extfieldsofstudy: Biology Medicine

text:
PTEN/MMAC1 Mutation and Frequent Loss of Heterozygosity Identified in Chromosome 10q in a Subset of Hepatocellular Carcinomas

Frequent allelic losses on chromosome 10q have been reported in several types of cancers, suggesting the presence of a putative tumor suppressor gene(s) on the chromosomal arm. We examined loss of heterozygosity (LOH) on chromosome 10q in 37 hepatocellular carcinomas (HCC) using eleven dinucleotide microsatellite markers, spanning the entire chromosome arm of 10q. Twelve (32%) out of 37 informative cases showed allelic losses of at least one locus on 10q and eight tumors showed a partial deletion of 10q. Analysis of deletion mapping of these eight cases identified two commonly deleted regions within the distal part of 10q (10q24‐q26), a 20‐cM interval flanked by D10S597 and D10S216 and a 24‐cM interval flanked by D10S216 and D10S590. Moreover, we detected a somatic missense mutation (Met→Val) of a candidate tumor suppressor gene PTEN/MMAC1, located at 10q23.3, in one HCC with LOH of 10q. Our findings indicated the presence of putative tumor suppressor gene(s) in the distal region of 10q that might be involved in the development and progression of HCC. Inactivation of PTEN/MMAC1 gene located outside the commonly deleted region of 10q might also play an important role in a subset of HCCs.

A new putative tumor suppressor gene PTEN/MMAC1 was recently isolated from the chromosomal region 10q23.3 and somatic mutations of this gene have been reported in a variety of human cancers such as glioma, endometrial cancer, breast cancer, thyroid cancer, lung cancer, bladder cancer and prostate cancer. [22][23][24][25][26][27][28][29] This gene was also identified as being responsible for two hamartoma syndromes; Cowden disease and Bannayan-Riley-Ruvalcaba syndrome. [30][31][32] Although frequent LOHs on 10q have been reported in HCC using a limited number of markers, chromosome 10q is among the least characterized in HCC. 7,8) Therefore, we examined in the present study the involvement of PTEN/ MMAC1 mapped to 10q23.3, in the progression of HCC. Analysis of mutations of this new putative tumor suppressor gene in HCC DNAs was performed by single strand conformation polymorphism (SSCP) and direct sequencing analyses.

MATERIALS AND METHODS
Specimens and DNA extraction Thirty-seven pairs of tumors and corresponding non-cancerous tissues were obtained from 37 patients with HCC at the Department of Surgery II, Osaka University Medical School. All specimens had been previously examined histopathologically to confirm the diagnosis. Tumors were histologically classified as well differentiated, moderately differentiated, poorly differentiated, and undifferentiated carcinoma, according to the typing scheme of the Japanese Liver Cancer Society. 33) After surgical removal, specimens were carefully dissected and stored at −80°C until extraction of DNA. High-molecular-weight genomic DNAs were extracted from frozen tissues according to the method described previously. 34) LOH analysis using microsatellite markers Ten primer sets for polymerase chain reaction (PCR) amplification of microsatellite markers were selected to span the whole chromosome arm according to the Centre d'Etude du Polymorphism Humain (CEPH)/Genethon linkage map. 35) We also used D10S2491, a newly isolated microsatellite marker from the locus of PTEN/MMAC1. 23) The linear order of CA-repeat microsatellite markers used is (centromere)-D10S561-D10S202-D10S2491-D10S564-D10S192-D10S597-D10S587-D10S216-10S212-D10S590-D10S212-D10S555-(telomere) ( Table I). One of each pair of primer sets was labeled with [γ-32 P]ATP by using T4 polynucleotide kinase (Promega, Madison, WI). Genomic DNA (100 ng) was amplified by PCR in a 10-µl reaction mixture containing 1× PCR buffer (6.7 mM Tris, 16.6 mM ammonium sulfate, 6.7 mM EDTA, 10 mM βmercaptoethanol), 20 pmol each of [γ-32 P]ATP-labeled primer and non-labeled primer, 1 U of Taq DNA polymerase, 0.8 mM dNTP mixtures, and 1.5 mM of MgCl 2 . Thirty-five PCR cycles were performed with each cycle consisting of 30 s at 94°C, 30 s at 50-55°C, and 30 s at 72°C in a Gene Amp PCR system 9600 (Perkin-Elmer Cetus, Norwalk, CT). Five microliter volume of each solution, mixed with an equal volume of loading dye (95% formamide, 10 mM EDTA, 0.02% xylene cyanol and 0.02% bromophenol blue), was denatured and then electrophoresed in 6% polyacrylamide gel containing 7.7 M urea and 32% formamide. Gels were dried and exposed to X-ray film for 4-48 h. Determination of LOH Evaluation of LOH was performed by comparing the intensity of two alleles in informative cases using a scanning densitometer. Tumors were scored as exhibiting LOH if there was an absence or >50% reduction in intensity of one allele in the tumor sample compared to the normal allele. Mutation analysis SSCP analysis was performed by PCR amplification of each of the nine exons, corresponding to the coding region of PTEN/MMAC1 gene. PCR primers were designed in introns to amplify each whole exon and intron-exon boundary. The nucleotide sequences of PCR primers have been described previously 36) except primers for exon 5, which were designed as follows: forward primer, 5′-CTTATTCTGAGGTTATCTTTTTACC-3′; reverse primer, 5′-CTCAGAATCCAGGAAGAGGA-3′. PCR reactions were performed in 10-µl solutions containing 50 ng of genomic DNA, 2 pmol of each primer set, and 0.5 U of Taq I polymerase. SSCP analysis was performed using 5% nondenaturing polyacrylamide gels (acrylamide:N,Nbisacrylamide 99:1) containing 10% glycerol. After electrophoresis for 16 h at 4°C, the gels were stained with Silver Green II (TaKaRa, Otsu) and scanned with a fluorescence image analyzer (FMBio II Multi-View, TaKaRa). PCR products showing a mobility shift in SSCP analysis were subjected to sequence analysis using a Direct Sequencing Kit ("BioDye," PE Applied Biosystems, Warrington, UK).

LOH analysis of HCC DNAs from 37
HCCs were examined for LOH using a panel of eleven microsatellite markers spanning the whole region of chromosome 10q. Table I lists all dinucleotide repeat markers used for LOH analysis, as well as the frequency of LOH at each locus. The frequency of LOH was higher at the locus of the marker closer to the telomeric region of 10q. The frequency of LOH was highest at D10S212 and D10S590 (6 of 18, 5 of 15), and lowest at D10S561 (1 of 14, 7%). Four out of 26 (15%) tumors showed LOH at the PTEN/MMAC1 locus (D10S2491). All 37 cases were informative with two or more markers and 12 (32%) showed allelic losses of at least one locus on 10q. Among 12 tumors with LOH, eight showed LOH at some loci, whereas the others showed retention of heterozygosity, indicating partial or interstitial deletions of the long arm of chromosome 10 in these tumors. Representative autoradiograms of cases with partial or interstitial deletions are shown in Fig. 1. Tumor biopsy #117 showed LOH at D10S212, but heterozygosity at D10S216 (Fig. 1a). Tumor biopsy #127 showed LOH at D10S587, but retained heterozygosity at D10S590 (Fig.  1b). Fig. 2 summarizes the results of LOH analysis with 11 markers in 12 cases with LOH. All samples showed LOH at the distal part of 10q. Two commonly deleted regions in the distal part were located in a 20-cM interval flanked by D10S597 and D10S216, and a 24-cM interval between D10S590 and D10S216, as shown by bars with arrows in Fig. 2.    Clinicopathological data were available for 16 out of 37 HCC tumors. With respect to the grade of tumor differentiation, no LOH was detected in five moderately differentiated tumors, whereas six out of 11 poorly differentiated or undifferentiated HCC tumors showed LOH at loci on 10q. Mutation analysis of PTEN/MMAC1 in HCC To examine whether PTEN/MMAC1 is a candidate tumor suppressor gene for HCC, all tumors showing LOH at any locus on chromosome 10q were subjected to mutation analysis of the PTEN/MMAC1 gene by SSCP and direct sequencing. One (tumor biopsy #134) out of 12 tumors showed an abnormally shifted band on SSCP analysis of exon 2. A somatic missense mutation (Met→Val) was detected at codon 35 in DNA sequence analysis of this tumor (Fig. 3, a and b).

DISCUSSION
We have presented here a detailed LOH analysis of chromosome 10q in 37 HCC tumors. Frequent LOHs (33%) were detected especially in the distal part of 10q at loci D10S212 and D10S590. Furthermore, analysis of deletion mapping defined two commonly deleted regions within the distal part of 10q (10q24-26). Recent studies have shown that the distal region of chromosome 10q is commonly deleted in glioma, lung cancer, melanoma, prostate cancer, bladder cancer, and thyroid tumor. 12,15,16,20,37) These results indicate that there are putative tumor suppressor gene(s) on the distal region of 10q, which are involved in various types of carcinomas.
Recent studies have isolated a putative tumor suppressor gene, PTEN/MMAC1, from near the centromeric region of 10q (10q23.3). The PTEN/MMAC1-encoded protein exhibits a lipid phosphatase activity, which is thought to be responsible for its tumor suppressor function. [38][39][40] Somatic mutations of this gene have been reported in various type of human cancers. The above findings suggest that there are plural tumor suppressor genes on chromosome 10q. To examine further the involvement of the PTEN/MMAC1 gene in HCCs, we searched for mutations in 12 tumors showing LOH at loci on 10q. SSCP and mutation analysis detected a single somatic mutation (Met→Val) in exon 2 of PTEN/MMAC1 gene, in advanced stage HCC tumor (Tumor Stage III, undifferentiated type). According to LOH analyses using a panel of eleven microsatellite markers, although allelic status was not informative at the locus of PTEN/MMAC1 (D10S2491), tumor biopsy #134 showed LOH at all loci of flanking informative markers. These results suggest a loss of one allele at the PTEN/MMAC1 locus, and that a mutation of this gene in the remaining allele occurred in the tumor DNA, resulting in inactivation of both alleles. Although the function of the mutated PTEN protein is not yet clear, a point mutation at codon 35 was previously reported in juvenile polyposis coli and was not observed in 93 controls. 41) These facts indicate that the variant may be a tumor-associated change. In the present study, only four out of 26 informative cases (15%) showed LOH at D10S2491, isolated from the PTEN/MMAC1 locus, and the most frequent LOH was observed at the more distal part of 10q. These results indicated that plural tumor suppressor genes involved in hepatocarcinogenesis might exist on chromosome 10q. In


extfieldsofstudy: Chemistry Medicine

text:
A Computational Model for Quantitative Analysis of Cell Cycle Arrest and Its Contribution to Overall Growth Inhibition by Anticancer Agents

