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Quantification of carbohydrate concentration by colorimetric assay
Carbohydrate analyses were performed following the phenol assay protocol (Dubois et al. 1956). Briefly, 200 µl of the PS fraction were mixed with 200 μl phenol (5 %) and 1 mL sulphuric acid (98 %). Mixtures were then incubated for 35 min at 30 °C and the carbohydrate concentration was measured with a spectrophotometer at 488 nm (Milton Roy Spectronic Genesys 2). A calibration curve was prepared using glucose as standard.
Protein analyses were performed following the modified LOWRY assay protocol (Raunkjær et al. 1994, Frølund et al. 1996), using five reagents as described in Table II-1.
250 µl of the PS fraction were mixed with 250 µl of SDS (2 %) and 700 µl of solution 4, and mixtures were then incubated for 15 min at 30°C. 100µl of solution 5 were added to each tube and vortexed immediately. Mixtures were then incubated for 30 min at 30 °C. The protein concentration was measured with a spectrophotometer at 750 nm (Milton Roy Spectronic Genesys 2). A calibration curve was prepared using bovine serum albumine (BSA) as standard.
Amino acid composition of polymers by HPLC
Amino acids of the PS fraction were identified and quantified by HPLC. PS fractions were dialysed against distilled water (cut-off 12-14 kDa) and freeze-dried. 10 mg were then mixed with 200 µl of HCl (6N). The acid mixture was carefully degassed to reduce the level of oxidative destruction and proteins were then hydrolysed (24 h at 110°C) in vacuum using a sealed glass ampule. Ampules were then dried using a speedvac after hydrolysis. The resulting amino acids were then reconditioned in Pickering diluent prior to injection in the HPLC system. Amino acids were separated by ion-exchange HPLC using a high-efficiency sodium column (4 × 150 mm; Pickering Lab, LCTech, Dorfen, Germany) with a Waters 2695 separation module (Waters). The elution buffers and gradient conditions were those recommended by the manufacturer (Table II-2).
Separating amino acids were first subjected to post-column derivatization with Ninhydrin (Pickering Lab.) by using a PCX 5200 derivatizer (Pickering Lab.) and later detected on a Waters 2996 Photodiode as a UV module detector at 570 nm for all the amino acids containing a primary amine, and at 440 nm for the Proline which holds a secondary amine. Quantification was performed by repeated injections of standards over a range of dilutions to determine the relationship between peak area and standard concentrations. The relative abundance of each amino acid (%) was calculated from their respective concentration (µg.cm-2), and protein concentration was calculated from the total amino acid concentration.
Sugar and protein concentration dynamics on the different substrates
Sugar concentrations ranged between 0.674 and 14.628 µg∙cm-2 (Figure II-2) and were significantly different between dolomite Sorel cement and the two types of concrete (Van-der-Waerden: p = 0.0315, post hoc test: Cg-Ds: p = 0.049; Cg-Cw: p = 0.537; Ds-Cw: p = 0.0104). They increased gradually over the monitoring period on grey and white concretes samples to reach on average 7.904 ± 2.472 µg∙cm-2 and 10.546 ± 1.295 µg∙cm-2, respectively, at the end of monitoring. The concentration of sugars in dolomite Sorel cement samples increased slightly, with a maximum mean concentration of 2.733 ± 0.240 µg∙cm-2 at the end of monitoring. Protein concentrations ranged between 0.208 and 1.552 µg∙cm- 2 (Figure II-2) and followed approximately the same pattern as the sugar concentration over time. However, the concentrations did not differ between substrates (Van-der-Waerden: p = 0.322).
