Emmanuelle Bignon
email: emmanuelle.bignon@univ-cotedazur.fr

Natacha Gillet

Chen-Hui Chan

Tao Jiang

Antonio Monari

Elise Dumont

Recognition of a Tandem Lesion by DNA bacterial formamidopyrimidine Glycosylases Explored Combining Molecular Dynamics and Machine Learning

Keywords: MutM, DNA repair glycosylase, tandem lesion, molecular dynamics simulations

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

Recognition of a tandem lesion by DNA bacterial formamidopyrimidine glycosylases explored combining molecular dynamics and machine learning

Emmanuelle Bignon, Natacha Gillet, Chen-Hui Chan, Tao Jiang, Antonio

Monari, Elise Dumont

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Introduction

The chemical stability of DNA components is fundamental to maintain the genome stability, hence preventing unwanted mutations or cell death. Indeed, the accumulation of DNA lesions has been recognized as one of the principal causes of cancer development [START_REF] Basu | DNA damage, mutagenesis and cancer[END_REF] . Although DNA maximizes its stability through its helical structure, its constituting nucleic acids are constantly exposed to damaging agents, either endogenous or exogenous, that inevitably lead to the production of lesions. Among the different sources of DNA lesions, more common oxidative agents include free radicals or reactive oxygen species (ROS), UV light, and ionizing radiations. In addition to the chemistry of oxidatively-generated lesions, ionizing radiations also damage DNA, as the excess energy along a single track is deposited along localized regions of space. This in turn leads to an accumulation of DNA damages (oxidation of purines, abasic sites and strand breaks) in close spatial proximity, i.e. the so-called local multiple damaged sites (MDS). MDS have recently been observed by atomic force microscopy (AFM) [START_REF] Xu | Direct observation of damage clustering in irradiated DNA with atomic force microscopy[END_REF] . When two or more damaged nucleotides are present within one or two helical turns it is customary to refer of MDS as clustered lesions [START_REF] Sage | Radiation-induced clustered DNA lesions: Repair and mutagenesis[END_REF] . As a consequence of lesion formation, specific and highly efficient repair machineries exist that are able to recognize the presence of lesions in the genome and remove them to reinstate undamaged DNA strands [START_REF] Cadet | Oxidative DNA damage & repair: An introduction[END_REF][START_REF] Chatterjee | Mechanisms of DNA damage, repair, and mutagenesis[END_REF] . Failure in the regulation of DNA processes is also related to the development of debilitating pathologies such as Xeroderma pigmentosa [START_REF] Kraemer | Xeroderma pigmentosum[END_REF] or to incidence, development, and morbidity of various cancers. As a consequence DNA repair enzyme also constitute a most attractive therapeutic target [START_REF] Gavande | DNA repair targeted therapy: The past or future of cancer treatment?[END_REF][START_REF] Zhang | Role of the DNA damage response in prostate cancer formation, progression and treatment[END_REF] . Specific DNA repair pathways may depend on the organisms, and are also related to the kind of lesions, for instance for localized oxidatively-induced damages the base excision repair (BER) pathway is preferred [START_REF] David | Base-excision repair of oxidative DNA damage[END_REF][START_REF] Fortini | 8-oxoguanine DNA damage: at the crossroad of alternative repair pathways[END_REF] , while for more extended and bulky lesions, such as base dimerization, the nucleotide excision repair (NER) mechanism is favored. Yet this sophisticated repair mechanism has been reported to be strongly impaired when not only one but two adjacent DNA lesions are located on the same strand, the so-called tandem lesions. The formation of tandem lesions can derive from a single radical hit, and their biological impact is now well established. While their formation mechanism has been delineated [START_REF] Hong | Characterization and mechanism of formation of tandem lesions in dna by a nucleobase peroxyl radical[END_REF] , the reasons underlying their resistance to repair are more elusive and should be analyzed taking firmly into account specific structural modification. The most common oxidative tandem lesions feature two adjacent oxidized nucleobases. In the following we will specifically consider 8-oxoguanine (8-oxoG) and an abasic apurinic/apyrimidinic site (Ap). This arrangement is particularly relevant also because Ap are also the most common outcome of ionizing radiations after excision of an entire nucleobase. [START_REF] Cadet | DNA base damage by reactive oxygen species, oxidizing agents, and uv radiation[END_REF] . Interestingly, Ap sites also represent key intermediates of the BER machinery and result from the action of DNA glycosylases before being further processed and removed by endonucleases. The presence of a tandem lesion, or more generally local multi damaged sites (MDS), that are the hallmarks of radiation chemistry [START_REF] Ward | Radiation and hydrogen peroxide induced free radical damage to dna[END_REF] , induces strong coupling between the lesions that in turn is translated into important structural deformations of the nucleic acid as compared to its ideal structure, i.e. either undamaged strand or sequences containing an isolated lesion. The unusual structural deformations induced by tandem lesions or MDS may also well justify their globally lower repair rate as compared to other lesions [START_REF] Bergeron | radicals induce an unexpected high proportion of tandem base lesions refractory to repair by DNA glycosylases[END_REF][START_REF] Georgakilas | Induction and repair of clustered DNA lesions: What do we know so far?[END_REF][START_REF] Gattuso | Repair rate of clustered abasic DNA lesions by human endonuclease: Molecular bases of sequence specificity[END_REF][START_REF] Bignon | Correlation of bistranded clustered abasic DNA lesion processing with structural and dynamic DNA helix distortion[END_REF] . To cope with their frequency, canonical DNA lesions benefit from a most efficient repair. For instance, 8-oxoG, which is well-known to mismatch with adenine and hence is potentially mutagenic [START_REF] Noguchi | The mutagenic potential of 8-oxog/single strand break-containing clusters depends on their relative positions[END_REF] , is repaired by formamidopyrimidine DNA glycosylase. The latter recognizes the presence of 8-oxoG in the genome and specifically binds at the damaged site [START_REF] Morland | Human DNA glycosylases of the bacterial fpg/mutm superfamily: an alternative pathway for the repair of 8-oxoguanine and other oxidation products in DNA[END_REF] . Many studies have contributed to dissect the mode of action of the bacterial glycosylase MutM in presence of a single 8-oxoG in particular concerning the recognition of the lesion [START_REF] Serre | Crystal structure of the lactococcus lactis formamidopyrimidine-DNA glycosylase bound to an abasic site analogue-containing DNA[END_REF][START_REF] Amara | Insights into the DNA repair process by the formamidopyrimidine-DNA glycosylase investigated by molecular dynamics[END_REF][START_REF] Rosa | Global deformation facilitates flipping of damaged 8-oxo-guanine and guanine in DNA[END_REF] . MutM [START_REF] Qi | Entrapment and structure of an extrahelical guanine attempting to enter the active site of a bacterial DNA glycosylase, mutm[END_REF][START_REF] Michaels | a protein that prevents g c→t a transversions, is formamidopyrimidine-DNA glycosylase[END_REF] has been shown to recognize 8-oxoG among other oxidatively-induced lesions and to subsequently proceed to its extrusion initiating the base excision process [START_REF] David | Base-excision repair of oxidative DNA damage[END_REF][START_REF] Fortini | 8-oxoguanine DNA damage: at the crossroad of alternative repair pathways[END_REF] . The mechanisms of recognition [START_REF] Fromme | DNA lesion recognition by the bacterial repair enzyme mutm[END_REF] and extrusion [START_REF] Qi | Entrapment and structure of an extrahelical guanine attempting to enter the active site of a bacterial DNA glycosylase, mutm[END_REF][START_REF] Fromme | Structural insights into lesion recognition and repair by the bacterial 8-oxoguanine DNA glycosylase mutm[END_REF][START_REF] Qi | Encounter and extrusion of an intrahelical lesion by a DNA repair enzyme[END_REF] of 8-oxoG have been scrutinized through a series of techniques, including molecular modeling and simulations, and are now relatively well characterized. Recently, Simmerling et al. [START_REF] Li | A dynamic checkpoint in oxidative lesion discrimination by formamidopyrimidine-DNA glycosylase[END_REF] , while recognizing the role of the damaged base flipping in favoring its recognition, have also pinpointed the existence of preliminary recognition steps correlating with the rapid sliding of MutM along the DNA strand that is incompatible with a recognition mechanism based on the systematic flip of all the bases. In addition, the same authors have also identified that 8-oxoG flips preferably through the major groove. The free energy required for the extrusion of 8-oxoG in extrahelical position has also been estimated by La Rosa and Zacharias [START_REF] Rosa | Global deformation facilitates flipping of damaged 8-oxo-guanine and guanine in DNA[END_REF] , also taking into account the contributions due to the DNA global bending and twisting. A most important feature of MutM efficiency has been traced back to the crucial M77, R112, and F114 amino acids triad -see Figure 1-A. Indeed, it permits to disrupt 8-oxoG interactions within the DNA helix by intercalating above the 8-oxoG position, thereby facilitating its extrusion towards the active site. Besides, other several important MutM residues (K60, H74, Y242, K258, and R264) are known to stabilize the DNA helix by interacting with its backbone [START_REF] Fromme | Structural insights into lesion recognition and repair by the bacterial 8-oxoguanine DNA glycosylase mutm[END_REF] . Interestingly, iron-sulfur cluster cofactors can play an important role in lesion processing by repair enzymes [START_REF] Fuss | Emerging critical roles of fe-s clusters in dna replication and repair[END_REF] including glycosylases (as shown for MutY and its homologs [START_REF] Nuñez | Fe-s clusters and muty base excision repair glycosylases: Purification, kinetics, and dna affinity measurements[END_REF] ), highlighting the finely-tuned redox mechanisms which unfortunately require very complex techniques to be modeled. On the other hand, several studies have addressed the behavior of tandem lesion-containing oligonucleotides, either from a biochemical and repair perspective [START_REF] Georgakilas | Induction and repair of clustered DNA lesions: What do we know so far?[END_REF] or from a structural point of view [START_REF] Hazel | Nmr solution structures of bistranded abasic site lesions in DNA[END_REF] , also relying on molecular modeling and 2/13 simulations [START_REF] Bignon | Correlation of bistranded clustered abasic DNA lesion processing with structural and dynamic DNA helix distortion[END_REF][START_REF] Fujimoto | Molecular dynamics simulation of clustered DNA damage sites containing 8-oxoguanine and abasic site[END_REF][START_REF] Cleri | Mechanical evolution of DNA double-strand breaks in the nucleosome[END_REF] . Globally, the different approaches agree in pointing out a strong effect of closely spaced lesions in modifying the structure and dynamics of the oligonucleotide. In addition, strong sequence effects, depending both on the relative position of the cluster lesions and on the nearby undamaged bases contribute to the complexity of the global landscape. The interaction of MDS-containing oligonucleotides with repair enzymes and in particular both E. Coli and human endonucleases [START_REF] Gattuso | Repair rate of clustered abasic DNA lesions by human endonuclease: Molecular bases of sequence specificity[END_REF][START_REF] Harrison | Multiply damaged sites in DNA: Interactions with Escherichia coli endonucleases III and VIII[END_REF] has been reported. The perturbations exerted by the secondary lesion on the protein/DNA contact regions, and the consequent decrease in its repair efficiency, as observed for some particular tandem lesions, have also been highlighted [START_REF] Gattuso | Repair rate of clustered abasic DNA lesions by human endonuclease: Molecular bases of sequence specificity[END_REF][START_REF] Georgakilas | Induction and repair of clustered dna lesions: what do we know so far?[END_REF][START_REF] Karwowski | The influence of (5 r)-and (5 s)-5, 8-cyclo-2-deoxyadenosine on udg and hape1 activity. tandem lesions are the base excision repair system's nightmare[END_REF][START_REF] Karwowski | The influence of single, tandem, and clustered dna damage on the electronic properties of the double helix: A theoretical study[END_REF][START_REF] Lomax | Efficiency of repair of an abasic site within dna clustered damage sites by mammalian cell nuclear extracts[END_REF] . However, no analysis of the structural behavior of MutM in presence of tandem DNA lesions has been reported, despite the relevance that such lesions may assume in conditions of strong oxidative stress or ionizing radiations.