Most anticancer agents induce cell cycle arrest (cytostatic effect) and cell death (cytotoxic effect), resulting in the inhibition of population growth of cancer cells. When asynchronous cells are to be examined, the currently used flow cytometric method can not provide checkpoint‐specific and quantitative information on the drug‐induced cell cycle arrest. Hence, despite its significance, no good method to analyze in detail the mechanism of cell cycle arrest and its contribution to overall growth inhibition induced by an anticancer agent has yet been established. We describe in this study the development of a discrete time (Markov model)‐based computational model for cell cycle progression/arrest with transition probability (TPi) as a model parameter. TPi was calculated using model equations that include easily measurable parameters such as the fraction of cells in each cell cycle phase and population doubling time. The TPi was then used to analyze checkpoint‐specific and quantitative changes in cell cycle progression. We also used TPi in a Monte‐Carlo simulation to predict growth inhibition caused by cell cycle arrest only. Human SCLC cells (SBC‐3) exposed to UCN‐01 were used to validate the model. The model‐predicted growth curves agreed with the observed data for SBC‐3 cells not treated or treated at a cytostatic concentration (0.2μM) of UCN‐ 01, indicating validity of the present model. The changes in TPi indicated that UCN‐01 reduced the G1‐to‐S transition rate and increased the S‐to‐G2/M and G2/M‐to‐G1 transition rates of SBC‐3 cells in a concentration‐ and time‐dependent manner. When the model‐predicted growth curves were compared with the observed data for cells treated at a cytotoxic concentration (2 μM, they suggested that 22% out of 65% and 32% out of 73% of the growth inhibition could be attributed to the cell cycle arrest effect after 48 h and 72 h exposure, respectively. In conclusion, we report here the establishment of a novel method of analysis that can provide checkpoint‐specific and quantitative information about cell cycle arrest induced by an anticancer agent and that can be used to assess the contribution of cell cycle arrest effect to the overall growth inhibition.

Most anticancer agents are designed to induce cell cycle dysregulation in cancer cells, in order to cause cell cycle perturbation or arrest (cytostatic effect) and eventually cell death (cytotoxic effect). Growth inhibition, i.e., reduction in growth rate of a cell population following drug treatment in comparison to untreated cells, is the result of both effects. Neither experimental nor mathematical methods have been established yet that would separate and quantify the degree of growth inhibition caused by each of these two effects separately. The reason is two-fold. First, there was no experimental method available to quantify cell death (apoptosis), because there was no surrogate marker known to be proportional to the number of cells removed from the population in a given time period. Second, no mathematical method was available to calculate the level of growth rate reduction directly caused by cell cycle arrest. A flow-cytometry DNA histogram of asynchronously growing cells only provides limited information such as changes in the cell cycle distribution of a cell population. Using these data alone, detailed information, including the identification of the affected cell cycle checkpoints and the degree of the changes, could not be determined. Without the checkpoint-specific and quantitative data, no mathematical model could be applied, and the reduction in growth rate directly caused by cell cycle arrest could not be estimated.
The goal of the present study was to establish a mathematical method to assess the respective contributions of cell cycle arrest and cell kill effect to the overall growth inhibition induced by an anticancer agent. In order to achieve this goal, a checkpoint-specific and quantitative parameter was required. We developed a cell cycle-based computational model that uses a quantitative model parameter, the transition probability (TP i ) for cell cycle progression at each cell cycle checkpoint. The TP i was calculated using model equations and cell cycle distribution data obtained from flow cytometry and population doubling time. Using TP i in a Monte-Carlo simulation, the growth of a cell population grown under cell cycle arresting conditions was predicted. Therefore, the difference between the observed growth of an untreated cell population and the model-predicted population growth for treated cells represents the growth inhibition resulting from the cytostatic effect of cell cycle arrest. Note that cell number reduction by cell death is not included in the simulation. Thus, the model simulation would overestimate the growth of a population when there is cell death in the population. Hence, the difference between the model-predicted and the observed growth rate of a treated cell population represents the growth inhibition resulting solely from the cytotoxic effect of cell kill. The relative contribution of the cytostatic and the cytotoxic effects of anticancer agents to the overall growth inhibition can thus be determined.
To demonstrate the validity and applicability of the present model, a new anticancer agent, UCN-01 (7hydroxystaurosporine), [1][2][3] was used as a model drug. UCN-01 is an indolocarbazole derivative currently under phase I/II studies in the US and Japan. UCN-01 was selected as a model drug for the present study, because it is understood that UCN-01 targets unique aspects of cell cycle regulation in comparison to other anticancer agents. 4) UCN-01 has been shown to inhibit progression through G 1 5) and to abrogate the G 2 checkpoint of cells exposed to DNAdamaging agents. 6,7) Recently, UCN-01 has been studied extensively to assess its cell cycle modulatory effect in combination with other chemotherapeutic agents. 6,[8][9][10] As demonstrated towards the end of this paper, our method can easily generate the growth curve of a cell population during or after various drug exposures, as long as experimental data such as cell cycle distribution after drug exposure and population doubling time of untreated cells are provided. By using the discrete time Markov model and numerical simulation, the present model eliminates complicated mathematical work and does not require comprehensive statistical understanding. Therefore, the present model is very useful not only to provide the checkpointspecific and quantitative information about cell cycle arrest, but also to assess the contribution of cell cycle arrest effect to the overall growth inhibition induced by an anticancer agent in vitro.

MATERIALS AND METHODS
Drugs and reagents UCN-01 and staurosporine were kindly provided by Kyowa Hakko Kogyo Ltd. (Tokyo).
UCN-01 was dissolved in dimethylsulfoxide and diluted with culture medium. Other drugs and reagents, unless otherwise noted, were purchased from Sigma Chemical Co. (St. Louis, MO). Cell culture A human small cell lung cancer (SCLC) cell line SBC-3 was obtained from Dr. Kimura (Okayama University, School of Medicine, Okayama). A human leukemia cell line HL-60 was obtained from ATCC. Cells were maintained in RPMI1640 supplemented with 10% heatinactivated fetal bovine serum, 100 mg/ml streptomycin, and 100 units/ml penicillin in a humidified air containing 5% (v/v) CO 2 at 37°C. MTT growth inhibition assay The 3-(4,5-dimethyl-2thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay of the growth inhibitory effect of UCN-01 was carried out as described previously. 11) Cells were seeded at a density of 2000-4000 cells/well 24 h prior to drug exposure. The cells were then treated with various concentrations of UCN-01 for up to 72 h. The absorbance of the reaction mixture was measured at 562 and 630 nm and the IC 50 was determined as the drug concentration required to reduce the absorbance to 50% as compared to the control in each test. Determination of population growth rate and cell cycle distribution Cells were plated at 1×10 6 to 5×10 6 cells per plate in 100 mm or 150 mm petri dishes at least 24 h prior to drug exposure. Following drug exposure, the cells were harvested at predetermined times and suspended as single cells in phosphate-buffered saline (PBS). An aliquot was used to determine total cell number using a Coulter Counter (Coulter Electronics Ltd., Luton, UK) and viable cell number by trypan blue exclusion. The rest of the cells were washed with ice-cold PBS, fixed in 5 ml of 50% methanol/PBS and stored at −20°C for up to 7 days until further analysis. For cell cycle analysis, 5×10 6 fixed cells were washed once in 5 ml of 30% methanol/PBS and twice in 5 ml of PBS. They were then re-suspended in 1ml of boiled RNase A (1 mg/ml) solution. After a 30 min incubation at 37°C, the cells were washed in PBS and stained with 2 ml of ethidium bromide (50 µg/ml) for 20-30 min on ice and protected from light. The cell cycle distribution was analyzed by flow cytometry (FACScan, Becton Dickinson Immunocytometry Systems, San Jose, CA) and cell cycle analysis software (Modfit, Verity, Topsham, ME).

Model development
Smith and Martin's cell cycle model: To describe population heterogeneity with respect to cell cycle times, a plausible hypothesis is the transition probability model of Smith and Martin. 12) This model assumes that cell cycle time variability resides predominately in the G 1 phase. The cell cycle progression is divided into a probabilistic Astate, which is contained within the G 1 phase, and a deterministic B-phase, which encompasses the remainder of the G 1 phase and the S, G 2 and M phases. The concept of transition probability, P (h −1 ) is defined as where α(t)=% cells remaining undivided at time t and ∆t=unit time.
This model has been verified in subsequent experiments [13][14][15] and the concept of transition probability has been successfully employed in many cell cycle studies, e.g., measuring G 1 exit rate and estimating DNA synthesis rate, indicating the feasibility of using the Smith and Martin's model in cell cycle analysis. (2) all cells are in cycle, i.e., no cells killed and no arrest in G 0 phase; (3) distribution of cell numbers in a cell cycle follows the age structure of a simple exponential population. 16) Model equations: According to the model assumptions and parameter definitions, mass balance equations for cell numbers in each cell cycle phase were written as follows.
where N i (t)=N total (t)⋅F i (t). N G1 (t), N S (t) and N G2/M (t) denote the number of cells in G 1 , S and G 2 /M phase at time t, respectively. TP G1 , TP S , and TP G2/M denote the transition probability from G 1 to S, S to G 2 /M, G 2 /M to G 1 , respectively. Eqs. (4.1) to (4.3) were solved for TP i , Based on a discrete-time Markov model, TP i (t) is defined for a discrete time interval of 1 h (∆t=1 h), and TP i (t) can be calculated when F i (t) and k(t) are provided. F i (t) is obtained from flow cytometry and k(t) either from the actual growth rate constant for control cell population or from the relative growth rate estimated by numerical simulation for treated cell populations as described below. (see 'Estimation of growth rate constant (k)'). Estimation of growth rate constant (k): In order to estimate the growth rate constant (k) of treated cells, similar mass balance equations for F i can be written when dividing Eqs. (4.1) to (4.3) by N total (t) as follows.
As for TP i , F i (t) is a constant within the time unit of 1 h. TI G1 , TI S and TI G2/M denote the relative transition probabilities (transition index) of transition from G 1 to S, S to G 2 /M and G 2 /M to G 1 phase, respectively, i.e., the sum of these TI i values is equal to 1 at any given time interval. The sum of F G1 (t), F S (t) and F G2/M (t) also equals 1 by definition, hence, the Eqs. (6.1) to (6.3) can be solved for TI i as shown below.
Using these equations and Steel's age structure theory (assumption #3), the relative cell population growth was simulated for each experimental time interval and the relative growth rate constant was estimated from the slope of these simulated growth curves (Appendix A). Fig. 1C shows the algorithm for the population growth simulation. Note that TI i was used instead of TP i in the estimation of the growth rate constant.

