apparent conflict arises between the finding of slowed BOHB oxidation in KPD and the finding that there is no difference in the rate of oxidation in the TCA cycle when the groups are challenged with a similar flux of oxidative substrate (^13^C~1~-acetate). This apparent paradox may be explained because our measurements specifically demonstrate that KPD show no defect in the rate of removal of CO~2~ from isocitrate (at the step of isocitrate dehydrogenase) in the TCA cycle, and this leaves open the possibility of kinetic defects distal to that step in the TCA cycle and therefore in the rates at which reduced energy intermediates are delivered for oxidative phosphorylation. The data suggest that there may be such a kinetic defect at the level of α-ketoglutarate dehydrogenase associated with more rapid decarboxylation of leucine, as evidenced by the accumulation of glutamate and reduced levels of acylcarnitines of TCA intermediates beyond that step.

This study combines for the first time an unbiased metabolomics approach and a targeted kinetics approach to specify the pathophysiology of a novel diabetic syndrome (Fig. 6). Umpierrez et al. (36) previously explored the pathophysiology of ketosis-prone type 2 diabetes, a condition identical to unprovoked A^−^β^+^ KPD. They reported no ketosis or β-cell decompensation following prolonged infusion of fatty acids, supporting the concept that proneness to ketosis in KPD is not due to excessive fatty acid flux to the liver.

Defective oxidation of ketones and accelerated leucine catabolism are key defects of fasting energy metabolism that underlie the distinctive syndrome of unprovoked A^−^β^+^ KPD. The findings indicate the need to investigate mitochondrial function and BCAT and BCKDH regulation in the skeletal muscle of KPD patients. These defects are associated with pronounced changes in glutamine/glutamate metabolism; interestingly, elevated glutamate predicted type 1 diabetes in a longitudinal metabolomics study (37). Clinically and metabolically, patients with A^−^β^+^ KPD manifest features of both type 1 and type 2 diabetes. The present data explain how overweight patients with a type 2 diabetes phenotype and adequate β-cell reserve are prone to developing unprovoked ketoacidosis. The coupled metabolomics-kinetics approach should be useful in elucidating the pathophysiology of other phenotypes of diabetes.

## ACKNOWLEDGMENTS

This study was supported by National Institutes of Health (NIH) R21-DK-082827 (to A.B.), the Diabetes and Endocrinology Research Center (P30-DK-079638) at Baylor College of Medicine, NIH RO1-DK-056689 (to F.J.), funds from the U.S. Department of Agriculture, Agricultural Research Service under Cooperative Agreement Number 58-6250-6001, and NIH PO1-DK-58398 (to C.B.N.).

No potential conflicts of interest relevant to this article were reported.

S.G.P. and F.J. helped to design the kinetic studies, analyzed data, and edited the manuscript. J.W.H. performed mass spectrometric analyses and analyzed data. I.C. recruited subjects and implemented the stable isotope protocols. J.R.B. and R.D.S. performed metabolomic analyses and reviewed the manuscript. D.I. performed biochemical assays. R.N. and K.O. maintained clinical care of the study subjects and edited the manuscript. C.S.H. performed autoantibody testing of the study subjects and reviewed the manuscript. C.B.N. helped with study design, supervised the metabolomic analyses, and edited the manuscript. A.B. designed and obtained funding for the study, implemented the kinetic protocols, analyzed data, and wrote the manuscript. A.B. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Parts of this study were presented in poster form at the 71st Scientific Sessions of the American Diabetes Association, San Diego, California, 24–28 June 2011.

The authors thank Elizabeth Fraser, Vy Pham, and Toni Oplt (all from Baylor College of Medicine) for technical assistance; Resa Labbe-Morris, RN, and the nursing staff of the Baylor College of Medicine General Clinical Research Center for meticulous attention to protocol and care of the study subjects; Varsha Patel, RPh, and the Investigational Pharmacy (The Methodist Hospital Research Institute, Houston, TX) for stable isotope preparation; Drs. Heinrich Taegtmeyer (University of Texas Health Sciences Center, Houston, TX), William Mitch, and Arun Sreekumar (both from Baylor College of Medicine) for helpful discussions; and all of the study subjects.

