Àlex Bayés

Louie N Van De Lagemaat

Mark O Collins

Mike D R Croning

Ian R Whittle

Jyoti S Choudhary

Seth G N Grant

Prof. S G N Grant

Characterisation of the proteome, diseases and evolution of the human postsynaptic density

The hPSD is a key structure for nervous system disease and behaviour.

Synapses are fundamental structures linking nerve cells and it is essential to characterise human synapse proteins to understand the extent and relevance of synapses to human disease and behavior, and to identify new diagnostic and therapeutic approaches. From neocortical biopsies of nine adults (Supplementary Table 1), the hPSD was isolated (Supplementary Fig. 1a) and proteomic profiling using LC-MS/MS provided comprehensive protein identification: of a total of 1461 proteins (total hPSD), 748 were detected in all three replicates (consensus hPSD; Supplementary Table 2; used for all subsequent analysis, except where explicitly stated otherwise). These data provide a novel resource and are freely available in the G2Cdb database (www.genes2cognition.org/HUMAN-PSD).

This proteomic data allows the first systematic analysis of the disease impact of mutations in hPSD proteins. Annotation of monogenic diseases from the Online Mendelian Inheritance in Man (OMIM) database 1 was chosen for this purpose because it is primarily derived from linkage studies, which are statistically more robust and less prone to artifacts than targeted association studies. These annotations revealed 269 diseases that result from mutations in 199 hPSD genes (14% of all hPSD genes). 133 of these diseases (~50%) are primary nervous system disorders (Supplementary Tables 3 and4). Of these, ~80% were central and ~20% were peripheral nervous system disorders. Similar proportions were seen in the consensus hPSD: 155 diseases arose from mutations in 110 genes and 82 were nervous system diseases. Although the number of brain diseases involved in the hPSD is striking, overall these arise from only 14% of hPSD genes, therefore we expect many more mutations and diseases to be discovered by future human disease genome projects.

To classify these diseases the International Classification of Disease (ICD-10) was used, and from 22 ICD-10 chapters, four represented hPSD diseases: neurology (chapter VI), psychiatry (V), developmental (XVII) and metabolic (IV) diseases (Fig. 1a). These included common neurodegenerative diseases (Alzheimer's, Parkinson's, Huntington's), adult and childhood cognitive disorders such as mental retardation, motor disorders such as ataxia or dystonia, epilepsies and many rare diseases.

Each disease class is defined by similar clinical phenotypes and these are systematically categorised in the Human Phenotype Ontology (HPO) 2 . Because HPO phenotypes are linked to gene mutations (via OMIM) we could use gene set enrichment analysis (see Supplementary Methods) to identify the phenotypes most relevant for the hPSD compared to non-PSD brain expressed proteins. As shown in Figure 1b and Supplementary Table 5, we found the hPSD was significantly enriched in 21 neural phenotypes primarily involving cognition and motor functions. Some were represented by large sets of genes, such as mental retardation (40 genes) and spasticity (20 genes), indicating subsets of the hPSD proteome play key roles in these functions. The enrichment observed in general disease phenotypes (Fig. 1b) such as neurological abnormality indicates the hPSD is a neuronal structure with a disproportionately high density of neural disease susceptibility.

A powerful way to extend this analysis of hPSD function is to examine the role of hPSD gene orthologs in mice, an important animal model of human disease.

Moreover, there is a wider range of behavioural, physiological and anatomical phenotypes that can be investigated for enrichment in the hPSD. From a database analogous to HPO (Mammalian Phenotype Ontology, MPO) 3 , the hPSD was enriched in 77 neural phenotypes (compared to other sets of brain proteins, Supplementary Table 6), which included cognitive and motor phenotypes, consistent with the enrichments found in the HPO analysis (Fig. 1b and Supplementary Table 7). The MPO also bridges to cellular level data, where we found neuronal morphology (83 genes), neurodegeneration (23 genes), synaptic transmission (38 genes) and plasticity (41 genes) were hPSD enriched phenotypes. The sets of genes underpinning these enriched phenotypes include components of known signalling mechanisms; for example in the learning/memory/conditioning gene set (57 genes), the N-methyl-D-aspartate (NMDA) receptor and PSD-95 interacting proteins were present, along with other proteins that are potentially involved with the same signalling process.

We next examined the conservation of hPSD protein coding sequences between humans, other primates (chimpanzee, Pan troglodytes and macaque, Macaca mulatta) and rodents (mouse, Mus musculus and rat, Rattus norvegicus) (Supplementary Table 8) using the dN/dS ratio; dN measures the rate of amino acid substitution and dS reflects the background rate of neutral DNA change 4 . When comparing human and mouse, which evolved from lineages that diverged from a last common ancestor (LCA) ~90my ago, the median dN/dS for hPSD genes was very significantly (p < 10 -148 ) less than the whole genome (protein coding genes only) (Supplementary Fig. 2b). A similar highly significant conservation in hPSD proteins was found in other pairs of compared species: human/chimp (LCA~6 my), human/macaque (LCA~30 my) and mouse/rat (LCA ~20 my) 5 showing the purifying selection (conservation) was not unique to the human lineage (Supplementary Fig. 2c-e).

Since it has been reported that general brain proteins evolved slower than proteins in other tissues [6][7][8] , we asked if hPSD conservation was a reflection of broader conservation observed in brain proteins. Unexpectedly, the hPSD was significantly more conserved than all three non-PSD brain expressed gene sets [9][10][11] (Fig. 2a, Supplementary Fig. 3 andTable 9). The extent of the hPSD conservation was further revealed by comparison with 7 other neuronal subcellular structures (including mitochondria, endoplasmic reticulum and nucleus), which were all less conserved (Fig. 2b, Supplementary Table 10). Since postsynaptic complexes are rich in protein interactions 12 , and a slower rate of evolution has been reported for proteins with many interactions 13 , we constructed an hPSD interaction network (Supplementary Fig. 4). Highly interconnected (hub) proteins and proteins in the multiprotein complexes bound to PSD-95 14 showed significantly lower dN/dS than other hPSD proteins (Fig. 2c, Supplementary Table 11). These data indicate that hPSD structural organisation plays a role in the conservation of hPSD protein sequence.

This study reveals the human PSD has a high degree of molecular complexity with over 1000 proteins and deepens our understanding of synaptic disease biology by showing that combinations of proteins regulate the phenotypes of over 130 brain diseases. It is possible, and indeed likely, that the proteins identified represent an overall synaptic parts list, with subsets of synapses containing subsets of these proteins. Further sensitive and high-resolution proteomic work will be necessary to expand our knowledge of regional and microscopic synapse heterogeneity. Our data provides a valuable resource and template for investigating human synapse function and suggests new diagnostic and therapeutic approaches. Neuron. human cortical neuron transcriptome 9 ; brain, whole mouse brain proteome; astrocyte, human astrocyte transcriptome 10 ; and human genome. Hs, human; Mm, mouse.

HPO and MPO phenotype IDs in brackets after each named phenotype. b-e, Median dN/dS of genome, total hPSD and consensus hPSD for pairs of species (indicated above graph). p values between genome and tPSD or cPSD indicated in each comparison. Supplementary Figure 3. hPSD constraint relative to other brain proteins a-d. hPSD is constrained relative to other brain proteins. Cumulative frequency plots of dN/dS distributions for human PSD and non-hPSD brain subsets and whole genome. The hPSD was more highly constrained than non-hPSD brain proteins measured using 4 datasets: a, human cortex proteome (p < 10 -6 ); b, human cortical neuron transcriptome (p < 10 -9 ); c, mouse brain proteome (p < 10 -29 ) and d, mouse brain proteome of transmembrane proteins (p < 10 -11 ). e,f. hPSD is constrained relative to other subcellular structures.

