Effects of the Pitch Surface on Displacement of Youth Players During Soccer Match-Play

Abstract The aim of this study was to analyze the effect of different pitch surfaces (artificial turf, natural turf and dirt field) on positioning and displacement of young soccer players (age: 13.4 ± 0.5 yrs; body height: 161.82 ± 7.52 cm; body mass: 50.79 ± 7.22 kg and playing experience: 3.5 ± 1.4 yrs). Data were collected using GPS units which allowed to calculate spatial distribution variability, assessed by measuring entropy of individual distribution maps (ShannEn). Ellipsoidal areas (m2) representing players’ displacement on the pitch, centred on the average players’ positional coordinates, were also calculated, with axes corresponding to the standard deviations of the displacement in the longitudinal and lateral directions. Analysis of variance (ANOVA) was used to evaluate differences between pitch surfaces and across players’ positions. There was significant effect in positioning (η2 = 0.146; p < 0.001) and displacement (η2 = 0.063; p < 0.05) by the players between pitch surfaces. A dirt field condition induced an increase in the players’ movement variability, while players’ displacement was more restricted when playing on artificial turf. Also, there were significant effects on positioning (η2 = 0.496; p < 0.001) and displacement (η2 = 0.339; p < 0.001) across players’ positions. Central midfielders presented the greatest movement variability and displacement while fullbacks showed the lowest variability. Subsequently, the results may contribute to implement strategies that optimise players’ performance in different surface conditions.

Introduction
Youth soccer matches are played on natural and artificial turf surfaces. However, in certain regions of Africa and southern Europe many soccer matches are still being played on a dirt field surface. Although we know this reality, there are still many uncertainties concerning the influence of each pitch surface on the tactical demands of young players in match-play.
Soccer players' performance is influenced by tactical, technical, physical and psychological factors (Stølen et al., 2005). More specifically, Impellizzeri and Marcora (2009) suggested that the tactical factor is one of the most important in the analysis of soccer matches. However, since the nature of a soccer match is highly dynamic and complex, assessing players' tactical performance can be a demanding task . This is why some researchers suggest that the analysis of variability of players' motion can be seen as an interesting method to identify and classify their performance from a tactical viewpoint (Couceiro et al., 2014). To assess it, a new set of technologies, such as those based on video tracking and global positioning systems (GPS) have contributed to new insights into the analysis of soccer players' performance either in training or during a game (Cummins et al., 2013). Portable GPS devices provide spatial-temporal data with the reasonable accuracy for tracking players trajectories on the pitch (Coutts and Duffield, 2010;Gray et al., 2010). Positional data are critical to better understand players physical, technical or tactical demands (Cummins et al., 2013;Stølen et al., 2005). Moreover, they also enable to analyse the interactions between player's trajectories, contributing to understanding the intricate motion patterns of players, which are critical in the analysis of tactical performance (Silva et al., 2014a).
Previous studies have demonstrated that analysis of how the players manage the space (Bartlett et al., 2012), measuring the action zone variability, may be a viable method to assess the players tactical performance (Gréhaigne, 2011;Gréhaigne et al., 2001). For instance, Jones and Drust (2007) suggested that if players restricted their action zones to specific pitch zones, this could be interpreted as a more structured game, played according to the players' positions and specific functions. Other studies have suggested that players' spatial distribution variability on the pitch can be a relevant concept to quantify their individual movements from a tactical perspective (Silva et al., 2014a;Silva et al., 2015). For instance, a study conducted by Silva et al. (2014a) concluded that national-level soccer players presented higher variability than regional-level players when playing small-sided games in smaller areas, which may indicate that players of different skills respond in a different manner to the same task constraints. According to the aforementioned authors, greater uncertainty in the players' behavioural modes during smallsided games may be associated with more variable action zones. Thus, it seems relevant to measure the variability to capture and interpret the meaning of situations that occur during soccer matches.
Typical measures of variability from spatial-temporal data include the range, standard deviation or the coefficient of variation, along with central tendency measures (mean, median, and mode). These linear quantities are complemented with more sophisticated analysis, such as the one based on entropy values obtained from player's spatial distribution maps (Couceiro et al., 2014;Silva et al., 2014a;Silva et al., 2015). The entropy, originally described by Shannon (1948), is a non-linear variable that can be used to express the uncertainty of locating the player in a specific region of the soccer pitch (Silva et al., 2014a). Normalized entropy ranges from 0 to 1, i.e., from highly predicted positions of the players on the pitch to highly variable or unpredicted positions (Silva et al., 2014a). This is why to the authors previously mentioned, entropy can be a valuable tool to quantify players' performance from a tactical perspective.
Players' relative positioning on the pitch, centred on their average positional coordinates, with axes corresponding to the displacement's standard deviations in the longitudinal and lateral directions of the pitch, can also provide additional information on tactical behaviour (Silva et al., 2014b;Yue et al., 2008). In this context, a study by Silva et al. (2014b) analysed the distribution of players' movement coordinates in the longitudinal and lateral pitch directions and confirmed that national-level soccer players displayed differentiated distributions in the longitudinal direction, while regional-level players tended to play on very similar longitudinal coordinates of the pitch, only varying their positioning along the lateral direction. In addition, Yue et al. (2008) concluded that forwards constituted the positional role with the largest range of movements compared with other positions, which means that they have to move in order to provide pass solutions to team mates as well as to escape from the opponent's zone.
Few studies have examined the effect of the pitch surface on players' demands during competitive soccer matches. To date, it has been demonstrated that a pitch surface neither influences tactical performance (Santos et al., 2013), nor the players' movement patterns (Andersson et al., 2008). However, Folgado et al. (2007) showed that the number of successful passes was higher in the natural turf condition compared to the sand surface. In addition, Andersson et al. (2008) showed that ball possession and the number of passes increased 20% in the artificial turf condition compared to the natural turf.
Despite the importance of these studies, it has not yet been investigated whether the pitch surface influences the positioning and displacement of young soccer players. This information could provide coaches and sports managers with an opportunity to adapt and © Editorial Committee of Journal of Human Kinetics overcome the specific constraints of the pitch surfaces on the tactical profile of the players and respective teams. Moreover, it may maximize the effectiveness of training sessions and implement strategies that could improve players' tactical performance during match-play.
The players and, ultimately the team, are expected to behave in a way that reflects the concept of game designed by coaches. However, the players' intentional actions can be constrained by the effect of the surface where the soccer match is performed. In this context, it is relevant to analyze the player's displacement to adapt and overcome possible constraints imposed by the surface. In this sense, this insight may provide additional information about the specific requirements of each surface and a prerequisite for coaches to improve tactical performance of players and teams, either in training or a game context. Therefore, this study aimed (1) to analyze the displacement variability patterns of players on artificial turf (AT), natural turf (NT) and a dirt field (DF) and; (2) to determine whether there were differences in the spatial distribution variability across players' positions. It was hypothesized that the three pitch conditions would induce differences in positioning and displacement of the players.

Participants
Sixty-six male U-14 soccer players, organized into three teams of twenty-two participated in the study (age: 13.4 ± 0.5 years; body height: 161.82 ± 7.52 cm; body mass: 50.79 ± 7.22 kg). Players were selected according to the coach's subjective appraisal of their ability. All players competed at a regional-level exhibiting match and training experience of 3.5 ± 1.4 years. The U-14 age group was chosen because 40% of soccer matches were still played on a dirt field, at the regional championship U-14 (AF Porto, Portugal). The participants' (teams and players) selection was conducted in accordance with the following criteria: 1) teams and players registered at the Porto Football Association championship; 2) teams and players from the same competitive level. All players and their coaches were informed about the research procedures, requirements, benefits and risks, and written informed consent was obtained from parents before the study began. The study protocol followed the guidelines outlined in the Declaration of Helsinki and was approved by the local Ethics Committee.

Measures
The positional data were used to calculate: (1) players' spatial distribution variability, assessed by measuring the entropy of individual spatial distribution maps (Shannon, 1948;Silva et al., 2015). These maps were obtained from discretization of the pitch into sectors of 1 m 2 , allowing to calculate the amount of time spent in each sector, normalized to total match duration to produce spatial probability distributions. In this way, a normalized value of entropy, ranging from 0 to 1, was calculated to quantify the uncertainty of locating each player in a specific location of the pitch. A low entropy value (near zero) indicating a sharply peaked distribution, suggests the player's position can be easily predicted. On the other hand, a high entropy value (near 1) corresponds to an uniform distribution and suggests the player exhibits high spatial distribution variability or that its position is highly variable and unpredictable (Silva et al., 2015). Taking into consideration the participant's experience, the entropy was also related with team tactical performance. Thus, teams with players with high entropy values were interpreted as using a game style that promoted positional exchanges between players and more diversified tactical functions. On the other hand, teams with players with low entropy values were interpreted as using a game style based on more consistent displacement and more specific tactical functions; (2) the covered area by players in the pitch surface, assessed by measuring the area of ellipses representative of players' pitch displacement, centred on the average positional coordinates of the players, with axes corresponding to the standard deviation of displacement in the longitudinal and lateral directions of the pitch (Zengyuan et al., 2008). Through elliptic forms we evaluated qualitatively the main directions of the players movements and their distribution and relative positioning on the pitch (Silva et al., 2014b). The ellipse areas were also calculated to provide quantitative information of the space predominantly used by each player during soccer matches.

