Increase in Muscle Mitochondrial Biogenesis Does Not Prevent Muscle Loss but Increased Tumor Size in a Mouse Model of Acute Cancer-Induced Cachexia
2012

X Wang
A M Pickrell
T A Zimmers
C T Moraes
Increase in Muscle Mitochondrial Biogenesis Does Not Prevent Muscle Loss but Increased Tumor Size in a Mouse Model of Acute Cancer-Induced Cachexia

PLoS ONE
73334262012Received November 28, 2011; Accepted February 12, 2012;Editor: Funding: National Institute of Health and the Muscular Dystrophy Association. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. *
Cancer-associated cachexia is a complex metabolic condition characterized by the progressive loss of body fat and deterioration of muscle mass. Although the cellular and molecular mechanisms of cachexia are incompletely understood, previous studies have suggested mitochondrial dysfunction in murine models of cancer cachexia. To better understand the metabolic shift in cancer-induced cachexia, we studied the effects of enhanced oxidative capacity on muscle wasting using transgenic mice over-expressing Peroxisome Proliferator-Activated Receptor gamma Co-activator-1a (PGC-1a) in skeletal muscle in a Lewis lung carcinoma-implanted model. Increased mitochondrial biogenesis was observed in the skeletal muscle of tumor-implanted mice. However, these increases did not prevent or reverse muscle wasting in mice harboring tumors. Moreover, tumor size was increased in muscle PGC-1a over-expressing mice. We found similar levels of circulating inflammatory cytokines in tumor-implanted animals, which was not affected by increased muscle expression of PGC-1a. Our data indicated that increased mitochondrial biogenesis in skeletal muscle is not sufficient to rescue tumor-associated, acute muscle loss, and could promote tumor growth, possibly through the release of myokines.

Introduction

Clinically, cachexia is defined as ''a complex metabolic syndrome associated with underlying illness and characterized by loss of muscle with or without loss of fat mass'' [1]. It has been found in many chronic or end-stage diseases such as AIDS, tuberculosis, and cancer [2]. Up to 50% of untreated cancer patients experience progressive loss of fat and lean body mass without starvation, a complex syndrome referred to as cancerinduced cachexia [3]. The presence of wasting is usually associated with intolerance to treatment, poor quality of life and high mortality in patients [4].

Although extensive studies have been carried out during the last decade, the underlying mechanisms causing cancer cachexia are still not fully understood. One of the leading theories is that tumorderived factors are responsible for the degradation of body mass, including the muscle [2]. It is widely accepted that proinflammatory cytokines play a key role in all pathways that lead to hyper catabolism and weight loss associated with cancer cachexia [5]. The presence of systemic inflammation is usually linked to worse prognosis in the patients [6].

Cancer cachexia causes systemic changes in patients' metabolic profile in order to support tumor development. It has been reported that mitochondrial dysfunction in the skeletal muscle, including decreased oxidative phosphorylation (OXPHOS) capacity and disrupted mitochondrial dynamics, is involved with systemic inflammation and skeletal muscle wasting [7]. The peroxisome proliferator-activated receptors (PPARs) transcription factors family and their modulator PPAR-gamma co-activator-1a (PGC-1a) are the master regulators of mitochondrial biogenesis and energy metabolism [8]. Mitochondrial uncoupling proteins (UCPs) 1, 2, and 3 are upregulated in atrophying muscle; and metabolic abnormality with increased proteolysis in the muscle has been implied in cachectic patients [9,10]. The activation of proinflammatory cytokine TNFa-induced NF-kB was shown to decrease promoter transactivation and transcriptional activity of regulators of mitochondrial biogenesis (PGC-1a, PPARa, and TFAM) and affect downstream oxidative markers (citrate synthase, and cytochrome c oxidase) [11].

Clinical interventions have been developed for general symptom management of this devastating condition; however, these measures are only palliative without specifically targeting the causing factor of cachexia and the outcomes are not satisfactory [12].

In this study, we investigated the potential therapeutic effect of increasing mitochondrial biogenesis by overexpressing PGC-1a in the skeletal muscle in a transgenic mouse model of cancer cachexia. Our results indicate that increased mitochondrial biogenesis in the muscle was not sufficient to alter the levels of proinflammatory cytokines and prevent the muscle loss associated with tumor implantation. Moreover, the increase in muscle PGC-1a may also have the side effect of promoting tumor growth.

Results

Tumor-inoculated transgenic MCK-PGC-1a mice maintain increased mitochondrial biogenesis in gastrocnemius and quadriceps

Transgenic MCK-PGC-1a mice over-express PGC-1a in the skeletal muscle, driven by the muscle creatine kinase (MCK) promoter [13]. We observed an increase of Ppargc1a mRNA levels of 13-fold in the gastrocnemius, a muscle composed of similar levels of Type I (oxidative) and Type II (glycolytic) fibers and 19fold in the quadriceps, a muscle composed mostly of Type II fibers, in 4-month-old tumor-free transgenic MCK-PGC-1a mice ( Figure 1A). A similar increase was observed for tumor bearing mice ( Figure 1A). When we determined the steady state levels of PGC-1a protein in gastrocnemius and quadriceps homogenates, we observed a marked increase in transgenic mice compared to controls, with or without tumors. The results for gastrocnemious and quadriceps were essentially identical for both genotypes ( Figure 1B, C and not shown).

PGC-1a is a transcriptional coactivator that upregulates the transcription of nuclear-coded mitochondrial proteins, stimulating mitochondrial biogenesis [14,15,16]. We quantified the levels of mitochondrial proteins in 4-month-old MCK-PGC-1amice and found significantly higher levels of several mitochondrial markers in mice without or with tumors ( Figure 1B and 1C). The levels of mtDNA in both gastrocnemius and quadriceps were elevated in PGC-1a expressing mice ( Figure 2A). In concordance with signs of increased mitochondrial biogenesis, we observed significant increases in citrate synthase (CS) and cytochrome c oxidase (COX) activities both in gastrocnemius and quadriceps ( Figure 2B, C).

Interestingly, we noticed a differential regulation of another member of the PGC-1 family, PGC-1b in gastrocnemius and quadriceps in tumor inoculated animals. While it remained unchanged in the gastrocnemius tissue, Ppargc1b mRNA levels were significantly decreased in quadriceps tissue of tumor-bearing MCK-PGC-1amice ( Figure 2D, E). Accordingly, the expression levels of transcription factors Ppara and Pparg were significantly increased in gastrocnemius but not in quadriceps muscle ( Figure 2D, E).

Over-expression of MCK-PGC-1adoes not protect against cancer-induced muscle loss

Previously, our laboratory showed that over-expression of PGC-1ain skeletal muscle protected and slowed down the progression of mitochondrial myopathies and age-induced sarcopenia [17,18]. We hypothesized that with increased mitochondrial function, our MCK-PGC-1a mice would be more resistant to muscle wasting by reversing the metabolic changes contributing to cachexia.