# REFERENCES

[^1]: S.G.P. and J.W.H. are joint first authors.


abstract: A report on the Conference on Systems Biology of Mammalian Cells, Dresden, Germany, 22-24 May 2008.
author: Hanspeter Herzel; Nils Blüthgen
date: 2008
institute: 1Institute for Theoretical Biology, Humboldt University, Invalidenstrasse, 10115 Berlin, Germany; 2Institute of Pathology, Charité, Schumannstr., D-10117 Berlin, Germany
title: Mathematical models in mammalian cell biology

Systems biology has been defined in many ways, emphasizing various aspects of interdisciplinary research. At the recent symposium in Dresden on systems biology of mammalian cells, this emerging field was presented as a fruitful symbiosis of genomics, imaging, and high- and medium-throughput technologies with data analysis and mathematical modeling. Here we focus on selected applications of mathematical modeling in mammalian cell biology - a challenging but promising branch of systems biology.

# Mathematically modeling the circadian clock

One application of mathematical models is in analyzing the workings of the mammalian circadian clock. About 20,000 synchronized neurons in the suprachiasmatic nucleus (SCN) control daily rhythms of physiology, metabolism and behavior. In addition, almost all peripheral tissues of mammals and even cell lines contain cellular clocks, in which endogenous oscillations of mRNA and protein abundance rhythms with a period of about 24 hours are driven by intracellular feedback loops involving clock genes such as *Per1-2*, *Cry1-2*, *Clock* and *Bmal1*. Post-translational events such as phosphorylation of clock proteins contribute to the delay in negative feedback and thus are crucial for the dynamics of circadian rhythms.

One of us (HH) reported that a combination of experiments in the lab of Achim Kramer and mathematical modeling led to a deeper understanding of the molecular mechanisms underlying human circadian behavior. In the first example, it was reported that the molecular explanation for a human behavioral disorder called familial advanced sleep phase syndrome (FASPS), which leads to a 4-hour advance in sleep and wakefulness, can be attributed to a point mutation in the circadian *Per2* gene. This mutation leads to a phosphorylation defect of the PER2 protein, changing its stability and subcellular localization in a cell culture model for FASPS. This cell culture model nicely recapitulates the 4-hour phase advance of human behavior by showing advanced rhythms of clock gene expression. Other phosphorylations of PER2, however, have partly opposite effects. Mathematical modeling integrated these experimental data and proposed a dynamical model with differential roles of PER2 phosphorylation sites for circadian dynamics.

In the second example, human skin fibroblasts from extreme chronotypes (that is, either 'night owls' or 'morning larks') have been used to characterize intrinsic circadian properties of these cells. Although for a large part of the subjects a good correlation between behavioral phase (that is, 'morningness' or 'eveningness' assessed by a questionnaire) and period of clock gene rhythms in skin fibroblasts (assayed by live-cell imaging using luciferase-based reporters) could be found, some subjects have normal circadian periods in their cells, but do display extreme behavioral phases. Computer models here helped to explain these phenotypes by suggesting that the amplitude and input sensitivity of the cellular oscillators should be experimentally investigated.

# Quantifying apoptosis and signaling cascades

Another impressive example of mathematical modeling and quantitative experimentation going hand-in-hand is the analysis of apoptotic pathways. Heinrich Huber (Royal College of Surgeons, Dublin, Ireland) reported the monitoring of cytochrome *c* release during apoptosis at a resolution of seconds, using confocal and FRET-based imaging techniques. In this way the onset of mitochondrial outer membrane permeabilization in individual HeLa cells was monitored. Combining this imaging approach with mathematical modeling allowed the identification of two separate kinetic phases: an 'ignition phase' during which the mitochondria were not yet fully permeabilized, and a second phase of cytochrome *c* redistribution.