Design and procedures
During three weeks, always on Sunday, a Journal of Human Kinetics -volume 65/2018 http://www.johk.pl total of 9 soccer matches were performed and analysed, in the following condition: week (1) -3 matches on artificial turf; week (2) -3 matches on natural turf; week (3) -3 matches on a dirt field. The teams and players who participated in the study were always the same and all soccer matches were played using 1-4-3-3 tactical structure, the most frequent in Portuguese youth teams (Rebelo et al., 2014). The players were classified according to their playing positional role: 1) central defenders (DC, n = 12); 2) centre forwards (CF, n = 6); 3) central midfielders (CM, n = 18); 4) wide midfielders (WM, n = 12); 5) fullbacks (FB, n = 12). The goalkeepers participated in the matches, but were excluded from the analysis. The matches were played according to soccer rules, except match duration (30min, without breaks) and players' substitution (not allowed). The pitch size was adjusted to standardize the measure for all conditions (length: 100 m, width: 64 m). Six extra soccer balls were always available near the goalposts and on the side of the pitch for prompt replacement when the ball left the playing area. All matches were proceeded by a planned, standardised warm up of 15 min comprising running activities, smallsided games and stretching. Following this period, the players simulated a match during two periods of 2 min, interspersed by 1 min of passive recovery. All games were played between 9 and 11 a.m., under similar climatic conditions. Each player carried a global positioning tracking device (Qstarz, Model: BT-Q1000eX) that recorded his 2D positional coordinates at a sampling frequency rate of 10 Hz. The GPS device was placed on the upper back of the player (using an appropriate harness). The playing surfaces were calibrated with the coordinates of four GPS devices stationed in each corner for approximately 4 min. The absolute coordinates of each corner were calculated as the median of the recorded time series, providing robust measurements to typical fluctuations of the GPS signals. These absolute positions were also used to define the reference Cartesian coordinate systems for each pitch, with its origin placed at the pitch centre. GPS longitudinal and latitudinal (spherical) coordinates were converted into Euclidean (planar) coordinates with the Haversine formula (Sinnott, 1984). Fluctuations in players positions were reduced using a moving average filter with a time scale of 0.2 s and data resampling was employed to synchronize the time series of all players within each soccer match (Silva et al., 2014a;Silva et al., 2015). All data were recorded in Microsoft Office Excel 2007 (Microsoft Corporation, USA) and subsequently exported to SPSS Statistics, version 22.0 (SPSS Inc., Chicago, USA). MatLab software (R2014a, Math works Inc., USA) was used to process and analyse the data.

Statistical analysis
Results are expressed as means ± standard deviations. A two-way analysis of variance (ANOVA) with repeated measures was employed to evaluate the differences in the described variables between each pitch surface. The Mauchly's test of sphericity was performed on all data to verify any violations of sphericity that were corrected through the Greenhouse-Geisser adjustment (Bathke et al., 2009). Effect sizes were reported as partial eta squared (η 2 ) obtained with the ANOVAs, following Cohen's guidelines (Cohen, 1988): (i) 0.01≤ η 2 < 0.06 -small effect; (ii) 0.06 ≤ η 2 < 0.14 -moderate effect; and (iii) η 2 ≥ 0.14 -large effect. Post hoc analysis was performed using the Bonferroni adjustment. All statistical analyses were carried out using SPSS Statistical Analysis Software (SPSS Inc., Chicago, USA) version 22.0 for Windows.

Spatial distribution
The entropy values were higher on the dirt field compared to the other pitch surfaces (Figure 1). ANOVAs yielded a main effect for pitch surfaces F(2.110) = 9.417; p < 0.001, η 2 = 0.146; = 0.977. Post-hoc analysis revealed significant differences between a dirt field and artificial turf (p = 0.019) and natural turf (p < 0.001). Between the artificial turf and natural turf no significant differences were found (p = 0.708). Figure 2 presents an example of spatial distribution maps of players for each pitch surface, highlighting lower variability in spatial distribution for the natural turf surface than other surfaces. Figure 3 illustrates the ellipse areas, centred on the average of players' positional coordinates, with semi-axes that correspond to the standard deviation of displacement in the longitudinal and lateral directions on each pitch © Editorial Committee of Journal of Human Kinetics surface. The ellipse areas of the artificial turf show less eccentricity and are more overlapping compared to other surfaces.

Relative positioning
ANOVAs yielded a significant effect of the pitch surface on the mean area covered by the players; F(2.110) = 3.667, p < 0.05, η 2 = 0.063; = 0.664 (Figure 1). Post-hoc analysis showed significant differences between the dirt field and artificial turf (p = 0.012). However, no differences were found between natural turf and the dirt field (p = 0.582) nor between natural turf and artificial turf (p = 0.627).

Spatial distribution and relative positioning across players' positions
The differences in spatial distribution and a mean area covered by players across their positional role are presented in Table 1. Significant differences in ShannEn values were observed across players' positions in all pitch surface conditions; F(4.55) = 13.51; p < 0.001, η 2 = 0.496; = 1.000 (Table 1). Specifically, central midfielders presented the greatest ShannEn values compared with other players' positions, while fullbacks showed the lowest values (p < 0.05). The differences were most evident on the dirt field.
With regard to the mean area covered, significant differences were observed across players' positions on artificial turf; F(4.55) = 4.465; p = 0.003 and the dirt field; F(4.55) = 4.425; p = 0.004, respectively. No differences were found on natural turf (Table 1). Central midfielders presented the greatest mean area covered, while fullbacks the lowest (p < 0.05).

Discussion
The aim of this study was to analyze displacement and positioning of players during soccer matches performed on artificial turf, natural turf and a dirt field. For this purpose, variability of the respective spatial distribution was evaluated and the mean area covered by each player was quantified.

Figure 1 Mean values for Shannon entropy and the covered area of players on each pitch surface. Error bars represent standard deviation.
Journal of Human Kinetics -volume 65/2018 http://www.johk.pl

Figure 3 Players' elliptical areas on each surface. Black and grey ellipses depict the players' major displacement of each team, respectively. Lateral (y-axis) and longitudinal (x-axis) axes depict pitch coordinates.
© Editorial Committee of Journal of Human Kinetics

Spatial distribution variability
The major findings were that the action zones of players were more restricted on artificial turf and natural turf than on a dirt field surface, which reveals more uncertainty in the behavioural modes of players on a dirt field. The results showed that players explored more variable areas of the pitch on the dirt field, which can be confirmed by the spatial distribution of players in different pitch conditions (Figure 2). This is probably due to increased irregularity that the dirt field usually presents, reflecting, therefore, more unstable behaviours. Thus, the players' spatial distribution seems to provide relevant information about the tactical roles or style of play for each match-play condition, as observed by Silva et al. (2015).
The Shannon entropy values showed that players' spatial distribution variability was significantly different in the three surfaces, especially between the dirt field and other surfaces. In general, during pitch surface manipulations, the higher spatial distribution variability was verified on the dirt field, which may result from greater flexibility of movement that players perform to adapt to the surface conditions and ball trajectories, probably more unpredictable on this surface. Thus, the greater Journal of Human Kinetics -volume 65/2018 http://www.johk.pl variability observed on the dirt field expresses the continuous effort of the players to adapt to the characteristics of this surface, which seems to induce a greater number of unexpected events and specific restrictions in the game profile. This is non-systematic variability, which reflects unintentional movements or actions that probably were not previously defined. Such characteristics of movement or actions may constraint tactical behaviour of players and teams, contributing, for instance, to inducing undefined tactical functions to players as well as a more individualistic team game profile (Folgado et al., 2014). On the other hand, artificial turf and natural turf reflected less variability, characterized by being under control of players and expressing their intentional and systematic movements or actions. Accordingly, the game profile on these surfaces was more structured from the tactical point of view, where the players demonstrated a more balanced relationship between lateral and longitudinal movements (Silva et al., 2015). In this context, it can be hypothesised that the pitch surface characteristics affect players' behaviour, constraining their variability of movements and actions. In addition, the nature and properties of the surface also seem to influence characteristics of the game (Andersson et al., 2008;Stiles et al., 2009), which was reflected in the trend to a direct and less structured game profile on the dirt field compared to artificial turf and natural turf.

Relative positioning
Considering the mean area covered by players, our results indicate that the dirt field surface presented a larger mean area than other surfaces, especially in relation to artificial turf. While we are not aware of any comparable data in the literature, it can be hypothesized that the natural turf, but especially dirt field surface, induces an increase in the mean area covered by players. Probably, this trend may result from effects that these surfaces may induce on certain indicators, such as the velocity or trajectory of the ball as well as players' running performance, impairing in this way players' movement on the pitch.
Analyzing the ellipse shapes, it was found that artificial turf condition induced less eccentricity and was more overlapping compared to the natural turf and dirt field (Figure 3), which reflects a more balanced configuration of different sections of the pitch. This elliptical configuration suggests a greater coordination between players, reflecting a more structured and collective game profile (Bartlett et al., 2012). In this context, it seems that artificial turf promotes a tactically more balanced game, expressed in smaller eccentricity of the shapes configuration on this surface, which was revealed by longitudinal and lateral movements with a similar amplitude. This evidence was not that clear on other pitch surfaces, especially on the dirt field, where the longitudinal displacement of players in the pitch was more pronounced. Thus, it can be hypothesised that the dirt field surface induces a game profile dominated by long passes rather than a tactically elaborated game concept, where ball circulation predominates (Silva et al., 2014b;Stiles et al., 2009). This information could be valuable for coaches that intend to improve tactical performance of the players and respective teams.