After confirming this robust enhancement in mitochondrial biogenesis, we examined whether over-expression of PGC-1a could provide protection against muscle loss in our tumorigenic model. We followed the weight of the MCK-PGC-1a and wildtype mice, with or without tumor, as an indicator of general health after inoculating tumor cells. We observed no significant difference between groups until post-injection day-12 and -13, where MCK-PGC-1a tumor mice had a significant increase in percentage body weight as compared to all 3 other groups of mice ( Figure 3A).

To understand where this increase in body weight for the MCK-PGC-1a tumor mice came from, we examined the body weight and extracted tumor weight. We found that tumor-free body weight was significantly decreased in tumor-bearing mice of both genotypes, and no major differences between the transgenic and wild-type mice ( Figure 3B). However, the tumors extracted from MCK-PGC-1a mice were approximately 50% larger than controls ( Figure 3C). When plotting the changes of body weight against tumor size, we noted a positive correlation in wild-type mice, which was disrupted in MCK-PGC-1a tumor mice ( Figure 3D). With this evidence, we concluded that the total body weight gain for tumor-bearing MCK-PGC-1a mice was caused by the increase in tumor mass and not a gain in muscle weight. However, despite having larger tumors, MCK-PGC-1a mice did not lose more weight than tumor bearing controls.

The weights of both gastrocnemius and quadriceps were mildly, but significantly decreased in wild-type tumor mice, indicating our model induced muscle loss ( Figure 3E). However, there was no difference in either muscle group as compared between tumorinoculated transgenic and wild-type mice ( Figure 3E). We also quantified the concentration of pro-inflammatory cytokine IL-6 in the serum. As expected, IL-6 levels were dramatically increased in tumor-bearing mice of both genotypes compared to tumor-free mice, but we did not find significant differences between the two genotypes ( Figure 3F). Thus, MCK-PGC-1a did not seem to protect against muscle loss or to lower systemic IL-6 levels at this endpoint.

Discussion

In this study, we utilized MCK-PGC-1a mouse model to study the effect of increased mitochondrial biogenesis on cancer-induced muscle loss. We found increased mitochondrial biogenesis in the muscle of MCK-PGC-1a tumor mice compared to WT tumor mice. Surprisingly, we found that the increased expression of PGC-1a in muscle did not prevent muscle loss. This was an unexpected result, as muscle PGC-1a was shown by our group and others to confer broad protection to different conditions associated with muscle degeneration [17,19].

Although the causes of cachexia are still poorly understood, increased degradation and decreased synthesis of muscle proteins by the proteasome system appears to play a major role [20]. Systemic inflammation seems to mediate this mechanism in cancer-induced cachexia [5]. Rosenberg and colleagues proposed that high serum levels of TNFa and IL-1b were the causes for weight loss in rheumatoid cachexia [21]. Systemic cytokine-driven inflammatory response in AIDS patients with concurrent active infections has also been associated with cachexia [22]. The model of subcutaneous tumor implantation also shows an increase in circulating IL-6 levels associated with muscle and total body weight loss. However, in our experiments the IL-6 levels were not different between wild-type and MCK-PGC-1a mice ( Figure 3F). Therefore, we speculate that although PGC-1a can protect against muscle loss associated with intrinsic metabolic dysfunctions [17,19], it is less effective in precluding muscle loss caused by extrinsic inflammatory signals.

The MCK-PGC-1a mouse model has been extensively studied by our group and others in various myopathies. Besides the transgenic strategy, PGC-1a can also be induced by endurance exercise [23,24]. Both increased muscle PGC-1a expression and exercise have been shown to ameliorate systemic inflammation [9,19,25]. As part of combinatory therapy, exercise concordant with patient's physical conditions is usually suggested during clinical intervention [26]. In our model of cancer-induced cachexia experimental animals die within 2 weeks after tumor inoculation. The mice developed mild muscle loss within a 13-day time frame with the tumor appearing as early as day 5 after inoculation. Although PGC-1a overexpression did not ameliorate the mild muscle loss in our acute tumorigenic model, we believe future experiments should examine the potential therapeutic effect of increased mitochondrial biogenesis in more severe or chronic models of cancer-induced muscle wasting.

Although the reason for this negative result is not known, we also found that the tumors were approximately 50% larger in mice overexpressing muscle PGC-1a. One possible explanation for this observation is that skeletal muscle can secrete myokines, such as IL-6, which could have growth promoting activity [27]. This was an expected observation that warrants further exploration with different tumor types and models.

In summary, our work demonstrated that stimulating mitochondrial biogenesis was not sufficient to prevent or reverse muscle loss during acute cancer-induced muscle wasting. Moreover, we found evidence that PGC-1a expression in muscle can lead to the development of larger tumors.

Methods

Animals

The generation of MCK-PGC-1a transgenic mice was previously described [13]. Female animals for analysis were pure C57BL/6J MCK-PGC-1a mice with age-matched littermate controls. All mice procedures were performed according to a protocol approved by the University of Miami: Institutional Animal Care and Use Committee (#10-071). Mice were housed in a virus-antigen-free facility at the University of Miami: Division of Veterinary Resources under a 12 hr light/dark cycle at room temperature and fed ad libitum with standard rodent diet. The endpoint of the study was set when the physical condition for more than 50% of remaining tumor-bearing mice was evaluated as critical and euthanized within 24 hours.

Tumor Inoculation

Female wild-type mice and MCK-PGC-1a transgenic littermates (n = 8/group) at 4 month of age were injected subcutaneously with 10 6 Lewis lung carcinoma cells in 100 mL sterile vehicle, phosphate-buffered saline (PBS), between the shoulder blades. Controls were injected in the same manner and location with PBS (n = 7 for wild-type mice, n = 5 for MCK-PGC-1a mice). The weights of the animals were recorded and their general health was monitored daily after tumor inoculation. Individuals deemed in poor condition unable to survive until the endpoint of study were euthanized and excluded from further analysis.

Spectrophotometer Assays

OXPHOS assays were performed as previously described [28]. In brief, homogenates from quadriceps and gastrocnemius were prepared using a tissue homogenizer (Omni) in PBS plus protease inhibitor cocktail (Roche) on ice. Samples were centrifuged at 800 g for 5 minutes and the supernatant of the homogenate was added to a buffer containing 10 mM KH 2 PO4, 1 mg/mL BSA, 120 mM lauryl maltoside, and 2 mM cytochrome c reduced with sodium hydrosulfite in. The mixtures were followed at 550 nm with the absorption reading taken every 11 seconds for 2 minutes at 37uC. 240 mM potassium cyanide was used to inhibit the reaction to ensure slope was specific to cytochrome c oxidase (COX). The slopes were normalized by protein concentration determined by Bradford assay.