A highlight of the conference was the opening lecture by Douglas Lauffenburger (Massachusetts Institute of Technology, Cambridge, USA). He asked how information about extracellular cues is encoded in the intracellular signaling network and causes a specific cellular response - in this case apoptosis. His group stimulated cells with different levels of tumor necrosis factor α, insulin, and epidermal growth factor (EGF), and measured phosphoprotein levels distributed across five kinase pathways as well as four apoptotic outputs. This impressive dataset showed that the response is not encoded in a single pathway, but that the information is distributed over the signaling network. It also enabled a comparison of different modeling strategies, including principal component analysis (PCA), fuzzy logic, and differential equations. The combination of these approaches led to interesting insights into the time-dependent role of the kinase IKK in the NF-κB pathway in inducing the apoptotic response and cross-talk mediated via autocrine loops involving transforming growth factor α and interleukin 1.

The question posed by Lauffenburger as to how information about extracellular cues is encoded in intracellular signaling pathways was addressed throughout the conference. Takashi Naka (RIKEN, Yokohama, Japan) presented data from breast cancer cell lines showing that EGF and heregulin induce broadly overlapping immediate-early gene expression patterns despite triggering different cellular responses. Naka speculated that the specificity of response is generated during late-phase signaling by feed-forward loop duration decoding through transcription factors such as c-Fos, which generate waves of expression of secondary, more specific transcriptional regulators depending on the length of the signal. In his closing talk, Hans Westerhoff (Free University, Amsterdam, The Netherlands) pointed out that the length of the signal in a pathway, and thereby the biological response, can be selectively regulated by the expression of phosphatases. He illustrated this with experimental data from growth-factor stimulated mammalian cells, where inhibition of the kinase in the MAP kinase (MAPK) pathway leads to smaller amplitudes of the signal, whereas inhibition of the phosphatases leads to an increase in the durations. This behavior has been previously postulated by Reinhard Heinrich using mathematical models of kinase cascades.

Leonidas Alexopoulos (Massachusetts Institute of Technology, Cambridge, USA) presented data from large-scale experiments on the response of the signaling network of primary and transformed hepatocytes following treatment with various cytokines and small-molecule inhibitors, alone and in combination. Using linear regression, he could work out alterations in the signaling network that were caused by oncogenic transformation. Again, these alterations were distributed in the network and not confined to a single pathway. As a consequence of the distributed information in the network, conventional single-target therapies could have limited efficacy. In this regard, Westerhoff pointed out that targeting the signaling network would require novel therapeutic agendas, such as targeting multiple nodes in the network, and that these targets might not necessarily be close to the original mutation in the network. Responses to such combinatorial inhibitor treatment can also be used to reverse-engineer the network, as demonstrated by Sven Nelander (Memorial Sloan-Kettering Cancer Center, New York, USA). He and colleagues applied combinations of pharmacological inhibitors to perturb mitogenic and pro-apoptotic signaling pathways in breast cancer cells and measured the activity of the pathways' components. From these data they were able to infer feedbacks within the pathway, and the use of combinatorial inhibition allowed for the inference of nonlinear interactions ('synergies') in the pathways. The method may be applicable to the design of targeted combination therapies for cancer.

# Mathematical models in pattern formation

There is a long history of applying partial differential equations to spatio-temporal pattern formation in morphogenesis, as pioneered by Alan Turing and Hans Meinhardt. Advanced imaging techniques such as fluorescence recovery after photobleaching (FRAP) now allow the quantification of diffusion-reaction dynamics. Frank Jülicher (Max Planck Institute of the Physics of Complex Systems, Dresden, Germany) has combined modeling with experimental data to describe morphogen gradients in the wing imaginal disc of the fruit fly. He reported that quantitative studies reveal that morphogen transport in the tissue is coupled to cellular kinetic processes such as ligand-receptor binding, endocytosis of ligand-receptor pairs and the recycling of ligands to the cell surface. The resulting nonlinear transport equations guarantee a robust gradient profile to regulate the expression of genes in a manner that depends on the distance to the source of the morphogen.