Spatial distribution and covered area across players' positions
Since it is well established that players' performance can change as a result of gradual changes in the player's action capabilities or changes in playing conditions (Fajen et al., 2009), we expected differences in spatial distribution and the mean area covered by the players across their playing positions. Our results confirmed that spatial distribution and the covered area were significantly different across players' positions (Table 1). Central midfielders showed the highest variability and mean area covered, especially on the dirt field surface. These findings are in contrast to Moura et al. (2015) who observed that external midfielders and external defenders presented the greatest variability across positions. The participants' characteristics and study design might explain the observed differences (in the aforementioned study national-team players were assessed during the UEFA European Championship). Also, the presented values (Table  1) suggest that central midfielders constitute the positional role with greater variability of movements and, therefore, with greatest influence on the actions of the game, as shown in previous research (Dellal et al., 2012). Moreover, the main role of central midfielders is to control the pitch's centre, which can induce the greatest movement amplitudes in the longitudinal and lateral © Editorial Committee of Journal of Human Kinetics directions compared to the other positional roles (Silva et al., 2014b). On the other hand, fullbacks showed the lowest spatial distribution variability and mean area covered, in all pitch surface conditions. This trend suggests that fullbacks reveal behaviours and movements more predictable than other positional roles, which, according to Moura et al. (2015), could facilitate a precise evaluation of their performance by their opponents. Such as fullbacks, central defenders also presented a smaller mean area covered, especially on the dirt field surface. These indicators seem to confirm that central defenders are the least participatory players during a game. This may result from the inherent specificity of the tactical role, with offensive-defensive actions that may induce a smaller amount of forward, backward, and sideways movement. If the number of tasks to be performed by the players increases, it will induce greater unpredictability to the individual performance of the players as well as greater complexity to the game profile of the team, i.e., it will increase variability. On the other hand, if the number of tasks to be performed by the players decreases, it will induce greater predictability to the individual performance of the players as well as smaller complexity to the game concept of the team, i.e., it will decrease variability. This information can be a key factor to the coaches in order to adapt the training requirements to the characteristics of the players and their positional roles.

Conclusions
This study concludes that young soccer players perform more unstable and random movements in the dirt field, suggesting that this surface can be used by coaches as a useful tool for training players' technical and tactical skills along their learning pathways. On the other hand, the artificial turf promotes a more structured style of play from a tactical viewpoint, demonstrating that it can be an excellent alternative to the natural turf to perform formal games, which can be used by national governing bodies to implement effective formal soccer activities in their youth development system (Ford et al., 2012). Our findings also showed that there were significant differences across the positional roles of players in the range of movement and displacement, suggesting that coaches should pay attention to the design of their training sessions and to constructing training units according to the requirements and adaptations of the respective positional role. Future studies could identify other specific conditions that may influence players' tactical behaviours, such as the players level (elite and non-elite), different age groups and different team formations (play systems), to provide deeper understanding of the players' actions and tactical relations during games.


extfieldsofstudy: Chemistry Medicine

text:
The rational design of affinity-attenuated OmCI for the purification of complement C5

Complement component C5 is the target of the mAb eculizumab and is the focus of a sustained drug discovery effort to prevent complement-induced inflammation in a range of autoimmune diseases. The immune evasion protein OmCI binds to and potently inactivates C5; this tight-binding interaction can be exploited to affinity-purify C5 protein from serum, offering a vastly simplified protocol compared with existing methods. However, breaking the high-affinity interaction requires conditions that risk denaturing or activating C5. We performed structure-guided in silico mutagenesis to identify prospective OmCI residues that contribute significantly to the binding affinity. We tested our predictions in vitro, using site-directed mutagenesis, and characterized mutants using a range of biophysical techniques, as well as functional assays. Our biophysical analyses suggest that the C5–OmCI interaction is complex with potential for multiple binding modes. We present single mutations that lower the affinity of OmCI for C5 and combinations of mutations that significantly decrease or entirely abrogate formation of the complex. The affinity-attenuated forms of OmCI are suitable for affinity purification and allow elution under mild conditions that are nondenaturing or activating to C5. We present the rational design, biophysical characterization, and experimental validation of affinity-reduced forms of OmCI as tool reagents to enable the affinity purification of C5.

Complement component C5 is a large, 188-kDa protein that is integral to the complement system. As an eventual consequence of activation of any of the classical, lectin, or alternative pathways, C5 is cleaved into two functionally distinct moieties (1,2). The smaller anaphylatoxin subunit C5a signals via C5aR, a pro-inflammatory G-protein-coupled receptor (3,4). The cleavage of C5a triggers a major conformational change in the C5d-CUB-MG8 superdomain, forming the metastable C5b subunit (5). The conformationally primed C5b can then assemble with C6-C9 to form the membrane attack complex, which results in the targeted lysis of pathogenic or damaged cells (6 -8).
Intriguingly, parasites have also developed strategies of targeting C5 to evade lysis by the complement system. Two species of tick, Ornithodoros moubata and Rhipicephalus appendiculatus, have independently evolved salivary proteins that inactivate C5 when secreted during feeding (9, 26 -29). The two proteins, dubbed OmCI and RaCI after their respective species of origin, are of comparatively low molecular weight and are distinct in both sequence and structure.
A crystal structure of the C5-OmCI-RaCI ternary complex (9) shows that both proteins bind at opposing ends of the C5d domain of C5, anchoring it to neighboring domains. These sites are distant from the eculizumab and cobra venom factorbinding sites on the MG7 domain (10,30) and may prevent C5 activation by a different inhibitory mechanism.
OmCI, a 17-kDa protein exhibiting the eight-stranded, antiparallel ␤-barrel fold characteristic of the lipocalin family, binds with the C5d and CUB domains of C5 (9,26). Although it does not appear to have bonded interactions with the neighboring, highly mobile C345C domain, such is their proximity that OmCI may still influence its conformational sampling.
As a lipocalin, OmCI has evolved to interact with small hydrophobic molecules (31). In addition to preventing release of C5a, OmCI also sequesters the proinflammatory molecule leukotriene B4 (LTB4), 2 making it a potent inhibitor of the early inflammatory response (27). A crystal structure of the OmCI-LTB4 complex shows the aliphatic chain of LTB4 bound within a conical binding cavity in the center of the ␤-barrel fold (28). The pocket is comparatively promiscuous, also binding palmit-oleic and elaidic fatty acids. OmCI can bind LTB4 and C5 simultaneously in a noncooperative manner (28).
OmCI has broad cross-species reactivity and is being developed commercially in a modified form as Coversin (32), primarily targeting patients with the R885H mutation, a perturbation to the eculizumab epitope that renders them resistant to treatment with the mAb (33,34).
Akin to C3 and C4, C5 is comparatively abundant in serum at a concentration of 75 g/ml (35). It expresses poorly in recombinant systems and is usually purified from serum. The standard protocol involves a serum-precipitation step, followed by two sequential ion-exchange purifications and a gel-filtration step (36,37). Alternative purification approaches have been described, including hydrophobic interaction chromatography and immunoadsorption (38,39). Immunoadsorption is incumbent on the immunization of animals to generate a polysera that, unless a mAb is used, may not be a specific capture mechanism, with only a proportion of the sera being specific to C5 and with the potential for inconsistent performance caused by batch variation. An additional complication is that changes in pH have been shown to activate C5 and subsequently mild, nonactivating elution conditions must be found to break the interaction with the antibody (37).
The C5-OmCI interaction has been previously exploited to purify C5 and OmCI complexes for crystallography (9). OmCI expresses extremely well in bacterial, yeast, or mammalian cells, with a yield of 0.4 g/liter reported in yeast (28). It binds C5 with high affinity and, with suitable modifications to modulate affinity, could be an ideal recombinant tool with which to purify C5 from serum.
The crystal structure allows computational energy calculations to guide the selection of residues to be attenuated using mutations to alanine or glycine. These rationally designed mutants may display differing affinities but also changing susceptibility to pH or ionic conditions, which may be exploited to create a specific recombinant reagent for affinity purification. If discovered, these reagents would markedly simplify the purification of complement C5 and aid academic and industrial research.

In silico design of affinity-impaired OmCI mutants
Using information from the crystal structure of the C5-OmCI-RaCI complex, we designed single residue mutations to reduce OmCI-C5 affinity using Molecular Operating Environment (MOE) software (40,41) (Fig. 1). Mutations were ranked by the dAffinity score, a surrogate for binding free energy. Mutations predicted to lower the energy of the interaction more than 1 kcal/mol were found (Table 1).
Notably, our analysis has selected charged residues that appear to participate in salt bridges or hydrogen bonds. Two hydrophobic residues, Met-134 and Leu-140, were also identified. When mutating hydrophobic residues, we chose glycine mutations rather than alanine to make a nonclashing mutation where hydrophobicity was entirely absent from the side chain.

Validation of affinity-impaired OmCI mutants
To test our predictions, we made the following single residue mutations: R47A, H117A, M134G, L140G, E141A, H164A, and D167A. In addition, a control F36W mutation was made. This mutation is patented as abrogating LTB4 binding without altering the affinity for C5 (42). We tested these OmCI mutants by SPR in single-cycle kinetics experiments, with the aim of identifying a smaller panel of mutants to profile as a purification The two OmCI residues that contribute significantly to binding affinity, Glu-141 and Arg-47, are shown. C shows the residues identified from our in silico mutagenesis study as contributing significantly to binding affinity, with D showing the 180°view.
The rational design of affinity-attenuated OmCI reagent. Single-cycle kinetics is performed by sequentially injecting increasing concentrations of analyte over the sensor chip, in the absence of any regeneration steps to remove bound analyte from the previous injection.
The sensorgrams could be fitted using a single-site binding model but were better explained by including additional parameters in a heterogenous ligand model. When using the single-site binding model, comparatively low standard errors (S.E.), Ͻ0.2%, were observed when fitting the rate constants. However, visually, the model appeared to poorly estimate the on rate (k on ), overestimating the k on at lower concentrations and underestimating at higher concentrations (Fig. S1).
Accordingly, we opted to rank the mutations based on changes in off rate (k off ), which was paramount for affinity purification (Table 1). Two mutations, E141A and M134G, had a significant effect on the stability of the OmCI-C5 complex. The E141A mutation increased the k off value of the complex by ϳ29-fold and the M134G mutation by ϳ229-fold. However, we were concerned that the M134G mutant displayed low binding, relative to the amount of immobilized material, suggesting that the protein had either been expressed with low activity or partially inactivated through amine coupling to the sensor chip. Other mutations had only modest effects on k off . The control F36W mutation, which is distal to the interface with C5, showed a small increase in k off value and displayed the same stoichiometric ratio of binding as WT OmCI. Our sensorgrams suggest that the OmCI-C5 interaction is complex, and we chose to investigate further in multicycle kinetics experiments, which are described later.

