T Douki

S Adar

Chapter 7: UV-induced formation of DNA damage in cells and their mutational consequences

Ultraviolet radiation induces a wide variety of damage in DNA that can originate from either photochemical reaction triggered by absorption of photons by DNA bases or oxidation processes mostly induced by photosensitization. The nature of DNA damage is dependent of numerous factors such the wavelength, the sequence, the chromatin structure or the cell type. The present chapter provides a summary of the available quantitative information of the yield of UV-induced DNA damage as well as the most recent pieces of information bases on state-of-the-art techniques such as next generation sequencing. The mutational consequences of the DNA photoproducts are briefly discussed.

Table of Contents

Introduction

Information gathered over the years in model systems and isolated DNA made possible the identification the most relevant types of DNA damage that could be generated in cells upon exposure to UV radiation. They are either dimeric photoproducts involving adjacent pyrimidine bases or oxidatively generated lesions such as strand breaks and oxidized bases (Fig. 7.1). In the early 1960's, radioactivity-based assays were developed to quantify thymine-containing cyclobutane pyrimidine dimers (CPDs). The subsequent development of immunoassays made possible the collection of large amounts of information in various cellular systems and in skin not only on CPDs but also on pyrimidine (6-4) photoproducts (64-PPs) and their Dewar valence isomers (Dewars) 1,2 . More specific methods based on molecular biology approaches 3 or mass spectrometry detection 4 provided data on the individual formation of each dimer at the different bipyrimidine sites. In the recent years, the breakthrough made in next generation sequencing provided an unprecedented picture on the formation and repair of DNA in the different region of chromatin [START_REF] Adar | Proc. Natl. Acad. Sci[END_REF][START_REF] Hu | Proc. Natl. Acad. Sci[END_REF] . Information on oxidatively generated lesions was mostly obtained through electrophoretic-based assay such as the comet assay [START_REF] Collins | [END_REF] or alkaline elution 8 . These methods were primarily developed for the quantification of strand breaks but could be extended to damaged bases by the use of purified DNA N-glycosylases that recognize well-defined classes of damage. Some specific lesions like 8-oxo-7,8-dihydroguanine (8-oxoGua) were also quantified by chromatographic approaches. The purpose of this chapter is to combine all these pieces of information, with emphasis placed on the most recent results but without neglecting relevant earlier data, in order to provide insight into the distribution of the most frequent UV-induced damage. Factors such as the cell type, the spectrum of the incident light and the specificity of the formation of damage in skin are also discussed.

Last, a short overview of link between DNA damage and mutagenesis is provided, especially in the light of recent next generation sequencing data. UVB mostly leads to the formation of pyrimidine dimers while UVA is at the origin of a strong oxidative stress. However, as discussed in the present chapter, the boundary between the two wavelengths ranges is less tight that long believed.

UV-induced pyrimidine dimers in cellular DNA

UV radiation is an efficient DNA damaging agent but the yield and the nature of the photodamage strongly depend on the wavelength of the incident photons. Major attention has been paid to the UVA (320-400 nm) and UVB ranges (280-320 nm), which represent more than 95% and less than 5% of solar UV, respectively. Information is also available on the DNA damaging effects of UVC light (100-280 nm), which is closer to the UV absorption maximum of DNA at 260 nm.

Wavelength-dependent formation of pyrimidine dimers by UVB

CPDs and UVB radiation

The most efficient portion of the solar spectrum for the induction of pyrimidine dimers is UVB. This is easily explained by the fact that the wavelength of the most energetic photons in solar UV is closer to the maximal absorption of DNA (260 nm).

Consequently, DNA base may absorb these photons and reach excited states, thereby opening the way to photochemical reaction as addressed by Martinez-Fernandeza and Improta in chapter 2 of this volume. Numerous data are available for CPDs. In cultured cells, the reported yields for broadband UVB lamps varies from 0.01 to 0.06 CPD/10 5 bases per J/m 2 9-12 . The differences between these values can be explained by both the use of different analytical tools, the cell type and the emission spectrum of the UVB sources. The latter point is a major issue because the yield of the pyrimidine dimers varies along the UVB range. Action spectra in cultured human fibroblasts 13 show that the yield of CPD is in a 1 / 0.4 / 0.06 ratio at 280, 290 and 300 nm, respectively. In CHO cells, ratio of 1 / 0.22 / 0.01 were determined for the yield of CPD at 290, 300 and 310 nm 8 . These values show that the slightest increase in wavelength leads to a significant decrease in the yield of CPDs. They explain the difference in DNA damaging properties of sunlight depending on the hour the day, the latitude and the longitude.

When the intensity on the UV radiation decreases as the result of the filter effect of the atmosphere, the ratio between the intensities of UVB and UVA is reduced 14,15 .

Differences in emission spectra is also of outmost importance when comparing results obtained by different teams using different irradiator.

Distribution of pyrimidine dimers in UVB irradiated cells

Most of the data discussed above are related to CPDs measured as a whole by immunoassays or to the sole TT CPD quantified by specific techniques. Yet, work on model systems and isolated DNA has shown that CPDs are formed at the four bipyrimidine sequences 4,[START_REF] Varghese | Photophysiology[END_REF] . These works also showed that, because of steric constraints, only the cis,syn diastereoisomer of CPD is detected in double-stranded DNA while other derivatives are produced in dinucleoside monophosphates and singlestranded DNA. Similar observations were made in cells with the following decreasing order of frequency for the cis,syn CPD: TT > TC > CT > CC 10,[START_REF] Tommasi | [END_REF][18][19][20][21][START_REF] Mouret | Proc. Natl. Acad. Sci[END_REF][START_REF] Rochette | [END_REF][24][25] . The ratio between the yields of these CPDs is 10 / 5 / 3 / 1, respectively. 64-PPs are also produced in significant amount by UVB radiation, although in a 3 to 5 lower yield than CPDs 9,10,12,26 . In UVB-irradiated cultured cells, they are formed in detectable amounts only at TT and TC sites in a 1 to 5 ratio 9,10,21,[START_REF] Mouret | Proc. Natl. Acad. Sci[END_REF]24,27 . CT and CC 64-PPs 21 have been detected

in small amounts only upon UVC-irradiation of isolated DNA. The ratio between CPD and 64-PP is also dependent on the involved pyrimidine bases, with an approximate ratio of 10 at TT and 1 at TC sites. The ratio between all these different bipyrimidine photoproducts is constant from one mammalian cell type to the other. This reflects that the photochemistry of DNA is hardly modulated by the cellular environment. As detailed below, recent works based on the detection of pyrimidine dimers at the nucleotide resolution by next generation sequencing led to similar results [START_REF] Hu | Proc. Natl. Acad. Sci[END_REF][28][START_REF] Mao | Proc. Natl. Acad. Sci[END_REF][START_REF] Premi | Proc. Natl. Acad. Sci[END_REF][START_REF] Sriramachandran | [END_REF] .

The only parameter that was found to drastically affect the mean distribution of CPDs in cells is the GC content of DNA 32 . This mostly concerns bacteria since all eukaryotic cells exhibit similar proportions of G/C and A/T bases pairs. Interestingly, this study of the impact of the GC content of DNA on the formation of pyrimidine dimers led to the conclusion that TC rather than TT dinucleotides are the most reactive sites in DNA. On a local scale, the reactivity of bipyrimidine sites can be modulated by the methylation of cytosine, an important epigenetic event. Evidence are accumulating for an increased formation of damage at methylated CpG island 33 , which is associated with increased mutagenesis [START_REF] Tommasi | [END_REF][START_REF] Rochette | [END_REF]34,35 .

It may be mentioned than UVB may also trigger a secondary photoreaction within 64-PPs: the conversion into a Dewar valence isomer of the pyrimidone ring [36][37][38][39] This photoreaction has long been neglected because UVA radiation was thought not to be absorbed by DNA. Yet, precise spectroscopic studies showed that DNA was actually a chromophore for UVA with an absorption at 350 nm respectively 10 4 and 10 3

times lower than at 280 and 300 nm 43 . Formation of CPDs in cellular DNA upon exposure to UVA has first been reported in bacteria 44 . Since then, similar results were obtained in cultured rodent cells 8,12,45,46 as well as in human fibroblasts 47 , keratinocytes [START_REF] Mouret | Proc. Natl. Acad. Sci[END_REF]48 and melanocytes 49 . The same observation was made in whole human skin both in vivo 50 and ex vivo [START_REF] Mouret | Proc. Natl. Acad. Sci[END_REF]24,51 . The ratio between the yield of CPDs after exposure to UVB or UVA is in the same range than the difference in absorption (Table 7.1). This value differs from one work to the other, which can be explained by differences in emission spectra of the UV sources. The importance of this parameter is clearly illustrated in action spectra where values of 60 were found between the ratio at 365 nm compared to either 290 nm or 310 nm in CHO cells 8 . Similarly, a ratio of 6

was determined for the yield of CPD in keratinocytes exposed at 302 nm compared to either 365 or 334 nm 47 . The correlation between the yield of CPDs and the DNA absorption, combined with the observation of CPD in UVA-irradiated isolated DNA, strongly suggests that a direct photochemical rather than a photosensitized process is involved in the UVA-induction of CPD 12,[52][53][54] . A last worth mentioning feature of the UVA photochemistry of DNA is that 64-PPs are at the best minor photoproducts. This can be explained by the change in the nature of the excited states generated when the wavelength of the absorbed photons increases from UVB to UVA 55 .

