Julie Lasselin

Sophie Layé

Sandra Dexpert

Agnès Aubert

Concepcion Gonzalez

Henri Gin

Lucile Capuron
email: lucile.capuron@bordeaux.inra.fr

Fatigue symptoms relate to systemic inflammation in patients with type 2 diabetes

Keywords: Fatigue, Brain, Behavior, and Immunity Fatigue Dimensions Diabetes Types 1 and 2 Inflammation Cytokines C reactive protein Interleukin-6 Neopterin Adiposity

Fatigue is frequent in patients with diabetes and this symptom appears to be more prominent in type 2 rather than type 1 diabetic subjects. Chronic inflammation represents one characteristic of type 2 diabetes that may contribute to fatigue symptoms. This possibility was assessed in a sample of 20 type 2 diabetic patients relatively to a group of 20 type 1 diabetic subjects. Specific dimensions of fatigue, including general fatigue, physical fatigue, reduced activity, mental fatigue and reduced motivation, were assessed using the Multidimensional-Fatigue-Inventory (MFI). Biological assays comprised the measurement of serum inflammatory markers [high-sensitive C-reactive-protein (hsCRP), high-sensitive interleukin-6 (hsIL-6), high-sensitive tumor-necrosis-factor-a (hsTNF-a) and neopterin]. Clinical parameters including indexes of adiposity were collected. In comparison to type 1 diabetic subjects, patients with type 2 diabetes exhibited higher fatigue scores, notably in the dimensions of general fatigue, physical fatigue and reduced activity, together with greater levels of inflammatory markers that correlated with indexes of adiposity. Regression analyses controlling for diabetes duration, insulin treatment status, glycemic control and fasting status, indicated that levels of inflammatory markers, in particular hsIL-6, hsCRP and neopterin, were associated with MFI fatigue dimensions in type 2 diabetic patients. Mediation analyses revealed that adiposity did not significantly account for the relationship of inflammatory markers with fatigue scores albeit coefficient regressions decreased somewhat when this variable was controlled for in regression models. These findings indicate that systemic low-grade inflammation relates to fatigue symptoms in patients with type 2 diabetes and suggest the involvement of inflammatory processes in the pathophysiology of diabetes-related fatigue.

Introduction

Fatigue represents a common complaint of patients with diabetes as it can be reported in up to 60% of patients [START_REF] Drivsholm | Symptoms, signs and complications in newly diagnosed type 2 diabetic patients, and their relationship to glycaemia, blood pressure and weight[END_REF][START_REF] Fritschi | Fatigue in patients with diabetes: a review[END_REF]. This symptom not only impacts the patient's quality of life and compliance to treatment, but it is also associated with an increased risk of disease complications [START_REF] Fritschi | Fatigue in patients with diabetes: a review[END_REF]. In a recent comparative study assessing fatigue symptoms in patients with type 1 and type 2 diabetes, we found that fatigue was particularly prominent in patients with type 2 diabetes and concerned primarily physical aspects rather than mental components. In patients with type 1 diabetes, fatigue scores were comparable to scores measured in healthy volunteers [START_REF] Lasselin | Fatigue and cognitive symptoms in patients with diabetes: relationship with disease phenotype and insulin treatment[END_REF].

The pathophysiological mechanisms leading to the development of fatigue symptoms in patients with type 2 diabetes remain largely unknown. While many biological and psychological factors may contribute, several lines of evidence point to the possibility that inflammatory factors are also involved. In support of this notion, recent data indicate that blockade of the inflammatory cytokine interleukin (IL)-1b with the monoclonal anti-IL1b antibody, XOMA052, partly improves motor fatigue in patients with type 2 diabetes [START_REF] Cavelti-Weder | Inhibition of IL-1beta improves fatigue in type 2 diabetes[END_REF]. The ability of pro-inflammatory cytokines (e.g., IL-6, IL-1b, tumor necrosis factor (TNF)-a) to act on the brain and to induce behavioral symptoms, including fatigue, is well documented in both experimental and clinical studies [START_REF] Capuron | Immune system to brain signaling: neuropsychopharmacological implications[END_REF][START_REF] Dantzer | From inflammation to sickness and depression: when the immune system subjugates the brain[END_REF][START_REF] Kelley | Cytokine-induced sickness behavior[END_REF][START_REF] Majer | IFNalpha-induced motor slowing is associated with increased depression and fatigue in patients with chronic hepatitis C[END_REF]. At the clinical level, the involvement of inflammation in the development of fatigue symptoms has been shown in patients with chronic medical conditions, including cancers, multiple sclerosis and chronic fatigue syndrome [START_REF] Bower | Fatigue and proinflammatory cytokine activity in breast cancer survivors[END_REF][START_REF] Flachenecker | Cytokine mRNA expression in patients with multiple sclerosis and fatigue[END_REF][START_REF] Fletcher | Plasma cytokines in women with chronic fatigue syndrome[END_REF][START_REF] Heesen | Fatigue in multiple sclerosis: an example of cytokine mediated sickness behaviour?[END_REF][START_REF] Orre | Levels of circulating interleukin-1 receptor antagonist and C-reactive protein in longterm survivors of testicular cancer with chronic cancer-related fatigue[END_REF][START_REF] Raison | Association of peripheral inflammatory markers with chronic fatigue in a population-based sample[END_REF]. Moreover, studies conducted in our group have shown that inflammation plays a major role in the development of fatigue symptoms in medically ill patients treated with cytokines, notably interferon-a [START_REF] Capuron | Neurobehavioral effects of interferon-alpha in cancer patients: phenomenology and paroxetine responsiveness of symptom dimensions[END_REF][START_REF] Capuron | Basal ganglia hypermetabolism and symptoms of fatigue during interferon-alpha therapy[END_REF]. Similar results were found in the elderly subject (Capuron et al., 2011a). All of these conditions share in common the chronic activation of immune/inflammatory processes. Similar to these conditions, diabetes is characterized by a chronic, lowgrade, inflammatory state. While this activation of inflammatory processes has been described in both type 1 and type 2 phenotypes [START_REF] Basu | Type 1 diabetes is associated with increased cyclooxygenase-and cytokine-mediated inflammation[END_REF][START_REF] Devaraj | Increased monocytic activity and biomarkers of inflammation in patients with type 1 diabetes[END_REF][START_REF] Pickup | Plasma interleukin-6, tumour necrosis factor alpha and blood cytokine production in type 2 diabetes[END_REF][START_REF] Su | Circulating proinflammatory cytokines and adiponectin in young men with type 2 diabetes[END_REF], increased levels of circulating inflammatory markers were found to be significantly more prominent in type 2 diabetes [START_REF] Alexandraki | Cytokine secretion in long-standing diabetes mellitus type 1 and 2: associations with low-grade systemic inflammation[END_REF][START_REF] Pham | Pro-and anti-inflammatory cytokines in latent autoimmune diabetes in adults, type 1 and type 2 diabetes patients: action LADA 4[END_REF]. Whereas increased inflammatory markers, notably C-Reactive Protein (CRP), were found to relate to long disease duration and diabetes complications in type 1 diabetes [START_REF] Devaraj | Evidence of increased inflammation and microcirculatory abnormalities in patients with type 1 diabetes and their role in microvascular complications[END_REF][START_REF] Treszl | Elevated C-reactive protein levels do not correspond to autoimmunity in type 1 diabetes[END_REF], systemic inflammation in type 2 diabetes is believed to originate, at least partially, from the adipose tissue [START_REF] Mraz | The effect of very-lowcalorie diet on mRNA expression of inflammation-related genes in subcutaneous adipose tissue and peripheral monocytes of obese patients with type 2 diabetes mellitus[END_REF][START_REF] Pedersen | Circulating levels of TNF-alpha and IL-6-relation to truncal fat mass and muscle mass in healthy elderly individuals and in patients with type-2 diabetes[END_REF][START_REF] Sam | Relation of abdominal fat depots to systemic markers of inflammation in type 2 diabetes[END_REF]. Adiposity is a distinguishing characteristic of type 2 diabetes that may easily promote the development of chronic inflammation, as adipocytes and infiltrated macrophages in the adipose tissue have the ability to secrete pro-inflammatory factors [START_REF] Trayhurn | Adipokines: inflammation and the pleiotropic role of white adipose tissue[END_REF][START_REF] Wellen | Obesity-induced inflammatory changes in adipose tissue[END_REF]. Interestingly, systemic inflammation was found in many studies to be associated with an increased risk of developing type 2 diabetes, suggesting that inflammatory processes may also contribute to the pathophysiology of the disease [START_REF] Duncan | Low-grade systemic inflammation and the development of type 2 diabetes: the atherosclerosis risk in communities study[END_REF][START_REF] Festa | Elevated levels of acutephase proteins and plasminogen activator inhibitor-1 predict the development of type 2 diabetes: the insulin resistance atherosclerosis study[END_REF][START_REF] Hu | Inflammatory markers and risk of developing type 2 diabetes in women[END_REF][START_REF] Schmidt | Markers of inflammation and prediction of diabetes mellitus in adults (Atherosclerosis Risk in Communities study): a cohort study[END_REF][START_REF] Spranger | Inflammatory cytokines and the risk to develop type 2 diabetes: results of the prospective population-based European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study[END_REF]. Given the well documented role of inflammation in the development of fatigue in chronic or immune-based conditions [START_REF] Bower | Cancer-related fatigue: links with inflammation in cancer patients and survivors[END_REF][START_REF] Capuron | Immune system to brain signaling: neuropsychopharmacological implications[END_REF][START_REF] Miller | Neuroendocrine-immune mechanisms of behavioral comorbidities in patients with cancer[END_REF], it appears highly possible that systemic inflammation also contributes to fatigue symptoms in patients afflicted with diabetes, notably of type 2 phenotype.

The objective of this study was to assess the inflammatory status of patients with type 2 diabetes comparatively to a group of type 1 diabetic subjects and to evaluate the involvement of systemic low-grade inflammation in fatigue symptoms in the same population.