Simulation of cell population growth curve:
The simulation of cell population growth over time was performed using TP i (t) calculated from Eqs. (5.1) to (5.3), F i (t) obtained from cytometry and the same algorithm shown in Fig. 1C (Appendix B). In order to obtain a smooth profile instead of stepwise changes over the sampling time intervals, the TP i for inbetween sampling times was estimated by interpolation assuming linear changes over time.
The present model assumes no cell death during cell cycle progression as mentioned above, hence, the model simulation represents a reduction in number of cells resulting from cell cycle arrest or disturbed cell cycle progression only. The model should underestimate the growth inhibition in the presence of cell death (number of cells killed) and the difference between the model-predicted and the observed growth curve of a treated cell population represents the growth inhibition resulting from cell death in the population.

Cytotoxicity and cell cycle arrest induced by UCN-01
In order to decide the drug concentration to use in our population growth inhibition study, the anti-proliferative effect of UCN-01 against SBC-3 cells was determined by MTT assay after 48-and 72-h drug exposures (data not shown). The greater cytotoxicity was seen for the longer  (Table I).
After 48 h exposure at 0.2 and 2 µM UCN-01, an accumulation in G 1 and a reduction in S phase were observed, and the changes were greater at the higher drug concentration, which was consistent with a previous study. 17) A decrease in the G 2 /M fraction occurred to the same extent at both concentrations. When cells were exposed to 0.1 to 2 µM UCN-01 for 34 h, the decrease in the G 2 /M phase fraction seemed to reach maximum at 0.1-0.2 µM, whereas other cell cycle arrest effects continued to increase up to the highest drug concentration tested (Table I). Significant changes in the cell cycle distribution were observed as early as 12 h post exposure, and a plateau was reached after 24 h for the 0.2 and 2 µM drug concentrations (symbols in Fig. 2, A and B). Model validation Using the TP i (described in 'Model development'), the cell cycle distribution (Fig. 2, A and B) and cell population growth (Fig. 2, C and D) of SBC-3 cells with or without drug exposure were simulated. The predicted data were compared with the observed data for validation of the present model.
Excellent agreement between the observed and the model-predicted cell cycle distribution was obtained for cells exposed to both 0.2 and 2 µM UCN-01 over 72 h (Fig. 2, A and B). Good agreement was also shown for population growth of cells with no drug treatment for 48 h (Fig. 2C) or for 72 h (Fig. 2D). These data indicate the for Jurkat cells exposed to no drug ( , ), 100 nM ( , ) and 300 nM ( , ) UCN-01. The raw data were obtained from the reference. 19) Note that the y-axis is in log scale and that the SD of most data points is smaller than the symbol size. validity of the model to predict cell population growth using cell cycle distribution data in the absence of significant cell death.
When cells were treated with 0.2 µM UCN-01, no significant difference in population growth between untreated and treated cells was seen until 34 h and 22% decrease in cell number was observed after 48 h exposure (Fig. 2C). The reduced growth rate was well-predicted by the model simulation indicating that the growth inhibition (22%) may be fully accounted for by the cell cycle arrest effect, not by cell death. For cells exposed to 2 µM UCN-01, significant reduction in population growth (i.e., decreased slope of treated compared to untreated cells) was observed at 24 h and the later time points (Fig. 2D). However, the model-predicted growth curve showed a significant reduction in cell number only at 48 and 72 h compared to the observed growth curve of untreated cells. These data indicate that the significant growth inhibition resulting from cell cycle arrest occurred after 48 h exposure, and the growth inhibition shown until 34 h may be accounted for by cell death, not by cell cycle arrest. Out of 73% growth inhibition shown at 72 h, 32% and 41% is accounted for by cell cycle arrest and cell death, respectively (as determined from the cell numbers at 72 h: 97.4×10 6 cells for observed control ( ), 66×10 6 cells for model-prediction of treated population ( ), and 26.3×10 6 cells for observed treated population ( ) (Fig. 2D). By comparing the model-predicted and the observed experimental data in SBC-3 cells (Fig. 2, A to D), the following information can be inferred. (i) UCN-01 induced cell cycle arrest as early as at 12 h after exposure (Fig. 2, A and B), but significant growth inhibition directly resulting from cell cycle arrest was not observed until 34 h post drug exposure regardless of drug concentration, which indicates that there is a delay between apparent changes in cell cycle distribution and subsequent population reduction, (ii) At 2 µM, the contribution of cell death to the overall growth inhibition decreased significantly after 48 h exposure, i.e., the slope of the observed growth curve gradually increased and approached that of the model-predicted curve: observed and model-predicted exponential growth rate constants for the 48-72 h interval were 0.0174 and 0.0224 h −1 , respectively, whereas at earlier time intervals, the observed growth rate constants were 2.3-to 2.5-fold lower than the predicted rates. (iii) The kinetics of growth inhibition resulting from cell death seemed different between 0.2 µM (no significant cell kill until 48 h) and 2 µM (significant cell kill after 12 h). This may be interpreted in terms of different cell death rates induced by the two different drug exposure conditions, as reported recently for 5-FU. 18) Collectively, these data indicate that the present model is very useful in analyzing the relationship of cell cycle arrest or cell death with overall growth inhibition. It can thus provide detailed information about the cytotoxic mechanism of anti-proliferative agents. Model application (Fig. 2E) In order to demonstrate the applicability and usefulness of the present model, a set of literature data was re-analyzed. 19) For TP i calculation and growth simulation, the necessary data consisted of the cell cycle distribution changes over time ( Table 2 in ref. 19) and the growth inhibition-time profile determined by viable cell number counting (Fig. 1 in ref. 19). The present TP i model successfully predicted the growth of untreated Jurkat cells (Fig. 2E). In cells exposed to 100 nM UCN-01, the observed-and model-predicted data agreed until up to 18 h, but the model simulation overestimated the population growth at 24 h (Fig. 2E). This indicated that the growth inhibition resulted from the cell cycle arrest effect of UCN-01 during the first 18 h, and that significant cell death occurred only after 24 h of drug exposure at this drug concentration. At 300 nM, cell kill affected the population growth rate of Jurkat cells 12 h post exposure. At the end of 24 h drug exposure, 35% and 60% growth inhibition was observed in Jurkat cells exposed to 100 and 300 nM UCN-01, of which 22% and 37% could be attributed to drug-induced cell cycle arrest (Fig. 2E), respectively.  Fig. 2A and 2B, Table 2 in ref. 19). However, growth inhibition caused by cell cycle arrest was observed at an earlier time in Jurkat cells, i.e., 24 h in Jurkat cells vs. 48 h in SBC-3 cells. These data suggest that the mechanism underlying the cell cycle arrest in Jurkat cells is more effective in inducing growth inhibition.
Under drug exposure conditions causing significant growth inhibition (>50% inhibition), the contribution of cell cycle arrest to the overall growth inhibition was greater for Jurkat cells in comparison to SBC-3 cells. Sixty percent growth inhibition was seen for SBC-3 cells treated with 2 µM UCN-01 for 48 h vs. 7% for Jurkat cells treated with 300 nM UCN-01 for 24 h. Under these conditions, 22% and 37% of growth inhibition could be attributed to the cell cycle arrest effect of UCN-01 in SBC-3 and Jurkat cells, respectively (Fig. 2E). These data again indicate that Jurkat cells are not only more sensitive to the overall growth inhibitory effect of UCN-01, which may be due to primed apoptosis, but also that they are more prone to cell cycle arrest-mediated growth inhibition than the solid tumor cell line SBC-3.
Looking at the mechanism of cell cycle arrest in more detail (Table II), the data show that UCN-01 induced a significant decrease (74%) in TP G1 with a concomitant increase in TP S (29%) and TP G2/M (13%) in SBC-3 cells, resulting in G 1 accumulation and S and G 2 /M reduction. In contrast, a significant decrease (71%) in TP S with an increase (40%) in TP G2/M was induced in Jurkat cells, resulting in G 2 /M phase abrogation. Although an apparently similar change in the cell cycle distribution is observed, the underlying mechanism may be different, as shown by the TP i analysis. Different mechanisms of druginduced cell cycle arrest may in turn explain the different rates and degrees of the contribution of cell cycle arrest to the overall growth inhibition.

DISCUSSION
For many anticancer agents with classical mechanisms of action such as DNA alkylation and microtubule binding, apoptotic cell death has been considered a secondary event resulting from drug-induced macromolecular damage and subsequent cell cycle arrest. However, recent studies have suggested that the signal transduction pathways leading to cell cycle arrest or cell death, especially apoptosis, may be independent. For example, it has been shown that paclitaxel-induced cell death occurs via a signaling pathway independent of microtubules and G 2 /M arrest. 20,21) It has been difficult to study each pathway independently of the other, because the two phenomena often occur simultaneously and no method had been available to assess their respective contributions to the overall growth inhibition under certain drug treatment conditions. As mentioned in the introduction section, cell death including apoptosis is an elimination process for which there is no cumulative index for convenient use. In contrast, cell cycle arrest is relatively easier to study using flow cytometric DNA histogram analysis. The present study was undertaken to develop a novel method that can extract quantitative information from cell cycle distribution data (fraction of cells in each phase) and also exploit it for the assessment of growth inhibition directly resulting from cytostatic effects of anticancer agents. Here, we report the development of a novel computational model that can be used to predict growth inhibition resulting from cell cycle arrest, so that one can easily evaluate the contribution of the counterpart, cell death, to the overall growth inhibition under given cytotoxic conditions.
Transition probability theory, proposed by Smith and Martin 12) and widely used in many modeling studies, is the basis for the present model as described under "Materials and Methods": the main model parameter, TP i was derived from Smith and Martin's transition probability theory and successfully used to analyze in detail the mechanism of cell cycle arrest in the present study. Another key element of the present model is the consideration of the age structure of a simple exponential population by Steel. 16) In the simulation of a growth curve, the transitions of cells between cell cycle phases were modeled as a probabilistic procedure (i.e., based on TP i , Appendix B). However, to estimate the growth rate constant, the relative transition probabilities, TI i , were used (Appendix A). In this numerical simulation, the distribution of cells within each phase also determines the transition rate of cells and thus eventually the growth rate of the population. In other words, the number of cells transiting per one transition event depends on the distribution of cells at each age (or step) within the phase. Without the consideration of the age structure, the model simulation would produce false growth patterns, e.g. unrealistic fluctuations in the cell cycle distribution during cell population growth.
The UCN-01-induced growth inhibition measured in terms of viable cell count showed some difference from the MTT assay data. The 0.2 µM treatment (IC 40, 48 h by MTT) showed only 22% growth inhibition by viable cell Table II Fig. 2 for viable cell counts). In order to explain this discrepancy, we examined the effect of various seeding densities on MTT assay results. By increasing the seeding density from 2000 cells/well to 4000 cells/ well, the IC 50, 48 h was increased from 0.32 to 0.9 µM. By increasing the density to 4000 cells/well good agreement of percent growth inhibition was achieved between the MTT vs. direct cell count data, i.e., 20% (vs. 22%) and 75% (vs. 73%) growth inhibition was determined for the 48-h ecxposure at 0.2 µM, and the 72-h exposure at 2 µM, respectively (MTT data not shown, see Fig. 2 for viable cell counts). These data suggest that the UCN-01-induced cytotoxicity is significantly influenced by the seeding density, which may be due to the decreased intracellular drug concentration secondary to the depletion of the drug in the medium, as has been shown for paclitaxel. 22) In summary, a novel computational model (we would like to call it the "cytostatic TP i model") was developed and validated for detailed analysis of cell cycle arrest and prediction of the population growth of cancer cells under cytostatic conditions. The growth inhibition predicted by the TP i model represents the decrease in population growth resulting directly from a cell cycle perturbation effect, i.e., cell cycle block. The present model uses the discrete time Markov model, and hence, it avoids mathematical complexity. It provides a simple new method to assess the relative contributions of cell cycle arrest and cell kill to the overall anti-proliferative effect of anticancer agents.
We propose that this novel method can be applied to study in detail the mechanisms of cell cycle arrest and its contribution to overall growth inhibition induced by new anticancer agents and/or new combination therapies. A template simulation program is included in the appendix to aid researchers who are interested in using this model in their studies.