Dark CPDs

In addition to the formation of CPDs resulting from the weak but real absorption of UVA photons by DNA, UVA was reported to trigger a delayed formation of CPDs. This phenomenon was first reported in cultured melanocytes 56 and further extended to keratinocytes 57 . In both cell types, an oxidative pathway seems to be involved as shown by the inhibiting effects of antioxydants. Mechanistic studies have been performed in melanocytes. In this cell type UVA, and to a lesser extent UVB, were found to induce the formation of dark CPDs in the hours following irradiation of pigmented melanocytes but not of melanocytes originating from albino mice 56 .

Interestingly, not only TT but also the mutagenic TC and CT CPDs were formed by this delayed mechanism. Evidence for a role of oxidation products of melanin and melanin precursor was provided by the replication of this effect on model systems involving oxidizing species, melanin derivatives and isolated DNA. A proposed mechanism is thus the formation of endoperoxide by UVA-induced oxidative stress that would then decompose into excited ketones. Such a mechanism has been previously described for oxidatively generated lesions 58 . It could induce the formation of CPDs by triplet energy transfer if the energy of the excited ketone is large enough. This pathway remains yet to be completely established in order to explain why C-containing dark CPDs are produced in significant amounts while photosensitized triplet-triplet energy transfer leads to the overwhelming formation of TT-CPD [59][60][61] . The biological relevance of this process was assessed by the observation of dark CPDs in human skin in vivo 62 . Their formation was observed even at the highest UVA wavelengths but not in the visible range 63 . An interesting phenomenon is the fast decrease in the peak of CPDs following exposure 56,57,62 64 . This strategy has been used for many years, with a first germicidal action spectra published as early as 1946 65 and confirmed since then by countless works. The maximal efficacy of UVC radiation is observed at 260-265 nm. In practical applications, equipment use the easily available 254 nm-emitting high-pressure mercury lamp. Its efficacy at this wavelength is approximately 90% of that at 260 nm. Because 254 nm radiation exhibits severe risks for skin and eyes [START_REF] Scheer | Opinion on biological effects of UV-C radiation relevant to health with particular reference to UV-C lamps[END_REF] , very recent alternatives have been proposed based on higher energy UVC, mostly at 222 nm. Because they exhibit a lower penetration in tissues, these photons are expected to be less damaging than 254 nm. It remains though to determine whether this represents an industrial advantage since all sterilization devices are protected. One novelty would be to use 222 nm light to directly sterilize areas of the human body but much deeper investigation on the harmlessness of these technologies would be necessary. More local application such as surgery site are yet interesting [START_REF] Fukui | [END_REF] .

DNA damage in the UVC range

Exposure to 254 nm radiation exhibits its germicidal properties because it induces large amounts of damage in the genome of bacteria, yeasts and viruses. This is clearly documented by early works on the formation of pyrimidine dimers in DNA [START_REF] Varghese | Photophysiology[END_REF][START_REF] Wang | Photochemistry and Photobiology of Nucleic Acids[END_REF] . This was mostly motivated by the availability of mercury lamps emitting photons with a wavelength close to the maximal absorption of DNA. Today, UVC is mostly used in biological studies as a convenient tool for the fast production of significant amounts of DNA damage. Because it is more efficiently absorbed than UVB, UVC leads to a larger yield of photodamage. The ratio in UVC-induced yield with respect to UVB is around 10 in bacteria 11 . This trend is different in human skin because UVC is strongly absorbed by the stratum corneum and the upper layer of the cutaneous tissue. The yield of CPDs was found to be similar at 260 and 300 nm in the epidermis and almost 1 order of magnitude lower in the dermis [START_REF] Ikehata | [END_REF] . Interestingly, the same decrease in the action spectrum in the UVC range compared to UVB is observed for skin cancer 70 .

The proportion between the frequencies of the different photoproducts is the same in the UVC range than with UVB 21 . It should be stressed that this analogy holds only for low doses of UVC. Indeed, at this wavelength, secondary photoreactions can take place, in particular for CPDs. The 260 nm absorption of pyrimidine bases is lost upon formation of the cyclobutane dimer but the resulting CPDs keep an absorption band at approximately 230 nm with residual absorption at 254 nm. CPDs are thus able to absorb photons at this wavelength, which triggers a photoreversion reaction into the original bases 71,72 . As a result, the dose-dependent formation is not linear but reaches a plateau 21,73 . This phenomenon does not take place with UVB that is not absorbed by CPDs. Because photoreversion of 64-PPs does not occur, the ratio between CPDs and 64-PPs decreases as the UVC dose increases. An additional issue is that Ccontaining CPDs are more susceptible to photoreversion than TT CPD 21,72 . Therefore, the ratio between the levels of the four possible CPDs also varies at large UVC dose.

Although most data on UVC-induced photodamage in double stranded B-DNA were obtained at 254 nm, data are available for lower wavelengths for isolated DNA. It was thus shown that the yields of CPD at 220 and 240 nm represent 80 and 40% of that at 260 nm, respectively 74 . In the 170-200 nm region, where DNA exhibits a second absorption maximum, the yield of CPD is slightly larger than that determined at 260 nm 74 . In skin, exposure to 222 nm did not lead to formation of CPDs, in contrast to UVB and, to a lesser extent, to 254 nm radiation 75 . Works carried out on isolated DNA provided information on the effect of higher energy UVC, typically 195 nm. At this wavelength, direct ionization of DNA bases is highly efficient and leads to damage of oxidative origin mostly observed at guanine bases. Their yields is a 6-fold larger yield than that of CPDs 76,77 .

Photochemistry in bacterial spores

Like bacteria, yeasts and other microorganisms, bacterial spores are killed upon UVC sterilization as the result of massive induction of DNA damage. A major difference is the nature of the induced photoproducts. In contrast to all other living cells, exposure of spores to UV radiation does not lead to the formation of CPDs and 64-PPs but to a dimer involving two adjacent thymine bases, 5-(-thyminyl)-thymine also known as the spore photoproduct (SP) 78 . This specific photochemistry is explained by the unusual A-like form of DNA in spores [START_REF] Nicholson | Proc. Natl. Acad. Sci[END_REF][START_REF] Nicholson | [END_REF][81] . This conformation is induced by the highly dehydrated core environment and the complexation of large amounts of small acid soluble proteins to DNA. In addition, the spore is loaded with large concentrations of dipicolinate that favour the formation of SP 82,83 . Like CPDs, SP is produced in maximum yield at the maximal absorption of DNA 84 . However, it is produced at higher energy (222 nm) in significant yield 85 . Formation of SP is believed to explain the photoresistance of spores since a very efficient radical-SAM (S-adenosyl-methionine) repair enzyme is present in the spore and rapidly reverts SP upon germination 86 .

Oxidatively generated DNA damage in cells

Emphasized has been placed in part 2 on the formation of pyrimidine dimers. Sunlight is also known to induce DNA damage through oxidative pathways. The underlying mechanisms have been documented by many works on isolated DNA. Formation of oxidatively generated lesions was also determined in vitro.

UVB and oxidative stress

Although often discussed for the effect of UVA, oxidatively generated DNA lesions are also relevant in the effects of UVB. The relative yield of this class of damage with respect to CPDs is yet very much in favour of the latter. For instance, the difference in yields of formamidopyrimidine glycosylase (Fpg)-and T4 endonuclease V (T4endoV)sensitive sites, corresponding respectively to oxidized guanines and CPDS, is approximately 1000 in CHO cells in the 290-320 nm range 8 . Similar results were reported in human fibroblasts 47 . The underlying mechanisms, that may involve excitation of catalase 87 , cyclooxygenase and reduced nicotinamide adenine dinucléotide phosphate (NADPH) 88,89 , remain to be fully elucidated. An interesting recent result, extensively discussed in the chapter 3 by D. Markovitsi in this volume, is the possible direct ionization triggered by absorption of UV photons by DNA bases, even below the energy corresponding to the ionization threshold [90][91][92] .

Main UVA-induced photooxidation pathways

UVA is only poorly absorbed by DNA and does not provide enough energy to induce photoionization of DNA components. Therefore, all oxidation reactions induced in UVA-irradiated cells arise from photosensitized reactions. In these processes, UVA photons are absorbed by endogenous cellular components 93 that, upon excitation, trigger oxidation reactions and release of oxidizing species. Numerous works on model systems and isolated DNA has provided valuable mechanistic information.

