Luc-Matthieu Fornecker
email: luc-matthieu.fornecker@chru-strasbourg.fr

Leslie Muller

Frédéric Bertrand

Nicodème Paul

Angélique Pichot

Raoul Herbrecht

Marie-Pierre Chenard

Laurent Mauvieux

Laurent Vallat

Seiamak Bahram

Sarah Cianférani

Raphaël Carapito

Christine Carapito

Multi-omics dataset to decipher the complexity of drug resistance in diffuse large B-cell lymphoma

The prognosis of patients with relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) remains unsatisfactory and, despite major advances in genomic studies, the biological mechanisms underlying chemoresistance are still poorly understood. We conducted for the first time a large-scale differential multi-omics investigation on DLBCL patient's samples in order to identify new biomarkers that could early identify patients at risk of R/R disease and to identify new targets that could determine chemorefractoriness. We compared a well-characterized cohort of R/R versus chemosensitive DLBCL patients by combining label-free quantitative proteomics and targeted RNA sequencing performed on the same tissues samples. The cross-section of both data levels allowed extracting a sub-list of 22 transcripts/proteins couples whose expression levels significantly differed between the two groups of patients. In particular, we identified significant targets related to tumor metabolism (Hexokinase 3), microenvironment (IDO1, CXCL13), cancer cells proliferation, migration and invasion (S100 proteins) or BCR signaling pathway (CD79B). Overall, this study revealed several extremely promising biomarker candidates related to DLBCL chemorefractoriness and highlighted some new potential therapeutic drug targets. The complete datasets have been made publically available and should constitute a valuable resource for the future research.

Introduction

Diffuse large B-cell lymphoma (DLBCL) is the most frequent subtype of non-Hodgkin lymphoma (NHL) and is a clinically and biologically heterogeneous disease. The anthracycline-based regimen R-CHOP (rituximab, cyclophosphamide, doxorubicine, vincristine and prednisone) is still considered as the standard of care for first-line treatment with approximately 60% of the patients achieving a complete response. The prognosis of patients with primary refractory or early-relapsed (R/R) disease is particularly poor with a median overall survival below one year. Because of the acquisition of chemoresistance, only a fraction of R/R patients can be cured with salvage therapies. [START_REF] Crump | Outcomes in refractory diffuse large B-cell lymphoma: results from the international SCHOLAR-1 study[END_REF] Recent advances in molecular biology, genetics and high throughput -omics technologies have led to a better understanding of the biology of this disease and the distinction of several subtypes of DLBCL. [START_REF] Swerdlow | The 2016 revision of the World Health Organization classification of lymphoid neoplasms[END_REF] Based on the cell-of-origin classification, the two major molecular subgroups are germinal center Bcell-like (GCB) and activated B-cell-like (ABC) DLBCL that notably differ in their clinical outcomes. [START_REF] Alizadeh | Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling[END_REF] Cytogenetic studies have highlighted the major importance of MYC, BCL2 and BCL6 rearrangements. [START_REF] Sarkozy | Double-hit and double-protein-expression lymphomas: aggressive and refractory lymphomas[END_REF] In parallel, the mutational landscape of DLBCL has been extensively studied, demonstrating the intratumoral heterogeneity and allowing the identification of recurrent somatic mutations, some of which provide promising opportunities for new drug developments. [START_REF] Bohers | Activating somatic mutations in diffuse large B-cell lymphomas: lessons from next generation sequencing and key elements in the precision medicine era[END_REF] However, the mechanisms underlying the resistance to treatment still remain poorly understood and robust biomarkers for the early identification of patients at risk of R/R disease are still lacking.

Mass spectrometry-based proteomics has benefited from an instrumental and methodological revolution over the last two decades. Today, global label-free quantitative proteomic studies enable the identification and quantification of thousands of proteins and provide new opportunities for an indepth characterization of complex proteomes. [START_REF] Doll | Region and cell-type resolved quantitative proteomic map of the human heart[END_REF] As a complement to the static picture revealed by genome sequencing, the comprehensive analysis of the proteome that is dynamic provides crucial information on protein expression to decipher complex biological processes. To date, no data are available in the literature focusing on the proteomic characterization of R/R DLBCL.

In this context, we conducted a large-scale differential proteomic investigation of R/R versus chemosensitive DLBCL patients in order to identify new potential biomarkers related to resistance to treatment and to better understand the biological mechanisms underlying chemoresistance. This proteomic investigation was combined with a quantitative transcriptomics experiment performed on the same samples to correlate genes expression and their impact at the proteomic level.

Results and discussion

We performed for the first time a large-scale differential multi-omics study on DLBCL patient's samples in order to search for new biomarkers that could help to early identify patients at risk of R/R disease and to better understand the biological mechanisms underlying chemorefractoriness. In the context of our current knowledge from the literature, a detailed study of some promising new biomarkers is provided below, demonstrating the high value of the present proteogenomic dataset.

Fresh-frozen tumour tissues were collected at the time of diagnosis, before any treatment, for 8 chemorefractory and 12 chemosensitive DLBCL patients who were uniformly treated in first-line with rituximab and an anthracycline-based chemotherapy regimen in a single institution. Patients were considered as chemorefractory if they had a stable or progressive disease after first-line (n=6), or if they relapsed less than one year after having achieved a complete response (n=2). Patients who achieved a complete response and did not relapse thereafter, with a minimal follow-up of at least 24 months after the end of treatment, were considered as chemosensitive. Chemorefractory patients were most likely to have an aggressive disease according to the age-adjusted International Prognostic Index (aaIPI) with 87% aaIPI 2-3 in the chemorefractory group and 42% in the chemosensitive group (p=0.04). There was a trend for a higher proportion of patients presenting with a disseminated disease (Ann Arbor stage 3-4) in the chemorefractory group compared to the chemosensitive group (87% vs 50% respectively, p=0.08). RNA could be extracted from the same tissue samples that were used for proteomics analysis for 17 patients (7 chemorefractory and 10 chemosensitive). In both groups, the majority of patients were classified into Germinal Center B-Cell-like (GCB) molecular subtype (72% of the chemorefractory patients and 70% of the chemosensitive patients) as determined by rapid reverse transcriptase multiplex ligation-dependent probe amplification assay (RT-MLPA). [START_REF] Mareschal | Accurate Classification of Germinal Center B-Cell-Like/Activated B-Cell-Like Diffuse Large B-Cell Lymphoma Using a Simple and Rapid Reverse Transcriptase-Multiplex Ligation-Dependent Probe Amplification Assay: A CALYM Study[END_REF] The mean percentage of tumor-cells, determined by morphological examination and immunohistochemistry, was 75%, and was ≥ 70% in 16/20 samples. Only one case had a low percentage of tumor cells (20%) but this sample corresponded to a particular subtype of DLBCL (T-cell/histiocyte-rich large B-cell lymphoma). Patient's characteristics are summarized in table 1 and a detailed description of the 20 patients is provided in Supplementary Table 1.

Overall, the combined proteomics analysis of the 20 samples resulted in the identification of 4774 unique protein groups (proteins which cannot be unambiguously identified by unique peptides are grouped in one protein group and quantified together). Pairwise comparisons of all samples against each other resulted in a high Pearson coefficient correlation (average r = 0.89) demonstrating a high quantitative accuracy and a high similarity in the global proteomes. The statistical analysis with the peptide-level Robust Ridge Regression model (MSqRob) allowed the relative quantification of 3101 proteins between the two groups of patients, with 586/3101 (18.9%) being significantly differentially abundant with a false discovery rate < 5%. Among these differentially abundant proteins, 246 were overexpressed in chemorefractory patients and 340 overexpressed in chemosensitive patients (Supplementary Table 2).

Transcriptomic analysis was performed on 17 samples (these 17 samples are indicated in supplementary table 1). It allowed the quantification of 17695 transcripts across the 17 samples. For the 4774 previously identified proteins, the transcript counterpart was also identified by RNAseq in 4338/4774 (90.8%). With an adjusted p-value < 0.1, 244 transcripts were differentially abundant between the two groups of patients (Supplementary Table 3).

Among the 3101 quantified proteins with MSqRob, 2965/3101 (95.6%) were also quantified at the transcriptomic level. The combination of transcriptomics and proteomics data thus resulted in 2965 transcripts/proteins commonly quantified at both levels. When focusing on the 246 proteins overexpressed in chemorefractory patients, only 24 had a high fold-change at the proteomic level (log2FC (R vs S) > 1) and low at the transcriptomic level (log2FC (R vs S) < 1). Conversely, 16 proteins had a low fold-change at the proteomic level (log2FC (R vs S) < 1) and high at the transcriptomic level (log2FC (R vs S) > 1) When focusing on the 340 proteins overexpressed in the chemosensitive patients, 23 had a high fold-change at the proteomic level (log2FC (R vs S) < -1) and low at the transcriptomic level (log2FC (R vs S) > -1). Conversely, only 5 proteins had a low fold-change at the proteomic level (log2FC (R vs S) > -1) and high at the transcriptomic level (log2FC (R vs S) < -1).

By considering only the significantly differentially expressed proteins between the two groups of patients, only 22/586 (3.8%) were found to be also differentially expressed at the transcriptomic level.

In all but one case, the variation direction was similar between the two methods with 16/22 trancripts and proteins overexpressed in chemorefractory patients (log2FC (R vs S) > 0), and 5/22 trancripts and proteins overexpressed in chemosensitive patients (log2FC (R vs S) < 0) (Table 2). Only one discordant case was observed with Complement C3 that was overexpressed in chemorefractory patient at the proteomics level but overexpressed in chemosensitive patients at the transcriptomics level. The crosssection of both data levels allowed the extraction of a sub-list of 22 differentially abundant transcripts/proteins couples (Table 2). Indoleamine 2,3-dioxygenase 1 (IDO1) was overexpressed in chemorefractory patients (Figure 1A). This enzyme is involved in the degradation of the amino acid tryptophan. L-kynurenine, one of the metabolites resulting from tryptophan degradation, has the ability to inhibit T-cell proliferation and to induce T-cell death, contributing to an immunosuppressive microenvironment. [START_REF] Frumento | Tryptophan-derived catabolites are responsible for inhibition of T and natural killer cell proliferation induced by indoleamine 2,3-dioxygenase[END_REF] Expression of IDO1 evaluated by immunohistochemistry was already found to be positive in one third of DLBCL cases and was associated with a worse response rate and a worse 3-year overall survival after first-line therapy with R-CHOP. [START_REF] Ninomiya | Indoleamine 2,3-dioxygenase in tumor tissue indicates prognosis in patients with diffuse large B-cell lymphoma treated with R-CHOP[END_REF] From a therapeutic point of view, IDO1 represents a novel immune checkpoint target.