Median mouse-human dN/dS shown for hPSD and other subcellular structures profiled by (e) transcriptome from human neocortical neurons and (f) mass spectrometry based proteomics in liver. All structures showed a median dN/dS above the hPSD: (e) nucleus p< 10 -20 ; plasma membrane p = < 10 -14 ; golgi p < 10 -6 ; cytosol p <10 -10 ; ER p < 10 -17 ; Early Endosome p = 0.0052; and Mitochondria p < 10 - 36 ). (f) nucleus p= 0.24; recycling endosome p= 0.028; plasma membrane p= 0.0057; golgi p < 10 -4 ; cytosol p <10 -11 ; ER Golgi Vesicle p < 10 -4 ; Early Endosome p = 0.0009; ER p < 10 -7 and Mitochondria p < 10 -29 ). Significance: p< 0.05 (*), p< 0.01 (**), p< 0.001 (***).

  The postsynaptic density from human neocortex (hPSD) was isolated and 1461 proteins identified. hPSD mutations cause 133 neurological and psychiatric diseases and show enrichment in cognitive, affective and motor phenotypes underpinned by sets of genes. Strong protein sequence conservation within mammalian lineages, particularly in hub proteins, indicates conserved function and organisation in primate and rodent models.

Fig. 1 .

 1 Fig. 1. hPSD Diseases and Enriched Phenotypes a. Distribution of hPSD nervous system diseases in 4 ICD-10 chapters (left chart) with Chapter VI expanded (right chart) to show further subclassifications. b. Representative human and mouse phenotypes enriched in the hPSD. Categories (bold) of human and mouse phenotypes with number of genes (hPSD genes). Heatmap compares enrichment of these phenotypes in 4 gene sets relative to the enrichment of the hPSD (shown in red). All other gene sets showed lower enrichment (darker colors with black representing no enrichment).

Fig. 2 .

 2 Fig. 2. hPSD sequence conservation a. Cumulative frequency plot of dN/dS values for hPSD and non-PSD genes expressed in cortical neurons 9 , and human genome. hPSD neuronal genes are more constrained than non-PSD neuronal genes (p < 10 -11 ). b. Median mouse-human dN/dS shown for hPSD and subcellular structures expressed in cortical neurons 9 . Significance: p < 0.05 (*), p< 0.001 (***). c, Boxplots of dN/dS distribution in hPSD hub proteins (>15 interactions, n = 23), non-hubs (≤15 interactions, n = 725) and the tandem affinity purification of PSD-95 complex 14 .

Acknowledgements

We thank C.P. Ponting, P.J. Brophy. R.A.W. Frank, N.H. Komiyama, M.V. Kopanitsa, T.

J. Ryan and members of the Genes to Cognition Programme for critical comments on the manuscript and discussions. AB is supported by EMBO and the European Commission. LVL, MOC, MDRC, JSC and SGNG are supported by the Wellcome Trust.

IRW is supported by Scottish Higher Education Funding Council and by grants from MRC, NIH and Melville Trust. This study was approved by Lothian Region Ethics Committee /2004/4/16, and informed consent was obtained from all donors. We thank the tissue donors, without whom this study would have been impossible.

Author Contributions

IRW provided brain samples; AB, MOC, JSC performed proteomic analysis. AB performed OMIM, ICD-10 and evolutionary analyses, LVL performed network and enrichment analyses. LVL, MDRC, AB and SGNG wrote the manuscript.

Author Information

No competing interests

Supplementary Table Legends

Supplementary Table 1: Information about Biological Samples.

Patient code, age, sex and reason for surgery are shown. Also included is the precise cortical location of each biopsy and in what hPSD fractionation it was used. Supplementary Table 2: Protein Identifications and Proteomic Data.

For each identified protein several identification (ID) numbers from biological databases are given: G2Cdb ID, International Protein Index (IPI) ID, Approved Gene Name from HUGO Genome Nomenclature Committee (HGNC), HCNC ID, Ensembl ID, Entrez Gene ID, OMIM (Gene) ID and UniProt ID. The number of total and uniquely identified peptides for each replicate is also provided. Proteins found with two or more peptides in all replicates are classified as members of the consensus hPSD. Supplementary Table 3: Supplementary Table 4: OMIM Diseases Identified Among Total hPSD Genes.

For each protein causing an OMIM disease the following information is given: G2Cdb ID, Approved (HGNC) Gene Symbol. Also shown is whether it is present in the consensus PSD (found in all three proteomic replicates), if it causes a Peripheral Nervous System Disease or a Central Nervous System Disease or a Peripheral and Central Nervous System Disease or a Non Nervous system Disease. In addition, OMIM Disease ID, OMIM Disease Description, Disease age of onset, ICD-10 Category for CNS Disease. and ICD-10 Sub-Category for CNS Diseases. Neoplasms and other diseases caused by somatic mutations have been excluded from the list. Supplementary Table 5: Human Neural Phenotype gene set enrichment analysis.

All phenotypes of neural origin from Human Phenotype Ontology (HPO) are included. For each HPO phenotype the HPO ID and description are given. The number of genes from the genome or the hPSD causing each phenotype is shown under the Genome and hPSD columns. Phenotype overrepresentation in the hPSD is with respect to the genome, and approved (HGNC) symbols for relevant hPSD genes are given. For each background dataset (mouse brain proteome, human cortical neuron transcriptome, and human astrocyte), number of phenotype genes present in the non-hPSD portion of the background dataset are listed in the 'dataset' column. Overrepresentation of phenotype genes in the background sets is computed relative to the genome in the column 'Overrep (dataset/ genome)'. Pvalue of overrepresentation of the phenotype in the hPSD relative to the background set is denoted 'P-value (hPSD > Other)' and is computed based on the observed overrepresentation in the hPSD and compared to the overrepresentation that would be observed if the hPSD were only as overrepresented in the particular phenotype as the background set. P-values were calculated using binomial statistics and corrected using the Benjamini-Hochberg false discovery rate procedure at α = 0.05. Supplementary Table 6: Brain datasets used in phenotype enrichment analysis.

For each of the three sets of brain molecules (mouse brain proteome, human cortical neuron transcriptome and human astrocyte transcriptome) all Ensembl identifiers are given.

Supplementary Table 7: Mammalian Neural Phenotype gene set enrichment

Analysis.

All phenotypes of neural origin from Mammalian Phenotype Ontology (MPO) are included, as well as two manually defined phenotypes representing synaptic transmission and synaptic plasticity. For each MPO phenotype the MPO ID and description are given. The number of genes from the genome or the hPSD causing each phenotype is shown under the Genome and hPSD columns. Phenotype overrepresentation in the hPSD is with respect to the genome, and approved (HGNC) symbols for relevant hPSD genes are given. For each background dataset (mouse brain proteome, human cortical neuron transcriptome, and human astrocyte transcriptome), number of phenotype genes present in the non-hPSD portion of the background dataset are listed in the 'dataset' column. Overrepresentation of phenotype genes in the background sets is computed relative to the genome in the column 'Overrep (dataset/ genome)'. P-value of overrepresentation of the phenotype in the hPSD relative to the background set is denoted 'P-value (hPSD > Other)' and is computed based on the observed overrepresentation in the hPSD and compared to the overrepresentation that would be observed if the hPSD were only as overrepresented in the particular phenotype as the background set. P-values were calculated using binomial statistics and corrected using the Benjamini-Hochberg false discovery rate procedure at α = 0.05. Supplementary Table 8: Comparison of dN/dS between Genome and hPSD. Supplementary Table 9: Mouse and Human Brain Datasets dN/dS Analysis.

Genes from the human genome, mouse brain proteome, human cortical neuron transcriptome and human cortex proteome for which there was dN/dS information were divided into those included in the hPSD and those absent from it. The cumulative dN/dS distributions are computed for each dataset. Supplementary Table 10

Supplementary Figure Legends

Supplementary Figure 4. hPSD protein interaction network

Proteins in the network are coloured by their dN/dS ratio. dN/dS ranges from 0.0 (no non-synonymous amino acid changes) to 0.048 (median dN/dS in the PSD) to 0.117 (genomic median). HGNC symbols of hub proteins are shown.