Patients and methods

Participants

This study included 20 patients with type 2 diabetes and 20 patients with type 1 diabetes recruited from the service of diabetology at the Haut-Lévêque Hospital in Bordeaux, France. Type 2 diabetic patients had a personal history of overweight, 10 of them were treated with insulin and the remainder 10 patients were either treated with oral antidiabetic medications (N = 6) or untreated (N = 4). All type 1 diabetic patients were treated with insulin, they had no personal history of overweight and they had shown significant weight loss at the onset of the disease. Clinical variables including the medical history of patients, duration of diabetes, glycated hemoglobin (HbA1C) levels (as indexes of diabetes control), treatment status, body mass index (BMI) and waist circumference (as indexes of adiposity) were collected in all participants. Patients with current major depressive disorder, as assessed by a structured clinical diagnostic interview with the Mini-International-Neuropsychiatric-Interview (MINI) [START_REF] Sheehan | The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10[END_REF], patients treated with antidepressants or neuroleptics, and patients suffering from chronic inflammatory medical conditions were excluded.

The study was approved by the local Committee for the Protection of Persons (CPP Bordeaux). All participants were adults and written informed consent was obtained from each of them after reading a complete description of the study.

Biological measurements

Blood samples were obtained for the measurement of serum concentrations of the inflammatory markers, high sensitivity (hs)CRP, hsIL-6 and hsTNF-a, as well as neopterin as marker of macrophage activation. Samples were centrifuged (10 min, 1000g, 4 °C) after clotting and sera were stored at À80 °C until the assays. Biological markers were assayed by enzyme-linked immunosorbent assay (ELISA) according to the manufacturer's specifications (hsIL-6 and hsTNF-a: R&D Systems, Minneapolis, Minnesota; hsCRP: CYT298, Millipore, Billerica, Massachusetts; neopterin: IBL International, Hamburg, Germany). Intra-and inter-assay variability were respectively ±7.4% and ±7.8% for hsIL-6, ±5.3% and ±8.4% for hsTNF-a, ±4.6% and ±6.0% for hsCRP, 3.6-6.8% and 7.6-10.3% for neopterin. Sensitivities were 0.039 pg/mL for hsIL-6, 0.106 pg/mL for hsTNF-a, 0.20 ng/mL for hsCRP and 0.7 nmol/L for neopterin.

Assessment of fatigue

Fatigue was assessed using the Multidimensional Fatigue Inventory (MFI), a 20-item self-report questionnaire measuring five dimensions of fatigue corresponding respectively to general fatigue, physical fatigue, mental fatigue, reduced activity, and reduced motivation [START_REF] Smets | The Multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue[END_REF]. Each dimension contains four items, scored from 1 to 5 with higher scores corresponding to greater levels of fatigue.

Data analyses and statistics

Raw values for the biological markers, hsIL-6, hsCRP and hsTNFa, were log-transformed because of non-normality. Characteristics of participants were compared across diabetes phenotype (type 1 versus type 2 diabetes) using two-sample t-tests for continuous variables and Chi-square tests for categorical variables. MFI fatigue scores were compared across groups using analysis of covariance (ANCOVA) controlling for insulin treatment status, diabetes duration and HbA1C levels. Due to logistic and/or medical constraints inherent to clinical setting and to the situation of diabetes, the collection of blood samples in fasting conditions was possible only in 30% of study participants. In order to control for this variation across subjects, analyses performed on biological markers were therefore systemically adjusted for fasting status in addition to insulin treatment status, diabetes duration and HbA1C levels. Three participants exhibited extreme (outlier) values (>3 SD above the mean) for at least one inflammatory marker and thus were excluded from analyses performed on biological data. Factor (principal component) analysis with varimax rotations was performed on biological markers to assess their interconnections and to eventually extract one composite inflammatory component. The relationship of adiposity (i.e., BMI and waist circumference) with inflammatory markers was estimated using Bravais-Pearson correlations performed in the whole population under study and separately in each diabetes phenotype. Multivariate regression analyses entering biological markers as predictors in separate models were performed to assess the relationship of levels of inflammatory markers with fatigue symptom dimensions in each diabetes phenotype. Models were adjusted for diabetes duration, HbA1C levels, insulin treatment and fasting status. Contribution of adiposity to the relationship between systemic inflammation and fatigue symptoms was assessed using the method recommended by [START_REF] Baron | The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations[END_REF]. According to this method, mediation takes place when (1) the initial variable is significantly associated with the outcome; (2) the initial variable is significantly correlated with the mediator; (3) the mediator has a significant effect on the outcome, when controlling for the initial variable; and

(4) the relationship between the initial variable and the outcome is reduced or no longer significant, when controlling for the mediator. The significance of the mediation (indirect effect) was tested using both conservative (Sobel) and non-parametric tests (bootstrapping resampling method as described by [START_REF] Preacher | Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models[END_REF]. Statistical analyses were performed with SPSS Statistics version 19. All probabilities were two-sided with the degree of significance set at p < 0.05.

Results

Characteristics of participants

As shown in Table 1, there was no significant difference between type 1 versus type 2 diabetic patients in terms of age and gender. Duration of diabetes (time since diagnosis) was longer in type 1 diabetic patients compared to type 2 diabetic subjects. Moreover, HbA1C levels were lower (indicative of a better control of diabetes) in type 1 diabetic patients. Finally, adiposity markers (i.e., BMI, waist circumference) were higher in type 2 diabetic patients compared to type 1 diabetic subjects.

Inflammatory markers

Overall, levels of circulating inflammatory markers were higher in type 2 diabetic patients compared to type 1 diabetic subjects (Fig. 1). More specifically, patients with type 2 diabetes exhibited significantly higher concentrations of hsCRP, hsTNF-a and neopterin (respectively, F(1, 31) = 9.8, p < .01; F(1, 31) = 5.1, p < .05 and F(1, 31) = 4.4, p < .05). Similarly, concentrations of hsIL-6 were higher in type 2 diabetic patients compared to type 1 diabetic subjects, but at a statistical trend level only (F(1, 31) = 3.6, p = .07). Factor analysis performed on inflammatory markers in the whole population under study revealed that these markers were interconnected and composed altogether one composite inflammatory dimension explaining 49% of the variance.

As expected, BMI and waist circumference correlated significantly with the composite measure of inflammation and with levels of individual inflammatory markers in the whole population under study, with increased adiposity indexes being related to higher levels of inflammation (Table 2 and Fig. 2). Further correlational analyses performed separately in each of the two diabetic populations revealed that these correlations were primarily significant in type 2 diabetic patients (Table 2).

Given the association of BMI and waist circumference with inflammatory markers in type 2 diabetic patients, complementary analyses using multivariate regression models were performed to assess the contribution of these variables to the relationship of type 2 diabetes with systemic inflammation (Table 3). Consistent with results presented in Fig. 1, type 2 diabetes was significantly associated with levels of inflammatory markers. This association did not remain significant when controlling for BMI or waist circumference, suggesting that adiposity was one major determinant of systemic inflammation in type 2 diabetes.

Fatigue symptoms and association with adiposity

Compared to type 1 diabetic patients, patients with type 2 diabetes exhibited higher MFI total scores (F(1, 35) = 8.8, p < .01), particularly in the dimensions of general fatigue, physical fatigue and reduced activity (F(1, 35) = 12.7, p < .01, F(1, 35) = 12.2, p < .01 and F(1, 35) = 7.6, p < .01, respectively) (Table 4), consistent with previous findings [START_REF] Lasselin | Fatigue and cognitive symptoms in patients with diabetes: relationship with disease phenotype and insulin treatment[END_REF]. Scores of mental fatigue and reduced motivation were comparable across the two diabetic populations (F(1, 35) = 2.2, p = .15 and F(1, 35) = .55, p = .46, respectively).

No significant relationship was found between fatigue symptoms and BMI or waist circumference in patients with type 1 diabetes. In type 2 diabetic patients, however, BMI was associated with the specific dimensions of mental fatigue and decreased motivation (b = .770, p < .05 and b = .768, p < .05, respectively). No significant association was found with the dimensions of general fatigue, physical fatigue and reduced activity. Similarly, no significant relationship was found between waist circumference and fatigue symptoms in type 2 diabetic patients, except with the dimension of mental fatigue at a marginal statistical level only (b = .715, p = .05). Mediation analyses revealed that systemic inflammation (hsIL6, hsCRP and composite inflammation in particular) significantly accounted for the associations of BMI with scores of mental fatigue and reduced motivation, as these associations did not remain significant when controlling for inflammatory markers (e.g., b = .382, p = .33 for mental fatigue and b = .290, p = .45 for reduced motivation, with control of composite inflammation). The largest mediating effect was obtained with hsIL6 (Sobel: Z = 1.85, p = .06 for mental fatigue and Z = 2.21, p < .05 for reduced motivation). Similar results were obtained with the nonparametric bootstrapping resampling method (result not shown).

Relationship between inflammation and fatigue symptom dimensions

Separate multivariate regression analyses controlling for diabetes duration, insulin treatment, HbA1C levels and fasting status were conducted to assess the relationship of inflammatory markers with fatigue scores. While no significant relationship was found in patients with type 1 diabetes, analyses revealed significant associations between inflammatory markers and fatigue symptom dimensions in patients with type 2 diabetes. In those patients, the composite measure of inflammation and the specific inflammatory markers of hsIL-6 and hsCRP were associated with MFI total fatigue scores, notably with the dimensions of mental fatigue and reduced motivation, with higher levels of inflammation predicting greater fatigue scores. HsCRP and hsIL-6 were also positively associated with the dimension of reduced activity, but at a statistical trend level only. A significant association was also found between neopterin and scores of general fatigue in type 2 diabetic patients, with higher neopterin levels predicting more intense general fatigue in these patients (Table 5).

Given the significant associations found between adiposity (BMI in particular) with both inflammation and fatigue symptoms, mediation analyses were conducted to test the indirect effect of adiposity on the relationship of inflammatory markers with fatigue scores in type 2 diabetic patients. Although some of the regression coefficients decreased somewhat when controlling for BMI in a Waist circumference value was missing in three type 2 diabetic patients.

multiple regression models (Table 5), mediation analyses revealed that, overall, this variable did not significantly mediate the relationship of systemic inflammation with fatigue symptoms in type 2 diabetic patients (Sobel; all p > .05), supporting the independent and direct effect of inflammatory markers on fatigue scores. Similar results were obtained with respect to waist circumference and when using non-parametric bootstrapping resampling method (results not shown).

