Alyssa Imbert

Nathalie Vialaneix

Julien Marquis

Julie Vion

Aline Charpagne

Sylviane Metairon

Claire Laurens

Cedric Moro

Nathalie Boulet

Ondine Walter

Grégory Lefebvre

Jörg Hager

Dominique Langin

Wim Hm Saris

Arne Astrup

Nathalie Viguerie
email: nathalie.viguerie@inserm.fr

Armand Valsesia
email: armand.valsesia@gmail.com

Network analyses reveal negative link between changes in adipose tissue GDF15 and BMI

Keywords: Adipocyte, macrophage, weight loss, network analyses, low calorie diet, inflammation

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

extracted RNA, CL performed RT-qPCR on RNA of AT isolated cells, CM analyzed GDF15 plasma data, OW setup and directed NIHS biobank, GL helped with bioinformatics analyses, WHMS and AA: designed the DiOGenes clinical study, AV, NVig, WHMS, JH, and DL: designed the transcriptomics studies, AV and NVig directed and supervised the whole study, AI, NVia, NVig and AV performed all statistical analyses, interpreted the results and wrote the manuscript with input from all authors; AV and NVig had primary responsibility for the final content; and all authors read and approved the final manuscript.

Clinical Trial Identifier: NCT00390637 GEO deposit: GSE141221 Supplemental Material DOI: 10.6084/m9.figshare.12030486 Precis A data driven approach in obese humans during and after calorie restriction revealed adipose tissue GDF15 gene expression upregulated during weight loss with a cluster of macrophages genes.

Introduction

Combining biomarker and phenotypic data provides valuable opportunity to identify signatures of diseases as well as response to treatments or lifestyle interventions, which may have implications for understanding biology and clinical management [START_REF] Leon-Mimila | Relevance of Multi-Omics Studies in Cardiovascular Diseases[END_REF][START_REF] Valsesia | Distinct lipid profiles predict improved glycemic control in obese, nondiabetic patients after a low-caloric diet intervention: the Diet, Obesity and Genes randomized trial[END_REF][START_REF] Musaad | Biomarkers of Obesity and Subsequent Cardiovascular Events[END_REF].

Excess adiposity is associated to numerous comorbidities including metabolic complications such as insulin resistance (IR), type 2 diabetes (T2D), and cardiovascular diseases (CVD) [START_REF] Dixon | The effect of obesity on health outcomes[END_REF][START_REF] Haslam | Obesity[END_REF]. Adipose tissue (AT) is the main lipid storage organ of the human body. Through active secretory functions, it may directly, or indirectly via cognate receptor, influence the activity of other metabolic organs such as liver and skeletal muscle. AT secreted proteic factors, so-called adipocytokines are produced by either adipocytes, precursor cells or resident AT macrophages. These cell types modify gene expression in response to AT expansion or reduction. Furthermore, an AT dysfunction leads to an altered secretory profile and is associated with increased inflammation and fibrosis [START_REF] Vegiopoulos | Adipose tissue: between the extremes[END_REF]. The plasticity of AT during weight loss has been assessed by several gene expression studies, including transcriptome-wide analyses [START_REF] Armenise | Transcriptome profiling from adipose tissue during a low-calorie diet reveals predictors of weight and glycemic outcomes in obese, nondiabetic subjects[END_REF] and targeted candidate approach [START_REF] Viguerie | Determinants of Human Adipose Tissue Gene Expression: Impact of Diet, Sex, Metabolic Status, and Cis Genetic Regulation[END_REF]. Yet, changes occurring following weight loss and their link with weight regain still remain far from complete [START_REF] Bolton | Molecular Biomarkers for Weight Control in Obese Individuals Subjected to a Multiphase Dietary Intervention[END_REF].

Additionally, very little is known about the relationship between weight regain, AT gene expression changes, and other clinical readouts such as indices of insulin resistance, and markers of CVD. So far, most studies have focused on pairwise assessment between a given transcript and a single clinical readout. De facto, this limits our understanding of the physiological changes. Systems biology aims to dissect complex relationships across the multiple scales of organization that characterize biological systems [START_REF] Smet | Advantages and limitations of current network inference methods[END_REF][START_REF] Montastier | System model network for adipose tissue signatures related to weight changes in response to calorie restriction and subsequent weight maintenance[END_REF]. In particular, networks have proven useful to unravel the complex relations (regulation, co-regulation) existing between gene expression profiles under various environmental conditions [START_REF] Civelek | Systems genetics approaches to understand complex traits[END_REF]. They are also a powerful approach to provide a global and comprehensive image of the systems functioning related to complex traits by studying jointly multiple clinical parameters [START_REF] Peters | A functional genomics predictive network model identifies regulators of inflammatory bowel disease[END_REF][START_REF] Le | Identification and replication of RNA-Seq gene network modules associated with depression severity[END_REF].

In this study, we aim to characterize AT gene expression changes during a two-phase dietary intervention in overweight and obese subjects using unsupervised and hypothesis-free methods. This allowed us to identify modules of co-regulated genes related with clinical parameters pertaining to weight regain, insulin-resistance and risk factors for developing CVD.

Material and Methods

Ethics

All studies were performed according to the latest version of the Declaration of Helsinki. Local ethics committees approved all procedures that involved human participants and written informed consent was obtained from all participants.

Randomized dietary intervention study design

The DiOGenes study ( 15) is a pan-European, multi-center, randomized controlled dietary intervention program (NCT00390637). A CONSORT flowchart describing the intervention is shown in Figure 1A. In this study, 938 overweight/obese, non-diabetic subjects followed a low-calorie diet (LCD) for eight weeks using a meal replacement product (Modifast 800kcal/d, Nutrition et Santé, France). Subjects achieving at least 8% of body weight loss were then included in a 6-month randomized dietary intervention (DI) and were assigned to one of five ad libitum maintenance diets, consisting in low protein/low glycaemic index, low protein /high glycaemic index, high protein/low glycaemic index, high protein/ high glycaemic index, and control according to current national dietary guidelines.

Abdominal subcutaneous AT biopsies were obtained by needle aspiration, about 10 cm from the umbilicus, under local anesthesia after an overnight fast. Plasma and AT samples were stored at -80°C until processing. BMI, total plasma lipid levels, waist circumference and HOMA-IR were obtained at baseline, upon weight loss and study termination. Lipid levels and HOMA-IR were quantified following an overnight fast.

DiOGenes transcriptome analyses

Total RNA was extracted from AT samples as previously described [START_REF] Viguerie | Determinants of Human Adipose Tissue Gene Expression: Impact of Diet, Sex, Metabolic Status, and Cis Genetic Regulation[END_REF]. RNA samples were then quantified with a fluorimetric method (Ribogreen, Thermo Fischer) and their integrity evaluated on a fragment analyzer (Advanced Analytical). Good quality RNA was available for 471 individuals at clinical investigation day (CID)1 (baseline), 330 at CID2 (at the end of the 8-w LCD) and 250 at CID3 (at the end of the 6-month DI) (Figure 1B). After sample randomization, 500 ng RNA was loaded on multi-well plates, dried by vacuum concentration, and resuspended into 5 µL nuclease free water.

Sequencing libraries covering the 3'-end of messenger RNA were prepared using the QuantSeq 3' mRNA-Seq Library Prep Kit from Lexogen, strictly following the manufacturer's recommendations.

The optimal number of PCR cycles (15 cycles) was empirically evaluated by quantitative PCR. Libraries were all quantified with a fluorimetric method (Picogreen, Thermo Fischer) and their size pattern evaluated on a fragment analyzer (Advanced Analytical). Libraries were pooled equimolar by 96 and clustered at a concentration of 9 pmol on 4 lanes of single read sequencing flow cells (Illumina).

Sequencing was performed for 65 cycles on a HiSeq 2500 (Illumina) using the SBS v4 chemistry (Illumina).

After demultiplexing with bcl2fastq (standard parameters), sequencing reads were trimmed with BBDuk (BBTools version 35.85, Bushnell B., sourceforge.net/projects/bbmap/) using the parameters k=13, ktrim=r, forcetrimleft=11, mink=5, qtrim=t, trimq=10, minlength=20, rcomp=f, and providing the sequence of the QuantSeq adaptors. Mapping to the human genome (built GRCh38.p2) was performed with RNA STAR (16) (version 2.3.0e and using default parameters). Gene count was performed with HTSeq (17) (version 0.5.4p3, with the parameters mode=intersection-nonempty, stranded=yes, a= 10, and type=exon). The annotation file used was based on GENCODE (18) release 25 but was filtered for transcripts not classified as pseudogenes and with a transcript support level "1" (for high quality annotation) or "NA" (for single exon transcripts). Only seven samples did not reach the manufacturer's recommended criteria of 3M sequencing reads. Those samples were reprocessed.

