Benjamin Vandendorpe

Carole Durot

Loïc Lebellec

Marie-Cécile Le Deley

Dienabou Sylla

André-Michel Bimbai

Kocéila Amroun

Fabrice Ramiandrisoa

Abel Cordoba

Xavier Mirabel

Christine Hoeffel

David Pasquier

Stéphanie Servagi-Vernat

Dr Stéphanie Servagi Vernat

Prognostic value of the texture analysis parameters of the initial computed tomographic scan for response to neoadjuvant chemoradiation therapy in patients with locally advanced rectal cancer

Keywords: Computed Tomography, texture analysis, prognostic value, neoadjuvant therapy, rectal cancer

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

Prognostic value of the texture analysis parameters of the initial computed tomographic scan for response to neoadjuvant chemoradiation therapy in patients with locally advanced rectal cancer

Benjamin Vandendorpe, Carole Durot, Loïc Lebellec, Marie-Cécile Le Deley, Dienabou Sylla, André-Michel Bimbai, Kocéila Amroun, Fabrice Ramiandrisoa, Abel Cordoba, Xavier Mirabel, et al.

To cite this version:

Benjamin Background and Purpose Baseline contrast-enhanced computed tomography (CT)-derived texture analysis in locally advanced rectal cancer could help offer the best personalized treatment. The purpose of this study was to determine the value of baseline-CT texture analysis in the prediction of downstaging in patients with locally advanced rectal cancer.

Patients and methods

We retrospectively included all consecutive patients treated with neoadjuvant chemoradiation therapy (CRT) followed by surgery for locally advanced rectal cancer.

Tumor texture analysis was performed on the baseline pre-CRT contrast-enhanced CT examination. Based on the selected model of downstaging with a penalized logistic regression in a training set, a radiomics score (Radscore) was calculated as a linear combination of selected features. A multivariable prognostic model that included Radscore and clinical factors was created.

Results

Of the 121 patients included in the study, 109 patients (90%) had T3-T4 cancer and 99 (82%) had N+ cancer. A downstaging response was observed in 96 patients (79%). In the training set (79 patients), the best model (ELASTIC-NET method) reduced the 36 texture features to a combination of 6 features. The multivariate analysis retained the Radscore (odds ratio [OR]=13.25; 95% confidence interval [95% CI], 4.06-71.64; p<0.001) and age (OR=1.10/1 year; 1.03-1.20; p=0.008) as

Introduction

The standard treatment for locally advanced rectal cancer is neoadjuvant chemoradiation therapy (CRT) followed by total mesorectal excision (TME) with a 6-10-week interval between these two treatments [1] [2]. Patients with a complete pathological response (pCR) after CRT have better long-term outcomes than those without a pCR. pCR might be indicative of a favorable biological tumor profile with a lower propensity for local or distant recurrence and an improved survival [START_REF] Maas | Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data[END_REF]. The tumor response to CRT may influence the prognosis after surgery. Beddy et al reported that the 5-year disease-free survival after CRT was significantly better in tumor regression patients than in those with no tumor regression [START_REF] Beddy | A simplified tumor regression grade correlates with survival in locally advanced rectal carcinoma treated with neoadjuvant chemoradiotherapy[END_REF]. Computed tomography (CT) is commonly used for tumor staging and dosimetric radiotherapy study. It may provide prognostic information based on texture analysis (TA) [START_REF] Marusyk | Intra-tumour heterogeneity: a looking glass for cancer?[END_REF]. TA is an emerging method that can reflect the degree of tumor heterogeneity. It is indeed difficult to visually assess tumor heterogeneity on a CT scan, because of photon noise and inter-observer variability in the interpretation of imaging. CT texture analysis software can address these drawbacks and quantify heterogeneity in a defined area objectively [6] [7]. CT texture analysis is an important research focus in many types of cancer [START_REF] Ciaravino | CT Texture Analysis of Ductal Adenocarcinoma Downstaged After Chemotherapy[END_REF][9] [START_REF] Chaddad | Novel Radiomic Features based on Joint Intensity Matrices for Predicting Glioblastoma Patient Survival Time[END_REF]. A recent study predicted complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer using radiomics from initial CT using a deep neural network approach involving 1683 radiological features [START_REF] Bibault | Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer[END_REF]. Textural analysis of baseline 18 FDG PET/CT provides strong independent prognostic factors of survival in patients with locally advanced rectal cancer [START_REF] Lovinfosse | FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer[END_REF].

MRI-based texture analysis of rectal cancers may be associated with poor response to pre-operative treatment and could potentially help in patient selection for individualized therapy [START_REF] Jalil | Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long-term survival in locally advanced rectal cancer treated by chemoradiotherapy[END_REF]. Contrast-enhanced CT of the thorax, abdomen, and pelvis is always performed on patients with rectal cancer before the start of CRT and during follow-up, whereas MRI is only indicated for patients with low and middle rectal tumors.

The present study aimed to develop and validate a prognostic model that includes a radiomics score based on baseline contrast-enhanced CT texture analysis, performed by a single expert radiologist, for downstaging in patients with locally advanced rectal cancer.

Patients and methods

Patients

This retrospective bicentric study complies with the "reference methodology" MR003 adopted by the French Data Protection Authority (CNIL) and patients did not object to the use of their clinical data for the research purpose.

All consecutive patients with locally advanced rectal cancer who received CRT based on fluorouracil followed by surgery between 2011 and 2017 at the Oscar Lambret Centre or Jean Godinot Institute were eligible for this study. Patients were identified from the hospital databases. CRT involved delivery of 45-50 Gy in 5 fractions per week combined with administration of capecitabine only, both capecitabine and metformine, or both leucovorine and 5-fluorouracil (LV-5FU2). Patients with a metastatic disease at baseline, who underwent a different chemotherapy regimen, or who were finally not operated on were not eligible for participation in this study.

Additionally, patients for whom texture analysis of the baseline CT scan was not feasible or pathological evaluation of the surgical specimen was not performed, and patients who refused to participate in the study were excluded. Clinical data were retrospectively extracted from the hospital medical records.

Study endpoint

The study endpoint of the prognostic factor analysis was downstaging (yes versus no), which is defined as a decrease in T-staging and or N-staging without an increase of the second component, when comparing the pathological staging (ypTNM) to the initial staging (TNM).

Candidate variables evaluated in the prognostic models were age, initial tumor size, initial tumor and nodal staging (TNM 7 th edition), initial staging, time interval between CRT and surgery (≤ 7 versus > 7 weeks), tumor site, World Health Organization Performance status (WHO: 0 versus 1), and radiomic features.

Texture analysis

Tumor texture analysis was performed on contrast-enhanced CT images acquired at a portal phase (diagnostic or dosimetric CT), using TexRAD ® software (TexRAD ® Ltd), by a single expert radiologist. A radiologist specialized in gastro-intestinal imaging chose the CT slice where the tumor was best visualized. The chosen slices were then exported in DICOM format and anonymized. The region of interest was manually delineated around the tumor. CT texture analysis was performed in a twostep process including image filtration and statistical quantification. The principle behind texture analysis is detailed in Supplementary data-1. Five spatial scale image filtrations (SSF) features were selectively extracted corresponding to different anatomic scales: fine (SSF2, object radius of 2 mm), medium (SSF3-5, object radius of 3-5 mm), and coarse (SSF6, object radius of 6 mm) texture scales, by using a Laplacian of Gaussian special band pass filter (Figure 1). In addition, the unfiltered images (SSF0) corresponded to conventional CT images. For each filtration, six measures were used to summarize the distribution of pixels and tumor heterogeneity:

mean grey-level intensity (mean), standard deviation (sd), entropy (irregularity of pixel intensities in space), mean positive pixel (mpp), skewness (symmetry of the histogram distribution), and kurtosis (measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution) [START_REF] Miles | CT texture analysis using the filtration-histogram method: what do the measurements mean?[END_REF].

Statistical analysis

The association between clinicopathologic data and downstaging was assessed, using the chi-square or Fisher exact test for categorical variables and the Student ttest for continuous variables. Radiomics features were also compared between the two groups. Additionally, correlation between the 36 radiomic features was evaluated (correlation matrix).

The prognostic model was developed on a training sample, and then tested on a "test" sample. Although this study was bicentric and involved two independent cohorts, a random sampling of the population was needed to obtain a training set representing 2/3 of the overall study population, as only 19 patients were recruited in the second center.

Considering the high-dimensional data and correlation between these data, selection of radiomic features associated with downstaging was first performed using penalized logistic regression in the training set [START_REF] Sauerbrei | Selection of important variables and determination of functional form for continuous predictors in multivariable model building[END_REF]. We searched the best penalized regression between the Ridge (α=0), ELASTIC-NET (with α varying from 0.1 to 0.9), and least absolute, shrinkage, and selection operator (LASSO; α=1) methods. A seven-fold cross validation provided the α-value and minimum value of the tuning parameter λ of these methods and maximized the area under the curve (AUC) of the receiver operating characteristic curve (ROC). Based on the selected model, a radiomics score (Radscore) was calculated as a linear combination of selected features weighted by their regression coefficients.

The association of the radiomics score (Radscore) with downstaging was assessed in the training sample and in the overall cohort by using the Student t-test. The ROC curve was plotted to estimate the AUC in the training data set.

Univariable logistic regression analysis was performed to select variables for the first step of the multivariable model (p<0.20). Fractional polynomial models were used to evaluate log-linearity of continuous variables. A step-wise selection provided the best regression model based on Akaike's information criterion. All odds ratios (OR) were estimated with their 95% confidence interval (95% CI).

Performance of the final model built on the training set was then evaluated on the test data set through the ROC curve and the corresponding AUC with its 95% CI.

The calibration of the final model was estimated with the Hosmer-Lemeshow test (a significant test statistic implies that the model does not calibrate perfectly) [START_REF] Kramer | Assessing the calibration of mortality benchmarks in critical care: The Hosmer-Lemeshow test revisited[END_REF].