Discussion

Results from the present study indicate a higher prevalence of fatigue symptoms together with increased signs of systemic inflammation in patients with type 2 diabetes relatively to type 1 diabetic subjects. Consistent with our previous data [START_REF] Lasselin | Fatigue and cognitive symptoms in patients with diabetes: relationship with disease phenotype and insulin treatment[END_REF], fatigue symptoms in type 2 diabetic patients manifested primarily in the dimensions of general fatigue, physical fatigue and reduced activity. Interestingly, significant associations were found between fatigue symptom dimensions and circulating levels of inflammatory markers in type 2 diabetic patients, suggesting the involvement of inflammatory processes in the development of type 2 diabetes-related fatigue. To our knowledge, this is the first time that such an association is shown in a diabetic population.

In line with previous reports [START_REF] Alexandraki | Cytokine secretion in long-standing diabetes mellitus type 1 and 2: associations with low-grade systemic inflammation[END_REF][START_REF] Pham | Pro-and anti-inflammatory cytokines in latent autoimmune diabetes in adults, type 1 and type 2 diabetes patients: action LADA 4[END_REF], low grade inflammation was found to be more prominent in type 2 diabetic patients compared to type 1 diabetic

Bravais-Pearson correlations (R).

Abbreviations: BMI: body mass index; hsCRP: high sensitive C-reactive protein; hsIL-6: high sensitive interleukin-6; hsTNF-a: high sensitive tumor necrosis factor-a. The factor composite inflammation corresponds to the first factor extracted from the principal component analysis performed on biological markers. This factor was saturated by each biological marker (e.g., hsCPR, hsIL-6, hsTNF-a and neopterin) and explained 49% of the variance. * p < .05. ** p < .01. *** p < .001.

# p = .08.

subjects. In patients with type 2 diabetes, systemic inflammation was characterized relatively to type 1 diabetic subjects by increased circulating concentrations of hsCRP, hsIL-6 and hsTNF-a together with increased levels of neopterin, indicative of macrophage activation. These biological variations were interconnected suggesting the activation of common inflammatory pathways.

The finding that neopterin was higher in type 2 diabetic patients comparatively to type 1 diabetic subjects is in accordance with recent data showing greater concentrations of this markers in a larger group of type 2 diabetic patients in comparison to healthy subjects [START_REF] Baris | Alterations in L-arginine and inflammatory markers in type 2 diabetic patients with and without microalbuminuria[END_REF]. Interestingly, systemic inflammation in patients with type 2 diabetes correlated with adiposity markers Multivariate regression analyses. All models were adjusted for diabetes duration, HbA1C levels, insulin treatment and fasting status (covariates). HsCRP, hsIL-6 and hsTNF-a were log-transformed due to non-normality. Separate effects of covariates on the outcomes of interest are given for information. Abbreviations: hsCRP: high sensitive Creactive protein; hsIL-6: high sensitive interleukin-6; hsTNF-a: high sensitive tumor necrosis factor-a. BMI: body mass index; HbA1C: glycated hemoglobin. The factor composite inflammation corresponds to the first factor extracted from the principal component analysis performed on biological markers. This factor was saturated by each biological marker (e.g., hsCPR, hsIL-6, hsTNF-a and neopterin) and explained 49% of the variance.

Composite

(e.g., BMI and waist circumference), with increased adiposity associating with higher inflammation. Moreover, the relationship of type 2 diabetes with systemic inflammation was significantly reduced when controlling for adiposity markers (i.e., BMI or waist circumference) suggesting that adiposity was a major determinant of type-2 diabetes related inflammation. This result supports the notion that low-grade systemic inflammation in type 2 diabetes originates, at least partially, from the adipose tissue and is consistent with previous reports documenting associations between circulating levels of inflammatory markers and abdominal fat mass in cohorts of type 2 diabetic subjects and in the elderly afflicted with type 2 diabetes [START_REF] Pedersen | Circulating levels of TNF-alpha and IL-6-relation to truncal fat mass and muscle mass in healthy elderly individuals and in patients with type-2 diabetes[END_REF][START_REF] Sam | Relation of abdominal fat depots to systemic markers of inflammation in type 2 diabetes[END_REF].

The finding that systemic low-grade inflammation was associated with fatigue symptom dimensions in patients with type 2 diabetes is particularly original, and suggests the involvement of inflammatory processes in the pathophysiology of diabetes-related fatigue. This result is in accordance with the well documented role of inflammation in the development of behavioral alterations, including fatigue [START_REF] Capuron | Immune system to brain signaling: neuropsychopharmacological implications[END_REF][START_REF] Dantzer | From inflammation to sickness and depression: when the immune system subjugates the brain[END_REF][START_REF] Kelley | Cytokine-induced sickness behavior[END_REF][START_REF] Majer | IFNalpha-induced motor slowing is associated with increased depression and fatigue in patients with chronic hepatitis C[END_REF]. The association of inflammatory markers with fatigue symptoms was found in multivariate regression models controlling for diabetes duration, insulin treatment status and HbA1C levels, attesting of the strength of the relationship and indicating that it did not rely on confounding factors nor it was explained by variations in diabetes length, treatment options and/or glycemic control. Moreover, mediation analyses revealed that adiposity did not significantly account for the relationship of inflammatory markers with fatigue scores in type 2 diabetic patients, albeit this variable correlated with both inflammation and specific fatigue dimensions in those patients and while coefficient regressions decreased somewhat when adiposity was controlled for in regression models. This finding highlights the possibility that the few associations that were measured between BMI and fatigue scores in type 2 diabetic patients were due to the interrelationship of adiposity with inflammatory markers. Moreover, this result suggests that, while related to adiposity, systemic inflammation has a specific and independent/direct effect (unmediated by adiposity characteristics) on fatigue symptoms in type 2 diabetes. Among the biological markers that were assessed in this study, hsIL-6 and hsCRP were those showing the strongest connections with fatigue scores, notably with the dimensions of mental fatigue, reduced motivation and reduced activity, in type 2 diabetic patients. This finding is consistent with previous reports (some of them emanating from our group) indicating the involvement of IL-6 and/or CRP in fatigue symptoms and neurobehavioral alterations in patients afflicted with chronic conditions (e.g., metabolic disorders, chronic fatigue syndrome, cancers) [START_REF] Bower | Inflammation and behavioral symptoms after breast cancer treatment: do fatigue, depression, and sleep disturbance share a common underlying mechanism?[END_REF][START_REF] Capuron | Depressive symptoms and metabolic syndrome: is inflammation the underlying link?[END_REF]Capuron et al., , 2011b;;[START_REF] Collado-Hidalgo | Inflammatory biomarkers for persistent fatigue in breast cancer survivors[END_REF][START_REF] Fletcher | Plasma cytokines in women with chronic fatigue syndrome[END_REF][START_REF] Orre | Levels of circulating interleukin-1 receptor antagonist and C-reactive protein in longterm survivors of testicular cancer with chronic cancer-related fatigue[END_REF][START_REF] Orre | Higher levels of fatigue are associated with higher CRP levels in diseasefree breast cancer survivors[END_REF] and in healthy models of chronic low grade inflammation such as normal aging (Capuron et al., 2011a;[START_REF] Valentine | The associations of adiposity, physical activity and inflammation with fatigue in older adults[END_REF]. Similar associations have been reported in healthy individuals [START_REF] Cho | Prospective association between C-reactive protein and fatigue in the coronary artery risk development in young adults study[END_REF][START_REF] Rief | Overnight changes of immune parameters and catecholamines are associated with mood and stress[END_REF][START_REF] Thomas | Sleep depth and fatigue: role of cellular inflammatory activation[END_REF]. Interestingly, higher neopterin concentrations were also found to relate to increased scores of general fatigue in the type 2 diabetic population from the present study. Surprisingly, albeit type 2 diabetic patients exhibited high scores of physical fatigue, no significant relationship was found between physical fatigue and levels of inflammatory markers. This result may be explained by the relatively small samples of diabetic subjects recruited in the present study. This limitation may have lead to reductions in statistical power, notably in linear regression models that controlled for multiple covariates with potential confounding effect. This limitation warrants further investigations on larger diabetic populations to comfort findings from the present study.

The mechanisms by which systemic inflammation may participate in the physiopathology of fatigue symptoms in patients with type 2 diabetes remain to be elucidated. Such mechanisms may include specific interactions between metabolic and inflammatory processes leading to subsequent alterations in brain chemistry (e.g., neurotransmitter and endocrine systems) and neurocircuitry involving notably the basal ganglia and fronto-thalamic loops [START_REF] Capuron | Immune system to brain signaling: neuropsychopharmacological implications[END_REF][START_REF] Majer | IFNalpha-induced motor slowing is associated with increased depression and fatigue in patients with chronic hepatitis C[END_REF][START_REF] Rocca | FMRI changes in relapsing-remitting multiple sclerosis patients complaining of fatigue after IFNbeta-1a injection[END_REF]. In addition, enzymatic variations responsible for alterations in the metabolism of neurotransmitters may be involved as inflammatory factors have potent effects of the activity of the enzymes, indoleamine-2,3-dioxygenase (IDO) and GTP cyclohydrolase-1 (GTP-CH1), involved in the biosynthesis of noradrenalin, dopamine and serotonin. Further clinical and experimental studies are needed to specifically address this question.

One question remains regarding the possibilities to prevent and/or treat diabetes-related fatigue -and subsequently improve patients' quality of life -using interventions to reduce inflammation. The association measured in the present study between adiposity and inflammatory markers supports the idea that reduced inflammation may be achieved by weight loss strategies, as shown in previous reports [START_REF] Viardot | The effects of weight loss and gastric banding on the innate and adaptive immune system in type 2 diabetes and prediabetes[END_REF]. Nutritional strategies may be also envisaged to modulate inflammation and its central/behavioral effects in patients with type 2 diabetes. Omega-3 polyunsaturated fatty acids (n À 3 PUFA) may represent good candidates for such a strategy, as these nutrients have been shown to exert potent anti-inflammatory effects [START_REF] Calder | N-3 polyunsaturated fatty acids, inflammation, and inflammatory diseases[END_REF]. Relevant to diabetes, n À 3 PUFA supplements were found to downregulate inflammation-related genes in the adipose tissue of women with type 2 diabetes [START_REF] Kabir | Treatment for 2 mo with n 3 polyunsaturated fatty acids reduces adiposity and some atherogenic factors but does not improve insulin sensitivity in women with type 2 diabetes: a randomized controlled study[END_REF]. In addition, these nutrients have been shown to reduce or prevent insulin resistance in experimental and clinical studies, although results in clinical settings are less consistent than in animal studies (Poudyal et al., 2011 review;[START_REF] Storlien | Fish oil prevents insulin resistance induced by high-fat feeding in rats[END_REF]. Finally, exercise training may represent another alternative since this strategy was found to reduce inflammatory markers in overweight individuals with type 2 diabetes [START_REF] Kadoglou | Exercise reduces resistin and inflammatory cytokines in patients with type 2 diabetes[END_REF].