Designing a suitable C5 purification reagent ligand based on combined OmCI mutations
To further lower affinity, we modeled combinations of mutations. Again, we used MOE to simulate changes in binding energy ( Table 1). Because of the potential for synergy, we retained mutations that showed little effect in this second round of designs and based our final selection on the output of our MOE analysis.
As predicted, combining mutations further abridge and, in some cases, entirely abrogate binding to C5. Combining either the H164A or the R47A mutation with E141A produced OmCI variants with k off 197-973-fold faster than WT OmCI, characteristics favorable for application as affinity-purification reagents, retaining fast association with C5, but with markedly increased dissociation.
The largest effect was observed in the E141A/R47A/H164A/ L140G quadruple mutant, which produced a square wave sensorgram, more characteristic of the fast binding kinetics of a low-molecular weight chemical fragment than a proteinprotein interaction. The k off value of this protein was ϳ5,500fold higher than WT OmCI. The M134G mutation completely prevented binding when used in combination with E141A and R47A.

Structural and functional analysis
Circular dichroism-To look for gross changes in the secondary structure, we used CD. We tested the E141A/R47A and E141A/H164A double mutants, alongside their constituent single mutants: E141A, R47A, and H164A ( Table 1). The M134G mutant, which markedly increased k off value but significantly lowered binding, was also tested. The CD traces were closely comparable with WT OmCI, except for M134G, which showed a marked loss of structure at 217 nm consistent with an increase in random coil. Loss of the positive peak at 190 nm was also observed, suggesting a loss of overall secondary structure (Fig. S2).
Differential Scanning Calorimetry-We assessed the thermal stability of the mutants, their ability to refold after denaturation, and their capacity to stabilize C5 when complexed using differential scanning calorimetry (DSC). C5 displays a biphasic unfolding; the main peak unfolded at 60°C, displaying a shoulder at 69°C, whereas a smaller, more stable domain or conformer unfolded at 77°C (Table 2 and Fig. 2). Upon complexing with OmCI, the main peak was stabilized significantly with a ⌬T m ϩ8.6°C. The mutants also increase the thermal stability of The rational design of affinity-attenuated OmCI the main peak, with the degree of stabilization reducing in agreement with their attenuated affinity.
The second peak may be stabilized upon OmCI binding, but the effect is smaller. The area under the peak, corresponding to the enthalpy of unfolding remains broadly the same, and the relationship to the affinity of the OmCI construct used is not as clear as observed with stabilization of the main peak.
The OmCI mutants are themselves typically less thermally stable than WT OmCI (Table 1, Fig. 2, and Table S1). The most destabilizing OmCI mutation was M134G, followed by R47A and E141A. The remaining mutants had comparatively modest ⌬T m of less than Ϫ3°C. Of note, the E141A/R47A double mutant was more thermally stable than WT OmCI, despite the single R47A and E141A mutations being among the most destabilizing.
We performed a cycle of cooling prior to a second melt to assess the ability of the proteins to refold after denaturation. The peak area corresponds to the enthalpy of unfolding (⌬H), and by taking a ratio of the two peaks, a ⌬⌬H or percentage of refolding can be calculated. As a large, complex protein with numerous disulfide bonds, C5 was unable to refold, but a proportion of OmCI could recover and be remelted at a T m almost identical to the first cycle. There was an approximately 72% recovery for WT OmCI with 47-69% recovery of the mutants. Consistent with the CD data, the M134G mutant was largely unable to refold with only 24% of protein recovered.
SPR: Multicycle kinetics-Because of our observations when fitting data in our single-cycle kinetics experiments, we looked for further evidence of complex binding kinetics in the C5-OmCI interaction. To obtain more detailed kinetic data for our analysis, we performed multicycle kinetics experiments by SPR. Multicycle kinetics performs a single injection of a single sample concentration within a cycle, regenerating the surface after each injection to dissociate bound material. To exclude heterogeneity in the immobilization steps, OmCI was uniformly immobilized in a fixed orientation, using biotin conjugated to an N-terminal AVI tag.
These data were explained by a single-site binding model, with the fitting of k on generating broadly acceptable S.E. values. The refractive index changes upon binding did not suggest that the binding stoichiometry exceeded 1:1. However, consistent with our single-cycle experiments, the single-site binding model again appeared to underestimate k on at low concentrations and overestimate at higher concentrations (Fig. 3). This suggests that observations in single-cycle kinetics experiments were not the result of heterogenous immobilization to the sensor chip and may indicate additional complexity in the OmCI-C5 binding interaction.
We report individual rate constants and K D values for the mutant OmCI proteins binding to C5 (Table 1, Fig. 3, and Table  S2). The WT protein exhibited antibody-like affinity, exceeding the 1.0EϪ5 s Ϫ1 limit for accurate determination of k off by SPR (43). In our experiments, the K D of WT OmCI is Ͻ100 pM, at least 10-fold lower than previously reported (28). The E141A single mutant displayed a Ͼ7-fold decrease in affinity (K D of 0.7 nM), and the E141A/H164A double mutant displayed a Ͼ40fold decrease in affinity (K D of 4 nM). Finally, the E141A/R47A double mutant displayed a Ͼ130-fold decrease in affinity (K D of 13 nM), relative to WT. Importantly, these decreases in affinity were mediated by increases in the k off values.
Complement activation ELISA-We tested the mutants in an alternative pathway (AP) activation ELISA to establish the extent to which affinity-abridged forms of OmCI could inhibit formation of the MAC (Table 1, Table S3, and Fig. S3). The single mutants and WT OmCI were potent inhibitors, displaying steep Hill slopes, indicating that they had reached the tight binding limit of the assay. The double mutants E141/R47A and E141A/H164A showed Ͼ10and Ͼ5-fold losses of potency, respectively. Despite the variation in potency, all the mutants could effectively inhibit formation of the MAC at sufficiently high concentrations.

Affinity purification of C5 with the E141A/H164A double mutant
Having engineered faster dissociating forms of OmCI, we began to develop methods for the affinity purification of C5 from serum. OmCI proteins were biotinylated via an N-terminal tag and captured on a streptavidin column. Fractionated serum was applied to the column and conditions for the elution of C5 were tested.
With WT OmCI (k off ϭ Ͻ1.0EϪ5 s Ϫ1 ), C5 was captured but would not elute with either 2 M NaCl or 3 M MgCl 2 . Using a pH gradient, C5 was eluted at approximately pH 2.8, where it formed a milky precipitate, which could be reversed upon neutralization with 1 M Tris.
Working to the "Goldilocks principle," we hoped to identify a mutant with the ideal kinetic profile to permit capture of C5 but with a suitably attenuated k off to allow the interaction to be destabilized and eluted under mild conditions. We ran small scale purifications, trialing the mutants as capture reagents in order of their faster dissociation. The E141A single mutant (k off ϭ 3.0EϪ04 s Ϫ1 ) would not permit elution with either NaCl or MgCl 2 . The abridged affinity had only a modest effect on the pH gradient, with C5 still eluting at a pH of ϳ2.8.
The E141A/H164A double mutant (k off ϭ 4.0EϪ03 s Ϫ1 ) was the next mutant trialed as an affinity-purification reagent. With this mutant, C5 was captured and then eluted with either 2 M NaCl or 3 M MgCl 2 . C5 did not respond favorably to elution with high NaCl, irreversibly precipitating when eluted at 2 M and eluting at 1 M in a broad, flat peak. Conversely, 3 M MgCl 2 gave a sharp, symmetrical elution peak with no visible precipitation in the fractions. We tested lower concentrations of MgCl 2 and found we could elute C5 using a 2 M solution, but with a 1 M solution tailing of the peak was observed.
After a gel-filtration step, we tested the purified C5 to ascertain its quality and functional activity (Fig. 4). The final material The rational design of affinity-attenuated OmCI was pure by SDS-PAGE, the thermal melt was consistent with commercially available C5 by DSC, and it bound to WT OmCI with high affinity, Ͻ100 pM, in a multicycle kinetics experiment. Crucially, the material was functionally active and restored activity to C5-depleted serum in an AP activation ELISA, with a pEC 50 of 7.1 Ϫlog M. The E141A/R47A double mutant (k off ϭ 3.2EϪ02 s Ϫ1 ) proved to have too fast a dissociation rate to retain C5 on the column prior to elution. C5 displays a biphasic unfolding, the main peak has a slight shoulder and unfolds at 60.3°C, with a smaller and more stable peak unfolding at 77.1°C. Complexing C5 with OmCI stabilizes the main peak, in proportion to the binding affinity (A). A smaller stabilization effect, ⌬T m ϭ ϩ3°C, is observed on the second peak but without clear correlation to the affinity of OmCI construct (B). C shows DSC thermograms for OmCI proteins. The first round of melting is shown in black with the second melt shown in orange. The peak area corresponds to the enthalpy of unfolding (⌬H) and by taking a ratio of the two peaks a ⌬⌬H or percentage of refolding can be calculated.