Prof. Dr Johan P Mackenbach
email: j.mackenbach@erasmusmc.nl

Willem J Meerding

Anton E Kunst

ECONOMIC COSTS OF HEALTH INEQUALITIES IN THE EUROPEAN UNION

Background

In order to support the case for intersectoral policies to tackle health inequalities, we have explored the economic costs of socioeconomic inequalities in health in the European Union,

Methods

Using recent data on inequalities in self-assessed health and mortality covering most of the European Union, we calculated the health losses due to socioeconomic inequalities in health by applying a counterfactual scenario in which the health of those with lower secondary education or lower (roughly 50% of the population) would be improved to the average level of health of those with at least higher secondary education. We then calculated various economic effects of those health losses: health care costs, costs of social security schemes, losses to Gross Domestic Product through reduced labour productivity, and the monetary value of total losses in welfare.

Results

Inequalities-related losses to health amount to more than 700,000 deaths per year, and 33 million prevalent cases of ill-health, in the European Union as a whole. These losses account for 20% of the total costs of health care, and 15% of the total costs of social security benefits. Inequalities-related losses to health reduce labour productivity and take 1.4% off GDP each year. The monetary value of health inequalities-related welfare losses is estimated to be €980 billion per year, or 9.4% of GDP.

Conclusion

Our results suggest that the economic costs of socioeconomic inequalities in health in Europe are substantial. As this is a first attempt at quantifying the economic implications of health inequalities, the estimates are surrounded by considerable uncertainty, and further research is needed to reduce this uncertainty. If our results are confirmed in further studies, the economic implications of health inequalities warrant significant investments in policies and interventions to reduce them.

What this paper adds

What is already known on this subject?

• In all countries with available data, significant inequalities in health exist between socioeconomic groups.

• Tackling these health inequalities requires action in many policy areas, including policy areas outside the health care system.

• A fruitful dialogue with policy areas outside the health care system is likely to be facilitated if the economic costs of health inequalities are known.

• Existing analyses of the costs of ill-health focus on average health, and we have therefore explored the economic costs of socioeconomic inequalities in health.

What does this study add?

• Inequalities-related losses to health amount to more than 700,000 deaths per year, and 33 million prevalent cases of ill-health, in the European Union as a whole.

• Inequalities-related losses to health reduce labour productivity and take 1.4% off GDP each year.

• The monetary value of health inequalities-related welfare losses is estimated to be €980 billion per year, or 9.4% of GDP.

• Inequalities-related losses to health account for 20% of the total costs of health care, and 15% of the total costs of social security benefits.

• If our results are confirmed in further studies, the economic implications of health inequalities warrant significant investments in policies and interventions to reduce them.

Introduction

In recent years there has been growing attention to the potential economic benefits of improvements in population health. This is far from new: historically, one of the origins of the public health movement lies in the awareness that the prosperity of nations is partly dependent on the health of their populations [START_REF] Rosen | A history of Public health[END_REF]. But this awareness has received a new stimulus from the publication in 2001 of the report of the WHO Commission on Macroeconomics and Health, which demonstrated that health improvement can be seen as a key strategy for income growth and poverty reduction in low-and middle-income countries [START_REF]Commission on Macroeconomics and Health, Working Group 5: Improving Health Outcomes of the Poor[END_REF]. This report was followed in 2005 by an overview of evidence concerning the impact of health on the economy in high-income countries, particularly the European Union [START_REF] Suhrcke | The contribution of health to the economy in the European Union[END_REF]. Both this and a later follow-up report by the same authors on the costs of ill health in the European region [START_REF] Suhrcke | Economic costs of ill health in the European region[END_REF] concluded that there are good economic arguments for investing in health -if Europe is to become more competitive globally, greater investments in human capital are necessary. It took the view that health should not only be seen as a 'capital good', which improves productivity and economic output, but also as a 'consumption good', which should be valued in itself as one important component of total 'welfare'.

Most analyses of the relationship between health and the economy focus on average health, but health is very unevenly distributed across society. In all countries with available data, significant inequalities in health exist between socioeconomic groups, in the sense that people with lower levels of education, occupation and/or income tend to have systematically higher morbidity and mortality rates [START_REF] Mackenbach | Health inequalities: Europe in profile[END_REF]. These health inequalities are one of the main challenges for public health, and there is a great potential for improving average population health by eliminating or reducing the health disadvantage of lower socioeconomic groups. This requires an active engagement of many policy sectors, not only of the public health and health care systems, but also of many other policy areas, including education, social security, working life, city planning, et cetera [START_REF] Whitehead | A typology of actions to tackle social inequalities in health[END_REF].

A fruitful dialogue between the public health and health care sector on the one hand, and other policy areas on the other hand, is likely to be facilitated if the economic costs of health inequalities are known, and if the economic benefits of reducing health inequalities can be made clear. What would be the economic impact of improving the health of groups with a lower socioeconomic status to that of more advantaged sections of the population? Answering this question is currently impossible, because it requires insight in the costs and effects of policies which will reduce health inequalities, and this is not yet available [START_REF] Macintyre | Using evidence to inform health policy: Case study[END_REF]. A first step in this direction, however, would be to quantify the current economic losses which health inequalities generate. In the study reported in this paper we have tried to estimate the total inequalities-related losses to population health in the European Union, and from there on made estimates of the impact of these health losses on labour productivity and Gross Domestic Product and on total welfare, as well as on health care costs and costs of social security benefits.

Data and methods

The levelling up approach

Socioeconomic inequalities in health usually present themselves as a gradient, characterized by a systematic increase of the rates of morbidity and mortality as one moves down the social ladder [START_REF] Adler | Socioeconomic status and health. The challenge of the gradient[END_REF]. Part of this association is likely to be causal, in the sense that lower positions in the social hierarchy expose people to a variety of health risks (unfavourable living and working conditions, psychosocial factors, health behaviours, …) [START_REF] Marmot | Social determinants of health[END_REF]. This suggests that socioeconomic inequalities in health can be reduced by improving the life situations of people with lower levels of education, occupation or income, e.g. by giving people with lower socioeconomic positions a higher education or income, or by changing their lifestyles and living conditions. Most analyses of opportunities for reducing health inequalities conclude that policies and interventions should aim for an 'upward levelling' of health inequalities, by which the higher rates of morbidity and mortality of the lower socioeconomic groups are reduced to the level of more advantaged groups in society [START_REF] Whitehead | Levelling up: a discussion paper on concepts and principles for tackling social inequalities in health[END_REF].

We have used this 'upward levelling' perspective to determine how much ill-health in the population is attributable to the fact that not everybody has a high level of education, a high occupational class, or a high income level. Our approach is closely related to the measure of 'Population-Attributable Risk' (PAR) [START_REF] Ezzati | Selected major risk factors and global and regional burden of disease[END_REF], which is often used in epidemiology to quantify the burden of ill health or premature mortality associated with specific risk factors such as smoking and overweight. In a similar way, the PAR approach can be used to estimate how much ill-health in the population is attributable to the fact that not everybody has a high level of education, a higher occupational class, or a high income level [START_REF] Mackenbach | Measuring the magnitude of socio-economic inequalities in health: an overview of available measures illustrated with two examples from Europe[END_REF]. In order to calculate the impact of health inequalities on over-all population health we will compare the current situation in European countries to the hypothetical situation that everyone would have the health status corresponding to a high socioeconomic position. We will not attempt to specify how such a situation could realistically be achieved, but just use this counterfactual situation to estimate the losses to population health which are currently generated by health inequalities. We will use a simple dichotomy between low and high socioeconomic status, in which 'high' socioeconomic positions are conservatively defined as roughly the upper 50% of the population. Socioeconomic status will be indicated by educational level. Our preference for this indicator is in part based on pragmatic reasons, because educational level is the only indicator of socioeconomic position available in different types of data sets for most European countries. A theoretical advantage is that educational level is established before full adulthood, and therefore is less sensitive to reverse causality. Recent studies have shown that educational inequalities in mortality and morbidity are indeed likely to reflect a causal effect of education on health outcomes [START_REF] Lleras-Muney | The Relationship Between Education and Adult Mortality in the United States[END_REF].