One-electron oxidation reactions

A first photosensitized process involves electron abstraction from DNA bases and is known as "type I photosensitization". This photoreaction has been found to be efficient with all nucleic bases studied as isolated monomer when the oxidation potential of the excited photosensitizer is large enough. However, when taking place in double stranded DNA, one-electron oxidation leads to the sole formation of damage at guanine bases [94][START_REF] Cadet | Photoinduced Phenomena in Nucleic Acids Ii: DNA Fragments and Phenomenological Aspects[END_REF][START_REF] Cadet | [END_REF] . This is explained by the much lower ionization potential of this base compared to other DNA components. Consequently, radical cations produced on T, C or A migrate within DNA to guanine residues where the guanine radical cation gives rise to oxidation products. Those are mostly 8-oxoGua and 4-hydroxy-5formamidopyrimidine (FapyGua). Secondary oxidation products of 8-oxoGua can be produced at large dose but not under biologically relevant condition.

Singlet oxygen

Rather than reacting with DNA components, photosensitizers in their excited state may transfer their energy to dioxygen. Unlike most molecules, the ground state of dioxygen is a triplet state, which limits its direct reactivity with DNA components. However, transfer of energy from the triplet excited state of the sensitizers converts it into the highly reactive singlet oxygen ( 1 O2) in a "type II photosensitization reaction". This species, that is not a radical, readily reacts with double bonds in organic molecules leading to the formation of endoperoxides and dioxetanes. It can also lead to the formation of hydroperoxide through the ene-reaction and oxidize sulphur atoms. In DNA, only guanine reacts with 1 O2 with formation of an endoperoxide that further leads to the formation of 8-oxoGua as the only reaction product 94,[START_REF] Cadet | Photoinduced Phenomena in Nucleic Acids Ii: DNA Fragments and Phenomenological Aspects[END_REF]97 .

Reactive oxygen species arising from the superoxide anion radical

Superoxide anion (O2• -) is produced in large amounts in UV-irradiated cells as the result of enzymatic process or by release from mitochondria 98 . Superoxide anion is poorly reactive with DNA but is at the origin of more damaging species. Upon dismutation, either catalysed by redox metals or performed by the superoxide dismutase enzymes, O2• -gives rise to hydrogen peroxide H2O2. Hydrogen peroxide is also unable to directly damage DNA. However, reduced metal ions like Fe 2+ or Cu + may convert it into the highly reactive hydroxyl radical •OH. In this reaction, metal ions are oxidized into Fe 3+ or Cu 2+ . The reducing environment of cells, mostly resulting from the large concentration in glutathione, allows the conversion of these ions back to their reduced forms able to react again with H2O2. This process is worsened by the release of iron from its storage proteins upon UVA irradiation 99,100 . The high reactivity of •OH leads to the formation of a wide series of damage to the four DNA bases 99,100 . Purines Endonuclease III that recognize oxidized purines and oxidized pyrimidines, respectively. T4endoV allows the detection of CPDs. In all reported studies, oxidized purines, most likely 8-oxoGua, are produced in larger amounts than direct strand breaks over the entire UVA range 8,103 . Additional works showed that oxidized pyrimidines are minor lesions 103 . The distribution of DNA oxidatively generated lesions in UVA-irradiated cells differs from that induced by the hydroxyl radicals produced by -rays and from that induced by singlet oxygen produced by a specific sensitizer. The ratio between the relative yield of direct strand breaks, Fpg-sensitive sites and Endo-III sensitive sites were 1/0.47/0.16, 0.37/1/0.4 and 1/0.25/0, respectively 103 . This observation, combined with the fact that type I photosensitization is believed to be only weakly involved in the formation of oxidatively generated DNA lesions 97 , led to the conclusion that 80% of the DNA damaging oxidizing species are singlet oxygen and 20% hydroxyl radicals 103 . It should be stressed that the yield of oxidatively generated lesions is not the same in all cell types. For example, melanocytes are more sensitive than other cutaneous cells upon UVA irradiation as the result of the presence of photosensitizing pheomelanin 49, 104-107 .

8-OxoGua being the most frequent oxidatively generated lesion, it can be used as a probe to compare oxidative stress and CPD formation in the UVA range. Using broadband sources and either enzymatic or chromatographic assays, CPDs have been detected in larger amounts in all cell types including CHO cells and human keratinocytes and melanocytes. The ratio between the amounts of CPD and 8-oxoGua is approximatively 5 [START_REF] Mouret | Proc. Natl. Acad. Sci[END_REF]46,48 , with the exception of melanocytes where the larger yield of 8-oxoGua leads to a ratio of 1.5 49 . This ratio is yet wavelength-dependent as shown in CHO cells where the values are 10 at 340 nm, 5.5 at 365 nm and 1 at 390 nm 8 . It can thus be concluded from these series of results that oxidatively generated lesions cannot be considered as the most frequent damaging events of the nuclear DNA in UVA-irradiated cells. Of course, this does not rule out that oxidative stress exhibits major effects on other biomolecules and in other cell compartments.

Spectral composition and photoproduct distribution

Most of the data available on the DNA damaging of sunlight were obtained with pure UVB or UVA sources. Some more environmentally relevant works used simulated sunlight that gathered the effects of both spectral ranges. However, only limited amounts of information are available on the possible combined effects of different wavelength ranges. The photobiological responses may not be additive and lead to antagonistic or synergistic effects on the induction of some types of DNA damage.

Occurrence of such phenomena would emphasize the need to take into account the emission spectra of the UV source. This is partially done when weighted action spectra are used but this approach relies only on additivity. Evidence are already available in field of DNA damage of such complex spectral effects 108 .

Formation of Dewar valence isomers

Dewar valence isomers 36 are produced when 64-PPs absorb a UV photon and undergo an intramolecular electrocyclisation in their pyrimidone ring 37 . The latter moiety exhibits a maximum absorption at 325 nm in TT and CT 64-PP and at 315 nm in TC and CC 64-PP 38,39,109 . Consequently, UVB is expected to efficiently induce this reaction. This is actually the case in model systems like small oligonucleotides but much less in isolated and cellular double-stranded DNA, at least at biologically relevant doses 12,21 . This difference is explained by the overwhelming presence of normal bases that preferentially absorb UVB photons, which are then not available for the isomerization of 64-PPs 110 . Interestingly, 64-PPs exhibit a residual absorption in the UVA range. The ratios between the areas under the curve of the absorption spectrum in the UVB (240-320 nm) and UVA (320-380) are 0.76 for TT 64-PP and 0.44 for TC 64-PP. By comparison, the corresponding value is only 0.02 for DNA. These figures shows the much lower shielding effect of 64-PP by normal DNA bases in the UVA than the UVB range. This ability of UVA at photoisomerising 64-PPs was put forward already in the first article reporting the structure of Dewar in dinucleoside monophosphate 38 .

Since then, several works using different quantification tools have shown that Dewars are present in significant amounts in cells exposed to simulated sunlight, namely a combination of UVB and UVA 12,46,111,112 . Additional support for the biological relevance of Dewar isomer came from the observation of its formation in cells exposed to natural sunlight 113,114 and from its unambiguous detection in marine microorganisms collected in the ocean 115 . Recently, the extent of photoisomerisation of 64-PPs in isolated DNA exposed to sunlight was reported to depend on the season 116 , following the UVB/UVA ratio. It is difficult to provide a value for the ratio between the amount of 64-PPs and Dewars generated in DNA by exposure to natural or simulated sunlight. Indeed, the proportion of Dewars increases at the expense of that of 64-PPs when the dose increases. All these results could be rationalized in terms of emission spectrum and absorption at the different wavelengths 110 .

UV-induced formation of sensitizers

Pyrimidine dimers are not the only types of photodamage impacted by the effects of two UV wavelengths ranges. Although this process has not been directly documented in cells, test tube experiments strongly suggest that UVB-mediated degradation of various biomolecules leads to the formation of photosensitizers that increase the extent of UVA-mediated oxidative stress. A first example are endogenously produced pterin derivatives 117,118 . Pterin is a well-known photosensitizer studied for its damaging properties of biomolecules and its application in photodynamic therapy [119][120][121] . Another source of photosensitizing photoproducts is tryptophan. Its main photoproduct is Nformylkynurenine that is known to exhibit photodynamic properties 122,123 . UVB irradiation of tryptophan also generates 6-formylindolo[3,2-b]carbazole (FICZ) which is also a photosensitizer 124 . The yield of FICZ is yet very low 125 and its role in UVAinduced oxidative stress is unlikely. Other combinations of wavelengths may enhance oxidative stress. For example, UVA-induced lipofuscin was reported to photosensitize keratinocytes to visible light 126 .

Impact of UVA on the repair of UVB-induced dimers

Several works have unambiguously shown that exposure to UVB, especially in a chronic or repetitive pattern, led to the enhancement of DNA repair capacities [127][128][129][130][131] and in particular of nucleotide excision repair, which handles pyrimidine dimers.

Evidence are also accumulating that UVA exhibits the opposite effects. Exposure to UVA prior to UVB has been shown to reduce the rate of removal of CPDs, both in keratinocytes 9 and melanocytes 132 . In vitro studies also showed that UVA, alone or in the presence of photosensitizer, induced oxidative damage to repair proteins 133 like RPA 134 or PCNA 135 . The combination of these two opposite impacts of UVB and UVA on DNA repair strongly suggests that the persistence of pyrimidine dimers could depend on the ratio between these two wavelength ranges in the spectrum of the incident light.