PCA was performed on log2 transformed count data to identify possible outliers and batch effects. This allowed to remove 3 atypical samples from the analysis (see Supplementary Figure S1). A second PCA without these atypical samples allowed to identify a possible blood contamination in most samples from UK. Samples with HBB larger than 20% (50 samples) were also removed from subsequent analysis, together with a remaining outlier (see Supplementary Figure S2). In addition, one plate with 79 samples had a higher variability than the other plates. Normalization was not able to correct this plate-effect but differential analysis with and without the samples from this plate gave very reproducible results (see Supplementary Figure S3). So we decided to remove the samples from this plate from the analysis as well. This resulted in using 918 samples from 556 unique individuals in the remaining of the analyses, distributed at the different time steps, as shown in Supplementary Figure S4.

Statistical analyses of QuantSeq data

Unless specified otherwise, all statistical analyses were performed using R (version 3.4) [START_REF] Core | R: A Language and Environment for Statistical Computing[END_REF]. The overall analysis strategy is summarized in Figure 2.

Differential analysis Pairwise time step analyses were performed to detect differentially expressed genes between two CID (i.e., contrasts: CID1 vs CID2, CID1 vs CID3 and CID2 vs CID3). For each analysis, raw count data were normalized using the TMM approach (20) implemented in the R package edgeR [START_REF] Robinson | edgeR: a Bioconductor package for differential expression analysis of digital gene expression data[END_REF]. Then, differentially expressed genes between the two conditions were extracted using a Negative Binomial test with a fixed effect for the individual and a log ratio test. Multiple test correction was performed with Benjamini-Hochberg (BH) False Discovery Rate (FDR) [START_REF] Benjamini | Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing[END_REF] within each contrast and significance was set at FDR 5%. Genes whose expression was found equal to 0 in more than 25% of the samples were removed from the analysis, as were genes whose expression was too low in one condition, resulting in an impossible estimation of their fold change (FC). In addition, since high dimensional data (n < p) would cause estimation issues for network inference methods, we further restricted our list of differentially expressed genes to those having an absolute log foldchange (FC) greater than log2(1.3).

Integration of clinical and transcriptome data Associations between clinical variables (BMI, total

lipid levels, waist circumference and HOMA-IR) and gene expression were tested using linear mixed effect models. Changes in gene expression (CPM log2 FC), gender and age were modelled as fixed effects; the center was modelled as a random effect. Adjustment for multiple testing was performed using BH correction.

Network inference and mining

Similarly to [START_REF] Montastier | System model network for adipose tissue signatures related to weight changes in response to calorie restriction and subsequent weight maintenance[END_REF], we performed a multi-step network inference to obtain a comprehensive model of the overall interactions between gene expressions and clinical variables. In such a model, nodes of the network correspond to genes or clinical variables and edges correspond to strong and direct interactions between changes in gene expressions and/or clinical variables between two-time steps.

Edges between genes and clinical variables were inferred using the aforementioned mixed effect models. Edges between genes were inferred using the graphical Lasso (GLasso [START_REF] Friedman | Sparse inverse covariance estimation with the graphical lasso[END_REF] as implemented in the R package huge) on the logFC expression of differentially expressed genes for each contrast. Unlike pairwise measure of associations, such as Pearson correlation coefficients, GLasso is based on partial correlations and provides a stronger criterion for dependency by adjusting for common co-expressed genes. This method is useful in order to filter out false positives by discovering only the most direct interactions. Tuning of the GLasso regularization parameter was performed using the RIC criterion (see Supplementary Methods). Finally, an unsupervised clustering of the nodes was performed for the three networks using the modularity (24) optimization method of Reichardt et al. [START_REF] Reichardt | Statistical mechanics of community detection[END_REF], as implemented in the R package igraph [START_REF] Csardi | The igraph software package for complex network research[END_REF]. This led to obtain strongly connected groups of genes and/or clinical variables for each network.

Pathway analysis

The biological functions represented by genes in each module were searched using Ingenuity Pathways Analysis (IPA) software version 7.5 (Ingenuity Systems, Redwood City, CA). Genes for which IPA reported location as "Extracellular Space" were considered to encode secreted factors.

The significance of canonical pathways was tested using the Fisher Exact test with the User Dataset of the 3 reference sets for each contrast.

AT cell isolation

The AT fractionation was performed as described in [START_REF] Curat | From blood monocytes to adipose tissue-resident macrophages: induction of diapedesis by human mature adipocytes[END_REF]. Briefly, after collagenase digestion (250 U/mL in phosphate-buffered saline (PBS), 2% bovine serum albumin (BSA), pH 7.4, volume/volume) of the AT for 30min at 37°C, the cell suspension was filtered through a 250 µm filter. The floating mature adipocytes were collected, washed 3 times and stored at -80 °C. The remaining stroma vascular fraction (SVF) was obtained after centrifugation. SVF cells were treated with erythrocyte lysis buffer (155 mmol/L NH4Cl; 5.7 mmol/L K2HPO4; 0.1 mmol/L EDTA; pH 7.3) followed by successive filtrations through 100, 70, and 40 -µm strainers. The viable recovered cells were counted and, after washing, the different SVF cells were isolated using an immunoselection/depletion approach utilizing magnetic microbeads coupled to specific CD antibodies (CD31, CD34, CD14) which are membrane cell markers to select the different SVF cell types. The preadipocytes are CD34positive cells (CD34+) and CD31-negative (CD31-) cells. The CD34-negative cells (CD34-) are immune cells (macrophages which are also CD14+ cells and lymphocytes which are CD14-cells) as described in [START_REF] Maumus | Native human adipose stromal cells: localization, morphology and phenotype[END_REF]. The cell extracts were collected and stored at -80°C until RNA extraction.

THP-1 cell culture

THP-1 cells were used as a human macrophage cell model. The cells were cultured in a humidified incubator at 37 °C with 5% CO2 in RPMI 1640 (Gibco) supplemented with 10% heat-inactivated FCS (VWR) and 100 units/mL penicillin, 100 μg/mL streptomycin and 10mM HEPES (Gibco). Cells were seeded at 5.5 × 10 5 cells/mL in 1 mL into 12 wells culture plates, then differentiated into M0 macrophage-like cells by stimulation with PMA 1 ng/mL (Sigma Aldrich) for four days followed by 48h without PMA. To alter the phenotype, macrophages were primed for 48h with fresh medium supplemented with LPS (2 ng/mL; Miltenyi Biotec) and IFN-γ (10 ng/mL; Miltenyi Biotec) to differentiate into the M1-like phenotype, or IL-4 (20 ng/mL; Miltenyi Biotec) to the M2-like phenotype. After washing, cell extracts were collected and stored at -80°C until RNA extraction.

Adipose tissue explants

AT explants were used for ex vivo studies. AT samples of about 400 mg obtained from needle biopsies in 11 overweight (mean BMI 27.7 kg/m 2 , SD 3.7) women aged 36.1 Y (SD 5.1) were cut into small pieces and incubated for 4h in 4ml of Krebs/Ringer phosphate buffer supplemented with 1g/L glucose and 20g/L BSA as described in [START_REF] Sramkova | Apolipoprotein M: a novel adipokine decreasing with obesity and upregulated by calorie restriction[END_REF].

RT-qPCR validation

Three hundred fifty-nine (359) individuals had good quality RNA at all 3 CIDs. The cDNA prepared from 500 ng of total RNA and processed using the using Superscript II reverse transcriptase (Invitrogen, St Aubin, France) in the presence of random hexamers were analyzed using the StepOne Plus Real-Time PCR system (Applied Biosystems, Carlsbad, CA) and TaqMan assays (Applied Biosystems) as described in Sramkova et al [START_REF] Sramkova | Apolipoprotein M: a novel adipokine decreasing with obesity and upregulated by calorie restriction[END_REF]. The Taqman assays were obtained from Applied Biosystems and the respective IDs were: GDF15 (Hs00171132_m1), SPP1 (Hs00959010_m1), CD68 (Hs00154355_m1), LIPA (Hs01548815_m1), CCL3 (Hs00234142_m1), PSMC4 (Hs00197826_m1), PUM1 (Hs01120030_m1), and 18S (Hs99999901_s1). The relative gene expression was calculated as 2 -ΔCt using PUM1 as reference gene for full AT samples, PSMC4 for THP1 cells or 18S for AT isolated cells data.

ELISA assays

GDF15 concentration in the buffer used for explants experiments was assessed using the ELLA SinglePlex assays (ProteinSimple, San Jose, California, USA). Plasma protein levels of GDF15 were measured in duplicate using human GDF15 ELISA kit (Human GDF-15 Quantikine ELISA Kit, Bio-Techne), following manufacturer's instructions. The GDF15 concentrations were calculated using sigmoidal standard curve fitted by nonlinear regression analysis for each test.