Several IDO1 inhibitors (epacadostat (INCB024360), indoximod, navoximod (GDC-0919) or BMS-986205) are now available but failed to demonstrate a therapeutic efficacy as a monotherapy.

However, several ongoing trials in various solid tumors (ovarian cancer, pancreatic cancer, squamous cell carcinoma of the head and neck, non-small cell lung cancer, metastatic renal-cell carcinoma for example) are currently evaluating IDO1 inhibitors in combination with other agents such as PD1 or PD-L1 inhibitors. [START_REF] Zhai | IDO1 in cancer: a Gemini of immune checkpoints[END_REF][START_REF] Vacchelli | Trial watch: IDO inhibitors in cancer therapy[END_REF] Although results from phase 2 studies were encouraging, such as with the combination of epacadostat and anti-PD1 in melanoma patients (Daud A. et al, ASCO 2018, #9511), recent results from phase 3 studies failed to confirmed these results [START_REF] Garber | A new cancer immunotherapy suffers a setback[END_REF] , suggesting that further analysis are warranted to better defined the subset of patients who are most likely to benefit from IDO1 inhibitors. No clinical trial are currently ongoing in order to evaluate the potential of IDO1 inhibitors in the context of R/R DLBCL.

We also demonstrated an overexpression of the chemokine C-X-C motif ligand 13 (CXCL13) in chemorefractory patients (Figure 1B). CXCL13, the unique ligand of CXCR5, is an inflammatory chemokine that contributes to generate a pro-inflammatory microenvironment in angioimmunoblastic T-cell lymphoma. [START_REF] Tripodo | Mast cells and Th17 cells contribute to the lymphoma-associated proinflammatory microenvironment of angioimmunoblastic T-cell lymphoma[END_REF] Moreover, it has been shown to be an adverse prognosis factor in advanced colon cancer. [START_REF] Qi | Expression features of CXCR5 and its ligand, CXCL13 associated with poor prognosis of advanced colorectal cancer[END_REF] In colon cancer cells, the CXCL13-CXCR5 axis participates in tumour growth and invasiveness by activation of the PI3K/AKT signalling pathway. [START_REF] Zhu | CXCL13-CXCR5 axis promotes the growth and invasion of colon cancer cells via PI3K/AKT pathway[END_REF] Our results indicate that CXCL13 could also play a key role in the microenvironment of DLBCL. Hexokinase 3 (HK3) is one of the four isoforms of hexokinase involved in the first step of the glycolysis pathway, converting glucose into glucose-6-phosphate. Glucose metabolism of cancer cells highly differs from that of normal cells. In cancer cells, pyruvate generated by glycolysis is converted into lactate via a phenomenon called "aerobic glycolysis" (Warburg effect). [START_REF] Vander Heiden | Understanding the Warburg effect: the metabolic requirements of cell proliferation[END_REF] Overexpression of hexokinase is crucial for cancer cells to produce enough ATP by aerobic glycolysis. Recently, hexokinase 2 was shown to be overexpressed in rituximab-resistant cell lines and to be associated with inhibition of mitochondrial-mediated apoptosis. [START_REF] Gu | Up-regulation of hexokinase II contributes to rituximab-chemotherapy resistance and is a clinically relevant target for therapeutic development[END_REF] In our study, hexokinase 2 was identified with a high degree of confidence (by 49 unique peptides covering almost 50% of its sequence) while its expression was not affected between the two groups of patients both at proteomic (log2FC=0.4, q-value=0.115) and transcriptomic (log2FC=0.7, adjusted p-value=0.58) levels. Interestingly, hexokinase 3 was also identified with a high degree of confidence (by 29 unique peptides covering 57% of its sequence), and its expression was significantly higher in chemorefractory patients at both proteomics (log2FC=01.7, qvalue < 0.001) and transcriptomics levels (log2FC=1.9, adjusted p-value=0.028) (Figure 1C). These results suggest that hexokinase 3 could play a key role in DLBCL chemorefractoriness.

Proteins S100 are involved in the regulation of proliferation, migration and invasion of cancer cells, and their dysregulation has been demonstrated in the majority of human cancers. [START_REF] Bresnick | S100 proteins in cancer[END_REF] Fifteen S100 family members were identified in our study by proteomic analysis (S100A2, S100A4, S100A6, S100A7, S100A7A, S100A8, S100A9, S100A10, S100A11, S100A12, S100A13, S100A14, S100A16, S100B and S100P) and 4 were found to be significantly overexpressed in chemorefractory patients: S100A4 (log2FC=0.9, q-value=0.003), S100A8 (log2FC=1.7, q-value<0.001), S100A9 (log2FC=1.9, q-value<0.001) and S100A11 (log2FC=0.5, q-value=0.007). Only S100A4 and S100A8 were significantly overexpressed at both transcriptomics and proteomics levels (Figure 1D and1E). S100A4 and S100A8 were already extensively studied in solid tumours and were found to be associated with tumour growth and metastasis. [START_REF] Fei | S100A4 in cancer progression and metastasis: A systematic review[END_REF][START_REF] Zha | S100A8 facilitates the migration of colorectal cancer cells through regulating macrophages in the inflammatory microenvironment[END_REF] However, few data are available in lymphoma with only S100A9 being described as associated with tumour growth and immune evasion. [START_REF] Cheng | Inhibition of dendritic cell differentiation and accumulation of myeloidderived suppressor cells in cancer is regulated by S100A9 protein[END_REF] Our data thus suggest that these proteins could be associated in DLBCL with a more aggressive disease and could participate in the development of resistance to treatment. Based on our results, targeting S100 proteins may represent a therapeutic potential for the treatment of R/R DLBCL.

Finally, the B-cell antigen receptor complex-associated protein beta chain (CD79B) was significantly under-expressed in chemorefractory patients both at proteomic (log2FC=-1.7, q-value=0.013) and transcriptomic (log2FC=-1.3, adjusted p-value=0.023) levels (Figure 1F). CD79B is necessary for the function of the B-cell receptor and somatic genetic alterations in the CD79B gene participate in the constitutive activation of the NF-kB pathway, in particular for the ABC DLBCL subtypes. CD79B mutations have been reported in 23% of R/R ABC DLBCL. [START_REF] Morin | Genetic Landscapes of Relapsed and Refractory Diffuse Large B-Cell Lymphomas[END_REF] However, few data are available which investigate the level of CD79B expression. This protein expression level could eventually affect the efficacy of the anti-CD79B antibody-drug conjugate (polatuzumab vedotin) that has recently emerged as a potential active drug in R/R DLBCL. [START_REF] Palanca-Wessels | Safety and activity of the anti-CD79B antibody-drug conjugate polatuzumab vedotin in relapsed or refractory B-cell lymphoma and chronic lymphocytic leukaemia: a phase 1 study[END_REF] We used the gene ontology (GO) (http://www.geneontology.org/) and PANTHER database [START_REF] Mi | PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements[END_REF] to perform enrichment analysis based on the Gene Ontology -Biological Process (GO-BP) annotations in our proteomics dataset. This enrichment analysis indicated that proteins significantly overexpressed in chemorefractory patients were particularly enriched in GO-BP associated with inflammation and immune response, as well as the coagulation cascade. At the opposite, proteins significantly overexpressed in chemosensitive patients were enriched in GO-BP associated with ribosome biogenesis and ribosomal RNA (rRNA) processing (Table 3).

These results suggest that cancer-related inflammation and disturbed immune response may play an important role and contribute to chemorefractoriness in DLBCL. The role of cancer-related inflammation in the development and progression of tumors, as well as in patient outcomes, has been recognized since many years. [START_REF] Diakos | Cancer-related inflammation and treatment effectiveness[END_REF] In addition to CXCL13 and S100A8 proteins, that were previously discussed, the other proteins from our dataset that were found to be significantly overexpressed at proteomics and transcriptomics level in chemorefractory patients and involved in inflammation or immune response are Granzyme H, Granzyme K, Complement C3 and Alpha-1-antitrypsin. Our results also suggest that a local activation of coagulation may contribute to treatment-resistance and tumor progression, and are consistent with previously published works having already highlighted the role of blood coagulation in cancer progression. In particular, it has been shown that blood coagulation enzymes in the tumor microenvironment played a role in solid-tumor progression and metastasis. [START_REF] Repetto | Coagulation and fibrinolysis in gastric cancer[END_REF][START_REF] Lima | Activation of blood coagulation in cancer: implications for tumour progression[END_REF] These results highlight the potential major role played by the microenvironment in tumor progression and drug-resistance in DLBCL. The strong interactions between cancer-cells and their surrounding microenvironment have been already largely studied, and targeting the microenvironment offers now novel therapeutic perspectives in cancer. [START_REF] Hui | Tumor microenvironment: Sanctuary of the devil[END_REF][START_REF] Wu | Tumor microenvironment and therapeutic response[END_REF] In this study, proteomic and transcriptomic analysis were performed on whole tissue sections, thus allowing the analysis of DLBCL-cells and their microenvironment. One major drawback of this approach is that these two compartments cannot be analysed separately, but this type of approach could be considered as an opportunity, in the context of a biomarker discovery study, to provide the most exhaustive list of potential new biomarkers.

However, in this context, validation studies are a crucial need in order to determine more precisely the relative contribution of both compartments that are tumor-cells and microenvironment. To achieve such an objective, immunohistochemistry could be considered as a method of choice.