For citrate synthase (CS) activity assay, the supernatants were added to a buffer containing 50 mM Tris-HCl pH 7.5, 20 mM acetyl CoA, 10 mM 5, 59-dithiobis-(2-nitrobenzoic acid), and 0.1% triton X-100. The assay was performed at 30uC with 50 mM A. mtDNA copy number of gastrocnemius and quadriceps of 4-months-old MCK-PGC-1a and wild-type mice, without and with tumor implantation (n = 5/group). B-C. COX and CS enzymatic activity of gastrocnemius (B) and quadriceps (C) muscle homogenates normalized to protein from tumor MCK-PGC-1a and age-matched controls (n = 5/group). D-E. mRNA levels (DDCt) of Ppara, Pparg, Ppard, and Ppargc1b in gastrocnemius (D) and quadriceps (E) normalized to Gapdh at 4-months-old tumor MCK-PGC1-a and aged-matched controls (n = 4/group). Error bars are mean 6 SEM. doi:10.1371/journal.pone.0033426.g002 oxaloacetate (OXA) to start the reaction. Readings were obtained every 11 seconds for 3 minutes. The slopes taken before adding OXA were extracted from the slope with OXA. Normalization was as above.

mRNA Isolation and Reverse Transcriptase PCR Dissected quadriceps and gastrocnemius muscle tissues were submerged in TRIzolH (Sigma/Invitrogen). Tissues were homogenized with a hand-held rotor homogenizer (VWR), and RNA was extracted by chloroform phase separation. We used 1 mg of RNA for reverse-transcription reaction using the iScript cDNA synthesis kit according to the manufacturer's protocol (BioRad).  The following primer pairs were used for the quantification of mtDNA copy number in total DNA (extracted with phenol: chloroform phase separation): Nd1 (59-CAGCCTGACCCA-TAGCCATA, 59-ATTCTCCTTCTGTCAGGTCGAA), Actb (59-TCACCCACACTGTGCCCATCTACGA, 59-CAGCG-GAACCGCTCATTGCCAATGG). Comparative Ct method was used to determine the relative abundance of genes of interest or mtDNA [29].

Real-time PCR

Western Blotting Analysis

Protein extracts were prepared from the quadriceps and gastrocnemius muscles that were homogenized with a hand-held rotor (VWR) in PBS containing protease inhibitor cocktail (Roche). Samples were then snap frozen in liquid nitrogen and stored in 280uC until used. Upon use, homogenates were diluted 1:10 with RIPA buffer (62.5 mM Tris-HCl pH 7.4, 150 mM NaCl, 1% NP-40, 0.25% SDS, 1 mM EDTA, with protease inhibitors and phosphatase inhibitors added freshly) and sonicated briefly. Homogenates were then centrifuged at 15,0006 g and the supernatant was collected. Proteins were quantified using Bradford assay. Equal amount of protein were loaded onto a 4-20% SDSpolyacrylamide gradient gel (BioRad). The gel was blotted on Polyvinylidene Fluoride (PVDF) membrane (BioRad).

Membranes were blocked in Odyssey blocking solution (LI-COR Biosciences) diluted 1:1 with PBS for 1 hour at room temperature. Primary antibodies used were OXPHOS rodent cocktail (Mitosciences), a-tubulin (Sigma), PGC1-a (Santa Cruz), SDHA (Mitosciences), b-actin (Sigma), cytochrome c (Mitosciences), porin (Mitosciences), and UQCRC1 (Mitosciences). Primary antibody was incubated overnight at 4uC. Secondary antibodies used were either infrared conjugated antibodies antirabbit-700 or anti-mouse-800 (Rockland) at manufacturer-suggested concentrations. Secondary antibodies were incubated for 1 hour at room temperature. Blots with infrared secondary antibodies were visualized with Odyssey Infrared Imaging System (LI-COR Biosciences). Optical density measurements were taken using the Gel-Pro Analyzer software.

Treadmill

Endurance was evaluated using a six lane treadmill with motivation grid designed for rodents (Columbus Instruments). Animals were given one training day to adapt to the equipment and motivation grid. On the test day, mice were required to run at a speed of 8 m/min for 5 minutes and the number of falls onto the motivation grid was recorded for each mouse.

Serum IL-6 Quantifications

Blood was taken from the left ventricle of deeply anesthetized mice before euthanized. Blood was allowed to clot on ice, and serum was isolated at 1,0006 g in a bench top centrifuge (Eppendorf 5424) for 15 minutes at 4uC. An additional centrifugation step of the serum at 10,0006 g for 10 minutes at 4uC was performed for complete platelet removal. Serum was used in BD cytometric bead array mouse inflammation cytokine kit according to the manufacturer's instructions (BD Biosciences). Samples were analyzed on a BD LSRFortessa cell analyzer (BD Biosciences).

Statistical Analysis

All results were expressed as means 6 STDEV. Significance of the differences was evaluated by 2-way ANOVA followed by Bonferroni post-test for experiments with more than 2 groups or by unpaired Student t-test between 2 groups. Differences were considered significant when p,0.05 (*), 0.001,p,0.01 (**), p,0.001(***).

Figure 1 .
1Tumor inoculated transgenic MCK-PGC-1a mice maintain upregulation of PGC-1a and mitochondrial markers. A. mRNA levels (DDCt) of Ppargc1a in gastrocnemius and quadriceps normalized to Gapdh at 4 months-of-age in MCK-PGC-1a and controls without and with tumor implantation (n = 4/group). B, C. Left panel: Representative Western blotting analysis of steady state levels of PGC-1a and mitochondrial proteins (complex II subunit SDHA, complex III subunit UQCRC1, and complex I subunit NDUFB8) at 4 months-of-age from muscle homogenates of MCK-PGC-1a and controls. Right panel: Optical density (O.D.) quantification of proteins of interest normalized toa tubulin (n = 3/group in B, n = 4/ group in C). Error bars are mean 6 SEM. doi:10.1371/journal.pone.0033426.g001

Figure 2 .
2Tumor injected transgenic MCK-PGC-1a mice maintain upregulation of mtDNA levels and mitochondrial enzymes activity.

Maxima SYBR Green/ROX qPCR master mix (Fermentas) was used according to manufacturer's directions to perform realtime PCR. Primers used for the cDNA quantification were: Ppargc1a(59-CTGCGGGATGATGGAGACA, 59-AGCAGC-GAAAGCGTCACA), Ppargc1b (59-TGGCCCAGATACACT-GACTATG, 59-TGGGCCTCTTTCAGTAAGCT), Ppara (59-TTCCCTGTTTGTGGCTGCTAT, 59-CCCTCCTGCAAC-TTCTCAATGTAG), Pparg (59-CGGAAGCCCTTTGGTGA-CTTTA, 59-GCGGTCTCCACTGAGAATAATGAC), Ppard

( 59 -
59ACCGCAACAAGTGTCAGTAC, 59-CTCCGGCATCC-GTCCAAAG), and Gapdh (59-TGCACCACCAACTGCTTAG, 59-GGATGCAGGGATGATGTTC).