Statistical analysis was conducted with Stata software (version 15.0) and R software (version 3.5.1). The packages used in R in this study are reported in Supplementary data-2. Unless otherwise mentioned, the reported statistical significance levels were all two-sided, with statistical significance set at 0.05.

Results

Patient characteristics

Of the 179 patients who were eligible for participation in the study, 121 were finally included: 102 from the Oscar Lambret Centre and 19 from the Jean Godinot Institute (Supplementary data-3). Patient and tumor characteristics are presented in Table 1.

The median age of the cohort was 62 (range, 28-82) years, and there were more men (n=70, 58%

Radiomic features

The radiomic features are summarized in a correlation matrix (Supplementary data-5).

Overall, there were few correlations between extracted features from unfiltered images (SSF0) and extracted features from filtered images (SSF 2-6). Conversely, for the filtered images (SSF 2-6), each measured parameter (mean, sd, entropy, mpp, skewness, kurtosis) was highly correlated between the different filtrations.

Some features were associated with the downstaging status (Supplementary data-6). As expected, a good performance was observed in the training data set, with an AUC of 0.90 (95% CI: 0.83-0.97). In the test data set, the AUC was estimated to be 0.70 (95% CI: 0.48-0.92) (Figure -3). The Hosmer-Lemeshow test yielded a nonsignificant statistic (p=0.89), which suggested that there was no departure from perfect fit.

Regarding the calibration in the test sample, we found a nonsignificant statistic (p=1.0), likely related to a lack of power.

Discussion

We developed a prognostic model for downstaging including a radiomics score based on baseline contrast-enhanced CT texture analysis performed by a single expert radiologist in patients with locally advanced rectal cancer. We validated the radiomics score on a test population, which is recommended in good practices but is rather uncommon in the literature [START_REF] Limkin | Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology[END_REF]. The advantage of CT is that it is commonly used for tumor staging and dosimetric radiotherapy study.

Tumors are heterogeneous on gross and cellular levels, as well as on genetic and phenotypic levels, with spatial heterogeneity in cellular density, angiogenesis, and necrosis. This heterogeneity may affect prognosis and treatment, as more heterogeneous tumors may be associated with more biologically aggressive behavior and increased resistance to treatment. This heterogeneity cannot be taken into account by profiles based on the analysis of tumors performed on a sample of the tumor volume. Furthermore, the most abundant cell type might not necessarily predict the properties of mixed populations [START_REF] Marusyk | Intra-tumour heterogeneity: a looking glass for cancer?[END_REF]. Texture analysis provides an objective and quantitative assessment of tumor heterogeneity by analyzing the distribution and relationship of pixel or voxel grey levels in the image [START_REF] Lubner | CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges[END_REF]. It is performed on a sample of the tumor, but this sample is chosen to be as representative as possible of the tumor heterogeneity. CT texture analysis was performed in the largest crosssectional area of the lesions instead of in the whole tumor. Whole-tumor analysis may allow a more precise evaluation of tumor heterogeneity and may also improve reproducibility. However, underestimation of entropy values by using the largest cross-sectional area seems to be less than 7% when compared whole-tumor analysis [START_REF] Ng | Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis?[END_REF]. Moreover, single slice CT-based texture analysis is a more simple tool to be used in daily routine practice. Texrad software has already proven its value in other tumor settings such as melanoma, or hepatocellular carcinoma [20][21].

Studies using MRI [START_REF] Liu | Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer[END_REF] and PET/CT [START_REF] Lovinfosse | FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer[END_REF] with logistic regression or a simpler artificial neural network to predict treatment response have been conducted, and these studies have reported a good accuracy (AUC=0.9756, for the MRI model). Using a non-penalized Cox modelling, Jalil et al. observed that a lower mpp at fine texture from pre-treatment MRI texture analysis was an independent predictor for overall survival, disease-free survival, and relapse-free survival [START_REF] Jalil | Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long-term survival in locally advanced rectal cancer treated by chemoradiotherapy[END_REF]. Bibault et al. recently conducted a study using deep learning and radiomics to predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer using 1683 features and reported an AUC of 0.72 (0.65-0.87). The primary endpoint they considered, i.e. complete response, can be useful to promote organ preservation, but is not useful to propose an intensification of the therapeutic regimen, because it does not differentiate responders from non-responders. In addition, they used a different statistical approach to model the texture features, which were based on 1683 parameters. Notably, the sample size is slightly larger in our study (121 versus 95)

and we split the study population to validate the model in a test set. Finally, the region of interest was not delineated by a radiologist specialized in rectal cancer [START_REF] Bibault | Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer[END_REF].

After a TME, 20% of patients have sexual complications, 44% consider the functional result after surgery as bad because of stool fractionation, urge incontinence, or real fecal incontinence [START_REF] Panis | Surgical treatment of rectal cancer: is the price to be paid too high?[END_REF], which is why a prospective American study suggests that the quality of life (QoL) of patients may be better after an abdominoperineal resection that after a TME [START_REF] Grumann | Comparison of quality of life in patients undergoing abdominoperineal extirpation or anterior resection for rectal cancer[END_REF]. Constant impotence and definitive colostomy occur as functional consequences of TME and of the pelvic radiation. The anterior resection syndrome is now considered as a real handicap, with an associated score [START_REF] Emmertsen | Low anterior resection syndrome score: development and validation of a symptom-based scoring system for bowel dysfunction after low anterior resection for rectal cancer[END_REF]. For the abovementioned reasons, many studies are attempting to identify a solution to avoid a systematic surgery.

Some studies have tested a strategy of therapeutic intensification for select patients.

GRECCAR-12 trial and PRODIGE-23 trial compare neoadjuvant CT with folfirinox followed by preoperative CRT versus preoperative CRT alone in patients with locally advanced rectal cancer in terms of 3-year disease-free survival.

Our final model, which included Radscore and age, provides interesting information that could be used to develop a personalized therapeutic strategy. CT-based texture analysis could allow upstream selection of the poor responders who may benefit from intensification of neoadjuvant treatment. Therefore, our model, after a validation of our model, which includes the Radscore, in a larger study, we could use it to predict downstaging, and exercise a wait-and-see approach if a cCR is confirmed after CRT.

Conversely, if no downstaging is predicted, we could propose neo-adjuvant chemotherapy or higher radiation dose after validation of this approach with the GRECCAR12 (NTC02514278) and PRODIGE23 (NCT01804790) studies.

Our study has some limitations. It was a retrospective study with a relatively small size. Following the recommendations, the study population was split into a training set and a test set, representing 2/3 and 1/3 of the whole sample. Even if the estimated AUC in the test set (AUC=0.70) is deemed satisfactory, the confidence interval is wide. This should be considered as an exploratory study and validation on a larger and independent cohort is needed. In addition, as all patients were treated with chemoradiation therapy, we cannot disentangle the prognostic value from the predictive value of the model. Inter-observer reproducibility was not taken into account, as the CT slice was chosen by only one expert radiologist. CT texture analysis was performed in the largest cross-sectional area of the tumor and not on the whole tumor, which is probably more representative of the tumor heterogeneity.

We did not include the infiltration of the mesorectal fascia and the tumor regression grade (TRG) into the variables in the model. Indeed, regarding infiltration of the mesorectal fascia, some patients had undergone their imaging examinations (CTscan and MRI) outside of the University Hospital and the imaging results would have been missing for too many patients. Moreover, we know that CT is limited for the assessment of circumferential margin, particularly in case of low rectal tumors. TRG is predictive of therapeutic response in rectal cancer patients after chemoradiotherapy. However, TRG classification is not routinely used. Furthermore, various TRG systems have been suggested, and the categorization is prone to relevant interobserver variability [START_REF] Chetty | International study group on rectal cancer regression grading: interobserver variability with commonly used regression grading systems[END_REF]. For example Kim et al compared the prognostic validity of four different TRG systems in order to identify the ideal TRG system [START_REF] Kim | What Is the Ideal Tumor Regression Grading System in Rectal Cancer Patients after Preoperative Chemoradiotherapy?[END_REF].

The modified Dworak TRG system for evaluation of entire tumors including regional lymph nodes is a better predictor of survival than current TRG systems for evaluation of the primary tumor alone [START_REF] Dworak | Pathological features of rectal cancer after preoperative radiochemotherapy[END_REF].

Finally, pre-treatment carcinoembryonic antigen (CEA) level >5 ng/mL is significantly associated with a lower pathological complete response rate [START_REF] Tural | The relation between pathological complete response and clinical outcome in patients with rectal cancer[END_REF]. However, this association was not considered in our analysis because the CEA level was not available for all patients.

In conclusion, our study presents a prognostic model for downstaging status after CRT, which includes a radiomics score based on pre-treatment contrast-enhanced CT, in patients with locally advanced rectal cancer. This may be a useful tool to select patients with an initially poorer downstaging probability and to propose to them an intensification of treatment via neo-adjuvant chemotherapy or higher radiation dose. After prospective evaluation of this approach in a randomized clinical study, this method could be implemented to better personalize treatment. (B) ELASTIC-NET coefficient profiles of the 36 texture features. A coefficient profile plot was generated against the log (λ) sequence. A dotted line was drawn at the value selected in cross-validation (λmin + 1 se), resulting in 6 nonzero coefficients.

These plots correspond to the ELASTIC-NET model with α=0.9. the performance that would be obtained at random.

Tables Table 1: Patient and treatment characteristics, overall and according to the response to chemoradiotherapy (downstaging).