In conclusion, the present findings indicate that systemic lowgrade inflammation is associated with fatigue symptom dimensions in patients with type 2 diabetes and suggest the involvement of inflammatory processes in the pathophysiology of diabetes-related fatigue.

Funding

This study was supported by subventions from the INRA, Region Aquitaine, and by the European Community (6th framework program) (Grant No. IRG2006-039575, LC). Separate multivariate regression analyses before (model 1) and after adjustment for BMI (model 2). All models were adjusted for diabetes duration, HbA1C levels, insulin treatment and fasting status (covariates). Inflammatory markers were entered in separate models. HsCRP and hsIL-6 were log-transformed due to non-normality. The mediating effect of BMI on the relationship between inflammatory markers and fatigue symptoms was tested according to method described in [START_REF] Baron | The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations[END_REF]. The Sobel test was used to test the significance of the mediation. Separate effects of covariates on the outcomes of interest are given for information.

Abbreviations: MFI: Multidimensional Fatigue Inventory; hsCRP: high sensitive C-reactive protein; hsIL-6: high sensitive interleukin-6; hsTNF-a: high sensitive tumor necrosis factor-a; HbA1C: glycated hemoglobin. The factor composite inflammation corresponds to the first factor extracted from the principal component analysis performed on biological markers. This factor was saturated by each biological marker (e.g., hsCPR, hsIL-6, hsTNF-a and neopterin) and explained 49% of the variance.

Fig. 1 .

 1 Fig. 1. Concentrations of biological markers in patients with type 1 diabetes () versus type 2 diabetes ( ). Abbreviations: hsCRP: high-sensitive C-reactive protein; hsTNF-a: high-sensitive tumor-necrosis-factor-a; hsIL-6: high-sensitive interleukin-6. Data are shown as mean (+SEM). Raw values for the biological markers, hsIL-6, hsCRP and hsTNF-a were log-transformed for statistical analyses, but shown in the figure as geometric means (from raw values) for information and readability purposes. ÃÃ p < .01; Ã p < .05; # p = .07.

Fig. 2 .

 2 Fig.2. Relationships between adiposity and inflammatory markers in patients with type 1 diabetes ( ) versus type 2 diabetes ( ). Abbreviations: hsCRP: high-sensitive Creactive protein; hsTNF-a: high-sensitive tumor-necrosis-factor-a; hsIL-6: high-sensitive interleukin-6. The displayed correlations were obtained in the whole population under study. Values for hsCRP, hsIL-6 and hsTNF-a were log-transformed for statistical analyses.

Table 1

 1 Characteristics of study participants.

		Type 1 diabetes Type 2 diabetes
	Sample size (N)	2 0	2 0
	Age, mean (SD)	51.8 (14.1)	50.3 (13.5)
	Women, n (%)	5 (25)	3 (15)
	Time since diabetes diagnosis, years (SD) 17.8 (13.9)	9 (6.7) *
	HbA1C, % (SD)	7.5 (1.8)	9.7 (2.4) **
	BMI, kg/m 2 (SD)	25.8 (4.2)	31.4 (5.2) ***
	Waist circumference, cm (SD) a	91.8 (12.0)	108.6 (13.3)

*** Analyses were performed using two-sample t-tests for continuous variables and chi-square tests for categorical variables. Abbreviations: BMI: body mass index; HbA1C: glycated hemoglobin. * p < .05 versus type 1 diabetic patients. ** p < .01 versus type 1 diabetic patients. *** p < .001 versus type 1 diabetic patients.

Table 2

 2 Relationship of adiposity (e.g., body mass index and waist circumference) with inflammatory markers.

		All participants		Type 1 diabetes	Type 2 diabetes
		BMI	Waist circumference	BMI	Waist circumference	BMI	Waist circumference
	hsCRP	.494 **	.480 **	.067	.115	.747 ***	.712 **
	hsIL-6	.445 **	.470 **	.163	.242	.528 *	.446 #
	hsTNF-a	.421 **	.424 **	.369	.292	.232	.312
	Neopterin	.205	.331 *	.073	.194	.193	.334
	Composite inflammation	.569 ***	.611 ***	.257	.323	.656 **	.688 **

Table 3

 3 Influence of adiposity on the relationship between type 2 diabetes and inflammatory markers.

	Predictors	Inflammatory markers
		hsCRP			hsIL6			hsTNF-a			Neopterin		Composite inflammation
		b	F	p	b	F	p	b	F	p	b	F	p	b	F	p
	Initial model
	Type 2 diabetes	.561	9.8	<.01	.381	3.6	.07	.427	5.0	<.05	.368	4.4	<.05	.616	10.8	<.01
	BMI	.495	12.3	<.01	.470	9.8	<.01	.458	10.0	<.01	.276	3.7	.06	.612	19.6	<.001
	Waist circumference	.474	10.5	<.01	.463	9.7	<.01	.439	8.8	<.01	.342	5.9	<.05	.614	19.8	<.001
	Model controlling for BMI
	Type 2 diabetes	.282	1.5	.23	.001	.01	.99	.099	.2	.68	.255	1.2	.29	.211	.9	.36
	Model controlling for waist circumference
	Type 2 diabetes	.304	1.6	.21	À.020	.01	.94	.115	.2	.64	.150	.4	.53	.184	.6	.43
	Separate effect of covariates
	-Diabetes duration	À.085	.2	.62	À.255	2.4	.13	.131	.6	.44	.294	3.0	.08	À.018	.01	.92
	-HbA1C levels	À.185	1.2	.27	.150	.8	.38	À.030	.03	.86	À.394	6.4	<.05	À.128	.6	.45
	-Insulin treatment	.318	3.9	.06	À.250	2.3	.14	À.243	2.2	.15	À.057	.1	.74	À.079	.2	.64
	-Fasting status	.243	2.2	.15	À.238	2.1	.16	À.077	.2	.65	.061	.1	.72	À.017	.01	.92

Table 4

 4 Fatigue symptoms in type 1 versus type 2 diabetes.

		Type 1 diabetes	Type 2 diabetes
	MFI total score, mean (SD)	43.8 (13.7)	52.6 (11.3) **
	Fatigue symptom dimensions
	General fatigue, mean (SD)	9.7 (3.6)	12.4 (2.9) **
	Physical fatigue, mean (SD)	9.2 (2.9)	12.0 (3.0) **
	Reduced activity, mean (SD)	8.5 (3.4)	11.0 (2.8) **
	Mental fatigue, mean (SD)	8.4 (3.2)	8.7 (3.0)
	Reduced motivation, mean (SD)	8.1 (3.4)	8.6 (2.7)
	ANCOVA controlling for insulin treatment status, diabetes duration and HbA1C
	levels.
	Fatigue symptom dimensions were assessed using the Multidimensional Fatigue
	Inventory (MFI).

** p < .01 versus type 1 diabetic patients.

Table 5

 5 Relationship of inflammatory markers with fatigue symptoms in patients with type 2 diabetes.

	Fatigue symptoms	Predictors	Model 1			Model 2 (adjusting for BMI)		Mediation Sobel		Separate effects of covariates
		Inflammatory markers	b	F	p	b	F	p	Z	p		b	F	p
	Total fatigue	hsCRP	.755	5.4	<.05	.553	2.0	.18	.88	.38	Diabetes duration	.242	.9	.35
		hsIL6	.650	11.6	<.01	.677	5.3	<.05	À.12	.90	HbA1C levels	.025	.01	.92
		hsTNFÀa	.059	.04	.84	À.217	.6	.46			Insulin treatment	.322	1.7	.21
		Neopterin	.451	2.3	.16	.305	1.1	.32			Fasting status	.293	1.4	.25
		Composite inflammation	.610	6.7	<.05	.499	2.2	.17	.47	.64
	General fatigue	hsCRP	.222	.6	.45	.177	.2	.63			Diabetes duration	.496	4.9	<.05
		hsIL6	.256	1.9	.19	.362	1.6	.23			HbA1C levels	À.091	.1	.73
		hsTNFÀa	.042	.04	.85	À.024	.01	.92			Insulin treatment	.503	5.1	<.05
		Neopterin	.471	6.0	<.05	.473	4.9	<.05	À.03	.98	Fasting status	.520	5.6	<.05
		Composite inflammation	.304	2.3	.16	.405	2.0	.19
	Reduced activity	hsCRP	.698	3.5	.09	.748	2.6	.13	À.20	.84	Diabetes duration	.085	.1	.75
		hsIL6	.483	3.6	.08	.711	3.5	.09	À.79	.43	HbA1C levels	.151	.3	.56
		hsTNFÀa	À.150	.2	.63	À.329	.9	.35			Insulin treatment	.108	.2	.68
		Neopterin	.052	.02	.88	À.034	.01	.93			Fasting status	.059	.05	.82
		Composite inflammation	.360	1.4	.26	.378	.7	.41
	Mental fatigue	hsCRP	.903	8.8	<.05	.623	3.3	.09	1.23	.22	Diabetes duration	.009	.01	.97
		hsIL6	.707	14.9	<.01	.600	4.6	.05	.52	.61	HbA1C levels	À.075	.1	.78
		hsTNFÀa	.155	.3	.60	À.172	.4	.53			Insulin treatment	.375	2.4	.14
		Neopterin	.408	1.7	.22	.204	.5	.48			Fasting status	.295	1.4	.25
		Composite inflammation	.689	9.2	<.05	.468	2.2	.17	.97	.33
	Reduced motivation	hsCRP	.930	9.1	<.05	.663	3.6	.09	1.17	.24	Diabetes duration	À.077	.1	.77
		hsIL6	.765	20.2	.001	.728	7.7	<.05	.19	.85	HbA1C levels	.252	1.0	.33
		hsTNFÀa	.225	.6	.45	À.081	.09	.78			Insulin treatment	.074	.1	.78
		Neopterin	.427	1.8	.20	.225	.6	.46			Fasting status	À.029	.01	.91
		Composite inflammation	.745	11.5	<.01	.577	3.5	.09	.76	.45