In this study, we present the first high-throughput multi-omics study in DLBCL. Over the past two decades, genomics and transcriptomics have largely dominated in cancer research, in particular with the recent advent of next-generation sequencing (NGS) technologies. In 2000, gene-expression profiling allowed the clear distinction of two molecular DLBCL subtypes, namely Activated B-Cell (ABC) and Germinal Center B cell-Like (GCB) subtypes. [START_REF] Alizadeh | Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling[END_REF] The development of NGS technologies resulted in the recent publication of exome sequencing in 1001 DLBCL patients allowing to depict the nearly complete mutational landscape in DLBCL and the identification of 150 driver genes. [START_REF] Reddy | Genetic and Functional Drivers of Diffuse Large B Cell Lymphoma[END_REF] This in-depth and extensive molecular characterization of DLBCL at genomic and transcriptomic levels also recently led to the proposal of novel molecular classifications in DLBCL identifying subgroups of patients with distinct clinical behaviour and prognosis. [START_REF] Chapuy | Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes[END_REF][START_REF] Schmitz | Genetics and Pathogenesis of Diffuse Large B-Cell Lymphoma[END_REF] More recently, MS-based proteomics has emerged as an important tool for the characterization of DLBCL. Various methodological approaches have been used with an aim to exploring various aspects of the disease, such as pathogenesis, subtypes classification or therapeutic issues. Super-SILAC-based approaches have demonstrated the ability to distinguish DLBCL subtypes according to their cell of origin in patient-derived DLBCL cell lines as well as on tumor samples from patients. [START_REF] Deeb | Super-SILAC allows classification of diffuse large B-cell lymphoma subtypes by their protein expression profiles[END_REF][START_REF] Deeb | N-linked glycosylation enrichment for in-depth cell surface proteomics of diffuse large B-cell lymphoma subtypes[END_REF][START_REF] Deeb | Machine Learning-based Classification of Diffuse Large B-cell Lymphoma Patients by Their Protein Expression Profiles[END_REF] So far, few proteomics studies have addressed the drug-resistance challenge in DLBCL. This issue has been first addressed by a proteomic study (two-dimensional gel electrophoresis with MALDI-TOF/TOF-MS analysis) aiming to identify differentially expressed proteins expressed by DLBCL cells with high or low sensitivity to chemotherapy after in vitro exposure to the CHOP regimen compounds. Nineteen differentially expressed proteins were identified between the two groups. Among these differentially expressed proteins, immunohistochemical analysis performed in DLBCL tissue samples from 98 patients confirmed a higher expression of Glutathione S-transferase (GSTP1) and Heat shock protein beta-1 (HSPB1), and a lower expression of Ezrin (EZR) and Pleckstrin (PLEK) in patients with relapse or progressive disease after CHOP chemotherapy. [START_REF] Liu | Identification of differentially expressed proteins in chemotherapy-sensitive and chemotherapy-resistant diffuse large B cell lymphoma by proteomic methods[END_REF] In another study, by using a SILAC-based quantitative proteomic approach on 10 DLBCL patients selected by their response to treatment (5 patients with primary refractory disease or early relapse, and 5 patients considered cured), 87 proteins, among a total of 3027 successfully quantified proteins, were differentially expressed between the two groups of patients with 21 overexpressed in refractory patients. The authors could demonstrate an up-regulation of proteins involved in the regulation of the actin cytoskeleton in chemosensitive patients. [START_REF] Ruetschi | SILAC-Based Quantitative Proteomic Analysis of Diffuse Large B-Cell Lymphoma Patients[END_REF] This work was pursued by using a tandem mass tag (TMT)-based quantitative proteomic approach performed on microdissected samples obtained from formalin-fixed paraffin-embedded tissues. This study allowed identifying 102 DA proteins and the authors could confirm the up-regulation of proteins involved in actin regulation in chemosensitive patients. Interestingly, they managed to highlight a potential role for ribosomal proteins in treatmentresistance as these proteins were largely represented in those found to be overexpressed in chemorefractory patients. [START_REF] Bram Ednersson | Expression of ribosomal and actin network proteins and immunochemotherapy resistance in diffuse large B cell lymphoma patients[END_REF] In comparison with these previously published works, our study points out the potential role of the microenvironment in drug-resistance in DLBCL. However, we observed one discrepancy between our study and the study published by Bram Ednersson et al regarding the potential role of ribosomal proteins. In our study, ribosomal proteins and ribosome biogenesis appeared over-represented in chemosensitive patients, while it was the opposite in the work of Bram Ednersson et al. There is no obvious explanation, but it remains hazardous to make a direct comparison between two exploratory studies that differ in several technical and methodological aspects such as the tissue used for protein extraction (fresh-frozen vs formalin-fixed paraffin-embedded, whole-tissue vs microdissection), proteomic workflow and quantification (label-free vs super-SILAC) and statistical analysis. Nevertheless, these two studies point out a potential role for ribosome proteins in drugresistance and, therefore, warrant continued research to clarify the role of these proteins in treatmentresistance of DLBCL.

In conclusion, this study revealed several extremely promising biomarker candidates associated with chemorefractoriness, related to tumour metabolism, microenvironment, BCR signalling pathway, hence highlighting new potential therapeutic drug targets. The combination of multilevel -omics datasets is very useful to reduce lists of thousands of candidates to a robust subset of significant targets, as well as to cross-validate candidates by different techniques. Further studies will be necessary to validate these findings in a larger and independent cohort of patients. However, the present work already provides greater insights in the underlying mechanisms of chemoresistance in DLBCL, supported by a publically available dataset.

Materials and Methods

Patients selection

Patients were selected among the fresh-frozen tissue-sample collection available from the "Centre de ressources Biologiques des Hôpitaux Universitaires de Strasbourg". We retrospectively analyzed the treatment and outcome of each patient. Only patients for whom a tissue-sample collected at the time of diagnosis and treated in first-line with the combination of anti-CD20 monoclonal antibody and an anthracycline-based regimen were selected. Patients were considered as chemorefractory if they had a stable or progressive disease after first-line, or if they relapsed less than one year after having achieved a complete response. Patients who achieved a complete response after first-line and did not relapse thereafter, with a minimal follow-up of at least 24 months after the end of treatment, were considered as chemosensitive.

Samples handling

All samples were obtained by a surgical resection or radiological-guided biopsy from a tumor mass.

Proteomic and transcriptomic analysis were performed on the same specimens that were used for the diagnosis of the disease. After collection, all samples were stored at -80°C, without conservative medium, until protein or RNA extraction. The neoplastic content in each sample was determined by a pathological review of all cases, based on morphology and immunohistochemistry for distinguishing B-cells and T-Cells.

Proteomics analysis

Sample preparation

Proteins were extracted from ~10mg of fresh frozen tumor tissues in a lysis buffer containing 62.5 mM Tris HCl pH 6.8, 2% SDS and 10% glycerol. Protein concentration was determined with DC TM method (Bio-Rad) according to manufacturer's instructions. For each sample, 20 µg of proteins were used for tube-gel preparation, as previously described. [START_REF] Muller | Benchmarking sample preparation/digestion protocols reveals tube-gel being a fast and repeatable method for quantitative proteomics[END_REF] Briefly, 7.5% acrylamide/Bis-acrylamide, and 0.25 µL TEMED were added for a final volume of 100 μL. Ammonium persulfate (2.50 µL) was added to initiate polymerization. After fixation with 50% ethanol/3% phosphoric acid, tube-gels were cut in 2 mm sections and each section in ~2 mm 2 pieces. The gel pieces were washed and the cysteine residues were reduced by adding 10 mM DTT for 30 min at 60 °C and 30 min at room temperature, and alkylated by adding 55 mM IAA for 20 min in the dark. The gel pieces were then washed three times by adding 50/50 (v/v) 25 mM NH4HCO3/acetonitrile (ACN). After two dehydrations with ACN, the proteins were cleaved in an adequate volume to cover all gel pieces with a modified porcine trypsin (Promega) solution at a 1:80 (w/w) enzyme:protein ratio. Digestion was performed overnight at 37 °C. Tryptic peptides were extracted twice under agitation, first with 60% ACN in 0.1% FA for 1 h then with 100% ACN for 1 h. The excess of ACN was vacuum dried, and the samples were resolubilized with H2O/ACN/FA (98/2/0.1 v/v/v).

NanoLC-MS/MS analysis

The nanoLC-MS/MS analysis was performed on a nanoAcquity UPLC device (Waters Corporation, Milford, USA) coupled to a Q-Exactive Plus mass spectrometer (Thermo Fisher Scientific, Waltham, Massachusetts, USA). Peptide separation was performed on an ACQUITY UPLC BEH130 C18 column (250 mm × 75 μm with 1.7 μm diameter particles) and a Symmetry C18 precolumn (20 mm × 180 μm with 5 μm diameter particles, Waters). The solvent system consisted of 0.1% FA in water (solvent A) and 0.1% FA in ACN (solvent B). Samples (equivalent to 800 ng of proteins) were loaded into the enrichment column over 3 min at 5 μL/min with 99% of solvent A and 1% of solvent B. The peptides were eluted at 450 μL/min with the following gradient of solvent B: from 1 to 35% over 120 min and 35 to 80% over 1 min. The 20 samples were injected in randomized order. The MS capillary voltage was set to 1.8 kV at 250 °C. The system was operated in Data Dependent Acquisition mode with automatic switching between MS (mass range 300-1800 m/z with R = 140,000, Automatic gain control (AGC) fixed at 3 x 10 6 ions and a maximum injection time set at 50 ms) and MS/MS (mass range 200-2000 m/z with R = 17,500, AGC fixed at 1 x 10 5 and the maximal injection time set to 100 ms) modes.

The ten most abundant peptides were selected on each MS spectrum for further isolation and higher energy collision dissociation fragmentation, excluding unassigned and monocharged ions. The dynamic exclusion time was set to 60 s.

Data analysis

Raw data obtained for each sample were processed using MaxQuant (version 1.5.5.1). Peaks were assigned with the Andromeda search engine with full trypsin specificity. The isoform-containing human database used for the search was extracted from the UniProtKB-SwissProt database (26 sept 2016, 42,144 entries). The minimum peptide length required was seven amino acids and a maximum of one missed cleavage was allowed. Methionine oxidation was set as a variable modification and peptides with modified methionines, as well as their unmodified counterparts, were excluded from protein quantification. Cysteine carbamidomethylation was set as a variable modification to account for the potential propionamide modifications of cysteine residues. Cysteine propionamidation was thus also set as a variable modification. For protein quantification, the "match between runs" option was enabled. The maximum false discovery rate was 1% at peptide and protein levels with the use of a decoy strategy. We used the "peptides.txt" files exported from MaxQuant for further statistical analysis with the peptide-level Robust Ridge Regression model (MSqRob) R-package. [START_REF] Goeminne | Experimental design and data-analysis in labelfree quantitative LC/MS proteomics: A tutorial with MSqRob[END_REF] Statistical analysis were performed for all quantified proteins and transcripts, independently of the number of patients in whom the proteins and/or transcripts were quantified.