  Abbreviations: TA: texture analysis; SSF: spatial scale filtration ; Mpp: mean positive

Figure 1 :Figure 2 :

 12 Figure legendsFigure 1: Texture analysis method

Figure 3 :

 3 Figure 3: Receiver operating characteristic (ROC) curves of the final model in

4 2 6 25

 46 ) p-value of the comparison test between ypT0N0M0 versus other 5

  CI): odds ratio (95% confidence interval) * Backward step-wise selection was applied by using Akaike's information criterion as the stopping rule. The final model includes only Radscore and age, leading to the corresponding estimates (OR, 95% CI, and p-value). ** In the multivariable model, the estimates (OR, 95% CI, and p-value) for initial tumor size and initial nodal staging were computed by separately adding each variable to the final model. RadScore, age, and initial tumor size were entered in the model as continuous variables, after checking the loglinearity. The estimated odds ratios correspond to a 1-unit increment (OR associated with a 1 unit of Radscore, a 1 year of age, or with 1 cm for tumor size).

Figure 1

 1 Figure 1

Figure 2 ABFigure 3

 23 Figure 2

  of radiomics score (Radscore) in the training data set As expected, there was a significant difference in Radscore between patients with a downstaging and those without a downstaging, in the entire cohort (p<0.001) (Table-1 and Supplementary data-8) and in the training data set (p<0.001). The AUC in the training sample was good: 0.86 (95% CI: 0.73-0.98).

	Development of a prognostic model for downstaging status in the training data set
	In univariable analysis, the potential factors associated with the downstaging status
	were Radscore, age, initial tumor size, and initial nodal staging. The multivariate
	logistic regression retained the Radscore (OR=13.25; 95% CI, 4.06-71.64; p<0.001)
	and age (OR=1.10/1 year; 95% CI, 1.03-1.20; p=0.008) (Table-3) as independent
	prognostic factors, whereas initial tumor size and initial nodal staging were no longer
	associated with downstaging (p-value=0.99 and 0.98, respectively).
	Radiomics feature selection and radiomics score (Radscore) building
	Based on the training set (79 patients), the best model (ELASTIC-NET with α=0.9)
	reduced the 36 texture features to a combination of 6 potential prognostic factors
	(Figure 2A, 2B): mean0, mpp0, mean2, entropy4, kurtosis5 and mean6. The formula
	of the resulting Radscore is defined in Supplementary data-7.

Validation

Performance of the final prognostic model in the training and test data sets

Table 2 :

 2 Association of chemoradiotherapy, surgery, and response to treatment with 2 downstaging 3 -value of the comparison tests between the two groups (downstaging yes versus no)

	1
	Characteristics		Downstaging	Total	p-value (1)
		No		Yes
		N=25		N=96		N=121
	Sex						0.48
	Male	16 22.9%	54 77.1%	70
	Female	9	17.6%	42 82.4%	51
	Age (years)						0.12
	Median -(Range)	64.0 (28.0-80.0) 61.5 (38.0-82.0) 62.0 (28.0-82.0)
	Centre						0.99
	Lille	21 20.6%	81 79.4%	102
	Reims	4	21.1%	15 78.9%	19
	Performance Status						0.99
	WHO-0	21 21.2%	78 78.8%	99
	WHO-1	4	18.2%	18 81.8%	22
	Tumor site						0.22/0.36 (2)
	Low rectum	14 24.1%	44 75.9%	58
	Middle rectum	8	14.5%	47 85.5%	55
	High rectum	3	37.5%	5	62.5%	8
	Initial tumor size (mm), 1 missing data						0.78
	Median -(Range)	45.0 (9.0-80.0)	50.0 (4.5-120.0) 50.0 (4.5-120.0)
	Tumor infiltration						0.85
	Mesorectum	21 21.0%	79 79.0%	100
	Mucosal	0	0.0%	1	100.0%	1
	Muscular	3	25.0%	9	75.0%	12
	Regional organ (prostate/uterus)	0	0.0%	4	100.0%	4
	Serosal	1	25.0%	3	75.0%	4
	Initial tumor staging (T)						0.66/0.71 (3)
	T2	3	25.0%	9	75.0%	12
	T3	22 21.0%	83 79.0%	105
	T4	0	0.0%	4	100.0%	4
	Initial nodal staging (N)						0.054/0.076 (4)
	N0	8	36.4%	14 63.6%	22
	N1	12 14.5%	71 85.5%	83
	N2	5	33.3%	10 66.7%	15
	Nx	0	0.0%	1	100.0%	1
	Initial staging TNM (5)						0.18
	T2N0M0	1	25.0%	3	75.0%	4
	T2N1M0	2	25.0%	6	75.0%	8
	T3N0M0	7	38.9%	11 61.1%	18
	T3N1M0	10 13.9%	62 86.1%	72
	T3N2M0	5	35.7%	9	64.3%	14
	T3NxM0	0	0.0%	1	100.0%	1
	T4N1M0	0	0.0%	3	100.0%	3
	T4N2M0	0	0.0%	1	100.0%	1
	Initial staging UICC						0.087
	Stage 2	8	33.3%	16 66.7%	24
	Stage 3	17 17.5%	80 82.5%	97

Table 3 :

 3 Univariable and multivariable analyses of downstaging in the training set (79 patients, 62 with downstaging).

			Univariate analysis	Multivariate analysis*
	Characteristics	OR (95% CI)	p-value	OR (95% CI)	p-value
	Radscore	7.96 (2.30-31.98) <0.001 13.25 (4.06-71.64)	<0.001
	Age (year)	1.05 (1.00-1.10)	0.054	1.10 (1.03-1.20)	0.008
	Initial tumor size (cm)	1.19 (0.95-1.55)	0.140	1.00 (0.71-1.43)	0.99**
	Initial tumor	T2	1	0.489
	staging	T3-4	1.68 (0.33-6.95)
	Initial nodal	N0	1	0.163	1	0.98**
	staging	N+	2.45 (0.66-8.54)		0.98 (0.17-4.97)
	Initial	Stage 2	1	0.223
	staging	Stage 3	2.17 (0.59-7.38)
	Time	≤ 7 weeks	1	0.871
	interval	> 7 weeks	1.10 (0.31-3.48)
	CRT-Surgery
	Tumor site	Low/Middle	1	0.636
		High	0.66 (0.13-4.91)
	Performance	WHO 0	1
	status	WHO 1	2.19 (0.53-14.94)

Acknowledgements

The authors thank Mohammed Sedki, from INSERM Villejuif, for assisting us with the statistical analysis.


Isabella O Zelano
email: zelano@crpg.cnrs-nancy.fr

Christophe Cloquet

Fabrice Fraysse

Shuofei Dong

Noemie Janot

Guillaume Echevarria

Emmanuelle Montargès-Pelletier

Noémie Janot

Guil- Laume Echevarria

Emanuelle Montargès-Pelletier

The influence of organic complexation on Ni isotopic fractionation and Ni recycling in the upper soil layers

Keywords: Ni, Isotopic fractionation, Complexation, Organic matter, Hyperaccumulating plants

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

Introduction

Being a biologically active trace metal, Ni is thought to have had a crucial role in the early Earth [START_REF] Willaims | Evolution was chemically constrained[END_REF][START_REF] Domagal-Goldman | Organic haze, glaciations and multiple sulfur isotopes in the Mid-Archean Era[END_REF] and during the last decade nickel isotopes have attracted increasing interest as a new tool to investigate biogeochemical processes at Earth's surface. The biogeochemical processes controlling Ni isotopic fractionation in surface layers are, however, not yet well understood and require further investigations. In the Earth's crust, Ni is mostly present under the +2 oxidation state (Ni 2+ ) [START_REF] Fujii | Density functional theory estimation of isotope fractionation of Fe, Ni, Cu, and Zn among species relevant to geochemical and biological environments[END_REF] and undergoes redox reactions mainly through anthropogenic processes (Ni metallurgy) (Ratié et al., 2015a). Thus, it is assumed that redox processes cannot be responsible for the different Ni isotopic signatures observed in terrestrial samples as opposed to other metals, such as Fe, Cu and Cr [START_REF] Wiederhold | Metal stable isotope signatures as tracers in environmental geochemistry[END_REF]. Although the current literature data about Ni isotopic compositions are still quite limited, they highlight an important dispersion of δ 60 Ni values among the different studied Earth's surface samples (Ratié et al., 2015a). Indeed, a relatively small Ni variation in isotopic composition of terrestrial samples has been measured, including eruptive rocks (basalts) and continental sediments, with an average δ 60 Ni value of +0.15‰ ± 0.24‰ [START_REF] Cameron | A biomarker based on the stable isotopes of nickel[END_REF]. In contrast with those abiotic materials, an isotopic fingerprint between -0.44 ± 0.20‰ and -1.46 ± 0.08‰ was observed for Ni-containing methanogen bacteria in respect to the culture medium, suggesting a high potential of Ni stable isotopes as biomarkers of methanogenesis on the early Earth [START_REF] Cameron | A biomarker based on the stable isotopes of nickel[END_REF][START_REF] Cameron | Nickel stable isotopes as biogeochemical tracers[END_REF][START_REF] Cameron | A first analysis of metallome biosignatures of hyperthermophilic archaea[END_REF]. Studying for the first time the isotopic composition of dissolved Ni in ocean and river waters, [START_REF] Cameron | Heavy nickel isotope compositions in rivers and the oceans[END_REF] pointed out a nickel isotopic mass balance incoherence. On one hand, they reported an average δ 60 Ni value of 1.44‰ ± 0.15‰ in a homogeneous set of seawater, consistent with the range of the δ 60 Ni values measured for organic-rich marine sediments (0.2‰ to 2.5‰) [START_REF] Porter | Characterising the nickel isotopic composition of organic-rich marine sediments[END_REF] and for the ferromanganese crust (0.9‰ to 2.5‰, with an average of δ 60 Ni = 1.6 ± 0.8‰) [START_REF] Gall | Nickel isotopic compositions of ferromanganese crusts and the constancy of deep ocean inputs and continental weathering effects over the Cenozoic[END_REF], both representing the principal output of Ni from the oceans [START_REF] Gall | Nickel isotopic compositions of ferromanganese crusts and the constancy of deep ocean inputs and continental weathering effects over the Cenozoic[END_REF]. On the other hand, dissolved Ni in river waters, supposed to be a considerable Ni ocean input, presented highly variable isotopic compositions, with δ 60 Ni ranging between 0.29‰ and 1.34‰. Thus the δ 60 Ni values measured for continental waters are not high enough to explain δ 60 Ni values in seawater, even though they are up to 1‰ heavier than the reported values for continental silicate rocks [START_REF] Cameron | Heavy nickel isotope compositions in rivers and the oceans[END_REF], suggesting a major influence of weathering processes on Ni isotopic fractionation. Few results have been reported about Ni isotope fractionation due to weathering [START_REF] Gall | Nickel isotopic compositions of ferromanganese crusts and the constancy of deep ocean inputs and continental weathering effects over the Cenozoic[END_REF]Ratié et al., 2015b). For instance, Ratié et al. (2015b) investigated the effects of weathering on lateritic profile of ultramafic sites, showing that formation of Ni-bearing clays and Fe-oxides induced a depletion of Ni heavy isotopes in the solid phases as well as their export in the dissolved phase, Δ 60 Ni soil-bedrock up to -0.47‰. These results are consistent with the δ 60 Ni values measured in the exchangeable pool of an ultramafic soil, the labile pool, which were higher than those of the corresponding bulk sample (average Δ 60 Ni exch-tot = 0.29‰ (Ratié et al., 2015b)). Those data were also in good agreement with Ni sorption/precipitation experiments onto ferrihydrite, where a preferential adsorption of light isotopes onto the solid phase was observed, Δ 60/58 Ni dissolved-sorbed = + 0.35 ± 0.10‰ [START_REF] Wasylenki | Ni isotope fractionation during sorption to ferrihydrite: implications for Ni in banded iron formations[END_REF][START_REF] Wang | Experimental constraints on reconstruction of Archean seawater Ni isotopic composition from banded iron formations[END_REF].