In this work, we take advantage of the existing knowledge of 8-oxoG recognition by MutM to investigate the structural and dynamic impact of the presence of a second, adjacent lesion, namely an Ap site. Relying on all-atom, explicit-solvent molecular dynamics [START_REF] Gattuso | Repair rate of clustered abasic DNA lesions by human endonuclease: Molecular bases of sequence specificity[END_REF][START_REF] Pérez | Frontiers in molecular dynamics simulations of DNA[END_REF] we simulate the structural and dynamical behavior of a MutM:DNA complex -see Figure 1-B. We consider both the situation in which only a single lesion (8-oxoG) is present and compare it to the one in which the adjacent guanine base dG20, present in the X-ray structure (PDB ID 3GO8), has been in silico mutated to an Ap site -see Figure 1. We clearly show that the presence of the tandem lesion induces important structural deformations to the DNA that significantly perturb the protein/nucleic acid interaction pattern, hence being susceptible to alter the 8-oxoG extrusion. We extensively describe the changes in the 8-oxoG lesion structural signature upon the presence of an adjacent Ap site and the perturbation of the interaction network with MutM, which contribute to ultimately diminish the recognition efficiency.

Results

We report the structural and dynamical properties of the bacterial MutM interacting with a 13-bp DNA sequence harboring either a single 8-oxoG at the position 19 (as found in the crystal structure PDB ID 3GO8) or 8-oxoG coupled with an Ap site at position 20, along two replicas reaching 1µs MD simulation time each. The numbering of the nucleic acids used hereafter corresponds to the one in Figure 1-B; the numbering of MutM residues refers to the crystallographic structure (PDB ID 3GO8).

Tandem lesions impact the interaction network around 8-oxoG

The interaction network as found in the MutM:DNA crystal containing a single 8-oxoG lesion is stable along our MD simulations. A most important structural feature in MutM is its intercalation triad, consisting of the M77, R112 and F114 residues. Those three amino acids are located around the 8-oxoG in the minor groove, weakening the stabilizing interactions of the lesion within the double-helix to facilitate its extrusion. R112 interacts with the complementary dC7, while M77 and F114 intercalate directly above 8-oxoG and disrupt the stable π-stacking with the adjacent base-pair -see Figure 2-A. These interactions are persistent along the entire MD simulations of the singly-damaged system. F114 is involved in π-staking with dG20 over 91.8% of the time series, with the distance between heavy atoms of their aromatic rings averaging at 5.35±0.01 Å. The rest of the time, F114 interacts transiently with dC7 facing dG20 and their aromatic rings maintain a distance of 6.73±0.01 Å -see Figure 3. M77 intercalates between OG19 and dG20, as its terminal methyl group remains at 4.80± 0.01 Å of the N9 atom involved in the N-glycosidic bond, and is ideally positioned to act on OG19 desoxyribose moiety to drag it outwards [START_REF] Fromme | Structural insights into lesion recognition and repair by the bacterial 8-oxoguanine DNA glycosylase mutm[END_REF] .

R112 side chain amino groups form H-bonds with the nitrogen and carbonyl of dC8 over 76.8% of the simulation time, the distance between these two atom groups being of 5.12±0.01 Å. Several other amino acids have been identified to stabilize the MutM:DNA complex by interacting with the negatively-charged phosphate groups of the backbone namely K60, H74, Y242, K258, and R264. These interactions are stable in our simulations and the highly conserved R264 forms strong H-bonds between OG19 and dG20 phosphate groups -as shown in Figure S1. Noteworthy, R264 is known to play a role in 8-oxoG extrusion [START_REF] Li | A dynamic checkpoint in oxidative lesion discrimination by formamidopyrimidine-DNA glycosylase[END_REF] . The structural behavior of MutM:DNA(8-oxoG) observed here corroborates the hypothesis of a highly dynamic system, whose functional flexibility is known to be central to ensure its biological role through the recognition and extrusion of 8-oxoG [START_REF] Amara | Functional flexibility of bacillus stearothermophilus formamidopyrimidine DNA-glycosylase[END_REF][START_REF] Landová | Conformational changes of DNA repair glycosylase mutm triggered by DNA binding[END_REF] .

The dG20 → Ap20 mutation induces a clear perturbation of this well-characterized interaction network. A first consequence is the perturbation of the dynamics of the intercalation triad. The presence of the abasic site involves, in the first 100 ns of simulation, a rapid reorientation of R76 situated just above the intercalation triad. R76 side chain turns towards the damaged site, and is found closer to OG19, at 7.78±0.02 Å vs. 11.84±0.02 Å observed in the singly 8-oxoG-containing duplex. R76 does not interact directly with OG19 but rather positions itself in the gap between the Ap site and the facing dC7, bridging the two residues through stable H-bonds as reported in Figure 2-B. The distance between the R76 guanidinium nitrogens and the dC7/Ap20 H-bond acceptor atoms lies at 3.00±0.01 Å and 3.02±0.04 Å, respectively, in the tandem-damaged MutM:DNA complex. Comparatively, the dC7-R76 distance is of 8.14±0.03 Å in the singly damaged system -see Figure 3-B. The reorientation of R76 reshapes the canonical interaction network of the intercalation triad, which is globally shifted downwards the duplex. F114 is pushed away from position 20 and comes closer to the opposite strand, the dC7-F114 distance drops to 5.65±0.01 Å, although its strong cation-π interaction with R76 avoids direct stacking with dC7. Additionally, the distance between the R76 guanidinium extremity and the F114 aromatic ring is of 3.93±0.01 Å vs. 7.93±0.02 Å in the singly-damaged system, while the interaction of R112 with the estranged dC8 is destabilized. In presence of tandem lesions, R112 lies further from dC8 (5.93±0.02 Å) than what is observed for the singly-damaged complex (5.12±0.01 Å). The intercalation of M77 is prevented in presence of the tandem lesion since its terminal methyl group rotates away from OG19:N9 (5.31±0.01 Å), while the interaction with M77 corresponds to a more rigid binding mode, with the formation of a H-bond between the sulfur atom and one hydrogen of OG19. The specific interaction patterns emerging in case of R76 and F114 amino acids appears as crucial to designing a recognition pattern that prevents those residues to dive to fill the void in the nucleic acid/protein interface to the absence of an entire nucleobase. Even though further studies should be required to confirm its specific role, this can suggest that those two residues have an important, and rather unique, effect in assuring repairing of clustered damages. Overall, interactions between the DNA backbone and MutM tend to be stronger upon tandem damages than in the singly damaged duplex. The H-bond between K60 and the phosphate at position 20 is stronger as witnessed by the -NH 3 + . . . P distance that is reduced to 5.72±0.02 Å vs. 7.51±0.06 Å for the singly-damaged system, as well as the interaction of H24 with dA21 (NH -P distance of 4.64±0.01 Å vs. 5.25±0.03 Å with the isolated 8-oxoG) and Y242 H-bond with OG19 (OH -P distance of 5.45±0.03 Å vs 6.14±0.05 Å). However, R264 interaction with the DNA helix is strongly perturbed: in the singly-damaged system, R264 forms H-bonds with OG19 and dG20 phosphates (CZ -P distance of 4.86±0.04 Å and 4.52±0.03 Å, respectively) which are disrupted upon the presence of the additional Ap site (CZ -P distance of 8.711±0.07 Å and 7.17±0.05 Å, respectively). The R264 position experiences important fluctuations in the tandem-damaged complex, and can form H-bond with OG19:O8 -see Figure 2-B. the R264:CZ -OG16:O8 distance is below 4 Å for 42% of the simulation time in the tandem-damaged complex, while in the singly-damaged complex such short distance amounts to 11% only -see Figure S2.