Statistical analyses of RT-qPCR and ELISA data

Gaussian distribution was tested using the D'Agostino & Pearson test. As data normality was not fulfilled, comparison of expression distribution between groups was performed using Kruskall-Wallis or Friedman test for unpaired or paired data respectively and Dunn's multiple comparison test.

Linear regression was performed with change in BMI as dependent variable.

Results

Baseline characteristics and overall dietary intervention outcome

In this report, we investigated gene expression changes within a subset of both men and women that had followed a two-phase dietary intervention (see Methods and Figure 1A) and for which RNA samples from AT biopsies were available (Figure 1B). At baseline, subjects were on average 41 years old, with a mean BMI close to 35 kg/m 2 and a mean HOMA-IR at 2.93 (Table 1). Upon LCD, individuals achieved, on average, 11% weight loss and upon study termination (6-months after weight loss) 10.8% weight loss. Both genders achieved similar weight loss relative to baseline (p=0.2376). These characteristics are representative of all 918 enrolled subjects in the DiOGenes study.

Differential gene expression analysis

Upon quality control of the sequencing data (see Methods and Figure 1B), we assessed which genes were differentially expressed between each paired clinical intervention time points (CID).

After filtering of low-quality genes, 6,290 genes were found differentially expressed for the contrast CID1/2, 5,263 for the contrast CID1/3 and 4,461 for the contrast CID2/3. This resulted in a list of 9,156 unique genes that were found differentially expressed for at least one of the contrasts, among which 1,228 were found differentially expressed for the three contrasts, as shown in Supplementary Figure S5. Among these genes, 541 had an absolute log FC > log2(1.3) for the contrast for CID1/2, 470 for the contrast CID1/3 and 661 for the contrast CID2/3 (for a total of 1,160 unique genes, used for network inference as shown in Supplementary Figure S5). The list of expressed genes with results on filtering and differential analysis can be found as supplementary material (Supplementary File S1).

Replication with RNASeq data

Taking advantage of previously generated RNAseq data for the CID1/2 contrast for 191 subjects [START_REF] Armenise | Transcriptome profiling from adipose tissue during a low-calorie diet reveals predictors of weight and glycemic outcomes in obese, nondiabetic subjects[END_REF], we compared the RNAseq and QuantSeq technologies. Based on expression levels from 19,938 genes, we found a very strong correlation in expression fold-changes during LCD (Pearson r 2 =70% with 95% CI [69.7%-71.2%], see Supplementary Figure S6A). Next, we attempted to replicate the 541 genes found differentially expressed with the QuantSeq analyses during LCD. At FDR 5%, 481 of those genes replicated with RNAseq analysis (Supplementary Figure S6B), thereby demonstrating a 90% replication rate between the two technologies.

Network analyses reveal GDF15 as a novel partner in adipose tissue inflammation related genes

We used a system biology approach to investigate the link between AT gene expression and clinical parameters during a 2-phase DI including a LCD and the subsequent 6-month weight follow-up.

The first network analysis investigated the link between changes in AT gene expression and clinical parameters during the LCD-induced weight loss phase (see Figure 3). The network was centered on a clinical parameter, BMI, with both positive and negative relationship to a bunch of genes. Clustering of the nodes (representing genes or clinical parameters with significant changes) of the graph was performed. It revealed 5 modules with respectively 111, 89, 41, 131, and 131 nodes, with 3 of these modules including at least one clinical parameter (See Supplementary File S2 for a full description of the modules). Most of the modules contained more than 70% of down-regulated genes, except for the module containing BMI that exhibited 89% of up-regulated genes. The lists of genes associated to clinical variables in each module are displayed in the Supplementary File S3.

Pathway analyses of the genes were therefore performed for each module (supplementary Table S1).

Total cholesterol, LDL-cholesterol and HOMA-IR were included in the same module which contained 108 genes. "Cell development" and "lipid metabolism" were the top biological functions and pathways represented. The changes in the mRNA level for 28 genes were positively connected to changes in total cholesterol (and 6 genes with negative association). The change in LDL-cholesterol was connected to change in mRNA level for 3 genes: SSTR2 (positively), U1 and U1.1 (negatively). These 3 genes were also connected to total cholesterol in the same manner. The lincRNA RP3.483K16.4 was the single gene connected, positively, to HOMA-IR. The module including waist circumference contained 130 genes with 85% positively connected to waist circumference, indicating that the highest was the reduction in waist circumference the greatest was the downregulation of expression of these genes. "Lipid metabolism" was the top biological function represented. Four genes encoded secreted proteins (APOC4, EGFL6, SEMA3C, TNMD). The BMI centered module included 130 genes among which 80 were connected to BMI and, for 91%, with negative relationship (Supplementary Figure S7A). "Inflammatory response" was the top biological function represented with 51 genes. Fourteen genes encoded secreted factors (CCL3, CHI3L1, CHIT1, FCGBP, FCGR3A, FCMR, GDF15, IFI30, PILRA, PLA2G7, SDC4, SPP1, TREM2, ZG16B), among which 12 were negatively connected to BMI indicating that the highest was the decrease in BMI, the greatest was the upregulation of expression of these genes.

Our two subsequent network analyses investigated changes during the weight follow-up (CID2/3) and the overall DI (CID1/3). We identified 7 modules (with size 20-83 nodes) for CID1/3 and 8 modules (size 6-142) for CID2/3 (see details in Supplementary File S2). Both networks exhibited a module containing BMI as biological variable. For each contrast, the biological functions represented in the modules with BMI were "organismal injuries" and "lipid metabolism" (supplementary Table S1). The "lipid metabolism" pathway was representative of the module containing HDL for CID2/3. Regarding these two contrasts (CID1/3 and CID2/3), the modules containing the BMI were composed of genes representing different biological functions (lipid metabolism and organismal injuries, respectively). Also, both networks contained a module with a cluster of co-regulated genes composed of CCL3, CD68, GDF15, LIPA, and SPP1 (Supplementary Figures S7B andS7C).

The cluster was also found in the module containing BMI from the LCD-induced weight loss phase (Supplementary Figure S7A). This observation led us to focus on GDF15 which was recently reported as a nutritional stress marker [START_REF] Patel | GDF15 Provides an Endocrine Signal of Nutritional Stress in Mice and Humans[END_REF], and the four other genes of the cluster (CCL3, CD68, LIPA, and SPP1) which are markers for AT macrophages [START_REF] Klimcakova | Macrophage gene expression is related to obesity and the metabolic syndrome in human subcutaneous fat as well as in visceral fat[END_REF].

Validation using RT-qPCR

Validation of findings used RT-qPCR and a larger subset of the DiOGenes study (590 individuals, 116 men and 474 women at baseline, including 352 individuals with paired samples regarding all contrasts).

The negative association between the change in AT GDF15 gene expression and the change in BMI was confirmed during LCD (contrast CID1/2). A low positive association was found during weight follow-up, but not across the whole dietary intervention (Supplementary Figure S8).

All five genes of the cluster of macrophage markers had relative expression significantly different between all pairwise time-contrasts (and with FDR adjusted p-values < 0.05). Notably all genes marked a strong up-regulation during LCD, followed with a down-regulation during the following 6month weight control phase and with expression levels significantly lower than their baseline levels (Figure 4). Reports of GDF15 in AT are scarce, and none describe the cell type of origin. Therefore, we compared GDF15 expression between isolated adipocytes and stroma vascular cells isolated from human AT together with gene expression of CCL3, CD68, LIPA, and SPP1.

Compared to isolated adipocytes, GDF15 mRNA levels were higher in the stromal fraction, mainly in pre-adipocytes and macrophages (Figure 5). The four other genes were predominantly expressed in macrophages.

We next investigated the expression profile of these 5 genes according to phenotypic profile of the human macrophages cell line THP-1. Once induced to M0 macrophages, the THP-1 cells were polarized to M1-like (pro-inflammatory) or M2-like (anti-inflammatory) phenotype. All genes exhibited differential expression between M1 and M2 THP-1 cells, with CD68, GDF15, and LIPA having higher expression in M2 than M1 macrophages, while CCL3 and SPP1 mRNA levels were higher in M1 cells (Figure 6).

Analyses of GDF15 protein

Next, we sought to confirm the observed AT GDF15 mRNA levels at the protein levels in AT and blood. The secretion of GDF15 by AT was assessed using subcutaneous AT explants from 11 overweight women. GDF15 concentration in the media from the AT explants was 440.9 ± 114.2 pg/mL (mean ± SD, data not shown). Changes in circulating GDF15 was investigated in a subset of 28 individuals with both plasma samples, clinical and AT RT-qPCR data available at all CIDs. At baseline plasma GDF15 was 473.5 ± 184.9 pg/mL. No significant change in plasma GDF15 was found across all phases of the dietary intervention (p= 0.4908, Supplementary Figure S9A). A positive association between plasma GDF15 and GDF15 gene expression in adipose tissue was observed at baseline (Supplementary Figure S9B), but no correlation between change in AT GDF15 expression and variations in plasma concentrations was found (p-value > 0.5). The negative association between change in plasma GDF15 and change in BMI during the LCD was previously reported [START_REF] Laurens | Growth and Differentiation Factor 15 is secreted by skeletal muscle during exercise and promotes lipolysis in humans[END_REF], but no significant association was found during weight follow-up or across the whole DI (p-value > 0.4) (data not shown).