Up to now, no data is available concerning Ni isotopic fractionation after complexation with soil organic matter. Nevertheless, metals present a high affinity for humic substances, which are a dominant phase influencing metal bioavailability. Moreover, even though it is widely assumed that biogeochemical cycle of metals is influenced by biotic processes, only few data have been published on the role of living organisms on Ni isotopic fractionation [START_REF] Deng | Nickel and zinc isotope fractionation in hyperaccumulating and nonaccumulating plants[END_REF][START_REF] Estrade | Weathering and vegetation controls on nickel isotope fractionation in surface ultramafic environments (Albania)[END_REF]. A fundamental example is hyperaccumulators, that are plants characterized by a unique capability to accumulate extremely high concentration of metals in their biomass [START_REF] Van Der Ent | Multi-element concentrations in plant parts and fluids of Malaysian nickel hyperaccumulator plants and some economic and ecological considerations[END_REF]. Most of those plants are nickel hyperaccumulators, with a nominal threshold con-centration fixed at 1000 μg g -1 foliar Ni [START_REF] Reeves | Tropical hyperaccumulators of metals and their potential for phytoextraction[END_REF]. Replenishment of available Ni pools in soils through the biogeochemical recycling represents, therefore, a considerable contribution to Ni global cycle. This contribution is even more important in the context of ultramafic areas, which are among the most important Ni continental reservoirs, and where hyperaccumulators mainly grow. Hyperaccumulators found in ultramafic complexes of Mediterranean Region are mainly in the genus of Alyssum (Brassicaceae), while several different families are present in tropical ultramafic outcrops, e.g. Rinorea bengalensis (Violacee), Phyllantus securinegioides (Phyllanthaceae), etc. ( [START_REF] Van Der Ent | Multi-element concentrations in plant parts and fluids of Malaysian nickel hyperaccumulator plants and some economic and ecological considerations[END_REF]. Despite the increasing attention dedicated to hyperaccumulating plants, suitable for soil remediation and agromining activity (Van der Ent et al., 2015), the ecological reasons for nickel hyperaccumulation are still not well understood [START_REF] Van Der Ent | Multi-element concentrations in plant parts and fluids of Malaysian nickel hyperaccumulator plants and some economic and ecological considerations[END_REF]. The role of those plants in nickel cycle is, however, crucial as they represent, together with the precipitation of secondary minerals, the major sink for metals. Moreover, the potential contribution of vegetation on Ni isotopic composition in the upper soil horizon and its influence on Ni exportation towards aqueous compartments has been advanced [START_REF] Estrade | Weathering and vegetation controls on nickel isotope fractionation in surface ultramafic environments (Albania)[END_REF]. However, the discrimination of stable isotopes of Ni within plants through accumulation processes has just been unraveled with a preliminary study so far [START_REF] Deng | Nickel and zinc isotope fractionation in hyperaccumulating and nonaccumulating plants[END_REF][START_REF] Estrade | Weathering and vegetation controls on nickel isotope fractionation in surface ultramafic environments (Albania)[END_REF]. Similarly, it is recognized that plant litter degradation is one of the main source of dissolved major and trace elements in rivers [START_REF] Fraysse | Experimental study of terrestrial plant litter interaction with aqueous solutions[END_REF][START_REF] Pokrovsky | Fe-Al-organic colloids control of trace elements in peat soil solutions: results of ultrafiltration and dialysis[END_REF], but no data has been published concerning the study of Ni isotopic fractionation potentially occurring during litter degradation process. A complete investigation of Ni cycle, including plant uptake and plant recycling, deserves, therefore, a greater attention. [START_REF] Deng | Nickel and zinc isotope fractionation in hyperaccumulating and nonaccumulating plants[END_REF] performed experiments on plants grown in hydroponic conditions and showed a preferential uptake of light Ni isotopes by plants. In another study, plants naturally grown on ultramafic systems were investigated reporting a whole-plant isotopic composition heavier than the soil, with Δ 60 Ni whole plant-soil up to 0.40‰, but lighter than the bioavailable pool, Δ 60 Ni DTPA-rhizosoil up to 0.89‰ [START_REF] Estrade | Weathering and vegetation controls on nickel isotope fractionation in surface ultramafic environments (Albania)[END_REF]. This supports the hypothesis that the bioavailable Ni in soil presents a heavy isotopic signature compare to the total soil, and that plants probably take up the lighter fraction of isotopes from this heavy pool.

A considerable number of processes are known to involve Ni binding to organic molecules, such as mobilization and uptake of nutrients by plants and microorganisms, detoxification processes by plants, microbial proliferation and dissolution of soil minerals [START_REF] Marschner | Marschner's Mineral Nutrition of Higher Plants[END_REF][START_REF] Jones | Organic acids in the rhizosphere -a critical review[END_REF]. Among all the organic ligands produced by biological entities and slow degradation of biological material in soils, waters and sediments, the principal group consists of molecules bearing carboxylate functional groups. Those organic ligands commonly occur as low-molecularweight molecules, such as acetate, malate, citrate, oxalate, and as macromolecules, such as humic substances [START_REF] Strathmann | Speciation of aqueous Ni(II)-carboxylate and Ni(II)-fulvic acid solutions: combined ATR-FTIR and XAFS analysis[END_REF][START_REF] Thurman | Organic Geochemistry of Natural Waters[END_REF]. The isotopic composition of Ni in plants can be, then, strongly correlated to the potential isotopic fractionation induced by complexation with organic ligands involved in uptake mechanisms by root cells [START_REF] Deng | Nickel and zinc isotope fractionation in hyperaccumulating and nonaccumulating plants[END_REF], in translocation and storage mechanisms in aerial parts of the plant, as well as by complexation with organic ligands present in soil solutions. The quantification of potential Ni isotopic fractionation when reacting with organic ligands is, therefore, necessary to elucidate the role of organic matter in the biogeochemical cycle of Ni.

In the present study, we aimed to determine the extent of Ni isotopic fractionation due to interaction with Purified Humic Acid (PHA), used as a proxy surrogate for reactive soil organic matter, and with oxalic and citric acids, both being representative of low-molecular weight organic molecules, involved in Ni plant uptake and storage processes [START_REF] Jones | Organic acids in the rhizosphere -a critical review[END_REF]. To this purpose, the Donnan Membrane Technique (DMT) [START_REF] Temminghoff | Determination of the chemical speciation of trace metals in aqueous systems by the Wageningen Donnan membrane technique[END_REF] was used to separate free Ni (Ni 2+ ) from the Ni bound to organic ligands (Ni-L) after complexation reaction. The DMT system consists of a donor and an acceptor solution divided by a negatively charged membrane, only permeable to free metal. The DMT has often been used to study metal interaction with organic and inorganic ligands, both in laboratory and field applications [START_REF] Kalis | Measuring free metal ion concentrations in situ in natural waters using the donnan membrane technique[END_REF]. However, the combination of this technique and the investigation of metal isotopic fractionation as a consequence of metal-ligand interaction has only been applied to Zn [START_REF] Jouvin | Zinc isotopic fractionation: why organic matters[END_REF] and Cu [START_REF] Ryan | Copper Isotope Fractionation During Equilibration With Natural and Synthetic Ligands[END_REF] so far. In this work, the combined effects of pH and degree of complexation between Ni and organic ligands on metal isotopic fractionation were investigated. This approach allows unravelling the influence of Ni interaction with organic molecules on Ni isotopic composition, which is still missing for a correct interpretation of Ni biogeochemical cycling. Moreover, in a second part of the work, the role of vegetation recycling in the upper soil layers was studied simulating, in controlled conditions, leaf degradation of two hyperaccumulators: Alyssum murale, a small shrub from temperate area, and a tropical young tree of Rinorea bengalensis, potentially growing up to 25 m high. This experiment aims to furnish new information necessary to introduce biological processes contribution in Ni isotopic cycle. Up to now, indeed, nothing is known, qualitatively and quantitatively, about how plant accumulation and release processes can modify Ni isotopic signature. To this purpose, Ni concentration and isotopic signature in leached solutions were monitored as a function of time, and results were interpreted on the basis of Ni speciation in plant leaves previously reported in literature, and on isotopic signature of Ni 2+ and Ni-L, determined with DMT system.