Figure 3 .
3Over-expression of MCK-PGC-1a does not protect against cancer-induced cachexia. A. Percentage body weight over two weeks after tumor cell or saline injection for MCK-PGC-1a or controls at 4 months-of-age, numbers of animals as labeled. B. Percentage tumor-free body weight at 2 weeks after tumor inoculation of MCK-PGC-1a and age-matched controls, numbers of animals as in panel A. C. Weight of tumor (grams) extracted from site of injection 2 weeks after tumor inoculation (n = 5 for wild-type, n = 7 for MCK-PGC-1a). D. Linear regression modeling relationship between changes in percentage body weight and the weight of tumor of MCK-PGC-1a and age-matched tumor-bearing controls, numbers of animals as in panel C. E. Weight of gastrocnemius and quadriceps (grams) of saline-injected and tumor-inoculated groups of MCK-PGC-1a or age-matched wild-type mice, numbers of animals as in panel A. F. Concentrations of serum IL-6 in control and tumor-bearing wild-type and MCK-PGC-1a mice (n = 5/group). Error bars are mean 6 SEM. doi:10.1371/journal.pone.0033426.g003
March 2012 | Volume 7 | Issue 3 | e33426
PLoS ONE | www.plosone.org
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Reproductive Biology and Endocrinology
8452010BioMed Central Research
Background: Ovarian stimulation in women with polycystic ovary syndrome (PCOS) increases the risk for perinatal complications. Ovulation induction by laparoscopic ovarian drilling (LOD) might improve the overall pregnancy outcomes. The aim of our study was to assess the adverse events or effects on pregnancy of LOD and clomiphene citrate (CC) stimulation in patients who received metformin.Methods:Setting: Academic research institution. We retrospectively analyzed the courses of 40 spontaneous pregnancies after LOD for CC-resistance, 40 pregnancies after CC stimulation, and 40 pregnancies after metformin treatment alone. Patients in the LOD and the CC groups had been pre-treated with Metformin. Primary outcome parameters were: the rate of multiple pregnancies; the rate of early pregnancy losses/miscarriages; the development of gestational diabetes, pregnancy-induced hypertension, and preeclampsia/HELLP-syndrome; premature delivery; and birth weight.Results:The rate of twin pregnancies did not differ between the CC group (12.5%), the LOD group (7.5%), and the metformin only group (2.5%, p = 0.239). Seventeen women suffered an early miscarriage. There were no differences with regard to the rates of gestational diabetes, pregnancy-induced hypertension, preeclampsia, and preterm delivery. By analyzing all pregnancy complications together, the overall pregnancy complication rate was highest in the CC group (70.0%, 28/40), followed by the LOD group (45.0%, 18/40), and the metformin only group (47.5%, 19/40; p = 0.047).Conclusions: CC, but not LOD, increases the complication rate in pregnant patients who received metformin.

Background

Despite the fact that polycystic ovary syndrome (PCOS) remains one of the most common female endocrinopathies, with an incidence of 5-10% in women of reproductive age, the etiology, pathogenesis, and even treatment of infertile women who suffer from PCOS are still the subject of much controversy [1,2]. Clomiphene citrate (CC) is presumed to be the first-line therapy, since it is known to result in higher ovulation, conception, pregnancy, and live-birth rates when compared with metformin; the combination of both drugs did not result in a significant benefit [3,4].

As second-and third-line therapies, gonadotropin stimulation and laparoscopic ovarian drilling (LOD) are recommended for CC-resistant anovulatory PCOS patients. To date, five randomized controlled trials have compared the effectiveness of LOD with that of gonado-tropin use, and all demonstrated similar pregnancy and live birth rates for both treatment options [5][6][7][8].

Ovarian stimulation has been shown to increase the risk for perinatal complications. Even non-in vitro fertilization assisted reproductive techniques lead to a higher risk for multiple pregnancies, prematurity, low and very low birth weight, transfer to the neonatal intensive care unit and various neonatal morbidity parameters [9]. Thus, it has been proposed that ovulation induction by LOD may improve the overall outcomes of pregnancies in PCOS patients [10]. A comparison of pregnancy outcomes of women with PCOS who had undergone LOD and non-PCOS pregnancy controls demonstrated a significantly higher risk of hypertensive disorders and gestational diabetes mellitus (GDM) for the PCOS group [11]. However, whether LOD might prevent pregnancy complications in women with PCOS remains hypothetical [10].

The aim of our retrospective study was to evaluate the pregnancy outcome after LOD compared to pregnancies after metformin treatment and CC stimulation.

Methods

Definition

PCOS was diagnosed according to the revised European Society of Human Reproduction and Embryology (ESHRE) and the American Society for Reproductive Medicine (ASRM) criteria of 2004, which were based on the Rotterdam criteria [2]. CC resistance was defined as the absence of developing follicles after ovarian stimulation with 150 mg clomiphene citrate/day given for five days beginning on the second day of the menstrual cycle.

Patient collective

In a retrospective cohort study, we included all CC-resistant women with PCOS who underwent LOD at the Department of Gynecology and Obstetrics of the Medical University of Vienna, Austria, between January 2001 and December 2008, and who became spontaneously pregnant within one year after the operation. Of a total of 125 patients who underwent LOD, all necessary data were available for 75 patients (60.0%). Forty women became spontaneously pregnant after LOD and were included in this analysis, whereas women in need of CC stimulation after LOD were excluded (Group 1).

Of a total of 125 PCOS women who had become pregnant after CC stimulation at our department between January 2003 and December 2008, 51 were lost to followup (40.8%). Of the remaining 74 patients, 40 age-matched PCOS women were assigned to the CC group (Group 2).

A total of 109 PCOS women had become pregnant after metformin therapy alone at our department between January 2003 and December 2008. In these patients, metformin had been used as a first-line monotherapy for PCOS, since these patients wanted an improvement in both metabolic and reproductive function and were not on a fast track toward becoming pregnant. Thirty-two patients were lost to follow-up (29.4%). Of the remaining 77 patients, 40 age-matched PCOS women were assigned to the metformin group (Group 3). For details on patient characteristics, see Table 1.

At our department, infertile women with PCOS undergo a first-line treatment with metformin alone in case that there is no urgent need to establish pregnancy, or combined CC stimulation and metformin treatment. Based on individual decisions with regard to the patient's needs, LOD is recommended after a minimum of 3 or a maximum of 6 cycles of CC stimulation has failed to induce ovulation. Thus, all of our patients (Groups 1, 2, and 3) had been treated with metformin for at least two months, with a minimum dose of 1500 mg/day as a standard, first-line therapy before the pregnancy was established. As soon as the pregnancy was diagnosed, the metformin therapy was discontinued.

