Lars Klareskog

Leonid Padyukov

Lars Alfredsson

Ph.D Bo Ding
email: bo.ding@ki.se

Henrik Källberg

GEIRA: Gene-Environment and Gene-Gene Interaction Research Application

Keywords: environment and gene-gene interaction research application GWAS, interaction, program

HAL is a

Introduction

The most common human diseases have a complex aetiology involving both genetic and environmental factors. Genome-wide association studies (GWAS) applying a selected set of single nucleotide polymorphism (SNP) covering the entire genome have recently become popular and quite successful methods in search for genetic determinants of common diseases. However, the genetic variants discovered so far account for only a small portion of common disease heritability, indicating that there still are many genes or loci with small effects to be discovered. The possibility exists that a gene's effect is more easily detected within a framework that accommodates gene-environment and gene-gene interactions. For example, a gene's overall effect might be too small to detect with any reasonable sample size, or a genetic effect is entirely dependent on an environmental exposure and may show a substantial effect when a specific environmental factor or another genetic variant is present. Results from many association studies have not been replicated, possibly due in part to the omission of incorporating interactions among disease associated loci in the analysis where allele frequencies for interacting genes or environmental exposures may have differed in the investigated populations [START_REF] Cardon | Association study designs for complex diseases[END_REF]. In fact, gene-environment interactions and gene-gene interactions are increasingly recognized phenomena in the field of human genetics [START_REF] Cardon | Association study designs for complex diseases[END_REF][START_REF] Moore | The ubiquitous nature of epistasis in determining susceptibility to common human diseases[END_REF][START_REF] Sing | Dynamic relationships between the genome and exposures to environments as causes of common human diseases[END_REF][START_REF] Ta | Genetics, statistics and human disease: analytical retooling for complexity[END_REF]. This pinpoints the needs for the development of a computation tool for detecting gene-environment and gene-gene interactions in a genome-wide fashion, i.e. a tool that allows the analysis of interaction between a specific environmental or genetic factor and a large number of SNPs.

In statistics, the term "interaction" is used to refer to departure from the underlying form of a statistical model. Because there are various statistical models, interaction does not have a consistent meaning [START_REF] Rothman | Epidemiology. An introduction[END_REF]. Interaction is often assessed by entering a product term into the linear or multiplicative regression model. For example, in a logistic regression model a product term is estimated as departure from multiplicativity of odds ratios. Interaction in epidemiology commonly refers to the situation where the joint effect of two risk factors on disease risk differs from the sum of their independent effects. This concept of interaction has a consistent meaning and is particularly applicable in the context of the sufficient cause model as described by Rothman [START_REF] Rothman | Epidemiology. An introduction[END_REF][START_REF] Rothman | Causes[END_REF]. A sufficient cause is defined as minimal sets of actions, events, or states of nature that together initiated a process that inevitably resulted in the outcome. For a particular outcome there would likely be many different sufficient causal mechanisms. In the sufficient cause context, interaction is defined as two causes being component causes of the same sufficient cause. It can be shown that in the absence of interaction in this sense, disease rates are additive [START_REF] Rothman | Epidemiology. An introduction[END_REF][START_REF] Rothman | Modern epidemiology[END_REF][START_REF] Vanderweele | Sufficient cause interactions and statistical interactions[END_REF]. It can also be shown that when the joint effect of two contributory causes of a disease exceeds the sum of their independent effects (on the additative scale) then sufficient cause interaction is present (but not vice versa). Rothman has argued that interaction as departure from additivity better reflects biologic interaction [START_REF] Rothman | Epidemiology. An introduction[END_REF][START_REF] Rothman | Modern epidemiology[END_REF]. He presents three measures of biologic interaction: RERI, the relative excess risk due to interaction; AP, the attributable proportion due to interaction; and S, the synergy index [START_REF] Rothman | Epidemiology. An introduction[END_REF][START_REF] Rothman | Causes[END_REF][START_REF] Rothman | Modern epidemiology[END_REF][START_REF] Rothman | Synergy and antagonism in cause-effect relationships[END_REF][START_REF] Rothman | The estimation of synergy or antagonism[END_REF][START_REF] Hallqvist | How to evaluate interaction between causes: a review of practices in cardiovascular epidemiology[END_REF][START_REF] Andersson | Calculating measures of biological interaction[END_REF]. These measures are defined as follows: Where RR 11 is the relative risk in the exposure category when both risk factors are present, RR 10 is the relative risk for the first risk factor in the absence of the second and RR 01 is the relative risk for the second risk factor in the absence of the first. Those who are unexposed to both the first and the second risk factor are used for reference category, i.e., RR 00 = 1. Thus, there are three relative risks to be estimated. If there is no interaction, RERI and AP equals 0 and S equals 1. For the calculation of confidence interval there are a few options [START_REF] Hosmer | Confidence interval estimation of interaction[END_REF][START_REF] Assmann | Confidence intervals for measures of interaction[END_REF]. In the current version of GEIRA program, we use Hosmer and Lemeshow's delta method, which is a straight forward Taylor expansion of the variances and covariances based on Wald-type statistics using approximate variance estimators. The calculation will be faster in terms of computation time compared with the re-sampling based bootstrap method [START_REF] Assmann | Confidence intervals for measures of interaction[END_REF] in the context of genome-wide scale.

The limited number of SNPs that come out from the screening can then easily be recalculated with confidence limits based on e.g. Assmann's method [START_REF] Assmann | Confidence intervals for measures of interaction[END_REF].

In the literature on gene-environment and gene-gene interactions, there is no definite agreement on which computational methods for calculations of interactions are most appropriate in which contexts [START_REF] Gorwood | Gene-environment interactions in addictive disorders: epidemiological and methodological aspects[END_REF][START_REF] Weinberg | Less is more, except when less is less: Studying joint effects[END_REF][START_REF] Mahdi | Specific interaction between genotype, smoking and autoimmunity to citrullinated alpha-enolase in the etiology of rheumatoid arthritis[END_REF]. Irrespective of what choice of method that is preferred, there is a definite need for better computational methods for quantification of interactions. The primary goal of present article is to provide tools for large scale interaction calculation for several of the most utilized methods for detection of interactions. We have chosen to use the term "additive interaction" referring to Rothman's "biologic interaction" and "multiplicative interaction" referring to the product term in a multiplicative model.. In the program, multiplicative interaction is estimated by inclusion of an interaction term in a logistic regression model while the estimation of additive interaction is based on the three measures, i.e., RERI, AP and S as described above.

Methods

SAS version 9.

2 for windows and R version 2.6.2 were used to develop the GEIRA program.

FEATURES

The program analyzes genome-wide SNP data or any list of bi-allelic genetic markers or dichotomous environmental factors in a case-control study setting using toward the single risk factor of choice ("major" risk factor). It includes simple data processing steps, which makes it easy and user-friendly and enables users to modify and extend the program.

After user defined single factor of choice and other variables are determined, the program will run automatically. All relevant measures for both additive interaction and multiplicative interaction will be included in the output in format of a table. Users can easily generate a ranked top list of SNPs based on certain user defined criteria. GEIRA consists of six steps which are described as follows (Figure 1):

Step 1 (Data importing): Here we assume that some other software package has previously been used to quality control genome-wide genotype data, including dataset filtering on the basis of SNP genotype call rate, minor allele frequency, Hardy-Weinberg equilibrium, individual missing genotype, outliers, etc. The program will read in transposed PLINK format data files, i.e., TPED (containing SNP and genotype information where one row is a SNP) and TFAM (containing individual and family information where one row is an individual) (see PLINK documentation for details. http://pngu.mgh.harvard.edu/~purcell/plink/pdf.shtml). In addition to TPED and TFAM files, a covariate file containing covariate information (including an environmental variable or a major genetic variable of choice) is needed.

Step 2 (Risk allele assigning): A minor allele is determined using all subjects (both cases and controls). A risk allele is determined by comparing the minor allele frequency (MAF) in cases and controls. If the MAF in cases is greater than or equal to that in controls, the minor allele is assigned to the risk allele. If the MAF in cases is less than that in controls, the major allele is assigned to the risk allele (Figure 2 Step 4 (Interaction calculation): Users can choose one of these models, i.e., a dominant, recessive or a co-dominant model. Calculate estimates for both additive and multiplicative interactions, incorporating all estimates into one output table.

Step 5 (Supervising module): Steps 1-4 should be executed in proper order. We therefore created a supervising module that will pass the correct parameters to each step in order.

Step 6 (Adjustment for multiple testing): P-value adjustments using Bonferroni, Sidak (A technique slightly less conservative than Bonferroni) and False Discovery Rate (FDR) are corrected for total tests performed and are calculated using simple functions of the raw p-values.