Discussion
In silico and biophysical analysis of the C5-OmCI interaction has identified residues that contribute significantly to the high binding affinity. We show that the OmCI-C5 interaction is complex and higher affinity than previously thought, and we demonstrate the utility of affinity-attenuated OmCI as a purification reagent. The in silico mutagenesis, performed using MOE, identified seven residues that could significantly alter binding energy, including five charged residues that potentially contribute to the formation of either salt bridges or hydrogen bonds.
The C5-OmCI-RaCI crystal structure suggests there are two ionic interactions between C5 and OmCI, arising from a bidentate salt bridge between Glu-141 OmCI and Arg-1226 C5 . Experimentally, E141A was the most effective single mutation, which highlights the importance of the salt-bridge interactions.  A shows the elution of C5 from an E141A/H164A OmCI column using isocratic elution with 2 M MgCl 2 , after one column volume the material elutes in a sharp and broadly symmetrical peak. B shows elution of affinity-purified C5 from a gel-filtration column as a single peak with a small high-molecular weight peak. C is a reduced and nonreduced SDS-PAGE gel of the material, before and after affinity purification and gel filtration (5 g of the final C5 sample is loaded). A thermal melt of the protein by DSC is shown in D, with a T m of 59.9°C for the main peak. The ability of the material to restore complement activation to C5-depleted serum, with a pEC 50 of Ϫ7.1 Ϫlog M, is shown in E. Finally, binding of our purified C5 to WT OmCI is shown in a multicycle kinetics experiment, with a K D Ͻ100 pM in F.

The rational design of affinity-attenuated OmCI
The other polar residues lack suitably charged opposing residues on C5 with which to make ionic interactions and instead make interchain hydrogen bonds. In the structure, His-164 OmCI and His-117 OmCI appear to make hydrogen bonds with the backbone carbonyl groups of Pro-1221 C5 and Ser-1236 C5 , respectively. Two residues appear to make bidentate hydrogen bonds: Asp-167 OmCI with the backbone amide groups of Arg-955 C5 and Arg-956 C5 ; and Arg-47 OmCI with the side chain carbonyl and amide groups of Asn-1221 C5.
Experimentally, the rank order of affinity of the hydrogen bonding residues was broadly as per the predictions in MOE, with the K D of H164A Ͻ D167A Ͻ H117A. However, our analysis in MOE overestimated the impact of the H164A mutation relative to removal of the E141 salt bridge and underestimated the importance of the R47A mutation. The H164A mutation was predicted to be the most attenuating single mutation, but it was found to be less effective than either E141A or R47A.
Two hydrophobic residues, Met-134 OmCI and Leu-140 OmCI , were also identified in the MOE analysis. Both are adjacent to charged residues (Asp-135 OmCI and Glu-141 OmCI , respectively), and may have been identified as either lowering the desolvation or conformational entropy of their polar neighbors. We had predicted that L140G would be the third most effective mutation, but as a single mutation, it was largely ineffective and made only a modest contribution when used in combination. The single M134G mutation virtually attenuated binding, but CD and DSC data, suggest this is likely to be due to instability arising from a loss of secondary structure. Met-134 is at the end of a ␤-sheet, and the insertion of a flexible glycine residue may be poorly tolerated in this position.
Met-134 marks the end of BH-␣3, a predominantly hydrophobic loop on which L140G is situated, connecting the large ␣3 helix of OmCI to the ␤-barrel fold. In complex with C5, this loop nestles in a hydrophobic channel on the C5d domain, bordered by Ala-1216 C5 -Val-01218 C5 and Ser-123 C5 -Val-1239 C5 . Although the region is hydrophobic, interchain hydrogen bonds appear to form between the backbone moieties. These are not side-chain interactions and subsequently could not be easily investigated by mutagenesis, but they may still contribute significantly to the free energy of binding, with the hydrophobic environment ensuring a low desolvation entropy.
Finally, the control mutation, F36W OmCI , showed only a slight increase in k off value, as expected. Although this mutant still displayed high affinity binding to C5, this may indicate a subtle allosteric effect arising from this distal mutation.
Overall the structure-based in silico approach was effective in designing mutations, reducing the number of mutants that had to be made and tested. Although there were some notable outliers in the ranking, there is a reasonable correlation with our experimental data.
When considering binding modes, our single-cycle kinetics experiments suggested the potential for a complex interaction that although displaying refractive index changes consistent with a 1:1 stoichiometry was not best described with a one-site binding model. We performed multicycle kinetics, with sitespecific immobilization of OmCI via an N-terminal tag and observed similar outcomes when fitting data using the singlesite binding model, suggesting that the complex kinetics are not the result of a heterogenous immobilization. Both the C5d and CUB domains interact with OmCI, and this creates the potential for multiple binding modes should an interaction with either domain be made first.
C5 displays a biphasic unfolding when thermally denatured. A main peak unfolds at 60°C, with a shoulder at 69°C, and a second smaller unfolding occurs at 77°C. The shoulder of the main peak observed in C5 may be of interest, because it closely matches the Tm of the main peak in the C5-WT OmCI complex. This may suggest that OmCI is selecting a naturally occurring C5 conformation in which C5 is resistant to cleavage by the C5 convertases.
The smaller unfolding at 77°C may also arise from a conformer of C5. If this is the case, the small size of the peak, corresponding to the enthalpy of unfolding, would suggest that the conformation is infrequently sampled. Although OmCI may bind to and stabilize the conformer, increasing T m , the ⌬H remains broadly consistent, which suggests it is not a conformation that OmCI selects for or induces.
Alternatively, this could be a more stable domain that is structurally distinct from the MG domains, which constitute a large proportion of C5, potentially either the C5d, CUB, or C345C domains. Complexing with OmCI appears to stabilize this domain by ϩ3°C, but there is not a clear correlation to affinity when complexed with mutant OmCI. The lack of correlation to affinity could indicate an interaction between C5-OmCI that is not within the binding interface, as seen in the crystal structure, and therefore is not attenuated by our mutations provided full occupancy is maintained.
When looking at the charge profile of the surface of C5 and OmCI, we noted a positively charged cavity between the C5d and CUB domains (Fig. S4). The surface of OmCI is negatively charged, and there may be further complementary charge interactions with this cavity that are prevented by Phe-1631 C5 protruding from the C345C domain and occluding the cavity (Fig. 1A). The position and proximity of the flexible C345C domain appears to be the result of crystal contacts between C5 molecules, rather than a specific interaction with the OmCI protein, suggesting that this positively charged region on C5 may be accessible in solution.
When testing our mutants in AP ELISA, we did not see reductions in E max as a result of removing contacts, which would have indicated allosteric inhibition. Our mutants behave in the functionally competitive manner of the WT, which could indicate a competitive or steric mechanism for OmCI inhibition.
Our affinity-attenuated forms of OmCI are useful tools for the purification of C5; they are highly expressed and able to refold after complete denaturation at 100°C. It is fortunate that the mutations decreased k off specifically, with only modest effects on k on , and as such are ideal capture reagents for affinity columns. Our final purification reagent, E141A/H164A OmCI, is thermally stable and resilient to denaturation. The elution conditions this mutant has enabled are comparatively gentle, with no evidence of C5 activation with short-term exposure to 2 M MgCl 2 . This reagent will be a valuable tool for the purification of C5 to support research and drug discovery.

Experimental procedures
Design of mutations MOE (2016.08) was used to virtually mutate individual OmCI residues to alanine in the crystal co-complex structure of OmCI-C5 (Protein Data Bank code 5HCC). The effect of these changes on the C5 interaction were scored using a binding freeenergy prediction (dAffinity). Alanine and glycine mutations that lowered dAffinity by more than 1 kcal/mol were selected for experimental validation.

Construct design
A DNA sequence, comprising the WT OmCI reference sequence (Uniprot entry Q5YD59), with a N-terminal AVI-tag peptide sequence (GLNDIFEAQKIEWHE) and polyhistidine tag, was designed using Vector NTI. In total, 15 gene variants were designed with single or double mutations to validate the in silico affinity predictions. The amino-acid sequence of the OmCI proteins can be found in Fig. S5. Custom synthesis and cloning into a mammalian expression vector was performed by ATUM.

Small-scale expression and purification
Plasmid DNA for each construct was amplified using Qiagen Plasmid Plus Giga kits and quantified by A 260 . Individual 50-ml ExpiHEK 293 cell cultures, at 3 ϫ 10 6 cells/ml, per construct, were set up using Expifectamine 293 Transfection kits (Invitrogen), as per the manufacturer's instructions. The cells were cultured for 4 days and centrifuged at 4000 rpm for 1 h.
The supernatants were filtered through a 0.22-m sterifilter, and each sample was purified on 1.0 ml of nickel-Sepharose Excel capture resin (GE Healthcare). The following modifications were made to the manufacturer's protocol: after binding, affinity captured protein was washed with 20ϫ resin volume using buffer A (0.5 M NaCl, 0.02 M imidazole, PBS, pH 7.3). Protein samples were then eluted using buffer B (0.5 M NaCl, 0.25 M imidazole, PBS, pH 7.3) as ten 2-ml fractions. Postelution, the proteins were concentrated, and buffer was exchanged using 10-kDa spin concentrators (MerckMillipore). The protein concentration was determined by A 280 , and the samples were analyzed by SDS-PAGE before storage at Ϫ80°C.

Bir-A biotinylation of AVI-tagged OmCI
Biotinylation of the n-terminal AVI tag was performed using a Bir-A labeling kit (Avidity), as per the manufacturer's instructions. OmCI protein was buffer exchanged in 10 mM Tris, pH 8.0, using 2-ml Zeba desalting columns (Thermo Fisher) and diluted to 40 M. Bir-A enzyme was added at 11.625 g/ml, and BioMix B was added at 10% (v/v). An overnight incubation at room temperature was performed. Prior to use, unreacted biotin was removed using a PD-10 desalting column (GE Healthcare).