Discussion

In this study, we investigated gene expression changes in AT, during a two-phase dietary intervention in overweight/obese, non-diabetic subjects. Our systems biology approach enables to relate such changes with changes in different clinical parameters associated to obesity or comorbidities (BMI, waist circumference, total lipid levels, and HOMA-IR). In a discovery phase, we implemented the QuantSeq technology (33) that enables to substantially reduce the sequencing costs (by about 5x only in term of reagent costs), while keeping high-quality transcriptomic profiling for all Human protein-coding genes. This allowed us to assess a large RNA collection (>1,000 samples stemming from a clinical intervention). Re-analysis with nearly 400 samples using Illumina RNASeq demonstrated a 90% replication rate. Also, our validation phase, using RT-qPCR in >1,000 samples further reproduced our initial findings, thereby validating the QuantSeq technology.

Through a network-based approach, we identified BMI as a key node, indicating that this specific clinical outcome has a major link with gene expression. Also, there was one specific transcriptomic cluster including the GDF15 gene, whose expression change during weight loss was linked with BMI change. Of note, GDF15 was not identified in previous differential gene expression analyses following LCD-induced weight loss [START_REF] Armenise | Transcriptome profiling from adipose tissue during a low-calorie diet reveals predictors of weight and glycemic outcomes in obese, nondiabetic subjects[END_REF]. GDF15 (growth differentiation factor 15)/MIC-1 (macrophage inhibitory cytokine-1)/NAG-1 (nonsteroidal anti-inflammatory drug-activated gene) is a member of the transforming growth factor β (TGF-β) superfamily, that was first identified as a blocker of macrophage activation [START_REF] Bootcov | MIC-1, a novel macrophage inhibitory cytokine, is a divergent member of the TGF-beta superfamily[END_REF]. Its expression is ubiquitous and circulating concentration range is high [START_REF] Mullican | Uniting GDF15 and GFRAL: Therapeutic Opportunities in Obesity and Beyond[END_REF]. Recently, GDF15 has generated considerable attention in the field of obesity and weight control. Notably, targeting GDF15 for the treatment of obesity and anorexia is the subject of several studies [START_REF] Mullican | Uniting GDF15 and GFRAL: Therapeutic Opportunities in Obesity and Beyond[END_REF][START_REF] Frikke-Schmidt | GDF15 acts synergistically with liraglutide but is not necessary for the weight loss induced by bariatric surgery in mice[END_REF][START_REF] Mullican | GFRAL is the receptor for GDF15 and the ligand promotes weight loss in mice and nonhuman primates[END_REF][START_REF] Yang | GFRAL is the receptor for GDF15 and is required for the anti-obesity effects of the ligand[END_REF][START_REF] Hsu | Non-homeostatic body weight regulation through a brainstem-restricted receptor for GDF15[END_REF][START_REF] Emmerson | The metabolic effects of GDF15 are mediated by the orphan receptor GFRAL[END_REF].

Preclinical studies in mice showed that GDF15 suppresses food intake [START_REF] Johnen | Tumor-induced anorexia and weight loss are mediated by the TGF-beta superfamily cytokine MIC-1[END_REF] and recombinant GDF15 administration lowers body weight [START_REF] Tsai | The MIC-1/GDF15-GFRAL Pathway in Energy Homeostasis: Implications for Obesity, Cachexia, and Other Associated Diseases[END_REF]. Also, GDF15 can directly increase thermogenesis and improve insulin sensitivity [START_REF] Tsai | Treatment with the TGF-b superfamily cytokine MIC-1/GDF15 reduces the adiposity and corrects the metabolic dysfunction of mice with diet-induced obesity[END_REF]. In human, a rise in blood levels was reported with acute exercise [START_REF] Laurens | Growth and Differentiation Factor 15 is secreted by skeletal muscle during exercise and promotes lipolysis in humans[END_REF][START_REF] Kleinert | Exercise increases circulating GDF15 in humans[END_REF] and exercise training [START_REF] Zhang | Exercise training promotes a GDF15-associated reduction in fat mass in older adults with obesity[END_REF]. Regarding weight loss, during or after termination of calorie restriction, an increase was observed in obese individuals upon bariatric surgery [START_REF] Kleinert | Effect of bariatric surgery on plasma GDF15 in humans[END_REF], and 2 weeks metformin-induced weight loss [START_REF] Coll | GDF15 mediates the effects of metformin on body weight and energy balance[END_REF]. Small-scale dietary studies also showed no (PMID: 32020057 DOI: 10.1038/s41430-020-0568-9) or slight increase in plasma GDF15 following light or drastic calorie restriction, for 48h or over 28 days [START_REF] Patel | GDF15 Provides an Endocrine Signal of Nutritional Stress in Mice and Humans[END_REF]. This was also confirmed in serum upon verylow calorie diet [START_REF] Dostálová | Increased serum concentrations of macrophage inhibitory cytokine-1 in patients with obesity and type 2 diabetes mellitus: the influence of very low calorie diet[END_REF]. In addition, T2D patients exhibited lower GDF15 plasma levels 6 months after the termination of an 8-week very-low calorie diet (PMID: 33925808 PMCID: PMC8146720 DOI: 10.3390/nu13051465). Hence, circulating GDF15 is considered as a nutritional stress marker [START_REF] Patel | GDF15 Provides an Endocrine Signal of Nutritional Stress in Mice and Humans[END_REF].

Only few studies investigated the ATs [START_REF] Ding | Identification of macrophage inhibitory cytokine-1 in adipose tissue and its secretion as an adipokine by human adipocytes[END_REF][START_REF] Šrámková | Expression of lipogenic markers is decreased in subcutaneous adipose tissue and adipocytes of older women and is negatively linked to GDF15 expression[END_REF]. In our study, we found that GDF15 is released by AT and GDF15 gene expression in AT was up-regulated upon an 8-week low calorie diet, in association with change in BMI and independently of change in plasma lipid profile or insulin resistance. The baseline plasma level was in range with other studies [START_REF] Mullican | GFRAL is the receptor for GDF15 and the ligand promotes weight loss in mice and nonhuman primates[END_REF]. No significant change was found in plasma (p>0.4). This might be due to the low power of the plasma study (only 28 individuals were investigated), or a low contribution of AT to circulating GDF15 compared to other tissues, as suggested by the lack of association between changes in AT GDF15 expression and variations in circulating GDF15. In AT, we noticed that the GDF15 up-regulation was only transient, as GDF15 gene expression levels were significantly reduced in the 6-month following the acute weight loss phase despite sustained weight loss, compared to baseline. Importantly, AT GDF15 mRNA levels were significantly reduced at study termination compared to baseline. However, plasma GDF15 remained steady. This indicates that AT GDF15 upregulation is induced by a negative energy balance. Also, it tends to suggest that this is linked to AT remodeling during LCD, and that upon the acute weight loss phase, the metabolic improvements (both in term of weight loss and insulin sensitivity) relates to generally higher GDF15 levels. It is to be noticed that GDF15 induces lipolysis as recently reported [START_REF] Laurens | Growth and Differentiation Factor 15 is secreted by skeletal muscle during exercise and promotes lipolysis in humans[END_REF].