Study design

The primary objective of the study was to assess the adverse events or effects on pregnancy of LOD and CC stimulation in patients who received metformin and in patients who received metformin only therapy. The following parameters were analyzed: the rate of multiple pregnancies; the rate of early pregnancy losses/miscarriages; the development of GDM, pregnancy-induced hypertension (PIH), and preeclampsia/HELLP-syndrome; premature delivery; and birth weight.

GDM was diagnosed using the 75 g, 2 h oral glucose tolerance test (OGTT), routinely performed in the third trimester. Patients were diagnosed with GDM if one of the following parameters reached or exceeded the 97.5 percentile: fasting venous blood glucose concentration (4.5 mmol/l), at 1 h (9.1 mmol/l) and 2 h (7.9 mmol/l). The indications for OGTT in the second trimester were as follows: glucose in any of the monthly urine samples; GDM in a previous pregnancy; diabetes in the immediate family; and fetal macrosomy (> 2 standard deviations). PIH was defined as gestational hypertension (blood pressure >140/90 mmHg without proteinuria at a gestational age >20 weeks on two or more occasions at least 6 h apart). Preeclampsia was defined as blood pressure >140/ 90 mmHg in combination with proteinuria >0.3 g/24 h after 20 weeks gestation. The HELLP-syndrome was defined as hemolysis, elevated liver enzymes (serum LDH ≥ 600 IU/L or total bilirubin ≥ 1.2 mg/dL), and low platelet count (≤ 100.000 cells/μL). Premature delivery was considered as delivery between the 22nd and 37th week of gestation.

Furthermore, we evaluated the following parameters from retrospective chart review: age; body mass index (BMI); sexual hormone levels including luteinizing hormone (LH), follicle stimulating hormone (FSH), testosterone, androstenedione, and oral glucose tolerance test as measured at the initial visit before any PCOS-related medication; diabetes mellitus; and pre-existing essential arterial hypertension and liver diseases before pregnancy was established.

Surgical technique of laparoscopic ovarian drilling

LOD was achieved with standard laparoscopy under general anesthesia. The procedure was performed after establishing a CO 2 pneumoperitoneum. The laparoscope was inserted through the umbilicus using a 5 mm trocar. Under direct supervision, two 5-mm adjuvant trocars were inserted subsequently.

Thirty-one of the reported patients who underwent LOD were treated with bilateral LOD using a monopolar electrocoagulation technique, as previously reported [12]. Nine patients were operated with a monopolar hook electrode (GK 375R, B. Braun Aesculap, Maria Enzersdorf, Austria) and 5-10 incisions of 2-3 mm length in the ovarian capsule.

The study was performed in accordance with the "Good Scientific Practice Standards" set forth by the Medical University of Vienna, which are based on the ethical standards of the revised Helsinki Declaration of 2008. The study was approved by the Institutional Review Board of the Medical University of Vienna (IRB number: 1121/2009).

Statistical analysis

Variables are described by frequencies and mean ± standard deviation (SD). Statistical analysis was performed using the chi-square test, the likelihood-quotient, univariate variance analysis, and the Kruskal-Wallis test. Differences were considered statistically significant if p < 0.05. Statistical analysis was performed using SPSS 15.0.1 for Windows (SPSS Inc., 1989.

Results

Patient characteristics

Details about patient characteristics are listed in Table 1. The duration of metformin therapy before pregnancy was longest in the LOD group, followed by the CC group (p < 0.001). For the LOD patients, the time between the operation and the last menstrual bleeding before conception was 6.1 ± 2.9 months. These patients had been stimulated for a minimum of 3 and a maximum of 6 cycles before the operation. In patients who became pregnant after CC stimulation, the mean number of stimulations was 1.6 ± 0.8. Pregnancies were established after a stimulation with 50 mg and 100 mg in 30 and 10 patients, respectively. None of the hormonal parameters were found to differ between the three groups. No woman had suffered from diabetes mellitus or any kind of liver disease pre-existing before pregnancy was established. The incidences of essential arterial hypertension were distributed equally (p > 0.05).

Pregnancy outcome

Details on early pregnancy courses and outcomes can be seen in Table 2. There was no case of ovarian hyperstimulation syndrome. The rate of twin pregnancies was highest in the CC group (12.5%), followed by the LOD and the metformin only groups (7.5% and 2.5%, respectively). However, the differences were not statistically significant (p = 0.239). All other patients had singleton pregnancies. Seventeen women suffered from an early miscarriage. In two patients, the pregnancy was terminated because of fetal malformations: one 26-year-old woman from the LOD group for fetal anencephaly in her 17 th gestational week; and one 26-year-old woman from the CC group for fetal Down syndrome in her 16 th gestational week. One woman from the LOD group suffered a late pregnancy loss in her 22 nd gestational week. Table 3 shows details outcomes of the 100 pregnancies remaining after the 24 th gestational week. The highest rate of preterm deliveries before the completed 37 th week of gestation was found for the CC group (19.4%), followed by the LOD and metformin only groups (8.8% and 5.7%, respectively). Again, the differences were not significant (p = 0.200). One male child was delivered by caesarean section in the 25 th week of gestation due to preterm, prelabor rupture of membranes and intra-amniotic infection. The child died at the neonatal intensive care unit a few days after delivery. No other children suffered from any morbidities in the postpartum period.

The mode of delivery was a caesarean section in 38.2% (13/34), 51.6% (16/31), and 34.3% (12/35) for groups 1, 2 and 3, respectively. Birth weight did not differ between groups 1, 2, and 3 (3243.1 ± 628.9 vs. 3261.3 ± 740.9 vs. 3073.3 ± 631.9, respectively; p = 0.253).

Overall pregnancy complication rate

By analyzing all pregnancy complications together, i.e., early miscarriage, fetal malformations, late pregnancy loss, development of PIH, preecmlampsie/HELLP-syndrome, and GDM, as well as preterm delivery, the following distribution was found: 70.0% (28/40) for the CC group; 45.0% (18/40) for the LOD group; and 47.5% (19/ 40) for the metformin only group (p = 0.047).

Discussion

In this retrospective study of 120 pregnancies after different treatment regimes for infertility in PCOS women, we found no significant differences in pregnancy outcomes, except for the overall complication rate. However, there was some trend toward higher early miscarriage, twin pregnancy, and preterm delivery rates for pregnancies after CC stimulation.

With regard to general patient characteristics, such as age, parity, type of infertility, body mass index, hormonal parameters and the incidence of pre-existing arterial hypertension, there were no differences between the three study groups. However, we observed a significantly longer intake of metformin in the LOD group, followed by the CC and metformin only groups. This is due to the fact that metformin is given as the standard first-line therapy for many anovulatory, young PCOS patients. In the event that metformin therapy failed to induce ovulation, women were subsequently treated with CC stimulation for a maximum of six cycles. Only CC-resistant women underwent LOD. However, metformin therapy was continued during CC stimulation and before and after LOD. Thus, LOD patients had the longest metformin intake.