This first local analysis suggests that the singly-vs. tandem-damaged 13-bp duplex present different interaction patterns, with non trivial changes in the binding mode and its dynamics. In order to probe more extensively the structural and dynamic consequences of dG20 → Ap20 substitution, we have relied on a recently-proposed machine-learning protocol [START_REF] Fleetwood | Molecular insights from conformational ensembles via machine learning[END_REF] to identify other residues possibly implied in the recognition mechanism.

Systematic assessment of interacting residues through machine-learning protocol

In order to probe the residues that exhibit important interactions with the DNA duplex, a machine-learning protocol based on the principal components analysis (PCA) was set up. The latter allows to generate a "footprint" of the residues that are particularly involved in MutM:DNA bonding -see Figure 4. A score function, in the following referred to as "importance", is attributed to each residue: the higher the score, the higher the contribution to the MutM:DNA complex stabilization. Using a threshold of 0.04 of importance, 47 and 61 residues out of 273 single out in the singly and tandem-damaged system, respectively.

The three residues of the intercalation triad (i.e. M77, R112 and F114) show a slightly higher contribution in the tandem-(0.043, 0.042, 0.045) than in the singly-damaged system (0.041, 0.038, 0.037). R76 and E78, adjacent to M77 in the MutM sequence, also present high values. As highlighted by the visual inspection of our MD trajectories, in the tandem-damaged system, R76 flips towards the lesion site to compensate for the nucleobase removal at position 20 by bridging Ap20 to the facing dC7 through strong H-bonds. The importance score for R76 is 0.045 with tandem lesions vs 0.037 with isolated 8-oxoG, corroborating the significant role of this residue in MutM:DNA binding upon the presence of Ap20, in line with the newly-formed and very stable H-bonds with the lesion site. E78 importance is higher than the threshold in both tandem-(0.042) and singly-(0.043) damaged systems. This residue interacts with R112, contributing to the H-bonds network in the vicinity of the lesion. Adjacent to F114, G115 importance in the stabilization of the complex is also enhanced upon dG20 → Ap20 mutation (0.042 in tandem vs. 0.033 with isolated 8-oxoG). Additional visual inspection of the MD trajectories reveals that G115 forms a strong H-bond with R76, helping in maintaining the latter intercalated between Ap20 and the facing dC7. Over the five key residues reported to anchor the phosphate DNA backbone (K60, H74, Y242, K258, and R264 [START_REF] Gilboa | Structure of formamidopyrimidine-DNA glycosylase covalently complexed to DNA[END_REF] ), only the closest to OG19 are associated with importance scores above the threshold of 0.04: Y242 (0.040 and 0.052 for the singlyand tandem-damaged system), K258 (0.037 and 0.040) and R264 (0.051 and 0.061). The G173 and Y176 residues also contribute to the H-bonding with the DNA backbone. N174 shows high importance values, 0.040 and 0.053 for singly-and tandem-damaged systems, this is due either to its interaction with the damaged site backbone or through indirect coupling with R264, as previously described in the literature [START_REF] Qi | Entrapment and structure of an extrahelical guanine attempting to enter the active site of a bacterial DNA glycosylase, mutm[END_REF] . I175 is involved in hydrophobic interactions with Y242 that in turn interacts with OG19 backbone. Among other residues whose contribution is above the threshold, R35 forms H-bonds with either dC8 or dC23 backbone, P130 and C166 interact with dT22:P, D165 and E150 maintain the 5'-terminus backbone of the DNA strand 1 (in the dG1 and dT2 surroundings), L164 stabilizes the position of the key-residue R264, while G263 and G265 form H-bonds with R264 or directly with the DNA backbone, and K258 interacts with the dC17 phosphate. Of note, the Q166C mutation kept from the crystal should have only a shy impact on the interaction with the DNA backbone, as methionine and glutamine share similar side chain length, we can safely hypothesize that a glutamine would have been pinpointed by the neural network analysis, probably with a higher contribution. Globally the PCA analysis clearly reveals that the protein residues comprised between the position 210 and 237 exhibit the highest values of importance. They correspond mostly to a large, flexible loop, comprising the residues 221-234 prior to the C-terminus that is prone to disorder, but also known to have an implication for DNA recognition despite being spatially far from the double-helix [START_REF] Qi | Encounter and extrusion of an intrahelical lesion by a DNA repair enzyme[END_REF] . Amino acids at the N-terminus also show significant contributions to the MutM:DNA bonding. The proline located at the very end of the N-terminal region has an important catalytic role since it reacts with the C1' atom of the Importance of the contribution of residues to the MutM:DNA complex bonding for the singly-damaged (blue) and the tandem-damaged (orange) systems. The threshold value above which the importance of the residue for the stabilization of the complex is considered as significant is 0.04. Some of the key-residues as well as the flexible loop region are pinpointed by the arrows. Contributions of amino acids to the bonding are mostly higher upon 8-oxoG/Ap combination, suggesting a more rigid complex upon local multiple damage sites than with an isolated 8-oxoG.

deoxyribose sugar moiety of the 8-oxoguanine to form a Schiff base, and hence it induces the cleavage of the N-glycosidic bond which constitutes the first step of the repair process [START_REF] Sadeghian | Ribose-protonated dna base excision repair: A combined theoretical and experimental study[END_REF][START_REF] Kreppel | Base-independent dna base-excision repair of 8-oxoguanine[END_REF] . Adjacent to P2, the vicinal E3 is also known to play a role in MutM catalytic efficiency [START_REF] Sadeghian | Ribose-protonated dna base excision repair: A combined theoretical and experimental study[END_REF][START_REF] Kreppel | Base-independent dna base-excision repair of 8-oxoguanine[END_REF] . Interestingly, the contribution of these two residues to the MutM:DNA stability decreases from 0.054 in the singly-damaged to 0.044 for the tandem-damaged complex, hence corroborating a subtle reduction of the excision efficiency. Other residues of the N-terminus (L4, P5, E6) also show a drop in their contribution upon dG20 → Ap20 mutation.

The residues which single out in this PCA analysis match very well with the ones evidenced by previous works on MutM [START_REF] Serre | Crystal structure of the lactococcus lactis formamidopyrimidine-DNA glycosylase bound to an abasic site analogue-containing DNA[END_REF][START_REF] Amara | Insights into the DNA repair process by the formamidopyrimidine-DNA glycosylase investigated by molecular dynamics[END_REF][START_REF] Qi | Entrapment and structure of an extrahelical guanine attempting to enter the active site of a bacterial DNA glycosylase, mutm[END_REF][START_REF] Fromme | DNA lesion recognition by the bacterial repair enzyme mutm[END_REF][START_REF] Fromme | Structural insights into lesion recognition and repair by the bacterial 8-oxoguanine DNA glycosylase mutm[END_REF][START_REF] Amara | Functional flexibility of bacillus stearothermophilus formamidopyrimidine DNA-glycosylase[END_REF] . Our machine-learning post-processing allows to disentangle a complex interaction pathway, which is already well-established for 8-oxoG-containing DNA [START_REF] Landová | Conformational changes of DNA repair glycosylase mutm triggered by DNA binding[END_REF] but perturbed upon the presence of tandem lesions as revealed by the present simulations. It allows to generate an exhaustive map of residues showing importance for the protein-DNA interactions, beyond the simple visual investigation based on the data from the literature. Noteworthy, the nucleic acid importance score in the MutM:DNA bonding is enhanced upon the presence of Ap20, denoting again a more constrained oligonucleotide -see Figure S3.

Mechanical and dynamic properties of the DNA strand

In order to assess the mechanical and dynamic properties of the DNA strand, the MD trajectories were post-processed with the Curves+ program [START_REF] Lavery | Conformational analysis of nucleic acids revisited: Curves+[END_REF] to evaluate the structural parameters of the double helix.

The first signature of the B-helix is often the bend angle, which reaches typical values around 51.55±0.34 • upon interaction with MutM for the singly-damaged oligonucleotide. Such extreme values for bending are typical [START_REF] Friedman | Detection of damaged DNA bases by DNA glycosylase enzymes[END_REF][START_REF] Sugahara | Crystal structure of a repair enzyme of oxidatively damaged DNA, mutm (fpg), from an extreme thermophile, thermus thermophilus hb8[END_REF] and necessary to facilitate the extrusion of the lesion towards the enzyme active site. The presence of the Ap site at position 20 is not sufficient to perturb the global bending of the 13-bp oligonucleotide (49.12±38 • ), but rather induces local deformations. Structural parameters of the dC8-OG19 basepair are particularly impacted -see Table 1. Importantly, the backbone parameters 'Bend', 'Tip' and 'Inclination' are lower when the Ap site is present at position 20, denoting a straighter portion of DNA helix than what is normally found in the canonical single-damaged MutM:DNA complex -see Figure 5 and Figure S4. The values monitored for these parameters are of 8.64±0.03 • , 13.96±0.13 S5.