The identification of GDF15 as a factor released by the AT led to the observation that this adipokine is produced by both adipocytes and stromal cells, with higher GDF15 expression in subcutaneous than visceral AT and expression negatively associated with body fat mass in both fat depots [START_REF] Ding | Identification of macrophage inhibitory cytokine-1 in adipose tissue and its secretion as an adipokine by human adipocytes[END_REF]. In human AT, GDF15 expression was reported as a marker of oxidative stress negatively associated to lipogenic gene markers [START_REF] Šrámková | Expression of lipogenic markers is decreased in subcutaneous adipose tissue and adipocytes of older women and is negatively linked to GDF15 expression[END_REF]. In the present study, changes in GDF15 mRNA levels are positively associated with changes in expression of macrophages markers (CCL3, CD68, LIPA, SPP1) in all contrasts. We show that GDF15 expression arises from two human AT cell types, preadipocytes and macrophages, while expression of the four genes (CCL3, CD68, LIPA and SPP1), all co-expressed with GDF15, only displayed macrophages-specific profiles. Macrophages exhibit phenotypic heterogeneity and plasticity, depending on their microenvironment. These cells originate from circulating monocytes and infiltrate tissues where they play various functions including tissue cleanup and repair. The M1 and M2 classification is an oversimplification of a continuum in activation states, and individual markers may fail to specify such polarization phenotype [START_REF] Martinez | The evolution of our understanding of macrophages and translation of findings toward the clinic[END_REF]. M1-like macrophages promote AT inflammation and insulin resistance; while M2 macrophages have an anti-inflammatory role [START_REF] Appari | Metabolic Regulation of Adipose Tissue Macrophage Function in Obesity and Diabetes[END_REF]. We found that GDF15 was more expressed in M2-like macrophages. This is consistent with a mice study showing that GDF15 expression was previously reported suppressed in M1-like macrophages [START_REF] Jung | Reduced oxidative capacity in macrophages results in systemic insulin resistance[END_REF]. GDF15 also enhances the oxidative function of macrophages, leading to polarization into an M2-like phenotype [START_REF] Jung | Reduced oxidative capacity in macrophages results in systemic insulin resistance[END_REF]. Interestingly our data show that preadipocytes which are part of the SVF cells, and not differentiated adipocytes, also express GDF15 at similar level than macrophages. While preadipocytes GDF15 expression studies remain scarce [START_REF] Šrámková | Expression of lipogenic markers is decreased in subcutaneous adipose tissue and adipocytes of older women and is negatively linked to GDF15 expression[END_REF][START_REF] Yanagitai | Phenylenediamine derivatives induce GDF-15/MIC-1 and inhibit adipocyte differentiation of mouse 3T3-L1 cells[END_REF], these cells can be reprogrammed through dietary-induced weight loss and contribute to improvement of the metabolic syndrome [START_REF] Rossmeislová | Weight Loss Improves the Adipogenic Capacity of Human Preadipocytes and Modulates Their Secretory Profile[END_REF]. Further studies are needed to elucidate the potential contribution of preadipocytes to weight loss and related metabolic dysfunction improvements through GDF15 expression.

We also report different expression profiles of the macrophage markers associated to GDF15. SPP1 and CCL3 expression levels were higher in M1-like macrophages. Osteopontin, encoded by SPP1, is an important component of immune response and inflammation [START_REF] Kahles | Osteopontin: A novel regulator at the cross roads of inflammation, obesity and diabetes[END_REF]. SPP1 expression is positively associated to AT macrophages accumulation [START_REF] Bertola | Elevated expression of osteopontin may be related to adipose tissue macrophage accumulation and liver steatosis in morbid obesity[END_REF], and osteopontin plays a role in the development of insulin resistance [START_REF] Kiefer | Osteopontin expression in human and murine obesity: extensive local up-regulation in adipose tissue but minimal systemic alterations[END_REF]. CCL3 encodes MIP-1α, a member of the CC chemokine family that is produced by a variety of cells, including resident and recruited macrophages [START_REF] Hill | A decade of progress in adipose tissue macrophage biology[END_REF]. Conversely, LIPA and CD68 expression levels were higher in M2-like macrophages. LIPA, encodes the lysosomal acid lipase protein that breaks down cholesteryl esters and triglycerides in human macrophages. Its expression and activity have been reported to be decreased in the metabolic syndrome [START_REF] Lin | Association and interaction of APOA5, BUD13, CETP, LIPA and health-related behavior with metabolic syndrome in a Taiwanese population[END_REF]. CD68 encodes a membrane protein marker and in our analyses, we observed its expression in both M1 and M2 macrophages (with slightly higher levels in the latter population). This is consistent with previous reports on CD68, that document it as a general marker of macrophages, whose expression is directly linked with the number of macrophages, and that associates with both pro-and anti-inflammatory markers [START_REF] Fjeldborg | Human Adipose Tissue Macrophages Are Enhanced but Changed to an Anti-Inflammatory Profile in Obesity[END_REF].

In obese individuals, weight loss induces a decrease in pro-inflammatory and an increase in antiinflammatory factors [START_REF] Cancello | Reduction of macrophage infiltration and chemoattractant gene expression changes in white adipose tissue of morbidly obese subjects after surgeryinduced weight loss[END_REF]. The up-regulation of these five genes during LCD may be a hallmark of the beneficial effect of calorie restriction-induced weight loss on AT inflammation. Lending support to this hypothesis, a recent study showed that treatment of obese mice with GDF15 improves the oxidative function of AT macrophages and reverses insulin resistance [START_REF] Jung | Reduced oxidative capacity in macrophages results in systemic insulin resistance[END_REF]. As no significant change in circulating GDF15 was found, the present study indicates a paracrine/autocrine role of GDF15 within AT. The study cannot provide evidence on whether the enhanced GDF15 expression during LCD originates from preadipocytes or macrophages, however, a strong co-regulation of both M1 and M2 macrophages markers was found. GDF15 appears as an anti-inflammatory marker. In addition to the decrease in the anti-lipolytic insulin, the pro-lipolytic GDF15 locally produced within AT may contribute to LCD-induced weight loss [START_REF] Laurens | Growth and Differentiation Factor 15 is secreted by skeletal muscle during exercise and promotes lipolysis in humans[END_REF]. The fatty acids produced by adipocytes could thereby induce transient local inflammation.

In summary, we identified an AT signature as a cluster of macrophage-related genes, through a transcriptome-wide systems biology approach. Specifically, a module including GDF15 was identified; while GDF15 is currently the focus of targeted studies, it demonstrates the validity of our approach to identify potentially relevant biomarkers of clinical improvements during dietary intervention. And indeed, our approach highlighted a novel macrophage signature composed of genes co-regulated with GDF15.

Legends for Figures

Figure 1 :

 1 Figure 1: Flowcharts of dietary intervention and expression studies. (A) Flowchart of the DiOGenes

Figure 2 :

 2 Figure 2: Workflow of the samples, data and network analysis. CID, clinical investigation day; DEG,

Figure 3 :

 3 Figure 3: Global network for changes in gene expression and bioclinical parameters during low calorie

Figure 4 :

 4 Figure 4: Adipose tissue gene expression using RT-qPCR at all time-points of the dietary intervention.

Figures Figure 1

 1 Figures

Figure

  Figure 2

2

 2 Figure 2

Table 1

 1 Cohort characteristics /-12.75 103.90 +/-12.19 114.10 +/-11.13 1.45E-14 1.59E-13 Number corresponds to mean value +/-standard error. The t-test compares differences between men and women. BMI: Body Mass Index, FDR: False Discovery Rate, HOMA-IR: Homeostasis model assessment of insulin resistance, LCD: Low caloric diet.

	Tables
		All (n=416)	Women (n=291)	Men (n=125)	P-value	FDR
	Age, y	41.01 +/-6.35	40.36 +/-6.39	42.52 +/-6.01	0.0012	0.0018
	Baseline weight, kg	99.69 +/-17.14	95.59 +/-15.75	109.37 +/-16.44 1.42E-13 7.78E-13
	Baseline BMI, kg/m 2	34.76 +/-4.87	34.83 +/-4.98	34.59 +/-4.63	0.6298	0.6298
	Baseline HOMA-IR	2.93 +/-2.20	2.73 +/-2.25	3.38 +/-2.04	0.0056	0.0069
	Baseline total	5.02 +/-0.93	4.93 +/-0.89	5.23 +/-0.98	0.0043	0.0059
	cholesterol, mmol/L
	Baseline LDL, mmol/L 3.14 +/-0.83	3.03 +/-0.78	3.38 +/-0.87	2.19E-04 4.81E-04
	Baseline HDL, mmol/L 1.28 +/-0.31	1.33 +/-0.32	1.15 +/-0.27	4.00E-08 1.47E-07
	Baseline waist circumference, cm 106.89 +Percentage of weight -11.06 +/-2.74	-10.68 +/-2.41	-11.90 +/-3.22	0.0004	0.0008
	loss during LCD
	Percentage of weight	-10.79 +/-5.97	-10.50 +/-5.83	-11.59 +/-6.32	0.2160	0.2376
	loss at study
	termination

This work was supported by Inserm, Paul Sabatier University, the Innovative Medicines Initiative Joint Undertaking (grant agreement n° 115372), and the Commission of the European Communities (FP6-513946 DiOGenes). DL is a member of Institut Universitaire de France; JM, AC, SM, OW, GL, JH and AV are full-time employee at Nestlé; WHMS reports having received research support from several food companies such as Nestlé, DSM, Unilever, Nutrition et Santé and Danone as well as Pharmaceutical companies such as GSK, Novartis and Novo Nordisk; he is an unpaid scientific advisor for the International Life Science Institute, ILSI Europe. AA reports grants and personal fees the submitted work, and royalties received for the book first published in Danish as 'Verdens Bedste Kur' (Politiken; Copenhagen, Denmark), and subsequently published