DNA damage at the nucleotide resolution in cellular DNA

The advent of genome sequencing opens new avenues for the analysis of UV-induced pyrimidine dimers. In recent years, several techniques were developed for the identification and study of CPDs and 64-PPs at single-nucleotide resolution (Fig. 7.2).

UV dimers have been mapped in bacteria 136 , yeast [START_REF] Mao | Proc. Natl. Acad. Sci[END_REF]137,138 , and human genomes [START_REF] Hu | Proc. Natl. Acad. Sci[END_REF]28,[START_REF] Premi | Proc. Natl. Acad. Sci[END_REF][START_REF] Sriramachandran | [END_REF]139 . Three major molecular approaches have been applied to pinpoint the damage positions. The first relies on damage-specific endonucleases to fragment DNA, and identifies damage sites based on the nucleotides at the fragment ends. CPDseq, adduct-seq, and most recently GLOE-seq rely on the T4endoV and were therefore only applied to CPDs [START_REF] Mao | Proc. Natl. Acad. Sci[END_REF][START_REF] Premi | Proc. Natl. Acad. Sci[END_REF][START_REF] Sriramachandran | [END_REF] . In contrast, excision-seq and UVDE-seq 137, 138 use the UV damage endonuclease (UVDE) and can map both CPDs and 64-PPs. However, this technique requires very high damage levels and has not been applied to mammalian genomes. The second approach, used by HS-Damage-seq [START_REF] Hu | Proc. Natl. Acad. Sci[END_REF] , relies on blocking of DNA polymerases by the pyrimidine dimers. Here, damaged DNA from cells is subjected to in vitro primer extension, and the position of the lesion is identified again based on the fragment end. Using anti-CPD and anti-64-PP antibodies allows discrimination and mapping of the two damage types separately. The third and last approach is most straightforward, but is so far limited in its application. This approach relies on identification of the damage-sites directly during single-molecule real-time (SMRT)

sequencing. Pyrimidine dimers significantly block DNA polymerization and will alter polymerase kinetics. Direct sequencing of dimers was published in 2011 using synthetic oligonucleotides 140 and has not significantly advanced since, suggesting a difficulty of scaling to genomic sequences. The recent RADAR-seq method 136 may overcome these hurdles since it bypasses the necessity to directly sequence dimers:

it uses T4 endonuclease IV to nick at damage site and in vitro synthesis to replace the dimer with a stretch of modified nucleotides prior to SMRT sequencing. However, RADAR-seq reduces the resolution of damage detection and has so far only been applied to in vitro irradiated bacterial DNA.

Analysis of the specific pyrimidines composing CPDs, performed with different methods, cell types, and UVC or UVB doses all showed a preference for TT>TC>CT>CC [START_REF] Hu | Proc. Natl. Acad. Sci[END_REF][28][START_REF] Mao | Proc. Natl. Acad. Sci[END_REF][START_REF] Premi | Proc. Natl. Acad. Sci[END_REF][START_REF] Sriramachandran | [END_REF] . HS-Damage-seq, excision-seq and UVDE-seq have also been used to map 64-PP in human and yeast genomes, and have found damages to form preferentially in TC>TT>CT>CC [START_REF] Hu | Proc. Natl. Acad. Sci[END_REF]137,138 . These preferences are consistent with the previous bulk DNA analyses. While UVDE will excise multiple photoproduct types, in UVDE-seq, UV-irradiated DNA was pre-treated with photolyase to remove CPDs. This allowed the identification of not only 64-PP but also non-canonical TA and AC photoproducts 138 . In addition to identifying the specific nucleotides composing the dimers, sequencing-based methods also provide the immediate sequence context of dimers. Thus, these methods show enrichment of CPD damage formation within Ttracks of DNA, and the preference for 64-PP damage formation in TC or TT sequences flanked by a C upstream and an A downstream of the damaged site [START_REF] Hu | Proc. Natl. Acad. Sci[END_REF]137 . In the large mammalian genomes, obtaining a quantitative high-resolution map of every damageable bipyrimidine site requires extremely high sequencing coverage.

Most of the studies to date have not produced such high-coverage maps. Instead, in order to analyze the chromosomal context of damage formation, studies average damage densities over aggregated sequences. These include the study of damage formation over transcription start sites of genes, well-positioned nucleosome dyads or transcription factor binding sites:

Damage formation in genes

In general, the major determinant of CPD damage formation is the frequency of pyrimidine dimers, primarily TT sequences. This frequency is not uniform in the genome, and specifically in genes. TT sequences are depleted near the transcription start sites of genes, and asymmetrically distributed between the transcribed and nontranscribed strands, resulting in overall lower damage levels on the transcribed strands of genes 141 .

Damage formation in nucleosomal DNA

Nucleosome positions are generally associated with a depletion of TT pairs that could lead to lower levels of damage 142 . Analysis of CPD formation after normalizing to the underlying TT frequencies in yeast and human genomes has shown that the rotational setting of the DNA in the nucleosome affects CPD damage formation: a 10bp periodicity in CPDs is observed, with peaks in the outward facing rotational settings [START_REF] Mao | Proc. Natl. Acad. Sci[END_REF]143 . Control experiments with genomic DNA irradiated in vitro did not produce this effect confirming that this periodicity is indeed a product of nucleosome binding. This increase in damage frequency at specific rotational settings within a nucleosome can be explained by the conformational changes in DNA that result in the adjacent pyrimidine bases assuming a favorable angle for dimer formation. This aspect is discussed by Gillet at al. in chapter 6 of this book.

Damage formation at transcription factor binding sites

Transcription factor binding sites were identified as sites of elevated UV mutagenesis in melanoma. Several studies assessed damage formation at transcription factor binding sites, focusing on bipyrimidines contained within or adjacent to the consensus binding motifs. In yeast, Abf1 and Reb1 binding sites were associated with lower CPD frequencies [START_REF] Mao | Proc. Natl. Acad. Sci[END_REF] . In humans, some transcription factors were associated with reduced, while others with elevated damage frequencies [START_REF] Hu | Proc. Natl. Acad. Sci[END_REF]144 . These altered damage frequencies were absent in irradiated naked DNA or at unbound motif-sites in cells, indicating they are a direct result of binding. Like in nucleosomes, binding can alter DNA confirmation and result in favorable or unfavorable angles for dimerization. Three independent studies identified ETS1 binding sites as hotspots for damage formation, that were associated with subsequent melanoma mutation hotspots 28,[START_REF] Premi | Proc. Natl. Acad. Sci[END_REF]139 .

Higher mutation rates could be a result of higher damage, but could also be the product of lower repair efficiency. Indeed, a recent study suggests that it is primarily the lower repair efficiency at transcription factor-bound sites that promotes mutagenesis 144 .

Repair of UV damages is extensively discussed by John J. Wyrick in chapter 12 of this volume. A key factor in untangling the relative contributions of damage formation versus repair will be to understand how damages affect protein binding in chromatin, including nucleosomes and transcription factors, a subject far less studied to date.

UV-induced DNA damage in skin

Most of the information discussed above were obtained in in vitro models. The amount of information in skin is less abundant, mostly because of limitation in sample size and technical issues. However, data obtained by immunological assays and in particular immunohistochemistry, and chromatographic techniques provided insight on the impact of the organization of the cutaneous tissues on the formation of photodamage.

The vast majority of the information available concerns pyrimidine dimers. Their results

show that the skin morphology drastically affects the level of DNA damage (Fig. 7.3).

It should also be stressed that eyes represent another target of solar UV. The formation and repair of CPDs have only been investigated recently 145-148 .

Yield of lesions in skin

UVB and UVA induce the formation of CPDs in both keratinocytes and melanocytes in the same yield in skin 50 . Interestingly, the mean value of the yield of CPDs induced by UVB in DNA of the epidermis is lower than in culture of keratinocytes, which are the most frequent cell types in this skin compartment [START_REF] Mouret | Proc. Natl. Acad. Sci[END_REF] . This can be first explained by the protective effect of the stratum corneum. An additional explanation is the shielding effect of the different layers of keratinocytes in the epidermis 112 . This effect is wavelength-dependent with a decrease in the CPDs yield per cell layer 15 faster at 260 nm than at 230. Determination of accurate quantitative data also showed that the yield of CPDs was roughly 2 and 4 orders of magnitude lower at 320 and 365 nm, respectively, compared to 290 nm in both dermis and epidermis [START_REF] Ikehata | [END_REF] . In the same study, 64-PPs were found to be produced in a constant ratio from 260 to 310 nm, and then strongly decrease at higher wavelengths. In contrast to UVB, UVA is poorly absorbed by the stratum corneum and penetrates through the keratinocytes layers without significant attenuation. These observations explain why the ratio between the yield of CPD in the UVB and UVA ranges is lower in skin than in cultured cells [START_REF] Mouret | Proc. Natl. Acad. Sci[END_REF]24,48 . It should be stressed that immunohistochemistry results showed that the level of UVA-induced CPDs was slightly higher in the basal layer than in layers located above. 149 . This suggested the occurrence of a reflexion of UVA takes place at the dermal-epidermal junction.

Effect of phototype on the formation of CPDs

Phototypes is a convenient way to stratify the different skin types, which mostly reflect ethnic origins. In Caucasian, the risk of burning and the ability to tan upon exposure to sunlight has led to the definition of phototypes I (fair skin) to IV (brown skin).