Application to real genome-wide data

Rheumatoid arthritis (RA) is a complex autoimmune disorder with both genetic and environmental influences on the disease pathogenesis [START_REF] Klareskog | Rheumatoid arthritis[END_REF]. Family aggregation and twin studies have estimated a genetic component of approximately 50% [START_REF] Cornelis | New susceptibility locus for rheumatoid arthritis suggested by a genome-wide linkage study[END_REF][START_REF] Macgregor | Characterizing the quantitative genetic contribution to rheumatoid arthritis using data from twins[END_REF]. Smoking is an established risk factor for RA [START_REF] Klareskog | Rheumatoid arthritis[END_REF][START_REF] Klareskog | Mechanisms of disease: Genetic susceptibility and environmental triggers in the development of rheumatoid arthritis[END_REF]. We applied GEIRA to the Swedish Epidemiologic Investigation of Rheumatoid Arthritis (EIRA) GWAS data (Illumina 300K). EIRA is an ongoing population-based case-control study aiming at studying geneenvironment interactions in rheumatoid arthritis. The study design and population description were detailed previously [START_REF] Plenge | TRAF1-C5 as a risk locus for rheumatoid arthritis--a genomewide study[END_REF][START_REF] Stolt | Quantification of the influence of cigarette smoking on rheumatoid arthritis: results from a population based case-control study, using incident cases[END_REF]. A total of 1,147 autoantibody to citrullinated protein antigens (ACPA)-positive rheumatoid arthritis cases and 1,079 age, gender and residence area matched controls were used for calculation.

In order to demonstrate the use of the GEIRA program, we chose to investigate interactions between a large number of genetic variants available in cases (ACPApositive RA) and controls as well as data on the environmental exposure smoking in the same cases and controls. We used already published GWAS data on these cases and controls as our basic genetic information [START_REF] Plenge | TRAF1-C5 as a risk locus for rheumatoid arthritis--a genomewide study[END_REF][START_REF] Ding | Different patterns of associations with anti-citrullinated protein antibody-positive and anti-citrullinated protein antibodynegative rheumatoid arthritis in the extended major histocompatibility complex region[END_REF]. We conducted an extra quality control procedure in PLINK [START_REF] Purcell | PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses[END_REF], thereafter 301,238 autosomal SNPs remained for analysis and were used as an input in the GEIRA program. The environmental factor, smoking status, was coded as 1 for ever smoker and 0 for never smoker. From the analysis, a total of 15 SNPs were found to be statistically significantly interacting with smoking on the additive scale. Of these 15 SNPs, two are from HLA region (HLA-DQA2 and HLA-DRA). The genome-wide significance defined as a P for AP of 1.66 x 10 -7 was used for claiming a statistical significance. This application took about 7 hours to complete on a desktop with two Intel Xeon processors running at 3.2 GHz and a RAM of 32G. The operating system was Windows Vista Business (64-bit). The further results of these experiments together with biological interpretation of the findings are submitted separately for publication (submitted).

Discussion

The GEIRA program aims at detecting gene-environment as well as gene-gene interactions, and in this first report, we describe both the generic algorithms, and their application in a specific case of gene-environment interaction. The main effect of each SNP can be estimated using other whole-genome association toolsets, such as PLINK [START_REF] Purcell | PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses[END_REF]. The results from the GEIRA program can be considered as a first screening step for selecting potential candidate gene-environment (or gene-gene) interactions across the genome. SNPs on the top of the list can be furthered examined by follow-up studies, such as replication in another independent population, dense mapping, and so on. Adjustment for confounding factors can be easily done by assigning the macro variables, covar_cat or covar_cont, to the confounding variables (see user's manual for details). For adjustment of multiple testing, an additional step (step 6) for correction for multiple testing was included in the program. Notwithstanding the fact that the step 2 is developed to ensure that the odds ratio (OR) for the risk allele is greater than 1, the OR associated with the genetic factor might still be less than 1 among subject unexposed to the environmental factor. In this case, the interpretation for interaction should be cautious.

AP is an estimate of the proportion of disease, which is due to interaction among persons with both exposures. If RR 11 < [(RR 10 + RR 01 )-1], AP will be negative. In this case the joint effect from the simultaneous presence of the two factors is less than what is expected by summing their independent effects. The factors then partly balance each others effects when simultaneously present. In this case, the factors in question may have antagonistic effects. We advise users to read the user's manual of the program for more details. Displays in the output window are suppressed and contents in the log window are saved in an external directory so that users can trace the program running process if there are some unexpected results in the outputs. To analyze only certain chromosomes, users only need assign a chromosome number of interest to the macro variable chr and run the program. It takes only 7 to 35 minutes, depending on which chromosome selected, to complete the analyses for a single chromosome. This program can be easily modified to estimate genome-wide gene-gene interactions. In this case, the only parameter needed to be changed is the environmental variable (envir). The environmental variable should be replaced by a dichotomous genetic variable (SNP, CNV, haplotype etc.) coded as 0 (unexposed) and 1 (exposed) with the remaining parameters to be unchanged. When applying to the very large GWAS data, parallel computing on a multiple CPUs/clusters server is strongly recommended.

In future versions of GEIRA, we will extend the interaction estimation to one continuous and one dichotomous variable, and further to two continuous variables. The algorithm and the program GEIRA presented in this article offers a powerful, userfriendly tool for performing interaction analyses with whole-genome data.

RERI = RR 11 -

 11 RR 10 -RR 01 + 1, AP = RERI/ RR 11, S = [RR 11 -1]/[(RR 10 -1) + (RR 01 -1)]

  and user's manual for details Step 3 (Data converting): The raw genotype dataset will then be converted into a dataset based on a dominant, recessive or co-dominant model, according to the following coding: Assuming C is the minor allele and also the risk allele. Dominant model coding: A_A→0, A_C→1, C_C→1 Recessive model coding: A_A→0, A_C→0, C_C→1 Co-dominant model coding: A_A→0, A_C→1, C_C→2

Figure 1 .

 1 Figure 1. A pictorial representation of the GEIRA algorithm.

Figure 2 .

 2 Figure 2. Detailed algorithm for step 2 (risk allele assignment). Example 1 illustrates a

Figure 1

 1 Figure 1

Figure 2

 2 Figure 2

Acknowledgments

This work has been financially supported by the Swedish Medical Research Council [to L.A.], the Swedish King Gustaf V's 80-year foundation [to B.D.], the Swedish Ulla and Gustaf af Uggla Foundation Research Grant [to B.D.], the Swedish COMBINE program [to L.K.], Swedish council for working life and social science COFAS [to H.K.].

Conflict of interest statements: None declared


Asier Minondo
email: aminondo@deusto.es

Does comparative advantage explain countries' diversification level?

Keywords: product proximity, export diversification, international trade, comparative advantage

This paper analyses whether the products in which a country has comparative advantage can explain its exports' diversification level. We argue that specialisation endows countries with some specific skills and assets; in some cases those skills and assets can easily be redeployed in other products and facilitate diversification, whereas in other cases skills and assets are more difficult to redeploy and offer scant diversification possibilities. Based on countries' comparative advantage and an index of product proximity, we construct a metric for countries' diversification possibilities. Using non-parametric and parametric techniques, we show that this metric is a very strong and robust predictor of countries' actual diversification level, even when we control for differences in income across countries. These results point out that diversification may not be an automatic outcome of development.

Introduction

Export diversification has recently become a hot topic in the economic research agenda [START_REF] Carrère | Trade Diversification, Income and Growth: What Do We Know?[END_REF]Newfarmer et al. 2009). There are old and new reasons that may explain this interest in export diversification. On the one hand, diversification is considered a key policy objective for countries specialised in natural resources (Sachs and Warner, 2001). On the other hand, countries seeking to accelerate economic growth through exports should determine whether diversification, rather than the intensive margin, is the best route to achieve this goal [START_REF] Besedeš | The Role of Extensive and Intensive Margins and Export Growth[END_REF]. In addition to that, some models suggest that countries can increase their growth rate if they diversify into products where learning by doing is larger [START_REF] Matsuyama | Agricultural Productivity, Comparative Advantage and Economic Growth[END_REF] or into rich-country products (Hausmann et al., 2007). In any case, other models warn that diversification may be hampered due to the costs involved in discovering the new export products [START_REF] Hausmann | Economic development as self discovery[END_REF].

If exports diversification has a positive effect on economic growth, a relevant question is what determines its level. Some scholars show that diversification can be an outcome of the development process. [START_REF] Imbs | Stages of Diversification[END_REF], Koren and Tenreyro (2007), [START_REF] Cadot | Export Diversification: What's Behind the Hump[END_REF] and [START_REF] Klinger | Breaking into New Markets. Emerging Lessons for Export Diversification, The World Bank[END_REF] find that countries grow through two stages of diversification. At low levels of income growth is accompanied by an increase in the level of diversification; however, once countries reach a certain level of income further growth is accompanied by re-concentration. In contrast, Parteka andTamberi (2008) and[START_REF] De Benedictis | Overall trade specialization and economic development: countries diversify[END_REF] conclude that growth is always accompanied by an increase in the level of diversification. 1The contribution of this paper is to present and test an alternative explanation of the differences in export diversification across countries. We argue that countries' diversification levels may be determined by the products in which it has comparative advantage. The link between comparative advantage and diversification is established through the concept of product proximity developed by [START_REF] Hidalgo | The Product Space Conditions the Development of Nations[END_REF]. These authors show that some products, such as electronics, tend to be exported along with a large range of different products; in contrast, other commodities, such as oil, tend to be exported alone. According to these authors, those differences are related to the skills and other assets, such as technology, capital or institutions, needed to produce each product. For example, the manufacturing of electronic products demand skills and assets that can easily be deployed in a large range of additional manufactures (e.g. to master the logistics of the components that are assembled in a factory); however, the extraction of oil demands skills and assets that are more difficult to redeploy in other products (e.g. to master the operation of a drilling rig). Due to these differences, countries that happen to develop comparative advantage in products that are close to other products can diversify more easily than countries that happen to develop comparative advantage in products that are in the periphery of the product space.