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On the number of reproductives contributing to a half-sib progeny array

Summary We address various statistical aspects of biological parentage in multi-oﬀspring broods that arise via multiple paternity or multiple maternity and, hence, consist of mixtures of full- and half-sibs. Conditioned on population genetic parameters, computer simulations described herein permit estimation of: ( 1 ) the mean number of oﬀspring needed to detect all parental gametes in a brood and (2) the relationship between the number of distinct parental gametes found in a brood and the number of parents. Results are relevant to the design of empirical studies employing molecular markers to assess genetic parentage in polygynous or polyandrous species with large broods, such as are found in many ﬁshes, amphibians, insects, plants and other groups. The utility of this approach is illustrated using two empirical data sets.

Introduction
Many animal and plant species with polygamous mating systems may produce individual broods that consist of a mixture of full-sib and half-sib offspring. In such cases, the clutch may be the product of multiple paternity (e.g. Cobbs, 1977 ;Griffiths et al., 1982 ;Baker et al., 1999) or multiple maternity (e.g. DeWoody et al., 1998).
Biologists are interested in the number of parents and their relative contributions to half-sib progeny arrays for several reasons, including assessments of multiple insemination (Levine et al., 1980), brood parasitism and other reproductive behaviours (Jones & Avise, 1997 a, b ;Coltman et al., 1998 ;DeWoody et al., 1998 ;Fitzsimmons, 1998 ;Imhof et al., 1998 ;Jones et al., 1998 ;Kellogg et al., 1998 ;Baker et al., 1999). However, challenging statistical and sampling issues arise in using molecular genetic markers to estimate parental contributions to a half-sib brood.
A half-sib clutch may be evidenced if, in the progeny array, more than four alleles are present at any one locus (Levine et al., 1980). In a typical molecular analysis of a potential half-sib clutch, gametic genotypes or haplotypes (' gametotypes ') tracing to the unshared parent(s) can be deduced by subtracting each progeny's diploid genotype from that of the known parent. In a single-locus assessment (and barring de no o mutation), the minimum number of unshared parents who contributed to a half-sib brood is simply the smallest integer value greater than or equal to one-half the number of different gametotypes inherited by progeny from those unshared parents (Kellogg et al., 1998). The difference between this minimal estimate and the true number of unshared parents is some function of how often adults share alleles. Normally, the two values are expected to be identical only in hypothetical cases where each allele in the parental population is unique. Some microsatellite loci may approach this ideal, but even highly polymorphic markers fall short of overcoming the limitations of face-value empirical estimates of parental contributions to a clutch. Thus, a remaining question is how many parents actually contributed to a progeny array.
Alleles shared among parents can also complicate decisions about how many progeny must be sampled from a half-sib cohort to detect all parental gametes present. Family sizes in many insects, amphibians and plants are far larger than can be analysed feasibly in the laboratory (Nason et al., 1996 ;Fletcher et al., submitted). In many fish, for example, several thousand embryos may be present in a single nest (Taborsky, 1994). How many of these should be sampled to provide reasonable assurance of detecting all parents who contributed to the brood ?
Likelihood-based methods for estimating re-mating frequency and sperm displacement have recently been developed for Drosophila (Harshman & Clark, 1998), but these authors assume a geometric decline in the relative contributions of each successive unshared parent. This assumption may not be valid in many organisms with external fertilization. Other programs that ' assign ' putative parents to a brood (e.g. Smouse & Meagher, 1994 ;Marshall et al., 1998) require genotypes of each potential parent. We have developed simulation programs that, in the absence of exhaustive genotypic data, allow estimation of (1) the number of parents that contributed to a half-sib progeny array and (2) the number of offspring that must be sampled from an array to detect at least one gamete from each parent.

Materials and methods
The first part of this section will outline the general methods underlying our models.
Suppose one has reasonable empirical estimates of gene frequencies at various neutral loci within a population. In principle, one can then use these frequencies to randomly generate ' individuals ' whose genotypes are products of Hardy-Weinberg equilibrium. Pairs of these individuals can then be randomly drawn from the population and ' mated ' such that the resulting full-sib progeny arrays are products of conventional Mendelian inheritance. If half-sib progeny arrays are desired, one must add the parameter of reproductive skew (i.e. the proportion of the shared parent's offspring which stem from each unshared parent).
We used this logic to devise computer programs which address the two questions outlined in Section 1. Each program repeatedly generates half-sib broods in which diploid genotypes of all sampled offspring and their one shared parent are known, but genotypes of unshared parents are not required (Table 1 ; Fig. 1).

(i) Sampling regimes
The first program calculates two statistics and their associated variance. These two statistics are monitored one locus at a time, and the most informative locus from a suite of loci is used to determine appropriate sample sizes from the progeny arrays. The first statistic, n, is the number of offspring needed per clutch to detect all marker-unique gametes from the unshared parents of the clutch (Fig. 1). That is, if four distinct alleles at a locus are present among three unshared parents, n`is the mean sample size from the clutch needed to detect all four of these parental . The second statistic, n*, attempts to account for this problem of nonunique alleles. The parameter n* is the number of offspring per clutch needed to observe all true gametotypes (not merely those detected by available markers) from unshared parents of the clutch. Thus, the minimum value of n is equal to the number of distinct alleles in a brood contributed by unshared parents at the most polymorphic locus, whereas the minimum value of n* is equal to twice the number of unshared parents. For example, if two mothers of genotype AB and BC contribute to a half-sib progeny array, the minimum value of n is 3, whereas the minimum value of n* is 4. It should be clear that n will always be less than or equal to n*.
Distributions of n and n* values were generated via computer by sampling from a half-sib progeny array hundreds or thousands of times. By sampling new progeny arrays (created by re-sampling parents from the initial adult population), each resulting brood is independent and statistics can be averaged across arrays. Thus, sampling issues are examined both within and among broods. These simulated sample sizes (n`and n`*) can then be used as guidelines for empirical sample sizes of progeny.

(ii) Parental assemblage size
Assuming that half-sib progeny arrays can be generated in the manner described above, gametotypes can in principle be used to determine the number of parents. Repeated sampling of progeny arrays generated by a fixed number of parents should produce a distribution of the number of distinct gametotypes represented in each array. For example, if six females   Table 1) that characterize the adult population. The shared and unshared parents are randomly chosen from the adult population, and progeny arrays (broods or clutches) are then created. Diploid genotypes are shown at two unlinked loci (upper-and lower-case letters, respectively). BROOD reports the mean, variance and confidence intervals around the number of progeny that must be sampled to detect the genetic contributions of each parent (see text). GAMETES and HAPLOTYPES utilize knowledge of the shared parent's genotype to deduce the genetic contributions (i.e. ' gametotypes ') of the unshared parents. The number of gametotypes contributed by parental assemblages of various size are then used to estimate the number of parents that contributed to a progeny array (see text).
contribute equally to a singly-sired brood, simulations may determine that 95 % of the time between eight and 10 gametotypes are detected at the most polymorphic locus. These distributions can then be constructed for various parental ' assemblage ' sizes, where each assemblage consists of the one shared parent and x unshared parents, where x assumes all integer values between 1 and some explicit maximum. Once gametic distributions are generated, the process can be inverted such that assemblage size now Imagine a species where one sex (say the male) mates with up to 15 individuals of the opposite sex, each of whom is parent to an equal number of progeny in a brood. Fifteen different assemblages then are created : one male mated with one female, with two females, with three females, and so on up to one male mated with 15 females. The parents in each assemblage are chosen at random from the adult population and a brood is created. A sample from this brood is analysed and the number of distinct gametes recorded. New parents then are sampled from the adult population and the process repeated with the same assemblage size. Finally, the entire protocol is repeated for each different assemblage size.
If specific distributions are used to define the contributions of unshared parents to the brood (i.e. the reproductive skew), the entire discrete parameter space can in principle be explored through simulations. Such an extension would provide a maximumlikelihood estimate of the true parental assemblage size as determined by Monte Carlo simulations.