The role of the sugar moiety, and in particular its ideal locking, could be crucial to explain the recognition of the damaged nucleic acid. Indeed, while AP sites have a propensity to assume extrahelical positions, they are also characterized, at least in naked configuration, to a strong variability and a coexistence of diverse conformations. The specific interactions developed in the enzyme conformation can, as a consequence, be crucial for the lesion recognition. Qi et al 27 reported a change in puckering values upon oxidation of a guanine residue. 8-oxoG would exhibit a C4'-exo puckering while a canonical dG ribose moiety would harbor a C2'-endo conformation. This would promote the recognition of 8-oxoG by MutM. In our simulations, the frequency of the C2'-endo conformation of OG19 is increased by the presence of tandem lesion compared to a single 8-oxoG (42.4% and 21.3%, respectively). However, rather than the C4'-exo puckering (2.9% and 7.6% for tandem-and single-damaged), the C1'-exo is the main or second preponderant conformation (42.5% and 61.5%) -see Figures S6. Interestingly, the different trends of the 8-oxoG phase angle are correlated to differences in the chi angle values, which can reach 180 • with a tandem lesion -see Figure S7. Besides, the distribution of epsilon-zeta angle characterizing the BI or BII conformational state of the 8-oxoG backbone is broader with a single lesion, further underlining the enhanced stiffness upon the presence of a tandem lesion -see Figure S8. Likewise, the distribution of the distance between the O8 and C5' of the 8-oxoG shows different trends for single and tandem lesions, highlighting again the difference of structural signature -see Figure S9. Concerning the inter base-pair parameters (dC8-OG19/dG9-dC18) and parameters of the adjacent dG9-dC18 base-pair, DNA structural values are comparable for single-and tandem-damaged systems and in agreement with previous works [START_REF] Rosa | Global deformation facilitates flipping of damaged 8-oxo-guanine and guanine in DNA[END_REF] -see Figures S10, S11 and S12. As could be expected though, the absence of the nucleobase at position 20 upon mutation to Ap site influences the stability of the canonical stacking that is usually conserved in the singly-damaged complex. This highlights the blurrier structural signature exhibited by the tandem-damaged DNA helix, which is another criteria that might affect the interaction with the surrounding amino acids, hence the efficiency of the 8-oxoG extrusion by MutM.

Discussion

MutM, the bacterial analog of the human OGG1, is responsible for the recognition and repair of the utmost common 8-oxoG lesion. The MutM:DNA interface has been investigated by NMR, X-ray and molecular dynamics simulations, probing the key residues that play a crucial role in the most specific recognition of 8-oxoG, but also of other DNA lesions 21, 23, 25-28, 40, 50-53 . An intercalation triad (M77, R112, F114) has been characterized, and several other residues are known to be essential in MutM:DNA interactions and 8-oxoG extrusion, guiding the lesion towards the N-terminal proline responsible for the Schiff base formation. Intrahelical insertion of a single F114 wedge residue [START_REF] Li | A dynamic checkpoint in oxidative lesion discrimination by formamidopyrimidine-DNA glycosylase[END_REF][START_REF] Brooks | Recent advances in the structural mechanisms of {DNA} glycosylases[END_REF][START_REF] Nelson | Two glycosylase families diffusively scan DNA using a wedge residue to probe for and identify oxidatively damaged bases[END_REF] is marked and allows a slow scanning of the double helix by MutM and analog enzymes. Among MutM key-residues, R264, located in the Zn-finger domain, is highly conserved and important for 8-oxoG extrusion [START_REF] Fromme | Structural insights into lesion recognition and repair by the bacterial 8-oxoguanine DNA glycosylase mutm[END_REF][START_REF] Qi | Encounter and extrusion of an intrahelical lesion by a DNA repair enzyme[END_REF] . N174 also plays a key-role and its mutation leads to the perturbation of the R264 contacts [START_REF] Qi | Entrapment and structure of an extrahelical guanine attempting to enter the active site of a bacterial DNA glycosylase, mutm[END_REF][START_REF] Li | A dynamic checkpoint in oxidative lesion discrimination by formamidopyrimidine-DNA glycosylase[END_REF] . Besides, the flexible loop prior to the C-term is known to be essential for the 8-oxoG recognition by folding over the lesion in a capping process [START_REF] Fromme | DNA lesion recognition by the bacterial repair enzyme mutm[END_REF][START_REF] Qi | Encounter and extrusion of an intrahelical lesion by a DNA repair enzyme[END_REF] . While the recognition and repair of single 8-oxoG by MutM are well documented, their perturbation upon the presence of tandem lesions is very poorly understood. However, it has been shown that ionizing radiations can lead to the formation of tandem lesions [START_REF] Watanabe | Spectrum of Radiation-Induced Clustered Non-DSB Damage -A Monte Carlo Track Structure Modeling and Calculations[END_REF] , rendering 8-oxoG refractory to excision by glycosylases [START_REF] Bergeron | radicals induce an unexpected high proportion of tandem base lesions refractory to repair by DNA glycosylases[END_REF][START_REF] Lomax | 8-OxoG retards the activity of the ligase III/XRCC1 complex during the repair of a single-strand break, when present within a clustered DNA damage site[END_REF] . Such local multiple damaged sites are highly mutagenic and increase the risks of cancer development [START_REF] Wood | Mechanistic studies of ionizing radiation and oxidative mutagenesis: genetic effects of a single 8-hydroxyguanine (7-hydro-8-oxoguanine) residue inserted at a unique site in a viral genome[END_REF][START_REF] Moriya | Single-stranded shuttle phagemid for mutagenesis studies in mammalian cells: 8-oxoguanine in DNA induces targeted GC -> TA transversions in simian kidney cells[END_REF] . They can also be cytotoxic as their error-prone repair can result in the formation of deleterious double-strand breaks [START_REF] Vignard | Ionizing-radiation induced DNA double-strand breaks: a direct and indirect lighting up[END_REF][START_REF] Thompson | Recognition, signaling, and repair of DNA double-strand breaks produced by ionizing radiation in mammalian cells: the molecular choreography[END_REF] . Noteworthy, the high toxicity of the DNA lesions induced by ionizing radiation is also exploited for the development of cancer (radio)-therapies [START_REF] Baskar | Cancer and radiation therapy: current advances and future directions[END_REF] . In this context, we investigated the structural impact of tandem lesions on the interactions between MutM and a 13-bp oligonucleotide harboring the 8-oxoG lesion at position 19. Using molecular modeling and machine-learning analysis, we highlighted a structural re-organization of MutM canonical interaction network around 8-oxoG upon the presence of an adjacent abasic site at position 20.

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The interaction network involving the intercalation triad and the damage is perturbed by the dG20 → Ap20 mutation. The MutM:DNA interactions are more pronounced, leading to a more rigid system, which could explain the difficulty of MutM to process such local multiple damaged sites. While in the simulation of the singly-damaged system, the classical interaction patterns are observed, the presence of an additional Ap site results in the rotation of R76 that provokes a shift of the intercalation triad. Noteworthy, as R76 is poorly conserved in MutM sequences from different organisms [START_REF] Fromme | Structural insights into lesion recognition and repair by the bacterial 8-oxoguanine DNA glycosylase mutm[END_REF][START_REF] Landová | Conformational changes of DNA repair glycosylase mutm triggered by DNA binding[END_REF] , one cannot rule out the possibility of a different reorganization around the intercalation triad. First observations of our MD trajectories allowed to describe the re-shaping of the MutM:DNA interaction patterns -see Figure 2 and3. The structural analysis of the DNA oligonucleotide also reveals changes in the local conformation of the lesion site (see Figure 5 and Table 1), which might jeopardize the efficiency of 8-oxoG recognition by the enzyme.

In order to go beyond the visual observation of MutM:DNA interactions, we applied machine learning (ML) techniques to provide an extensive map of these contacts. ML methods have gained enormous amount of attention in recent years. Their power in finding important information out of large amount of data has been exploited by the biochemistry community, many interesting applications have been showcased in the literature. Recently, Fleetwood et al. [START_REF] Fleetwood | Molecular insights from conformational ensembles via machine learning[END_REF] have demonstrated its capability in learning ensemble properties from molecular simulations and providing easily interpretable metrics describing important structural or chemical features. The machine-learning analysis of our trajectories is based on the demystifying package from Fleetwood et al. [START_REF] Fleetwood | Molecular insights from conformational ensembles via machine learning[END_REF] . Residues highlighted as providing a significant contribution to the MutM:DNA bonding by the PCA analysis are in agreement with data from the literature. Comparison of the residues importance in MutM:DNA interactions upon single or tandem lesions allowed to pinpoint the changes in the interaction patterns, which concern the most important features of MutM -see Figure 4. Apparently in contradiction with common chemical sense, the PCA analysis revealed that the dG20 → Ap20 mutation leads to stronger, more stable interactions between the two macromolecules. The contribution of the residues involved in DNA anchoring is almost systematically increased in the tandem-damaged system. Nucleic acids also exhibit stronger interactions with MutM in the case of the tandem lesion, which overall suggests that the presence of a second damage somehow results in a more rigid complex than when an isolated 8-oxoG is present. The difference of structural signatures between the single and tandem lesions is further highlighted by the perturbation of the 8-oxoG backbone parameters upon addition of the adjacent abasic site. However, the global rigidity of the tandem-damaged MutM:DNA complex can actually be 8/13 counterproductive for repair since it has been evidenced that flexibility of the DNA strand is a key feature correlating with 8-oxoG removal [START_REF] Landová | Conformational changes of DNA repair glycosylase mutm triggered by DNA binding[END_REF] . This consideration is also further reinforced by the fact that conversely, the catalytic N-terminal residues are less involved in the MutM:DNA complex stability in the case of the tandem lesion. This is also the case for the 211-234 loop region which is known to play a key-role in 8-oxoG extrusion. Hence, the presence of the Ap site alongside the 8-oxoG lesion impacts the canonical structural behavior of these two important MutM regions, which might also contribute to the lower repair efficiency.