To our knowledge, this is the first study to compare pregnancy outcomes after different treatment regimes, including LOD for anovulatory PCOS women. To date, only one trial has focused on pregnancy outcomes after LOD compared to pregnancies after spontaneous ovulation and conception in healthy women. The authors of that study reported a significantly higher risk of hypertensive disorders and GDM in the LOD group [11]. In our patient collective, similar incidences of GDM of about 30% were found for the three study groups. Nonetheless, the overall GDM rate of 30.7% reflects the increased risk for GDM in patients with PCOS compared to healthy women [10]. We observed a trend toward a higher risk of PIH and preeclampsia/HELLP syndrome that was somewhat more profound in the CC group in our study collective. However, these data failed to reach statistical significance (p > 0.2). Whether this is caused by possibly too small sample sizes cannot be concluded from this data. It is known that, even after LOD, the rates of pregnancy-induced hypertensive disorders are increased when compared to healthy controls [11].

Although statistically not significant, we observed the highest rate of twin pregnancies after CC stimulation. This is in accordance with the recent literature reporting that CC stimulation leads to slightly increased rates of multiple pregnancies when compared to pregnancies after spontaneous conception. The multiple pregnancy rate after CC stimulation in PCOS women has been reported to be less than 10% [2,10]. The rate of twin pregnancies after CC stimulation of 12.5% in our study may be considered similar.

Rates of early pregnancy loss of up to 40% have been reported for PCOS women [13]. As mentioned, treatment with ovulation-inducing medications might be responsible for this increased risk for early pregnancy loss. This is in accordance with the observation that CC stimulation leads to a significantly higher incidence of miscarriage compared to spontaneous pregnancies in subfertile women [14]. Similarly, we found the highest rate of early miscarriage in the CC group in our study (20%), whereas rates of about 10% were observed in the LOD and metformin only groups.

The rate of preterm deliveries before the 37 th completed week of gestation was highest for pregnancies after CC stimulation (about 20%). Again, these differences were not significant, perhaps due to the small sample size. There were no differences in birth weight and neonatal morbidity or mortality.

We analyzed the overall risk for any kind of pregnancy complication, and found a significantly higher risk in the CC group (70%) than in the other groups (about 45%). This might be an indication that pregnancy after LOD is somewhat superior to that after CC stimulation in PCOS patients. However, these data have no validity with regard to patients who have undergone LOD, but who are still in need of CC stimulation subsequently in order to become pregnant. In further studies, we will focuse on this group of patients.

The additive effect of combined metformin and CC or metformin and LOD may be a confounding factor. We consider this a study weakness.

Conclusions

CC, but not LOD for CC resistance, somehow increases the complication rate in pregnant patients who received metformin. However, since our study does not demonstrate a significant benefit of LOD treatment for CC resistance compared to CC stimulation concerning the major outcome parameters except for the overall complication rate, LOD cannot be recommended as a first-line therapy for anovulatory PCOS women under metformin treatment. One has to consider that LOD might lead to a decrease in the ovarian reserve due to a surgical destruction of the ovarian capsule and/or to adhesion formation, both possibly resulting in impairment of fertility. Thus, LOD must remain a second-or third-line treatment option [15][16][17][18]. Currently, not only ovulation and pregnancy rates but also pregnancy outcome, live birth rate, and baby take-home rate are considered the ultimate clinical endpoints for studies on infertility treatment. Thus, larger, prospective, randomized trials are necessary in order to compare pregnancy outcomes after different treatment regimes for anovulatory infertile PCOS women and to finally evaluate the value of LOD.

Table 1 :
1Patient characteristics *Age at conception; + Prim. = primary; # Sec. = secondary; ‡ statistically significant; + before initiation of any PCOS-related treatmentPregnancies
after LOD
(n = 40)

Pregnancies
after CC
stimulation
(n = 40)

Pregnancies
after metformin
therapy only
(n = 40)

p

Table 2 :
2Early pregnancy outcomePregnancies after
LOD
(n = 40)

Pregnancies after CC
stimulation
(n = 40)

Pregnancies after
metformin therapy
only
(n = 40)

p

Twin pregnancies
3/40 (7.5%)
5/40 (12.5%)
1/40 (2.5%)
0.239

Early miscarriage
(<20 th week)

4/40 (10.0%)
8/40 (20.0%)
5/40 (12.5%)
0.410

Pregnancy termination
for fetal malformations

1/36 (2.8%)
1/32 (3.1%)
0
0.441

Late pregnancy loss
(20 th -24 th week)

1/35 (2.9%)
0
0
0.331

Table 3 :
3Late pregnancy outcome PIH = pregnancy induced hypertension; + GDM = gestational diabetes mellitus; # IGDM = insulin-dependent gestational diabetes mellitus; ‡ statistically significantPregnancies after
LOD
(n = 34)

Pregnancies after CC
stimulation
(n = 31)

Pregnancies after
metformin therapy
only
(n = 35)

p

PIH*
1 (2.9%)
4 (12.5%)
4 (11.4%)
0.262

Preeclampsia/HELLP
syndrome

4 (11.8%)
4 (12.5%)
1 (2.9%)
0.238

GDM +
10 (29.4%)
10 (31.3%)
11 (31.4%)
0.980

IGDM #
5 (14.7%)
5 (15.6%)
5 (14.3%)
0.988

Preterm delivery <37 th
gestational week

3 (8.8%)
6 (19.4%)
2 (5.7%)
0.200

*
All authors conceived and designed the study. JO, SW, CK, JCH, and KM supervised the data collection, assisted in the analysis, and drafted the manuscript; JO and SW assisted in collection and maintenance of the data; all authors assisted in conception, design, and analysis, and edited the manuscript. All authors read and approved the final manuscript.Author DetailsDepartment of Gynecologic Endocrinology and Reproductive Medicine, Medical University of Vienna, Vienna, AustriaCompeting interestsThe authors declare that they have no competing interests.Authors' contributions
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Pregnancy outcome in women with polycystic ovary syndrome comparing the effects of laparoscopic ovarian drilling and clomiphene citrate stimulation in women pre-treated with metformin: a retrospective study. Ott, Reproductive Biology and Endocrinology. 845Cite this article asCite this article as: Ott et al., Pregnancy outcome in women with polycystic ovary syndrome comparing the effects of laparoscopic ovarian drilling and clomiphene citrate stimulation in women pre-treated with metformin: a ret- rospective study Reproductive Biology and Endocrinology 2010, 8:45


Lifemap: Exploring the Entire Tree of Life

Damien M De Vienne *email@example.com
Laboratoire de Biométrie et Biologie Evolutive
UMR5558
Univ Lyon
Université Claude Bernard Lyon 1
CNRS
Villeurbanne, LyonFrance

Lifemap: Exploring the Entire Tree of Life
COMMUNITY PAGE
The Tree of Life (ToL) is meant to be a unique representation of the evolutionary relationships between all species on earth. Huge efforts are made to assemble such a large tree, helped by the decrease of sequencing costs and improved methods to reconstruct and combine phylogenies, but no tool exists today to explore the ToL in its entirety in a satisfying manner. By combining methods used in modern cartography, such as OpenStreetMap, with a new way of representing tree-like structures, I created Lifemap, a tool allowing the exploration of a complete representation of the ToL (between 800,000 and 2.2 million species depending on the data source) in a zoomable interface. A server version of Lifemap also allows users to visualize their own trees. This should help researchers in ecology and evolutionary biology in their everyday work, but may also permit the diffusion to a broader audience of our current knowledge of the evolutionary relationships linking all organisms.