Surface plasmon resonance: Single-cycle kinetics
Experiments were performed using a Biacore 8K (GE Healthcare). The OmCI mutants were immobilized on a CM5 chip using amine coupling. To achieve a minimal immobilization both flow cells were activated using EDC/NHS at a 1:2 molar ratio (flow rate, 10 l/min; contact time, 30 s). Solutions of OmCI at 1 g/ml were prepared in pH 4.5 Sodium-acetate buffer and immobilized on flow cell two only (flow rate, 10 l/min; contact time, 420 s). Finally, ethanolamine was applied to both flow cells (flow rate, 10 l/min; contact time, 420 s). A final immobilization level of ϳ50 response units was obtained.
Single-cycle kinetics were measured using a 9-point, 3-fold titration of C5 (CompTech) from 500 nM in HBS-EP buffer. A high flow rate of 40 l/min was used, with a contact time of 230 s and a dissociation time of 900 s. Binding to the reference surface was subtracted, and the data were fitted to a single-site binding model using Biacore evaluation software.

Differential scanning calorimetry
DSC was performed using a Malvern Microcal VP Capillary DSC. Samples of C5 (CompTech) and OmCI protein were diluted to 1 mg/ml in PBS. For complexing experiments, C5 and OmCI were mixed in 1:1.25 molar ratio and incubated for a minimum of 6 h at 4°C. Samples were heated from 10°C to 100°C at a rate of 1°C/min. The injection speed was 50 l/s with three filling strokes used. Six buffer reads were measured prior to running samples, and a clean, consisting of three cycles of 20-min detergent incubations at 80°C, was performed after each sample. For refolding experiments, after cooling for 1 h, a single rescan was performed under the same analysis parameters. The ;Data were analyzed using Origin 7.0 software and fitted using a non-two-state, cursor-initiated model.

Circular dichroism
OmCI protein was buffer exchanged into 20 mM phosphate, pH 7.4, 150 mM NaF using 0.5-ml Zeba desalting columns (Thermo Fisher). CD spectra were acquired using a scan range of 185-260 nm, with a step size of 0.5 nm, a time-per-point of 1 s, and a bandwidth of 1 nm. The concentration of protein was 0.3 mg/ml, and the cuvette pathlength was 1.0 mm. An air blank was measured and automatically subtracted, and a buffer blank was measured and manually subtracted from the spectra.

Surface plasmon resonance: Multicycle kinetics
Multicycle kinetics were measured using a Biacore 8K (GE Healthcare). Site-specifically labeled OmCI protein was diluted to 5 nM in HBS-EP. Both flow cells of an SA streptavidin chip were prepared by performing three sequential injections of 1 M NaCl, 20 mM NaOH (flow rate, 10 l/min; contact time, 60 s). OmCI was then injected over flow cell 1 (flow rate, 10 l/min; contact time, 60 s). Final immobilization levels in the range of 20 -100 response units were obtained. A wash of the fluidics with a 1:1 mixture of isopropanol and 1 M NaCl, 20 mM NaOH was performed, prior to running samples.
A 7-point, 3-fold titration of C5 (CompTech) was prepared in HBS-EP buffer from 250 nM. This was injected at a flow rate of 30 l/min, with a contact time of 240 s and a dissociation time of 3600 s. After each injection, the surface was regenerated with two sequential injections of 0.1 M citric acid monohydrate (pH 2.0) with a contact time of 30 s and flow rate of 30 l/min.
Binding to the reference surface was subtracted, and the data were fitted to a single-site binding model, using Biacore evaluation software. When fitting data, curves at concentrations Ͼ18.5 nM (20 -200ϫ, K D ) were excluded from the analysis to maximize curvature and increase the accuracy of fitting k on .

The rational design of affinity-attenuated OmCI Complement activation ELISA
An alternative pathway complement ELISA (Wieslab) was run as per the manufacturer's instructions. 10-point, 3-fold titrations of OmCI were made from 5 M in human serum (TCS Bioscience) and allowed to incubate for 30 min at room temperature, prior to receiving a 1 ⁄ 18 dilution into assay diluent. The diluted samples were immediately plated onto the assay plate.
For experiments with purified C5, C5 was titrated from 5 M in C5-depleted serum (Comptech), immediately diluted 1 ⁄ 18 into assay diluent, and plated. The data were analyzed using a fourparameter logistic fit in Prism 7.02.

Ammonium-sulfate precipitation
Human serum (TCS Bioscience) was thawed in a 37°C water bath. The serum was cooled to 4°C, and 60 ml of a saturated ammonium sulfate solution was added dropwise per 100 ml of serum, with gentle stirring. The precipitate was collected by centrifugation at 10,000 ϫ g for 30 min at 4°C. The pellet was resuspended to the original serum volume using PBS, 10 mM EDTA, and a second round of precipitation was performed. Prior to loading on the column, the sample was filtered through a 0.22-m sterifilter.

Purification of C5 using affinity-attenuated OmCI
Using an Akta pure (GE Healthcare), biotinylated E141A/ H164A OmCI was diluted to 20 M, and 2.0 ml was injected onto a 1-ml Hi-Trap streptavidin HP column (GE Healthcare) at a flow rate of 0.5 ml/min.
The column was equilibrated with 5ϫ column volume of PBS. Ammonium sulfate precipitated serum was loaded at a flow rate of 0.5 ml/min, and a 5ϫ column volume wash with PBS at a flow rate of 1 ml/min was performed. C5 was eluted and collected in fractions, using an isocratic elution of 5ϫ column volume of 20 mM Tris, 2 M MgCl 2 , at a flow rate of 1 ml/min. Immediately after elution, the fractions were pooled and injected onto a HiLoad 16/600 Superdex 200-pg gel-filtration column (GE Healthcare) that had been pre-equilibrated with PBS. The column was run at 1 ml/min, and all peaks were collected. The peaks were analyzed by SDS-PAGE, and those with a molecular weight consistent with C5 monomer were pooled and stored at Ϫ80°C.


extfieldsofstudy: Medicine Biology

text:
Transposable Element Expression in Acute Myeloid Leukemia Transcriptome and Prognosis

Over half of the human genome is comprised of transposable elements (TE). Despite large-scale studies of the transcriptome in cancer, a comprehensive look at TE expression and its relationship to various mutations or prognosis has not been performed. We characterized the expression of TE in 178 adult acute myeloid leukemia (AML) patients using transcriptome data from The Cancer Genome Atlas. We characterized mutation specific dysregulation of TE expression using a multivariate linear model. We identified distinct patterns of TE expression associated with specific mutations and transcriptional networks. Genes regulating methylation was not associated with significant change in TE expression. Using an unpenalized cox regression analysis we identified a TE expression signature that predicted prognosis in AML. We identified 14 candidate prognostic TE transcripts (TEP) that classified AML as high/low-risk and this was independent of mutation-based and coding-gene expression based risk-stratification. TEP was able to predict prognosis in independent cohorts of 284 pediatric AML patients and 19 relapsed adult AML patients. This first comprehensive study of TE expression in AML demonstrates that TE expression can serve as a biomarker for prognosis in AML, and provides novel insights into the biology of AML. Studies characterizing its role in other cancers are warranted.

Supplementary figure 2.
Module average dysregulation for multiple predictive features. The x-axis depicts the predictive features. The yaxis depicts the modules constructed by network analysis. The module dysregulation average was summarized from the significant altered non-TE, predicted by the multiple regression (supplement figure 1), that corresponded to the given module.

Acquisition of RNA sequencing fastq files for AML
The RNAseq data was obtained from The Cancer Genome Atlas (TCGA) via dbGAP access.
The transcriptome data for the 284 pediatric AML (TARGET 1 ) and relapsed AML 2 were also obtained through dbGAP.

Normalization of TE and non-TE in TCGA
We calculated transcript abundances using Kallisto 3  We considered normalized expression of TE and non-TE by collapsing all read counts with a minimum read cutoff of 16, as recommended the sequencing quality control consortium (SEQC) 21 . The non-TE and TE were normalized together as a source data using voom 5  heteroscedasticity, and the normalized counts were then saved in log 2 counts per million (CPM).

Normalization of TE and non-TE in TARGET
The cytogenetic variables included in the pediatric TARGET patient profile were t(8;21), inv (16)

Normalization of TE and non-TE in MSKCC
For the 19 relapsed AML patient profiles, the following predictive features were used in normalizing nonTE/TE expression and survival analysis. Demographic variables were age, and sex. Mutational variables were NPM1 point mutations, FLT3 point mutations. Cytogenetic variables were deletion 5 on the q-arm, deletion of 7 on the q-arm, Y deletion, trisomy 21, t(6;9), and inversion 16. Other variable white blood counts were also used.

Multivariate linear model: altered expression of transposable elements in TCGA
We fit the normalized TE expression in an empirical Bayesian linear model and identified altered expressed TE (using TE transcripts), using Benjamini-Hochberg FDR cutoff of 0.05, un-adjusted p.value threshold of 0.01, minimum absolute log-fold change of 0.5, and hierarchical multiple testing strategy across features as contrasts 6 .
The altered expressed TE (AE-TE) were then identified which satisfied the previously described filtering criteria for each corresponding feature such as mutations, cytogenetics, blood related factors, and clinical/demographic factors resulting in significant effects per feature. The total numbers of AE-TE were plotted in a barplot for each predictor variable. We compared the linear model to a random model by sampling randomly the predictor design matrix and re-fitting an empirical Bayesian random mutational model 7 .
The statistically significantly AE-TE identified after hierarchical testing were summarized by averaging the estimated coefficients for each corresponding AE-TE within each biotype. In other words, for each TE biotype, the significant AE-TE estimated coefficients from the linear model were averaged. The biotype information was identified using Arkas 4 , which includes RepBase annotation information at the transcript level. We hence were able to identify each TE transcript biotype dysregulation for each mutational predictor. Each of these was depicted using an R package 'riverplot'.