Faten Nasri

Manel Zouari

Mohamed Kharrat

Maher Dammak

Florence Vacandio

Marielle Eyraud²

Structural, Micromechanical and Tribological Characterization of Zn-Ni Coatings: Effect of Sulfate Bath Composition

Keywords: Zn-Ni coatings, Electrodeposition, Sulfate bath, Sn, Tribological behavior

Zn and Zn-Ni alloy coatings were electrode-posited on mild steel from sulfate-based bath containing Sn as additive. The effect of Ni content on the microstructure, morphology, micro hardness and the tribological behavior of these coatings were studied and discussed. Adding Sn in the sulfate bath had a significant effect on the surface morphology, particularly on the Zn-8 wt% Ni coatings. By increasing the Ni concentration from 8 to 14 wt%, the X-ray patterns showed that the phase structure of Zn-Ni alloy coatings was changed from g-phase Ni 3 Zn 22 to c-phase Ni 5 Zn 21 . The plastic deformation and delamination were found to be wear mechanisms for the investigated coatings. While the Zn-14 wt% Ni alloys had the best wear resistance, Zn films had the most severe wear volume loss and the highest friction coefficient. * The Zn-14 wt% Ni alloys are harder than Zn and Zn-8 wt% Ni films.

* The stabilized friction coefficient of Zn-14 wt% Ni coatings is found to be more reduced than that of Zn and Zn-8 wt% Ni coatings.

* For Zn-Ni coatings, the steady values of the friction coefficient are comparable due to their nodular surface morphology.

* The Zn layers have a significant worn surface that become less and less important by increasing the Ni content.

* The Zn-14 wt% Ni alloys offer the best wear resistance ability amongst all the studied coatings thanks to its high micro hardness and fine-grained nodular morphology.

Introduction:

In recent years, the zinc coatings have been found to exhibit a high corrosion resistance to steel components. However, the corrosion protection of Zn coatings is not sufficient due to their high electrode potential with respect to steel [1][2][3]. For this reason, various investigations tend to improve the zinc deposit's properties. In fact, some research works have investigated the current mode of effect on the zinc electrodeposition. They have found that the use of a pulse current instead of direct current allows the enhancement of the corrosion resistance of the zinc coatings [4][5][6]. Actually, this improvement is attributed to the refinement of the zinc layers' morphology. According to Wasekar et al. [4], the pulse reverse current increasingly enhances the zinc coatings' corrosion resistance by improving the zinc microstructure and forming a compact corrosion product. Furthermore, the incorporation of an element from the Fe group such as Ni, Co, Fe or Sn is used as an alternative to ensure a better corrosion performance than pure Zn [7][8][9][10][11][12]. These Zn alloys are recognized by their great corrosion inhibition, thermal stability and mechanical properties in comparison with plated Zn [13][14][15][16]. Among Zn alloys, the Zn-Ni films are considered as the most significant ones due to their high corrosion resistance and flexibility along with low hydrogen embrittlement possibility [17]. In the light of the advantages already mentioned, much importance has been paid on the baths used in the Zn-Ni alloy coatings' plating. To begin with, the chloride baths have been widely used and been recently found to be characterized by their poor deposit distribution on the substrate [18]. Thus, the current alternatives tend to use the sulfate baths. Indeed, several research works are investigating the electrodeposition additives and parameters to be used with these baths to improve the Zn-Ni coatings' properties. In this respect, some studies have been focused on optimizing the parameters of sulfate baths to obtain Zn-Ni coatings with a nanocrystalline structure and investigate the phases formed during the electrodeposition. According to these studies, the current density has a significant effect on the Zn-Ni coatings' properties [19]. Indeed, they have found that the corrosion protection performance increases with the rise of current density. Therefore, the best corrosion resistance corresponds to the Zn-Ni coatings with 18.9 wt% Ni and c-phase Ni 5 Zn 21 . It is evident that nickel exhibits anomalies in electrodeposition and the increase in current density does not mean further corrosion protection performance. I

In the same context, other researchers have studied the Zn-Ni electrodepositions by varying the bath temperature, stirring speed and Ni 2+ /Zn 2+ ratio, in order to obtain Zn-Ni coatings with a single c phase [18]. They have deduced that Ni content of coatings has a remarkable effect on the structure, morphology and micro hardness of Zn-Ni coatings. According to them, the Zn-10 wt% Ni coating pre-sent a single phase Ni 5 Zn 21 towards 40 °C and 80 mA/cm 2 . Actually, the improvement of the coatings' plating is not limited to the bath parameters, the additives and the use of reverse-pulse are also found as solution. In this context, Feng et al. [20] have also investigated the electrodeposition parameters to obtain a Zn-Ni coating with a single c phase, but have used more complicated alkaline bath containing some additives and DMH as the complexing agent. As a result, they have found that the coatings with 13 or 16 wt% Ni content and single phase c are obtained with nanostructure of about 26 nm grain sizes and the Zn-13 wt% Ni coating present the best corrosion resistance. Yogesha et al. [21] have focused on the optimization of the acid chloride-based bath containing thiamine hydrochloride (THC) as brightener to electrodeposit Zn-Ni coatings. They have deduced that the brightener addition changes the Zn-Ni alloys composition. The wear resistance investigation and improvement of the Zn-Ni alloy coatings have also been of interest to the researchers in order to make these coatings applicable on steel fasteners used for automotive and aerospace industries [START_REF]F1941-00 Standard Specification for Electrode-posited Coatings on Threaded Fasteners (Unified Inch Screw Threads (UN/UNR)[END_REF][START_REF] Sriraman | [END_REF]. Panagopoulos et al. [24] have used a sulfate bath to obtain a Zn-14 wt% Ni nanocrystalline alloys to investigate their tribological behavior. They have shown that the counterpart material has a main effect on the wear mechanism. They have found that a surface delamination is discovered as a chief wear mechanism by using steel pins which got changed due to severe shearing against alumina ball. Additionally, Sriraman et al. [START_REF] Sriraman | [END_REF] have investigated the tribological behavior of the Zn, Zn-Ni, Cd and Cd-Ti coatings. The Zn-Ni coatings are electrodeposited from commercial Zn-Ni plating solutions with commercial additives. Based on these authors' results, Zn-Ni alloys have the highest micro hardness and consequently the superior resistance. Tafreshi et al. [25] have studied the effect of the Zn-Ni alloys' Ni content on the structure and tribological behavior by comparing them with pure Zn layers. Equally important, they have revealed that the micro hardness increases with the increase of the Ni content from 11 to 17 wt% by presenting different intermediate phases (-phase Ni 3 Zn 2 , -phase Ni 5 Zn 21 , -Zn-Ni). For tribological performance, they found that Zn-14 wt% Ni alloys represented the best wear resistance in terms of wear loss and friction coefficient, whereas, the Zn layer had the worst wear resistance property amongst all the coatings. Despite its diversity, research works on the Zn-Ni alloys that have been electrodeposited via sulfate bath are limited. The limitations mainly concern the analyses of the bath additives and Ni content's effects on the coating microstructure and then its micromechanical and tribological responses.

In the present work, Zn and Zn-Ni coatings with different Ni contents were electrodeposited on mild steel substrate using sulfate-based bath containing Sn as additive. The effects of Sn additive on the coating's microstructures were analyzed and discussed. The evolutions of the structural, micromechanical and tribological properties of Zn-Ni coatings with respect to the Ni con-tents were investigated.

Materials and Methods

Electrodeposition Process

The substrate material used in this study was mild steel. All specimens were machined to the dimensions of 30 9 15 9 20 mm3. The chemical composition of the substrate was as follows: 0.17% C, 1.4% Mn, 0.045% S, 0.045% P and 98.34% Fe. Furthermore, the mild steel samples were mechanically polished with 180, 400 and 1000 grit SiC paper. The substrates were activated in HCl and rinsed with ionized water and ethanol.

For both pure Zinc and Zn-Ni coatings, mild steel substrate was used as a cathode and pure zinc specimen as an anode, within an electrochemical cell. Following each pure Zinc and Zn-Ni electrodeposition, the coated steel specimens were rinsed with distilled water and ethanol. The surface roughness of all the samples were measured via a tactile profilometer.

Coatings Characterization

The surface microstructure of pure Zn and Zn-Ni alloy coatings were examined with a Scanning Electron Microscope connected with an EDS facility. Moreover, the crystallographic structure of the elaborated coatings was investigated using an X-ray diffractometer (XRD) Brucker-SIEMENS D5000 with Cu k radiation (= 0.15440 nm) and a graphite monochromator.

The average crystallite size of the samples was calculated from the peak width at the half maximum of the peak (), using the Debye-Scherrer equation [13]: (1) where  is the wavelength of the X-ray radiation,  is the Bragg angle of the peak, and  is the angular width of the peak at full-width at half maximum (FWHM).