(iii) Simulation parameters
We first tested the relative importance of various biological parameters on the sample sizes of offspring (n and n*) necessary to detect distinct parental gametes in a brood. First, the numbers of unshared parents and the relative contributions of each were varied while the number of loci, alleles and allele frequencies were held constant. Next, the number of unshared parents and their relative contributions were held constant while the numbers of alleles and their relative frequencies were altered. Then, numbers of alleles and of loci were varied to determine their effects on n and n*.
The level of polymorphism was adjusted by varying the number of alleles per locus and the allele frequency distributions. Allele frequency distributions were   (2), number of alleles (25 and 15, respectively) and allele frequencies were held constant, such that 40 % of the alleles contributed equally to 80 % of the gametic pool (see text). ' Contribution ' refers to the fraction of the brood attributable to the most successful unshared parent ; other unshared parents contributed equally to the remainder of the brood. designated such that a fraction of the alleles at a locus contributed equally to a fraction of the gametic pool while the remaining alleles contributed equally to the remainder of the gametic pool. For example, if 40 % of the alleles contributed equally to 80 % of the gametic pool (and the other alleles contributed equally to the remaining 20 %), then for the five-allele case, two alleles each have a frequency of 0n4 and the other three alleles have a frequency of 0n0667 each. A second series of simulations under specified conditions (Table 2) was used to estimate numbers of unshared parents that contributed to a brood. Distributions were then constructed showing the relationship between numbers of unshared parents and the observed numbers of gametes or haplotypes given these population genetic conditions. These distributions were then pooled and inverted as described earlier, thus creating new distributions that illustrate how the deduced number of parents changes based on different numbers of distinct gametes or haplotypes observed within a brood.

Results
We developed three computer programs designed to estimate : (1) the number of parents that contributed to a half-sib progeny array and (2) the number of offspring that must be sampled from an array to detect at least one gamete from each parent. The results from each program are presented in turn, and potential applications of the programs are examined in light of empirical data. A flowchart illustrating the relationships between the programs is presented in Fig. 1.

(i) BROOD
Our program for determining necessary sample sizes is termed BROOD. We employed two statistics (n and n*) to measure the effects of polymorphism on sampling regimes for parentage assessment of half-sib broods. These statistics differ in that n is with respect to alleles that are defined by a particular genetic marker, whereas n* is with respect to quintessential alleles. Thus, n approaches n* as the number of differentiable allelic states in the population approaches infinity. BROOD simulations illustrate the effects of genetic polymorphism and the number of unshared parents on these two statistics (Figs. 2-4). As expected, n`increased as the number of distinct alleles in the population increased (Fig. 2). The difference between n`and n`* decreased as the level of marker polymorphism increased (Fig. 2). This is understandable because, in principle, n`should approach n`* as allelic variation becomes large.
Uniform allele frequency distributions in the adult  Table 2). population did not entail substantially smaller n`* than skewed or highly skewed distributions (Fig. 2). This result, which may seem counterintuitive, arises because allele frequencies in a population have no bearing on Mendelian inheritance within a brood (e.g. if one parent of a clutch is heterozygous, about 50 % of those offspring receive each of the two alleles regardless of allele frequencies in the adult population). However, skewed allele frequencies did decrease n`slightly (Fig. 2). This results from the fact that under HWE, skewed as opposed to equitable allele frequencies give rise to more homozygous parents so that on average fewer alleles are found in each brood. The second analysis (Fig. 3) supports the obvious notion that n`and n`* (the number of offspring that must be sampled from a brood) increase as more unshared parents contribute to a half-sib brood. Notice that n`asymptotes once the parental assemblage size is large enough to contain all allelic variation found in the population as a whole, whereas n`* increases without bound as the assemblage size increases. The simulations also support an intuitive notion that when the relative contributions of multiple unshared parents to a brood are skewed (as opposed to equitable ; Keller & Reeve, 1994), more progeny must be tested to detect all parental gametes present. This sample size escalated dramatically if one or more of the unshared parents produced less than about 10 % of the brood. For example, with four unshared parents, the upper bound on the 95 % confidence interval for n`increased from about 39 when all parents contributed equally to a brood to more than 92 when parental contributions were highly skewed.
Finally, all else being equal, n`and n`* tended to increase as more loci or more alleles were monitored (Fig. 4). For example, with 25 alleles each at either two or four loci, n`increased from about 51 to 61 ; and, with two loci each with either 10 or 25 alleles, nì ncreased from about 43 to 51. Note, however, that nà nd n`* do not increase proportionately with the number of loci. For instance, with 25 alleles at each locus, n`* is about 66 at four loci and only 79 at 10 loci (Fig. 4). This disproportionate increase arises because genotypes were sampled from individuals, such that once a genotype is determined at the first locus, alleles at all other loci are sampled simultaneously.

(ii) GAMETES and HAPLOTYPES
The next two programs, termed GAMETES and HAPLOTYPES, will illustrate the concepts behind the simulations. In their current formulation, both programs assume an equal contribution of gametes from each unshared parent of a brood. Our empirical data on fish mating systems suggest that such uniform distributions may be reasonable as a first approximation, as for example when each of several females deposits a clutch of eggs in a male's nest. However, other distributions may be appropriate for different organisms (e.g. Harshman & Clark, 1998).
In addition to generating distributions of gametotype counts, each program also records the most likely number of unshared parents (parental assemblage size) for a given number of different gametes in a brood. GAMETES uses the single most informative locus from a suite of loci, whereas HAPLOTYPES incorporates multi-locus gametic data by permutating alleles across unlinked loci in the progeny array. Thus, for HAPLOTYPES the maximum possible number of distinct haplotypes from a single parent is 2 L , where L is the number of loci, and will always be greater than the number of gametes detected by GAMETES if L 1. The maximum number of haplotypes in a progeny array is, thus, equal to the number of parents multiplied by 2 L . Hence, GAMETES can be viewed as a special case of HAPLOTYPE where L l 1.
One might expect that allele frequency distributions would play a large part in determining the estimated number of parents contributing to a brood. However, Shown are the number of outcomes wherein an indicated number of unshared parents contributed a given number of marker-identified gametes or haplotypes to a brood. Note that each distribution was created not as an inverse of a single assemblage distribution shown in Fig. 6, but as a grand total of all assemblage distributions ranging in size from 1 to 15. Thus, the size of the sample space varies (unlike Fig. 5).
in our simulations these estimates appear to be influenced more by the number of alleles than by the distributions per se. For example, nearly-normal allele frequency distributions similar to those found in the empirical studies of DeWoody et al. (1998) and Jones & Avise (1997 a, b) gave similar results in the current analyses to those from the L-shaped empirical distributions reported by Luikart et al. (1998). Likewise, imprecise estimates of allele frequencies in the adult population did not strongly affect our estimates of the mean number of gametes or haplotypes in a progeny array (or the deduced numbers of parents), presumably because few parents have the rare alleles that would most often be missed in a population survey due to sampling error.
Examples of graphical results from the GAMETES and HAPLOTYPE simulations are shown in Figs. 5 and 7, and compilations of these respective classes of information are summarized in the corresponding Figs. 6 and 8. Fig. 7 shows the distributions of markeridentified gametes or haplotypes detected in broods with various numbers of parents and the populationgenetic and sampling conditions specified (Table 2). From Fig. 8 it can be seen that gametic numbers (A and B) and haplotype numbers (C and D) in a brood both tended to increase with larger numbers of unshared parents. Furthermore, Fig. 8 D shows, for example, that the confidence interval around the mean extended from 12-16 haplotypes when there were four unshared parents of a brood to 29-37 haplotypes when there were 10 unshared parents. This means that 95 % of the time, 10 unshared parents are expected to produce between 29 and 37 haplotypes.
In true empirical studies one is more likely to know the number of gametes contributed by unshared parents than to know the number of parents. Fig. 7 illustrates how the deduced number of parents can vary given different numbers of parental gametes or haplotypes detected in a brood. Except in cases where extremely few unshared parental gametes were present in a brood, the deduced number of parents spans a range of values. For example, Fig. 7 F shows that under the conditions described, the presence of 21 haplotypes in a brood suggests six, seven or eight unshared parents, with relative likelihoods given by the peak heights. Thus, the most likely number of unshared parents is six, an outcome eight times more likely than the second most probable number (seven unshared parents). Fig. 8 compiles such information and shows the 95 % confidence intervals.
Interestingly, one can also note from Fig. 8 that whereas GAMETES and HAPLOTYPES give similar results in terms of the mean number of unshared parents, the variance around the mean is much smaller when data from multiple loci (i.e. HAPLOTYPES) is utilized. Note too that the single-locus approach is particularly unstable under low-polymorphism conditions ( Fig. 8 A).

(iii) Applications to real data
To exemplify how these programs might assist in the design and interpretation of empirical research on parentage assessment of half-sib broods, empirical case studies will be presented for two fish species : the redbreast sunfish (Lepomis auritus ;DeWoody et al., 1998) and the tesselated darter (Etheostoma olmstedii ; DeWoody et al., unpublished data). In both species, individual males tend nests into which one or more females may lay eggs, and a primary question is how many females have contributed to a nest of embryos or fry whose father often is known. For both species, progeny cohorts and nest-attendant males were collected and assayed for microsatellite markers from each of multiple nests, and maternal gametes were deduced by subtraction. These two examples were chosen for illustration here because one represents a high-polymorphism and the other a low- polymorphism situation with respect to the microsatellite loci employed. For the redbreast sunfish, two highly polymorphic loci (18 and 22 alleles each ; frequencies described in DeWoody et al., 1998) were employed in the parentage assessments, and the number of embryos assayed per nest (25 nests) ranged from 10 to 175, with mean of 40. Using observed allele frequencies in the adult population, and assuming equal maternal contributions to a brood, BROOD simulations show that nì s about 27 (upper 95 % CI, 48) and n`* is 33 (upper 95 % CI, 54). Thus, the empirical sample sizes originally employed indeed were sufficiently large (on average) to detect most if not all of the maternal alleles present within a particular nest under these conditions.
For the sunfish nests fathered by a single male, DeWoody et al. (1998) concluded from direct counts of deduced maternal gametes that minimally, between two and six mothers contributed to a brood. For example, one nest (LA12) had six different maternal alleles among 50 embryos sampled at one locus, meaning that no fewer than three females spawned in that nest. Single-locus simulations show that the ' adjusted ' estimate of mothers for this nest was 3n8, with 95 % confidence interval spanning three to six. Similarly, multi-locus simulations show that for another nest (LA28 phase B) with 16 di-locus haplotypes among 50 embryos, the adjusted number of mothers was 5n3 (95 % CI, 4-8), whereas the minimum number based on direct genotypic count was four. Two points are evident from such examples. First, statistically adjusted numbers of deduced parents are larger than the minimal estimates from the direct-count method. Secondly, in these cases the adjusted estimates are close to the face-value estimates and do not alter biological conclusions appreciably.
In a continuing study of the tesselated darter, at the time of writing three microsatellite loci have been characterized, and polymorphism is relatively low in the adult population (Table 3). In this case, the darter broods have not yet been assayed genetically, so simulations based on the observed allele frequencies were conducted over a range of assemblage sizes to 1 5 3 Fig. 9. Effects of the number of unshared darter parents on n and n* when all other parameters were held constant. Empirically derived allele frequencies at the three darter loci are described in Table 3. ascertain whether our sample sizes of offspring were appropriate. Results are shown in Fig. 9. For example, if we assume that two unshared parents contribute equally to each darter nest, we find that n`is 7n3 and n`* is 9n0. Likewise, assuming seven unshared parents, these means are about 28 and 48, respectively. Thus, unless the number of unshared parents contributing to a half-sib darter nest is more than about seven, then samples of 50 offspring per nest should be adequate to capture parentage patterns in these fishes with the available markers.