Multivariate linear model: altered expression of ENSEMBL non-TE TCGA
Normalized transcripts of non-TE were examined separately from TE. Non-TE were identified using ENSEMBL gene identifiers, which included coding gene, pseudogene, long noncoding RNA, short noncoding RNA elements, and small nuclear RNA. Arkas provided the annotation information and data stored of the experimental data. The multivariate linear model, and significant effects were similarly modeled as with TE; we used hierarchical filtering of the multivariate model coupled with Benjamini-Hochberg FDR filtering q.value threshold of 0.05, un-adjusted p-value 0.01 threshold, and minimum absolute log fold change threshold of 0.5.

Coding gene network association table with TE transcript biotypes
In order to comprehensively study the correlation patterns between TE and other transcripts including coding gene pathways, we constructed a weighted gene correlation network table 8 . We first constructed a non-TE expression network using WGCNA using soft-thresholding power of 6 after confirming that scale free topology and mean-connectivity scores were above 80%, which suffices for diverse nodal connectivity. The normalized non-TE transcript expression in log 2 CPM was used for ENSEMBL gene-IDs described earlier. The minimum genes for each eigengene cluster was set to 10, the maximum was set to 4000. We used 'blockwise' module construction, and module 50 is defined as the set of genes that remained un-connected in the networking context.

Prognostication of AML using TE transcript expression in TCGA:
We used all patients in TCGA to normalize. We prepared the TE expression of all patients by normalizing the collapsed estimated counts with respect to ENSEMBL transcript IDs using limma-voom 11 as previously described. We then subset the training and testing cohort after normalization. The corresponding survival times along with expiration status of each patient was obtained from the data acquisition. The matching survival data for each data split was matched by patient.

Identification of 14 transposable element prognosticators via nested sampling
We identified the 14 candidate transposable element prognosticators through nested sampling in TCGA (N=178). For each nested sample division, the TCGA subjects were divided 60% nested training cohort, and 40% nested testing cohort. Each nested cohort division ensured that selected covariates were prevalent within the subdivision proportional to the population prevalence of the corresponding covariate. The following covariates were frequency matched: TP53, FLT3,

Evaluation of TEP
The survival analysis was evaluated in three ways.
1. We implemented multivariate Cox regression using a ridge regression 13 and the Cstatistic was evaluated using cross-fold validation. For TCGA intra cohort validation, we used 3-fold cross validation (N=37).
2. Utilizing compound covariate prediction method, (identifying patient risk scores as a linear combination of TEP expression and TEP hazard estimates), the patient risk summary scores were cut into 2 risk groups using a quantile cut at the 75 th quartile. A Kaplan-Meier (KM) survival model was performed using the 2 groups, and a Wald test statistic was calculated on the classification of patient risk summary scores.

Simple sign averaging method for risk discrimination
Simple sign averaging of Cox hazard estimates was shown to perform no worse than LASSO, or ridge regression 15 . We calculated the sign average estimated from the TCGA intra cohort validation using the final TEP. Using the sign averages of the Cox hazard estimates, we perform a Kaplan-Meier (KM) survival analysis on the intra cohort validation set (N=37), TARGET, and

Cox proportional hazards model with random biotype effects TCGA
The TEP were fit into a multivariate Cox model with random effects which assumed a Gaussian distribution for the random effects 16  The 'blood' features were BM/PB blast %, and WBC. All variables were then mean centered and fit into a Cox proportional hazards model, each variable's contribution to the multivariate concordance index is depicted in Figure 6. The survival plots were drawn using the R package 'survminer' (Alboukadel, 2017, https://CRAN.R-project.org/package=survminer).

Validation in TARGET Pediatric AML
We validated the 14 TEP expression using 284 pediatric AML patients. The expression data was normalized using limma-voom. The design matrix considered mutation and cytogenetic information that was 5% prevalent in the TARGET cohort. The normalized TEP expression were fit into a Cox model 15 and the hazards estimates were used to identify patient risk scores as previously described. The patient risk summary scores classified patients into 2 groups based on a quantile cut at the 75 th quartile. The patient risk group classification was then fit into a KM survival plot depicted in Figure 3C.
The signed averages of the TARGET TEP hazard estimates were used as an alternative discrimination. The signed averages were computed as previously described and depicted in C and D). The C-statistic was calculated using 5-fold cross validation from the Cox model 15 .

Validation in Adult Relapsed AML Memorial Sloan Kettering Cancer Center (MSKCC) data set
We normalized the expression of MSKCC 19 relapsed AML patients using voom. The 14 TEP normalized expression was similarly used in a Cox model 15 , and the patient risk classification groups were identified. The KM survival model was constructed, and depicted in Figure 3D.
The Wald test statistic was computed from the KM model and depicted in supplementary figure   4F.
The effects of age, and sex covariates were examined in MSKCC. The patient ethnicity information was not available. The ANOVA was performed similar to TARGET and TCGA.
The were summarized into a group categorical covariate using 'coxme'.

TCGA stratified survival models based on mutational covariates
Using mutation status of 178 patients and their corresponding event free survival times (EFS), we predicted survival using Cox model variant that used ridge penalty 15 . The mutational status in the Cox model variant were centered, but not scaled 13 . Survival statistical testing was performed consistently as before. We thus identified 96 low risk patients determined by mutation covariates, and 82 patients as high risk based on mutations alone.
Using the 14 TEP and the 96 low-risk patients classified by mutations, we performed survival analysis using Cox model to sub-stratify the low-risk mutation based cohort. The TEP expression of the mutation based low-risk patient cohort was transformed into a categorical variable. The principal components of the 14 TEP expression 15 corresponding to the patients within the lowrisk cohort were cut at the 75 th quartile. The categorical TEP features were centered and hazards were estimated from a Cox model. This model was evaluated in a Kaplan-Meier survival model as previously described. The summary statistics, and statistical testing of the survival analysis was performed consistently as before. Although dichotomizing the continuous TEP covariates is not necessary, we attempted to negatively bias the TEP's predictive power for a conservative analysis.
High risk (N=82) patients classified by mutations were also sub-stratified. The TEP substratification was performed identically as the low-risk analysis.

TCGA stratified survival models based on cytogenetic covariates
Similar to mutational risk classifications, we identified risk groups by compounding the Cox model hazard estimates derived from cytogenetics previously described. Each cytogenetic risk cohort was analyzed similarly to the mutation sub-stratification analysis using the 14 TEP.

TCGA stratified survival models based on nonTE expression covariates
The nonTE expression of the 178 TCGA patients were used to identify patient risk groups. The 'voom' normalized expression was performed, and the principal components were identified.
The first 14 PCs were used as covariates in the Cox model 15 and were cut into a categorical variable using the 75 th quartile. These categorical features were centered, and then used in a Cox model and the concordance index was computed using a 3-fold cross validation. The nonTE risk groups were identified using the resultant hazard estimates that were compounded with the categorical non-TE PCs variables.
Similar to the identification of risk cohorts using the nonTE expression, the principal components of the TEP were similarly utilized. The nonTE based low-risk patient cohort was used as the subjects for sub-stratification analysis. The principal components of the subjects' corresponding 14 TEP expression were dichotomized into a categorical variable by cutting at the PCs 75 th quartile. These categorical variables were centered and used as the features in the Cox model which identified hazard estimates per each categorical variable. Compounding the Cox covariates with the estimated hazards determined 2 sub-stratified risk groups. These 2 risk groups were used in a Kaplan-Meier survival model ( Figure 4E).
The high-risk cohort identified by nonTE expression was modeled similarly ( Figure 4F).

TEP associated gene predictors identified through penalized regression
The normalized nonTE expression was used as the independent predictors for each of the 14 TEP (response variable) using 'glmnet' 18 . The LASSO (Gaussian family) was used with 10 fold crossvalidation (10). The largest 'lambda' within 1 error of the minimum cross validated error was used. The scaled predicted response was derived from the glmnet model which was compared to the scaled observed TEP expression. Figure 5 depicted the gene predictors which modeled the corresponding TEP expression (R 2 >0.5).
Previously the nonTE network identified module membership for each nonTE. The module membership for the selected TEP predictors was identified using the nonTE network ( Figure 2).

Functional annotation of TEP associated predictors
The DAVID 19 bioinformatic resource was used to identify GO function annotations associated with genes from module 2 and 3. We screened the enriched GO functions using an FDR cutoff of 0.05 and a Fisher p.value threshold of 0.05. GO biological processes (BP), molecular function (MF), and cellular components (CC) were identified for module 2 and 3.


extfieldsofstudy: Medicine Chemistry

s2fieldsofstudy: Biology Chemistry

text:
HMGB1 deficiency reduces H2O2‐induced oxidative damage in human melanocytes via the Nrf2 pathway

Abstract Oxidative stress leads to melanocyte death and has been implicated in the pathogenesis of vitiligo. The nuclear factor, E2‐related factor 2 (Nrf2), is a critical transcription factor in protecting cells from oxidative damage. High‐mobility group box 1 (HMGB1) is a chromatin‐associated nuclear protein and an extracellular damage‐associated molecular pattern molecule. Extracellular HMGB1 released from activated immune cells, necrotic or injured cells, becomes a proinflammatory mediator through binding to cell‐surface receptors of responding cells. In this study, we investigated the role of HMGB1 from melanocytes in the response to oxidative stress and the mechanism involved. We showed that HMGB1 is expressed by primary normal human epidermal melanocytes (NHEMs). H2O2 treatment increased cytoplasmic translocation and extracellular release of HMGB1. HMGB1 knockdown by small interfering RNA (siRNA) led to decreased apoptosis of NHEMs. HMGB1 inhibition enhanced the expression of Nrf2 and its target genes. The expression of Nrf2 and its downstream antioxidant genes was downregulated after the supernatant of H2O2‐treated NHEMs was added to HMGB1‐deficient cells. HMGB1 knockdown by siRNA suppressed the expression of the autophagosome marker, LC3, and enhanced p62 expression. Coimmunoprecipitation with Keap1 showed a reduced Nrf2‐Keap1 interaction and an increased p62‐Keap1 interaction under oxidative stress. These data demonstrated that external stimuli (eg, oxidative stress) may trigger autocrine HMGB1 translocation and release by melanocytes, suppressing the expression of Nrf2 and downstream antioxidant genes to induce melanocyte apoptosis, and thereby participate in the pathological process of vitiligo.