Materials and methods

All reagents were prepared using ultrapure water (18.2 MΩ cm -1 ). Nitric (HNO 3 ) and hydrochloric (HCl) acids were of supra-pure grade. The Humic Acid (PHA) was extracted from peat soils collected in São Paulo State, Brazil, purified following the International Humic Substances Society (IHSS) procedure. Element standard solutions were supplied by TechLab (1000 μg mL -1 , in a 2 to 5% HNO 3 ). Nickel solution for DMT experiments was diluted and a working solution at pH = 5 was prepared for avoiding a drastic decrease of pH in HA solution.

Nickel speciation with Donnan Membrane Technique

Experiments were conducted by using the DMT to quantify a potential Ni isotopic fractionation resulting from metal interaction with small organic acids and with PHA. The procedure applied in this study was modified from the one described by [START_REF] Pan | In-situ measurement of free trace metal concentrations in a flooded paddy soil using the Donnan membrane technique[END_REF].

System set-up

The system is made of two solutions, a donor and an acceptor, separated by a cation exchange membrane. The donor side consisted of 250 mL of Ni(NO 3 ) 2 aqueous solution, containing Ca(NO 3 ) 2 as supporting electrolyte, and the selected organic ligand. The acceptor solution was inside a cylindrical poly(methyl methacrylate) (PMMA) cell filled with 10 mL of the same Ca(NO 3 ) 2 electrolyte solution, and directly immersed in the donor solution from which it was separated by two cation-exchange membranes (supplied by VWR). The membranes are negatively charged and only the free hydrated cation can permeate through it, allowing the equilibration of free hydrated ion Ni 2+ between the two sides. The small volume of the acceptor side was chosen to reduce the equilibration time and the quantity of Ni passing from donor to acceptor, avoiding, therefore, any alteration of the equilibrium in the donor solution [START_REF] Ryan | Copper Isotope Fractionation During Equilibration With Natural and Synthetic Ligands[END_REF]. Since the equilibration time for divalent cations was previously estimated to be between 48 and 72 h [START_REF] Temminghoff | Determination of the chemical speciation of trace metals in aqueous systems by the Wageningen Donnan membrane technique[END_REF][START_REF] Weng | Determination of the free ion concentration of trace metals in soil solution using a soil column Donnan membrane technique[END_REF], an equilibration time of 72 h was adopted.

As a preliminary step, membranes were washed to remove any undesired cations from their surface. Membranes were, therefore, immersed for at least 30 min in the following solutions: 1 M ethylene-diaminetetraacetic acid (EDTA), 18 MΩ water, 0.1 M HNO 3 , 18 MΩ water. A 1 M Ca(NO 3 ) 2 was also used to saturate membrane surfaces. Each step was repeated twice. Finally, membranes were conditioned with 2 mM Ca(NO 3 ) 2 solution. PMMA vessels were washed twice in 5% HNO 3 and rinsed with 18 MΩ water.

Preliminary tests for isotopic fractionation due to Ni adsorption onto membranes

As already reported in literature for Zn and Cu [START_REF] Jouvin | Zinc isotopic fractionation: why organic matters[END_REF][START_REF] Ryan | Copper Isotope Fractionation During Equilibration With Natural and Synthetic Ligands[END_REF], the DMT can be a useful tool to study isotopic fractionation of a metal due to the interaction with a ligand. This is possible only if no isotopic fractionation is induced by metal sorption onto the membranes. Preliminary experiments were, therefore, conducted to verify this assumption by immersing three membranes in 100 mL of 5 μM Ni(NO 3 ) 2 aqueous solution and three membranes in 100 mL 5 μM Ni in 2 mM Ca(NO 3 ) 2 . After three days of interaction, membranes were recovered and washed three times with 20 mL of 1 M HNO 3 , and Ni concentration and isotopic composition were measured in the final and initial solutions. In a second step, the complete DMT device was tested in absence of ligand in the donor side, made of 600 mL 5 μM Ni(NO 3 ) 2 solution in 2 mM Ca(NO 3 ) 2 . After three days of interaction, acceptor and donor solutions were recovered, all membranes were washed as described above and Ni concentration and isotopic composition were measured in all solutions. No significant difference was observed between the δ 60 Ni of the membrane wash and the original Ni solution, neither in the preliminary tests, nor between donor, acceptor and membrane washing solutions (Table S1 of Supplemental Information (SI)).

Ni binding to organic ligands and Ni speciation calculation

Experimental conditions were chosen on the base of speciation calculations performed using Visual MinteQ v. 3.0 code released by USEPA.

In all experiments, Ni concentration was 50 μM in 2 mM Ca(NO 3 ) 2 . The pH value was adjusted with NaOH or HNO 3 in the donor solution. For each different ligand concentration at least two replicates were performed. The ligand concentrations were 0.01 mM (pH = 5-7) for oxalic acid, 0.01 mM (pH = 5-7) and 0.1 mM (pH = 7) for citric acid. For both 0.01 mM oxalic and citric acid, about 10% of Ni was expected to be complexed, while for 0.1 mM citric acid, > 65% was predicted. No experiment was conducted with 0.1 mM oxalic acid because of the oversaturation of Ca-oxalate complexes. In all investigated cases, the formation of a monodentate complex between Ni and ligand was predicted. Solutions of PHA were prepared at 40, 80 and 110 mg/L (pH = 7), and 30%, 50% and 60% of metal was expected to be complexed. Nickel interaction with PHA was predicted using the NICA-Donnan model, in which the NICA equation discriminates metal specifically bound to the humic acid through its carboxylic and phenolic groups, and the Donnan equation quantifies the metal bound through electrostatic interaction [START_REF] Kinniburgh | Metal ion binding by humic acid: application of the NICA-Donnan model[END_REF][START_REF] Benedetti | Metal ion binding to humic substances: application of the non-ideal competitive adsorption model[END_REF]. The proton-binding parameters (b, logK H,1 , logK H,2 , n H,1 , n H,2 , Q max,1 and Q max,2 ) values used were the ones determined specifically for the material used in this work. Heterogeneity parameters p 1 and p 2 and ion-binding parameters (Ca-and Ni-) values used were the generic ones for humic acids [START_REF] Milne | Generic NICA-Donnan model parameters for metal-ion binding by humic substances[END_REF].

Plant leaf degradation experiment

Degradation experiments were performed on leaves of the hyperaccumulator plants Alyssum murale, supplied by ENSAIA-LSE, Université de Lorraine, cultivated on the ultramafic area in Pojskë, Pogradec, South-East of Albania [START_REF] Bani | In-situ phytoextraction of Ni by a native population of Alyssum murale on an ultramafic site (Albania)[END_REF], and on leaves of Rinorea Bengalensis, collected in the ultramafic area of Sabah, Borneo, Malaysia. Experiments were conducted on leaves dried in oven at moderate temperature (40 °C), to avoid drastic transformations of plant tissue and to reduce artificial effects on metal release. A. Murale leaf samples were delivered cut in pieces of about 0.5 cm, deriving from different plants. On the contrary, R. bengalensis leaves derived from one young plant of about 1.5 m high and leaves were cut in pieces of about 2-4 cm. 80 mg of dry sample were put in a 50kD Spectra/Por ® 7 dialysis membrane, filled with 10 mL of 5•10 -4 M NaNO 3 solution. Dialysis membranes were then set in a 250 mL Teflon beaker filled up with 5•10 -4 M NaNO 3 solution and the beaker solution was gently agitated with a magnetic stirrer, to enhance solution exchange through the dialysis membrane. The reactors were plunged in a thermostated water-bath at 25 ± 0.2°C. The input solutions were injected in the reactors by a peristaltic pump with a flow rate of 0.5 mL min -1 . Experiments were performed with 5•10 -5 M NaN 3 as a biocide on R. Bengalensis to inhibit the microbial respiratory activity, and without and with the biocide on A. Murale, to highlight the potential kinetic and isotopic differences induced by microbial activity. Measuring major and minor element concentrations in the output solutions as a function of time monitored the reaction progress. For each sampling time, between 20 mL and 40 mL of solution were collected and pH and conductivity were measured before acidification with few μL of concentrated HNO 3 .

Metal concentration

Metal concentrations in DMT solutions and in leaching solutions of degradation experiments were determined with X-Series Inductively Coupled Plasma Mass Spectrometer (ICP-MS Quadrupole) supplied by Thermo Fisher Scientific. Leaves of A. Murale and R. Bengalensis and DMT solutions containing PHA (preliminary dried), were acid digested with 3 mL of aqua regia for 24 h at 140°C, and recovered with 0.3 M HNO 3 .

Ni isotopic measurements

Before Ni isotopic determination, samples were subjected to a purification procedure necessary to remove sample matrix. The applied protocol was the one fully detailed by [START_REF] Estrade | Weathering and vegetation controls on nickel isotope fractionation in surface ultramafic environments (Albania)[END_REF], based on the works of [START_REF] Cameron | A biomarker based on the stable isotopes of nickel[END_REF] and [START_REF] Gueguen | Nickel isotope variations in terrestrial silicate rocks and geological reference materials measured by MC-ICP-MS[END_REF]. Briefly, a known amount of sample solution was evaporated (acid digested when needed) to get the 1-2 μg of Ni. Samples were then dissolved again in 3M HNO 3 and mixed with a double-spike solution containing an equal amount of 61 Ni and 62 Ni isotopes, in order to have a spike/sample ratio of 1.15. After over-night equilibration, samples were dried down and redissolved in 1 mL 6M HCl before purification. Two successive chromatographic separations were performed, using the anion-exchange resin AG1-X8 and a Ni-specific resin, supplied by Triskem Inc., France, which complexes Ni 2+ with dimethylglyoxime between pH 8 and 9. A procedural blank was prepared and measured in each analytical session and estimated to contain between 5 and 15 ng of Ni. Nickel isotopic signature in all samples was measured by MC-ICP-MS (Neptune plus, Thermo Scientific) equipped with an Apex HF desolvation introduction system. All samples and standards were diluted to 200 μg L -1 in 0.3 M HNO 3 and measurements were performed in medium resolution. The use of a double Ni spike allowed the correction of the instrumental mass bias.