The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium database with the identifier PXD009089. [START_REF] Deutsch | The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition[END_REF]

Transcriptomics analysis

RNA isolation and sequencing

Total RNA was isolated from fresh frozen tumor tissues with the with TRIzol reagent (Invitrogen, Carlsbad, California, USA). RNA extraction could not be performed for 3 patients (samples #5, #12 and #15 in supplementary table 1) because of the lack of enough tissue available. RNA integrity was assessed with the Agilent total RNA Pico Kit on a 2100 Bioanalyzer instrument (Agilent Technologies, Paolo Alto, USA). The sequencing library was prepared with the Ion AmpliSeq Transcriptome Human Gene Expression Panel (Thermo Fisher Scientific, Waltham, Massachusetts, USA) according to the manufacturer's protocol. [START_REF] Li | Comprehensive evaluation of AmpliSeq transcriptome, a novel targeted whole transcriptome RNA sequencing methodology for global gene expression analysis[END_REF] Briefly, after reverse transcription of total RNA, the cDNAs were amplified by multiplex PCR including a total of 20,812 amplicons. These amplicons were then partially digested, and after barcoded sequencing adapter ligation, the libraries were loaded at a concentration of 75 pM on an Ion PI IC 200 chip using the Ion Chef Instrument (Thermo Fisher Scientific, Waltham, Massachusetts, USA). Finally, the sequencing took place on an Ion Proton sequencer with the Ion PI IC 200 Kit, according to the manufacturer's instructions (Thermo Fisher Scientific, Waltham, Massachusetts, USA).

Analysis of RNA-sequence reads

The raw reads were processed by the Torrent Suite analysis pipeline and mapped to the human genome assembly hg19 AmpliSeqTranscriptome. The Torrent AmpliSeqRNA Plugin was used to generate raw read counts which were further used for differential analysis. An average of 3.5 million reads were generated per sample with 93.25% reads on target and an average of 11,005 amplicons covered by at least 10 reads. We applied the R Bioconductor package DESeq2 to identify genes that were differentially expressed. The gene selection was based on the adjusted p-value. All genes with an adjusted p-value lower than 0.1 were selected as differentially expressed. [START_REF] Love | Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2[END_REF] RNAseq raw data have been deposited in fastaq format in the EMBL-EBI ArrayExpress archive (https://www.ebi.ac.uk/arrayexpress/) with the accession number E-MTAB-6597.

Ethical Committee

Sample collection for further research analysis was approved by an Ethical Committee ("Comité de Protection des Personnes Est IV", Strasbourg, France) and all patients provided an informed consent according to the Declaration of Helsinki.

Figure legend

Figure 1. Top six differentially abundant proteins and genes between chemorefractory and chemosensitive patients. For each protein, dots represent the distribution of the intensities of the peptides quantified in each group of patients. For each gene, dots represent the log2 normalized read count distribution of the considered gene for each patient in each group.

Figure 1 .

 1 Figure 1.

Table 1 .

 1 Patients' characteristics.

		Chemosensitive	Chemorefractory	p value
	Age (years)			0.58
	Median	55	57
	range	18-79	31-73
	Sex, n (%)			0.55
	Male	9/12 (75)	5/8 (63)
	Female	3/12 (25)	3/8 (37)
	Ann Arbor Stage, n (%)			0.08
	1-2	6/12 (50)	1/8 (13)
	3-4	6/12 (50)	7/8 (87)
	aaIPI, n (%)			0.04
	0-1	7/12 (58)	1/8 (13)
	2-3	5/12 (42)	7/8 (87)
	Response to first-line, n (%)			NA
	Complete response	12/12 (100)	2/8 (25)
	Primary refractory	NA	6/8 (75)
	Number of treatment lines			NA
	Median	1	5
	Range	1	3-7
	Cell of origin, n (%)			0.93
	GC	7/10 (70)	5/7 (72)
	ABC	1/10 (10)	1/7 (14)
	Unclassifiable	2/10 (20)	1/7 (14)
	Abbreviations: aaPIP, age-adjusted International Prognostic Index; GC, Germinal Center B-Cell-Like;
	ABC, Activated B-Cell-Like; NA, Not Applicable

Table 2 .

 2 Sub-list of 22 differentially abundant transcripts/proteins couples at both transcriptomics and proteomics levels.

	Protein name	Gene name	Proteomics log2FC (R vs S)	q-value	Transcriptomics log2FC (R vs S) adjusted p-value
	C-X-C motif chemokine 13	CXCL13	2,9	0.006	1,6	0.019
	Indoleamine 2,3-dioxygenase 1	IDO1	2,9	<0.001	1,7	0.061
	Granzyme H	GZMH	2,7	0.028	1,7	0.047
	Protein THEMIS2	THEMIS2	1,2	0.015	1,3	0.067
	Granzyme K	GZMK	0,6	0.017	1,5	0.089
	Complement C3	C3	0,3	<0.001	-1,4	0.060
	Hexokinase-3	HK3	1,7	<0.001	1,9	0.028
	Superoxide dismutase [Mn], mitochondrial	SOD2	1,2	<0.001	1,6	0.039
	Protein S100-A8	S100A8	1,7	<0.001	1,7	0.066
	Protein S100-A4	S100A4	0,9	0.003	1,4	0.020
	PRA1 family protein 3	ARL6IP5	0,5	0.009	0,9	0.089
	rRNA 2'-O-methyltransferase fibrillarin	FBL	-0,4	0.006	-0,8	0.070
	40S ribosomal protein S18	RPS18	-0,3	0.011	-1,1	0.076
	40S ribosomal protein S6	RPS6	-0,3	0.043	-0,9	0.033
	40S ribosomal protein S12	RPS12	-0,4	0.009	-0,9	0.089
	Alpha-1-antitrypsin	SERPINA1	1,1	0.007	1,8	0.039
	Serpin B6	SERPINB6	1,2	<0.001	1,5	0.001
	Phosphatidylinositol 3,4,5-trisphosphate-dependent	PREX1	0,8	0.006	1,1	0.067
	Rac exchanger 1 protein
	Ceruloplasmin	CP	0,6	<0.001	1,7	0.083
	CD97 antigen	CD97	0,5	0.003	1,2	0.074
	B-cell antigen receptor complex-associated protein	CD79B	-1,7	0.013	-1,3	0.024
	beta chain
	Syntaxin-11	STX11	1,4	<0.001	1,2	0.070
	Abbreviations: Log2FC, log2 fold-change; R, chemorefractory patients; S, chemosensitive patients

Table 3 .

 3 Gene Ontology-Biological Process enrichment among the differentially expressed proteins using the PANTHER database system.

	Gene Ontology-Biological Process	Fold enrichment	p value	FDR
	Chemorefractory patients
	Fibrinolysis	8.5	0.0001	< 0.0001
	Regulation of complement activation	4.1	< 0.0001	0.0065
	Platelet activation	3.5	0.0002	0.0198
	Platelet degranulation	3.2	0.0001	0.0154
	Inflammatory response	3.2	< 0.0001	0.0001
	humoral immune response	3.1	< 0.0001	0.0051
	Neutrophil degranulation	2.5	< 0.0001	< 0.0001
	negative regulation of immune system	2.5
	process		0.0004	0.0034
	activation of immune response	2.4	< 0.0001	0.0018
	Chemosensitive patients
	Ribosome biogenesis	3.2	< 0.0001	< 0.0001
	rRNA processing	3.4	< 0.0001	< 0.0001
	FDR: False Discovery Rate

Acknowledgments

The authors acknowledge "Alsace contre le Cancer" for research grant and the "Centre de Ressources Biologiques" (CRB) from the University Hospital of Strasbourg for providing the samples. This work was supported by the "Agence Nationale de la Recherche" (ANR) and the French Proteomic Infrastructure (ProFI; ANR-10-INBS-08-03). This work was further supported by the Laboratory of Excellence ANR-11-LABX-0070_TRANSPLANTEX, the INSERM (UMR_S 1109), and the Cancéropole Grand-Est, all to SB; and the European regional development fund (European Union) INTERREG V program (project n°3.2 TRIDIAG) to RC and SB.

Author contributions

LMF, LMu, CC and RC conceived the project and designed the study. LMF, LMu and AP performed experiments. NP and FB performed statistical analysis. LMF, LMu, RC and CC wrote the manuscript. SB, SC, RH, MPC, LMa and LV analysed data and reviewed the manuscript.

The authors have declared no conflict of interest.


Geert Haarlemmer
email: geert.haarlemmer@cea.fr

Chamseddine Guizani

Suzanne Anouti

Maxime Déniel

Anne Roubaud

Sylvie Valin

Analysis and comparison of bio-oils obtained by hydrothermal liquefaction and fast pyrolysis of beech wood

Keywords:

Introduction

In the search for replacement of fossil liquid fuels, a wide panel of different conversion techniques have been proposed. These include hydrothermal liquefaction, fast (or flash) pyrolysis (aided by catalysts or not) and various gasification processes followed by catalytic fuel synthesis (Fischer-Tropsch amongst others). The yield and quality of the produced liquid fuel are extremely variable from process to process.

Hydrothermal liquefaction (HTL) converts biomass in subcritical water [START_REF] Kruse | Water -a magic solvent for biomass conversion[END_REF]. Typical operating conditions are 260-350 °C and 8-20 MPa. At lower temperatures, hydrothermal carbonisation is favoured, while higher temperatures, especially above the critical point, favour gasification. Hydrothermal liquefaction is particularly suited for wet resources as no preliminary drying is required. Wet resources that are often considered for this process include sewage sludge [START_REF] Hammerschmidt | Catalytic conversion of waste biomass by hydrothermal treatment[END_REF][START_REF] Itoh | Production of heavy oil from sewage sludge by direct thermochemical liquefaction[END_REF], food processing residues [START_REF] Déniel | Hydrothermal liquefaction of food processing residues: application to blackcurrant pomace[END_REF][START_REF] Déniel | Energy valorisation of food processing residues and model compounds by hydrothermal liquefaction[END_REF] and algae [START_REF] Hognon | Comparison of pyrolysis and hydrothermal liquefaction of Chlamydomonas reinhardtii. Growth studies on the recovered hydrothermal aqueous phase[END_REF]. Dry resources have also been studied, including wood and forestry residues [START_REF] Minowa | Thermochemical liquefaction of Indonesian biomass residues[END_REF][START_REF] Zhong | A comparative experimental study on the liquefaction of wood[END_REF] and agricultural residues [START_REF] Pinkowska | Hydrothermal decomposition of rapeseed straw in subcritical water. Proposal of three-step treatment[END_REF][START_REF] Gan | Operating condition optimization of corncob hydrothermal conversion for bio-oil production[END_REF][START_REF] Goudriaan | Liquid fuels from biomass via a hydrothermal process[END_REF]. It has been extensively shown that additives can improve bio-oil yields [START_REF] Hammerschmidt | Catalytic conversion of waste biomass by hydrothermal treatment[END_REF][START_REF] Zhong | A comparative experimental study on the liquefaction of wood[END_REF].