To test the validity of this explanation, we build an index of countries' diversification possibilities based on the products in which they have comparative advantage and the proximity of those products to the rest of commodities. Using non-parametric and parametric techniques, we show that this index is a very strong and robust predictor of countries' actual diversification levels, even when we control for differences in GDP per capita across countries. These results point out that countries' diversification levels might not be an automatic outcome of countries' development process. The conclusions of our paper are in line with a recent study by [START_REF] Hidalgo | The building blocks of economic complexity[END_REF], who using network techniques show that countries that have comparative advantage in products in which few countries also have comparative advantage are endowed with a larger set of capabilities; this larger set of capabilities, in turn, allows countries to export a larger set of products.

The rest of the paper is organised as follows. Section 2 explains in detail the relationship between countries' comparative advantage and their exports diversification level. Section 3 presents the empirical analyses and Section 4 concludes.

The link between comparative advantage and diversification

To establish the link between the products in which a country has comparative advantage and the diversification level we draw on the concept of product proximity developed by [START_REF] Hidalgo | The Product Space Conditions the Development of Nations[END_REF]. These authors argue that several dimensions may influence the degree of relatedness between two products: similarities in the combination of productive factors, the characteristics of the technology used in production, the use of a specific component, the features of the final customers or the use of specific distribution channels. Due to the myriad of factors that may determine the relatedness between products, they use an outcome measure to calculate the degree of proximity between products. They argue that two products will be close to each other if countries tend to have revealed comparative advantage in both products.

Based on this idea they calculate proximity ( ) between product i and product j at year t as:

) | ( ), | ( min , , , , t i t j t j t i ijt x x P x x P
(1)

where P(x i,t | x j,t ) is the conditional probability of having revealed comparative advantage in product i given that the country has revealed comparative advantage in product j.

Based on this index and using network displaying techniques [START_REF] Hidalgo | The Product Space Conditions the Development of Nations[END_REF] are able to draw a product space map. This map shows that products are not evenly distributed: there are sections of the map with a high density of products, whereas other sections of the map are sparsely populated. Our argument is that these discontinuities in the product map are very important to determine countries' diversification opportunities. If a country happens to develop comparative advantage in a product which is close to a large number of other commodities, it will be easier for this country to diversify into new products. In contrast, if a country happens to develop comparative advantage in a very sparsely populated zone of the map, its diversification opportunities will be more scant. [START_REF] Hidalgo | The Product Space Conditions the Development of Nations[END_REF] provide evidence that diversification is governed by the relatedness between products. They show that countries tend to develop comparative advantage in those goods that are close to the products in which they have comparative advantage. According to this model, changes in a country's comparative advantage will lead to alterations in its diversification possibilities and, hence, on its exports diversification level.

The concept of product proximity can be rephrased in the framework of cones of diversification developed by [START_REF] Schott | One Size Fits All? Heckscher-Ohlin Specialization in Global Production[END_REF] and Xian (2007). These authors argue that as countries accumulate capital (and other productive factors) they move to new diversification cones. In these models countries also shift from a product to a nearby product; however, in this case the closeness between products is determined by how they combine the productive factors in the production process. In [START_REF] Hidalgo | The Product Space Conditions the Development of Nations[END_REF] the proximity index encompasses not only the similarity in the ratio in which factors of production are combined, but also other features that may influence the relatedness between products. In addition to that, [START_REF] Hidalgo | The Product Space Conditions the Development of Nations[END_REF] argue that some cones of diversification may encompass a larger number of goods than others. Countries that end-up in diversification cones that cover a larger number of goods will be able to diversify into more products than countries that end-up in diversification cones with a smaller number of goods. 2

To construct a metric for a country's diversification possibilities, we first, following [START_REF] Hausmann | The Structure of the Product Space and the Evolution of Comparative Advantage[END_REF], calculate an index of product centrality, which is defined as the average proximity of a product to the rest of products:

J Centrality j ijt it (2)
where J is the total amount of products. Second, based on this index, we calculate a country's diversification possibilities as the average centrality of products in which the country has revealed comparative advantage.

Empirical analyses

Data

We use data from the NBER World Trade Database to calculate product proximity indexes [START_REF] Feenstra | World Trade Flows: 1962-2000[END_REF]. This database offers data for SITC Rev. 2, 4-digit classification, that distinguishes 775 products. Countries' diversification possibilities are calculated for the 1980-2000 period. As we need to calculate countries' revealed comparative advantage to get countries' diversification possibilities, the sample of countries should be the same for the whole period. There are 91 countries that meet that criteria. 3 Based on the same sample, product proximity indexes are calculated at the beginning of the period: 1980. To avoid reverse causality, we calculate a product proximity index set for each country. A country's product proximity index set is calculated by removing that country from the sample that is used to calculate product proximity indexes. Data on countries GDP per capita in constant 2 As [START_REF] Cadot | Export Diversification: What's Behind the Hump[END_REF] point out, the diversification level may increase, temporarily, even when a country moves to a new diversification cone that encompasses the same number of goods. This may occur if incumbency advantages make the phasing-out of old products slower than the addition of new products to the export basket. 3 We exclude from the sample countries with a population of less than 3 million. purchasing power parity (PPP) are obtained from the World Bank's World Development Indicators.

Exports diversification index

Following [START_REF] Cadot | Export Diversification: What's Behind the Hump[END_REF], the diversification of a country's export structure is calculated using a Theil index:

4 i J i i x x J T ln 1 1 where J x J i i 1
where J is the total number of products and x i denotes the amount of exports of product i. The Theil index is inversely related with a country's diversification level: the larger the index the lower the diversification level. In addition to the Theil index, in the parametric estimations, to asses the robustness of our results, we also use other concentration indexes, such as the Herfindahl index and the Gini coefficient.

Non-parametric estimations

In the first set of estimations we use non-parametric techniques to analyse the relationship between our relevant variables: diversification, countries' centrality and GDP per capita. The advantage of non-parametric estimations is that they do not impose any prior functional form on the estimated relationship. For our analyses we use a lowess smoothing function.

First, as in previous studies, we analyse the relationship between GDP per capita and diversification. 5 Figure 1 presents the relationship between GDP per capita and the Theil index. As shown in the figure, we observe that the relationship between GDP per capita and concentration follows a convex curve: there is sharp reduction in concentration when GDP per capita rises from low income levels, but the slope becomes smoother when larger GDP per capita levels are reached. The lowest concentration level happens at around 24,000 PPP $, similar to the turning-point level found by [START_REF] Cadot | Export Diversification: What's Behind the Hump[END_REF]. From this GDP per capita onward there is almost a flat relationship between income and diversification. This result is not in line with the re-concentration process found by [START_REF] Imbs | Stages of Diversification[END_REF], Koren and Tenreyro (2007), [START_REF] Klinger | Breaking into New Markets. Emerging Lessons for Export Diversification, The World Bank[END_REF] and [START_REF] Cadot | Export Diversification: What's Behind the Hump[END_REF]; however, it is in line with the results in Parteka andTamberi (2008) and[START_REF] De Benedictis | Overall trade specialization and economic development: countries diversify[END_REF] who do not find either a two-stage process. 6Figure 2 presents the relationship between our index of countries' diversification possibilities, countries' centrality, and the Theil index. As expected, we find that countries with a larger centrality index have a lower concentration level. This result confirms that countries with revealed comparative advantage in goods that are close to a large number of other commodities are able to export a larger range of goods than countries that have revealed comparative advantage in goods that are in the periphery of the product space. To analyse whether the positive relationship between countries' centrality and diversification is governed by GDP per capita, Figure 3 presents the relationship between GDP per capita and centrality.

We can observe that there is, in fact, a positive relationship between both variables. However, we can also see that there are large differences in the level of countries' centrality for the same GDP per capita level. To sum up, non-parametric analyses show that both GDP per capita and centrality may explain countries' diversification levels. Hence, to determine what is the relative contribution of both variables to countries' diversification the next section presents the results of parametric estimations.

Parametric estimations

Table 1 and Table 2 present the results of the parametric estimations. In Table 1, following the re-concentration hypothesis, we assume a non-monotonic and non-linear relationship between diversification and GDP per capita. In order to capture this non-monotonic and nonlinear relationship, following previous studies, we introduce GDP per capita and the square of GDP per capita as independent variables. In Table 2, we assume that there is a monotonic relationship between diversification and GDP per capita. In order to capture this monotonic relationship we introduce the inverse of the GDP per capita as independent variable. This functional form is non-linear, as in the previous case; however, contrary to the previous functional form, it is monotonic. For each specification, we estimate four different models.