Our study provides an example of the predictive power of all-atom, MD simulations coupled to machine learning analysis, applied to a very challenging test-case. Indeed, the combination of oxidatively-generated DNA lesions embrace a combinatorial chemistry, with contrasted structural, mechanical and dynamic properties. Additionally, MutM is a very flexible protein [START_REF] Landová | Conformational changes of DNA repair glycosylase mutm triggered by DNA binding[END_REF] , certainly difficult to properly sample. The efficiency of our protocols gives perspectives for its extension towards other tandem systems and the investigation of sequence effects [START_REF] Noguchi | The mutagenic potential of 8-oxog/single strand break-containing clusters depends on their relative positions[END_REF][START_REF] Sassa | DNA sequence context effects on the glycosylase activity of human 8-oxoguanine DNA glycosylase[END_REF][START_REF] Sassa | Understanding the sequence and structural context effects in oxidative DNA damage repair[END_REF] . Furthermore, the biological significance of rationalizing this complex scenario is also unquestionable. Indeed, ionizing radiations can be satisfactorily exploited in cancer therapy, and the inhibition of repair enzyme by combined chemotherapy can prove a most valuable synergy in assuring the accumulation of lesions necessary to reach the apoptosis threshold. Understanding of the molecular mechanisms underlying DNA repair is thus crucial for also offering novel perspectives for cancer research.

Materials and Methods

All-atom molecular dynamics simulations

All MD simulations were performed with the Amber and Ambertools 2018 packages [START_REF] Case | San francisco[END_REF] . The starting X-ray structure of Bacillus stearothermophilus MutM was taken from the structure obtained by Verdine and coworkers [START_REF] Qi | Encounter and extrusion of an intrahelical lesion by a DNA repair enzyme[END_REF] , PDB ID code 3GO8. The crystallographic self-complementary ds-DNA is a 13-bp sequence d(GTAGATCCGACG).(CGTCCGGATCT) featuring 8-oxoG as the 19th nucleobase (in bold). It should be noted that the β F-α10 loop 217-237 of MutM, absent from the crystal structure, was reconstructed using Modeller [START_REF] Webb | Comparative protein structure modeling using modeller[END_REF] . The zinc atom present in the zinc-finger motif of MutM was kept and described with parameters taken from the Zinc Amber Force Field (ZAFF) developed by Merz and coworkers [START_REF] Peters | Structural survey of zinc-containing proteins and development of the zinc amber force field (zaff)[END_REF] . 19 potassium ions were added to neutralize the MutM:DNA complex, which was embedded in a 92x97x91 Å 3 TIP3P water molecules bath. The Amber ff14SB [START_REF] Maier | ff14sb: improving the accuracy of protein side chain and backbone parameters from ff99sb[END_REF] was used throughout, including the bsc1 force field corrections for the DNA duplex [START_REF] Ivani | Parmbsc1: a refined force field for DNA simulations[END_REF] .

The parameters for 8-oxoG and Ap site have been generated with a standard antechamber procedure embedded in Amber 18 [START_REF] Case | San francisco[END_REF] , as described in previous references [START_REF] Bignon | Interstrand cross-linking implies contrasting structural consequences for dna: insights from molecular dynamics[END_REF][START_REF] Bignon | Nucleosomal embedding reshapes the dynamics of abasic sites[END_REF][START_REF] Dumont | Singlet oxygen attack on guanine: Reactivity and structural signature within the B-DNA helix[END_REF] and in agreement to the literature. The abasic site was modeled as an alpha anomer, and the Q166C mutation in the crystal was conserved to start from a structure as close to experimental data as possible. Four 10,000 steps minimization runs were carried out on the initial MutM:DNA complex, imposing restraints on the amino and nucleic acids, that were gradually decreased from 20 to 0 kcal/mol/Å 2 along the four runs. The temperature was then raised from 0 to 300K in a 50 ps thermalization step, and afterwards kept constant using the Langevin thermostat with a collision frequency γln of 1 ps -1 . The system was subjected to a 1 ns equilibration run in the NPT ensemble. Finally, two replica of 1 µs production run were performed to sample the conformational ensemble of the system. The integration step was set to 2 fs and trajectory files were written every 100 ps. The Particle Mesh Ewald method was used to treat electrostatic interactions, with a 9.0 Å cutoff. RMSD were monitored to ensure the sampling of relevant conformational space -see Figure S13. A clustering was carried out for both systems using the cpptraj module of Amber, based on the RMSD of the proteic and nucleic acids. The pdb file of the representative frame of the main cluster for each MD trajectory is available in SI (mono_MD1.pdb, mono_MD2.pdb, tandem_MD1.pdb and tandem_MD2.pdb). The structural descriptors of the DNA helix were evaluated based on a postprocessing analysis with Curves+ [START_REF] Lavery | Conformational analysis of nucleic acids revisited: Curves+[END_REF] and other distances and RMSD values were monitored using Ambertools 63 on 1000 frames for each MD replicate. A description of the parameters (schemes and how they are computed) is available on curves+ website (http://curvesplus.bsc.es/helpar). Plots were generated using the ggplot2 package of R 71 and figures were rendered using the VMD software (http://www.ks.uiuc.edu/Research/vmd/) [START_REF] Humphrey | Vmd: visual molecular dynamics[END_REF] .

Machine-learning post-analysis

The Principal Components Analysis (PCA) is a covariance-matrix-based unsupervised learning technique that take the inverse distances between residues as input, and produces the eigenvalues and eigenmodes of this covariance-matrix. The eigenmodes can be interpreted as the modes of motion, and the corresponding eigenvalues are the amplitude. The principal components (eigenmodes with highest amplitudes) capture the major fluctuations of the system can in turn be used to find out the important features of the DNA/MutM interface. We also initially compare to a supervised approach, with the distance between dC7 and R76 as a labelling tag to discriminate between bounded and unbounded states. However, for this system, this "tag" turns out to be not discriminative enough and only single out R75 and F114, and the unsupervised learning turned out to be superior.

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Emmanuelle Bignon ,

 Bignon Natacha Gillet, Chen-Hui Chan, Tao Jiang, Antonio Monari, et al.. Recognition of a tandem lesion by DNA bacterial formamidopyrimidine glycosylases explored combining molecular dynamics and machine learning. Computational and Structural Biotechnology Journal, 2021, 19, pp.2861-2869. 10.1016/j.csbj.2021.04.055. hal-03326585

Figure 1 .

 1 Figure 1. (A) Cartoon representation of the bacterial MutM in interaction with a 13-bp double stranded DNA helix harboring 8-oxoguanine (OG19) as the 19th nucleobase -PDB ID code 3GO8 27 . The magnified section highlights the position of the catalytic triad (M77, R112, and F114 in green) and the residues interacting with the DNA backbone (in orange) around the damage. (B) Sequence of the 13-bp oligonucleotide, showing the position of the 8-oxoguanine (in red). In simulations with tandem lesions, dG20 is mutated in silico into an abasic site (Ap). (C) Chemical structure of the 8-oxoguanine and the abasic site lesions.

Figure 2 .

 2 Figure 2. Cartoon representation of MutM interacting with the DNA helix harboring a single 8-oxoG lesion (OG19, A) or tandem lesion 8-oxoG + Ap (OG19 and Ap20, B). H-bonds are depicted as dashed pink lines and the DNA structure is rendered transparent for sake of clarity. Upon local multiple lesions, the interaction pattern around 8-oxoG (OG19) is perturbed. The intercalation triad M77/R112/F114 is shifted down by R76 which comes to interact between Ap20 and the facing dC7, preventing M77 ad F114 intercalation above 8-oxoG. R264, normally interacting with the DNA backbone phosphates between positions 19 and 20, is now involved in hydrogen bonding with OG19 carbonyl.

Figure 3 .

 3 Figure 3. Distribution of relevant distances involving the intercalation triad (A) and R76 (B) upon a single 8-oxoG mutation (single) or Ap + 8-oxoG lesions (tandem). The presence of the Ap site at position 20 makes M77 and R112 move away from 8-oxoG and the facing dC8. Steric hindrance provoked by R76 movement pushes F114 closer to dC7. R76 comes closer to 8-oxoG and intercalates in the gap left by the abasic site, with formation of very stable H-bonds bridging Ap20 and the facing dC7. It also interacts with F114, preventing it to stack within the double-helix.

Figure 4 .

 4 Figure 4. Importance of the contribution of residues to the MutM:DNA complex bonding for the singly-damaged (blue) and the tandem-damaged (orange) systems. The threshold value above which the importance of the residue for the stabilization of the complex is considered as significant is 0.04. Some of the key-residues as well as the flexible loop region are pinpointed by the arrows. Contributions of amino acids to the bonding are mostly higher upon 8-oxoG/Ap combination, suggesting a more rigid complex upon local multiple damage sites than with an isolated 8-oxoG.