Phototypes V and VI are Asian and dark brown skins, respectively. A high skin phototype is known to be highly protective against skin cancer, as exemplified by studies carried out in areas harbouring fair-and dark-skinned populations 150-152 . On the average, protection factors of 20 and 70 against melanoma and carcinomas, respectively, were observed when skin of phototypes VI were compared to phototype II 153,154 . This was only partially explained by the decrease in the mean level of CPD in keratinocytes that was found to be only 10-times lower in skin of phototype VI compared to II following exposure to simulated sunlight or UVB 62,155 . The same values were observed for both immediate and dark CPDs 62 . The latter result further suggests that photooxidation of melanin is not the only mechanism at the origin of dark CPDs.

A better understanding of the impact of the phototype and of the melanin content was provided by experiments assessing the distribution of melanin and of the yield of CPDs.

These works unambiguously showed that the best protection against CPDs between dark and fair skins was observed in basal layer, where the largest proportion of melanin is stored in dark skin [155][156][157] . The basal layer, that harbours the cells at the origin of melanomas and basal cell carcinomas, is much more protected than the rest of the epidermis. It should be stressed that these strong protective effects are no longer observed when less extreme phototypes are concerned. The ratio between the yield of CPDs in phototype II and IV is only approximately 1.5 24,[158][159][160] . Another interesting data regarding pigmentation is that tanning of Caucasian skin affords only a weak protection of a factor 2 against the formation of CPDs 160-162 .

Mutagenic consequences of DNA photodamage

The biological consequences of DNA photodamage in terms of carcinogenesis are mostly driven by their ability to induce mutations. A first requisite for a lesion to be mutagenic is that it escapes DNA repair as observed for pyrimidine dimers. The mutagenicity of a photodamage also depends on its chemical structure and the resulting impact on the coding properties upon replication of DNA. The combination of all the features explain the mutation spectra observed in tumours with specific repair enzymes led to the conclusion that these mutations mostly result from CPDs rather than 64-PPs, which are very efficiently repaired. Another important feature is that CT transitions at TC sites are also predominant in cells exposed to UVA 45,174,175 in line with the predominant formation of CPDs in this wavelength range.

The bulk of these data helped defining a UV mutational signature where CT transition at TC sites and CCTT tandem mutations were predominant 176,177 .

In vitro mutational data are also available for oxidatively generated damage.

8-OxoGua, following misincorporation of adenine upon replication leads to GT transversion 178 . Information were also obtained on the Fapy derivatives that show that FapyGua leads to GT transversion while 4,6-Diamino-5-formamidopyrimidine (FapyAde) is poorly mutagenic 179 . Among thymine oxidation products, thymine glycols are strongly blocking lesions but poorly mutagenic. 5-Formyluracil also leads to a low mutation frequency 180 . In contrast, cytosine oxidation products are mutagenic, mostly following deamination 181,182 . The contribution of these mutations to UVA-mutational

signature is yet limited. The GT transversion typical of the most frequent oxidatively generated lesions is a minor mutation, with the exception of one work involving mutation analyses in tumours collected in the basal layer 183 . Whole genome sequencing analysis of the mutation induced by UVA in xeroderma pigmentosum variant human cells failed to detect a significant contribution of oxidative damage but rather the mutation signature of pyrimidine dimers 184 with frequent mutations at bipyrimidine sites. Mutation at adenine is also observed 185 while adenine oxidation products are not frequent upon UVA irradiation.

Next generation sequencing of human tumours

Sequencing of specific genes in human tumours such as p53 has confirmed the presence of the UV signature [186][187][188] . The development of modern and fast sequencing techniques, which revolutionized the field of genomic, provided a novel and outstanding perspective on the mutations underlying human cancers. Next generation sequencing techniques have been used to determine millions of mutations present in thousands of tumours collected from different types of cancers 189,190 . In the field of skin carcinogenesis, a first interesting result is that, together with lung cancers, melanoma is one of the cancer type accumulating the largest number of mutations per tumour [190][191][192] . The value is more than one order of magnitude larger than, for example, in leukaemia or medulloblastoma. This can be explained by the fact that skin is constantly exposed to sunlight and accumulates numerous DNA damage and their resulting mutations during the development of the tumour. An interesting output of this sequencing effort is the statistical analysis of the databank that made possible the determination of mutational signatures 193,194 available on-line from the COSMIC consortium 195 . Signatures bearing mutations at bipyrimidine sites (Table 7.2) are observed with a high frequency in melanoma 190,[196][197][198][199] , basal cell carcinoma 200,201 , squamous cell carcinoma 202 and cutaneous T cell lymphoma 203 . They are basically absent from other cancer types. In the field of melanoma, the same approach allowed to show that acral and mucosal melanomas do not exhibit the same signature 197,204 .

Another interesting information provided by these techniques is related to a mutational signature associated with oxidative stress and 8-oxoGua. In the COSMIC database, this signature is observed in one third of the breast, prostate and stomach cancers but not in skin cancer. A last comment regarding melanoma is the high frequency of a AT transversion leading to the BRAF V600E mutation 205 . This mutation does not take place at a bipyrimidine site and does not correspond to known oxidative lesions. In addition, it is also frequent in a number of internal cancers and may thus not be of photobiological origin. However, recent UVDE-seq results in UVC-irradiated yeast proposed a role for AT dimers 138 , although these photoproducts have been observed only in test tube experiments and in much lower yield than CPDs 206, 207 .

Chapter 7 :

 7 UV-induced formation of DNA damage in cells and their mutational consequences. .......................................................................................................... 1 7.1 Introduction ....................................................................................................... 5 7.2 UV-induced pyrimidine dimers in cellular DNA ................................................. 6 7.2.1

Figure 7 . 1 :

 71 Figure 7.1: Chemical structure of the main DNA damage induced by UV radiation.

are converted into 8 - 7 . 3 . 3 .

 8733 oxo and formamidopyrimidine derivatives upon attack at the C8 position. •OH also reacts efficiently with the C5-C6 double bond of pyrimidines, and leads to the formation of 5,6-dihydroxy-5,6-dihydro derivatives (thymine and cytosine glycols) and 5-hydroxycytosine. Oxidation reactions also take place on the methyl group of thymine with formation of 5-hydroxymethyl-and 5-formyl-uracil. •OH can also perform hydrogen atom abstraction from the deoxyribose moiety of the sugarphosphate backbone99, 100 . Consequently, it is one of the few species that can induce DNA strand breaks. The superoxide anion not only leads to the formation reactive oxygen species but also of nitrogen oxidizing derivatives. The most documented pathway involves its reaction with NO that leads to the formation of peroxynitrite HOONO and, after subsequent reaction with CO2, of nitrosoperoxy carbonate ONOOCO2 -. While the former compound damages DNA through its decomposition into •OH 101 , the latter gives rise to carbonate radical anion that is a one-electron oxidant102 . Oxidatively generated DNA damage in UVAirradiated cells Information on the formation of oxidation products in the DNA of UVA irradiated cells was mostly inferred from enzymatic assays associated with either electrophoretic analysis or alkaline elution. In these approaches treatment with purified DNA repair enzymes are added to the protocol in order to convert specific classes of DNA base damage into strands breaks that add to those directly produced by the irradiation. The most frequently used enzymes are bacterial Formamidopyrimidine glycosylase and

Figure 7 . 2 .

 72 Figure 7.2. The three major approaches to mapping UV induced dimers. A) Mapping UV dimers based on endonuclease cleavage sites. Prior to cleavage free 3'-OH groups are blocked in order to obstruct adapter ligation. Shown is the schematic of CPD-seq. Adduct-seq, GLOE-seq, excision-seq and UVDE-seq are similar to CPDseq but slightly modified. B) Mapping dimers based on the position at which a DNA polymerase is blocked in-vitro (Damage-seq). C) Direct detection of damages through SMRT sequencing. Red triangle: UV dimer. Red X: blocked DNA ends. Yellow circle: biotin. Figure generated with Biorender.com

Figure 7 . 3 :

 73 Figure 7.3: Penetration of UV radiation in human skin. The highly energetic UVC radiation are rapidly absorbed by the stratum corneum. UVB is absorbed in the epidermis and reaches the top of the dermis. In contrast, UVA is weakly attenuated and interacts with the whole dermis. In terms cell types, keratinocytes present in dense layers in the epidermis are much more affected by both UVA and UVB than fibroblasts sparsely spread in the dermal extracellular matrix.

7. 7 . 1 .