Prokaryotic communities: DNA extraction, sequencing and data analysis
Biofilm was scratched from the substrate using a scalpel and fibber glass filter (GF/F). The DNA was extracted from biofilm with the “DNeasy PowerBiofilm” Kit (Qiagen). 20 µl of extracted DNA (min: 3.5 ng.µl-1, max: 13.91 ng.µl-1, spectrophotometrically measured with Nanodrop) was sent to the Laboratory MR DNA (MR DNA, Shallowater, TX, USA) a full-service next generation sequencing service provider, that processed the amplification and sequencing of the samples. The V4-V5 region of the 16S rDNA gene was amplified with the primer sets 515f-Y (GTGYCAGCMGCCGCGGTAA) and 926r (CCGYCAATTYMTTTRAGTTT; (Parada et al. 2016)) with barcode on the forward primer. A PCR of 30 cycles with the HotStartTaq Plus mixing kit (Qiagen, Valencia, CA) was performed under the following conditions: 94 °C for 3 minutes, followed by 28 cycles at 94 °C for 30 seconds; 53 °C for 40 seconds and 72 °C for 1 minute, then a final elongation step at 72 °C for 5 minutes. After amplification, the PCR products of the different samples were monitored in a 2 % agarose gel to determine the success of the amplification and the relative intensity of the bands. Multiple samples were mixed in equal proportions based on their molecular weight and DNA concentrations, and then purified with calibrated Ampure XP beads. These pooled and purified products were used to prepare a DNA library following Illumina TruSeq DNA library. After the amplification and denaturation steps, the libraries were grouped and sequenced. 50 ng of DNA from each sample was used to prepare the libraries using the Nextera DNA Sample Preparation Kit (Illumina). The size of the library insert was determined by the Experion Automated Electrophoresis Station (Bio-Rad). The size of library inserts ranged from 300 to 850 bp (average 500 bp). The pooled library (12 pM) was loaded into a reagent cartridge 600 Cycles v3 (Illumina) and Sequencing was performed on a MiSeq according to the manufacturer’s instructions. Mock community DNA (ZymoBIOMICS, Ozyme) was used as a standard for subsequent analyses. The sequences data were processed using MR DNA analysis pipeline (www.mrdnalab.com, MR DNA). In short, sequences were joined and the barcodes and primers sequences were trimmed. Short sequences < 150 pb, the sequences with ambiguous base calls were deleted. The remaining sequences were denoised and clustered at 97 % sequence similarity to define Operational taxonomic units (OTUs). Singletons and chimeras were removed from analyses. Final OTUs were taxonomically classified using BLASTn against a database organized from GreenGenes, RDPII and NCBI (www.ncbi.nlm.nih.gov, DeSantis et al., 2006, http: // rdp.cme.msu.edu).
Temperature and sedimentation rates during the monitoring
The temperature was similar between sites during the biofilm monitoring (16.9 ± 0.4 C° at Monaco and 16.8 ± 0.4 C° at Roquebrune. During the macrofouling monitoring, the temperature was higher at Monaco than at Roquebrune in summer (2017_08) and autumn (2017_10) season (Student test p<0.01). Then, the temperature was similar between sites for winter (2018_02) and spring (2018_04) season (Figure III-3: A) Average temperature of the sea, B) average sedimentation rate during biofilm and macrofouling monitoring at Monaco and Roquebrune.. During the monitoring of macrofouling, the sedimentation rate was greater in Monaco than in Roquebrune (Figure III-3: A) Average temperature of the sea, B) average sedimentation rate during biofilm and macrofouling monitoring at Monaco and Roquebrune.). On both sites, the sedimentation rate increased drastically after six months of monitoring, especially at Monaco (Monaco: from 8.02 ± 0.06xxx g.m-².day-1 to 52.34 ± 1.02 g.m-².day-1, Roquebrune: from 4.84 ± 0.25 g.m-².day-1 to 15.14 ± 2.07 g.m ².day-1), peaked during the winter season (Monaco: 161.73 ± 0.04 g.m-².day-1, Roquebrune: 98.33 ± 6.94 g.m-².day-1) and then decreased at the end of the monitoring (Monaco: 91.55 ± 0.86 g.m-².day-1, Roquebrune: 54.22 ±0.12 g.m-².day-1) (Figure III-3: A) Average temperature of the sea, B) average sedimentation rate during biofilm and macrofouling monitoring at Monaco and Roquebrune.).
Sugars and proteins content in the Extracellular Polymeric Substances (EPS) of the biofilm
The sugars concentration ranged between 5.04 and 57.34 µg.cm-2 at Monaco, and between 4.23 and 24.15 µg.cm-2 at Roquebrune. At Monaco, it increased on each substrate over the sampling period and picked after 21 days of monitoring. At the end of the monitoring, it decreased drastically except on rock where it increased slightly (Figure III-6). In average, the concentration was found higher on the concrete than on the other substrates (Van der Waerden test: p= 0.03; post-hoc test: C – D: p= 0.01, C – R: p=0.03, D – R: p=0.78). At Roquebrune, it followed to a lesser extent the same trend (Figure III-6), and was lower on the dolomite than on the other substrates during the last two weeks of monitoring (Van der Waerden test: p= 0.002; post-hoc test: C – D: p=0.0006, C – R: p=0.9674, D – R: p=0.0006).