Our calculations were carried out in the three steps described below. All data apply to 2004, and are for the European Union as a whole (EU-25, before the recent enlargement to EU-27). Further details on sources of data, calculation procedures and methodological evaluations can be found in a full report [START_REF] Mackenbach | Economic implications of socioeconomic inequalities in health in the European Union[END_REF].

Estimation of the inequalities-related burden of ill-health and premature mortality

We first made estimates of the relative risk of mortality and self-reported ill-health of lower as compared to higher educational groups. These relative risk estimates were combined with information on absolute mortality and prevalence rates in the European Union in 2004, as available from the Eurostat website, to obtain mortality rates and prevalence rates according to educational level.

For mortality, we estimated the age-standardized mortality rate to be 36 percent higher in lower educated as compared to higher educated. This estimate was primarily derived from a study covering 10 western European populations representative of the EU-15 [START_REF] Huisman | Socio-economic inequalities in mortality among elderly people in 11 European populations[END_REF].

In this study, rate ratios were calculated per 10-year age group for men and women, for the age group 30-39 to 90+ years. We calculated the weighted average of the age-specific rate ratios, which was 1.31 for men and women together (who were given equal weight).

A correction had to be made for the exclusion of new EU member states in Eastern Europe. Taking into account that mortality inequalities are about two times as large in these countries (i.e. a rate ratio of about 1.61) [START_REF] Mackenbach | Socioeconomic Inequalities in Health in 22 European Countries[END_REF] and that these countries represent 17% of the population of EU-25, the rate ratio of 1.307 was adjusted to 1.36. The estimates were based on data that applied to the 1990s for Western European countries, and to about 2000 for Central and Eastern European countries. We assumed that the rate ratio of 1.36 had not substantially changed between these years and 2004.

For ill-health, the rate ratio of 1.45 was derived from a re-analysis of data from national health surveys carried out in the early 2000's. Micro data from these surveys were acquired for the "Eurothine" study [START_REF] Kunst | Tackling health inequalities in Europe: the Eurothine project[END_REF]. We selected data from 12 countries with identical survey questions on self-assessed health: Sweden, Norway, Denmark, England, Ireland, Belgium, France, Italy, Spain, Portugal, Hungary and the Czech Republic. For each country, we calculated the age-standardized prevalence of "less than good" selfassessed health for respondents with at least upper secondary education and for those with lower levels of education. For all countries together, the average prevalence rates were 43.7 and 28.9% for men with low and high educational levels, respectively. The corresponding values for women were 50.6 and 33.9%. These rates implied a prevalence rate ratio of 1.51 for men, and 1.49 for women, the average being 1.50. These estimates did not include children and a substantial part of the elderly population, among whom inequalities in health are relatively small. We calculated that their inclusion would reduce estimates of the magnitude of health inequalities by about 10%. We therefore adjusted the 1.50 estimate downwards to 1.45.

Based on the absolute mortality rates, we calculated the absolute number of deaths for low and high educated persons in the European Union in 2004, and the total years of life lost due to all deaths 2004. The latter measure was obtained by multiplying the former with 16.06 years, which was shown in our analyses to be the average number of years of life lost per death in the European Union.

Life table based measures express the burden of mortality and ill health in terms of the effect that current mortality and morbidity rates would have on the life of a hypothetical cohort of people exposed to these risks throughout their life. We calculated the life expectancy at birth according educational level, using period life tables for 2004. In addition, we calculated the "expectancy of life in poor health" per educational level, by multiplying the total life expectancy with the age-standardized prevalence of less-thangood health.

ECHP analysis of the economic effects of ill health in lower educational groups

In a second step, we determined the effects of ill-health in lower educational groups on earnings, assuming that salaries and wages reflect the value of a person's labour output, i.e. labour supply times labour productivity. We analyzed data of the European Community Household Panel (ECHP) conducted by Eurostat [18]. The ECHP is a survey based on a standardized questionnaire that involves annual interviewing of a representative panel of households and individuals of 16 years and older in each EU member state. We used data from the first to fifth wave, held in 1993 to 1997. Outcome measures (personal income, labour force participation etc) were taken from the fifth wave, while health variables were measured for waves 1, 3 and 5 respectively.

A key outcome parameter in our analysis was personal earnings, measured by personal income gained through work. We analyzed personal income in persons aged 16 to 64 years, excluding students, self employed workers, and unpaid family workers, and including those with zero income. Personal earnings were measured as gross monthly income, including salaries and wages, but not taking into account incomes from capital returns and social benefits. We supplemented the income analyses by studying the effect of health on labour market participation, number of hours worked, and hourly income.

These three variables were considered as key components that together help to understand the effects of health on personal income. Labour force participation and utilization of unemployment benefits and disability benefits was analyzed in men and women in the age group 16 to 64 years, excluding students. We measured the rate of labour force participation, considering as economically inactive those who worked less than 8 hours per week (generally these are housewives, long-term unemployed, workdisabled and early retired).

Health was measured by self-assessed general health, with 5 levels: very poor, poor, fair, good, and very good. We used loglinear regression models with control for 5-year age group, sex and their interaction. In addition, we controlled for country and for marital status, which both may confound estimates of the effect of health on economic outcomes.

We conducted many alternative analyses in order to test the robustness of the findings.

These included analyses with lagged effects of health as measured during previous waves on current income (which partly remove the effect of income on health, and more accurately measure the effect of health on income), analyses with other measures of health (i.e., presence of long-standing health problems with restrictions in daily activities, and having had to cut down daily activities due to health problems during the past 14 days), and analyses of spill-over effects of an individual's health on the income of their partner. While analyses with lagged effects of health showed that the cross-sectional associations between income and health are likely to overestimate the true effect of selfassessed health on personal income by 25-50%, the other two analyses show that our estimates do not represent the full effect of health on income (results not shown) [START_REF] Mackenbach | Economic implications of socioeconomic inequalities in health in the European Union[END_REF].

Because over-and underestimation appeared to be approximately equal in size, we present the results of the simplest (cross-sectional, unlagged) analysis.

Estimation of macro-economic effects of the burden of ill health and premature mortality in lower educational groups

In order to make a comprehensive estimate of the costs of the inequalities-related burden of ill health, we start from the notion that health is both a 'capital good' and a 'consumption good' [START_REF] Grossman | On the concept of health and the demand for health[END_REF]. As a 'capital good' health is an important component of 'human capital' (the value of human beings as means of production). Just like an adequate level of education, a good health status enables people to engage in formal and informal labour activities and to be productive. This value of health as a capital good can (partly) be captured by its effects on common economic measures such as labour participation, labour productivity and income.

The effects of health on personal income that were estimated made with the ECHP data (see above) were aggregated into an effect on Gross Domestic Product (GDP), taking into account that GDP consists of three components: (1) "compensation of employees" (gross earnings plus employers' social contributions); (2) "gross operating surplus and mixed income" (among which firm profits and earnings from self-employed persons); ( 3) "taxes less subsidies on production and imports". For the second component we assumed that 50% of firm profits can be ascribed to employees' productivity, and that the impact of a "levelling up" scenario on the gross earnings of self-employed persons will be 50% of the impact on employees' gross earnings.

The welfare loss of ill-health is not completely captured by the notion of health as a 'capital good', i.e. by its impact on income as defined in GDP. One arrives at a more complete estimate of this welfare loss when one considers that health is a 'consumption good' as well. As a 'consumption good', health directly contributes to an individual's 'utility' (economic language for 'happiness' or 'satisfaction'), because a good health status is enjoyable as such, and because a good health status enables individuals to enjoy work and leisure activities. The estimation of the value of health as a consumption good is, however, problematic as no market exists for health. In the literature different approaches for the valuation of health can be found, such as analyses of people's 'willingness to pay' in so-called contingent valuation studies [START_REF] Hirth | Willingness to pay for a quality-adjusted life year: in search of a standard[END_REF] and analyses of past allocation decisions of health care authorities [START_REF] Bootman | Principles of pharmacoeconomics[END_REF]. Unfortunately, although there is a consensus that health should be valued very highly, there is no consensus on a specific value of health.