Once measured, Ni isotopic composition in samples was normalized using standard bracketing method relative to NIST-986 and reported as δ 60 Ni:

δ Ni 60 =( Ni 60 /58 sample Ni 60 /58 NIST 986 -1)×1000(in ‰ ) (1)
The long term analytical reproducibility was assessed by measuring the used Ni stock solution all along the experiments, obtaining an average value of δ 60 Ni = -0.11 ± 0.05‰ (2SD, n = 25).

The external reproducibility was monitored by measuring ten times the reference material BHVO-2, obtaining δ 60 Ni = 0.05 ± 0.04‰ (2SD), in agreement with published values. When 2SD value associated to measurements was lower than long term analytical reproducibility, the value of 0.05‰ value was used.

System isotopic mass balance and calculation of Ni-L isotopic signature

The isotopic mass balance of the system was assessed by summing the contribution of all DMT system compartments, as reported in Eq. 2:

x I ⋅δ Ni I 60 = x A ⋅δ Ni A 60 + x D ⋅δ Ni D 60 + x M ⋅δ Ni M 60 x tot-meas
where x I is the initial amount (μmol) of Ni in the donor solution, x A , x D and x M are respectively the amounts (μmol) measured in acceptor, donor solutions and membrane rinsing solution, and x tot-meas is the amount of Ni (μmol) recovered in the entire DMT system (x A +x D +x M ). δ 60 Ni I , δ 60 Ni A , δ 60 Ni D and δ 60 Ni M are the isotopic compositions of Ni in the initial solution, and in acceptor, donor and membrane rinsing solutions after equilibration, respectively.

Despite membranes were previously conditioned as described above to limit Ni 2+ adsorption onto the negative charged sites, up to 25% of the initial amount of Ni was adsorbed onto the mem-branes (Table 1). This contribution must, therefore, be taken into account to calculate the isotopic fractionation of Ni due to its complexation with organic ligands, i.e. δ 60 Ni-L. For this purpose, the Eqs. 3-4, already used by [START_REF] Jouvin | Zinc isotopic fractionation: why organic matters[END_REF], were adopted:

x Dtot = x Mb 2 + x Dmeas (3)
where x Dtot is the total amount of Ni on the donor side (μmol), x Dmeas , is the amount of Ni (μmol) measured in the donor side, and x Mb , the amount of Ni (μmol) adsorbed onto the membranes, assumed to be half of Ni in the membrane leach, (x Mb /2). Eqs. 4 and 5 were, therefore, used to calculate δ 60 Ni-L:

x Dtot ⋅δ Ni D 60 =¿ (x Mb /2)⋅δ Ni Mb 60 +¿ x Dmeas F Ni2+ ⋅δ Ni 2+ 60 +¿ x Dmeas (1-F Ni2+ )⋅δ Ni-L 60 (4) δ Ni-L 60 =¿ [ x Dtot ⋅δ Ni D 60 -(x Mb /2)⋅δ Ni Mb 60 -x Dmeas F Ni2+ ⋅δ Ni 2+ 60 ] [ x Dmeas (1-F Ni2+ )]
where δ 60 Ni 2+ is the isotopic composition of Ni 2+ measured in the acceptor. F Ni2+ is the fraction of the free hydrated Ni 2+ in the donor side, estimated from [Ni 2+ ] measured in the acceptor and corrected for the effect of the ionic strengths [START_REF] Temminghoff | Determination of the chemical speciation of trace metals in aqueous systems by the Wageningen Donnan membrane technique[END_REF]:

[ Ni 2+ ] D [ Ni 2+ ] A = [Ca 2+ ] D [Ca 2+ ] A (6)
where All experiments were conducted at least in duplicate and measured three times each. The external reproducibility is reported as 2σ, (n = 3). The average values X of experimental duplicates x m are reported as:

X =x m ±δ xm (8)
where:

δ xm = ∑ x=1 N σ x √ N (9)
The standard deviation σ associated to Δ 60 Ni takes into account the error propagation, calculated as:

y=a+b , σ y= √ (σ a) 2 +(σ b) 2 (10)

Results and Discussion

Nickel-PHA interaction

Nickel complexation with PHA ranged between 30% and 57% (Table 1), which was in good agreement with Ni complexation rate predicted by NICA-Donnan model. The 110 mg PHA L -1 was the highest ligand concentration that could have been used without observing coagulation of macromolecules, provoked or enhanced by the high Ca concentration in solution.

As it can be observed on Fig. 1, no trend can be identified as a function of Ni complexation rate. The measured δ 60 Ni A and δ 60 Ni D (Table 2) both present not distinguishable values, within uncertainties, from the isotopic composition of the Ni starting solution (δ 60 Ni I = -0.11 ± 0.05, Table S1) and, consequently, δ 60 Ni-PHA could not be calculated.

In those experimental conditions, NICA-Donnan model predicted Ni interaction with PHA mainly through carboxylic sites binding (Table SI2). At pH = 7, the model predicts that contribution of phenolic sites remains under 1.5%, the electrostatic binding to PHA represents between 2% and 2.5% of total Ni and Ni inorganic complexes were negligible. [START_REF] Jouvin | Zinc isotopic fractionation: why organic matters[END_REF] repor-ted that no isotopic fractionation was measurable for Zn below pH = 6, but they calculated Δ 66 Zn PHA- freeZn = +0.24 ± 0.06‰ at pH = 7, when the contribution of phenolic groups became more relevant [START_REF] Jouvin | Zinc isotopic fractionation: why organic matters[END_REF]. Authors, therefore, attributed the observed isotopic fractionation to Zn interaction with phenolic groups, which have a higher binding constant (logK Zn-phenolic = 2.39) than carboxylic sites (logK Zn-carboxylic = 0.11). This hypothesis was also supported by the measured bond length ZneO for carboxylate (2.00 Å) and phenolate groups (1.91 Å), indicating a stronger interaction with the latter [START_REF] Karlsson | Complexation of zinc in organic SoilsEXAFS evidence for sulfur associations[END_REF]. In the case of Cu interacting with insolubilized HA (IHA), Wilcke (2010) reported Δ 65 Cu IHA- solution = +0.26 ± 0.11‰ but no effect of pH was observed. Moreover, they modeled Δ 65 Cu phenolic/ carboxylic-solution = +0.27‰ without any difference between the two types of sites even if Cu presents a larger difference between the two constants than Zn (logK Cu-phenolic = 6.85. logK Cu-carboxylic = 2.23) [START_REF] Milne | Generic NICA-Donnan model parameters for metal-ion binding by humic substances[END_REF]. Similarly, [START_REF] Ryan | Copper Isotope Fractionation During Equilibration With Natural and Synthetic Ligands[END_REF] reported Δ 60 Cu complex-free = +0.14 ± 0.11‰ between 8/16 Cu and Suwannee River Fulvic Acid, without any variation as a function of pH. They underlined, however, that Cu is known to have a higher affinity for humics than several other metals [START_REF] Pandey | Stability constants of metal-humic acid complexes and its role in environmental detoxification[END_REF] and they suggested that pH should only slightly influence Cu isotopic fractionation due to the strong binding constants over a wide pH range. However, a strong correlation between the stability constants of Cu complexes with natural and synthetic ligands and the magnitude of the isotopic fractionation was reported [START_REF] Ryan | Copper Isotope Fractionation During Equilibration With Natural and Synthetic Ligands[END_REF]. In the hypothesis that complexation rate constants are correlated to the magnitude of the isotopic fractionation, the absence or the small Ni isotopic fractionation observed in this study is consistent with the low generic values reported for phenolic and carboxylic groups [START_REF] Milne | Generic NICA-Donnan model parameters for metal-ion binding by humic substances[END_REF], logK Ni-phenolic = -0.26 and logK Ni-carboxylic = 1.0.

Ni-citric/oxalic acid interaction

Nickel concentration and isotopic signature measured in DMT system compartments are reported in Tables 12 plexed with organic ligand is between 10% and 20%. In those experiments, the measured δ 60 Ni A and δ 60 Ni D values were not distinguishable from the initial δ 60 Ni solution (-0.11 ± 0.05) within analytical uncertainty, neither as a function of pH, nor as a function of the ligand.

According to a study based on Fourier-transformed infrared (FTIR) and X-Ray absorption (XAS) spectroscopies, carboxylic acids with two or more space-close carboxylate functional groups, or with an alcohol donor group adjacent to the carboxylate, form a stronger interaction with Ni, than would do monocarboxylate or polycarboxylate functions separated by methylene groups [START_REF] Strathmann | Speciation of aqueous Ni(II)-carboxylate and Ni(II)-fulvic acid solutions: combined ATR-FTIR and XAFS analysis[END_REF]. Based on FTIR results, [START_REF] Strathmann | Speciation of aqueous Ni(II)-carboxylate and Ni(II)-fulvic acid solutions: combined ATR-FTIR and XAFS analysis[END_REF] observed a stronger interaction between Ni and oxalic acid than between Ni and citric acid [START_REF] Strathmann | Speciation of aqueous Ni(II)-carboxylate and Ni(II)-fulvic acid solutions: combined ATR-FTIR and XAFS analysis[END_REF]. The same conclusion was reached by the XAS study reported by [START_REF] Montargès-Pelletier | Identification of nickel chelators in three hyperaccumulating plants: an Xray spectroscopic study[END_REF]. As bond strength between metal and ligand is thought to influence metal isotopic fractionation [START_REF] Jouvin | Zinc isotopic fractionation: why organic matters[END_REF][START_REF] Ryan | Copper Isotope Fractionation During Equilibration With Natural and Synthetic Ligands[END_REF][START_REF] Wilcke | Copper Isotope Fractionation During Complexation With Insolubilized Humic Acid[END_REF], a different Δ 60 Ni bond-free value between Ni-citrate and Ni-oxalate is expected. However, the large predominance of Ni 2+ in the donor solution in these experimental conditions might have masked the isotopic fractionation and its different entity between the two ligands. It is, therefore, only possible to affirm that no difference between the two ligands can be observed for Ni complexation rates lower or equal to 20% in those settled experimental conditions.