Figure 5 .

 5 Figure 5. Distribution of three characteristic DNA helix intra base-pair parameters for dC8-OG19 over 2 µs MD simulation, for a single 8-oxoguanine (Single, red, top) and both 8-oxoguanine and Ap site (Tandem, blue, bottom). The structural signature of the tandem lesion-containing oligonucleotide is closer to canonical B-DNA than in the presence of a single 8-oxoG.

Table 1 .

 1 • , and 19.50±0.10 • , respectively in the singly-damaged system, Averaged values and standard errors of the oligonucleotide global bending and of dC8-OG19 base-pair structural parameters, for systems harboring either a single 8-oxoG (Single, left) or a tandem 8-oxoG+Ap lesion (Tandem, right). vs 7.59±0.04 • , 8.10±0.14 • , and 18.26±0.16 • in the tandem-damaged complex. Besides, several intra base-pair structural parameters are also found closer to the canonical B-DNA for the dC8-OG19 base-pair. Especially, the 'Buckle' and 'Propeller' drop from -16.13±0.19 • to -8.83±0.24 • and from 5.63±0.17 • to -0.01±0.09 • , respectively upon dG20 → Ap20 mutation. Other parameters (Opening, Shear, Stretch, and Stagger) show much less significant deviations -see Table1and FiguresS4 and

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Acknowlegements

Support from ENS de Lyon is gratefully acknowledged. This work was performed within the framework of the LABEX PRIMES (ANR-11-LABX-0063) of Université de Lyon, within the program "Investissements d'Avenir" (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR).


Cluster dynamics in the collapsing Soviet shipping network

Port and shipping dynamics during the Soviet period is not a very popular topic for scholars.

In their review of about 400 scientific articles on ports published in geography journals since 1950, [START_REF] Ng | The changing tides of port geography (1950-2012)[END_REF] could only find two papers focusing on socialist ports, one on Odessa published in 1964, and another on Poland in 1986. Ports and shipping do not constitute a common field of interest for specialists on Russia and socialist economies either.

Over the past decades, geographers such as Thorez (1998aThorez ( , 1998b) ) devoted their attention to socialist shipping trends [START_REF] Vigarié | La Mer et la Géostratégie des Nations[END_REF] documenting the changes affecting post-Soviet ports following the collapse of the Union of Soviet Socialist Republics (hereafter USSR).

While ports in Eastern Europe [START_REF] Ledger | East European shipping and economic change: a conceptual model[END_REF], and in the Baltic in particular [START_REF] Serry | Dynamiques du transport maritime en Baltique orientale[END_REF][START_REF] Thorez | Les enjeux portuaires de la Russie en mer Baltique[END_REF], as well as the special case of North Korea [START_REF] Ducruet | Going West? Spatial polarization of the North Korean port system[END_REF], received a lot of their attention, the bulk of research on ports and shipping concentrated on developed and developing countries in the capitalist world, one exception being China, given its rapid economic growth since the Open Door Policy (see [START_REF] Wang | Regional resilience and spatial cycles: Long-term evolution of the Chinese port system (221BC-2010AD)[END_REF]. Such works primarily insisted on the important changes and uneven adaptation of ports and shipping flows to the various influences of political transitions on trade activities (see Chapter 8 for an analysis of the Black Sea liner shipping network). Yet, a global approach to the way USSR ports and maritime networks experienced the collapse remains lacking today.

The aim of this chapter is to fill this gap and to place the topic within the broader, transdisciplinary field of network science. Network analysis has become a strong discipline, which is no longer limited to sociology as it applies to many areas, such as biology, geography or history [START_REF] Zreik | Statistical Analysis of Networks and Applications in Social Sciences[END_REF]. The USSR provides great potential for such research since it presents us with the case of a major shock affecting a vast economy, and the objective of testing its collapse on its internal and external maritime linkages. Many aspects that have become paramount in network analysis are, for instance, how spatial networks evolve in relation to distances and proximities of all kinds, and how disruption affects connectivity.

Another research pathway is to test such effects on the emergence of communities (or clusters) in a given network.

The most recent statistical methods for the modeling and processing of relational data are generally based on the stochastic block model (SBM) [START_REF] Wang | Stochastic blockmodels for directed graphs[END_REF]Nowicki and Snijders, 2001). The SBM model assumes that each vertex belongs to a latent group, and that the probability of connection between a pair of vertices depends exclusively on that group. Among the recent extensions of the SBM model is the random subgraph model (RSM) proposed by Jernite et al. (2013), which aims at modeling categorical edges using prior knowledge of a partition of the network into subgraphs. The subgraphs are assumed to be made of latent clusters, which have to be inferred from the data in practice. The vertices are then connected with a probability depending only on the subgraphs, whereas the edge type is assumed to be sampled conditionally on the latent groups.

In this work, we consider the methodology introduced in Zreik et al. ( 2016) that permits the inference of a dynamic version of the RSM model on real data. Our approach allows us to analyze the evolution of USSR-related maritime flows between ports of the world over ten years (1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996) through the traffic of ships. Our main hypothesis is that the topology and spatial distribution of flows, the emergence of clusters, and the impact of the collapse are determined by the political belonging of port nodes. This is questionable since shipping networks both reflect and transgress the border effects that characterize the markets that they help to connect.

The next section provides a review of port and shipping dynamics in Russian and Soviet ports based on the few available scholarly works on the subject. It also provides a methodological standpoint with preliminary analyses based on the definition of the USSR maritime ego network to identify main traffic trends. This is followed by the application of stochastic methods to reveal how the topology of this network has evolved according to the political belonging of port nodes before, during, and after the USSR collapse of the early 1990s. The conclusion discusses the contribution of this analysis to broader research on socialist economies, ports and shipping, and spatial network analysis in general.

Socialist ports and maritime transport: from bipolarization to globalization

General trends in USSR shipping

Traditionally, socialist economies are believed to develop through mainly continental relationships among preferential trade partners [START_REF] Vigarié | La Mer et la Géostratégie des Nations[END_REF], thereby neglecting maritime transport as a growth engine. Such a conception inherited from the Marxist-Leninist economy theory rapidly lost ground from the 1960s onwards, given the necessity for socialist powers to expand their relationships internally and with the rest of the world, whether for commercial or military purposes, through a growing network of friendly partners in the extended socialist and developing world (e.g. Cuba, Vietnam and Africa).

Thus, the Soviet Union gradually strengthened its global influence by competing with the capitalist world along all major maritime routes, as reflected in the continuous growth of its total port tonnage [START_REF] Vigarié | La Mer et la Géostratégie des Nations[END_REF] in the 1970s-1980s, despite cumbersome customs regulations and deficient port infrastructures. Yet, in the early 1980s a total 240 million tons of maritime trade remained coastal (16.5%), with socialist partners (32.1%), developing countries (6.1%), and capitalist countries (45.3%) (Thorez, 1998a).

The ambition of this chapter is to shed new light on the impact of a major geopolitical shock on the topology and spatial distribution of a spatial, -in our case, maritime-network. This shock is multifaceted. Its chronology is well known, with the dismantlement of the COMECON (Council for Mutual Economic Assistance) in June 1991, impacting strongly on Soviet foreign trade; the dissolution of the Warsaw Pact in July 1991; and the partition of the USSR itself in December 1991, immediately followed by the constitution of the Commonwealth of Independent States (CIS). Such events are both a cause and a consequence of the weakening Russian economy throughout the 1980s, leading to a sudden drop in trade exchanges, a disorganization of former relationships, and a growing debt, all resulting in a reorganization of ports and maritime networks [START_REF] Vigarié | La Mer et la Géostratégie des Nations[END_REF].

The aforementioned reorganization is well documented by the works of Thorez (1998aThorez ( , 1998b) ) and can be synthesized as follows: prior to the disintegration of the USSR, ports and maritime transport were characterized by a spatial and functional division of labor centered upon and organized from Moscow. Soviet shipping companies were specialized in certain trades and routes, each having their base or hub port at locations around the block from the Baltic Sea to the Far East. Certain large ports were multifunctional, while others remained specialized in specific commodities, mainly raw materials and intermediate inputs, given the late adoption of containerization in the USSR. The 1991 collapse had been a "brutal thunderstruck" (Thorez, 1998a, p. 13) forcing port authorities and shipping companies to rethink their activity in terms of hinterland supply chains and management in a context of falling demand and traffic decline. Such a decline -the whole USSR area concentrated no more than 3.5% of world maritime trade in 1994-was accentuated by the modal shift of traffic from sea to land (road, rail), especially towards West European transit hub ports, such as Antwerp, Rotterdam, and Hamburg, thereby causing a severe decline at a number of post-Soviet ports. The shrinking of maritime trade had been severe internally (coastal shipping) while former Soviet ports, including those in Russia, strove to diversify their traffic to compensate the loss of their formerly vast hinterland.

The shock was felt differently among countries and ports depending on their capacity to adapt to a new competitive and global environment and the importance given to maritime trade for domestic demand or transshipment; although in relative terms, maritime traffic volumes declined less (-42%) than total transport traffic (-49.6%) between 1990(-49.6%) between and 1995(-49.6%) between (Thorez, 1998b)). This is because certain ports recovered more quickly from the shock than others: positively in the Baltic and Far East regions, negatively in the Black Sea and in Russia itself. The process was characterized by many of the republics of the Soviet Union declaring their independence and being recognized as sovereign nation-states.