J.Lasselin et al. / Brain, Behavior, and Immunity 26 (2012) 1211-1219

J.Lasselin et al. / Brain, Behavior, and Immunity 26 (2012) 1211-1219


Zhang, N. ., Sauvage, C Manohar Chakrabarti

Na Zhang

Christopher Sauvage

Stephane Munos

José Blanca

Joaquin Canizares

María José Díez

Rhiannon Schneider

Michael Mazourek

Jammi Mcclead

Stéphane Muños

Jose Blanca

Joaquin Cañizares

Maria Jose Diez

Mathilde Causse

Esther Van Der Knaap

A cytochrome P450 regulates a domestication trait in cultivated tomato

Keywords: Tomato, fruit mass, QTL, SlKLUH, domestication

des établissements d'enseignement et de recherche

SlKLUH also modulates plant architecture by regulating number and length of the side shoots, and ripening time, and these effects are particularly strong in plants that transgenically down regulate SlKLUH expression carrying fruits of a dramatically reduced mass. Association mapping followed by segregation analyses revealed that a single nucleotide polymorphism in the promoter of the gene is highly associated with fruit mass. This single polymorphism may potentially underlie a regulatory mutation resulting in increased SlKLUH expression concomitant with increased fruit mass. Our findings suggest that the allele giving rise to large fruit arose in the early domesticates of tomato and becoming progressively more abundant upon further selections. We also detected association of fruit weight with CaKLUH in chile pepper (Capsicum annuum) suggesting that selection of the orthologous gene may have occurred independently in a separate domestication event. Altogether, our findings shed new light on the molecular basis of fruit mass, a key domestication trait in tomato and other fruit and vegetable crops.

Significance

Our study offers novel insights into the molecular basis of the regulation of tomato fruit mass, a critical trait in the domestication of fruit and vegetable crops. \body Plant domestication and artificial selection led to improved agricultural production resulting from dramatic increases in fruit and seed weight [START_REF] Darwin | The variation of animals and plants under domestication[END_REF]. At the start of the Neolithic era approximately 10,000 years ago, domestication of animals and plants accompanied the change in lifestyle from hunter-gatherer to a farming routine [START_REF] Diamond | Guns, Germs, and Steel[END_REF][START_REF] Smith | Seed plant domestication in eastern North America[END_REF]. Driven by the selection of alleles from wild relatives and those that arose after the initial domestication events, characters associated with the domestication syndrome such as larger fruit and seed are typically differentiating wild from cultivated forms [START_REF] Pickersgill | Domestication of plants in the Americas: Insights from mendelian and molecular genetics[END_REF]. Genome-wide genetic diversity analyses support the notion that the initial domestication of tomato was situated in Northern Peru and Ecuador [START_REF] Blanca | Variation revealed by SNP genotyping and morphology provides insight into the origin of the tomato[END_REF]. Selections from the red-fruited wild relative S. pimpinellifolium L. evolved into the semi-domesticated S. lycopersicum L. var cerasiforme bearing fruit of small to medium weight. S. l. cerasiforme was further domesticated in Mexico giving rise to the large fruited tomato S. lycopersicum var lycopersicum which to date is cultivated throughout the world [START_REF] Blanca | Variation revealed by SNP genotyping and morphology provides insight into the origin of the tomato[END_REF].

Tomato is an agriculturally important vegetable crop and is used as model for fruit development including ripening and morphological studies [START_REF] Klee | Genetics and control of tomato fruit ripening and quality attributes[END_REF][START_REF] Paran | Genetic and molecular regulation of fruit and plant domestication traits in tomato and pepper[END_REF][START_REF] Tanksley | The genetic, developmental, and molecular bases of fruit size and shape variation in tomato[END_REF]. Genetic studies have identified several QTLs associated with fruit mass in tomato, of which six loci (fruit weight1.1 [fw1.1], fw2.2, fw2.3, fw3.1/fw3.2, fw4.1 and fw9.1) are postulated to be major QTLs [START_REF] Grandillo | Identifying loci responsible for natural variation in fruit size and shape in tomato[END_REF]. The only cloned fruit mass gene from vegetable or fruit crops is FW2.2/Cell Number Regulator (CNR) [START_REF] Frary | fw2.2: a quantitative trait locus key to the evolution of tomato fruit size[END_REF]. The gene encodes a negative regulator of cell division and controls tomato fruit mass as well as organ size in other species, e.g. maize [START_REF] Frary | fw2.2: a quantitative trait locus key to the evolution of tomato fruit size[END_REF][START_REF] Guo | Cell Number Regulator1 affects plant and organ size in maize: implications for crop yield enhancement and heterosis[END_REF] and nitrogen-fixing nodule number [START_REF] Libault | A member of the highly conserved FWL (tomato FW2.2-like) gene family is essential for soybean nodule organogenesis[END_REF]. fw3.2 is the second major tomato fruit mass QTL, which explained 19 % of the phenotypic variance in a F 2 population derived from a cross between cultivated tomato accession 'Yellow Stuffer' and wild tomato accession 'LA1589' [START_REF] Van Der Knaap | The making of a bell pepper-shaped tomato fruit: identification of loci controlling fruit morphology in Yellow Stuffer tomato[END_REF][START_REF] Zhang | Fine mapping of fw3.2 controlling fruit weight in tomato[END_REF]. Despite the importance of fruit mass in the evolution of fruit and vegetable plants, and the numerous genetic loci that underlie the trait [START_REF] Paran | Genetic and molecular regulation of fruit and plant domestication traits in tomato and pepper[END_REF][START_REF] Grandillo | Identifying loci responsible for natural variation in fruit size and shape in tomato[END_REF][START_REF] Blas | Genetic mapping of quantitative trait loci controlling fruit size and shape in papaya[END_REF][START_REF] Costantini | Berry and phenologyrelated traits in grapevine (Vitis vinifera L.): from quantitative trait loci to underlying genes[END_REF][START_REF] Doganlar | Conservation of gene function in the Solanaceae as revealed by comparative mapping of domestication traits in eggplant[END_REF][START_REF] Eduardo | QTL analysis of fruit quality traits in two peach intraspecific populations and importance of maturity date pleiotropic effect[END_REF][START_REF] Zhang | Fruit size QTL analysis of an F-1 population derived from a cross between a domesticated sweet cherry cultivar and a wild forest sweet cherry[END_REF], cloning of domestication genes of fruit and vegetable crops has lagged behind that of the cereal crops. Therefore, insights into the molecular mechanisms that led to the transition of the fruit from small to large remains largely unknown.

Our current study focuses on fine mapping of a tomato fruit mass locus fw3.2 and cloning of the underlying gene. Association mapping, segregation analysis and transgenic studies led us to identify the putative molecular basis of fruit weight at this locus and a likely regulatory SNP in the promoter of the gene that is highly associated with fruit mass. Phenotypic evaluations demonstrated the cellular basis of increased fruit mass and pleiotropic effects associated with the locus. We also investigated the likely origin of the derived allele and the potential role of this gene in the regulation of fruit mass in other crop species.

Results

fw3.2 regulates fruit mass, delays ripening and modulates plant architecture, but not total yield per plant. To elucidate whether fruit mass differences were determined during flower or fruit development, we evaluated the ovary size at anthesis. Evaluations of the nearly isogenic lines (NIL) that differ for the allele at fw3.2 showed that the ovary perimeter at anthesis was similar while the mature fruit perimeter was significantly different. Further analyses revealed that in particular the pericarp and septum areas were significantly larger in the large fruited NIL fw3.2(ys) (Table 1, Fig. S1A -B). The pericarp of the mature fruit carrying the large fruit allele fw3.2(ys) showed an increase in cell number whereas cell size remained the same (Table 1, Fig. S1C -D). The time from anthesis to ripe fruit was prolonged in the fw3.2(ys) NIL (Table 1). In addition to fruit, fw3.2(ys) increased seed mass, although seed number was not significantly different (Table S1). Number of fruits were significantly higher in the lines carrying the small fruited fw3.2(wt) allele, while yield per plant was the same (Table 2). Additionally, fw3.2(wt) NIL contained more inflorescences and side shoots, including increased side shoot length (Table 2 andTable S1). The number of flowers per inflorescence remained the same. Retaining only 12 fruits per plant led to increased fruit weight in both NILs (Table S1) demonstrating that the increase in fruit mass controlled by fw3.2 was not due to changes in source-sink relationships.

Thus, the results demonstrate that increases in fruit mass coincide with a reduction of number of fruit per plant caused by a reduction in side shoot number and length yielding fewer inflorescences.

Fine mapping delimited the fw3.2 locus to a 24.4 kb region. Previously, the fw3.2 locus was fine mapped to a 51.4 kb region comprised of 7 candidate genes [START_REF] Zhang | Fine mapping of fw3.2 controlling fruit weight in tomato[END_REF]. An additional recombinant screen delimited the locus to a 24.4 kb region comprised of three candidate proteins: a cytochrome P450 (ORF6) belonging to CYP78A subfamily, an ABC transporter (ORF7) distantly related to PGP transporters, and a Kelch domain-containing protein of unknown function (ORF8). The fine mapping experiment excluded most of the ORF8 gene to only include the promoter, first exon and first intron until the NDF9 marker (Fig. 1, Table S2). Gene action analysis revealed the additive nature of the fw3.2 alleles suggesting that neither allele is a null (Table S2).