Pyrolysis is an ancient technique to produce charcoal from wood. By heating the wood in absence of an oxidising agent to 400-600 °C, it decomposes in charcoal, tar and gas. Fast pyrolysis (often referred to as flash pyrolysis) applies the same principle except that the heating rate is much higher (500-1000 °C s À1 ). Under these conditions the production of tar and condensable compounds are favoured at the detriment of char and gas [START_REF] Bridgwater | Fast pyrolysis processes for biomass[END_REF].

The quality of the bio-oils produced depends on the process but also on the resource. Typically hydrothermal oils are very viscous, strong smelling black oils with a relatively high heating value, up to 35-40 MJ/kg. Pyrolysis oils have a smoky odour, are less viscous and have a much lower heating value (similar to that of the parent biomass). Hydrothermal oils are typically proposed as a diesel substitute after catalytic upgrading [START_REF] Zhu | Techno-economic analysis of liquid fuel production from woody biomass via hydrothermal liquefaction (HTL) and upgrading[END_REF]. Pyrolysis oils are often thought of as an alternative for fuel oil.

The objective of this paper is to present bio-oils produced by hydrothermal liquefaction and pyrolysis of beech wood, and to characterise them as much as possible. In this study, we tested various analysis techniques on six different bio-oils from HTL and entrained flow pyrolysis processes. The objective is to determine values for some of the important properties qualifying a liquid fuel. We are interested here in determining some physical and chemical properties, by testing standard methods developed to characterise biodiesel and conventional fuels.

Review of analysis techniques

There are many standards for commercial fuels, for heating and transportation applications. Each application requiring fuel is typically designed for a particular fuel for optimal performance. Inversely, most fuels target a particular application. A fuel is characterised with properties relevant to its application, including manipulation, storage and combustion. There is a very large panel of standardized tests that are applied to different fuels that are commercially available. Some of these tests are relevant to potential applications concerning bio-oils.

All commercial fuels have criteria related to storage. The iodine value (standard EN 14111) is determined to indicate if biodiesels contain compounds with unsaturated long chains like fatty acids or their derivatives resulting from the conversion of lipids. This technique has already been applied to pyrolysis oils [START_REF] Wiggers | Biofuels from waste fish oil pyrolysis: continuous production in a pilot plant[END_REF]. The presence of unsaturated compounds can produce polymerisation reactions during storage. Iodine value measures the amount of unsaturation in the form of double bonds, which react with iodine compounds. The higher the iodine number, the more C@C bonds are present in the bio-oil. The total acid number (TAN) is used as an index of fuel acidity, one of the parameters to estimate corrosion. This is an important quality measurement to avoid corrosion risk to machinery and storage tanks (standards EN 14104 and ASTM D664). Biodiesel and pyrolysis oils are often characterised with this parameter [START_REF] Oasmaa | Norms, standards, and legislation for fast pyrolysis bio-oils from lignocellulosic biomass[END_REF][START_REF] Wang | Hydrothermal liquefaction of Litsea cubeba seed to produce bio-oils[END_REF].

One of the major fuel qualities is its heating value expressed by its Lower Heating Value (LHV) or Higher Heating Value (HHV) (standard NF EN 14918). Nazari et al. [START_REF] Nazari | Hydrothermal liquefaction of woody biomass in hot-compressed water: catalyst screening and comprehensive characterization of bio-crude oils[END_REF] report hydrothermal bio-oils with higher heating values in the range of 25-32 MJ kg À1 . Vaporisation is the first stage in the combustion process. Some applications require very small amounts of residual carbon after vaporisation. This is evaluated by measuring the Conradson Carbon Residue (CCR, standards EN ISO 10370 and ASTM D4530). It is obtained after heating a fuel, and is the main indicator to identify residual carbon. Transportation fuels such as petrol, jet fuel and diesel have more narrow specifications in terms of combustion properties, for example Research Octane Number (RON, standard ASTM D2699) and cetane number (standard EN ISO 5165 and ASTM D613). These latter two are of little interest for bio-oils.

Ultimate analysis is not usually a criterion for fuels but does give interesting insights to the quality of a fuel. Typical oxygen contents for hydrothermal oils vary in the literature. Hydrothermal oils from woody biomass in batch autoclave experiments contain 20-30% oxygen according to Nazari et al. [START_REF] Nazari | Hydrothermal liquefaction of woody biomass in hot-compressed water: catalyst screening and comprehensive characterization of bio-crude oils[END_REF], 34% according to Doassans-Carrère et al. [START_REF] Doassans-Carrère | Comparative study of biomass fast pyrolysis and direct liquefaction for bio-oils production: products yield and characterizations[END_REF] and Gan and Yuan report 28% oxygen content for hydrothermal oil obtained from corn cobs [START_REF] Gan | Operating condition optimization of corncob hydrothermal conversion for bio-oil production[END_REF]. Continuous liquefaction reactors produce biocrudes with lower oxygen content for similar biomasses, Elliot at al. report 12% [START_REF] Elliott | Hydrothermal liquefaction of biomass: developments from batch to continuous process[END_REF][START_REF] Tews | Biomass direct liquefaction options: technoeconomic and life cycle assessment[END_REF] while Hoffman et al. report 5% for oil produced in supercritical conditions. Pyrolysis oils contain more oxygen, around 50% on water free basis according to Doassans-Carrère et al. [START_REF] Doassans-Carrère | Comparative study of biomass fast pyrolysis and direct liquefaction for bio-oils production: products yield and characterizations[END_REF] while Zhang et al. report 35-40% [START_REF] Zhang | Review of biomass pyrolysis oil properties and upgrading research[END_REF].

Few authors presenting hydrothermal oils complete these results in terms of functional fuel properties such as viscosity, density, volatility and stability during storage. The properties of biooils can be easily compared with commercial standards. This directs the use of bio-oils towards certain applications for example transport or heating.

Viscosities of HTL bio-oils are rarely mentioned in the literature, high values were found up to 1000 Pa s [START_REF] Minowa | Thermochemical liquefaction of Indonesian biomass residues[END_REF]. Furthermore, according to Mohan et al. [START_REF] Mohan | Pyrolysis of wood/biomass for bio-oil: a critical review[END_REF] the viscosity of bio-oils increases due to the aging effect. It is well agreed that rheological properties of biooils change over time at elevated temperature by many factors like polymerisation, oxidation and others. For pyrolysis oils much more data is available. Studies on rheological properties and on the aging effects of bio-oil can be performed to give an indication on how bio-oil viscosity changes upon time depending on temperature and other storage conditions [START_REF] Oasmaa | Norms, standards, and legislation for fast pyrolysis bio-oils from lignocellulosic biomass[END_REF][START_REF] Oasmaa | Acidity of biomass fast pyrolysis bio-oils[END_REF]. The fuel pumpability, assessed via the measure of viscosity (standards EN ISO 3104 and ASTM D445), is another important parameter.

The characterisation of chemical species in bio-oils is often performed using separation techniques such as gas chromatography coupled with mass spectrometry (GC/MS). These techniques provide an indication of the chemical nature of the bio-oil but they do not allow the quantification of all of its compounds [START_REF] Staš | Overview of analytical methods used for chemical characterization of pyrolysis bio-oil[END_REF][START_REF] Mullen | Chemical composition of bio-oils produced by fast pyrolysis of two energy crops[END_REF].

Another major parameter is related to the volatility of the fuel. For storage purposes the product should be stable, vapour pressure expressed as the Reid Vapour Pressure (standard ASTM D323) should be inferior to a certain value (seasonal parameter). The boiling point range and the end of the distillation (standard ASTM D86) are also important parameters to be regulated.

Characterisation by a full distillation curve (standard ASTM D86) is rarely presented for bio-oils because they are available in small amounts at the laboratory scale. To understand the evaporation behaviour of bio-oils, other analysis techniques are applied, such as thermogravimetric analysis (TGA). TGA is applied to any type of sample that will undergo a mass change over time under the effect of temperature in a given atmosphere. The evaporation and thermal decomposition are part of the changes that will be detected by thermogravimetry. TGA is often used to predict the thermal behaviour of the major macromolecular components of biomass during their thermal conversion to biofuel [START_REF] Collura | Thermal behaviour of Miscanthus grasses, an alternative biological fuel[END_REF]. In other published works, TGA is used to simulate distillation or to show the volatility of products resulting from biomass conversion. The distillation of biodiesels produced from different vegetable oils as resources are compared in term of percentage of weight loss at different temperatures [START_REF] Gautam | Determination of important biodiesel properties based on fuel temperature correlations for application in a locomotive engine[END_REF]. Zhu et al. compared HTL bio-oils obtained from different operating conditions [START_REF] Zhu | Hydrothermal liquefaction of barley straw to bio-crude oil: effects of reaction temperature and aqueous phase recirculation[END_REF]. TGA can also be used as an alternative method to the Conradson carbon residue (CCR) method [START_REF] Ghetti | A rapid heating TGA method for evaluating the carbon residue of fuel oil[END_REF].

Materials and methods

Biomass and chemicals characteristics

The same biomass was used to produce oils by HTL and pyrolysis. The biomass is beech wood supplied by SPPS company. The raw wood is finely ground with particles having sizes ranging from tens to a few hundreds of microns. Narrow particle distributions were obtained by sieving. The wood water content was determined after drying at 105 °C according to the standard NF-EN-14774. Ash content was determined by burning the sample in air at 550 °C according to the standard NF-EN-14775. The ultimate analysis of the wood sample was performed by the SOCOR laboratory (France) to determine the carbon, hydrogen, nitrogen and sulphur content. These properties are summarized in Table 1.