Increasing, indeed, the concentration of citric acid to 0.1 mM, a larger fraction of Ni was complexed, reaching 70%, and the DMT compartment presented distinguishable values in isotopic composition (Fig. 1 andTable 2). However, the calculated values of δ 60 Ni-L and the consequent Δ 60 Ni - bound-free results were affected by a considerable incertitude, allowing only affirming that the isotopic fractionation of Ni induced by complexation with citric acid is lower than 0.2‰. As already mentioned above, the low solubility of Ca-oxalate prevented performing similar experiment (high Ligand/Ni ratio) with oxalic acid.

Alyssum murale and Rinorea bengalensis leaf degradation

Nickel content and isotopic composition in leaves of A. murale and R. bengalensis before degradation experiments are reported in Table S3-4. Element concentrations in leaves before leaching experiments and in leaching solutions are reported in Tables S5-8.

Nickel concentrations in leachate solutions of A. murale and R. bengalensis, and the corresponding Ni isotopic compositions are reported in Fig. 2a-b and Fig. 3a-b, and listed in Tables S3-4.

In the present study, leaching experiments on A. murale were conducted in presence and absence of NaN 3 . Nickel concentration was measured as a function of time and a mass balance was estimated assuming that [Ni] was constant between two consecutive samplings. Considering the flow rate of 0.5 mL min -1 multiplied for the time between two consecutive samplings, it was calculated that after one week the 75%-80% of Ni originally contained in leaves was leached out in absence and in presence of NaN 3 , respectively. At the end of the experiment, between 80% and the 90% of Ni was released, in absence and presence of the biocide, respectively. The continuous and the dotted lines in Fig. 2b indicate the average Ni isotopic compositions (triplicate) of A. murale leaves before degradation and the corresponding standard deviation, δ 60 Ni Alys = 0.19 ± 0.07‰. In addition, this value is close to the one measured by [START_REF] Estrade | Weathering and vegetation controls on nickel isotope fractionation in surface ultramafic environments (Albania)[END_REF] on aerial parts of A. murale specimen, collected in the same geographical area in Albania [START_REF] Estrade | Weathering and vegetation controls on nickel isotope fractionation in surface ultramafic environments (Albania)[END_REF]. During the entire experiment, δ 60 Ni value in leachate solutions overlapped, within the incertitude range, the δ 60 Ni initially measured for the pristine leaves. No significant difference was observed between experiments conducted in presence and absence of biocide. In these experimental conditions it was not possible, therefore, to estimate the contribution of microbial activity, which was expected to significantly influence both kinetics of metal release and isotopic signature of leachate.

In the case of R. bengalensis, leaching experiment was conducted only in presence of NaN 3 . Similarly to A. murale, > 80% of total Ni was leached out during the first 10 days, and after 30 days of leaching, about 90% of the total Ni was recovered. The average value of δ 60 Ni between 1 and 10 days was -0.20 ± 0.05‰, corresponding to δ 60 Ni of the original leaves, i.e. -0.19 ± 0.07‰. Interestingly, the solution leached between 10 and 30 days, clearly displayed an enrichment in lighter isotopes, with δ 60 Ni = -0.40 ± 0.05‰. The difference between average δ 60 Ni 10days and δ 60 Ni 30days was calculated, obtaining Δ 60 Ni 10-30day = 0.20 ± 0.05‰ and highlighting the presence of at least two pools: the main one, not presenting any fractionation, and a second one providing a release of light isotopes. Unfortunately, it was not possible to check the isotopic signature of Ni remaining in R. bengalensis leaves at the very end of those leaching experiments. However, to complete the mass balance, the 10% of Ni missed is expected to provide an isotopic composition enriched in heavy isotopes. During plant material degradation, the isotopic composition of released Ni could potentially present different values compare to Ni originally contained in the plant. Those differences can mainly depend on Ni speciation and localization in different leaf organs, on microbial activity during leaching experiments, and on isotopic fractionation induced by kinetic phenomena. Previous data on hyperaccumulators have reported an enrichment of heavy isotopes in litter, compared to tree leaves, for both Ni [START_REF] Wang | Experimental constraints on reconstruction of Archean seawater Ni isotopic composition from banded iron formations[END_REF] and Zn [START_REF] Karlsson | Complexation of zinc in organic SoilsEXAFS evidence for sulfur associations[END_REF]. Nickel localization has been largely investigated in Alyssum species and Ni was observed mainly concentrated in epidermal vacuoles and trichomes [START_REF] Mcnear | Application of quantitative fluorescence and absorption-edge computed microtomography to image metal compartmentalization in Alyssum murale[END_REF][START_REF] Broadhurst | Simultaneous hyperaccumulation of nickel, manganese, and calcium in Alyssum leaf trichomes[END_REF]. According to bulk and micro XAS investigations [START_REF] Montargès-Pelletier | Identification of nickel chelators in three hyperaccumulating plants: an Xray spectroscopic study[END_REF][START_REF] Mcnear | The hyperaccumulator Alyssum murale uses complexation with nitrogen and oxygen donor ligands for Ni transport and storage[END_REF], Ni in A. murale leaves was shown to be mainly stored as Ni-malate complexes. From similar studies using XAS data, Ni in R. bengalensis 11/16 was mainly present as associated with citrate, forming 1:1 complex, both in plant tissue and in transport fluids. However, differently from A. murale, in R. bengalensis the highest Ni concentrations have been observed in spongy mesophylls, and in lower and upper epidermis ( [START_REF] Van Der Ent | Nickel biopathways in tropical nickel hyperaccumulating trees from Sabah (Malaysia)[END_REF]. To authors'knowledge, there is no evidence of distinct Ni speciation in the different parts of the R. bengalensis leaf, which make difficult to link the observed fractionation to a distinct metal speciation. In the case of Zn in hyperaccumulator plants, a distinction has been proposed between Zn in tetrahedral coordination, forming apoplasmic cell wall complexes, and Zn in octahedral coordination, in the symplasm, suggesting a difference in δ 66 Zn values depending of Zn coordination and localization. Moreover, it has been suggested that a change in Zn speciation is luckily to take place readily from living to dead leaves, after which Zn was observed to be in tetrahedral form [START_REF] Aucour | Dynamics of Zn in an urban wetland soil plant system: coupling isotopic and EXAFS approaches[END_REF]. However, this hypothesis cannot be applied in the case of Ni, as the studies of Ni speciation in A. murale and R. bengalensis leaves evidenced that Ni was remaining in octahedral coordination. Furthermore, for both plants, there is no evidence of Ni in apoplastic space between cells, and Ni is assumed to be mainly stored in vacuoles. Thus in the case of A. murale leaching experiments, Ni is expected to be released in a rather homogeneous way, with no disruption due to different speciation and to the release of Ni strongly bound to cell wall. In the case of R. bengalensis, experimental results show that about 10% of Ni released from leaves is enriched in lighter isotopes. The reason of the observed Ni fractionation could lie in a distinct Ni speciation, hardly detected by XAS measurements in bulk mode, due to their very low concentration and due to the averaging effect of bulk mode spectroscopic measurements. Although no strong evidence can support the hypothesis of a distinct chemical speciation, for the last 10% Ni released, the observed isotopic fractionation could be correlated to distinct localizations and/or distinct chemical binding to the leaf cells. Then, it can be supposed that two distinct Ni pools are successively released, corresponding to tissues with distinct rate of degradation, and that might probably induce an isotopic fractionation. Beside this first hypothesis, the fractionation can also find an explanation through the formation of metabolic breakdown products during biomass degradation, reacting with simultaneously released Ni 2+ . Although such a phenomenon was not observed for A. murale, a successive degradation of these secondary Ni compounds could be responsible for the different isotopic composition of Ni 2+ released between 10 and 30 days in the case of R. bengalensis. However, to deeply decipher Ni behaviour during degradation, these results should be thoroughly discussed by supplementary data on Ni speciation in leachate solutions and in R. bengalensis leaves, at different steps of the leaching experiment.

Implication for the global Ni biogeochemical cycle

In order to use Ni isotopic signature as a tool to decipher Ni biogeochemical cycle on the Earth's surface layers, it is fundamental to identify and characterize mechanisms and processes that are responsible for Ni isotopic fractionation. Reported results aimed to furnish, for the first time, new data about Ni isotopic fractionation produced during interaction with organic matter and during the degradation of plant biomass.