Hardness and Tribological Testing

Hardness measurements were carried out by Vickers's micro hardness testing machine, using the diagonals method and P = 1 daN as applied load for 14 s. The mean value of at least five measurements conducted in different areas of each sample was considered. The tests were developed using a diamond indenter in the shape of a square pyramid with an angle between edges ' = 136°. For each test, the hardness Hv and the penetration h of the indenter were calculated from the measured value of the diagonal D footprint using the following expressions [START_REF] Carpentier | Caractérisation micromécanique de matériaux composites et anisotropes par indentation instrumentée[END_REF]:

Hv=1.854 (2) h= (3)
To characterize the tribological response of the coatings, a linear alternating motion tribometer was used. This device allowed us to put the coating surface in contact with a 100Cr6 ball counter body (15 mm in diameter, 680 Hv) under an imposed normal force Fn. Besides, the coated substrate was then driven in an alternating and translational movement produced by the combination of a gear motor and a rod/crank system. A force sensor was used to mea-sure the tangential force and a data acquisition system allowed a continuous recording of this effort. The tests were developed for a frequency of 1 Hz, a displacement amplitude of ± 7.5 mm, a maximum cycles number of 5000 and an imposed normal force of 6 N. Namely, five tests were performed for each of the considered coating. At the end of each friction test, the data processing could be traced back to the changes in the friction coefficient evolution with the number of cycles.

Thereafter, the worn surfaces of the coated specimen and the ball counter body were inspected using Scanning Electron Microscope (Philips series XL30) and Optical Microscope (Leica DM 2700 M). At last, surface profile in the transverse direction across the wear scar on the coated specimen was established using a tactile profilometer ''SJ-210''. On the basis of this profile, the volume loss due to the wear was calculated.

Results and Discussion

Effect of the Bath Composition on the Microstructure of Zn-Ni Coatings

Zn and Zn-Ni coatings were electrodeposited on mild steel substrate using sulfate-based bath containing Sn as additive. In order to explore the Sn effect on the Ni and Zn-Ni electrodepositions, a preliminary study was conducted. In the first part, we carried out a cyclic voltammetric analysis for which the platinum was used as an anode, the vitreous carbon as a cathode and a saturated calomel electrode (SCE) as a reference electrode. Both solutions used in this work contained NiSO 4 .6H 2 O, 0.8 M (solution1) and NiSO 4 .6H 2 O, 0.8 M; SnSO 4 , 8 10 -5 M (solution2). The curve presented in Fig. 1 shows a typical trend of the voltammograms of pure nickel (Red Curve) and pure nickel with Sn as an additive (Blue Curve) obtained at -1.2 V and 20 mv/s (scanning speed). Different voltammograms present a peak corresponding to the nickel cation's reduction during the go sweep. At the back sweep, a cross between the flow current and the return current, characteristic of a nucleation and three-dimensional growth, is observed. According to Fig. 1, it is noticed that the Sn promotes the reduction of the Ni and, most important, enhances the nucleation phenomenon. Therefore, the Sn has a significant effect on the Ni electrodeposition, despite its negligible concentration into the bath. In the second part of the preliminary study, typical Zn-Ni coatings were developed with and without Sn as an additive. The microscopic observations of these coatings are presented in Fig. 2 that shows cluster like appearance for both Zn-Ni alloy coatings. Nevertheless, for the Zn-Ni alloys with Sn as an additive, the clusters are more enlarged, mainly due to the three-dimensional growth of the Ni, and therefore promoting nucleation. These electrodepositions were conducted at -1.2 V, with a non-adjusted pH and without heating. The XRD diagrams of these two types of coatings are shown in Fig. 3. It is observed that the peaks became wider (the value of the angular width of the peak at full-width at half maximum () increases) by adding the Sn to the Zn-Ni coatings. This enlargement is due to the reduction of the grain size, which confirms the cyclic voltammetric study and the microscopic observations. From the identification of the phases in these coatings, it is found that the phase -(Ni 5 Zn 21 ) is well localized in the coatings with Sn addition than those without.

The bath used in the present work is the same as the one used earlier by Panagopoulos et al. [24] to obtain Zn-14 wt% Ni alloys. For the present study, the different concentrations and plating conditions are determined to obtain pure Zinc and Zn-8 wt% Ni alloys coatings. The chemical composition of the electrolyte bath solution as well as the plating conditions are illustrated in Table 1 in which 0.4 M and 0.8 M present the Nickel concentration into the bath for both alloy coatings. The surface microstructures of pure Zn and Zn-Ni alloys coatings are illustrated in Fig. 4. The surface of pure Zn layer presents hexagonal pellets morphology (Fig. 4a). Alloying Zn with Ni is able to change the morphology into a smooth nodular fine-grained structure (Fig. 4b). The indicative areas of the Zn-Ni surface are expanded in order to grasp the grain structure. Figure 4b0 shows a non-uniform surface containing hexagonal pellets with the appearance of nodules. As for Zn alloy coatings with ample amount of Ni, Fig. 4c shows the increase of the cluster's size to obtain surface uniformity with cauliflower morphology. Immediately, EDS analysis on the Zn-Ni coatings alloy shows that Zn-0.4 M Ni presents 8% of Ni in the composition of the alloy. Conversely, it is about 14% of Ni in the Zn-0.8 M Ni alloy coatings. The plating baths are very poor in the order of Sn ions compared to the presence of Zn and Ni ions. In short, the existence of Sn in the layers is undetectable with the EDS technique. On the other hand, Sn has a significant effect on the morphology of the coatings, resulting in the reduction of the alloys' grain size, which appears mainly for Zn-8 wt% Ni alloys. Compared to some research works [19], Zn-8 wt% Ni alloys obtained from a different bath, does not have the same surface morphology. In fact, according to them, the surface morphology seems to be more coarse-grained than that obtained in the present study. This can be explained by the fact that the Sn promotes the Ni nucleation, which in turn results in the grain size reduction [START_REF] Alfantazi | [END_REF].

Phase Structures

The phase structures of Zn and Zn-Ni coatings were determined by using XRD technique as demonstrated in Fig. 5. Accordingly, the main peaks of pure Zn layer are clarified at 2 of 36, 39 and 43°. Subsequently, the Ni addition to Zn reduces the intensity of the Zn film peaks, giving birth to new peaks. The Zn-8 wt% Ni alloy coating's XRD pattern present three main peaks at about 2 of 43, 54 and 69° corresponding to the  (Ni 3 Zn 22 ) phase. This phase has an hexagonal close packed (hcp) structure, it is rich in Zn and characterized by being soft and flexible [28]. The increase in the Ni content to 14 wt% lead to the disappearance of this phase into a new  (Ni 5 Zn 21 ) phase corresponding to the peaks at about 2h of 44, 62 and 79°. This  phase presents a body centered cubic (bcc) structure, whose flexibility is lower than the -phase [18,25]. Nevertheless, the increase in Ni and the decrease in Zn permits the domination of  (Ni 5 Zn 21 ) phase over (Ni 3 Zn 22 ) phase. This domination involves the morphology modification from hexagonal clusters to spherical nodules. The average grain sizes of the coatings that have been measured by Scherrer equation are about 7 lm, 50 nm and 37 nm for the pure Zn, Zn-8 wt% Ni and the Zn-14 wt% Ni, respectively.

Surface Roughness and Coatings' Thickness

An average roughness (Ra) of 1.6 ± 0.4 m and 1.4 ± 0.2 m is detected in the case of pure Zn and Zn-14 wt% Ni coatings, respectively, compared to the Zn-8 wt% Ni coatings which has lesser surface roughness (0.7 ± 0.2 lm). Overall, it seems that the plating time is determined to get 50 µm thickness, whose average for both pure Zinc and Zn-Ni alloy coatings is presented in Table 2.

The optical micrographs of the cross section (Fig. 6) reveals uniform thickness over the whole surface for the different coatings. This figure is obtained after chemical attack (CrO 3 (20 g) and Na 2 SO 4 (1.5 g) in 100 ml of distilled water) in order to increase substrate/coating contrast. According to these micrographs, the porosity of the Zn films seems to be inconsiderable. Nonetheless, the level of porosity increases in the case of Zn-Ni alloys. In fact, the porosity into the coatings rises, while increasing the Ni content. This observation can be attributed to the hydrogen release, which is very intense in the Ni containing solutions [29,30].

Micro hardness

The micro hardness outcomes are notably illustrated in Fig. 7 showing that the coatings' composition has a significant effect on the hardness. The penetration depth is found to be between 2.5 and 3.5 µm for the different coatings, which is significantly lower than the layer's thickness. It can be seen that the micro hardness of Zn-Ni alloy coatings increases with the rise of their Ni concentration. This supposition is explained by the fact that the hardness of Ni is greater than that of Zn [21] and by the former's ability to decrease the grain size which is a main factor to enhance the hardness. Indeed, the presence of smaller grains impedes the dislocation motion and results in an increase in the micro hardness [31].