Like others have done before us [START_REF] Suhrcke | The contribution of health to the economy in the European Union[END_REF][START_REF] Luce | The return on investment in health care: from 1980 to 2000[END_REF], we will loosely base our estimates on figures that were derived and proposed by the American economist Nordhaus [START_REF] Nordhaus | The health of nations: the contribution of improved health to living Standards[END_REF]. Although work is underway to generate similar figures for Europe, this was not yet ready when this paper was written (2010). On the basis of a review of willingness-to-pay studies Nordhaus settled on a value of $3.0 million (or appr. €2.3 million) per life saved, and a value of one current life-year of $100,000 (or appr. €77,000). The first figure can be used to indicate the monetary value of avoidance of death at adult age (about 40 years), while the second figure can be used to indicate the monetary value of an additional year of life lived now.

Nordhaus' estimate of €2.3 million per life saved is too high for our purposes, because it is based on estimates from labour market studies, which focussed on the economic importance of deaths among working-aged persons. The average loss of life years due to death at working age is considerably larger than the average loss of life years due to health inequalities in the general population. For Europe, we calculated a loss of about 16 years per death due to health inequalities, compared to about 40 years per death at working age. In order to account for this difference, we will assume that Nordhaus' estimate of €2.3 million should be reduced by 16/40 th , i.e. to €920,000 per death avoided.

In addition to these costs in terms of health as a 'capital good' and as a 'consumption good' we calculated two other, more tangible cost categories, namely health care costs and costs of social security benefits. These cannot be added to the previous cost categories, but should be seen as societal expenditures which are incurred in order to cope with the damage to health as a capital and/or consumption good. Health care costs were estimated from the multivariately adjusted relationship between self-assessed general health and GP visits, specialist visits and hospitalization rates as observed in the ECHP dataset, and from OECD data on health care costs, assuming that the extra use of physicians and hospitals related to the excess ill health in lower educated groups can be generalized to health care as a whole. Costs of social security benefits were estimated from the multivariately adjusted relationship between self-assessed general health and receipt of disability benefits and unemployment benefits as observed in the ECHP dataset, and from OECD data on the total costs of these benefit schemes.

Results

Table 1 presents our estimates of inequalities-related losses to health, in terms of mortality (deaths), morbidity (cases of fair/poor health), life expectancy (years of life lost), and expectancy of life with morbidity (number of years lived in fair/poor health).

The number of deaths that can be attributed to health inequalities is estimated to be 707 thousand (the difference between the 4,6 million deaths which currently occur each year in the EU-25 as a whole, and the 3,9 million which would occur in an 'upward leveling' counterfactual scenario). The number of life-years lost due to these deaths (now and in the future) is about 11.3 million. Similarly, the number of prevalent cases of ill-health that can be attributed to health inequalities is estimated to be more than 33 million. As the reference period is one year (i.e. 2004), this number is equal to the current number of person-years-lived-with-health-problems which can be attributed to health inequalities.

The lower part of the table presents estimates in terms of life table-derived measures. The estimated impact of health inequalities on average life expectancy at birth in the EU-25 for men and women together is 1.84 years, and that on life expectancy in fair/poor health is 5.14 years. This implies a reduction of the expectancy of life in good health by 6.98 years (the sum of 1.84 and 5.14 years).

In our analysis of the European Community Household Panel we observed large differences in the level of personal earnings according to the general health of people.

Persons with "very good" or "good" health had about 4 times higher earnings than those with "poor" and "very poor" health. The relative impact of health on personal income was larger for lower educated persons. In absolute terms, health had a greater impact on personal income among the higher educated, because of the higher overall levels of personal income of higher educated compared to lower educated (figure 1). In our analysis, the main cause of lower earnings among those with poor health was their lower labour force participation. People with "very poor" health were about 2 times less likely to participate in the labour force than those with "very good" health. To a lesser extent the number of hours worked among economically active persons and hourly wages contributed to differences in income between persons with good and poor health. The effects of health on labour force participation, number of hours worked and hourly wages were generally larger (in relative terms) among persons with lower educational level (table 2).

Using the results of the 'levelling up' counterfactual scenario, which would reduce the number of persons in "very poor" or "poor" health states with more than 33 million (table 1), we estimate the health inequalities-related losses to salaries and wages in the European Union to be 2.8% (table 3). This translates into €113 billion, or a 1% loss in GDP for the EU as a whole. The total GDP impact is likely to be larger because part of the added value of employees' labor productivity is also included in firm profits, and because our estimate does not include the economic impact of health among the selfemployed. The impact on total GDP of health losses through these two additional effects is estimated to be around 0.7%, or €28 billion for the EU as whole. The total costs of health inequalities in terms of health as a 'capital good' amount to €145 billion, or 1.35% of GDP.

The total costs of health inequalities in terms of health as a 'consumption good' are in the order of €1000 billion (€980 billion, or 9.4% of GDP, in table 3). In a 'levelling up' counterfactual scenario, the yearly number of deaths due to health inequalities is 707,000 (table 1). If a life saved would be valued at €920,000, the total value of these losses would amount to €650 billion. We estimated the number of life-years lost by these individuals to be 11.4 million (table 1). If these life-years would be valued at €77,000 each, and one would take a standard discount rate of 1.5% per annum over an average of 16 years to take into account that these life-years will not be gained immediately, the total value of this loss in life would amount to €778 billion. Thus, the two alternative approaches to value the costs of annual deaths both yield estimates of about €700 billion, or 6.7% of current GDP. Inequalities-related losses to self-assessed health were estimated to be about 23 million cases of "fair" health and 10 million cases of "poor" health (table 1). These numbers imply 4.3 million years of life-in-good-health lost, which is 40% of the mortality effect of 11.4 million years (see above), which adds another €280 billion to the costs of health inequalities.

The analysis of ECHP data confirmed that poor health was consistently related to GP visits, specialist visits and hospitalization rates. People with "very poor" health had more than 6 times more GP visits and more than 9 times more specialist visits than those with "very good" health, after adjustment for confounders. Virtually identical associations were observed within higher and lower educated groups. Our 'levelling up' scenario would decrease the number of GP visits and specialist visits by 16%, and the number of nights in hospital by 22%. We calculated the impact of health inequalities on health care costs to be €26 billion for physician services, and €59 billion for hospital services.

According to OECD data, physician visits and hospitalizations represent almost half of total health care costs [START_REF] Luce | The return on investment in health care: from 1980 to 2000[END_REF]. If the empirical results for physician visits and hospitalizations would apply to total health care, the total impact of health inequalities on health care costs would represent €177 billion, or around 20% of total health care costs in the EU-25 (table 3).

Our analysis of the ECHP panel data also confirmed that poorer health is strongly associated with receipt of disability benefits. People with "very poor" health on average receive about 20 times more disability benefits than those with "very good" health, after adjustment for confounders. Among lower educated groups, the effect of health on disability benefits is slightly smaller in relative terms. The association between poorer health and receipt of unemployment benefits was weaker, but in general, those with poor health received more unemployment benefits, after adjustment for confounders. On the basis of our 'upward levelling' counterfactual scenario we calculated that health inequalities account for 25% of the costs of disability benefits (representing €55 billion annually) and 3% of the costs of unemployment benefits (representing about €5 billion annually) in the EU as a whole (table 3). The total of €60 billion corresponds to 15% of the total costs of social security systems.

Discussion

Our estimates suggest that the economic costs of socioeconomic inequalities in health may be substantial. While the estimates of inequalities-related losses to health as a 'capital good' (leading to less labour productivity) seem to be modest in relative terms (1.4% of GDP), they are large in absolute terms (€141 billion). It is valuing health as a 'consumption good' which makes clear that the costs of socioeconomic inequalities in health are really huge: in the order of €1000 billion (or 9.4% of GDP). The separately calculated impacts on costs of social security and health care systems and health care, which are incurred in order to cope with these losses to health as a 'capital good' and/or as a 'consumption good', support these conclusions. Inequalities-related losses to health account for 15% of the costs of social security systems, and for 20% of the costs of health care systems in the European Union as a whole. It is important to emphasize that all these estimates represent yearly values, and that as long as health inequalities persist, these losses will continue to accumulate over the years. These estimates should be seen as a first attempt at coming to grips with these difficult issues. There are many uncertainties.