Introduction

An exhaustive knowledge of the evolutionary relationships linking all organisms (the whole biodiversity) would produce a tree-like structure, referred to as the Tree of Life (ToL). The decrease of DNA sequencing costs [1] associated with improved phylogenetic and phylogenomic methods for reconstructing phylogenetic trees [2][3][4] helped resolve, in the recent years, many parts of this ToL. The aggregation of these portions produces a phylogenetic classification scheme describing the evolutionary relationships among the species under consideration [5]. The NCBI taxonomy is one of these classifications, describing relationships among~1.4 million species (as of Oct 2016), that is widely used by biologists. Another version of the ToL, the Open Tree of Life (OTOL), was published recently [6]. It adds to the taxonomy some evolutionary information retrieved from published phylogenetic trees, in order to provide a more accurate and more comprehensive description of the ToL. It currently contains around 2.2 million species.

Despite huge ongoing efforts to assemble the ToL [6,7], no tool exists today to explore it entirely and interactively in a satisfying manner. More generally, the visualization of very large trees still remains a challenge [8]. A parallel has been drawn earlier between what has been achieved in cartography with the development of Google maps or OpenStreetMaps [9] (OSM), and what could be done for exploring the ToL [8,10]. The logic behind the use of the cartographic paradigm for visualizing a taxonomy is straightforward: like geographic entities (countries, regions, cities, etc.), taxonomic levels have names (kingdoms, families, classes, orders, genera, etc.) and are nested within each other. Consequently, it is possible to propose a visualization where zooming in increases the level of detail displayed while hiding upper-level information. This idea was first exploited in a tool called OneZoom [10] that allows visualizing, on demand, large trees by zooming and panning. This tool introduced the concept of deep zooming for visualizing large phylogenies (along with [11]), which motivated other research along the same line, including the present one. The main limitation of OneZoom that makes it inappropriate for visualizing the complete ToL is that the fractal representation it uses prevents the presence of multifurcations in the trees (one node connected to more than two descendants), which is very common (83% of the nodes in the NCBI taxonomy and 72% in the OTOL are not binary).

Three Versions of the Tree of Life

I developed Lifemap, a tool largely inspired by the technology developed for cartography that is free of the limitation described above. Its approach differs from that of OneZoom [10], both in the representation of the tree and in the way images are displayed and interacted with on the screen. This allows a fast and smooth exploration of the biggest tree ever proposed on a single page for exploration.

Lifemap uses a representation inspired by Treemaps, a method developed in the field of computer sciences in the early 90s for turning file system tree structures (directories in a computer) into a planar space-filling map [12] for clearer visualization. In Treemaps, directories are represented by rectangles that are recursively split in as many subrectangles as there are subdirectories, leading to a fully-filled map. The size of the rectangles is proportional to the number of files in each directory. This approach cannot be directly used to visualize the ToL because it is incompatible with the representations of links between the nodes, which is important in an evolutionary context where branches represent time and must be visible. I, however, kept the idea of filling the map recursively, using a base shape whose size is proportional (but with a square root transformation) to the number of elements in it. In Lifemap, this base shape is a half-circle, and the way these half-circles are arranged within and between each other ( Fig  1) ensures that there is space to draw all the branches and guarantees that the branches never intersect (unlike other solutions, [13]).

Lifemap comes in three versions that differ by the tree that is displayed and the information that is associated with tips and nodes when clicking. The general public version (Fig 2A) displays a reduced NCBI taxonomy obtained by removing nonidentified clades and all taxa below the species level. When clicking on nodes or tips, a short description and a picture are displayed ( Fig 2B). Pictures and text are obtained from Wikipedia. If no Wikipedia page exists or if a picture is lacking, the user can click on a link to contribute to Wikipedia for these specific taxa by creating a page, modifying the text, and/or adding a picture. Lifemap should thus help identify missing pages and improve the quality and quantity of pages dedicated to clades and species in Wikipedia.

The NCBI version, named "Lifemap NCBI," displays the whole NCBI taxonomy and is updated every week. When clicking on a node, the user can (i) get additional information about the current taxa (taxid, number of species), (ii) reach the NCBI web page corresponding to the node, and (iii) download the corresponding subtree in parenthetic format for further analysis. In this version, the user can also add a layer to the tree to visualize at each node the number of fully sequenced genomes.

The third version is named "Lifemap OTOL." It displays the latest OTOL synthetic tree and will be updated every time a new version is released. The information displayed when clicking on the nodes is similar to the one available in the two others: Wikipedia picture and description, taxonomy code, possibility to download the subtree in parenthetic format, and taxonomic sources of information for each node. Other information that can be displayed as layers on Lifemap will be added in the future in response to user's suggestions and requests in the different versions.

Finally, all three versions give the possibility to compute, visualize, and explore "paths" in the ToL (Fig 2C). This is done either by choosing a source and a destination taxa or by clicking the "view full ancestry" button associated with each node. In the latter case, the destination is set as the root of the tree. The path is computed instantly and highlighted on the tree. The most recent common ancestor (MRCA) is indicated with a marker, and the list of taxa encountered in the route from the source to the destination is returned.

A Virtual Machine for Exploring Any Tree

In addition to the three precomputed trees described above and available online, Lifemap comes as a virtual machine that can be downloaded and used to visualize any tree. A web page (Fig 3) available from the browser once the virtual machine is running allows the user to upload a tree file (Step 1), perform all the necessary computations on the server (Step 2), and explore the tree (Step 3). On the exploration page, the user has access to a search bar that allows finding and zooming to any node or tip based on its name (Fig 3).

Because Lifemap requires nodes to be named, and because this condition is not met for most trees, the server version of Lifemap automatically gives names to unnamed nodes. This is done by traversing the whole tree from tips to root and assigning each (unnamed) node the names of two of the direct descendants, separated by a "+" sign.