First, we pool all observations and estimate the model with simple OLS. Taking advantage of the panel structure of our dataset, second, we estimate a between-effects model; third, a fixedeffects model and fourth, to control for the possible endogeneity between the dependent and independent variables, a GMM model. 7 Finally, we estimate all the models with centrality as the only independent variable and, then, we introduce the GDP per capita coefficient, in its non-monotonic and monotonic specification, as an additional independent variable.

As there is much more variation in diversification across countries than within countries, the pooled OLS and the between-effects models have a much larger R-square than the fixedeffects model. We can observe that both in the non-monotonic (Table 1) and in the monotonic specification (Table 2), centrality has a negative and statistically significant coefficient in all models. These results point out that countries' diversification possibilities play a very strong role in determining countries' exports diversity. We can see, as well, that centrality has a negative coefficient and it is statistically significant even when we control for differences in GDP per capita across countries. In Table 1, we observe that GDP per capita has a negative coefficient and the square of GDP per capita has a positive coefficient, confirming the predictions of a non-monotonic relationship between income and diversification. However, GDP per capita and the square of GDP per capita are only statistically significant in the fixedeffects model. In the monotonic specification, the inverse of GDP per capita is always positive and statistically significant. As explained above, to control for endogeneity, in the last columns of Table 1 and Table 2 we estimate a GMM model. The (System) GMM model treats all covariates as potentially endogenous and include their lags and first-differences as instruments. We observe that in all specifications, centrality remains negative and statistically significant. Moreover, in all GMM estimations, we cannot reject the crucial assumption of no second-order correlation of the residuals.

To further analyse the robustness of our results we perform additional regressions analyses. show that country size influences countries' diversification possibilities. 8 If economies of scale are prevalent, smaller countries will have less opportunities to develop a large range of industries. Table 3a and Table 3b in the Appendix present the results of the estimations when we introduce a variable for country size. We use two variables to proxy country size: GDP and population. As expected, both population and GDP coefficients are negative, confirming that larger countries have more diversification opportunities than smaller countries; however, the population coefficient is not statistically significant in the non-monotonic specification's between-effects model and the GDP coefficient is only statistically significant in the monotonic-specification's pooled OLS model. The centrality coefficient has the correct sign and remains statistically significant in all estimations.

Next, we perform the econometric analyses using alternative measures for export concentration: the Herfindahl index and the Gini coefficient. One limitation of these indexes is that they are bounded between zero and one. Moreover, as shown in Figures 4a and4b, in our sample countries Herfindahl indexes are clustered around zero and Gini coefficients are clustered around one. As the censoring of data may lead to biased estimations, following [START_REF] Cadot | Export Diversification: What's Behind the Hump[END_REF], we also estimate the models using a logistic transformation of the Herfindahl and Gini coefficients. In the case of the Herfindahl index (Tables 4a and4b in the Appendix), centrality has always a negative coefficient and is statistically significant. We can observe that estimations with the logistic transformation (Panel B) yield better results than estimations with the standard coefficient. First, the fit of the model is always larger with the logistic transformation, specially in the non-monotonic specification. Second, in the nonmonotonic specification the logistic transformation yield the expected coefficients on GDP per capita and the square of GDP per capita in all estimations.

When the Gini coefficient is used as the dependent variable, the centrality index has the correct sign in all estimations. However, it is statistically not significant in five of the seven estimations of the fixed-effects model with the standard coefficient (Table 5a and Table 5b in the Appendix -Panel A). This results might be explained by the large degree of observations that are clustered around the value 1 (Figure 4b). In fact, when we estimate the fixed-effects model with the logistic transformation the centrality coefficient is always negative and statistically significant.

Conclusions

In this paper we show that the products in which a country has comparative advantage may play a very important role on countries' exports diversification level. We argue that countries that develop comparative advantage in commodities that demand skills and assets that can be easily redeploy in other products have more opportunities to diversify into new products than countries that develop comparative advantage in goods that demand skills and assets that are more difficult to redeploy. To test this hypothesis we calculate an index of countries diversification possibilities that combines countries' comparative advantage and proximity between products. Using non-parametric and parametric techniques we show that the diversification possibilities index is a strong predictor of countries actual diversification level, even when we control for differences in GDP per capita across countries. These results indicate that the products in which a country has comparative advantage play a very important role in explaining the level of export diversification; hence, export diversification might not be an automatic outcome of the process of development. 1980-20001980-20001980-20001980-20001980-20001980-20001980-20001980-2000 No. of No. of 1980-20001980-20001980-20001980-20001980-20001980-20001980-20001980-2000 No. of No. of 1980-20001980-20001980-20001980-20001980-20001980-20001980-20001980-2000 No. of No. of 1980-20001980-20001980-20001980-20001980-20001980-20001980-20001980-2000 No. of No. of 1980-20001980-20001980-20001980-20001980-20001980-20001980-20001980-2000 No. of No. of 1980-20001980-20001980-20001980-20001980-20001980-20001980-20001980-20001980-20001980-20001980-20001980-2000 No. of No. of 1980-20001980-20001980-20001980-20001980-20001980-20001980-20001980-2000 No. of No. of 1980-20001980-20001980-20001980-20001980-20001980-20001980-20001980-2000 No. of No. of 1980-20001980-20001980-20001980-20001980-20001980-20001980-20001980-20001980-20001980-20001980-20001980-2000 No. of No. of

First

  , previous studies, such as Parteka and Tamberi (2008) and Starosta de Waldemar (2010),

  Figure 1. Diversification and GDP per capita

Table 1 .

 1 Results of the regression analyses (Theil index -Non-monotonic specification)

		Pooled	Pooled	Between-effects Between-effects Fixed-effects Fixed-	GMM	GMM
		OLS	OLS				effects
	Centrality	-52.32	-46.16	-55.46	-52.72	-17.83	-17.75	-62.62	-53.89
		(-21.84)***	(-9.29)***	(-24.18)***	(-8.46)***	(-4.42)***	(-4.54)***	(-9.43)***	(-8.01)***
	GDP pc		-5.31x10 -5		-3.79x10 -5		-1.38x10 -4		-3.59x10 -5
			(-1.45)		(-0.74)		(-3.04)***		(-0.84)
	GDP pc squared		1.23x10 -9		1.26x10 -9		2.30x10 -9		1.03x10 -9
			(1.27)		(0.82)		(2.73)***		(0.96)
	Period

Table 2 .

 2 Results of the regression analyses (Theil index -Monotonic specification)

		Pooled	Between-effects Fixed-effects GMM
		OLS
	Centrality	-47.34	-51.95	-17.87	-56.54
		(-14.09)***	(-13.96)***	(-4.98)***	(-10.39)***
	Inverse GDP pc	351.39	226.87	453.55	323.79
		(2.39)**	(1.64)*	(3.10)***	(1.75)*
	Period	1980-2000	1980-2000	1980-2000	1980-2000
	No. of countries	91	91	91	91
	No. of obs.	1,864	1,864	1,864	1,864
	R-squared	0.74	0.79	0.16
	AB Test for AR(2) p-value				0.911

Note: Country clustered robust t-statistics in parentheses. ***, **, * statistically significant at 1%, 5% and 10% respectively.

Table 3a .

 3a Robustness analysis I (Countries size included as independent variable -Non-monotonic specification)

		Pooled	Between-	Fixed-	GMM	Pooled	Between-	Fixed-	GMM
		OLS	effects	effects		OLS	effects	effects
	Centrality	-41.85	-49.22	-16.80	-48.38	-45.65	-53.16	-17.48	-54.19
		(-7.96)***	(-9.25)***	(-4.55)***	(-7.01)***	(-9.18)***	(-8.45)***	(-4.50)***	(-8.59)***
	GDP pc	-8.11x10 -5	-6.67x10 -5	-1.25x10 -4	-5.22x10 -5	-5.73x10 -5	-4.40x10 -5	-1.39x10 -4	-2.68x10 -5
		(-2.19)**	(-1.53)	(-2.90)***	(-1.20)	(-1.59)	(-1.04)	(-2.98)***	(-0.67)
	GDP pc squared	1.87x10 -9	1.94x10 -9	2.09x10 -9	1.27x10 -9	1.47x10 -9	1.58x10 -9	2.43x10 -9	8.70x10 -10
		(1.98)**	(1.50)	(2.62)***	(1.19)	(1.56)	(1.26)	(2.65)***	(0.85)
	Population	-1.63x10 -9	-1.41x10 -9	-3.57x10 -9	-1.44x10 -9
		(-6.40)***	(-1.43)	(-2.33)**	(-5.11)***
	GDP					-9.08x10 -14	-9.61x10 -14	-1.08x10 -13	-1.08x10 -13
						(-1.54)	(-0.24)	(-1.52)	(-1.52)
	Period

Table 3b .