First, although there are abundant data on the magnitude of socioeconomic inequalities in mortality and morbidity in the European Union, the comparability of data is limited, and some countries still lack relevant data [START_REF] Kunst | Monitoring of socioeconomic inequalities in health in the European Union: guidelines and illustrations[END_REF]. As a result, our estimates of the magnitude of inequalities-related losses to health in terms of mortality and morbidity, life-years, and life expectancy with morbidity, as presented in table 1, have considerable margins of uncertainty. We think it likely that we have underestimated the inequalities-related losses to population health in the European Union, because of the measurement error inherent in the large-scale registries and surveys which we used for our calculations, and because of the fact that we have conservatively taken the upper half of the educational distribution as the reference category for our 'levelling up' scenario. Improved health monitoring systems are needed in the European Union to solve these problems.

Second, our estimates of the monetary value of inequalities-related losses to health as a 'capital good' have a number of uncertainties. These uncertainties relate to the fact that we had to ignore losses generated through other mechanisms than the health effects on personal income. We could not take into account differences in savings between people in poor and in good health, and we had to ignore the value of informal labor. We also could not take into account the effect of ill-health on educational careers [START_REF] Groot | Investing and earning. Costs and benefits of educational investments[END_REF]. These uncertainties also relate to the validity and precision of the losses to personal income as calculated in the ECHP data. We assumed that the lower wages of lower educated people reflect the lower value of their contribution to the production of goods and services, but the validity of this assumption could be debated. We assumed that upward biases (e.g. due to a reverse effect of income on health) and downward biases (e.g. due to the fact that we largely ignored mental health problems and spillover effects of health on the earnings of partners) in the estimated effects of ill-health on personal income cancelled each other out, but this is clearly a very rough assumption. There is also no guarantee that what has been found in a single data set will be reproduced in other data sets, but we think that on the whole our estimates are likely to be conservative. There is an urgent need for systematic reviews and/or meta-analyses assessing the (causal) effect of ill-health on earnings in the European Union.

Third, our estimates of the monetary value of inequalities-related losses to health as a 'consumption good' also have a number of uncertainties. There is no consensus among economists on the monetary value of health, and the accuracy of our estimates is therefore strongly dependent on the validity of Nordhaus' estimates for the value of a life saved and a current life-year lived [START_REF] Nordhaus | The health of nations: the contribution of improved health to living Standards[END_REF]. In the literature, widely different estimates have been reported [START_REF] Olsen | Theory versus practice: a review of 'willingness-to-pay' in health and health care[END_REF], but Nordhaus' estimates represent a conservative and well-documented choice and correspond reasonably well with some recent European estimates [START_REF] Bickel | Externalities of energy[END_REF]. They were derived for the year 1990, and because more recent estimates for the United States are considerably higher [START_REF] Hirth | Willingness to pay for a quality-adjusted life year: in search of a standard[END_REF],

we may well have underestimated the monetary value of inequalities-related losses to health. On the other hand, the monetary value of health in 2004 might be lower in Europe than in the United States, if only because Gross Domestic Product in the EU-25 in 2004 was about 35% lower -a difference which is also reflected in different recommended thresholds for determining cost-effectiveness of health technologies [29]. The 16/40 th fraction which we applied to Nordhaus' estimate of the value of a life saved was derived from an analysis of the actual life-years lost as a result of health inequalities in Europe, which is only 16/40 th of the life-years lost in Nordhaus'analysis. While one might argue that the value of life-years is not homogeneous across the life-course, we see no objective basis for such differentiation. Here again, there is an urgent need for more research in order to better support policy making in Europe, not only in the field of health inequalities, but in other health domains as well (e.g. health care policy, health research policy).

In order to illustrate the potential impact of these uncertainties on our findings we have performed a sensitivity analysis which is reported in table 4. We selected five alternatives. A more radical 'upward levelling' scenario, in which the health of the whole population would be lifted up to that of the highest 20% instead of the highest 50%, leads to substantially (i.e. about 60%) higher estimates of the economic costs of health inequalities. On the other hand, if we use more conservative estimates of the effects of education, e.g. to take into account that part of the observed associations may not be causal or that we only had mortality data for a part of the EU-25, all four cost categories (capital good, consumption good, health care, social security) go down. The estimates of the impact on health as a capital good go down too, if we would assume a smaller than observed effect of health on personal income, or would assume no effect of health on firm profits or mixed incomes. The estimate of the impact on health as a consumption good go down if we lower Nordhaus' value of €77,000 for a life-year by 35%, to take into account lower average GDP in the European Union as compared to the United States.

The range of values shown in table 4 illustrates that uncertainties are indeed substantial, but do not shed doubt on our over-all finding that the economic costs of health inequalities are likely to be substantial.

Our results suggest that investing in programs to reduce health inequalities may have important economic benefits. During the past two decades, socioeconomic inequalities in health have increasingly been recognized as an important public health issue throughout Europe [START_REF] Mackenbach | the European Network on Interventions and Policies to Reduce Inequalities in Health. Tackling socioeconomic inequalities in health: an analysis of recent European experiences[END_REF]. As a result, there has been a considerable research effort which has permitted the emphasis of academic research to gradually shift from description to explanation [START_REF] Siegrist | Social inequalities in health. New evidence and policy implications[END_REF]. And as a consequence of that, entry-points for interventions and policies have been identified, providing the building-blocks with which policy-makers and practitioners have begun to design strategies to reduce socioeconomic inequalities in health. Although relatively little is known yet about the effectiveness of these strategies, it is possible to make some educated guesses about their potential impact on the economic implications of health inequalities in the European Union. For example, if it would be possible to implement a number of equity-oriented anti-tobacco policies which would reduce the prevalence of smoking in the lower socioeconomic groups by 33%, while the prevalence of smoking in the higher socioeconomic groups would decline by 25% [START_REF] Giskes | Applying an equity lens to tobacco-control policies and their uptake in six Western-European countries[END_REF], our analyses suggest that a substantial impact would be generated. Not only would health inequalities be reduced considerably, but also some 7% of the economic costs of health inequalities through mortality and morbidity (including the costs of health care and social security benefits). Inequalities-related losses to health as a 'consumption good' through mortality would be reduced by between about €75 billion for the EU-25 as a whole, and inequalities-related losses to health as a 'capital good' would be reduced by almost €9 billion per year [START_REF] Mackenbach | Economic implications of socioeconomic inequalities in health in the European Union[END_REF].

The same could be true for policies targeting other 'downstream' determinants of health inequalities, like unfavourable working conditions or lack of access to good quality health care, and for policies targeting 'upstream' determinants of health inequalities, like barriers to higher education and income inequality. The effects of these policies are largely unknown, and the same applies to their costs. Some economists argue that income inequality is necessary for economic progress, so reducing health inequalities by income redistribution may entail high costs, even in terms of average population health [START_REF] Deininger | Economic growth and income inequality: re-examining the links[END_REF]. This is still largely uncharted scientific territory, but in view of the sheer numbers highlighted by our analysis we think it likely that a good economic case for reducing health inequalities can be made -in addition to the more self-evident moral case which has so eloquently been made by the World Health Organization's Commission on Social Determinants [START_REF]Closing the gap in a generation: Health equity through action on the social determinants of health[END_REF]. Source of observed mortality rate: Eurostat website. Mortality rates for the higher educated were derived from the observed mortality rate in the whole population, and a mortality rate ratio of 1.36 comparing lower educated and higher educated.

[b]

Absolute numbers were obtained by multiplying rates or years by the total population size of EU-25 in 2004 (=458,973,000).

[c]

Source of observed morbidity rate: Eurostat website. Prevalence rates for the higher educated were derived from the observed morbidity rate in the whole population, and the prevalence rate ratio of 1.45 comparing lower educated to higher educated.

[d]

Source of observed life expectancy: Eurostat website. We interpolated estimates available for 2000 and 2005. Life expectancy for the higher educated was derived from the same data as described in note [a].