Discussion

Lifemap should become a useful source of information for the general public interested in evolution and biodiversity, but also for education and research in various fields related to ecology, evolutionary biology, and genomics. In addition, the Lifemap virtual machine might help researchers manipulating very large trees to visualize their data. Lifemap is available online at Each clade is represented by a half-circle whose size depends on the relative number of species in the clade as compared to its sister clades at a given level. Note that these proportions are not respected at the tree root where the three superkingdoms are arbitrarily given the same size. Computation of the size of each half-circle is based on the angle they are associated with (α and β on the first panel): if n A and n B are the number of species in clades A and B, respectively, the angles in degrees are computed as follows: http://lifemap.univ-lyon1.fr. The general public version is also available as a mobile app for Android phones and tablets on the Play Store (as "Lifemap-Tree of Life"), and will soon be available for iOS devices and Windows phones.
a ¼ 180 Â ffiffiffiffiffi n A p =ð ffiffiffiffiffi n A p þ ffiffiffiffiffi n B p Þ and b ¼ 180 Â ffiffiffiffiffi n B p =ð ffiffiffiffiffi n A p þ ffiffiffiffiffi n B p

Materials and Methods

Pipeline

A pipeline was written in Python (v2.7) to perform all the actions to create Lifemap and keep it up-to-date. The pipeline uses the ETE 3 toolkit [14] for tree operations (traversal) and NCBI interrogation. Here are the main steps: • Retrieve NCBI taxonomy from the NCBI website (or OTOL taxonomy from the OTOL web site) and convert to Newick format.

• Only for the general public version: Remove all taxa whose name contains the words "unidentified," "Unidentified," "unclassified," "Unclassified," "environmental," "Environmental," "uncultured," or "Uncultured," and all taxa below the species level.

• While traversing the tree:

• Compute the coordinates of (i) nodes and tips, (ii) half-circles, (iii) half-circle centers where clade names are written at some zoom levels, (iv) rank names along the half-circles

• Write all these coordinates in a postgreSQL database that has PostGIS enabled • Write information associated with each node and tip into the Solr Search Engine v5.4.0 (scientific name, common name, coordinates, zoom level, rank name, taxid).

• Get additional information for each taxa (for now, the number of genomes fully sequenced), and write this information into Solr.

All Lifemap functions are freely available (Under the GNU General Public License) on GitHub at https://github.com/damiendevienne/Lifemap/, along with detailed descriptions of the functions, the configuration files, the requirements, etc. Readers are referred to this source of information for more details.

NCBI and OTOL data

The NCBI taxonomy data is retrieved from the NCBI ftp site (ftp://ftp.ncbi.nlm.nih.gov/pub/ taxonomy/) every time the tree is updated. This is done automatically by the ETE 3 toolkit [14] used in the pipeline described above. For the Lifemap OTOL version, the latest OTOL synthetic tree is downloaded from https://tree.opentreeoflife.org/about/synthesis-release/, and the associated taxonomy version is retrieved from https://tree.opentreeoflife.org/about/taxonomyversion/. Currently, the synthetic tree version 7.0 is used, along with the taxonomy version 2.10.

Tiling and image production

The zooming capability of the tool relies on the same principle and the same tools as OSM. The whole space is cut into square images (tiles) of 256 x 256 px and to each image at zoom n corresponds four images at zoom n + 1 (Fig 4). Images are generated when first requested by a user, thanks to the mod_tile open source apache module linked to the renderd tool (both part of the OSM suite of tools [15]) and cached on the server for further accession. The creation of the images is performed by mapnik v3.0.9 [16], which reads the database, formats the graphical elements with respect to a style sheet written for Lifemap, and returns the images to the server.

Note that the number of images is expected to grow exponentially. The level zero of Lifemap, where all three superkingdoms are visible, is formed by four images. Level one requires 16 images (4 2 ), level two requires 64 images (4 3 ), . . ., level n requires 4 n+1 images (Fig 4). The number of zoom levels required to go to the deepest species in Lifemap-OTOL in its current version is 52, so if all images were generated at all zoom levels, the number of images would be around 1.1 x 10 32 (S4 i for i in (1:53)), which is too big to be stored. However, the vast majority of these images are entirely black and are simply not generated, because images are generated on user's demand only, which solves the issue. The size of our servers is sufficient for all the images that are not entirely black to be generated and stored (estimated to be around 200Go for the different versions of Lifemap).

Map-browser interaction and mobile version

The websites on which Lifemap can be explored were written in HTML5, CSS, and Javascript, using the Leaflet v0.7 javascript library [17]. A mobile version of the tool was also written. The user is automatically redirected to this website if accessing Lifemap from a phone.

The creation of the mobile app was performed using PhoneGap [18], a tool that allows encapsulating an HTML5/CSS/Javascript website and all its dependencies into a fully featured mobile application that can be uploaded to Android, Apple, or Microsoft stores.

Fig 1 .
1Three successive zoom levels in Lifemap illustrating the half-circle representation employed.

Þ. The square root reduces the difference in half-circle sizes between very small and very large groups. At every level, the half-circles (clades) are randomly distributed within their parental half-circle. doi:10.1371/journal.pbio.2001624.g001 Fig 2. Screenshots of the Lifemap tool. (A) Example of the appearance of Lifemap when zooming to the primates order. (B) Example of information displayed in the general public version when clicking on a node. (C) Visualization of the path between two taxa. Lemur image credit: Mathias Appel, Flickr (https://flic.kr/p/ FKtBbU). doi:10.1371/journal.pbio.2001624.g002

Fig 3 .
3Screenshots of the Lifemap server. Visualizing a tree with the Lifemap server is a three-step process that is performed from the dedicated web page (top screen). Once the tree file has been uploaded and the computations have been performed, the tree can be explored (bottom screen). This last screenshot shows the appearance of Lifemap for a fully bifurcating tree with nodes automatically named (see text).doi:10.1371/journal.pbio.2001624.g003
PLOS Biology | DOI:10.1371/journal.pbio.2001624 December 22, 2016
AcknowledgmentsI thank Stéphane Delmotte and Bruno Spataro for help in the Lifemap servers preparation and the creation of the virtual machine, and all the members of the Laboratory of Biometry and Evolutionary Biology (LBBE, Lyon) for agreeing to beta-test the tool. Thanks also to Manolo Gouy, Laurent Duret, and Dominique de Vienne for insightful discussions and corrections to the first version of this text.
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Like in OSM, the image displayed at a given zoom level is composed of paving (or tiling) of small square images. To each of these square images at a given zoom level corresponds four images of the same size at the next level. 10.1371/journal.pbio.2001624.g004Principle of the tiling system used in LifemapFig 4. Principle of the tiling system used in Lifemap. Like in OSM, the image displayed at a given zoom level is composed of paving (or tiling) of small square images. To each of these square images at a given zoom level corresponds four images of the same size at the next level. doi:10.1371/journal.pbio.2001624.g004

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