 3b Robustness analysis I (Countries size included as independent variable -Monotonic specification)

		Pooled	Between-	Fixed-	GMM	Pooled	Between-	Fixed-	GMM
		OLS	effects	effects		OLS	effects	effects
	Centrality	-45.54	-49.98	-17.01	-52.63	-46.50	-51.35	-17.83	-54.56
		(-13.46)***	(-13.39)***	(-4.90)***	(-11.08)***	(-13.74)***	(-11.58)***	(-4.97)***	(-11.29)***
	Inverse GDP pc	412.20	294.82	344.98	311.13	347.48	226.24	440.35	331.07
		(2.75)***	(1.95)*	(2.85)***	(1.82)*	(2.37)**	(1.36)	(3.05)***	(1.83)*
	Population	-1.43x10 -9	-1.32x10 -9	-3.27x10 -9	-1.35x10 -9
		(-7.62)***	(-2.02)**	(-1.94)*	(-6.52)***
	GDP					-8.11x10 -14	-5.12x10 -14	-1.30x10 -13	-1.75x10 -14
						(-1.77)*	(-0.15)	(-1.44)	(-0.36)
	Period

Table 4a .

 4a Robustness analysis II (Herfindahl index -Non-monotonic specification)

		OLS	OLS	OLS	OLS	BE	BE	BE	BE
	Panel A: Standard
	Centrality	-6.43	-7.34	-7.14	-7.33	-6.74	-8.55	-8.44	-8.55
		(-9.07)***	(-6.14)***	(-5.51)***	(-6.09)***	(-8.16)***	(-5.45)***	(-5.66)***	(-5.93)***
	GDP pc		2.52x10 -6	1.22x10 -6	2.44x10 -6		5.95x10 -6	5.22x10 -6	5.87x10 -6
			(0.37)	(0.16)	(0.35)		(0.72)	(0.66)	(0.65)
	GDP pc squared		3.08x10 -11	6.08x10 -11	3.54x10 -11		1.15x10 -12	1.83x10 -11	5.59x10 -12
			(0.18)	(0.33)	(0.21)		(0.01)	(0.09)	(0.02)
	Population			-7.56x10 -11				-3.57x10 -11
				(-1.52)				(-0.26)
	GDP				-1.76 x10 -15				-1.35 x10 -15
					(-0.28)				(-0.10)
	R-squared	0.46	0.47	0.48	0.47	0.55	0.55	0.55	0.55
	Panel B: Logistic
	Centrality	-57.55	-54.89	-51.35	-54.30	-60.90	-64.06	-60.73	-63.44
		(-15.01)***	(-7.75)***	(-6.68)***	(-7.70)***	(-14.27)***	(-7.66)***	(-5.96)***	(-6.42)***
	GDP pc		-3.73x10 -5	-6.03x10 -5	-4.23x10 -5		-1.60x10 -5	-3.73x10 -5	-2.27x10 -5
			(-0.77)	(-1.18)	(-0.89)		(-0.24)	(-0.51)	(-0.37)
	GDP pc squared		1.10x10 -9	1.63x10 -9	1.38x10 -9		1.03x10 -9	1.54x10 -9	1.38x10 -9
			(0.82)	(1.20)	(1.04)		(0.51)	(0.70)	(0.74)
	Population			-1.34x10 -9				-1.08x10 -9
				(-4.17)***				(-1.58)
	GDP				-1.07 x10 -13				-1.06 x10 -17
					(-1.17)				(-0.28)
	R-squared	0.63	0.63	0.65	0.64	0.71	0.71	0.72	0.72
	Period

Table 4a .

 4a Robustness analysis II (cont.)

		FE	FE	FE	FE	GMM	GMM	GMM	GMM
	Panel A: Standard
	Centrality	-3.16	-3.08	-3.00	-3.04	-9.88	-8.68	-8.25	-8.42
		(-3.47)***	(-3.39)***	(-3.34)***	(-3.36)***	(-5.47)***	(-5.70)***	(-5.51)***	(-5.93)***
	GDP pc		-1.04x10 -5	-9.35x10 -6	-1.06x10 -5		7.17x10 -6	6.24x10 -6	7.42x10 -6
			(-2.00)**	(-1.87)*	(-1.97)*		(0.87)	(0.73)	(0.97)
	GDP pc squared		2.07x10 -10	1.89x10 -10	2.25x10 -10		-5.86x10 -11	-4.93x10 -11	-7.43x10 -11
			(1.98)*	(1.88)*	(1.95)*		(0.01)	(-0.24)	(-0.41)
	Population			-2.92x10 -10				-4.09x10 -11
				(-1.29)				(-0.78)
	GDP				-1.52 x10 -14				8.07 x10 -16
					(-1.48)				(-0.12)
	R-squared	0.08	0.09	0.09	0.09
	AB Test AR(2)					0.855	0.788	0.754	0.768
	Panel B: Logistic
	Centrality	-21.84	-21.39	-20.21	-20.77	-75.91	-66.54	-59.76	-65.18
		(-3.76)***	(-3.74)***	(-3.66)***	(-3.68)***	(-7.76)***	(-7.31)***	(-6.37)***	(-7.61)***
	GDP pc		-1.35x10 -4	-1.19x10 -4	-1.39x10 -4		-4.59x10 -6	-2.35x10 -5	-1.41x10 -6
			(-2.32)**	(-2.14)**	(-2.27)**		(-0.08)	(-0.41)	(-0.03)
	GDP pc squared		2.43x10 -9	2.17x10 -9	2.74x10 -9		5.77x10 -10	8.49x10 -10	5.64x10 -10
			(2.25)**	(2.12)**	(2.27)**		(0.39)	(0.57)	(0.39)
	Population			-4.51x10 -9				-1.09x10 -9
				(-2.18)***				(-3.09)***
	GDP				-2.58 x10 -13				-8.10x10 -14
					(-2.03)**				(-0.84)
	R-squared	0.09	0.11	0.13	0.12
	AB Test AR(2)					0.989	0.961	0.898	0.951
	Period

Table 4b .

 4b Robustness analysis II (Herfindahl index -Monotonic specification)

		OLS	OLS	OLS	BE	BE	BE	FE	FE	FE	GMM	GMM	GMM
	Panel A: Standard
	Centrality	-6.01	-5.86	-6.10	-6.58	-6.43	-6.73	-3.16	-3.12	-3.16	-8.82	-7.55	-7.92
		(-6.02)***	(-5.70)***	(-5.96)***	(-5.88)***	(-5.10)***	(-5.30)***	(-3.78)***	(-3.75)***	(-3.78)***	(-5.97)***	(-5.54)***	(-5.95)***
	Inverse GDP pc	29.55	34.52	29.97	10.14	15.37	10.29	60.93	55.53	60.38	32.33	22.76	32.32
		(0.91)	(0.91)	(0.82)	(0.31)	(0.39)	(0.24)	(2.00)**	(1.72)*	(1.97)*	(0.72)**	(0.53)	(0.76)
	Population		-1.17x10 -10			-1.02x10 -10			-1.63x10 -10			-1.15x10 -10
			(-3.21)***			(-0.60)			(-0.67)			(-2.84)***
	GDP			8.74 x10 -15			1.26x10 -14			-5.37x10 -15			2.33x10 -14
				(1.49)			(0.58)			(-0.75)			(2.15)**
	R-squared	0.47	0.47	0.47	0.52	0.53	0.53	0.10	0.10	0.10
	AB Test AR(2)										0.753	0.664	0.686
	Panel B: Logistic
	Centrality	-53.45	-51.87	-52.74	-58.84	-57.19	-58.08	-21.89	-20.86	-21.83	-67.12	-61.26	-62.25
		(-10.11)***	(-9.52)***	(-9.78)***	(-9.10)***	(-8.55)***	(-9.34)***	(-4.21)***	(-4.07)***	(-4.20)***	(-8.37)***	(-8.47)***	(-8.71)***
	Inverse GDP pc	289.80	343.28	286.51	133.17	190.19	131.80	531.19	401.04	510.92	303.67	258.00	314.94
		(1.43)	(1.64)	(1.41)	(0.49)	(0.70)	(0.66)	(2.51)**	(2.03)**	(2.45)**	(1.17)	(1.06)	(1.26)
	Population		-1.26x10 -9			-1.11x10 -9			-3.92x10 -9			-1.19x10 -9
			(-5.41)***			(-1.45)			(-1.79)*			(-4.78)***
	GDP			-6.83 x10 -14			-3.06x10 -14			-1.99x10 -13			-1.26x10 -14
				(-1.02)			(-0.09)			(-1.39)			(0.16)
	R-squared	0.64	0.65	0.64	0.71	0.72	0.71	0.11	0.12	0.12
	AB Test AR(2)										0.913	0.869	0.871
	Period

Table 5a .