Discussion
For large broods consisting of mixtures of full-sibs and half-sibs, the simulations developed here permit appraisals of : (1) the mean number of offspring that must be sampled to detect all parental gametes in a brood and (2) the relationship between the number of distinct parental gametes in a brood and the true number of parents. Thus, simulations can assist in the design of empirical research on several aspects of genetic parentage in species with polygynous or polyandrous mating systems. These programs are available as MATLAB source code (and will soon be available as C code) on our website at www.genetics.uga.edu\popgen\parentage.html.
With regard to the first objective mentioned above, these simulations should aid in the design of sampling strategies when brood size is too large to permit feasible laboratory assay of all progeny in a clutch (as is often true for highly fecund species such as many amphibians, fishes and insects). With regard to the second objective, the simulation approach provides an improvement over conventional procedures of merely using allelic counts to estimate the number of unshared parents of a brood (e.g. DeWoody et al., 1998 ;Kellogg et al., 1998). For example, in a half-sib brood displaying 10 single-locus gametotypes, the minimum number of unshared parents by the direct-count method is five. However, in the simulated distributions, these 10 gametes more often truly arose from six, seven or eight unshared parents (Fig. 7 B), and the 95 % confidence interval spans 5-10 parents (Fig. 8 B).
One major advantage to our approach is that potential unshared parents need not be sampled exhaustively because inferences are based largely upon the distribution of parental alleles in progeny arrays. Statistical approaches have been developed to resolve parentage issues in cases where genotypic data are exhaustive (i.e. Marshall et al., 1998), but our approach allows inferences in the absence of direct data on the unshared parents. Marshall et al. (1998) also correctly point out that typing errors are common in large-scale parentage studies ; such typing errors may potentially bias estimates of the number of parents contributing to half-sib progeny arrays.
Explicit analytical approaches to maximumlikelihood (ML) offer another avenue for estimating numbers of parents contributing to a half-sib clutch. However, it has proved difficult to derive ML equations that take into account multiple unshared parents and multiple loci (Harshman & Clark, 1998). Current simulation methods are far less intensive computationally, and thus may be useful to biologists studying parentage in organisms with large clutch sizes.


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Ceramide Directly Activates Protein Kinase C ζ to Regulate a Stress-activated Protein Kinase Signaling Complex*

We have previously shown that interleukin 1 (IL-1)-receptor-generated ceramide induces growth arrest in smooth muscle pericytes by activating an upstream kinase in the stress-activated protein kinase (SAPK) cascade. We now report the mechanism by which ceramide activates the SAPK signaling pathway in human embryonic kidney cells (HEK-293). We demonstrate that ceramide activation of protein kinase C ζ (PKCζ) mediates SAPK signal complex formation and subsequent growth suppression. Ceramide directly activates both immunoprecipitated and recombinant human PKCζ in vitro. Additionally, ceramide activates SAPK activity, which is blocked with a dominant-negative mutant of PKCζ. Co-immunoprecipitation studies reveal that ceramide induces the association of SAPK with PKCζ, but not with PKCε. In addition, ceramide treatment induces PKCζ association with phosphorylated SEK and MEKK1, elements of the SAPK signaling complex. The biological role of ceramide to induce cell cycle arrest is mimicked by overexpression of a constitutively active PKCζ. Together, these studies demonstrate that ceramide induces cell cycle arrest by enhancing the ability of PKCζ to form a signaling complex with MEKK1, SEK, and SAPK.

ceramide-activated protein kinase involved in growth control (8,9,10). Evidence suggests that PKC is directly activated by ceramide and not by diacylglycerol (DAG) (8,9,11). Several reports also relate ceramide-induced growth arrest to activation of stress-activated protein kinase (SAPK)/c-Jun N-terminal kinase, but the precise mechanism remains to be elucidated (6,12,13). Because ceramide has been shown to activate PKC and SAPK in the same cell type, it has been inferred, but not proven, that PKC is a critical element in ceramide-induced SAPK activation (11). It is likely that the site of action for ceramide is upstream of SAPK, because ceramide does not directly regulate immunoprecipitated SAPK activity in a cellfree system (7). We now demonstrate that ceramide-activated PKC recruits upstream components of the SAPK cascade into a signaling complex, resulting in growth arrest.
Activation of the SAPK signaling pathway is characterized by a cascade of protein kinases, which are recruited to the plasma membrane. Specifically, GTP-dependent activation of Rac or Cdc42 leads to recruitment of MEKK1 to the plasma membrane, where it is phosphorylated and activated (14). Activated MEKK1 directly phosphorylates and activates SEK, which in turn directly activates SAPK. Activation of Rac-1 by inflammatory cytokines or ceramides has been postulated to be one mechanism to activate the SAPK cascade (14 -16). Because PKC does not directly regulate Rac-1 (17), an alternative mechanism for activation of SAPK could be ceramide-induced PKC activation of MEKK1 and/or SEK. Thus, it is hypothesized that ceramide regulation of the SAPK pathway also is dependent on direct activation of PKC.
In this study, we demonstrate that ceramide directly activates both immunoprecipitated and recombinant human PKC. Upon ceramide activation, PKC interacts with MEKK1, SEK, and SAPK to inhibit insulin-like growth factor-1 (IGF-1)-induced cell growth. Together, these findings suggest a novel role of ceramide in regulation and assembly of multi-protein signaling complexes.
are adenovirus-transformed human embryonic kidney cells of tubule epithelial origin. These cells express functional IGF receptors and are an excellent model for growth factor-induced mitogenesis as well as inflammatory cytokine-induced growth-arrest (2,18). Western blot analyses revealed that HEK-293 cells express PKC␣, ⑀, and .
Western Blot Analysis-Western blot analysis using anti-PKC antibody was performed as described previously (7). Briefly, treated HEK-293 cells were washed in ice-cold Dulbecco's phosphate-buffered saline solution and lysed in 1 ml of lysis buffer (50 mM Hepes, 150 mM NaCl, 1 mM NaF, 1 mM EDTA, 1 mM EGTA, 1 mM NaVO 4 , 0.2% Nonidet P-40, 1 g/ml of leupeptin, pepstatin, and aprotinin). Cell lysates were cleared by centrifugation, and the Bio-Rad protein assay was performed to determine protein concentration. 40 g of protein lysate per sample was separated on a 10% SDS-PAGE gel and transferred to Hybond nitrocellulose membranes. The membranes were blocked in 5% nonfat milk in tris-buffered saline (TBS) for 1 h and then incubated with the primary anti-PKC antibody (1:1000 dilution in 5% nonfat milk TBS) for 2 h at room temperature. After incubation, the membranes were washed three times with TBS for 10 min each. The blots were then incubated with secondary horseradish peroxidase-conjugated goat antirabbit IgG antibody (1:5000 dilution in 5% nonfat milk in TBS) for 2 h at room temperature. The membranes were then washed three times with TBS. The PKC band was visualized by ECL and quantified using laser densitometry. A similar protocol was used for detection of PCNA, MEKK1, p-SEK, and SEK expression.
In Vitro Reconstitution Activity Assay for Immunoprecipitated PKC-Immunoprecipitation of PKC and the subsequent reconstitution activity assay was adapted from previous methods (7,19,20). Briefly, PKC was immunoprecipitated from HEK-293 lysates using 0.5 g of polyclonal rabbit anti-PKC antibody. After overnight incubation at 4°C, goat anti-rabbit IgG agarose was added, the mixture was rotated for 2 h, and the immunocomplex containing PKC was pelleted by brief centrifugation. After three washes, the pellets were resuspended in kinase buffer (50 mM Hepes, 100 mM NaCl, 10 mM MgCl 2 , 50 mM NaF, 1 mM NaVO 4 , 1 mM dithiothreitol, 0.1% Tween 20). The in vitro kinase reaction was initiated by addition of 40 g/ml phosphatidylserine/reaction, 10 mM MgCl 2 , 0.25 mM ATP (cold), and 1 Ci of [␥-32 P]ATP (10 mCi/mmol), and 10 g of histone IIIS as a substrate. Specified samples contained DAG (1,2-diolein) and/or C 18:1 -ceramide as co-factors. After 15 min of incubation at 37°C, the kinase reaction was terminated by addition of sample buffer and heated at 95°C for 5 min. Phosphorylated Histone IIIS proteins were then separated on 12% SDS-PAGE gels and transferred to Hybond nitrocellulose membranes. The bands corresponding to phosphorylated Histone IIIS were detected by autoradiography (Kodak X-OMAT). A similar protocol utilizing recombinant PKC (50 ng) was used to verify the in vitro effects of physiological ceramide on immunoprecipitated PKC.
In Vitro Reconstitution Activity Assay for Immunoprecipitated SAPK-In vitro SAPK kinase assays were performed as described previously (6). The bioactivity of immunoprecipitated SAPK was assessed by phosphorylation of c-Jun as exogenous substrate. The phosphorylated bands were detected by autoradiography.
Co-immunoprecipitation of PKC with SAPK Cascade Signaling Components-Lysates from treated HEK-293 cells were prepared as described previously and incubated with rabbit anti-SAPK1 (or anti-MEKK1) antibody for 12 h at 4°C. Goat anti-rabbit IgG antibody conjugated to agarose was added to each sample and incubated for 2 h at 4°C. Immunocomplexes were then pelleted by brief centrifugation and washed twice in lysis buffer. Immunoprecipitates were combined with sample buffer and heated at 95°C for 5 min, followed by separation on 12% SDS-PAGE gels. Proteins were transferred to Hybond nitrocellulose membranes and probed with anti-PKC antibody and, in some cases, anti-PKC⑀ antibody (1:1000 dilution in 5% nonfat milk in TBS). Subsequently, the membranes were incubated with the horseradish peroxidase-conjugated anti-rabbit IgG antibody (1:5000), and the bands corresponding to PKC or PKC⑀ were visualized by ECL. Equal loading of SAPK or MEKK1 was determined by reprobing the membranes with anti-SAPK or-MEKK1 antibodies.
For co-immunoprecipitation of PKC with phosphorylated SEK (p-SEK), a similar protocol was performed except that PKC was immunoprecipitated using anti-PKC antibody. Subsequent to SDS-PAGE electrophoresis and protein transfer to nitrocellulose, the membranes were probed with anti-p-SEK antibody (1:1000). The bands corresponding to p-SEK were visualized using ECL. Equal loading of PKC protein was determined by reprobing the membranes with anti-PKC antibody.
Transfection of HEK-293 Cells with Either Wild-type (WT), Dominant-negative PKC, or Constitutively Active PKC Constructs-HEK-293 cells were transiently transfected with either WT or mutant PKC constructs (a generous gift from Dr. J. Moscat) using Superfect (Qiagen). The wild-type construct is a full-length PKC in a pCDNA3 expression vector. The dominant-negative mutant construct is a kinasedefective mutant that contains a point mutation in the catalytic domain. The constitutively active PKC construct is the catalytic domain without the regulatory domain. Transfection efficiency was consistently ϳ40% as determined by green fluorescence protein co-transfection assay. Cells expressing the wild-type and dominant-negative mutant constructs were subjected to in vitro kinase assays to assess SAPK bioactivity, as described above. In addition, lysates from wildtype or constitutively active PKC-expressing cells were subjected to Western blot analysis to assess proliferating cell nuclear antigen (PCNA) expression. To verify that the transfections with constructs for PKC did not alter protein levels of other PKC isoforms, Western analyses were performed and PKC␣ or PKC⑀ expression did not change (data not shown).
Statistical Analysis-Independent t tests were used to determine the significant differences between groups. The p value of the individual components was adjusted for multiple comparisons by the Bonferroni method. The data were expressed as mean Ϯ S.E. All non-parametric data were analyzed by the Kruskal-Wallis test. In those experiments where the control optical density values were set to 100%, the S.E. for each of these control values was reported utilizing the non-transformed data.