Association mapping and genetic segregation analysis identified a potential regulatory SNP

in the promoter of ORF6. To further investigate which of the candidate genes may underlie the fw3.2 locus, we sought to identify the underlying nucleotide polymorphisms that were correlated with larger fruit. We conducted an association mapping study using a core collection of tomato genotypes demonstrated to have broad genetic diversity within red-fruited tomatoes and comprised of domesticated S. l. lycopersicum, the wild relative S. pimpinellifolium and semidomesticated S. l. cerasiforme accessions [START_REF] Ranc | A clarified position for Solanum lycopersicum var. cerasiforme in the evolutionary history of tomatoes (Solanaceae)[END_REF]. We sequenced approximately 7 kb of the fw3.2 locus and genotyped 115 markers inside and outside the fw3.2 region spanning 48.5 kb (Fig. 1 and Dataset S1). Accounting for kinship and population structure, we identified six SNPs that were significantly associated with fruit mass (Fig. 1 and Table S3). With the exception of one SNP, all were located in the upstream region of ORF6 and ORF7. The two most significant SNPs were found in or near ORF6, 512 bp upstream of the start of transcription (M9) and 72 bp from the stop codon in the 3´UTR (M42) (Fig. 1). Successful association mapping typically requires rapid linkage disequilibrium (LD) decay. However, low rates of LD decay were found around the fw3.2 locus, suggesting few natural recombination events in this region that would potentially compromise the interpretation of the association mapping results [START_REF] Zhang | Fine mapping and characterization of fw3.2, one of the major QTL controlling fruit size in tomato[END_REF]. Therefore, to determine the validity of the association of these SNPs to fruit mass, we performed intraspecific segregation analyses using populations derived from parents that showed few polymorphisms including M9 and three to five additional SNPs (Dataset S1 and Table S4). Genetic analyses using the F 2 and BC 1 F 2 populations showed segregation for fruit mass with the allele of M9 in all families in either one or both populations (Table S4). Importantly, family 12S74 segregated for M9 but not for M42 and showed that fruit mass segregated with the allele of M9 (Dataset S1 and Table S4). This finding demonstrated that the M9 SNP was more critical in regulating fruit mass than M42, lending support for the notion that the ORF6 promoter SNP may regulate fruit mass differences at fw3.2.

Comment citer ce document : Chakrabarti, M. ., Zhang, N. ., Sauvage, C., Munos, S., Blanca, J. ., Cañizares, J. ., Diez, M. J. ., Schneider, R. ., Mazourek, M. ., McClead, J. ., Causse, M., van Down regulation of ORF6 leads to reduced fruit mass. To further investigate the role of the candidate genes in regulating fruit mass, we down-regulated transcript accumulation of ORF6 and ORF7. Since expression of ORF6 and ORF7 was lower in the lines that carried small fruit (Fig. 2A), the constructs were transformed into the fw3.2(ys) NILs typically yielding large fruit. Down regulation of ORF6 transcript levels dramatically reduced fruit and seed mass (Fig. 2B -E and Table S5). Similar to the NILs, down regulation of ORF6 led to a higher number of side shoots and faster ripening (Table S5). The transgenic lines showed additional defects compared to the NILs, e.g. reduced plant height, smaller leaves and leaflets, and severely reduced seed number indicating that extensive down regulation of ORF6 led to phenotypes affecting the entire plant (Table S5). The RNAi-2 lines targeting the coding region led to less severe phenotypes, including slightly larger fruit with viable seeds. Even though overall seed number was significantly reduced, fruit with 50-100 seeds showed reduced fruit weight compared to the control with the same number of seed (Fig. S1H). Thus, when accounting for seed number, fruit mass is still reduced in lines that down regulate ORF6. Contrary to ORF6, down regulation of ORF7 using four amiRNA constructs did not show a correlation between reduction in fruit mass and transcript level (Table S7 and Fig. S1I), and the plants were indistinguishable from the wild type fw3.2(ys) NIL. Thus, it is unlikely that ORF7 underlies the fw3.2 locus. In light of these findings, we assumed that ORF6 underlies the fw3.2 locus and renamed it SlKLUH after the founding member of this subfamily of P450s [START_REF] Anastasiou | Control of plant organ size by KLUH/CYP78A5-dependent intercellular signaling[END_REF]. Expression of SlKLUH was the highest in vegetative meristems and in the young flower buds (Fig. 2F). Compared to SlKLUH, the expression of SlFW2.2/CNR was much lower in most tissues examined and differed in tissue specificity. Also, expression of SlKLUH was very high in developing seed and not in the growing pericarp, contrary to SlFW2.2/CNR (Fig. 2G). A and B, respectively) compared to S. pimpinellifolium (0.002874 and 0.004962) and S. l. cerasiforme (0.003203 and 0.004364) (Fig. 3A). The ratio of nucleotide diversity of S. l. lycopersicum to S. pimpinellifolium at SlKLUH was significantly lower compared to the average diversity for chromosome 3 [percentage of π S. lycopersicum /π S.pimpinellifolium for fragment A, B and chromosome 3 are 0, 3.9 and 56.9 %, respectively]. Tajima's D analysis of all accessions combined showed significant values for parts of the two sequenced fragments, ranging from -2.0851 to -1.5187 and from -2.1922 to -1.9117 for fragment A and B, respectively as compared to the average Tajima's D for chromosome 3 (-1.3247) (Fig. 3A). The findings of the molecular diversity analysis were also supported by the results from the phylogenetic analysis using the SlKLUH gene sequences. One cluster consisted of all S. pimpinellifolium and several S. l. cerasiforme accessions and displayed relatively high sequence diversity. The other cluster is comprised of the S. l. lycopersicum and the remaining S. l. cerasiforme accessions and showed limited genetic diversity (Fig. 3B). The derived allele designated by the M9 SNP was only found in a single subclade with some S. l. cerasiforme and all the cultivated tomato accessions, suggesting the mutation arose in the S. l. cerasiforme background. SNP in a tomato collection that covers the proposed trajectory of tomato domestication [START_REF] Blanca | Variation revealed by SNP genotyping and morphology provides insight into the origin of the tomato[END_REF]. The M9 SNP associated with increased fruit mass was already present at approximately 30% frequency in the Ecuadorian and Northern Peruvian S. l. cerasiforme that were closely related to the wild progenitor S. pimpinellifolium. The allele frequency increased slightly in the Mesoamerican S. l. lycopersicum accessions, and increased further in the S. l. lycopersicum landraces from Europe and elsewhere to become practically fixed in modern accessions (Fig. 3C and Table S6). This finding suggested that even though the derived allele arose early, selections for larger fruit weight may not have been as critical or that this allele was not selected early and only became relevant in recent times.

Possible origin of the M9 mutation and identification of fruit mass

Based on coinciding QTLs, it has been proposed that orthologous genes may have been selected independently in different crops [START_REF] Paran | Genetic and molecular regulation of fruit and plant domestication traits in tomato and pepper[END_REF][START_REF] Paterson | Convergent domestication of cereal crops by independent mutations at corresponding genetic loci[END_REF]. To evaluate whether chile pepper (C. annuum) may harbor a fruit mass QTL at a region that is syntenic to tomato fw3.2, we determined whether the pepper ortholog CaKLUH was associated with fruit mass in this species. A population of 106 F9 RILs derived from a cross between a large-fruited pepper and a small-fruited wild relative C. SlKLUH is most likely underlying fw3.2 and a critical SNP in its promoter is proposed to regulate fruit mass. ORF6 encodes a protein with the highest similarity to the Arabidopsis KLUH/CYP78A5 protein controlling vegetative, reproductive organ size and also seed size [START_REF] Anastasiou | Control of plant organ size by KLUH/CYP78A5-dependent intercellular signaling[END_REF][START_REF] Adamski | Local maternal control of seed size by KLUH/CYP78A5-dependent growth signaling[END_REF]. Other members of this subfamily of cytochrome P450 also control organ and plant size, e.g. CYP78A9, CYP78A6 in Arabidopsis and CYP78A11 in rice [START_REF] Fang | Maternal control of seed size by EOD3/CYP78A6 in Arabidopsis thaliana[END_REF][START_REF] Ito | Overexpression of a gene encoding a cytochrome P450, CYP78A9, induces large and seedless fruit in arabidopsis[END_REF][START_REF] Miyoshi | PLASTOCHRON1, a timekeeper of leaf initiation in rice, encodes cytochrome P450[END_REF]. Two additional members of this subfamily CYP78A27 and CYP78A28 control colony size in moss [START_REF] Katsumata | Involvement of the CYP78A subfamily of cytochrome P450 monooxygenases in protonema growth and gametophore formation in the moss Physcomitrella patens[END_REF]. This highly conserved function of CYP78A subfamily in regulating organ size led us to postulate that ORF6 was the most likely candidate gene for the fw3.2 QTL.

The first experiment to test this hypothesis was to identify SNPs that were significantly associated with fruit mass. Our expectations were that these SNPs would be found near the most likely candidate gene. The most significant SNPs identified from the association mapping were the M9 SNP located 512 bp upstream of the transcription start site and the M42 SNP located in the 3´ UTR at 72 bp downstream from the stop codon of ORF6. However, LD decay rate was low [START_REF] Zhang | Fine mapping and characterization of fw3.2, one of the major QTL controlling fruit size in tomato[END_REF], which was likely due to the lack of outcrossing and recombination events during domestication of tomato. Yet, when the M9 polymorphism arose, its effect on fruit mass could have been of functional significance. This pattern of few SNPs in large LD blocks has also been found in human [START_REF] Meyer | A functional variant at a prostate cancer predisposition locus at 8q24 is associated with PVT1 expression[END_REF][START_REF] Nakki | Allelic variants of IL1R1 gene associate with severe hand osteoarthritis[END_REF][START_REF] Nischwitz | More CLEC16A gene variants associated with multiple sclerosis[END_REF][START_REF] Uno | A genome-wide association study identifies genetic variants in the CDKN2BAS locus associated with endometriosis in Japanese[END_REF] and may be common in plants as well. The low LD yet significant association of the SNP with fruit mass led us to conduct segregation analyses which excluded the importance of M42 leaving M9 as the most significantly associated SNP of potential functional relevance, i.e. to regulate fruit mass. Interestingly, the M9 SNP was located within the third repeat of four 30 bp tandem repeats, with each repeat sharing 83 to 100% homology with its consensus sequence. The tandem repeat was located 440 bp to 559 bp upstream from the SlKLUH transcription start site. Moreover, M9 mutation is located within a putative cis element, related to the organ-specific element (OSE) found in nodulin and leghemoglobin genes in soybean and Vicia faba [START_REF] Sandal | A small family of nodule specific genes from soybean[END_REF][START_REF] Stougaard | Interdependence and nodule specificity of cis-acting regulatory elements in the soybean leghemoglobin-Lbc3 and N23 gene promoters[END_REF][START_REF] Vieweg | The promoter of the Vicia faba L. leghemoglobin gene VfLb29 is specifically activated in the infected cells of root nodules and in the arbusculecontaining cells of mycorrhizal roots from different legume and nonlegume plants[END_REF]. In the future, it will be interesting to assess the role of this putative cis-element in gene expression and to explore whether the tandem repeat has any bearing on the modulation of SlKLUH expression. Even though the M9 SNP appears most promising to regulate fruit mass, we can't exclude the roles of three other SNPs, M60, M74 and M82 in the regulation of fruit mass while additional SNPs that may be found in the remaining regions that were not sequenced cannot be excluded either.