Constructing the Soviet ego network: preliminary results

Despite our current knowledge of the impact of the disintegration of the USSR on ports and maritime transport, to date the relational or network perspective had not been contemplated. Such a major event in economic and political terms provides fertile ground for the study of the changing topology and geography of a spatial network. For that purpose, we constructed the (maritime) ego network of the USSR defined by internal (intra-USSR vessel movements) and external (vessel movements between USSR ports and other ports, and between the latter ports) relationships, based on the Lloyd's List database over the period 1987-1996. This corresponds to the definition of the ego network in social network analysis [START_REF] Wasserman | Social Network Analysis: Methods and Applications[END_REF]. Such a network places the USSR at the core of the analysis, leaving aside the rest of the global maritime network, (i.e., vessel movements between ports not at all connected with the USSR). The choice of the period (10 consecutive years) is justified by the need to verify the true impact of the shock that lies in the middle of the period (1991). Yet, it is acknowledged that the economic downturn of Russia and the USSR already started long before the collapse itself, namely, already in the late 1980s.

Overall, this methodology allows us to get closer to the shock and reduce the noise caused by unrelated factors. For each year under study, we compiled four complete months of vessel movements between such ports, taking into account not only adjacently connected ports but also intermediary stops along the whole voyage during the months of March, June, September, and December, to avoid the effect of trade seasonality. As a result, the spatial pattern of aggregated USSR shipping traffic during the study period reveals its concentration in the vicinity, along Russian, Soviet shores, especially in the Baltic and Black Seas for the largest (volume) and most specialized (share) nodes (Figure 19.1). Elsewhere, high shares of USSR traffic in total port traffic are clearly visible in partner, or politically close, countries, despite the distance: Cuba, Algeria and Vietnam, but also in small groups of ports in Peru, India, and Canada. Due to physical proximity and regardless of political affinities, certain nodes emerge on the world map, such as several specialized ports in Japan (Far East Russian trade), Helsinki and Hamburg, as the most specialized of the largest, non-Soviet nodes, as well as Rotterdam and Antwerp, which both act as multifunctional/transshipment ports as mentioned above.

[Figure 19.1]

We also measure traffic evolution per political group or subgraph: USSR, Eastern Block, other socialist ports, as well as non-aligned and capitalist ports (Figure 19.2). The "other socialist" group is composed of ports in countries having had socialist elements in their political constitution at a given point in time of their contemporary history. Our results confirm the aforementioned trends in several ways, namely: (1) total traffic of USSR ports; (2) traffic of each subgraph relative to the year 1991; (3) traffic between USSR and each subgraph relative to the year 1991; and (4) internal traffic of each subgraph. As expected and coinciding with the collapse, USSR ports witnessed a sharp slowdown in the years 1991-1992 after a period of growth, followed by rapid recovery, as seen over the broader 1977-2008 period based on the same database. Each subgraph went through distinct evolutions, however. Relative to the year 1991, USSR ports are the ones handling the highest traffic prior to the collapse, while Eastern Block ports already faced decline in 1989-1990, but recovered more rapidly than the USSR. Ports of the "other socialist" subgraph went through a slowdown comparable to the USSR after 1991 and before recovery. Interestingly, ports in non-aligned and capitalist countries did not much suffer from the demise of the USSR, given the stability of their growth over the period. Traffic evolution between the USSR and other subgraphs reveals complementary trends. For instance, based on the year 1991, USSR traffic preferentially connected "brother" countries prior to its collapse, while such traffic became relatively more important with the Eastern Block afterwards and also internally (intra-USSR).

Thus, the dissolution of the USSR affected long-distance relationships, such as those with "other socialist" countries to a greater extent and less so within its vicinity. The share of intra-group traffic shows that in this ego network flows among capitalist ports dominate around 80% on average despite a slight decline; non-aligned ports increased a noticeable share of mutual linkages, from about 35% to nearly 50%; while all socialist ports in the three other subgraphs exhibit a very low internal share, given their reliance on external, nonsocialist ports to connect with the rest of the world but also with each other. In fact, USSR traffic mainly connected capitalist ports (70% on average), followed by intra-Soviet traffic (10%), other socialist (8%), Eastern Block (6%) and non-aligned ports (6%), with little change over time.

[Figure 19.2]

The model of dynamic random subgraphs

The remainder of the analysis involves the application of the dynamic random subgraphs model (dRSM) [START_REF] Zreik | The dynamic random subgraph model for the clustering of evolving networks[END_REF] on a slightly different network whereby USSR ports and Eastern Block ports are grouped into the same subgraph, given their geographic and political proximity and their relatively low number (Figure 19.3). In addition, network analysis only applies to ports connected every single year during the period, thereby leaving aside a number of fluctuating ports. Despite much discrepancy due to the overwhelming importance of capitalist ports, our port sample is as follows: 48 USSR ports, 17 Eastern Block ports (65 in total for both), 77 other socialist ports, 93 non-aligned ports, and 584 capitalist ports. This sample consists of 819 ports within the 2,691 ports connecting the USSR during the period, which means about 30.4% of all ports, but mainly large traffic nodes.

[Figure 19.3]

In the following section, we describe the dynamic RSM (dRSM) model, as introduced in Zreik et al. ( 2016). Thus, we consider a set of T networks {G(t)}T t=1, where G(t) is a directed graph observed at time t and for which a partition P(t) of S subgraphs is also known. Each G(t) is represented by its N _ N adjacency matrix X(t) and N denoting is the number of nodes (assumed constant over time). No loops are considered. The edge X(t) ij , describing the relationship between nodes i and j is assumed to take its values in {0, … C} such that X(t) ij = c means that nodes i and j are linked by a relationship type c at time t and X(t) ij = 0 indicates the absence of relationship. Our goal is to cluster at each time t the N nodes into K latent groups with homogeneous connection profiles, (i.e. find an estimate at each time t of the binary matrix Z, which is such that Z(t) ik = 1 if at time t, the node i belongs to the class k, and 0 otherwise). In our maritime application the Z(t) ik = 1 if at time t, the port i belongs to the class k, and 0 otherwise; however the type of connections between ports is binary in this application, so X(t) ij = 1 means that port i and j are linked by a one or several ships at time t and X(t) ij = 0 indicates the absence of relationship.

The dRSM model at each time t

The network is assumed to be generated at each time t as follows: each vertex i is first associated to a latent class k with a probability depending on the subgraph to which it belongs. So, we assume that for a number K of latent groups given, the variable Z(t) i is drawn from a multinomial distribution of parameter _(t) si :

where _(t) s = (_(t) s1 ; : : : ; _(t) sK) is the vector of prior probabilities of the K latent groups in the subgraph s at time t and is such that PKk=1 _(t) sk = 1; 8 s 2 1; : : : ; S: On the other hand, we assume that the type of link between nodes i and j is sampled from a multinomial distribution depending on the latent vectors Z(t) i and Z(t) j , with _kl 2 [0; 1]C+1 and PC c=0 _klc = 1.

Modeling the evolution of random subgraphs

We present a hidden state, in the form of a state-space model to model the evolution of the subgraphs over time (Figure 19.4). So, we introduce a new latent variable (t) s , which is assumed to be distributed according to a normal distribution with mean B_(t) and covariance matrix _ :

[Figure 19.4] Note that this variable which was introduced in our model for characterizing temporal processes, could make the link between the proportions of subgraphs _(t) s at different times through a logistic transformation, in the form:

where C((t) s ) = PK 1 `=1 exp((t) s`) and _(t) sK = 1 PK 1 `=1 exp(_(t) s`).

The variable _(t) s is generated by sampling (k 1) of the components of (t) s according to normal distribution with mean B_(t) of dimension (K 1) and a covariance matrix _ of size (K 1) _ (K 1). The last component of the vector (t) s is arbitrarily set to zero. Due to that (t)s has a (K 1) degree of freedom. The rest of the modeling includes a classic state-space model for linear dynamic systems that evolves over time. The model is as follows:

where A and B are two transition matrices of size (K 1)_(K 1). The terms of noise !, u and v are supposed to be Gaussian such as ! _ N(0; _); v _ N(0; _); u _ N(0; V0): Finally, our model has three latent variables (_; ;Z), and is parameterized by _ = (_0; A;B;_; V0;_; _). This model is called the dynamic random subgraph model (dRSM). Figure 2 presents the graphical model for dRSM.

The inference of the model relies on variational EM bounds as an algorithm. The number of clusters is estimated using a BIC criteria. For more details, we refer to [START_REF] Zreik | The dynamic random subgraph model for the clustering of evolving networks[END_REF].

Main results

In this section, we apply the methodology associated with the dRSM model to the maritime flows data, which describes the varied traffic between 819 ports active in the world at each time t. Here, we concentrate on the period around the occurrence of this event in 1991, studying it over 10 consecutive years (1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996). As this event impacted Socialist countries more than others, we decided to analyze the aforementioned four subgraphs based on their political category. Therefore, the network is a directed network without loops and binary : C = 1 and Xt ij = 1 if i and j exchanged at least one ship during the period t, 0 otherwise, with t 2 f1; : : : ; 10g and S = 4, we looked for K = 6 clusters in the data.

By construction, the matrix _ veri_es _kl0 + _kl1 = 1; 8 (k; l) therefore, only _kl1 term, describing the connection probabilities given in Table 19.1.