Tribilogical Behavior

3.5.1 Friction Response Figure 8 shows the evolution of the friction coefficient of different Zn and Zn-Ni alloy coatings versus the sliding cycles' number. In fact, all Zn-Ni alloy coatings have the same friction coefficient evolution trend. During the running-in stage, a rapid increase in the friction coefficient is observed due to the increase in the contact area between the coating and the steel ball counterpart (Fig. 9). This growth is followed by a decrease in the friction coefficient until stabilization after some sliding cycles. Therefore, the establishment of a transfer film on the steel ball surface and a third body constituting the detached particles have been found to result in this decrease in the friction coefficient in Zn and Zn-8 wt% Ni coatings' case. However, for the Zn-14 wt% Ni alloys, a re-increase in the friction coefficient is noted after about 350 cycles, due to the transfer film loss. The steel ball counter body, which is in direct contact with the hardened coating, results in the increase in the frictional interaction between the contacting bodies. The results in Fig. 8 reveal that after 5000 sliding cycles, the pure Zn coating has the highest friction coefficient. The lowest micro hardness of the Zn film is in the reason behind the increase of the real contact area, and thus the significant elevation of the friction coefficient. Indeed, the Ni content has a significant effect on the friction coefficient that decreases with the increase in Ni content. For both Zn-Ni coatings, the steady values of the friction coefficients are comparable due to their nodular surface morphology.

3.5.2 Wear Response The optical and SEM micrographs of the Zn-Ni alloy coatings' wear track shows that several wear mechanisms are in motion (Figs. 10,11). The wear surface of the Zn and Zn-8 wt% Ni coatings are wider than the Zn-14 wt%Ni alloy coatings and seem to be deeper. After 5000 cycles, the width of the worn surface is about 2.5 mm for both Zn and Zn-8 wt% Ni coatings, but it is only about 1.8 mm for Zn-14 wt% Ni alloys.

Figure 10 shows optical micrographs of the worn sur-faces, evolution of the various coatings after 100 and 1000 cycles. Although the wear behavior for both Zn-Ni alloys seems to be similar, it is different from that of pure zinc. This is due to the morphology of the coatings and the presence of the nodules in the alloys. For pure Zn film, after 100 cycles, shear and delamination are identified as an initial wear mechanism. Furthermore, after 1000 cycles, plowing is located on the edges of the wear track, while the interior is the seat of a plastic deformation (Fig. 10a). For both Zn-Ni alloy coatings (Fig. 10b,c), after 100 cycles, the initial smoothening of the surface layer is activated with material removal pursued by wear debris accumulation. This accumulation is due to the trapping of the debris in the valleys of the nodules. After 1000 cycles, the wear develops and the nodules are clipped with the production of the oxide layers. According to Fig. 10b,c, the nodule crushing is more significant into the Zn-14 wt% Ni alloy wear scars (this is explained by their important size). However, their wear is less developed than the Zn-8 wt%Ni alloys.

By progressing the wear test for Zn and both Zn-Ni coatings (Fig. 11) until reaching 2500 cycles, the center of the wear track for Zn and Zn-8 wt% Ni coatings represents a severe adhesive wear mechanism with a large plastic deformation resulting from the re-deposition of the coatings' torn patches during the advancement of the wear test. By increasing the sliding distance from 2500 to 5000 cycles, similar phenomena are observed. For both cycles' number, wear seems to be more developed for the Zn coatings than for the Zn-8 wt% Ni alloys since the Zn films are less hard than the alloys.

Still in the same vein, by raising the Ni content from 8 to 14 wt%, the wear mechanism seems to be less prominent than pure Zn and Zn-8 wt% Ni. After 2500 sliding cycles, the plastic deformation with the beginning of the shear is observed. In fact, the detached debris already observed during the 100 and 1000 sliding cycles are entrapped between the coating and the steel ball counterpart forming a third-body layer. This layer interact with the underlying coating to form an abraded cracked surface. Then, and after 5000 sliding cycles, a hardened tribolayer with transverse cracks are observed towards the end of the wear test. Therefore, the Zn-14 wt% Ni alloy coatings have better wear resistance than the Zn film and the Zn-8 wt% Ni alloy coatings. Accordingly, this can be explained by the low friction coefficient and the high hardness found for the Zn-14 wt% Ni coatings. According to the Archard theory, wear resistance is proportional to the hardness of the material [START_REF] Archard | Zn and Zn-Ni alloy coatings Table 1: Sulfate baths used for the electroplating of pure Zn and Zn-Ni alloy coatings[END_REF].

The coatings' torn patches analyzed by XRD technique (Fig. 12) shows that the worn surface is composed of pure Zn and zinc oxide layers (ZnO) for the different investigated coatings. Nevertheless, the debris analysis of Zn-Ni alloy films reveal the presence of the intermetallic phase In fact, while increasing the Ni content, the oxide layers production decreases. As a result, the interaction between the third-body layer and the steel ball counterpart is expected to be relatively soft in the case of Zn-14 wt% Ni coating (Fig. 9). (Ni 3 Zn 22 ) and the intermetallic phase  (Ni 5 Zn 21 ) in the case of the Zn-8 wt% Ni and Zn-14 wt% Ni coatings, respectively. The oxidation degree seems to be more significant in the Zn coatings, case than the Zn-Ni alloys case. In fact, while increasing the Ni content, the oxide layers production decreases. As a result, the interaction between the third-body layer and the steel ball counterpart is expected to be relatively soft in the case of Zn-14 wt% Ni coating (Fig. 9).

The wear volume loss of pure Zn coatings and Zn-Ni alloy coatings after 2500 and 5000 sliding cycles are depicted in Fig. 13. It can be seen that the pure Zn coating have a high volume loss if compared to Zn-Ni alloys, for the two considered test durations. By incorporating 8 wt% of Ni to the Zn layer, the wear loss of the Zn-8 wt% Ni alloy coatings decreases slightly compared to that observed in case of pure Zn. Nonetheless, for the Zn-14 wt% Ni alloy coatings, the wear loss decreases to half after 2500 and 5000 sliding cycles. This newly found result may be linked to the micro hardness since the Zn-14 wt% Ni coatings are twice as hard as the Zn layers. Nevertheless, for all studied coatings, the wear volume loss is slightly affected by the sliding distance.

Therefore, the morphology and the micro hardness of the coatings have a significant effect on their tribological behavior. In fact, according to what have been seen, the tribological behavior of the Zn-Ni alloys at short cycles are practically similar, thanks to their nodular morphology. Nonetheless, during the progression of the wear test, a remarkable difference in terms of the wear mechanisms and volume is observed. This dissimilarity may be related to the reduced size of the nodules and the middling micro hardness of the Zn-8 wt% Ni alloys relative to the Zn-14 wt% Ni coatings. With 14 wt% of Ni in the coatings, the layer becomes harder with a typical nodular morphology. The morphology of these nodules and the micro hardness of these coatings are responsible for the reduced friction coefficient by the decrease of the real contact area. It can be concluded that the particular structure of the Zn-14 wt% Ni coating causes the entrapping of debris in the coating. Consequently, a third-body layer is formed, representing an intimate contact with the counterpart steel ball after removal of the transfer film. This direct contact results in lower wear volume [START_REF] Sriraman | [END_REF].

Conclusion

Based on the present study resulting in the electrodeposition of Zn and Zn-Ni alloy coatings on mild steel substrate using sulfate-based bath containing Sn as additive, the following points can be concluded: * Sn as additive promotes the Ni nucleation that leads to the grain size reduction. * Alloying Zn with Ni permitts the change in morphology of the coatings from hexagonal pellets to nodular fine-grained structure. The Zn-14 wt% Ni films show ''cauliflower'' morphology.

Figures

Fig. 1

 1 Fig. 1 : Cyclic voltammograms of pure nickel (red curve) and pure nickel with Sn as an additive (blue curve) at -1.2 V (color figure online)

  Fig. 1 : Cyclic voltammograms of pure nickel (red curve) and pure nickel with Sn as an additive (blue curve) at -1.2 V (color figure online)

Fig. 3 :Fig. 4 :Fig. 6 .Fig. 7 .Fig. 8 .Fig. 9 .Fig. 10 .

 34678910 Fig. 3: X-ray diffraction patterns of Zn-Ni alloy coatings: a without additive, b with Sn as additive

Acknowledgements

The authors gratefully acknowledge the help and support of Professor Mongi Feki from the Laboratory of Materials Engineering and Environment (National Engineering School of Sfax; Sfax; Tunisia).