 71 In vitro data of UV-induced mutagenesisUV-mutagenesis is investigated for years. The first works involved irradiation of plasmids further introduced and replicated in bacteria. These early studies showed that UVB and UVC mostly led to mutation at bipyrimidine sites. The subsequent works took advantage of progress in oligonucleotide synthesis to study the specific mutational properties of individual pyrimidine dimers with purified polymerases or following incorporation in plasmids and replication in cells[163][164][165][166][167] . It was thus observed that TT CPD was poorly mutagenic in contrast to TT 64-PP mostly because of differences in impact of the lesions on the local structure of DNA. In contrast to TT CPD, deaminated TC CPD (TU CPD) were highly mutagenic, leading to CT transitions because U codes like T during replication. The TC 64-PP was less mutagenic than the corresponding TT photoproduct168 . In contrast, the TT Dewar was more mutagenic than the TC Dewar and less than the TT 64-PP. It is worth mentioning that in mammalian cells, in addition to deamination41, 169 and structural modifications, mutagenesis of UV-induced photoproducts is largely explained by the role of translesional polymerases[170][171][172] , as discussed in the chapter 16 by Menck et al. in this book. Some of these enzymes, like polymerase , are referred to as "error-free" when bypassing pyrimidine dimers. They are thus expected to accurately incorporate A opposite T in TT CPDs, but also opposite U that would arise from C deamination in TC dimers. It should be stressed that experiments involving cells20 or mice 173 transfected

  Abbreviations :

  . This has been well documented in model systems but is observed in very low yield in cells exposed to biologically relevant doses of UVB. As discussed later in this chapter, formation of Dewar in cells requires both UVB and UVA. Another important secondary

reaction is the hydrolytic deamination of cytosine moieties in CPDs and CT and CC 64-PPs and Dewars

[40][41][42]

. This process leads to the formation of uracil-containing CPDs that play a role in mutagenesis, which is dominated by CT mutations at bipyrimidic sites as discussed later in this chapter. More information on this process can be found in chapter 8 by Taylor and colleagues in this book.

7.2.2 UVA-induced CPDs

7.2.2.1 Direct formation of CPD upon exposure to UVA

Formation of CPDs is not only induced by UVB but also by the less energetic UVA.

Table 7 .

 7

	1: Comparison of the induction of CPDs in various cell types exposed to UVB
	and UVA radiations. The reported results are the ratio between the yields of
		formation.
	Cell type	UV sources*	ratio	ref
	CHO cells	310 vs 365 nm 290 vs 365 nm	700 40000	8
	CHO cells	bb-UVB vs bb-UVA	700	12
	CHO cells	bb-UVB vs bb-UVA	2000	46
	Human skin fibroblasts	302 vs 365 nm 302 vs 334 nm	1000 150	47
	Human keratinocytes	bb-UVB vs bb-UVA	20000	9
	Human skin explants	bb-UVB vs bb-UVA	6500	22
	Human skin explants phototype IV	bb-UVB vs bb-	1100	24
	Human skin explants phototype II	UVA	1050
	*bb: broadband

3 UVC-induced DNA damage 7.2.3.1 Interest of UVC in photobiology UVC

  . This could be explained by a faster repair of dark than immediate CPDs. Formation of dark CPDs requires molecular contact between endoperoxides and bases. It is therefore favoured in open regions of chromatin where DNA repair is the most efficient. Further work is necessary to confirm this hypothesis that would help quantifying the biological role of dark CPDs. is absent from the sunlight reaching the surface of Earth and of limited interest in terms of health effects. However, the recent COVID pandemic has led to a regain of interest in the UVC-mediated disinfection technologies

7.2.

Table 7 .

 7 2: Proportion (in %) of different mutation signatures determined in the COSMIC* database for skin cancers. Results represents the ratio between the number of tumours exhibiting each signature and the number total number of tumour of each cancer. BCC: basal cell carcinoma, SCC: squamous cell carcinoma.


Maxime Ryckewaert

Maxime Metz

Daphné Héran

Pierre George

Bruno Grèzes-Besset

Reza Akbarinia

Jean-Michel Roger

Ryad Bendoula

Massive spectral data analysis for plant breeding using parSketch-PLSDA method: discrimination of sunflower genotypes

Keywords: Spectroscopy, Massive data, Digital Agriculture, Precision Agriculture, Chemometrics

In precision agriculture and plant breeding, the amount of data tends to increase. This massive data is becoming more and more complex, leading to difficulties in managing and analysing it. Optical instruments such as NIR Spectroscopy or hyperspectral imaging are gradually expanding directly in the field, increasing the amount of spectral database. Using these tools allows access to non-destructive and rapid measurements to classify new varieties according to breeding objectives. Processing this massive amount of spectral data is challenging. In a context of genotype discrimination, we propose to apply a method called parSketch-PLSDA to analyse such a massive amount of spectral data. ParSketch-PLSDA is a combination of an indexing strategy (parSketch) and the reference method (PLSDA) for predicting classes from multivariate data. For this purpose, a spectral database was formed by collecting 1,300,000 spectra generated from hyperspectral images of leaves of four different sunflower genotypes. ParSketch-PLSDA is com-

Introduction

In recent years, precision agriculture and plant breeding have tended to increase the quantity and complexity of phenotyping related data [START_REF] Mahlein | Plant disease detection by imaging sensors-parallels and specific demands for precision agriculture and plant phenotyping[END_REF][START_REF] Tripodi | Sensing technologies for precision phenotyping in vegetable crops: current status and future challenges[END_REF][START_REF] Awada | The adoption of automated phenotyping by plant breeders[END_REF]. Managing and analysing huge amounts of data are identified as a future bottleneck in phenotyping [START_REF] Tripodi | Sensing technologies for precision phenotyping in vegetable crops: current status and future challenges[END_REF]. Indeed, over the last few years, high throughput phenotyping (HTP) platforms in the laboratory or directly in the field have been flourishing [START_REF] Chawade | High-throughput field-phenotyping tools for plant breeding and precision agriculture[END_REF][START_REF] Shakoor | High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field[END_REF]. These platforms provide a monitoring of one or more phenotypic traits of the vegetation. This information can be obtained at different spatial, spectral or temporal scales depending on the studied level, which could be vegetation organ, individual or even population [START_REF] Dhondt | Cell to whole-plant phenotyping: the best is yet to come[END_REF][START_REF] Mahlein | Plant disease detection by imaging sensors-parallels and specific demands for precision agriculture and plant phenotyping[END_REF][START_REF] Mutka | Quantitative, image-based phenotyping methods provide insight into spatial and temporal dimensions of plant disease[END_REF].

The higher the spatial, spectral or temporal resolution, the larger the amount of data. Spectroscopy in the visible and near-infrared range (VIS-NIR) has proven to be relevant for providing useful information for vegetation monitoring.

Several plant phenotyping issues can be tackled with high spectral resolution measurements such as biochemical variable access [START_REF] Vigneau | Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in wheat[END_REF][START_REF] Jay | Estimating leaf chlorophyll content in sugar beet canopies using millimeterto centimeter-scale reflectance imagery[END_REF], disease [START_REF] Lu | Detection of multi-tomato leaf diseases ( late blight , target and bacterial spots ) in different stages by using a spectral-based sensor[END_REF][START_REF] Yu | Hyperspectral Canopy Sensing of Wheat Septoria Tritici Blotch Disease[END_REF] or stress detection [START_REF] Behmann | Detection of early plant stress responses in hyperspectral images[END_REF][START_REF] Lene | Determining the Influence of Water Deficiency on NPK Stress Discrimination in Maize using Spectral and Spatial Information[END_REF].

From a technological point of view, spectral acquisitions directly in the field have been made possible thanks to spectrometer miniaturisation [START_REF] Yan | Hand-held near-infrared spectrometers: Stateof-the-art instrumentation and practical applications[END_REF][START_REF] Krzysztof | Handheld near-infrared spectrometers: Where are we heading? NIR news[END_REF] or hyperspectral imager evolution [START_REF] Mishra | Close-range hyperspectral imaging of whole plants for digital phenotyping: Recent applications and illumination correction approaches[END_REF][START_REF] Fiorani | Future Scenarios for Plant Phenotyping[END_REF]. Associated with mobile vectors (such as UAV, tractor, pedestrian), these tools become HTP instruments and generate a large amount of spectral data. However, simple computations on this amount of data such as outlier detection or the use of pre-processing become difficult to perform and very time consuming [START_REF] Szymańska | Modern data science for analytical chemical data -A comprehensive review[END_REF]. Processing this massive amount of spectral data is challenging.

In chemometrics, most popular methods as Partial Least Square (PLS) [START_REF] Wold | PLS-regression: a basic tool of chemometrics[END_REF] are based on an assumption of linear relationship between spectral data and specific variables [START_REF] Mark | Chemometrics in spectroscopy[END_REF]. These methods are popular because of their good predictive performances and low computation time. Conversely, using these methods may not provide good prediction models when relationships between spectra and variable of interest are non-linear.