[Table 19.1]

Topological and functional trends

One first result is the identification of clusters composing the USSR ego network, based on their connection probabilities and intra vs. inter-class probabilities (Figures 19.5 and 19.6). This allows us to define clusters 1 and 3 as the "cores" of the network, as they connect all other clusters, connect each other, and have a noticeable amount of internal connectivity.

All other clusters connect these cores, but differ in terms of size (i.e. number of ports), while clusters 4 and 6 are the only ones among them to share a mutual connection. Clusters 2 and 5 have no mutual relationships, and cluster 2 has no internal connectivity. We can thus infer a strong opposition between the core and several peripheries that do not otherwise connect.

The rest of the analysis will investigate whether such a core-periphery pattern reflects the bipolarization of the world; if it reflects other logics; what is the role of USSR ports in such a pattern; and how did the political transition affect the geographic distribution of clusters over time.

[Figure 19.5] [Figure 19.6] Our main results point to a body of evidence relating to the specific evolution of each cluster depending on each subgraph (Figure 7). Overall, the USSR collapse is largely responsible for the sudden growth or decline of group proportions, especially for the years 1991-1993. Core clusters 1 and 3 that dominate the entire network exhibit very different evolutions during this transition. Cluster 3, the most central cluster, lost significance for USSR and Eastern Block ports in 1991[START_REF] Ledger | East European shipping and economic change: a conceptual model[END_REF], until a recovery in 1995-1996. By comparison, the same cluster gained ground for non-aligned ports and other socialist ports, and underwent a slight decline for capitalist ports. Thus, the demise of the USSR had been most detrimental with a direct effect on communist ports and perhaps on capitalist ports well-connected to the USSR and Eastern Block. The other core cluster 1 reveals another similarity between USSR/Eastern Block and capitalist ports during the collapse, with a growth as opposed to a decline for other socialist and non-aligned ports. In fact, the tight interdependence between communist and capitalist markets, despite their political opposition, led to similar effects during the shock in both blocks.

[Figure 19.7] When it comes to peripheral clusters, some of them display drastically different behavior from one subgraph to the other. The largest of these in terms of number of ports, cluster 4, registered a considerable decline in importance in all subgraphs except for the capitalist one.

The opposite trend occurred for cluster 5, bearing in mind that these two clusters only connect indirectly, through a different core cluster. The most peripheral cluster 2 increased its proportion in all subgraphs, while the smallest cluster, number 6, did not witness any particular trend over time. These evolutions shed new light on the topological and functional trends characterizing world ports and their connectivity during the disintegration of the USSR. In other words, the two cores of the network (clusters 1 and 3) have an opposite behavior during the collapse, with communist and capitalist ports losing importance in the main core (cluster 1) and gaining importance in the secondary core (cluster 3). The two large semi-peripheries (clusters 4 and 5) also show an opposition, but this time between capitalist ports and all other ports. Capitalist ports gain importance in the largest and best-connected cluster 4, while other socialist, non-aligned, and USSR-Eastern Block ports gain importance in cluster 5, which strongly connects the main core cluster 1. The most peripheral cluster 2 increased its importance mainly for other socialist and USSR-Eastern Block ports, suggesting that becoming peripheral had been mainly the case of socialist/communist ports.

Spatial trends

Based on the aforementioned topological and functional trends that characterize the evolution of the USSR ego network and its components, it is crucial to investigate the changing spatial embeddedness of identified clusters on the world map (Figure 19.8). The core cluster 1 is principally composed of European ports, including most of the largest gateways and hubs along the so-called North European Range (e.g. Le Havre, Antwerp, Rotterdam, and Hamburg) but also Felixstowe, Dunkirk, Liverpool, London, Bremerhaven, and a number of meridional ports like Algeciras, Bilbao, and Piraeus. These gateways and hubs are crucial for the USSR to connect to the rest of world although they belong to capitalist economies, because they act as modern transshipment nodes in the global shipping network not only for the USSR but also for an extended Europe as a whole. It is worth noting that many USSR and Eastern Block ports belong to the same cluster, featuring ports from Russia, Poland, East Germany, and Baltic States and more on the Scandinavian shores of the communist maritime system.

With the demise of the USSR, several of the aforementioned ports remained in cluster 1, regardless of whether capitalist (e.g. Le Havre-Hamburg range) or communist (e.g. St.

Petersburg); but the cluster took on a different geographic scope, with the addition of many more non-European ports (New York, Los Angeles, Hong Kong, Singapore, Japan, Brazil, Mediterranean ports) and a number of large communist ports (e.g. Odessa, Constantza). All other ports shifted to cluster 3, as mentioned above, composed mainly of small and medium-sized North European ports. One of the main effects of the USSR collapse on core clusters is thus the shift of a few large communist ports in the center of the system (cluster 1) together with the world's largest ports. The dislocation of the USSR thus provoked a displacement of the network's center of gravity towards the largest external hubs mainly, because of the loosening political significance of flows. All other communist ports shifted to other, more peripheral, clusters. Before looking at these clusters, it is interesting to note that cluster 6, which does not react to the collapse in any subgraph, is geographical by nature, (i.e. mainly composed of Asian and Oceanian ports). Certain Chinese and Vietnamese ports, which belonged to the "other socialist" subgraph and to the peripheral cluster 2 in the late 1980s, became fully part of cluster 6 after the collapse. Geographic proximity and regional integration effects thus gradually superseded former political factors influencing the fading pattern of Soviet trade between former brother countries.

The evolution of cluster 2, the most peripheral and extroverted cluster, is interesting as it shows a group of mainly small and medium-sized ports, with a sizeable proportion of communist ports (USSR and other socialist regions such as China, Vietnam, and North Korea), absorbing many more ports on all continents during the collapse, but without losing its original composition. This means that the collapse created more peripheral ports due to trade reorientation, but these ports maintained their linkages with the core of the system (cluster 1). Geographically, cluster 2 expanded mainly towards the Americas and the Mediterranean/Black Sea, but also to South Asia (e.g. India). The case of Cuba's main port, Havana, is emblematic of such a trend, which suggests a shrinkage of port and maritime activity during the collapse. This also explains why clusters 1 and 2 share a common feature: the growth of their respective proportions mainly in the "other socialist" subgraph, (i.e. in partner countries of the USSR).

Lastly, in relation to the evolution of semi-peripheries, clusters 4 and 5 also exhibit interesting geographic patterns and shifts. The populous and well-connected cluster 4, before and after the USSR collapse, is mainly composed of a spatially continuous region including medium-sized Latin American, African, and Mediterranean/West Asian ports.

Cluster 5 comprises mostly minor North European and Scandinavian ports. The break-up of the USSR provoked an inversion of these clusters in the years 1991-1993, as they replaced each other on the map. This reinforces the idea of shrinkage and dilatation of Soviet influence during the shock. The likely reason is that cluster 5 is the best connected to the core (cluster 1), so that the shock provoked a connectivity shift from the Old World (North Europe) to the New World (Latin America/Africa/West Asia). The smaller North European ports, well connected to their large European neighbors before the shock at least partly through USSR shipping, became suddenly better connected with the "secondary core" (cluster 3), it too becoming more "European" and more "socialist" (cf. Figure 19.7). Overall, the USSR collapse created a lock-in of small and medium-sized capitalist European ports together with their socialist counterparts, based on geographic proximity and high-density linkages, while the rest of the world, outside Europe, became the new core of the system, notwithstanding the permanent presence of a few large socialist ports.

[Figure 19.8]

Conclusion

The analysis of the Soviet ego network evolution using a recent statistical model called dRSM to analyze a dynamic network with stochastic methods shows a new step towards a better understanding of the influence of political and spatial factors shaping transport and communication systems. Our main results convey the idea that the diminishing importance of political factors in times of crisis gives more importance to geographic proximity and node hierarchy, when a given transport system reorients its trade activities and becomes more embedded in the global maritime network. The crucial importance of geographic proximity had already been underlined in previous studies using various clustering methods on shipping network data (see [START_REF] Bouveyron | Cluster identification in maritime flows with stochastic methods[END_REF], but this is the first time that political factors are introduced as a complementary -and perhaps more fertile-dimension.

Yet, if the structure of the USSR ego network and the effect of its dissolution could be well described through such methods as a vital and advanced complement to more classic descriptive statistics, one question remains: why did the network go back to its initial pattern? The short-lasting impact of three consecutive years (1991)(1992)(1993) is rather surprising, given the end of world bipolarization from those dates. Perhaps, running the same analysis over a longer period would better highlight more profound and structural changes affecting shipping flows. As mentioned earlier, in the late 1980s the USSR had already become a relatively open and highly maritime-oriented economy, compared with the early phases of its development, before and up to the 1960s-1970s. This explains why the observed changes are mainly conjectural, but they were profound. We could also learn from such a shock the relative influence of geography and politics on network structures and dynamics. Further research will refine the analysis by integrating links weights and perhaps focus on the diversity of cargo flows from bulks to containers, while enlarging the time span to better reveal such influences.

  Rawya ZREIK, César DUCRUET, Charles BOUVEYRON, Pierre LATOUCHE Pre-final version of the chapter published in Ducruet C. (Ed.) (2017) Advances in Shipping Data Analysis and Modeling. Tracking and Mapping Maritime Flows in the Age of Big Data, Routledge Studies in Transport Analysis, pp. 317-337.

FigureFigure 19 . 8 :

 198 Figure 19.1:. USSR-related traffic volume and share at world ports, 1987-1996

Acknowledgements

The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. [313847] "World Seastems".