Our results showed that, for the applied experimental conditions, where only carboxylic groups were involved in Ni complexation, Ni interaction with humic acids did not induce a measurable isotopic fractionation, despite the high affinity of humic acids for metals and their influence on metal mobility. Similarly, for small organic acids, a difference between oxalic and citric was expected on the basis of the stronger bonds formed between Ni and oxalate, but, in the settled experimental conditions, no direct correlation has been found between bond strength and the induced isotopic fractionation. Moreover, only for citric acid Δ 60 Ni citrate-free < 0.20‰ was calculated. Recently, a study about Ni distribution and speciation in a malaysian hyperaccumulating plant, R. bengalensis, highlighted the predominance of Ni-citrate complexes, both in plant tissues and transport fluids [START_REF] Van Der Ent | Nickel biopathways in tropical nickel hyperaccumulating trees from Sabah (Malaysia)[END_REF]. Other studies have evidenced Ni in hyperaccumulating plants univocally associated with carboxylic acids, in partic-ular with citric and malic acids, and only a small fraction presents as free ion [START_REF] Van Der Ent | Nickel biopathways in tropical nickel hyperaccumulating trees from Sabah (Malaysia)[END_REF][START_REF] Lee | Isolation and identification of a citrato-complex of nickel from nickel-accumulating plants[END_REF]. However, the fractionation induced by Ni-citrate formation as we demonstrated through DMT experiments is not high enough to justify Δ 60 Ni values in hyperaccumulators reported in literature. A study about Ni uptake by plants grown in hydroponic conditions [START_REF] Deng | Nickel and zinc isotope fractionation in hyperaccumulating and nonaccumulating plants[END_REF] reported, indeed, the enrichment in Ni light isotopes in all plants compared to starting media, Δ 60 Ni plant-solution = -0.90‰ to -0.21‰. This enrichment was attributed to the Ni low affinity transport system across root cell membranes, coherent with the absence of Ni high-affinity transporters in higher plants [START_REF] Deng | Nickel and zinc isotope fractionation in hyperaccumulating and nonaccumulating plants[END_REF]. The effect of Ni concentration gradient in the hydroponic solutions was also combined with kinetic isotopic fractionation, as heavy isotopes move more slowly. In parallel, a translocation of Ni light isotopes from roots to shoots was also observed, Δ 60 Ni shoot-root = -0.47‰. This fractionation was attributed to xylem loading processes. However, up to now, metal transporters involved in xylem loading have not been identified [START_REF] Clemens | A long way ahead: understanding and engineering plant metal accumulation[END_REF]. A comparison of non-hyperaccumulator and hyperaccumulator plants reports a range from 0.04 to 0.73‰ between roots and aerial parts, with the latter being enriched in lighter isotopes [START_REF] Estrade | Weathering and vegetation controls on nickel isotope fractionation in surface ultramafic environments (Albania)[END_REF]. However, in the same study, three different fractionation trends between roots and aerial parts, in three different species of hyperaccumulating plants were measured, making difficult to ascribe these trends to specific mechanisms. The different δ 60 Ni values were associated to the plant stage of growth, combined with storage and translocation mechanisms involving roots, leaves and phloem. Five principal molecular mechanisms can be mentioned describing metal hyperaccumulation within plants: i) mobilization and uptake from soil, ii) compartmentalization and sequestration in roots, iii) transfer to xylem, iv) transport to tissues, and v) storage in leaf cells [START_REF] Clemens | A long way ahead: understanding and engineering plant metal accumulation[END_REF]. All these steps are regulated by the presence of chelating molecules and selective transporters, which influence metal accumulation rate and potentially its isotopic composition. Organic acids, e.g. citric, are thought to be likely involved in metal sequestering in cell vacu-oles [START_REF] Callahan | Metal ion ligands in hyperaccumulating plants[END_REF]. Nickel complexation with citrate or malate [START_REF] Montargès-Pelletier | Identification of nickel chelators in three hyperaccumulating plants: an Xray spectroscopic study[END_REF][START_REF] Mcnear | The hyperaccumulator Alyssum murale uses complexation with nitrogen and oxygen donor ligands for Ni transport and storage[END_REF][START_REF] Van Der Ent | Nickel biopathways in tropical nickel hyperaccumulating trees from Sabah (Malaysia)[END_REF], therefore, could have been advanced as a possible explanation for isotopic fractionation observed in plants. However, results obtained with DMT system demonstrated that the observed fractionation does not take place during storage process. Other phenomena should be considered, such as Ni interaction with other organic molecules (e.g. during transfer processes) presenting different functional groups, probably at cell wall level, as this was evidenced in the case of Zn distribution [START_REF] Aucour | Dynamics of Zn in an urban wetland soil plant system: coupling isotopic and EXAFS approaches[END_REF]. Isotopic fractionation due to kinetic phenomena should also be taken into account. Results from leaf degradation experiments showed that during leaf decomposition, the most part (e.g. 80% for R. bengalensis) of released Ni presented δ 60 Ni value corresponding to the original leaves. As plants have previously been shown to take up light isotopes from the Ni heavy bioavailable pool, and it has been observed that light isotopes are translocated first from roots to leaves [START_REF] Deng | Nickel and zinc isotope fractionation in hyperaccumulating and nonaccumulating plants[END_REF][START_REF] Estrade | Weathering and vegetation controls on nickel isotope fractionation in surface ultramafic environments (Albania)[END_REF], plant biogeochemical recycling should represent a light isotope input into surface soils, depending on the degree of fractionation between roots and leaves. Even a small fractionation can have a relevant contribution, in particular in ultramafic area, where hyperaccumulators represent a considerable part of the endemic vegetation, and are able to move tremendous amount of Ni, e.g. almost 5 kg per tree of R. bengalensis [START_REF] Van Der Ent | Nickel biopathways in tropical nickel hyperaccumulating trees from Sabah (Malaysia)[END_REF][START_REF] Echevarria | Genesis and Behaviour of Ultramafic Soils and Consequences for Nickel Biogeochemistry[END_REF]. A quantitative estimation of Ni recycling is still poorly constrained, but reported results add further progress for a better comprehension of specific mechanisms regulating Ni cycle within plants.

Conclusions

Due to their role in Ni storage in leaf cells, the complexation with organic acids, e.g. citric acid, could have been advanced as a possible explanation for isotopic fractionation observed in plants. Although a difference between oxalic and citric acids was expected, DMT-based experiments did not highlight quantifiable differences between the two compounds, not allowing a correlation between bond strength and the induced isotopic fractionation. Surprisingly, humic acids, commonly reported as crucial reactants in soils and waters, did not provoke measurable Ni isotopic fractionation. Leaf leaching experiments evidenced, for R. bengalensis, a different isotopic composition of released Ni as a function of time, obtaining Δ 60 Ni 10-30day = 0.20 ± 0.05‰. This fractionation measured for one single leaching cycle, unraveled the influence of hyperaccumulating plants on Ni isotopic signature in surface soils and waters.

Indeed, although the fractionation observed during Ni release involved a minor percentage of the total Ni present in leaves, e.g. 10-20%, this fractionation cumulates to the fractionation occurring during Ni uptake from soil and inside the plant (Δ roots-leaves ). Obtained results help to elucidate some of the mechanisms involved in Ni cycle, but further investigations of specific processes, in individual or cumulative modes, are still needed to widen the knowledge of Ni cycle at larger scale.

Acknowledgments. Authors would like to thank Shui-Jiong Wang and an anonymous reviewer for their useful comments. Authors also thank Prof. Jose Paulo Pinheiro and Dr. Elise Rotureau for their help in setting DMT systems. Prof. Jose Paulo Pinheiro is also gratefully acknowledged for supplying PHA. Dr. Antonty Van der Ent is gratefully acknowledged for organising sampling campaign in Sabah rainforest. Dr. Baptiste Laubie and Prof. Aida Bani are acknowledged for supplying Alyssum murale leaves. The authors would like to thank Agence Nationale de la Recherche (ANR) project number ANR-14-CE04-0005-03 (AGROMINE) and ANR-10-LABX-21-01 (LABEX21) for funding. This is CRPG contribution N°2567.

Supporting Information. Supplementary data to this article can be found online at https:// doi.org/10.1016/j.chemgeo.2018.02.023.

  [Ni 2+ ] A , [Ca 2+ ] A and [Ca 2+ ] D are the concentration of Ni and Ca measured in the acceptor and donor sides, and [Ni 2+ ] D is the unknown concentration of free Ni in the donor. The isotopic fractionation between Ni bound to the organic ligand and the free hydrated Ni 2+ , Δ 60 Ni bound-free , was calculated as the difference between the calculated isotopic signature of organo-Ni complexes (δ 60 Ni-L) and the measured isotopic signature of free hydrated Ni 2+ (δ 60 Ni 2+ ), as follows: Δ 60 Ni bound -free = (δ 60 Ni-L) -(δ 60 Ni 2+ ) (7)

Fig. 1 .

 1 Fig. 1. Ni isotopic composition measured in acceptor (diamonds) and donor sides (empty circles), for citric acid (black), oxalic acid (green) and PHA (yellow), and calculated δ 60 Ni for Ni-Citrate (triangles), as a function of Ni complexation degree (%Ni-L). Grey dot lines represent 2σ uncertainty interval of the Ni starting solution, δ 60 Ni I = -0.11 ± 0.05.

  and represented on Fig. 1. Both at pH 5 and 7, in presence of 0.01 mM citric and oxalic acid, the percentage of Ni-com-9/16 Table 2. Nickel isotopic composition, δ 60 Ni, and relative external reproducibility (2σ, n = 3) in the Acceptor, Donor and Membrane leach, after reaction with organic acids (citric and oxalic) and with purified humic acids (PHA). The error associated with average values of experimental replicates was calculated as reported in Eqs. 8-9. The δ 60 Ni-L and Δ 60 Ni values are calculated according to Eqs. 4, 5-7; the propagation error was calculated according to Eq. 10.

Fig. 2 .

 2 Fig. 2. Alyssum murale leaf leaching experiments in presence (green points) and absence (black squares) of NaN 3 . a) Ni concentration (μmol L -1 ) in the leached solution as a function of time. b) δ 60 Ni value in the leached solution. The continuous and dot black lines indicate the average isotopic composition of leaves before leaching and the corresponding standard deviation.

Fig. 3 .

 3 Fig. 3. Rinorea bengalensis leaf leaching experiments in presence of NaN 3 . a) Ni concentration (μmol L -1 ) in the leached solution as a function of time. b) δ 60 Ni value in the leached solution. The continuous and dot black lines indicate the average isotopic composition of leaves before leaching and the corresponding standard deviation.

Table 1 .

 1 Nickel

-ligand complexation rate, Ni concentration in Acceptor and Donor sides, and in Membrane leach of DMT system, after reaction with organic acids (citric and oxalic) and with humic acids (HA) at different pH and ligand concentration.