The classical 'clock and wavefront model' proposed by Erik Christopher Zeeman in 1976 has been applied by Oliver Pourquié (Howard Hughes Medical Institute, Kansas City, USA) to study the vertebrate segmentation clock. This periodic pattern is established in the embryo during the formation of the somites. Somitogenesis involves an oscillator driving the dynamic expression of genes in the presomitic mesoderm from which the somites are derived. Microarray data indicated that the mutually exclusive activation of Notch/FGF and Wnt pathways coordinate the oscillator. The FGF pathway controls the positioning of the wavefront of gene expression and couples the spatio-temporal activation of segmentation to the posterior elongation of the embryo.

# How useful are mathematical models?

For small subsystems, dynamical models can be fitted directly to experimental time-course data. But despite extensive microarray, proteomics and imaging data, larger systems such as the mammalian cell-cycle machinery cannot yet be described in quantitative detail by mathematical models. In these cases, however, statistical approaches such as PCA, Bayesian networks and fuzzy logic help to extract useful information.

Presentations at the conference impressively illustrated that complex processes such as apoptosis or morphogenesis can be tackled with a combination of quantitative spatio-temporal data and modeling, thus giving hope that we might be able to make dynamic models of other more complex processes in future as quantitative data becomes available. The conference nicely illustrated that the collaboration of experimentalists and theoreticians helps to design appropriate experimental strategies for the analysis of mammalian cells. Therefore, we believe that the time is ripe to apply systems biology more widely in mammalian cells. Although many models will be semi-quantitative for the near future, they will help guide our experimental design and integrate our data.

### Acknowledgements

We thank Kieran Smallbone, Stefan Legewie and Achim Kramer for commenting on this report. Attendance of NB at the conference was made possible by an award from the MTZ Foundation.


abstract: A recently departed genome biologist discusses the impact factor of his life with St Peter at the Pearly Gates.
author: Gregory A Petsko
date: 2008
institute: 1Rosenstiel Basic Medical Sciences Research Center, Brandeis University, Waltham, MA 02454-9110, USA
title: Having an impact (factor)

*The time: Some time in the not-too-distant future*.

*The place: The entrance to The Pearly Gates. There are fluffy clouds everywhere. In the center is a podium with an enormous open book. A tall figure in white robes with white hair and beard stands at the podium. Approaching is a thin, middle-aged man with glasses and a bewildered expression. He is the soul of a recently deceased genome biologist*.

**GB**: My gosh is this...? Are you...? Am I really...?

**St Peter**: Yes, I'm St Peter. And yes, this is where souls such as yours enter heaven.

**GB**: Wow. I mean, I didn't expect to live forever, but still, this is something of a shock. (*Pauses*.) OK, I guess I can live with it. Uh, I mean ...

**St Peter**: I know.

**GB**: Well, at least I'm here. I'm not thrilled to be dead, but it's a relief to know I'm going to heaven.

**St Peter**: I'm afraid it's not that simple. We have to check.

**GB**: Check what?

**St Peter**: Your life history. (*He leafs through the enormous book*.) It's all here, you know.

**GB**: I'm sure it is. I can imagine you guys keep records that make PubMed seem like a stack of index cards. I'm a little surprised you don't use something more up-to-date, though.

**St Peter**: If you mean a personal computer, no - we don't. After all, they were invented elsewhere.

**GB**: You mean on earth?

**St Peter**: No, somewhere a lot warmer. (*He stops at a page*.) Here you are.

**GB**: Hey, I'm not worried. I was a good scientist, a good citizen, a good family man, I think, too. I never...

**St Peter**: Yes, yes, I'm sure, but you see, none of that matters. The only thing that matters is your IF.

**GB**: IF?

**St Peter**: Your impact factor. That's all we use now. If your IF is above 10, then you enter here. If it's lower, well...

**GB**: My impact factor? What the hell - oops, sorry - is that?

**St Peter**: It's something we borrowed from you science chaps on earth. Oh, we used to do it the hard way: send a fledgling angel down to check on your deeds; look at how your life affected your friends and family, consider your intentions versus your actions. All that sort of thing. It was tedious and required huge numbers of new angels, who have become somewhat scarce since free-market capitalism became all the rage down there. Then we noticed that you scientists never bothered to do anything like that. If you had to evaluate someone, all you did was look at this number called the impact factor. So we did the same thing. Now when anyone comes here, all we do is look up their number.