 5a Robustness analysis II (Gini coefficient -Non-monotonic specification)

		OLS	OLS	OLS	OLS	BE	BE	BE	BE
	Panel A: Standard
	Centrality	-2.49	-1.52	-1.20	-1.45	-2.70	-1.85	-1.50	-1.78
		(-13.12)***	(-5.76)***	(-4.82)***	(-5.82)***	(-6.80)***	(-6.80)***	(-4.31)***	(-6.56)***
	GDP pc		-4.73x10 -6	-6.77x10 -6	-5.27x10 -6		-3.90x10 -6	-6.12x10 -6	-4.67x10 -6
			(-2.17)**	(-3.38)***	(-2.67)***		(-1.68)*	(-2.32)**	(-1.76)*
	GDP pc squared		4.91x10 -11	9.65x10 -11	8.02x10 -11		4.37x10 -11	9.60x10 -11	8.35x10 -11
			(0.71)	(1.57)	(1.32)		(0.50)	(1.08)	(0.83)
	Population			-1.19x10 -10				-1.09x10 -10
				(-8.51)***				(-1.05)
	GDP				-1.18x10 -14				-1.21x10 -14
					(-2.11)**				(-0.28)
	R-squared	0.62	0.70	0.75	0.71	0.71	0.75	0.79	0.77
	Panel B: Logistic
	Centrality	-54.69	-46.20	-41.39	-45.74	-57.78	-52.92	-47.68	-52.38
		(-24.82)***	(-10.19)***	(-9.15)***	(-10.24)***	(-23.06)***	(-10.75)***	(-8.98)***	(-8.49)***
	GDP pc		-6.79x10 -5	-9.91x10 -5	-7.18x10 -5		-5.82x10 -5	-9.18x10 -5	-6.41x10 -5
			(-2.10)**	(-3.13)***	(-2.24)**		(-1.81)*	(-2.56)***	(-1.73)*
	GDP pc squared		1.48x10 -9	2.21x10 -9	1.70x10 --9		1.66x10 -9	2.44x10 -9	1.96x10 -9
			(1.82)*	(2.82)***	(2.12)**		(1.95)*	(2.50)**	(1.94)**
	Population			-1.82x10 -9				-1.65x10 -9
				(-6.82)***				(-2.32)**
	GDP				-8.37x10 -14				-9.30x10 -14
					(-1.45)				(-0.16)
	R-squared	0.77	0.78	0.81	0.78	0.83	0.83	0.86	0.84
	Period

Table 5b .

 5b Robustness analysis II (Gini coefficient -Monotonic specification)

		OLS	OLS	OLS	BE	BE	BE	FE	FE	FE	GMM	GMM	GMM
	Panel A: Standard
	Centrality	-2.25
		(-11.25)***
												-1.48x10 -9
												(-6.54)***
	GDP			-8.54x10 -14			-5.85x10 -14			-1.25x10 -13			-2.66x10 -14
				(-1.80)*			(-0.13)			(-1.47)			(-0.56)
	R-squared	0.79	0.81	0.79	0.84	0.86	0.84	0.22	0.24	0.23
	AB Test AR(2)										0.879	0.880	0.883
	Period

It is important to indicate that there are differences across studies with respect to the diversification index used and its absolute or relative nature.

The Theil index is derived from Shannon's measure of information entropy.

Due its very large PPP GDP per capita, United Arab Emirates behaves as an outlier and, hence, is excluded from the sample.

To analyse whether the differences in results were driven by the non-parametric technique used in the analyses, following these later authors, we also estimated a generalized additive regression model (GAM), with no changes in the shape of the fitted curves.

We do not present the results for the random-effects model because the Hausmann test rejects the null hypothesis of no correlation between the independent variables and country random effects.

Other less time-variant variables that may also influence diversification possibilities, such as rent-seeking or geography, are control for in the fixed-effects model estimation.


Shireen Kassam

Heidi Goenaga-Infante

Lenushka Maharaj

Crispin T Hiley

Simone Juliger

Simon P Joel

Methylseleninic acid inhibits HDAC activity in diffuse large B-cell lymphoma cell lines

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Selenium (Se) is a trace element that is fundamental to human health. While its therapeutic use in the cancer setting has mainly focused on chemoprevention [START_REF] Rayman | Selenium in cancer prevention: a review of the evidence and mechanism of action[END_REF], there is also evidence to support its use in established cancer [START_REF] Cao | Selective modulation of the therapeutic efficacy of anticancer drugs by selenium containing compounds against human tumor xenografts[END_REF]. Se exists in different chemical forms but organic, rather than inorganic, Se compounds are preferred as the latter are genotoxic [START_REF] Li | Superior in vivo inhibitory efficacy of methylseleninic acid against human prostate cancer over selenomethionine or selenite[END_REF]. Regarding diffuse large B-cell lymphoma (DLBCL), our department has reported that serum Se at diagnosis is predictive of outcome in patients with B-cell non Hodgkin lymphoma, the majority of whom had DLBCL [START_REF] Last | Presentation serum selenium predicts for overall survival, dose delivery, and first treatment response in aggressive non-Hodgkin's lymphoma[END_REF]. In addition, in vitro studies have demonstrated that high concentrations of Se are cytotoxic [START_REF] Last | The activity of methylated and non-methylated selenium species in lymphoma cell lines and primary tumours[END_REF], whilst non-toxic concentrations can sensitise cells to the cytotoxic effects of chemotherapeutic agents [START_REF] Juliger | Chemosensitization of Bcell lymphomas by methylseleninic acid involves nuclear factor-kappaB inhibition and the rapid generation of other selenium species[END_REF]. The precise mechanism of Se action is not clear and differs based on the chemical form, dose/concentration and tumour type. In DLBCL cell lines, Se has been shown to inhibit the activation of NF-κB [START_REF] Juliger | Chemosensitization of Bcell lymphomas by methylseleninic acid involves nuclear factor-kappaB inhibition and the rapid generation of other selenium species[END_REF]. These preclinical data have led to funding for a multi-centre clinical trial of high dose Se supplementation in the form of methylselenocysteine (MSC), in combination with immunochemotherapy, in patients with relapsed/refractory DLBCL.

Metabolism of organic Se compounds, such as MSC, is necessary for their anti-tumour activity, with the metabolite methylselenol thought to be crucial. This metabolite is highly reactive and volatile and therefore cannot be directly measured or quantified [START_REF] Ip | In vitro and in vivo studies of methylseleninic acid: evidence that a monomethylated selenium metabolite is critical for cancer chemoprevention[END_REF]. However, its methylation products, dimethylselenide and dimethyldiselenide, can be quantified by LC-MS methods [START_REF] Infante | Investigation of the selenium species distribution in a human B-cell lymphoma line by HPLC-and GC-ICP-MS in combination with HPLC-ESIMS/MS and GC-TOFMS after incubation with methylseleninic acid[END_REF]. MSC is directly converted to methylselenol in vivo by β and elimination reactions, mediated by lyase enzymes found mainly in the liver and kidney [START_REF] Cooper | Cysteine S-conjugate beta-lyases: important roles in the metabolism of naturally occurring sulfur and selenium-containing compounds, xenobiotics and anticancer agents[END_REF]. As MSC is therefore mainly a prodrug, the compound methylseleninic acid (MSA) has been developed for in vitro studies. This is a water soluble, stable selenium species that is readily taken up into cells and upon reaction with cellular thiols, such as glutathione, is reduced to methylselenol [START_REF] Ip | In vitro and in vivo studies of methylseleninic acid: evidence that a monomethylated selenium metabolite is critical for cancer chemoprevention[END_REF].

Recently, it has been reported that the organic Se compounds selenomethionine (SLM) and MSC can also undergo transamination reactions that generate α-keto acid Se metabolites [START_REF] Lee | Alphaketo acid metabolites of naturally occurring organoselenium compounds as inhibitors of histone deacetylase in human prostate cancer cells[END_REF][START_REF] Nian | Alpha-keto acid metabolites of organoselenium compounds inhibit histone deacetylase activity in human colon cancer cells[END_REF]. These metabolites have a similar structure to the known HDAC inhibitor butyrate, and were shown to inhibit histone deacetylase (HDAC) activity in human prostate [START_REF] Lee | Alphaketo acid metabolites of naturally occurring organoselenium compounds as inhibitors of histone deacetylase in human prostate cancer cells[END_REF] and colon [START_REF] Nian | Alpha-keto acid metabolites of organoselenium compounds inhibit histone deacetylase activity in human colon cancer cells[END_REF] cancer cell lines. HDACs regulate a large number of cellular processes through the deacetylation of both histone and non-histone proteins. One such process is angiogenesis, in which the expression of HIF-1 a key pro-angiogenic transcription factor, is partly regulated by HDACs [START_REF] Ellis | Targeting tumor angiogenesis with histone deacetylase inhibitors[END_REF], and the activity of the protein itself is dependent on deacetylation by HDAC4 and 6, as acetylation results in ubiquitination and degradation [START_REF] Qian | Class II histone deacetylases are associated with VHL-independent regulation of hypoxia-inducible factor 1 alpha[END_REF].

As MSA is not an amino-acid containing selenium species (unlike SLM and MSC) it is not a substrate for transaminases and cannot readily be converted to an a-keto acid, instead generating the volatile species methylselenol and more stable methylation products dimethylselenide and dimethyldiselenide [START_REF] Infante | Investigation of the selenium species distribution in a human B-cell lymphoma line by HPLC-and GC-ICP-MS in combination with HPLC-ESIMS/MS and GC-TOFMS after incubation with methylseleninic acid[END_REF]. This study reports for the first time that MSA inhibits HDAC activity in DLBCL cell lines, most likely attributable to the modification of cystine residues by methylselenol, and that this HDAC inhibition is associated with a decrease in cellular HIF-1 and VEGF secretion under hypoxic conditions.