[e]

Estimated on the basis of life expectancy and the prevalence of "less than good" health.

[f]

The total years of life lost were estimated by multiplying the total number of deaths due to inequalities (707,000) by an estimate of the average number of years of life lost per death (16.06 years). The impact of health inequalities was estimated on the basis of the estimates of inequalities-related losses to health as presented in table 1. The % estimates in column 3 were multiplied with column 1 to obtain an estimate of the absolute amounts in column 4.

[c]

In 2004, the average gross monthly income from wages and salary earnings in EU-25 was estimated to be €726 per person. Under the "levelling up" scenario, this average increases by 2.77 percent, to €746 per person. When only the impact of the "levelling up" scenario on gross earnings of employees is counted, the impact on total GDP will be 39% times 2.77%, or 1.1% of GDP. Through firm profits and income of self-employed persons, a "levelling up" scenario will increase the second GDP component by (gross operating surplus and mixed income, accounting for 38% of GDP) by 0.7%, which translates into 0.3% of GDP. [d] See main text.

D0027ee07JM.doc, 05-09-07 30

Figure 1

 1 Figure 1: association between health and earnings, per educational level

Table 1 .

 1 The population health impact of educational differences in mortality and morbidity in the EU-25 in 2004

	Total EU-25	Total EU-25:	Impact of
	population:	estimates	health
	observed rates	assuming rates	inequalities
	and numbers	of higher
	(1)	educated
		(2)	(1) -(2)

Table 3 .

 3 Estimates of the welfare impact of socioeconomic inequalities in health, EU-25 member states, 2004

	Total value [a]	Impact of health inequalities [b]
			As
			proportion
			(%)
	In billion	As % of	of each	In billion	As % of
	euro	GDP	item	euro	GDP

Table 4 .

 4 Results of sensitivity analyses.

	Impact of health inequalities (in billion euro)
	Standard estimates	Alternative 1:	Alternative 2:	Alternative 3:	Alternative 4:	Alternative 5:
	(as in table 3)		the health of people	educational	the effect of	health has no	one life-year is
			is "leveled up" to	differences in	health on income	effect on firm	worth 35% less
			the highest 20% of	health are 30%	is 30% smaller	profits and mixed	than the US based
			the population,	smaller than	than estimated	incomes	estimate of
			instead of the	empirically	using ECHP data		77,000 euro
			highest 50%	estimated
	Health as a capital good	141	226	108	99	113	n.a.
	Health as a consumption good	980	1568	754	n.a.	n.a.	637
	Health care costs	177	283	136	n.a.	n.a.	n.a.
	Social security benefits	60	96	46	n.a.	n.a.	n.a.

Same notes apply as to table 3. For explanation of alternative scenarios, see main text.

Acknowledgements

The study was supported by the European Commission under contract number SANCO/2005/C4/Inequality/01. The analyses of ECHP data were performed by Heleen van Agt.

Note to figure 1: Source: Analysis of the fifth wave of the European Community Household Panel, covering persons aged 16-64 in 11 countries in Western Europe, excluding students, self-employed workers, and unpaid family workers, and including those with zero income. Personal earnings were measured as gross monthly income, including salaries and wages, but not taking into account incomes from capital returns and social benefits. Results are based on a loglinear regression analysis controlling for age, gender, age*gender, cohabitational status and country.


Liam Edward Browne

Lishuang Cao

Helen E Broomhead

Laricia Bragg

William Wilkinson

R Alan North
email: r.a.north@manchester.ac.uk

P2X receptor channels show three-fold symmetry in ionic charge selectivity and unitary conductance

come

In the closed structure of the P2X cation channel, three α-helical transmembrane domains cross the membrane obliquely: in rat P2X2 receptors, these intersect at Thr 339 .

Replacing Thr 339 by lysine in one, two or three subunits progressively increased chloride permeability and reduced unitary conductance. This implies that the closedopen transition involves a symmetrical separation of the three subunits, and that Thr 339 from each contributes symmetrically to the open channel permeation pathway.

The conducting pore of ligand-gated ion channels is typically formed as a passage along the central axis of several subunits. ATP-gated channels (P2X receptors) are among the simplest such channels: in this case the central cation-selective channel is formed by the second of two transmembrane domains (TM2) from each of three subunits 1 . In the crystal structure of the closed zebrafish P2X4.1 receptor, these TM2 helices (from N334 to L361) cross the membrane at an oblique angle, such that the narrowest part of the channel is delimited by two helical turns from L340 to A347 2 . The corresponding region of the rat P2X2 receptor (I332 to T339) is a key determinant of the conducting properties of the open channel [3][4][5][6][7][8] . We introduced a lysine residue at this position (T339K) in one, two or three of the TM2 helices (Supplementary Methods) and determined that the open channel functions as a symmetrical trimer, in which each TM2 helix contributes equally to the permeation pathway.

The current evoked by ATP (Supplementary Methods) at wild type P2X2 receptors shows marked inward rectification 9 : in contrast, outward currents through P2X2[T339K] receptors were larger at positive holding potentials 7 (Fig. 1a). The concatemer with three wild type subunits (TTT) showed inward rectification similar to channels formed by the expression of single wild type subunits, and the rectification of the three-lysine concatemer (KKK) resembled that of the homotrimeric channel formed from single P2X2[T339K] subunits (Fig. 1a). Channels containing one or two lysine residues showed intermediate inward rectification (Fig. 1a and Supplementary Table ). There was no obvious position dependence among forms KTT, TKT, and TTK. Concatemers containing two T339K subunits all showed enhanced outward currents, although this was less for KKT than for KTK and TKK (Fig. 1a): of all the constructs, only KKT showed evidence of partial breakdown (Supplementary Fig. 1) and it is possible that wild type monomers were also formed.

Wild type P2X2 channels have negligible chloride permeability 1,10 , but substitution by lysine at T339 converted the P2X receptor channel from cation-selective to anion-preferring: P Cl /P Na increased from <0.1 for the wild type channel to ~2 for the T339K channel (Supplementary Table ). For concatemeric channels, the increase in chloride permeability was progressive with the number of lysines at this position (Fig. 1b). There was a strong correlation between the increase in outward current measured at 150 mV and the increase in chloride permeability (Fig. 1c). In other words, the large outward currents in P2X2[T339K] results from the increased inward movement of chloride ions when the cell is strongly depolarized. This shows that the electrostatic environment around T339 is critical for the charge selectivity of the permeating ions. Concatemers with only wild type or only T339K in each of the three subunits provided channels with unitary conductances similar to those observed with the corresponding monomers (Fig. 2b and Supplementary Fig. 2). Concatemers that contained one or two T339K subunits had unitary conductances not different from the intermediate levels observed with co-expression of monomers described above (Supplementary Fig. 2).

The progressive increase in positivity of the electrostatic field strength at this position by introduction of one, two or three positive charges converts the P2X2 receptor from tenfold cation-selective to a channel that prefers anions, and this accounts for the rectification previously reported 1,9 . Similar conversion of ion selectivity from cationic to anionic by mutagenesis is reported for some other ion channels (e.g. nicotinic receptors 11 and cyclic nucleotide-gated channels 12 ). Introduction of a single lysine into the trimeric concatemer reduced the unitary current by about 50%, and further lysines caused further stepwise reductions (Fig. 2 and Supplementary Fig. 2). There was no obvious change in open probability in T339K as compared to wild type channels, indicating that the T339K substitution did not change gating (e.g. by endowing the protein with a new voltagedependence).

It is currently thought that the P2X receptor pore opens by a separation 13 and counterclockwise rotation of each of the three TM2 helices, driven by forces transmitted through connecting rods passing through the ectodomain from three inter-subunit binding sites 14 . Our findings with concatenated channels strongly suggest that opening of P2X2 channels occurs by equivalent and symmetrical rearrangement of the TM2 helices. We conclude that the side chain of the residue that occludes the permeation pathway of the closed P2X receptor also contributes to the selectivity filter of the open channel (T339 in the P2X2 receptor)(Supplementary Fig. 3). This result is consistent with a symmetrical iris-like separation of the three TM2 helices: other recent evidence suggests that this is accompanied by a steepening and rotation of these helices 14,15 .