**GB**: A single number? Are you nuts? You can't sum up someone's whole life in a single number!

**St Peter**:You do. At least, you sum up their career that way, when you decide if they've published in the best journals or done the best work. It's how you work out who gets promoted and who's a star and who gets funded and...

**GB**: Yes, but it's a terrible idea! We should never have done it. It ruined European science in a matter of a few years, and then it spread to Australia, China and Japan, and finally to Canada and the US; and before too long, science was totally controlled by unimaginative bureaucrats who just used that number for everything. It was a disaster!

**St Peter**: That's not what St Garfield thinks.

**GB**: St who?

**St Peter**: St Eugene Garfield, PhD. He invented citation analysis, remember? He thought using the IF was a great idea - really, a logical extension of his own work creating the Citation Index. So we set it up: for example, I see here that you contributed regularly to several local charities.

**GB**: Of course. They do good work. I never did it because I thought it would get me into heaven, but...

**St Peter**: Just as well, because it won't. Local charities, you know. Small impact factor. Doesn't really add much to your total. Besides, how bad could the idea be? Why, the journal *Genome Biology* advertises its impact factor right at the top of their website. Didn't you use to write a column for them? (*He looks at the ledger again*.) Oh my, I see that won't add much to your total either.

**GB**: But that's all ridiculous! It's the whole problem I was trying to explain to you. That's like saying that a paper only has significant impact if it's published in *Nature*, *Science*, or *Cell*. Once you do that, then the impact factor of where you publish becomes a surrogate for the use of your own judgment. No one bothers to read anyone's papers when they're up for a fellowship or being considered for a job or for a promotion or having their grant proposal evaluated; all you do is look to see how many papers they've published in high-impact journals. No one considers whether the work was better suited to a more specialized journal or a journal where other work that puts it in context was published previously; no one considers whether those handful of high impact-factor journals have the best referees or whether they in fact may have a disproportionate number of incorrect papers because of the pressure to publish there. And look, over reliance on one stupid number gave a small bunch of editors enormous power over the careers of people who, for the most part, they never met or heard speak, and whose body of work they never read. It was probably the worst idea since General Custer thought he could surround the whole Sioux Nation with a couple of hundred troops.

**St Peter**: Ah, yes. St Sitting Bull still talks about that.

**GB**: Huh? (*Shakes himself*.) Look, once the impact factor dominated scientific judgments, creative people were doomed. Bureaucrats didn't need to know anything or have any wisdom; all they had to do was rely on arbitrary numbers. And now you're telling me you're doing that to determine who gets into heaven?

**St Peter**: Yes; it's a lot simpler. It doesn't matter if you were kind or tried hard or did good work or were pious or modest or generous. The only thing that matters is how big an impact we calculate you had.

**GB**: But that's just wrong! Look, maybe I could talk to the people who thought up that idea and pushed for its use. If I can just get in for a minute...

**St Peter**: Oh, they're not here. (*He waves his hand and an image appears on a cloud. It shows a huge pit of boiling sulfur and brimstone. In it, up almost to their necks, are a bunch of men in business suits*.) As you can see, they're in a warmer climate.

**GB**: Well, at least, that seems fair somehow. Wait a minute - is that George W Bush?

**St Peter**: Yes.

**GB**: But his impact factor should have been huge.

**St Peter**: Oh, the absolute value was off the charts. But we do take the sign into consideration...

**GB**: Then why is he only in brimstone up to his knees?

**St Peter**: Oh. He's standing on Dick Cheney's shoulders. (*The image vanishes*.) Now let's get back to you...

**GB**: But don't you see, the idea that you can determine someone's impact in the future from where they publish today is totally absurd. On that basis, God would have an impact factor of zero. I mean, He did his best work a long time ago; it has never been repeated by anyone; and all His ideas were published in a book, not in a peer-reviewed journal!