Ceramide Directly Stimulates PKC Bioactivity-Ceramide
has been shown to activate the SAPK cascade. However, the mechanism of activation has not been clearly defined. Because PKC has one cysteine-rich domain (CRD), which is suggested to interact with ceramide, but not diacylglycerol (DAG) (21), we hypothesize that ceramide-activated PKC regulates the SAPK cascade. Therefore, the ability of ceramide to directly and acutely affect the bioactivity of immunoprecipitated PKC from HEK-293 cells was assessed by performing in vitro reconstitution activity assays. The immunocomplexes were treated with physiological ceramide (C 18:1 -ceramide) and/or physiological DAG (1,2-diolein) for 15 min. Bioactivity was assessed by resolving radiolabeled phosphorylation of Histone IIIS. As shown in Fig. 1, A and B, the bioactivity of immunoprecipitated PKC was significantly increased (3-fold) by ceramide but not DAG treatment. When C 18:1 -ceramide-treated immunocomplexes were challenged with the addition of DAG, the bioactivity of PKC was significantly decreased. These results suggest an apparent reciprocal relationship between ceramide and DAG for PKC bioactivity.
To verify the stimulatory actions of physiological C 18:1 -ceramide on immunoprecipitated PKC, we performed direct in vitro kinase activity assay using purified, recombinant PKC (Fig. 1C). Consistent with the immunoprecipitated PKC kinase assay, physiological ceramide significantly activated recombinant PKC activity. Similar to results obtained in the immunoprecipitated PKC kinase assay, DAG treatment had no effect by itself but was able to reduce C 18:1 -ceramide-induced PKC activity. In addition, cell-permeable C 6 -ceramide mimicked the effect of physiological C 18:1 -ceramide to stimulate PKC activity in the cell-free assays (data not shown). In support of the specificity of the actions of ceramide on PKC, the inactive cell-permeable ceramide analogue, dihydro-C 6 -ceramide, had no effect on PKC activity. These studies, utilizing both immunoprecipitated and recombinant human PKC protein, confirm the hypothesis that bioactive ceramides directly activate PKC activity.
Ceramide Does Not Change PKC Expression-In addition to directly activating PKC, ceramide could also increase PKC activity by inducing PKC protein expression. We examined the PKC protein expression level by performing Western blot analysis using anti-PKC antibody. As shown in Fig. 2, when cycling (10% FBS) or non-cycling (serum-deprived, SD) HEK-293 cells were treated with cell-permeable C 6 -ceramide for 24 h, the protein expression level of PKC was not altered compared with the control cells without C 6 -ceramide treatment. These results demonstrate that ceramide regulates HEK-293 cells as a consequence of a direct activation of PKC and not up-regulation of PKC protein expression.
PKC Is a Necessary Component for Ceramide Activation of SAPK Activity-Because we demonstrated that ceramide directly activates PKC, we next examined whether ceramideinduced SAPK activity is dependent on PKC. As shown in Fig.  3, HEK-293 cells overexpressing the WT PKC construct resulted in an increase in C 6 -ceramide-stimulated immunoprecipitated SAPK bioactivity. In contrast, C 6 -ceramide-induced SAPK activity was blocked with a dominant-negative mutant of PKC. These results suggest that PKC is a necessary signaling component for modulating ceramide-mediated activation of SAPK bioactivity.
Ceramide Augments PKC⅐SAPK Interaction-To further define the mechanism by which ceramide activates PKC leading to SAPK complex formation, we next examined whether PKC associates with SAPK. Therefore, to document if ceramide induces a potential interaction between PKC and SAPK, we performed co-immunoprecipitation assays. As shown in Fig. 4, HEK-293 cells treated with C 6 -ceramide specifically increased PKC association with SAPK1. Because ceramide induces the translocation of PKC⑀ from the plasma membrane to the cytosol, an event consistent with inactivation (22), we also investigated if ceramide regulates PKC⑀⅐SAPK interaction as a negative control. Ceramide did not augment an association between PKC⑀ and SAPK. These results demonstrate that ceramide specifically induces an interaction between PKC and SAPK. . Control experiments showed that histone phosphorylation was dependent on exogenous phosphatidylserine (PS) (data not shown). For the immunoprecipitated assays, phosphorylated histone IIIS protein was resolved on a 12% SDS-PAGE gel electrophoresis, visualized by autoradiography, and quantified by laser densitometry. For the recombinant protein assays, the phosphorylated histone bands were excised and quantified by liquid scintillation counting. For both immunoprecipitated and recombinant PKC, C 18:1 -ceramide directly stimulated PKC activity. DAG itself had no effect on either immunoprecipitated or recombinant PKC but significantly reduced C 18:1 -ceramide-induced PKC activity. Results similar to C 18:1 -ceramide were observed for the cell-permeable C 6 -ceramide (data not shown). For the recombinant PKC assay, the inactive ceramide analogue, DH-C 6 -ceramide, did not increase PKC activity. A, a representative autoradiogram for the immunoprecipitated PKC assay. B, the quantification of the immunoprecipitated PKC bioactivity. C, the quantification of the recombinant PKC assays (mean Ϯ S.E.; n ϭ 3-4). Star, significantly different from vehicle control; p Ͻ 0.05, paired t-test. Sun, combination treatment is significantly different from individual treatment alone; p Ͻ 0.05, paired t-test. IGF-1 induces DAG generation, which we have previously shown to inhibit ceramide-activated PKC. Thus we investigated whether IGF-1 could diminish ceramide-induced PKC⅐SAPK interactions. Our studies document that IGF-1, in contrast to ceramide, does not induce PKC association with SAPK. In fact, ceramide-induced association between SAPK and PKC was diminished. These data further support the specificity of ceramide-activated PKC to form SAPK signaling complexes.
Ceramide Induces PKC Association with p-SEK and MEKK1-Our results imply a role for ceramide to modulate PKC interactions with upstream elements of the SAPK signaling cascade. Activated SEK is the immediate upstream dual specificity kinase that phosphorylates SAPK on threonine and tyrosine residues. It is possible that bioactive PKC may recruit and activate SEK through phosphorylation on Ser-219 and Thr-223. Therefore, we next investigated the ability of ceramide to induce an association between PKC and bioactive (phosphorylated)-SEK (p-SEK) by co-immunoprecipitation assays. As shown in Fig. 5, A and B, C 6 -ceramide or IL-1 treatment significantly increased (5-fold) the association of PKC with p-SEK in HEK-293 cells. Consistent with the effect of IGF-1 on ceramide-induced PKC⅐SAPK interaction, IGF-1 treatment also reduced both C 6 -ceramide-and IL-1-induced association of PKC with p-SEK. These results clearly demonstrate that ceramide regulates the activity of PKC and its interaction with p-SEK.
To further define the upstream SAPK signaling elements regulated by ceramide-activated PKC, we also investigated whether ceramide induces an association between PKC and MEKK1. As shown in Fig. 5, C and D, we observed a strong association of PKC with MEKK1 in response to ceramide or IL-1 treatment. Again, IGF-1 reduced both C 6 -ceramide-and IL-1-induced PKC⅐MEKK1 interactions. These results strongly suggest that the stimulatory action of ceramide on SAPK activation are a result of activated PKC interacting with MEKK1 as well as SEK.