METHODS

Cell lines, cell culture and treatment of cell lines

Four DLBCL cell lines were used. DoHH2, RL and SUD4 cells were obtained from Cancer Research UK cell services and the DHL4 cell line was obtained from the Dana Farber Cancer Institute (kind gift from Dr Margaret Shipp). Cells were maintained in RPMI-1640 culture medium (Sigma-Aldrich, Poole, UK) supplemented with 10% foetal calf serum, 100 units/ml penicillin and 100µg/ml streptomycin at 37°C in a humidified atmosphere with 5% CO 2 .

Cells were also cultured under hypoxic conditions (1% O 2 , 5% CO 2 , 94% N 2 ) at 37°C in a humidified atmosphere. Stock solutions of MSA and MSC 10mmol/L (PharmaSe ® Inc., Texas, USA) were prepared in deionised water and stored at -80°C prior to dilution in medium for use.

Western blotting

For whole cell protein extraction, cells were lysed using CelLytic™ MT cell lysis reagent (Sigma-Aldrich) and protein content determined using the BCA™ assay (Pierce, Illinois, USA). The method for nuclear protein extraction is given in 'Supplementary Data'. Twenty µg of lysate was resolved on pre-cast gels (Invitrogen, CA, USA) and then transferred to a polyvinylidene fluoride membrane using the iBlot™ gel transfer device (Invitrogen™). The following primary antibodies were used: acetylated histone H3 (Millipore, MA, USA), acetylated α-tubulin (Sigma-Aldrich), total histone H3, α-tubulin, GAPDH (Cell Signaling, MA, USA) p21 (Santa Cruz, CA, UK) and HIF1α (Abcam, Cambridge, UK) followed by HRP-conjugated secondary antibodies (Dako Ltd, Cambridge, UK). Bands were visualised using an enhanced chemiluminescence method (ECL reagent, GE Healthcare, Little Chalfont, UK).

Measurement of histone deacetylase activity

HDAC activity was measured using 96-well HDAC fluorimetric/discovery assay and HDAC fluorimetric cellular activity kits (Biomol, Plymouth Meeting, PA, USA) according to the manufacturers' instructions. The assay measures fluorescence emitted by the Fluor de Lys TM substrate when its acetylated lysine side chain is deacetylated. For the fluorimetric assay/discovery assay, HeLa nuclear extract (provided in the assay kit) was used as the source of HDAC activity and trichostatin A (TSA) was used as a positive control. For the HDAC fluorimetric cellular activity assay, 1x10 5 cells were plated in phenol-red free medium in a 96-well plate followed by the addition of MSA or TSA and the cell-permeable Fluor de Lys TM substrate. Control wells consisted of medium, without cells, to which substrate, developer and/or MSA were subsequently added. Fluorescence (excitation 350-380 nm, emission 440-460 nm) was measured on a POLARstar OPTIMA plate reader (BMG Labtech, Offenburgh, Germany). The values from the control wells were deducted from the test wells and the results expressed relative to the no inhibitor control. The addition of MSA to control wells did not significantly alter the fluorescence value.

Measurement of vascular endothelial growth factor (VEGF)

VEGF levels in cellular supernatants were measured using the MSD ® electrochemiluminescent cytokine assay (Meso Scale Discovery, Maryland, USA) according to the manufacturer's instructions. 25µl of the supernatant was added, in duplicate, to the wells of a custom-made 96-well microtitre plate, which was pre-coated with VEGF antibodies in addition to having an integrated electrode. The plate was incubated at room temperature for 1 hour with vigorous shaking. Following this, 25 µl of the detection antibody solution (1µg/ml) was added to each well and the plate again incubated at room temperature for 1 hour with vigorous shaking. Following 3 wash steps, the plate was analysed on a SECTOR™ Imager (Meso Scale Discovery) and light emitted at 620nm was measured. A standard curve was prepared from the cytokine calibrator solution provided with the assay kit.

Measurements were corrected for cell number and expressed relative to control (supernatant from cells cultured under normoxic conditions).

Determination of intracellular Se species.

Following the exposure of cell lines to MSA, cells were lysed as for western blotting. Cell lysates were then analysed by reverse-phase high performance liquid chromatography (Agilent 1200, Agilent technologies, Palo Alto, CA, USA) coupled to an Agilent 7500i inductively coupled plasma mass spectrometer (ICP-MS) for element-specific detection. For chromatographic and ICP-MS conditions, see 'Supplementary Data Table S1'.

RESULTS

The effect of MSA on protein acetylation and HDAC proteins in short term culture Time-and concentration-dependent experiments revealed similar effects in all four cell lines (data for SUD4 cells shown in Figure 1a). MSA concentrations as low as 1µM increased the acetylation of histone H3 and α-tubulin as early as 2 hours, an effect sustained until 24 hours.

The induction of acetylated histone H3 continued to increase out to 24 hours, while acetylated α-tubulin was typically maximal at 2 hours, showing a slight decrease at 24 hours.

In addition, MSA induced the expression of the cyclin-dependent kinase inhibitor p21, a known HDAC regulated gene, at 24 hours but by 48 hours the effect was less marked (Figure 1b). MSA did not alter the expression of class I (HDAC1), class IIa (HDAC4) or class IIb (HDAC6) HDAC proteins (Figure 1c).

The effect of MSA on HDAC activity

When MSA was incubated with HeLa isolated nuclear extract for 30 minutes and 2 hours, there was no inhibition of Fluor de Lys TM substrate deacetylation, suggesting that MSA, in this cell-free assay system, was not able to inhibit HDAC activity (data not shown). However, when HDAC activity was investigated in a cell-based assay, MSA did result in inhibition of HDAC activity in a concentration-dependent manner in all four cell lines, with 30μM MSA inhibiting HDAC activity by between 40-50% (Figure 1d). This suggests that cellular metabolism of MSA is required to generate an active metabolite.

To further investigate whether a cellular metabolite of MSA was responsible for this effect, the HDAC inhibitory activity of medium from DHL4 cells incubated with MSA for 2 hours was tested in the cell-free HDAC assay. Medium from DHL4 cells incubated with 10µM MSA was not able to inhibit Fluor de Lys TM substrate deacetylation, but medium from cells incubated with 30µM MSA had a significant HDAC inhibitory effect (20.6 ± 4.7 %; Figure 1e) relative to control. Medium incubated with 30µM MSA for 2 hours in the absence of cells had no effect on HDAC activity. These data support the hypothesis that a cellular metabolite of MSA is responsible for the HDAC inhibitory effect seen in cell based assays.

Determination of the intracellular Se metabolites formed after exposure of cells to MSA

RL and DHL4 cell lines were exposed to 20µM MSA for 1 and 2 hours. After incubation with MSA, three main Se-containing peaks were detected, the most abundant, identified as dimethylselenide, with a retention time of around 35 minutes (Figure 2b, peak C), a peak eluting at 60 minutes (peak D; subsequently identified as S-methylselenoglutathione [START_REF] Goenaga-Infante | HPLC with APEX-Q nebulisation ICP-MS and ESI MS/MS to compare selenium uptake and speciation in non-malignant cells and B cell lymphoma lines[END_REF]) and MSC (peak A, 5 minutes). A very small peak eluting at 11 minutes (peak B) was identified as SLM. In the RL cell line the same peaks were detected with the exception of MSC (data not shown) and the peak with the highest intensity was again dimethylselenide.

Effect of MSA on HIF-1α expression and VEGF secretion

Inhibition of HDAC activity has been shown to inhibit angiogenesis through effects on HIF-1α [START_REF] Ellis | Targeting tumor angiogenesis with histone deacetylase inhibitors[END_REF]. Therefore, the effect of MSA on HIF-1α expression and VEGF production was investigated after culture of RL and DHL4 cells under hypoxic conditions. Western blotting for HIF-1α in nuclear extracts showed that hypoxia induced the expression of HIF-1α, but this induction was suppressed by MSA (Figure 2c). Hypoxia also increased the production of VEGF by RL and DHL4 cell lines but this was inhibited by MSA in a concentrationdependent manner (Figure 2d)

The effect of MSC on protein acetylation MSC will be the Se compound used in the forthcoming clinical trial. Figure 2e shows that in RL and DHL4 cells, MSC increased the acetylation of histone H3 and α-tubulin and the expression of p21 in a concentration-dependent manner, suggesting that MSC, or a metabolite of MSC, also inhibits HDAC activity.