The second experiment was to transgenically down regulate the expression of the candidate genes, ORF6 and ORF7. Our expectation was that expression levels positively correlate with fruit mass since the fw3.2(wt) NIL exhibit smaller fruit and lower transcript levels. Reduced expression of ORF6, and not of ORF7, had pronounced effects on fruit mass, seed mass, side shoot number and fruit ripening which were traits that were also significantly altered in the NILs.

The severity of these phenotypes followed a gradient from fw3.2(ys), fw3.2(wt) to the RNAi line.

Thus, fine mapping, association mapping and functional segregation analyses of a highly significant SNP in the promoter of ORF6 combined with the plant transformation results and phenotypic evaluations strongly implied that ORF6 underlies the fw3.2 locus. The expression of ORF6, which we renamed SlKLUH, was particularly high in the developing seeds. However, the effect of increased fruit mass was found in the pericarp of the maternal tissues. Thus, it is plausible that a seed-derived signal might play a role in regulation of fruit mass by SlKLUH. A similar hypothesis has been proposed to explain the action of Arabidopsis KLUH [START_REF] Anastasiou | Control of plant organ size by KLUH/CYP78A5-dependent intercellular signaling[END_REF]. opening) leading to the initiation of seed development. Immediately following fertilization, the fruit undergoes a short period of cell division which is followed by a longer period of cell expansion until the final dimensions are reached [START_REF] Gillaspy | Fruits: a developmental perspective[END_REF][START_REF] Iglesias | Physiology of citrus fruiting[END_REF][START_REF] Marcelis | Cell division and expansion in the cucumber fruit[END_REF][START_REF] Xiao | Integration of tomato reproductive developmental landmarks and expression profiles, and the effect of SUN on fruit shape[END_REF]. The increase in fruit mass was due to growth processes taking place after fertilization, particularly of the pericarp and septum tissues.

The role of

These findings showed that the extra cell divisions led to enlarged fruit and a concomitant delay in ripening. Therefore, the delay in ripening was likely the result of an extension of the cell division stage resulting from increased expression. SlFW2.2/CNR also controls fruit mass by extending the cell division period [START_REF] Cong | Natural alleles at a tomato fruit size quantitative trait locus differ by heterochronic regulatory mutations[END_REF]. Thus, the increases in fruit mass resulting from domestication may operate on similar processes during fruit development yet through different mechanisms based on putative gene function. The lengths of other plant organs were not affected in the NILs, suggesting a specific role of the natural alleles of SlKLUH in the regulation of fruit and seed mass. Our results also indicated that plant architecture was affected implying a pleiotropic effect of SlKLUH on plant growth and development. Exploration of possible origin of M9 mutation using a wide array of tomato accessions showed that the increase in the derived allele frequency followed the same path as the proposed domestication of tomato and was consistent with the selection for a larger fruit along this process [START_REF] Blanca | Variation revealed by SNP genotyping and morphology provides insight into the origin of the tomato[END_REF]. However, the allele did not become fixed early during domestication of tomato, further supporting the notion that this region may not have been under strong selection during domestication. Even though the derived M9 allele was detected in low frequency in the Peruvian S. pimpinellifolium, without further study we cannot ascertain the validity of this finding and whether the allele may have originated in a wild relative.

Diversity of

Conclusion.

To date, only one gene underlying a fruit mass QTL is known [START_REF] Frary | fw2.2: a quantitative trait locus key to the evolution of tomato fruit size[END_REF]. Our study identified SlKLUH as the second major gene controlling fruit mass in a vegetable or fruit crop, shedding light on the molecular regulation of this trait in plants. Additionally, previous studies had shown an overlap of the fw2.2/cnr QTL in chile pepper, albeit that the effect of the pepper QTL was minor [START_REF] Paran | Genetic and molecular regulation of fruit and plant domestication traits in tomato and pepper[END_REF][START_REF] Rao | Mapping of yield-related QTLs in pepper in an interspecific cross of Capsicum annuum and C. frutescens[END_REF], whereas the existence of a pepper fw3.2 QTL was less certain. The findings from this study raise the possibility that KLUH may regulate fruit mass in chile pepper and potentially other crops.

Materials and methods

Plant materials and phenotypic evaluation. Plants were grown under field and greenhouse condition at OARDC, Wooster, Ohio in 2010 -2012, and a subset in Gainesville, Florida in 2011. Most experiments were conducted with 10 plants for fw3.2(ys) and fw3.2(wt) NILs and multiple independent lines for the ORF6_RNAi and ORF7_amiRNA constructs. Fruit and seed mass analyses in fw3.2 NILs, ORF6_RNAi and ORF7_amiRNA lines were performed with 20 ripe fruits per plant, unless fruit set was impaired. Side shoot number and lengths were measured at 56 and 70 days after sowing, whereas plant height and number of nodes were recorded at 35 and 56 days after sowing. Leaf attributes were measured on 12 th and 13 th leaves. Floral organ measurements were performed with five flowers per plant. For cell measurement of the ovaries at anthesis, three ovaries per plant and three sections per ovary were analyzed. For cell measurements at mature fruit stage, two fruits per plant and two sections per fruit were analyzed.

To test source-sink relationship, a total of 12 fruit plant were kept and fruit weight from these plants was compared with control plants with no fruit removal. All phenotypic evaluations were performed independently at least twice, except the fruit removal experiment which was done once.

Whole fruit, pericarp and cell measurements. Mature fruit perimeter, area of pericarp, septum, columella and placenta were analyzed using Tomato Analyzer (version 2.3) [START_REF] Brewer | Development of a controlled vocabulary and software application to analyze fruit shape variation in tomato and other plant species[END_REF]. For the cell measurements in the pericarp, thick sections were made with a razor blade, stained with 0.5% Toluidine Blue in 0.1% Sodium Carbonate solution (SPI, Electron Microscopy Supplies, West Chester, PA), photographed with a Leica MZFLIII dissecting microscope coupled to a digital camera (SPOT RT KE, Diagnostic Instruments, Inc. USA), followed by analysis with ImageJ software. For cell measurements of the ovary at anthesis, the tissue was fixed in 0.1M phosphate buffer (pH7.2) containing 3% glutaraldehyde and 2% paraformaldehyde. Samples were dehydrated in 25%-90% ethanol series, followed by infiltration in 50%-100% LR White resin (Electron Microscopy Supplies, Hatfield, PA) gradient at room temperature. Samples were embedded in 100% LR White for 24 hours at 55⁰C. For each ovary, 5µm transverse sections were stained and photographed and cell measurements were similar as described for the pericarp cell measurements.

Fine mapping, gene action and genetic segregation analysis. To identify additional recombinants, 178 seedlings of 09S69-68 line were screened, resulting in recombinant line 10S187-82. The progeny testing of the other line was from a previous study [START_REF] Zhang | Fine mapping of fw3.2 controlling fruit weight in tomato[END_REF].

For the genetic segregation analysis of the M9 SNP, we developed five F 2 and BC 1 F 2 populations each that were derived from parents from the core collection showing few polymorphisms in addition to M9. Alleles for the other known fruit weight/shape genes that were segregating in the populations were fixed such that only fw3.2 was segregating. For the progeny testing and segregation analysis, 10-13 plants per genotype [genotypes carrying homozygous fw3.2(ys) and homozygous fw3.2(wt) alleles] were grown and 20 ripe fruits representing the average per plant were weighed. The average fruit mass of each plant was contrasted between the genotypes and the significance of fruit mass segregation in each family was determined using student's t-test.

For gene action analysis, 10 plants each for fw3.2(ys), fw3.2(wt) and heterozygous for fw3.2 were analyzed. Gene action was represented as D/A, where D = Aa -(AA+aa)/2 and A = (AA-aa)/2.

Gene action experiments were repeated with the same NIL family grown in two separate fields.

Gene expression analysis.

Total RNA was extracted using Trizol® (Invitrogen Inc. USA) as recommended by the manufacturer. Northern blots were performed as described previously [START_REF] Xiao | A retrotransposonmediated gene duplication underlies morphological variation of tomato fruit[END_REF].

For analyzing ORF7 transcript accumulation, total RNA was isolated from mature leaves, genomic DNA was removed using TURBO DNA-free TM kit (Invitrogen TM ), followed by first strand cDNA synthesis and qPCR analysis using iQ TM SYBR Green Supermix (Bio-Rad, USA).

For Northern blot and qPCR analyses, the expression of eIF4α and CAC were used as internal control (for primers see Table S7). For RNA seq analysis, strand-specific libraries of approximately 250 bp fragments were prepared using 10 µg total RNA. Four replicates were generated for each tissue type and time point. Single end sequences of 51 bp were generated on an Illumina HiSeq2000 at Weill Medical College [START_REF] Zhong | High-throughput illumina strand-specific RNA sequencing library preparation[END_REF]. Datasets for the digital gene expression and transcriptome analysis of developing fruit using RNA seq can be accessed using NCBI SRA accession number SRA061767, SRA068200 and SRA091611 [START_REF] Huang | Genome-wide identification, phylogeny and expression analysis of SUN, OFP and YABBY gene family in tomato[END_REF]. Analyzed data can also be accessed at the Tomato Functional Genomics Database (http://ted.bti.cornell.edu/cgi-bin/TFGD).

Association of CaKLUH with fruit weight in chile pepper. To identify nucleotide polymorphisms, the 5´ upstream, intron, 3´downstream regions of the CaKLUH ortholog in pepper were sequenced in C. annuum cv. 'NuMex RNaky' and the C. frutescens accession BG 2814-6 [START_REF]QTL analysis for capsaicinoid content in Capsicum[END_REF]. Genomic DNA was extracted from 106 individuals from a F 9 RIL population derived from a cross between these parents. The intron SNP was developed into a CAPS marker using the primer pair fw3.2_pepper_F and fw3.2_pepper_R and restriction enzyme digestion with HaeIII. Ten fruit were dried and weighed from each line.