**St Peter**: Very funny. Go to hell.


author: Emil R. Unanue; Fumihiko UranoCorresponding author: Fumihiko Urano, <firstname.lastname@example.com>.
date: 2014-01
references:
title: Endoplasmic Reticulum: An Interface Between the Immune System and Metabolism

Endoplasmic reticulum (ER) is a busy cell organelle that participates in many important cellular tasks. It has been established that ER is involved in protein and lipid biosynthesis, calcium regulation, redox regulation, cell signaling, and cell death. Given the many vital and complex functions of ER, there is little wonder that its failure can trigger a range of diseases. Recent genetic and clinical evidence indicates that inherited or acquired dysregulation of ER homeostasis can give rise to genetic diseases, including Wolfram syndrome (which is characterized by juvenile-onset diabetes and neurodegeneration) and a number of common metabolic diseases including diabetes and atherosclerosis. Accelerating interest in the role of ER in metabolic disease has been fueled by recent reports showing pathways that link ER to inflammation. The role of ER as an interface between the immune system and metabolism is an emerging concept (Fig. 1). However, currently there is no treatment targeting ER for combating immunometabolic diseases. To fulfill this unmet medical need, we need to identify pathways and molecules that link the immune system to metabolism at ER.

ER dysfunction has been a suspect as a major pathogenic component of human chronic diseases, such as diabetes, atherosclerosis, and Wolfram syndrome (1–10). However, the precise role of ER in the etiology of these diseases is not clear. It has been recognized that inflammation plays a central role in chronic metabolic diseases, raising the possibility that ER is at the intersection of inflammation and metabolism (7,11–14). Over the past several years, this concept has been supported by genetic, experimental, and clinical evidence (10,15). One of the unmet scientific needs in this emerging field is to identify the pathways linking ER to production of inflammatory cytokines. Two molecular pathways linking ER to production of interleukin (IL)-1β, a major player in inflammation, have been recently identified. These are activating transcription factor (ATF) 5 and miR-17 (16,17). Both molecules are regulated by key regulators of the ER stress response, Perk and Ire1α.

In this issue, Iwasaki et al. (18) describe the molecular pathway linking ER to IL-6 production. Using DNA microarray and network analyses of macrophages, they show compelling evidence that ATF4, which is involved in the ER stress response, plays an essential role in IL-6 expression induced by various metabolic stresses, including ER stress. Furthermore, they reveal that the ATF4 pathway has a synergistic effect on the Toll-like receptor-4 signaling pathway, enhancing IL-6 expression. IL-6 has been shown to play crucial roles in insulin resistance and type 2 diabetes (19), raising the possibility that ATF4 signaling is a novel target for the treatment of metabolic diseases. The new findings from Iwasaki et al. (18) also suggest that ER-stressed macrophages may trigger autoimmune diseases through IL-6 production.

Compelling evidence indicates that ER is at the intersection of inflammation and metabolism and is therefore an attractive target for immunometabolic diseases. For example, recent evidence strongly suggests that ER dysfunction in antigen-presenting macrophages and β-cells triggers autoimmunity during the onset and progression of type 1 diabetes (20,21). Despite the underlying importance of ER dysfunction in these diseases, no current therapies target ER. The unmet scientific and medical need in the field of ER immunometabolism is to target the common molecular processes that are altered in ER diseases as a novel therapeutic discovery strategy. The strategy of performing clinical studies using drugs previously known to target ER, such as glucagon-like peptide 1 agonists and vitamin D, on patients with immunometabolic diseases should be explored (22,23).

## Article Information

**Funding.** F.U. is supported by grants from the National Institutes of Health (DK-067493, DK-020579, and UL1 TR000448), JDRF (47-2012-760, 17-2013-512), American Diabetes Association (1-12-CT-61), the Ellie White Foundation for Rare Genetic Disorders, and the Jack and J.T. Snow Scientific Research Foundation.

**Duality of Interest.** No potential conflicts of interest relevant to this article were reported.

# References