DISCUSSION

These experiments demonstrate for the first time that the organic selenium species MSA can inhibit HDAC activity in DLBCL cell lines. The deacetylation of histone proteins is mainly mediated by class I HDACs, while α-tubulin deacetylation is mediated by HDAC6, a class II HDAC. The concentration-dependent increase in the acetylation of both histones andtubulin strongly suggests that MSA inhibits both class I and II HDACs, although possibly with a different time-course as the acetylation change was more apparent at an early timepoint with a class II HDAC substrate. The HDAC inhibitory action of MSA was confirmed by an HDAC activity assay. However, in the cell-free assay, MSA was not able to inhibit the HDAC activity of HeLa nuclear extract. HDAC inhibition was only apparent when MSA was exposed to intact, viable cells and activity measured using a cell-based assay. This is likely to be due to the intracellular activation of MSA to methylselenol, which is the metabolite thought to be responsible for its anti-tumour effect [START_REF] Ip | In vitro and in vivo studies of methylseleninic acid: evidence that a monomethylated selenium metabolite is critical for cancer chemoprevention[END_REF]. Although medium from cells exposed to MSA was able to inhibit the HDAC activity of HeLa nuclear extract, the effect was small (30µM MSA inhibited HDAC activity by 21%). This is probably due to the volatile nature of MSA metabolites which are not retained in the cell medium [START_REF] Infante | Investigation of the selenium species distribution in a human B-cell lymphoma line by HPLC-and GC-ICP-MS in combination with HPLC-ESIMS/MS and GC-TOFMS after incubation with methylseleninic acid[END_REF]. Medium incubated with MSA alone, in the absence of cells, had no effect on HDAC activity in a subsequent cell based assay, confirming the Se species responsible for the effects observed is a cellular metabolite and not a breakdown product.

It is currently unclear whether this HDAC inhibitory activity is due to reversible binding of a Se species at the catalytic site on the enzyme, or an irreversible effect due to the redox modification of cysteine residues in HDAC proteins. The main species formed from MSA, (methylselenol, dimethylselenide and dimethyldiselenide) do not contain the classical features of an HDAC inhibitor, requiring a side chain to reach into the active site pocket and a Zn binding group, typically a carboxylic or hydroxamic acid [START_REF] Marks | Histone deacetylase inhibitors: A chemical genetics approach to understanding cellular functions[END_REF]. Moreover, MSA has been shown to inhibit other enzymes, notably PKC isoforms, through the redox modification of key cysteines [START_REF] Gundimeda | Locally generated methylseleninic acid induces specific inactivation of protein kinase C isoenzymes: relevance to selenium-induced apoptosis in prostate cancer cells[END_REF]. Although similar effects have not been reported with Se compounds and HDACs, reactive carbonyl species have recently been shown to inactivate HDAC enzymes through the modification of 2 conserved cysteine residues (Cys 261 and Cys 273 ), possibly representing a redox signalling response that would result in the transcription of a subset of genes through HDAC inhibition [START_REF] Doyle | Redox signaling, alkylation (carbonylation) of conserved cysteines inactivates class I histone deacetylases 1, 2, and 3 and antagonizes their transcriptional repressor function[END_REF]. There is therefore the potential for Se compounds to directly alter HDAC structure and thereby catalytic activity.

The intracellular Se metabolites formed after exposure of cells to MSA revealed that the major metabolite was dimethylselenide, the methylation product of methylselenol . As expected, methylselenol itself was not detected, due to its high volatility. However, the presence of dimethylselenide confirmed that methylselenol was the major metabolite formed and hence was likely to be responsible for HDAC inhibition. α-keto acid Se metabolites, reported by others to show HDAC inhibitory activity [START_REF] Lee | Alphaketo acid metabolites of naturally occurring organoselenium compounds as inhibitors of histone deacetylase in human prostate cancer cells[END_REF][START_REF] Nian | Alpha-keto acid metabolites of organoselenium compounds inhibit histone deacetylase activity in human colon cancer cells[END_REF], were not detected by the ICP-MS method used. The assay was not optimised for the detection of these α-keto acid species, although given this is a robust, highly sensitive method for trace element and elemental speciation analysis, with an extremely low detection limit and shown by others to detect a wide range of organic selenium compounds [START_REF] B'hymer | Selenium speciation analysis using inductively coupled plasmamass spectrometry[END_REF], it is likely that such metabolites would not have been detected if present.

Se compounds have also been reported to inhibit angiogenesis in xenograft models [START_REF] Bhattacharya | Tumor vascular maturation and improved drug delivery induced by methylselenocysteine leads to therapeutic synergy with anticancer drugs[END_REF].

Although we have not demonstrated a direct causal link between the activity of HDACs and the expression or activity of HIF-1 , other evidence suggests the two are associated. As outlined earlier, the regulation of HIF-1 expression involves HDACs [START_REF] Ellis | Targeting tumor angiogenesis with histone deacetylase inhibitors[END_REF], while HIF-1 activity is dependent on deacetylation by HDACs 4 and 6, which maintains the stability of the protein [START_REF] Qian | Class II histone deacetylases are associated with VHL-independent regulation of hypoxia-inducible factor 1 alpha[END_REF]. HIF-1 expression is also regulated by NF-k itself subject to acetylation/deacetylation modifications of the p65, and possibly p52, subunits which affects DNA binding [START_REF] Bendinelli | NF-kappaB activation, dependent on acetylation/deacetylation, contributes to HIF-1 activity and migration of bone metastatic breast carcinoma cells[END_REF]. We have previously reported the NF-k inhibitory activity of MSA [START_REF] Juliger | Chemosensitization of Bcell lymphomas by methylseleninic acid involves nuclear factor-kappaB inhibition and the rapid generation of other selenium species[END_REF].

Given that in the experiments now reported MSA, at similar concentrations to those required for HDAC inhibition, was able to inhibit HIF1α expression and VEGF secretion, HDAC inhibition may be a potential mechanism by which Se compounds inhibit angiogenesis in vivo.

HDAC inhibition by MSA requires relatively high concentrations, with 30µM for 2 hours resulting in 40-50% inhibition of activity across the four cell lines tested. HDACi currently in clinical trials inhibit activity in vitro at nanomolar concentrations [START_REF] Khan | Determination of the class and isoform selectivity of small-molecule histone deacetylase inhibitors[END_REF]. However, this Se concentration is potentially clinically achievable. In the phase I/II dose escalation study of SLM in patients with solid tumours, the highest dose level of 7200µM twice daily resulted in a mean plasma Se concentration after 8 days of 31µM/l [START_REF] Fakih | A Phase I and pharmacokinetic study of selenomethionine in combination with a fixed dose of irinotecan in solid tumors[END_REF].

MSA cannot be used clinically due to its high reactivity, thus the naturally occurring organic Se compounds, SLM and MSC, have been used as precursors of methylselenol.

Methylselenol is generated from MSC and SLM by the action of pyridoxal 5'-phosophate (PLP)-dependent β-lyases and γ-lyases respectively, predominantly in the liver and kidney [START_REF] Okuno | Cystathionine gamma-lyase contributes to selenomethionine detoxification and cytosolic glutathione peroxidase biosynthesis in mouse liver[END_REF]. To date, SLM has been the most widely used compound in clinical trials [START_REF] Vecchia | Nutrition and gastric cancer[END_REF][START_REF] Lippman | Effect of selenium and vitamin E on risk of prostate cancer and other cancers: the Selenium and Vitamin E Cancer Prevention Trial (SELECT)[END_REF], however, its conversion to methylselenol is not as efficient as that of MSC, as SLM can also be non-specifically incorporated into body proteins in place of methionine [START_REF] Rayman | Food-chain selenium and human health: spotlight on speciation[END_REF]. In addition, MSC has been shown to have a more potent anti-tumour effect [START_REF] Ip | Chemical speciation influences comparative activity of selenium-enriched garlic and yeast in mammary cancer prevention[END_REF][START_REF] Li | Superior in vivo inhibitory efficacy of methylseleninic acid against human prostate cancer over selenomethionine or selenite[END_REF], and therefore it is the compound that will be used in the forthcoming clinical trial. These experiments show that MSC can also inhibit HDAC activity in vitro, although only at relatively high concentrations. This is likely to be because tumour cells have only low β-lyase activity, thus generating only limited amounts of methylselenol. It is anticipated that when used in vivo much lower concentrations of MSC would be required to inhibit HDAC activity.

In summary, this work demonstrates that methylselenol can inhibit HDAC activity, an effect likely to induce other biological changes and potentially to contribute to the anti-tumour effects of Se in vivo.

ACKNOWLEGEMENTS

We thank the Medical Research Council and the Barry Reed Research Fund for supporting this work.

Figure 1

 1 Figure 1 (a) Protein acetylation in SUD4 cells exposed to MSA for 2 and 24 hours (b) p21 expression in DHL4 cells exposed to MSA (c) HDAC expression in RL and DHL4 cells exposed to MSA for 24 hours (d) HDAC activity in 4 DLBCL cells after exposure to MSA or TSA for 2 hours (e) Inhibition of HeLa nuclear HDAC activity by medium from DHL4 cells exposed to MSA for 2 hours. *p<0.05

Figure 2

 2 Figure 2 (a) Representative chromatogram of control DHL4 cells (b) Representative chromatogram demonstrating intracellular Se species generated after exposure of DHL4 cell to 20µM MSA for 2 hours Peak A is MSC, peak B is SLM, peak C is dimethylselenide and peak D is S-methylselenoglutathione (c) HIF1α expression in DHL4 ann RL cells exposed to hypoxia and MSA for 24 hours (d) VEGF levels in the supernatant of DHL4 and RL cells exposed to hypoxia and MSA for 24 hours (e) protein acetylation and p21 expression in RL and DHL4 cells exposed to MSC.