Ceramide-induced Growth Arrest Is Dependent on PKC-
The critical role of PKC in ceramide-and IL-1-mediated inhibition of cell growth was assessed in HEK-293 cells transiently transfected with either wild-type (WT) or constitutively active PKC. Cell cycle arrest was evaluated by proliferating cell nuclear antigen (PCNA) expression (Fig. 6). PCNA expression is used as a marker of cells entering the cell cycle at early G 1 and S phases. In cells transfected with the WT PKC construct, both IL-1 and its second messenger, ceramide, significantly reduced IGF-1-induced PCNA expression. This inhibitory effect of C 6 -ceramide on HEK-293 cell growth does not appear to be caused by necrosis as C 6 -ceramide, at concentrations up to 100 M, did not induce lactate dehydrogenase release (data not shown). In contrast to C 6 -ceramide, the inactive cell-permeable ceramide analogue, dihydro-C 6 -ceramide, did not have an inhibitory effect on either basal or IGF-1-induced PCNA expression. Most importantly, the mitogenic effect of IGF-1 was reduced in cells overexpressing the constitutively active PKC. Compared with wild-type PKC-expressing cells, a similar pattern of PCNA expression was observed in non-transfected or mock transfected cells (data not shown). Together, these re- sults suggest that PKC is necessary for IL-1 and/or ceramideinduced cell cycle arrest. Moreover, these data imply that PKC⅐SAPK complex formation is required for ceramide-induced growth arrest. DISCUSSION The concept of signaling complex formation determining the specificity and selectivity of cellular responses is an exciting new area in the field of cellular communication. Recent studies have shown that scaffold/adapter proteins serve to assemble multiple signaling proteins together in large scale aggregates. The importance of these scaffolding proteins, such as Juninteracting protein-1, kinase suppressor of Ras, and 14-3-3, in signal transduction pathways is only recently being appreciated (23)(24)(25)(26). Adding to this orchestrated regulation, we now elucidate a novel mechanism by which the sphingolipid metabolite, ceramide, can regulate protein-protein interactions between PKC and elements of the SAPK cascade, culminating in cell cycle arrest.
We demonstrate that ceramide selectively augments a signal complex formation of PKC with MEKK1, SEK, and SAPK. The fact that our co-immunoprecipitation experiments were performed in the presence of a non-ionic detergent (0.2% Nonidet P-40) suggests that direct protein-protein interactions are occurring as a consequence of ceramide activation of PKC, and presumably not by ceramide promoting hydrophobic associations of these proteins at the membrane. Based on this evidence, it is suggested that direct binding and activation of PKC by ceramide leads to recruitment and activation of upstream elements in the SAPK cascade resulting in cell cycle arrest. Other studies have postulated alternative mechanisms by which ceramide regulates the SAPK cascade. Ceramide has been shown to activate small molecular weight G-proteins that may couple inflammatory cytokine receptors with the SAPK cascade (14,15). Specifically, ceramide stimulates Rac-1, as well as Vav, a guanine nucleotide exchange factor for Rac (27). Another SAPK regulatory mechanism may involve ceramide activation of PP1 and PP2A phosphatases (28). Even though overexpression of a dual specificity threonine/tyrosine phosphatase, M3/6, diminishes ceramide-activated SAPK (29), other studies demonstrate that ceramide activates SAPK under conditions where ceramide also activates protein phosphatases (6,12). The role of ceramide-activated phosphatases to modulate ceramide-dependent PKC⅐SAPK interactions is of potential interest. Thus, ceramide may regulate several mechanisms mediating SAPK-induced cell cycle arrest.
The role of ceramide binding to, and activating, PKC is still somewhat controversial. Our studies, using both immunoprecipitated and recombinant human PKC, clearly demonstrate that ceramide, but not dihydroceramide, directly induces PKC bioactivity. Supporting our findings, ceramide has been shown to bind to PKC as determined by kinetic analyses and in vitro phosphorylation studies (9,30). In contrast, a radioiodinated photoaffinity-labeled ceramide analogue was unable to directly interact with immunoprecipitated PKC (31). These apparent contradictions in the literature may be due to structural differences in the ceramide analogues. Alternatively, the ability of ceramide to interact with the cysteine-rich lipid-binding domain (CRD) of immunoprecipitated PKC could be altered by co-immunoprecipitating proteins, such as Par-4, that also interact with this domain (21). It has been proposed that the single CRD of PKC may interact with ceramide, but not DAG (6,32). This may be due to the lack of a second CRD, which is observed in conventional and novel PKC classes, or to subtle differences in the loop structure of the PKC CRD that respond to the unique polar regions of ceramide (21). Although DAG does not directly activate PKC, it is suggested that DAG selectively inhibits PKC activity by antagonizing ceramide binding at the CRD. The fatty acyl groups of DAG may block this putative ceramide-binding domain in an analogous fashion to arachidonic acid blocking ceramide binding to PKC (9).
In our studies, we found that the actions of ceramide on PKC⅐SAPK interactions were inhibited by IGF-1 treatment. This somewhat surprising finding has several possible explanations. IGF-1-generated DAG may compete with ceramide at the putative ceramide binding site on PKC (21) or activate other PKC isotypes linked to mitogenesis. In addition, IGF-1 has also been shown to stimulate PKC through a phosphatidylinositol 3-kinase-dependent mechanism (33). This alternate mechanism to stimulate PKC may couple PKC to other mitogen-activated protein kinases (MAPK), such as extracellular signal-regulated kinases (ERK), which are more closely linked to mitogenesis. The role of sphingolipid-and polyphosphoinositide-derived second messengers to differentially regulate PKC⅐MAPK signaling complexes is an attractive theory that is beyond the focus of the current studies. Alternatively, ceramide may become phosphorylated by IGF-1 treatment, generating FIG. 4. Ceramide enhances PKC interaction with SAPK1. Protein interactions between select PKC isoforms and SAPK were assessed by Western analyses utilizing anti-PKC or -PKC⑀ antibodies on SAPK1 immunoprecipitates from HEK-293 cells. PKC isoforms, co-immunoprecipitated with anti-SAPK1 antibody, were detected with enhanced chemiluminescence and quantified by laser densitometry. HEK-293 cell monolayers in 12-well plates were made quiescent in Dulbecco's modified Eagle's medium supplemented with 0.5% FBS for 24 h. The monolayers were then treated with vehicle control (0.01% Me 2 SO), IGF-1 (50 ng/ml), C 6 -ceramide (10 Ϫ6 M), or IGF plus C 6 -ceramide for 5 min. The basal interaction between SAPK⅐PKC (2.19 Ϯ 0.21 arbitrary units) was slightly more than the interaction between SAPK⅐PKC⑀ (1.45 Ϯ 0.33). C 6 -ceramide enhanced interactions of PKC, but not PKC⑀, with the SAPK1 pathway. Equal loading of the samples was assessed by reprobing the blots with anti-SAPK1 antibodies. A, representative Western blots. B, the quantification of the PKC bands (mean Ϯ S.E.; n ϭ 3). Star, significantly different from vehicle control; p Ͻ 0.01, paired t-test. Sun, combination treatment is significantly different from individual treatment alone; p Ͻ 0.005, paired t-test.
ceramide-1-phosphate, a pro-mitogenic lipid (34). Another possibility suggests that IGF-1 treatment induces the activation of ceramidase, an enzyme that catalyzes the deacylation of ceramide to form the pro-mitogenic lipids, sphingosine or sphingosine-1-phosphate (35). Regardless of mechanism, IGF-1 cotreatment reduces the actions of inflammatory cytokines or ceramides to activate SAPK activity. However, this compensatory mechanism does not supersede the ability of ceramides to induce cell cycle arrest.
Our finding that ceramide-induced cell growth inhibition is a consequence of activated PKC coupling to elements of the SAPK cascade clearly suggests the critical role that specific PKC⅐MAPK signaling complexes play in cell cycle arrest. This is particularly intriguing, because PKC can be activated by both mitogenic and anti-mitogenic stimuli. Thus, the ability of activated PKC to interact with distinct MAPK signaling elements could explain the contradictory actions of PKC as a regulator of cell growth. For example, it was initially documented that PKC is required for maturation of Xenopus oocytes and for DNA synthesis in fibroblasts (36). Interactions FIG. 5. Ceramide induces PKC association with p-SEK and MEKK1. HEK-293 cell monolayers in 6-well plates were used to assess PKC interaction with bioactive phospho-SEK (p-SEK) (A and B) as well as MEKK1 (C and D). Cells were treated with C 6 -ceramide (C 6 -Cer, 1 M), IL-1 (20 ng/ml), and/or IGF-1 (50 ng/ml) for 5-10 min. To assess PKC⅐p-SEK interactions, the lysed cells were immunoprecipitated with the anti-PKC antibody and immunoblotted with an antibody that detects phosphorylated SEK. To assess PKC⅐MEKK1 interactions, the lysed cells were immunoprecipitated with an anti-MEKK1 antibody and immunoblotted with the anti-PKC antibody. Reprobing the blots with the appropriate antibody (anti-PKC for A and anti-MEKK1 for C) assessed equal loading. Positive and negative controls included whole cell lysates and cell-free samples, respectively (data not shown). Both C 6 -ceramide and IL-1 treatment led to a significant interaction between PKC and p-SEK as well as MEKK1, which was reduced by co-treatment with IGF-1. A and C, representative Western blots of three such FIG. 6. Ceramide-induced cell cycle arrest is dependent on PKC. HEK-293 cells were transfected with either wild-type or constitutively active PKC constructs. After transfection, cells were treated with ceramide (1 M), IL-1 (20 ng/ml), or dihydro-C 6 -ceramide (DH, 1 M) with or without IGF-1 (50 ng/ml) for 18 h. Western analysis was performed on the cell lysates to assess PCNA expression. Both C 6ceramide and IL-1 treatment significantly reduced IGF-induced PCNA expression in wild-type transfected cells. The specificity of the actions of ceramide was confirmed by the lack of an effect with dihydro-C 6 -ceramide. The constitutively active PKC-expressing cells mimicked the actions of C 6 -ceramide and IL-1 to induce cell cycle arrest in wild-type transfected cells. A, representative Western blots. B, the quantification of the PCNA bands (mean Ϯ S.E.; n ϭ 4). Star, significantly different from vehicle control; p Ͻ 0.05, paired t-test. Sun, significantly different from IGF-stimulated condition; p Ͻ 0.05, paired t-test. experiments each. B and D, the quantification of p-SEK and PKC protein levels in the complex, respectively (mean Ϯ S.E.; n ϭ 3). Star, significantly different from vehicle control; p Ͻ 0.01, paired t-test. Sun, combination treatment is significantly different from individual treatment alone; p Ͻ 0.05, paired t-test.
between PKC and the pro-mitogenic ERK cascades have been suggested, as a dominant-negative mutant of PKC suppressed activation of MEK and ERK by TNF␣ (37). However, recent studies suggest a growth inhibitory role for PKC. NIH-3T3 fibroblasts transfected with wild-type PKC are not tumorigenic (38). In fact, PKC has been reported to actually suppress the neoplastic transformation of fibroblasts mediated by the v-raf oncogene (39) and does not activate Raf-1 activity, an upstream kinase in the ERK cascade in vitro (40,41). These observations offer an explanation for inhibition of ERK bioactivity by ceramide (6,7,12,42). Thus, the ability of ceramide to preferentially couple activated PKC to upstream elements in the SAPK cascade, and not the ERK cascade, could be one possible mechanism for inducing cell cycle growth arrest.
We have shown that ceramide induces cell cycle arrest via selective interactions of PKC with elements of the SAPK cascade. Moreover, these interactions are modulated by the ability of ceramide to bind to and directly activate PKC. The role of ceramide to selectively induce PKC⅐SAPK complex formation may illustrate one mechanism by which a pro-inflammatory response can lead to cell growth arrest. This critical role of ceramide to directly activate PKC and regulate MEKK1⅐SEK⅐SAPK interactions is a novel hypothesis by which inflammatory cytokine receptor-induced ceramide formation may limit cellular proliferation in models of non-proliferative immunological renal diseases.