When dealing with a large amount of spectral data, complex structures and non-linear relationships can arise which may compromise linear regression approaches [START_REF] Dardenne | Multivariate Calibration and Chemometrics for near Infrared Spectroscopy: Which Method?[END_REF]. In practice, using a linear classifi-cation or regression to predict a complex database would lead to degraded results [START_REF] Bertran | Handling intrinsic non-linearity in near-infrared reflectance spectroscopy[END_REF][START_REF] Ni | Non-linear calibration models for near infrared spectroscopy[END_REF]. As a consequence, linear methods are challenged on large amounts of data. Furthermore, some methods, called local methods, exploit data based on a restricted neighbourhood of individuals which greatly improves prediction quality [START_REF] Dardenne | Multivariate Calibration and Chemometrics for near Infrared Spectroscopy: Which Method?[END_REF][START_REF] Pérez-Marín | Non-linear regression methods in NIRS quantitative analysis[END_REF][START_REF] Davrieux | LOCAL Regression Algorithm Improves near Infrared Spectroscopy Predictions When the Target Constituent Evolves in Breeding Populations[END_REF][START_REF] Tormod | Locally weighted regression and scatter correction for near-infrared reflectance data[END_REF]. These methods can be used to overcome non-linearity problems under the assumption that with a restricted neighbourhood, the relationship between spectra and variables becomes linear. The parSketch-PLSDA method has recently been proposed to implement a local approach to a large volume of data [START_REF] Metz | A "big-data" algorithm for KNN-PLS[END_REF]. Therefore, parSketch-PLSDA can be used to address the complex analysis of large amount of spectral data from phenotyping.

In this paper, we propose to study the use of the parSketch-PLSDA method to exploit a large amount of spectral data, generated from hyperspectral images of leaves of four different sunflower genotypes. Additionally, we compare this method with a reference method in an application of discrimination of different sunflower varieties.

Materials and methods

Biological material

Four sunflower genotypes (called A, B, C and D) were grown in a greenhouse at INRAE France in well-watered conditions by using a flood and drain system refilling water every 48 hours. All pots used the same potting soil (Pot Clay coarse, Floradur, Floragard). Water and lighting conditions were similar for each pot with a day-night cycle of 16h/8h. The temperature was 25 • C with a relative humidity in the greenhouse ranging from 50% to 60%. The greenhouse was equipped with multispectral lighting (450 nm, 560 nm, 660 nm, 730 nm and 6000 • K) controlled by Herbro automaton (GreenHouseKeeper) with Photosynthetically Active Radiation (PAR) set at 400 µmol/m2/s.

For the four selected genotypes, two potted plants (called P1 and P2) of each were grown. Four leaves were collected at the upper and middle parts of each plant, except for the genotype D where only two leaves of each plant were collected. Leaf petioles were immediately wrapped with water soaked paper before measurements. In total, 28 leaves were then collected and measured.

Spectral acquisitions

Spectral data of the prepared leaf samples were acquired in the reflectance mode by using a laboratory-based line scanning Hyperspectral Imaging System (HIS). The HIS system was composed of a linear halogen light (Haloline, Osram, 150 W), a translation rail (Linear Unit LES 4, Iselautomation, Germany), and a detection system. The sample was placed on a translation rail, synchronised with the acquisition software (NEO Hyspex, Norsk Elektro Optikk AS) which can record images when sample was scanned under the hyperspectral camera (NEO Hyspex VNIR-1600 with 30cm-objective, Norsk Elektro Optikk AS, Skedsmokorest, Norway). Spectral data were acquired in the 400 -1000 nm wavelength range with 3.7 nm intervals.

For each sample, the reflected light intensity (I s (λ)) was measured at each wavelength . Dark current image (I b (λ)) was also recorded for each measure. A white reference (SRS99, Spectralon ®) was used as a reference (I o (λ)) to standardize images from non-uniformities of all components of the instrumentation (light source, lens, detector). From these measurements, reflectance (R s (λ)) was calculated for each sample:

R s (λ) = I s (λ) -I b (λ) I 0 (λ) -I b (λ) (1)
For all hyperspectral images, vegetation pixels were selected to form a spectral data set. This selection was made by a threshold procedure (Fig. 1).

Indeed, vegetation and background pixels were easily identified by comparing their reflectance value at 800 nm to a threshold defined here at 30%. Leaf spectra were collected from the 28 hyperspectral images, representing more than 1,300,000 spectra.

Data analysis

Two methods were used to compare their ability to discriminate sunflower genotypes. Both methods were applied to a similar data set, called test set, built out of the spectra database. Calculations were performed with the R software (version 3.6.1 (Core Team, 2013)) and rnirs package (https:// github.com/mlesnoff/rnirs) was used for classical discrimination methods (PLSDA).

PLSDA method

The Partial Least Squares for Discrimination Analysis (PLSDA) [START_REF] Barker | Partial least squares for discrimination[END_REF] was used as reference method for classification. This method consisted of building models between multivariate data and a vector coding different classes (here, the four genotypes).

Multivariate data was represented by a matrix X of size (n, p) where n was the observation number and p the variable number. The n observations were identified by their corresponding class in the vector y of size (n,1) where values ranged from 1 to q, where q was the class number. The first step was to transform y into a dummy matrix Y of size (n, q) also called disjunctive table.

y =                        1 1 1 2 2 2 3 3 3                        → Y =                        1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1                        (2)
An example of a dummy matrix is given in equation 2 with nine observations belonging to three classes. The matrix Y contains binary values (0,1)

where each column corresponded to a class. For a given observation, the class-corresponding column has a value of 1 while other columns were equal to 0.

Then, a Partial Least Square (PLS) model [START_REF] Wold | PLS-regression: a basic tool of chemometrics[END_REF] was applied between X and Y. Y being multidimensional, the algorithm PLS2 adapted to the prediction of several responses was used. Finally, a linear discriminant analysis (LDA) [START_REF] Fisher | The Use of Multiple Measurements in Taxonomic Problems[END_REF] was applied between the PLS2 scores and Y.

ParSketch-PLSDA method

The other strategy was to apply the parSketch-PLSDA method [START_REF] Metz | A "big-data" algorithm for KNN-PLS[END_REF], an extension of the K-Nearest Neighbours (KNN)-PLSDA method for massive data processing [START_REF] Lesnoff | Comparison of locally weighted PLS strategies for regression and discrimination on agronomic NIR data[END_REF]. ParSketch-PLSDA was used to combine an indexation strategy (parSketch) and the PLSDA.

An approximation of the neighbourhood was defined for each spectrum to be classified. This neighbourhood was then used to compute a PLSDA model and to predict which class belong new spectra.

ParSketch was performed in three steps: dimension reduction, grid creation, neighbourhood approximation. Three method parameters (v, s, m)

were defined, corresponding to these three steps, and are described below.

First, a dimension reduction was achieved by calculating the matrix T corresponding to the sketch of the matrix X as follows:

T = XP (3)
Where P was a matrix of size (p,v) containing values of -1 or 1 according to a random selection. The first parameter of ParSketch (v), corresponding to the column number of P was then defined. The higher the value of v the better the approximation of the neighbourhood. However, the larger the value of v the longer the parSketch method computation time.

The second step corresponding to the grid creation process (see Fig. 2) was to segment the space (2d) formed by adjacent pairs of T columns. The number of segments (s) is the second parSketch parameter. The higher the value of s the better the approximation of the nearest neighbours. However, the greater the value of s, the smaller the number of neighbours. The last step to configure parSketch was to define the minimal number m of grids returned in the neighbour's search. This step corresponded to the neighbourhood approximation for the grid search process (see Fig. 3). The higher the value of m the better the approximation of the nearest neighbours.

Observations present in the same cell for at least m grid number are selected as neighbours of the individual to be predicted. However, the greater the value of m, the smaller the number of neighbours returned by parSketch method.

Figure 3: An illustrated example of grid search. For a new measure t(new) and a m value equals to 2, t(6) will be returned as a neighbour because it is present in two grids next to t(new), whereas t(1) will not be considered as a neighbour

Evaluation strategies and method parameterization

The data set was divided into two independent data sets: a calibration set and an independent test set . The calibration set was formed with the 14 images acquired on P1 plants and corresponding to about 650,000 spec-tra. The PLSDA model and parSketch-PLSDA method were both calibrated using all spectra of this calibration set.

The test set was formed with the other 14 images acquired on P2 plants (independent from P1). 1000 spectra were randomly selected in each image totalling 14,000 spectra for the test set. Spectra that could not be predicted by parSketch due to lack of neighbours were removed from the test set. In the end, the same test set were used for parSketch-PLSDA and for PLSDA.

For both methods, validation steps were performed on the calibration set in order to minimize overfitting.

To build the PLSDA model, the cross-validation step consisted of splitting the calibration data set into different blocks in order to calculate calibration and validation errors. This approach, also called k-fold validation [START_REF] Wold | Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models[END_REF][START_REF] Camacho | Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: theoretical aspects[END_REF] was carried out with five blocks repeated three times. Validation errors were then computed and led to the number of latent variables to be retained.

For parSketch-PLSDA, a parametrisation step was performed to configure the three parSketch parameters. This step was performed by analyzing distributions of returned neighbours according to two parSketch parameters: number of segments s and the common minimum grids m. Here, the number of random vectors v was set to a value of 20. Afterwards, a PLSDA model was established. The number of latent variables was optimized for a subset of the calibration set, called the validation set. This validation set was formed with four images of the calibration set by randomly selecting 1000 spectra in each image.