Biomass particles having a size ranging between 315 and 450 lm were used for the pyrolysis experiments. Hydrothermal experiments were performed with the same wood with particle sizes inferior to 75 lm.

For hydrothermal liquefaction experiments, distilled water was used to prepare the biomass slurry. Pellets of Sodium Hydroxide NaOH were purchased from Merck and used as received. Ethylacetate was purchased from Sigma-Aldrich and used as received.

In our experiments, all the reagents were analytical grade.

Hydrothermal liquefaction -experimental device and operating conditions 3.2.1. Batch reactor

Hydrothermal liquefaction experiments are performed in a 0.6 L stainless steel (SS316) stirred batch reactor (Parr Instruments). In a typical experiment, the reactor is filled with approximately 240 g of biomass slurry prepared from beech wood and distilled water at a constant dry matter to water ratio (15 wt% dry matter). Sodium hydroxide NaOH is added as an additive when needed. The pH of the mixture is measured before and after each experiment using a Scientific Instruments IQ170 pH meter. The autoclave is leak tested, purged and pressurized to 1 MPa with nitrogen gas, to ensure sufficient pressure for gas analysis after the reaction. The pressure inside the reactor is a function of the initial pressure, the reaction temperature and the amount of gas produced. The reactor is stirred at 600 rpm and is heated to the reaction temperature in about 35 min. For these experiments, the operating temperature is set at 300 °C. Once the reactor reaches the reaction temperature, it is held during a specified time within ±1 °C of the specified operating temperature (holding time). For these experiments a 60 min holding time is applied. After the holding time, the reactor is rapidly cooled to room temperature in 20 min by an air quench.

Product recovery

The content of the reactor is first filtered on a Buchner filter to separate the aqueous liquid from the raw residue. The raw organic residue is sticky, and removed from the reactor as best as possible. The reactor is then weighed and the weight difference with the empty reactor is counted as raw organic residue (wet). The raw organic residue is dried at room temperature under air circulation until a stable mass is obtained.

The raw organic residue is then extracted with ethyl-acetate; using a tenfold amount of solvent (w/w) to separate the bio-oil from the char. Bio-oil is recovered after evaporation of the solvent at room temperature under air circulation until a stable weight is obtained. It is assumed that no residual solvent is left in the bio-oil. The char is also dried at room temperature under air circulation until a stable weight is obtained. Weight loss of the raw residue after extraction and drying is used to determine the proportion of solvent-soluble organics in the raw residue, and therefore the bio-oil and char yields.

Fast pyrolysis -experimental device and operating conditions

The experimental device is a Drop Tube Reactor (DTR) located at the School of Mines in Albi, France. The device was previously used in several studies focusing on the pyrolysis and gasification of biomass [START_REF] Septien-Stringel | High temperature gasification of millimetric wood particles between 800 °C and 1400[END_REF][START_REF] Couhert | Failure of the component additivity rule to predict gas yields of biomass in flash pyrolysis at 950 °C[END_REF][START_REF] Billaud | Biomass gasification between 800 and 1400 °C in the presence of O 2 : drop tube reactor experiments and simulation[END_REF].

The experimental device consists of three main parts, the biomass injection system, the reactor and the collection of the reaction products. The reactor is an alumina tube inserted in a vertical heater with three independent heating zones. The dimensions of the tube are 2.3 m in length and 0.075 m in internal diameter. The heated zone is 1.2 m long. The reactor works at atmospheric pressure and can reach a maximum temperature of 1400 °C. The wood particles are continuously fed into the reactor using a gravimetric feeding system. The main gas stream (nitrogen) is preheated to the pyrolysis temperature before entering the reactor.

The solid char falls by gravity into a collection pot. Tars and light gases goes into a cold trap at À77 °C (isopropanol + solid CO 2 ), then in a filter lined with glass wool fibres. Tars are trapped after these two condensation stages while light gases are aspired towards a gas chromatograph for analysis.

This staged condensation allowed us to obtain two bio-oil fractions. The first one, obtained in the cold trap, has a high water content (aqueous fraction), while the second one which is obtained after the condensation in the glass wool fibres lined pot, contains much less water and mostly organic molecules (organic fraction). The focus of the present work is on the bio-oil organic fraction. The mass balance is obtained after weighing the char in the pot, the tar collection pot before and after the experiment and by calculating the amount of gases after analysis.

HTL and pyrolysis product analysis

Most of the techniques used in this paper are based on standard, well known, techniques. High viscosity and the deep black colour of hydrothermal oils impose some adaptations from the standard methods are required due to for example colour indicators.

Total acid number (TAN) and iodine value

These criteria are concerned with stability and corrosion during storage. The total acid number (TAN) and the iodine value are determined according to the generic (applicable to most fuels) European standards EN 14104 (2003) and EN 14111 (2003) respectively. The titration experiments were repeated three times for each method.

The total acid number is the number of milligrams of potassium hydroxide (KOH) that is required to neutralise the acidic functions present in one gram of bio-oil. It is expressed in mg KOH per gram of sample. To determine the TAN value, 50 mg of bio-oil sample was dissolved in 10 ml of isopropyl alcohol and then titrated by a KOH solution (0.02 mol l À1 ) using Phenolphthalein as a colour indicator. The standard EN 14104 suggests using 0.1 mol l À1 but working with a lower concentration allows using smaller samples to assure the transparency of the solution. Temperature at equivalent point was controlled according to the standard method.

Iodine value is the mass of halogen binding to double-bonds contained in 100 g of bio-oil. It is expressed in grams of iodine (I 2 ) per 100 g of the sample, according to the standard EN 14111. It is based on the reaction between a Wijs (ICl) solution and the

Thermogravimetric analysis

The thermogravimetric (TG) analysis (referred to as TGA) was conducted on a SETSYS SETARAM device using a platinum pan at atmospheric pressure. The oil is heated at 10 °C min À1 from 60 °C to 900 °C under nitrogen flow (50 ml min À1 ). The final temperature is maintained for 10 min and air is introduced (50 ml min À1 ) to perform the combustion stage. Sample mass of the analysed biooil are typically 5-6 mg. The TG curve shows weight loss as function of the temperature. This gives precious information on the volatility of the bio-oil and allows the evaluation of the carbon residue.

Higher heating value

The higher heating value HHV of bio-oils (typically samples of 0.3-0.5 g) is measured using a Parr 6200 bomb calorimeter (Parr Instruments Company).

Viscosity measurements

Dynamic viscosity of the bio-oils is measured using a rotational viscometer with a temperature controlled sample chamber. The viscometer was supplied by the Sheen society; it is composed of a sample cell placed in a thermostat adapter, and a range of different geometric spindles. A sample volume between 8 and 11 ml is required for each measurement depending on the spindle. The torque required to rotate the spindle at the required frequency is measured. The viscosity is calculated from torque measurement and the geometry of the spindle. In practice the measurement is reliable between 15% and 85% of the maximum torque, resulting in a limited operating window for each configuration.

Density

Density is measured by two different methods. For pyrolysis oils with a sufficiently low viscosity a known volume is filled with the oil and weighed. Hydrothermal oils are too viscous for the previous method to be accurate, due to presence of air bubbles perturbing the measurement. A special density module was used on a high precision balance that allows the density measurement of an object with an arbitrary shape. The method consists of weighing the sample directly followed by a weighing under water. The difference in weight is created by buoyancy and represents the sample volume.

Results

Conversion results

Hydrothermal liquefaction

Bio-oils have been produced by hydrothermal liquefaction at 300 °C, 60 min holding time. Two different operating conditions are presented here. In the first case (L1), HTL of beech wood was performed in water without any additive. In the second case (L2), 0.06 g of sodium hydroxide per gram of beech wood was added. In both cases the amount of raw residue (after drying) as well as the char and oil yields are presented (Table 2). As seen earlier in Section 3.2.2, the oil yield is the solvent soluble fraction. All yields are given relative to the initial mass of dry biomass used in the conversion. Each experiment was performed three times. The averaged value of the results is reported, the reported uncertainty is the span in the data rather than a standard deviation.

The presence of sodium hydroxide clearly does not have any influence on the final pH and on the gas production. The presence of a strong base favours the production of oil and appears to inhibit the formation of char. The produced gas mainly consists of carbon dioxide with traces of ethane, ethylene and propane. The total amount of organic molecules in the aqueous phase cannot be assessed directly. Due to oxygen transfers (hydration and dehydration reactions) between water and the biomass and the production of CO 2 , the mass balance can only be closed when the water present in the reactor is taken into account. The reported yield of the aqueous phase is calculated by difference as is common practice in this type of study. In practice, about 35% of the initial organic material is dissolved in the water phase.

Fast pyrolysis

Four pyrolysis oils were produced at different temperatures ranging between 450 °C and 600 °C. The particle size and gas residence time were maintained constant at 315-450 lm and 16.6 s respectively. The biomass feeding rate was set at 1 g min À1 . The pyrolysis tests durations were between 60 and 90 min at temperatures from 450 to 600 °C.

The results of fast pyrolysis tests are given in Table 3. The yield of the organic fraction (analysed in the present study) is always lower than that of the aqueous fraction. However, this organic fraction is of higher interest as it contains less water and a higher concentration of organics. The mass balances are relatively well closed. No duplicate runs were made. An assessment of all experimental uncertainties evaluated the experimental uncertainty at 10%.

When considering the effect of the temperature on the pyrolysis products distribution, one can see that the lowest temperature of 450 °C corresponds to the highest solid yield (40.6%) and the lowest gas yield (6.1%). At the highest temperature of 600 °C the gas production is maximised (51.5%), while the solid char yield is the lowest (8.2%). The production of bio-oil reaches a maximum at 500 °C (62.4%) and decreases at higher temperatures. These observations can be explained by the fact that the reaction kinetics of the wood pyrolysis reaction is relatively low at 450 °C and increases with the temperature. The characteristic pyrolysis time is about 2 s at 450 °C and decreases to 0.15 s at 600 °C leaning on the results of [START_REF] Gorton | Modeling pyrolysis oil production in an entrained-flow reactor[END_REF]. The cracking kinetics of the condensable species increases with temperature leading to a higher gas and lower biooil yields with increasing temperature.