Note: Supplementary information is available on the Nature Neurosicence website.

  Single channel recording showed that wild type rat P2X2 receptors open to a single conducting level in ATP (Fig. 2a)(27.3 ± 1.3 pS, n = 12). P2X2[T339K] had much reduced unitary currents (6.1 ± 0.6 pS, n = 7). The corresponding values when potassium was the main internal ion were 41.1 ± 3.3 pS (n = 7) and 6.1 ± 0.3 pS (n = 8), so we used internal potassium in subsequent experiments to discriminate more easily levels intermediate between wild type and T339K. Outside-out patches from cells transfected with both wild type and T339K cDNAs usually showed multiple conductance levels (Fig. 2a). In 9 of 44 patches a single open level was observed at 44 ± 1.6 pS; in 8 of 44 patches a single open level occurred at 7.2 ± 0.1 pS. In 11 patches, we observed three open levels (i.e. four peaks in the all points histogram), which corresponded in amplitude to wild type level, and two new intermediate levels (II: 14.4 ± 0.9 pS; III: 24.5 ± 1.1 pS). In 16 patches, we observed a single intermediate conductance level, corresponding in amplitude to either II or III (Supplementary Fig. 2).

Figure 1

 1 Figure 1 Lysine at 339 progressively increase chloride permeability and outward current. (a) Current-voltage plots for ATP-induced currents in ten cells expressing concatenated trimeric P2X2 receptors with one, two or three lysines at position 339. Currents are normalized: scales apply to all panels (actual currents at -150 mV were (pA): wild type (wt) 2000, T339K 700, KTT 3100, TKT 2900, TTK 2700, KKT 230, KTK 800, TKK 1800 pA, TTT 3300 and KKK 1900). ATP concentrations used were 10 or 30 μM (close to EC 50 ). (b) Reversal potential for ATP-evoked currents becomes dependent on the chloride concentration as lysines are introduced at position 339. Means ± s.e.m. (c) P Cl /P Na (determined from experiments in b) increases according to the number of lysines at position 339 and outward rectification increases proportionately (Pearson's r = 0.97).

Figure 2

 2 Figure 2 Lysine at 339 reduces single channel currents. (a) ATP (0.3 μM or 1 μM) activates single channels in outside-out patches from cells expressing cDNAs encoding wild type P2X2 (top), P2X2[T339K] (middle), and both (bottom) subunits. Bottom trace shows the intermediate current amplitudes: zero current/closed channel peak is truncated, and open arrowhead indicates the position of the third level (< 1 pA). Holding potential -120 mV. (b) Outside-out recordings of single channel activity in patches from cells expressing concatenated cDNAs. The amino acid at position 339 in each subunit of the trimer is indicated above each trace. ATP concentrations were 1 to 10 μM. Rightside records are allpoints histograms used to estimate unitary current amplitudes; zero level peaks are truncated.

ACKNOWLEDGMENTS

This work was supported by The Wellcome Trust.

Supplementary material contains:

Supplementary Figures: 3

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P2X receptor channels show three-fold symmetry in ionic charge selectivity and unitary conductance

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R. Alan North

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Supplementary Methods Supplementary methods Supplementary Table 1 Table 1. Rectification index and relative chloride permeability for monomeric and concatenated P2X2 subunits Supplementary Figure 1 Trimeric protein expression Supplementary Figure 2 Summary of unitary current amplitudes Supplementary Figure 3 Model of open P2X2 receptor pore


Cédric Mercier

Laetitia Dahan

Houcine L'

Nicolas Ouafik

Joseph André

Ciccolini

Cedric ; Mercier

Cédric Mercier

C Ciccolini

Mercier

N André

J Ciccolini
email: joseph.ciccolini@univmed.fr

Pharmacokinetics of gemcitabine in non-small-cell lung cancer patients: impact of the 79A>C cytidine deaminase polymorphism

Keywords: pharmacogenetics, gemcitabine, pharmacokinetics

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They conclude, from this genotypic-based study, that this polymorphism has little impact on gemcitabine pharmacokinetics eventually. Of note, in this study, CDA status was never evaluated using a functional test, and no clinical data (e.g., relationships between allelic variants and the incidence of severe toxicities) were made available to further confirm the little relevance 79A>C polymorphism had on outcome with gemcitabine. However, as fairly underlined by the authors, several experimental or clinical reports have previously evidenced the importance of the 79A>C mutation, both in term of impact on CDA activity, or as a biomarker predictive of toxicity/response with gemcitabine at the bedside [3-5]. In their paper, Maring et al. list a variety of possible confounding factors explaining this discrepancy, such as epigenetic deregulations, dose levels, sample size or lack of treatment uniformity in the patients they enrolled. In the light of our own expertise on genetic polymorphism affecting CDA and subsequently patients undergoing gemcitabine- based therapies, we do believe that the absence of impact of the 79A>C Maring et al.

have evidenced is not artifactual. Actually, we reached the same conclusion in a pilot study performed in animals as well as in 150 patients (130 adults, 20 children) designed to evaluated whether CDA genetic polymorphism was a culprit for early severe toxicities upon gemcitabine intake [2]. In our study, we proved that CDA deficiency led to marked alterations of the gemcitabine profile in animals, with subsequent unrecoverable haematological toxicities. In patients, the mutation was screened along with the 208G>A and 435T>C polymorphisms, and failed to be correlated with either CDA activity or clinical outcome. Conversely, sorting the same patients next on a CDA phenotypic status, rather than on a genotypic one, was fully predictive of those undergoing drug-induced severe toxicities, regardless of the dose administered, or the associated treatments. In this the recent study by Maring et al. is of interest, because it fully supports the hypothesis that the gemcitabine pharmacogenetics issue should be better addressed using functional tools, rather than with a candidate gene approach. Finally, we would to emphasize that the apparently negative conclusions of this paper should not deter physicians from moving toward a more personalized medicine with gemcitabine, since it all depends on the tools you use to custom treatment.

  with great interest the article entitled "Pharmacokinetics of gemcitabine in non-small-cell lung cancer patients: impact of the 79A>C cytidine deaminase polymorphism" by Maring J et al. which has been recently published in the European Journal of Clinical Pharmacology [1]. We would like to comment on the article by referring to our own non-clinical and clinical studies on evaluating the relationships between CDA status (either determined from a phenotypic or a genotypic basis) and pharmacokinetics or clinical outcome in individuals treated with gemcitabine [2]. Maring et al. studied the impact of the canonical 79A>C polymorphism in the cytidine deaminase (CDA) gene on the pharmacokinetics of gemcitabine and its main inactive metabolite 2′,2′-difluorodeoxyuridine (dFdU) in non-small-cell lung cancer (NSCLC)

  J, Dahan L, André N, Evrard A, Duluc M, Blesius A, Yang C, Giacometti S, Brunet C, Raynal C, Ortiz A, Frances N, Iliadis A, Duffaud F, Seitz JF, Mercier C. (2010) Cytidine deaminase residual activity in serum is a predictive marker of early severe toxicities in adults after gemcitabine-based chemotherapies. J Clin Oncol. 28:160-5. 3. Kirch HC, Schroder J, Hoppe H, Esche H, Seeber S, Schutte J (1998) Recombinant gene products of two natural variants of the human gene confer different deamination rates of cytarabine in vitro. Exp Hematol 26:421-425. 4. Gilbert JA et al (2006) Gemcitabine pharmacogenomics: cytidine deaminase and deoxycytidylate deaminase gene resequencing and functional genomics. Clin Cancer Res 12:1794-1803 5. Tibaldi C, Giovannetti E, Vasile E, Mey V, Laan A, Nannizzi S et al (2008) Correlation of CDA, ERCC1, and XPD polymorphisms with response and survival in gemcitabine/cisplatin-treated advanced nonsmal cell lung cancer patients. Clin Cancer Res 14:1797-1803.

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