Development of transgenic RNA interference lines for ORF6 and ORF7. Two RNAi hairpin constructs for ORF6, pMC2 corresponding to RNAi-3 transgenic lines and pMC3 corresponding to RNAi-2 transgenic lines were generated. RNAi-2 targets the coding sequence of SlKLUH, whereas RNAi-3 targets mainly the 3´UTR (Fig. 2B). Sense and antisense arms were cloned in the HindIII-XhoI and SacI-XbaI sites of a modified pKYLX80 vector, where XhoI and SacI sites are separated by a 150 bp ω-3 fatty acid desaturase intron. Expression cassettes between HindIII-XbaI were subcloned into binary vector pKYLX71 under a 35S promoter with duplicated enhancer [START_REF] Schardl | Design and construction of a versatile system for the expression of foreign genes in plants[END_REF]. Four artificial miRNAs specific for ORF7 were designed using WMD3-Web MicroRNA Designer [START_REF] Ossowski | Gene silencing in plants using artificial microRNAs and other small RNAs[END_REF] and were cloned into pKYLX71 binary vector. Primers for the RNAi and sequences for amiRNAs are shown in Table S7. All constructs were introduced in the fw3.2(ys) NIL 08S591-10 carrying an introgression of ~130 kb. Transformations were carried out at the Plant Transformation Core Research Facility at the University of Nebraska-Lincoln. Association mapping. For the association mapping, approximately 7 kb of the fw3.2 locus was sequenced and additional markers were used to genotype the region in 86 accessions of the core collection [START_REF] Ranc | A clarified position for Solanum lycopersicum var. cerasiforme in the evolutionary history of tomatoes (Solanaceae)[END_REF]. Genomic positions and scores of markers used in association analysis are presented in Table S3. Association analysis was done using MLM model of TASSEL2.1 software [START_REF] Bradbury | TASSEL: software for association mapping of complex traits in diverse samples[END_REF]. Q and K matrix were generated with STRUCTURE 2.2 [START_REF] Falush | Inference of population structure using multilocus genotype data: dominant markers and null alleles[END_REF] and SPAGeDi [START_REF] Hardy | SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels[END_REF], respectively. To generate Q and K matrix, 20 EST-SSR markers distributed throughout the genome were used and the data for these markers in the core collection was obtained from a previous study [START_REF] Ranc | A clarified position for Solanum lycopersicum var. cerasiforme in the evolutionary history of tomatoes (Solanaceae)[END_REF].

Molecular diversity, phylogenetic analysis and estimation of M9 allele frequency. For the molecular diversity analysis, fragment A (637bp) and fragment B (507bp) were sequenced in 157 (15 S. pimpinellifolium; 20 S. l. lycopersicum and 122 S. l. cerasiforme) and 172 lines (16 S. pimpinellifolium; 24 S. l. lycopersicum and 132 S. l. cerasiforme), respectively [START_REF] Ranc | A clarified position for Solanum lycopersicum var. cerasiforme in the evolutionary history of tomatoes (Solanaceae)[END_REF]

  der Knaap, E. . (Auteur de correspondance) (2013). A cytochrome P450 regulates a domestication trait in cultivated tomato. Proceedings of the National Academy of Sciences of the United States of America, 110 Manuscrit d'auteur / Author manuscript Manuscrit d'auteur / Author manuscript Manuscrit d'auteur / Author manuscript Version définitive du manuscrit publiée dans / Final version of the manuscript published in : Proceedings of the National Academy of Sciences of the United States of America (2013), Vol. 110, N°.42, p. 17125-17130, DOI: 10.1073/pnas.1307313110 Journal homepage: www.pnas.org/cgi/doi/10.1073/pnas.1307313110

  Comment citer ce document : Chakrabarti, M. ., Zhang, N. ., Sauvage, C., Munos, S., Blanca, J. ., Cañizares, J. ., Diez, M. J. ., Schneider, R. ., Mazourek, M. ., McClead, J. ., Causse, M., van der Knaap, E. . (Auteur de correspondance) (2013). A cytochrome P450 regulates a domestication trait in cultivated tomato. Proceedings of the National Academy of Sciences of the United States of America, 110 Manuscrit d'auteur / Author manuscript Manuscrit d'auteur / Author manuscript Manuscrit d'auteur / Author manuscript Version définitive du manuscrit publiée dans / Final version of the manuscript published in : Proceedings of the National Academy of Sciences of the United States of America (2013), Vol. 110, N°.42, p. 17125-17130, DOI: 10.1073/pnas.1307313110 Journal homepage: www.pnas.org/cgi/doi/10.1073/pnas.1307313110 Molecular diversity and phylogenetic analysis of SlKLUH. To examine the evolutionary history of the fw3.2 locus, DNA sequence variation of the segments spanning part of the coding region (fragment A) and promoter (fragment B) of SlKLUH were investigated. The SlKLUH region showed reduced nucleotide diversity (π) in S. lycopersicum (0 and 0.000195 for fragment

  QTL overlapping with fw3.2 in chile pepper. To further explore where the derived allele arose, we genotyped the M9 Comment citer ce document : Chakrabarti, M. ., Zhang, N. ., Sauvage, C., Munos, S., Blanca, J. ., Cañizares, J. ., Diez, M. J. ., Schneider, R. ., Mazourek, M. ., McClead, J. ., Causse, M., van der Knaap, E. . (Auteur de correspondance) (2013). A cytochrome P450 regulates a domestication trait in cultivated tomato. Proceedings of the National Academy of Sciences of the United States of America, 110 Manuscrit d'auteur / Author manuscript Manuscrit d'auteur / Author manuscript Manuscrit d'auteur / Author manuscript Version définitive du manuscrit publiée dans / Final version of the manuscript published in : Proceedings of the National Academy of Sciences of the United States of America (2013), Vol. 110, N°.42, p. 17125-17130, DOI: 10.1073/pnas.1307313110 Journal homepage: www.pnas.org/cgi/doi/10.1073/pnas.1307313110

  frutescens showed fruit mass segregated significantly with the large fruited allele of KLUH from the C. annuum parent [dry fruit weight (mean±SD, g) for groups carrying the C. frutescens and the C. annuum allele of fw3.2 are 3.21 ± 0.36 and 4.99 ± 0.34, respectively; P value < 0.0005]. These findings demonstrate that fw3.2 QTL is found in both tomato and chile pepper, and that KLUH may regulate fruit mass in two independently domesticated species. Discussion Comment citer ce document : Chakrabarti, M. ., Zhang, N. ., Sauvage, C., Munos, S., Blanca, J. ., Cañizares, J. ., Diez, M. J. ., Schneider, R. ., Mazourek, M. ., McClead, J. ., Causse, M., van der Knaap, E. . (Auteur de correspondance) (2013). A cytochrome P450 regulates a domestication trait in cultivated tomato. Proceedings of the National Academy of Sciences of the United States of America, 110 Manuscrit d'auteur / Author manuscript Manuscrit d'auteur / Author manuscript Manuscrit d'auteur / Author manuscript Version définitive du manuscrit publiée dans / Final version of the manuscript published in : Proceedings of the National Academy of Sciences of the United States of America (2013), Vol. 110, N°.42, p. 17125-17130, DOI: 10.1073/pnas.1307313110 Journal homepage: www.pnas.org/cgi/doi/10.1073/pnas.1307313110

  Comment citer ce document : Chakrabarti, M. ., Zhang, N. ., Sauvage, C., Munos, S., Blanca, J. ., Cañizares, J. ., Diez, M. J. ., Schneider, R. ., Mazourek, M. ., McClead, J. ., Causse, M., van der Knaap, E. . (Auteur de correspondance) (2013). A cytochrome P450 regulates a domestication trait in cultivated tomato. Proceedings of the National Academy of Sciences of the United States of America, 110 Manuscrit d'auteur / Author manuscript Manuscrit d'auteur / Author manuscript Manuscrit d'auteur / Author manuscript Version définitive du manuscrit publiée dans / Final version of the manuscript published in : Proceedings of the National Academy of Sciences of the United States of America (2013), Vol. 110, N°.42, p. 17125-17130, DOI: 10.1073/pnas.1307313110 Journal homepage: www.pnas.org/cgi/doi/10.1073/pnas.1307313110

  SlKLUH in regulating fruit mass, ripening and plant architecture. Fruit development starts with the fertilization of the ovules in the ovary at the time of anthesis (flower Comment citer ce document : Chakrabarti, M. ., Zhang, N. ., Sauvage, C., Munos, S., Blanca, J. ., Cañizares, J. ., Diez, M. J. ., Schneider, R. ., Mazourek, M. ., McClead, J. ., Causse, M., van der Knaap, E. . (Auteur de correspondance) (2013). A cytochrome P450 regulates a domestication trait in cultivated tomato. Proceedings of the National Academy of Sciences of the United States of America, 110 Manuscrit d'auteur / Author manuscript Manuscrit d'auteur / Author manuscript Manuscrit d'auteur / Author manuscript Version définitive du manuscrit publiée dans / Final version of the manuscript published in : Proceedings of the National Academy of Sciences of the United States of America (2013), Vol. 110, N°.42, p. 17125-17130, DOI: 10.1073/pnas.1307313110 Journal homepage: www.pnas.org/cgi/doi/10.1073/pnas.1307313110

  SlKLUH and origin of M9 mutation. Our findings indicate reduced nucleotide diversity in S. l. lycopersicum in the SlKLUH region compared to S. pimpinellifolium, and an overall reduced diversity compared to the entire chromosome. Parts of two sequenced regions around SlKLUH showed significant Tajima's D values, which may suggest an excess of low frequency SNPs in certain regions of the gene in tomato which is potentially the result of a population expansion after a bottleneck or a selective sweep. Taken together, these results support a selective pressure around the fw3.2 region but this selection is likely to have predated domestication because reduction is found in both S. pimpinellifolium and S. l. lycopersicum.

Fig. 1 .

 1 Figure legends

Fig. 2 .

 2 Fig. 2. Transcript accumulation and effect of SlKLUH knock down on fruit and seed mass. A.

Fig. 3 .

 3 Fig. 3. Molecular diversity and phylogenetic analysis of SlKLUH, and origin of M9 mutation. A.

Acknowledgements. We thank Dr. D. Choi (Seoul National University) for providing DNA sequence of chile pepper CaKLUH region; and Molecular and Cellular Imaging Center, OARDC, The Ohio State University for assistance with microscopy. This work is supported by the National Science Foundation Grant (IOS-0922661) to EVDK.