Physical properties of bio-oils

Physical properties are important parameters as they relate to transport and storage. In this study we present density and viscosity measurements. Key values are given in Table 4. For 450 °C the results are presented for the totally condensed phase with water included, and denoted with the -Liq extension. The -Org extension indicates the condensed organic phase. Each experiment was performed three times except for the viscosity that was measured only on one sample. The averaged value of the results is reported, the reported uncertainty is the span in the data rather than a standard deviation.

Fig. 1 presents the evolution of the viscosity of hydrothermal oils L1 and L2 with the temperature and the frequency of the viscometer. The rotating viscometer can operate with a range of frequencies. The actual frequencies depend on the minimum and maximum torque measurement on the motor. The viscosity of hydrothermal oil L1 was found to be very high at room temperature: at 30 °C the value was 700 Pa s and only the lowest frequency was possible to avoid excessive torque on the motor. At higher temperatures the viscosity decreases rapidly to 520 mPa s at 80 °C. For higher temperatures the full range of allowed frequencies was used. Hydrothermal oil L2 has a much lower viscosity but its evolution with the temperature is less pronounced. It was observed that the measured viscosity decreases with the frequency and therefore the shear rate. Towards higher frequencies the viscosity approaches an asymptotic value. The fluid is not perfectly Newtonian, something that is to be expected from this type of complex fluids. This means that the measured value of the viscosity measured is not a precise data but depends on the measuring device. It should be taken as an order of magnitude value. The fluid becomes more Newtonian with higher temperatures and the values are more precise.

The viscosities of pyrolysis oils (see Fig. 2) are much lower than those obtained after hydrothermal liquefaction. The viscosity decreases with increasing temperatures. As noted earlier, bio-oil P1 is the totally condensed phase from the fast pyrolysis experiments. Its viscosity is much lower than that of the other samples, due to the presence of a non-negligible amount of water. The presence of water is not desirable for energy recovery application; yet it decreases the oil viscosity and therefore enhances its pumpability. Bio-oils P2 to P4 are exclusively the organic fraction of the oil; the water phase is condensed independently. There appears to be no clear correlation between operating conditions and the viscosities of oil P2, P3 and P4. Of the three organic fractions P4 appears to have the lowest viscosity. An obvious explanation can be found in the fact that higher pyrolysis temperature produces lower molecular weight molecules. At 70-80 °C the viscosities converge to 20 mPa s.

The evaporation behaviour is evaluated by thermogravimetric analysis. Thermogravimetric analyses of bio-oils produced by hydrothermal conversion are presented in Fig. 3. To compare biooils with classic refinery streams, the thermogravimetric analyses of Iraqi heavy crude [START_REF] Barneto | Thermogravimetric monitoring of crude oil and its cuts in an oil refinery[END_REF], diesel oil [START_REF] Hashimoto | Evaporation characteristics of a palm methyl ester droplet at high ambient temperatures[END_REF] as well as a light cycle oil and a heavy gas oil from Brazilian crudes [START_REF] Gonçalves | Investigation of petroleum medium fractions and distillation residues from Brazilian crude oils by thermogravimetry[END_REF] are presented in the same graph. Pyrolysis bio-oils are presented in Fig. 4. Pyrolysis oils initially follow the diesel curve but after evaporating 50% of their mass the evaporation slows down significantly. This indicates that pyrolysis oils contain more high boiling point (high molecular weight) compounds. In all cases, especially for hydrothermal oils, the residual carbon after evaporation is much higher than for most refinery streams ranging between 11% to 13% for pyrolysis oils and 17% to 24% for hydrothermal oils.

The evaporation behaviour of the bio-oils is presented in a more quantitative way in Table 5. For pyrolysis oils, the volatility between 35 and 100 °C or 35 and 250 °C increases with increasing pyrolysis severity. Hydrothermal and pyrolysis oils have a comparable volatility below 100 °C, but hydrothermal oils appear to contain more high boiling compounds. Nazari et al. [START_REF] Nazari | Hydrothermal liquefaction of woody biomass in hot-compressed water: catalyst screening and comprehensive characterization of bio-crude oils[END_REF] performed a similar experiment for hydrothermal oil produced under similar conditions with birch wood. Oil was obtained from solvent extraction and solvent evaporation under reduced pressure. They found much higher evaporation temperatures; little activity was shown under 180 °C with a peak in the evaporation around 380 °C. At 400 °C 46% was evaporated while in our oils this degree of evaporation is achieved at 250 °C. It is difficult to comment on the reason for this difference, probably due to more severe solvent evaporation conditions.

Chemical properties of bio-oils

All oils have been analysed for a wide spectrum of properties such as total acid number (TAN), iodine value, heating value and residual carbon after evaporation. The results are presented in Table 6. The first major observation is that the heating value of hydrothermal oils is higher than that of pyrolysis oils and that they contain less water. Hydrothermal oils have a higher molecular weight than pyrolysis oils; this is confirmed by their higher viscosity and their tendency to produce residual carbon during heating and evaporation. Acidity of the oils depends much on the conversion process. The addition of a strong base in the HTL process significantly reduces the acidity of the oil (L2 compared to L1). The ash content of the bio-oils is negligible as the parent biomass contains very little ashes. Sodium added for the production of hydrothermal oil L2 is mainly found in the water phase.

Higher temperatures reduce the acidity of the pyrolysis oils. This may be due to dehydration and decarboxylation reactions of carboxylic acids, which are thermally favoured above 450 °C [START_REF] Hurd | Ketene from acetic acid[END_REF][START_REF] Szwarc | The kinetics of the thermal decomposition of acetic anhydride[END_REF]. Acetic acid can dehydrate to form water and acetic anhydride (reaction (1)). Moreover, thermal decomposition of acetic acid can yield formaldehyde along with carbon monoxide and ethylene (reaction (2)). Finally, acetic acid is able to undergo decarboxylation to form methane and carbon dioxide (reaction (3)). We observed in our experiments an increase of the formaldehyde yield, as well as of CO, CO 2 , CH 4 and C 2 H 4 (results not shown).

2CH 3 CO 2 H ! H 2 O þ ðCH 3 COÞ 2 O ð1Þ
2CH 3 CO 2 H ! 2CH 2 O þ 2CO þ C 2 H 4 ð2Þ
CH 3 CO 2 H ! CO 2 þ CH 4 ð3Þ
This same phenomenon can be observed when comparing the results presented in this paper with the results from Hoffman et al. [START_REF] Hoffmann | Co-processing potential of HTL biocrude at petroleum refineries -Part 1: Fractional distillation and characterization[END_REF]. These authors show that for hydrothermal oils produced from hardwood at 400 °C have a lower acidity, 37 mg KOH per gram bio-oil. Higher temperatures increase the iodine value of pyrolysis oils, corresponding to an increase of monomeric lignin derivatives with unsaturated side-chains (see also Fig. 6).

Fig. 5 shows the GC/MS analysis of the hydrothermal oils. The peaks in Fig. 5 are partially identified in Table 7. In practice there are many peaks, sometimes overlaying, which makes positive identification difficult. Only compounds with a positive identification are presented. The peaks of phenolic compounds (phenol, guaiacol and syringol) are less important, relative to the other compounds, in the oil produced in presence of sodium hydroxide. This is probably due to a solubilisation of these compounds in the water phase in the presence of a strong base. However, the presence of sodium hydroxide in the conversion process only moderately influences the chemical composition.

The thermogravimetric analysis shows that about 50% of the hydrothermal oil is evaporated at 250 °C, the maximum furnace temperature of the GC/MS. This means that only half of the oil is actually evaporated in the GC/MS and therefore analysed. Many high molecular weight compounds cannot be analysed. In the case of pyrolysis oil only 30% of the oil cannot be analysed.

The GC/MS analyses of pyrolysis oils are presented in Fig. 6. The peaks in Fig. 6 are partially identified in Table 7. We observe that many compounds are identified both in hydrothermal and pyrolysis oils. With increasing severity in the pyrolysis, some of the peaks of the larger molecules (cyclopentanone) are reduced and smaller molecules are favoured (formaldehyde and acetic acid). Thermogravimetric analysis showed that 30% of the pyrolysis oil is not evaporated at the maximum temperature of the GC/MS furnace. Many new molecules are present in the chromatogram at retention times between 30 and 37 min. It is possible (even likely) that these compounds are the results of cracking reactions of high-boiling point compounds, which were not visible previously. Some molecules like acetic acid, hydroxyacetone and formaldehyde are present in the four oils and have relatively constant peak areas, independent on the temperature. Some other molecules show significant changes as to their peak area. For instance, toluene only appears at 550 °C and shows an increase in its peak area at 600 °C. This molecule probably results from the cracking reaction of heavier molecules. A similar observation can be made for the o-xylene which also appears at 550 °C but has its peak area decreasing at 600 °C. This is probably due to thermal cracking. This molecule may give a molecule of toluene or benzene by losing one or two methyl groups respectively.

It can also be noted that monocyclic molecules such as phenol, guaiacol and dimethylphenol have their areas increasing with temperature. These are also likely outcome of the cracking reactions of larger molecules.

Other monocyclic molecules such dimethoxyphenol (syringol) and methoxyeugenol show lower peak areas with increasing temperature. A plausible explanation could be that such molecules would lose their side chains by thermal cracking to give simpler molecules such as phenol or toluene and more gas in the form of CO, H 2 , CH 4 and CO 2 , which would be consistent with our experimental observations.

Table 7

Identification of GC/MS peaks in hydrothermal oils (left) and pyrolysis oils (right).

Hydrothermal oils (Fig. 5) Pyrolysis oils (Fig.

Discussion

As mentioned in the introduction, commercial fuels are not generic and each responds to a specific need. Norms and standards are designed to make sure that the application operates correctly for any commercial fuel for that particular application. Table 8 presents the requirements for some commercial fuels and typical values for bio-oils found in this study. From the presented results it is clear that both hydrothermal and pyrolysis oils are far from a dropin commercial use.