In order to compare both methods, confusion matrices were obtained and percentages of precision and recall were calculated according to the following equations:

Precision = tp tp + f p (4) Recall = tp tp + f n (5)
Where tp, f p and f n corresponded to true positives, false positives and false negatives respectively. On the one hand, for a given class, precision value assessed the predictive quality of the model based on the proportion of well-classified observations among all observations that were classified in the same corresponding class. On the other hand, recall, also called sensitivity, evaluated the number of well-classified observations compared to the total number of observations of the given class. These two criteria are complementary to evaluate the model performances. These two figures of merit were expressed as percentages. The higher the values, the better the model performance. Reflectance spectra shown in Fig. 4a correspond to 1000 spectra per class randomly selected among all the data set. These spectra correspond to vegetation spectra [START_REF] Xu | A polarized hyperspectral imaging system for in vivo detection: Multiple applications in sunflower leaf analysis[END_REF] : specific hollows at 450 nm and 650 nm related to chlorophyll content, anthocyanin content at 550 nm; the red-edge towards 780 nm and a plateau in the near-infrared between 780 nm and 1000 nm. Besides, the main observed variability in the spectrum plot corresponds to an additive effect due to the scattering effect of the structure of the leaves. However, the number of spectra is too large to be able to describe difference between classes.

Results and discussion

Data visualization

A principal component analysis was applied to these spectra. Figure 4b shows the score plot of the two first components. The first component represents 88% of the spectra variability and 8% for the second component. On these two components, scores are uniformly distributed without any evident distinction between genotypes. The exploratory study of the spectra shows that there are no outliers and that there is no distinct group on the first two components.

Model calibration

PLSDA

Figure 5 shows the cross-validated error rate curve for PLSDA applied to all spectra of the calibration data set. The behaviour of the curve decreases continuously according to the latent variable (LV) number. for the PLSDA applied to all spectra of the calibration data set

A high value of LV number generally shows the complex structure of a data set. This is expected with spectral measurements on vegetation [START_REF] Metz | A "big-data" algorithm for KNN-PLS[END_REF]. With 16 LVs, an error rate with a value close to 12% is obtained. However, after 16 LVs the predictive performance gain is very small.

Consequently, the PLSDA model is set to 16 LVs.

ParSketch-PLSDA

ParSketch parameters s and m are studied according to the statistical distribution of the number of returned neighbours (Fig. 6). The number of neighbours decreases to a value close to zero when parameter values (s,m) increase. Indeed, on the one hand, an increase of number of segments s, the number of returned neighbours will be lower. And on the other hand, by increasing the minimum number of common grids m, the risk of not having neighbours is high. This global trend is expected [START_REF] Metz | A "big-data" algorithm for KNN-PLS[END_REF].

By contrast, when parameter values are low, the number of returned neighbours is high (close to 300 000 neighbours by individual to be predicted). This situation is not desirable, as it may cause problems related to computation time constraints. As a result, parameters m and s must be chosen to have a sufficient number of neighbours, neither too much nor too little. As several values are possible, four combinations of the parSketch parameters are selected (Table 1) to compare their model performances. Table 1 shows the retained values of the three parSketch parameters for these four combinations. The combination (a) was selected because the median number of neighbours is 1246. This low number of neighbours enables to quickly calibrate PLSDA models but it could be insufficient to have a good predictive quality. Indeed, a low median number of neighbours means that a large amount of observations do not have neighbour at all. For the combinations (b) and (c), higher numbers of neighbours are returned, with median values of 2903 and 9303 respectively. Finally, the highest median number of neighbours returned by parSketch is chosen with the combination (d) with a value equal to 27030. In this case, constraints in computation time might appear. Moreover, the linear relationship between spectra and class variable of a small neighbourhood might be lost.

Validation error curves for the four retained combinations for parSketch-PLSDA are shown in the figure 7. neighbours per sample to be predicted (cf. Table 1).

The model with the lowest predictive quality has a high number of neighbours. This degradation reflects a non-linear aspect of the data set. By further increasing the number of returned neighbours, prediction qualities of parSketch will be close to the PLSDA method on the whole data set.

Finally, for this validation set the optimal parameter combination is the combination (c). In this case, the number of latent variables is not easy to define. The number of latent variables is defined by a trade-off between the size of the model and the benefit of adding an extra dimension to the model.

The number of latent variables chosen is therefore 16.

Model testing

The PLSDA model has been calibrated with all the spectra of the calibration set. Then this model is applied to the test set defined previously and its prediction performances are assessed in Table 2. The percentage missings from recall values correspond to the prediction error for each class. The prediction error of the whole data set, corresponding to the average error, is close to 23%. It is expected to have value for the test error slightly higher than the 12% observed during calibration (see Fig. 5).

This means that the calibration set samples are representative of the test set despite their independence (as mentioned above, the test set corresponds to other plants of the same genotype). with parSketch-PLSDA (83% and 87% respectively) compared to PLSDA (76% and 77% respectively). Consequently, the model prediction error decreases to a value of 13%. This implies that parSketch-PLSDA model performs better than the reference discriminant strategy. This improvement in the classification results demonstrates the advantage of using a limited number of neighbours to create a model.

As the methods used are locally linear, this improvement confirms the hypothesis that with a limited number of neighbours, the problem becomes linear. The prediction improvement obtained with parSketch-PLSDA method highlights the presence of non-linear relationships between spectra and a class variable in the whole data set. which can be encountered when building a large spectral database.

Conclusion

In this study, we compared the two classification strategies on the same calibration and test data sets.

For both methods, classification results are encouraging and confirm the interest of VIS-NIR spectroscopy for variety discrimination. Results showed that parSketch-PLSDA method outperforms PLSDA by improving prediction qualities by 10%. The use of the parSketch-PLSDA procedure in the exploitation of massive spectral data is confirmed and shows the interest of using a close neighbourhood of the spectra to be predicted.

It would be interesting to test such methods on a larger number of genotypes. This increase in the spectral database can potentially lead to an increase in complexity hence reducing the data set quality. Therefore, it would be interesting, in perspective, to evaluate other methods dealing with non-linearity.

In the framework of plant breeding, hyperspectral imaging or field microspectrometers as tools for high-throughput plant phenotyping could be considered in real time with this method. In an applicative aspect, parSketch procedure is parallelisable, which shows the possibility of fast real-time prediction of a large amount of data. We used parSketch-PLSDA on spectral data for close-range plant phenotyping. Other applications to plant breeding (disease, biotic/abiotic stress) or other applications related to precision agriculture could be considered. More generally, this method can be applied to any other application in analytical chemistry or metabolomics.

Figure 1 :

 1 Figure 1: Pixel selection from a hyperspectral image of a sunflower leaf (a) image, (b) mask based on threshold values

Figure 2 :

 2 Figure 2: An illustrated example of grid creation with a segment number s = 3

Figure 4 :

 4 Figure 4: All data set including Genotype A (red), B (green), C (cyan) and D (violet): (a) spectra, (b) score plot of the first two principal components

Figure 5 :

 5 Figure 5: Evolution of the cross-validated error rate as a function of latent variables (LV)

Figure 6 :

 6 Figure 6: Distribution of the returned neighbours according to parSketch parameters s and m (number of segments and common minimum grids). Here, v (number of random vectors) parameter is fixed to 20

Figure 7 :

 7 Figure 7: Evolution of the validation error rate as a function of latent variables for the four parameter combinations of parSketch-PLSDA

Table 1 :

 1 Combinations of the selected parSketch parameters and the corresponding median number of returned neighbours

	Combination m v	s Median neighbour number

Table 2 :

 2 Confusion matrix for PLSDA (16 LVs)

		A	B	C	D	Recall (%)
	A	3120 121	339	250	81
	B	238 2812 619	154	74
	C	127	241 3463	75	89
	D	283	173	270 1213	63
	Precision(%) 83	84	74	72
	Precision and recall values are high for all classes A, B, C and D with
	values ranging from 72% to 84% for precision and from 63% to 89% for
	recall. Genotypes A and B have the highest precision values with values of
	83% and 84%, respectively. This means that 83% of spectra classified in
	genotype A, actually belong to genotype A. Few other genotypes are found
	in this class. The same argumentation holds true for 84% of genotype B
	spectra. For recall, genotypes A and C have the best values with 81% and
	89%, respectively. For these genotypes, spectra are mainly well-classified
	that is infrequently assigned to other classes.

Table 3 :

 3 Confusion matrix for parSketch-PLSDA with combination(c) (m = 7, v = 20,

	s = 12)
		A	B	C	D	Recall (%)
	A	3547 114	115	54	93
	B	40	3256 473	54	85
	C	58	211 3590	47	92
	D	51	112	260 1516	78
	Precision(%) 96	88	81	91
	Table 3 shows the parSketch-PLSDA prediction performance by giving
	percentages of precision and recall for each genotype. Genotypes A and D
	have the highest precision values with values of 96% and 91% respectively.
	Besides, genotypes A and C have the highest recall values with values of
	93% and 92% respectively. Genotype D has low recall values with both
	methods (63% for PLSDA and 78% for parSketch-PLSDA). This is probably
	due to the under-representation in the data set which may degrade the model
	calibration. Indeed, only two images were acquired per plant of genotype D
	compared to four images for the other genotypes.
	Finally, overall recall and precision values have increased by almost 10%

Acknowledgement

This work was conducted within the OPTIPAG Project supported by the grant ANR-16-CE04-0010 from the French Agence Nationale de la Recherche.