Proteins concentration ranged between 7.30 and 25.47 µg.cm-2 at Monaco, and between 5.45 and 27.53 µg.cm-2 at Roquebrune. At Monaco, it increased on each substrate from the beginning to pick after 21 days of monitoring. Then, at the end of the monitoring, it decreased slightly, except on the dolomite where it decreased more intensely (Figure III-6). As for sugars, the protein concentration was found higher on the concrete than on the other substrates (Van der Waerden test: p= 0.001; post-hoc test: C – D: p= 0.0004, C – R: p=0.0019, D – R: p=0.5961). At Roquebrune, it followed the same trend except on the rock, were it increased drastically after 21 days (Figure III-6), but no significance difference was found between substrates.
Table of contents :
RESUME
ABSTRACT
AVANT-PROPOS
REMERCIEMENTS
TABLE DES MATIERES
TABLE DES TABLES
TABLE DES ILLUSTRATIONS
I. INTRODUCTION GENERALE
1. CONTEXTE GLOBAL
2. HISTORIQUE ET DEFINITION DES RECIFS ARTIFICIELS
3. ETAT DE L’ART DES CARACTERISTIQUES INTRINSEQUES DES RECIFS ARTIFICIELS
4. COLONISATION DES RECIFS ARTIFICIELS
5. INFLUENCE DES CARACTERISTIQUES INTRINSEQUES DES RECIFS ARTIFICIELS SUR LA COLONISATION .
6. HYPOTHESES
II. CHAPTER 1: BIOFILM MONITORING AS A TOOL TO ASSESS THE EFFICIENCY OF ARTIFICIAL REEFS AS SUBSTRATES: TOWARD 3D PRINTED REEFS.
RESUME
ABSTRACT
1. INTRODUCTION
2. MATERIALS & METHODS
2.1. Sampling and site of monitoring
2.2. Pigment analysis
2.3. Extraction of Polymeric substances
2.4. Quantification of carbohydrate concentration by colorimetric assay
2.5. Amino acid composition of polymers by HPLC
2.6. Data analysis
3. RESULTS
3.1. Pigment analysis
3.2. Sugar and protein concentration dynamics on the different substrates
3.3. Amino acid composition
3.4. Principal Component Analysis
4. DISCUSSION
CONCLUSIONS
III. CHAPTER 2: COLONISATION OF ARTIFICIAL REEF SUBSTRATES: “THE HABIT DOES NOT MAKE THE MONK.”
RESUME
ABSTRACT
1. INTRODUCTION
2. MATERIALS & METHODS
2.1. Substrates
2.2. Monitoring and sampling
2.3. Biofilm analysis
2.4. Macrofouling identification
2.5. Heavy metals analyses
2.6. Statistical analyses
3. RESULTS
3.1. Temperature and sedimentation rates during the monitoring
3.2. Structure of the biofilm microbial communities
3.3. Sugars and proteins content in the Extracellular Polymeric Substances (EPS) of the biofilm
3.4. Macrofouling communities
3.5. Heavy metals in sinking and suspended POM and virgin substrates
4. DISCUSSION
CONCLUSION
IV. CHAPTER 3: STUDY THE COMPLEXITY OF ARTIFICIAL REEFS FROM THE MICRO TO THE MACRO SCALE
PART 1: TOWARD A PURPOSEFUL DESIGN OF ARTIFICIAL REEFS: A FIRST METHOD TO ASSESS THEIR STRUCTURAL COMPLEXITY AND HETEROGENEITY
RESUME
ABSTRACT
1. INTRODUCTION
2. MATERIAL & METHOD
2.1. The numerical object of artificial reefs and fictive objects
2.2. Normalization of the 3D CAD models and process of the input.
2.3. Point clouds and normals
2.4. Combination of modules
2.5. Evaluation of the structure
2.6. Data analysis
3. RESULTS
3.1. 3D fictive 3D CAD models
3.2. Evaluation of the structure of the AR modules
3.3. Combination of modules
4. DISCUSSION
CONCLUSION
PART 2: LINK BETWEEN MICROTOPOGRAPHY AND PHOTOSYNTHETIC BIOFILM PHYSIOLOGY AT DIFFERENT SCALES
RESUME
ABSTRACT
1. INTRODUCTION
2. MATERIEL & METHOD
2.1. Substrates of artificial reefs and natural rock
2.2. Characterization of the surface topography
2.3. Analyse of biofilm activity by Imaging PAM
2.4. scale
3. RESULTS
3.1. Characterisation of the surface of substrates at the global scale .
3.2. Characterisation of the biofilm activity with Imaging PAM and correlation with complexity indexes
4. DISCUSSION
CONCLUSION
RÉFÉRENCES