In the case of hydrothermal oils, industrial applications are still far. The viscosity is very high and this may not be acceptable for most applications. Viscosity for hydrothermal oils produced from blackcurrant pomace as presented by Anouti et al. [START_REF] Anouti | Analysis of physicochemical properties of bio-oil from hydrothermal liquefaction of blackcurrant pomace[END_REF] is much lower. Acidity is also very high, even when the oils are produced under basic conditions. In practice hydrothermal oils will need to be upgraded to respond to commercial applications [START_REF] Biller | Hydroprocessing of bio-crude from continuous hydrothermal liquefaction of microalgae[END_REF]. The results presented in this paper are valid for beech wood converted in batch reactors. Other reactor technologies may and will yield slightly different products. Projections towards other technologies and resources are risky.

Pyrolysis oils are also far from commercial fuels. The growing interest in pyrolysis oils and the (slow) start of commercial production has sparked off discussions to create standards for pyrolysis oils [START_REF] Oasmaa | Norms, standards, and legislation for fast pyrolysis bio-oils from lignocellulosic biomass[END_REF]. In practice due to the acidity and stability specific applications will be developed. Drop in applications seem far away without extensive upgrading.

The oxygen content as found in this study for both pyrolysis oils as hydrothermal oils are comparable to those found in previous studies as presented in Section 2. From the literature it appears that hydrothermal oil produced in batch reactors [START_REF] Nazari | Hydrothermal liquefaction of woody biomass in hot-compressed water: catalyst screening and comprehensive characterization of bio-crude oils[END_REF][START_REF] Doassans-Carrère | Comparative study of biomass fast pyrolysis and direct liquefaction for bio-oils production: products yield and characterizations[END_REF] contain more oxygen than oil produced in continuous reactors [START_REF] Elliott | Hydrothermal liquefaction of biomass: developments from batch to continuous process[END_REF][START_REF] Tews | Biomass direct liquefaction options: technoeconomic and life cycle assessment[END_REF]. A possible explanation may be found in the longer heating times, leading to some carbonisation before liquefaction takes place. Globally the hydrogen content of hydrothermal oils and pyrolysis oils are quite similar. The differences are in the ratio between oxygen and carbon, this being higher for pyrolysis oils. Hydrothermal oils may be better suited for catalytic hydro-deoxygenation. It is clear however that information on CHO alone is far from sufficient to evaluate bio-oils.

It is interesting to note that many of the same degradation products from lignin are found in both pyrolysis oils and hydrothermal oils. These products generate high iodine values. The limitation on the iodine value in biodiesel is due to unsaturations in the fatty acids leading to wax forming by polymerisation. It appears that the test is not specific, and sterically hindered double bonds are also indicated by this test.

Conclusions

This paper clearly shows that bio-oils are far from being commercial drop-in fuels. Both hydrothermal and pyrolysis oils suffer from a very high acidity. This means that storage and transfer facilities need to be adjusted. High iodine values also indicate potential storage problems. Upgrading to reduce the oxygen content (acidity), saturate double bonds (iodine value) and depolymerise high molecular weight compounds (viscosity) will be unavoidable for drop-in applications. Bio-oils present an interesting source of liquid fuels that merits the eventual development of specific applications and standards to guarantee a reliable operation of the application. In practice, hydrothermal oils from beech wood, as found in this study, will need to be heated before injection because of their high viscosity. The viscosity of the organic fraction of pyrolysis bio-oils is higher than that of commercial fuels. The presence of water (when mixing the aqueous and organic fractions) lowers significantly the viscosity. Water is however undesirable for combustion applications as it reduces the heating value. In addition, the aqueous and organic fractions are only miscible in certain amounts before phase separation occurs.

The addition of a strong base in the hydrothermal conversion process increases the oil yield, and improves the viscosity of the oil. However this is not sufficient and other catalysts should be considered. Increasing severity in the fast pyrolysis process decreases the acidity of the oil at the expense of an increased iodine values, and leads to an increase of lighter molecules at the expense of higher molecular weight compounds.

Fig. 1 .

 1 Fig. 1. Evolution of the viscosity of hydrothermal bio-oil L1 with temperature.

Fig. 2 .

 2 Fig. 2. Evolution of the viscosity of pyrolysis oils with temperature.

Fig. 3 .

 3 Fig. 3. Thermogravimetric analysis of hydrothermal bio-oils.

Fig. 4 .

 4 Fig. 4. Thermogravimetric analysis of pyrolysis bio-oils.

Fig. 5 .

 5 Fig. 5. GC/MS analysis of hydrothermal oils.

  4-Hydroxy-3-methoxyphenyl)-2-Propanone (vanilloylmethyl cetone or guaiacylacetone)

Fig. 6 .

 6 Fig.6. GC/MS analysis of pyrolysis bio-oils.

Table 1

 1 Proximate and ultimate analysis of the beech wood. -bonds contained in constituents of the bio-oil. Excess of the Wijs reagent becomes I 2 by adding potassium iodide (KI). The concentration of formed iodine I 2 is then determined by titration with sodium thiosulfate. Bio-oil samples of 90-100 mg were used for iodine value measurements. lm film thickness. The injection of 1 ll of bio-oil samples is performed on Split mode 10:1 with Helium as carrier gas. The injector temperature is maintained at 250 °C. The GC oven temperature is programmed from 45 °C (10 min) to 230 °C at a rate of 6 °C min À1 , and held at 230 °C during 9.17 min. It is then raised to 250 °C at a rate of 10 °C min À1 , held at 250 °C during 20 min. The transfer interface and the ion source in the mass spectrometer are maintained at 230 °C. Bio-oil samples dissolved in ethyl acetate are filtered with a 0.45 lm PTFE filter before analysis. The National Institute of Standards and Technology (NIST) mass spectral library is used to identify the compounds.

	Proximate analysis
	Moisture (wt%)	8.7
	Volatile matter (wt% dry basis)	84.3
	Fixed carbon (wt% dry basis)	15.2
	Ash (550 °C) (wt% dry basis)	0.80
	Ash (815 °C) (wt% dry basis)	0.46
	Ultimate analysis (wt% dry basis)
	C	49.1
	H	5.7
	N	<0.3
	S	0.05
	O (by difference)	44.5

double

Table 2

 2 Results of hydrothermal liquefaction experiments.

		L1 without additives	L2 with NaOH
	Initial/final pH	5.4/5.5	10.2/5.6
	Raw residue yield (dry)	44 (±1)%	48 (±2)%
	Char yield	30 (±2)%	19 (±1)%
	Bio-oil yield	14 (±2)%	28 (±2)%
	Gas yield	20 (±2)%	17 (±1)%
	Aqueous phase by difference	36 (±3)%	35 (±2)%

Table 3

 3 Product yields and mass balances for the different pyrolysis tests (uncertainty results estimated at 10%).

		Yields (%)				Closure
		Char Aqueous	Organic	Gas	Non-condensed	(%)
			fraction of	fraction of		water
			the oil	the oil
	450 °C 40.6	29.6	17.9	6.1 6.3	100.5
	500 °C 13.6	45.2	17.2	15.0 6.0	97.1
	550 °C 10.3	33.6	14.8	25.6 8.6	92.9
	600 °C	8.2	30.1	9.5	51.5 4.9	104.3

Table 4

 4 Physical properties of bio-oils (uncertainty is globally estimated at 10%, except when detailed).

	Oil	Density (22 °C) (kg m À3 )	Viscosity @ 40 °C (mPa s)	Viscosity @ 80 °C (mPa s)
	L1	1.14 (±0.02)	67,000 (±5000)	520 (±40)
	L2	1.14 (±0.02)	2200 (±200)	210 (±10)
	450 °C-Liq	1.1	13	7
	500 °C-Org	1.1	70	19
	550 °C-Org	1.1	43	20
	600 °C-Org	1.1	56	40

Table 5

 5 Fractions evaporated at different temperature ranges (uncertainty estimated at 1%).

	Oil	35-100 °C	35-250 °C	250-900 °C
	L1	17	50	31
	L2	12	44	33
	P1-Org	17	67	22
	P2-Org	17	69	18
	P3-Org	20	70	17
	P4-Org	26	71

Table 6

 6 Chemical properties of bio-oils.

	Oil	HHV (MJ kg À1 )	Residual carbon (%)	TAN (mg KOH per gram oil)	Iodine value	Water content (%)	C (%)	H (%)	O (%) a
	L1	27.0 (±0.5)	17 (±1)	67 (±4)	127 (±3)	<1	68.2 (±0.6)	6.8 (±0.1)	27.3
	L2	30.1 (±0.5)	24 (±1)	32 (±3)	126 (±3)	<1	64.8 (±0.3)	7.0 (±0.1)	24.0
	P1-Org	21.7 (±0.5)	11 (±1)	109 (±17)	163 (±7)	21 (±1)	NA	NA	NA
	P2-Org	21.4 (±0.5)	13 (±1)	89 (±14)	147 (±1)	11 (±1)	40.8	6.5	52.8
	P3-Org	23.3 (±0.5)	13 (±1)	45 (±6)	203 (±13)	10 (±1)	35.6	6.8	57.6
	P4-Org	25.1 (±0.5)	13 (±1)	46 (±5)	164 (±1)	7.8 (±0.1)	NA	NA	NA

a Calculated by difference.

Table 8

 8 Standards for several commercial fuels and typical values found for hydrothermal and pyrolysis oils.

		Diesel	Biodiesel	Marine heavy fuel oil	Hydrothermal oil	Pyrolysis oil
	Standard	NF EN 228	NF EN 14214	NF ISO 8217		In progress
	Density at 15 °C (kg m À3 )	820-845	860-900	920-1010	1.14	1.1
	Carbon residue (%)	<0.3	<0.3	<2.5-20	17-24	11-13
	Total acid number (mg KOH per gram of fuel)	<0.5	<0.5	<0.5	32-67	45-109
	Iodine value (g I2 /100 g of fuel)	NA	<120	NA	126-127	147-203
	Higher heating value (MJ kg À1 )	4 5	P35	NA	27-30	21-25
	Water content (%)	<0.02	<0.05	<0.033	<1	8-20
	Viscosity at 40 °C (mPa s)	3-4	<4	8.5-690	67,000	13-70

Acknowledgments

The authors would like to acknowledge financial support from the French Research National Agency ANR (LIQHYD project, Grant No ANR-12-BIME-003) and the CCIAG (Boil project). The authors are also grateful to Julien Roussely, Marine Blanchin, Hélène Miller and Sébastien Thiery for help on analysis of the products.