and -independent mechanisms, in hormonal, auto-, para-, or intracrine fashion. Tyrosinase are the key elements in the regulation of melanogenesis. The presence of a large-binding site of the transcription factor family in the gene demonstrates the complexity and precise control of melanogenesis. Within the context of the skin as a stress organ, melanogenic activity serves as a unique molecular sensor and transducer of noxious signals and as regulator of local homoeostasis. 2 Vitiligo is a common acquired skin depigmentation disorder characterized by the death of melanocytes. 3 The aetiology of vitiligo and the causes of melanocyte death are not fully understood, yet. The oxidative stress hypothesis suggests an imbalanced redox state of the vitiliginous skin. This results in dramatic production of reactive oxygen species (ROS), such as H 2 O 2 . ROS oxidize cell components, leading to melanocyte destruction and creating depigmented macules. 4 Increased levels of H 2 O 2 were detected in the epidermis of vitiligo patients. 5,6 The nuclear factor, E2-related factor 2 (Nrf2), is a regulator of cellular resistance to oxidants. The Nrf2-kelch-like ECH-associated protein 1 (Keap1) system is one of the major cellular defence mechanisms against oxidative and electrophilic stresses. [7][8][9][10] Under quiescent conditions, the transcription factor, Nrf2, is constitutively degraded through the ubiquitin-proteasome pathway. Its binding partner, Keap1, is an adaptor of the ubiquitin ligase complex that targets Nrf2. Under oxidative stress, Nrf2 is rapidly released from Keap1 and translocates into the nucleus, where it binds to ARE and induces the phase II antioxidant genes. 11 These genes encode haem oxygenase-1 (HO-1), catalase, superoxide dismutase, glutathione-S-transferase, glutathione peroxidase, NADH quinone oxidoreductase 1 (NQO1), glutamate-cysteine ligase catalytic subunit (GCLC), and glutamyl cysteine ligase modulator subunit (GCLM), which are important antioxidants in melanocytes. 12 p62/ SQSTM1 (hereafter referred to as p62) interacts with the Nrf2binding site of Keap1 and competitively inhibits the Keap1-Nrf2 interaction, which is responsible for the expression of a battery of genes encoding antioxidant proteins and anti-inflammatory enzymes. 13,14 Nrf2 is a critical transcription factor in protecting melanocytes from oxidative damage. 15 Dysfunction of the Nrf2 signalling pathway may result in increased sensitivity of vitiligo melanocytes to H 2 O 2 -induced oxidative insult. 6 High-mobility group box 1 (HMGB1) is a highly conserved nuclear protein involved in transcriptional activation and DNA folding in eukaryotic cells. 16

| Skin specimens and ethics statement
Normal skin specimens (for primary human melanocyte culture) were obtained from six healthy individuals who underwent circumcision.
In this study, foreskin was from Asiatic-Chinese yellow race donors.
The study was performed in accordance with the Declaration of Helsinki (1964) and its later amendments, and the protocol was approved by the Clinic Research Ethics Board of the First Affiliated Hospital of Xi'an Jiaotong University. All participants gave written informed consent prior to inclusion in the study.

| NHEM cultures
The epidermis was separated from the dermis after an overnight incu- It must be noted that melanocytes used for the experiments were lightly pigmented and dendritic morphology did not change significantly during relatively short periods of incubation including 6 and 12 hours after 0.5 mM H 2 O 2 treatment ( Figure S1).

| Antibodies and reagents
In this study, we used rabbit anti-human HMGB1, anti-human p62,

| Small interfering RNA gene silencing
The

| Statistical analysis
Student's t test was used to determine significance of between-group differences. The dose-response analyses were conducted by one-way ANOVA with Dunnett's multiple comparison test. All values are expressed as means ± SD, and differences were considered statistically significant when P < 0.05. All statistical analysis were performed by the GraphPad Prism (GraphPad Software Inc, San Diego, CA, USA).

| HMGB1 protein expression in NHEMs
The expression of HMGB1 in NHEMs is shown in Figure 1.
Immunofluorescence staining with anti-HMGB1 showed that HMGB1 is predominantly located in the nucleus of NHEMs.

| Cell viability after H 2 O 2 treatment
To investigate the viability of NHEMs in the presence of oxidative stress, we used H 2 O 2 to induce oxidative stress. As shown in

| H 2 O 2 treatment promotes cytoplasmic translocation and release of HMGB1 from NHEMs
HMGB1 is an abundant nuclear protein with proinflammatory activity that depends on its extranuclear function. To investigate the distribution of HMGB1 under oxidative stress, immunofluorescence staining was used with specific anti-HMGB1 antibodies. As shown in Figure 3A, untreated NHEMs primarily displayed nuclear localization of HMGB1. However, in cells treated with exogenous H 2 O 2 , there was an increase in the percentage of HMGB1 localization at the cytoplasm. To confirm HMGB1 cytoplasmic translocation, cytoplasmic and nuclear fractions of NHEMs were isolated and immunoblotted with antibodies specific for HMGB1 ( Figure 3C). Consistently, levels of HMGB1 in the cytoplasmic fractions were increased dramatically after treatment with H 2 O 2 ( Figure 3D). Furthermore, the levels of HMGB1 released into the culture medium were measured by western blot analysis ( Figure 3E)

NHEM apoptosis
HMGB1 release is a common mediator of the response to oxidative stress; consequently, we examined the role of HMGB1 in NHEMs under oxidative stress. We used siRNA targeting HMGB1, and HMGB1 knockdown efficiency was evaluated by western blotting ( Figure 4A). In addition, using flow cytometry we examined apoptosis of cells transfected with scrambled siRNA or HMGB1 siRNA 24 and 48 hours after H 2 O 2 treatment. As shown in Figure 4B, decreased apoptosis was observed in HMGB1 siRNA-transfected cells compared with scrambled siRNA-transfected cells. Collectively, these data suggest that HMGB1 plays a major role in promoting NHEM apoptosis.
F I G U R E 1 HMGB1 expression in NHEMs. Detection of HMGB1 by immunofluorescence using rabbit anti-HMGB1 antibody followed by Alexa488-conjugated secondary antibody (HMGB1, green; nucleus, blue). Scale bar = 10 μm. Three independent assays were performed F I G U R E 2 NHEM viability decreases after H 2 O 2 treatment. Melanocytes were exposed to H 2 O 2 at the indicated concentrations for 24 hours. A significant decline in cell viability was detected when the H 2 O 2 concentration was ≥0.5 mM. Data are from three independent assays and are expressed as means ± SD. **P < 0.01

| HMGB1 knockdown enhances the expression of Nrf2 and its target genes, which can be reversed by addition of H 2 O 2 -treated melanocyte supernatant or recombinant HMGB1 protein
Nrf2-ARE, a major antioxidant pathway, regulates oxidative stressrelated cytoprotective genes in melanocytes. We investigated whether knocking down HMGB1 affects the expression of Nrf2 and its down-

| HMGB1 silencing inhibits NHEM autophagy and enhances p62 expression
It has been shown that HMGB1 translocation to the cytoplasm is closely associated with HMGB1 autophagy 20 and that HMGB1 activated an autophagic response to oxidative stress. 22 The selective autophagy substrate, p62, activates the stress-responsive transcription factor, Nrf2, through inactivation of Keap1. 13 To study the mechanism by which HMGB1 knockdown changes Nrf2 and its downstream genes' expression, we evaluated the expression of LC3 and p62, two characteristic markers of cell autophagy, in control and HMGB1-knockdown cells. As shown in Figure 6A and B, H 2 O 2 increased the expression levels of LC3II in NHEM control cells. However, the LC3II expression level in the HMGB1 siRNA group decreased significantly. In contrast, the p62 expression level in the HMGB1 siRNA group was significantly higher than that in the control group ( Figure 6C and D).

| HMGB1 knockdown suppresses Nrf2-Keap1 interaction and induces p62-Keap1 interaction under oxidative stress
To study the mechanism of the HMGB1 knockdown effect on the expression of Nrf2 and its downstream genes, we used co-IP techniques

| DISCUSSION
HMGB1 is a DNA-binding nonhistone protein that acts as a transcription regulator participating in DNA replication, recombination, in the development of vitiligo and helps understand the mechanism of melanocyte death.
We further studied the mechanism of HMGB1 affecting the expression of Nrf2 and its downstream genes. Our results suggest that HMGB1 can be transferred from the nucleus to the cytoplasm under oxidative stress (Figure 3). The literature indicates that HMGB1 can activate autophagy under oxidative stress.
HMGB1 plays a location-dependent role in inducing autophagy. 20 Consistently, our results showed that after HMGB1 knockdown, autophagy was suppressed and p62 expression was increased. The competitive interaction of p62 with Keap1 can promote Nrf2 activation to play its antioxidant role. It has been reported that ATG7 knockout in mice leads to p62 accumulation, and then to enhanced expression of Nrf2 target genes in melanocytes. As shown in Figure 7, in HMGB1-deficient NHEMs, SQSTM1/p62 competed with Nrf2 for binding to its cytoplasmic anchor Keap1, leading to enhanced Nrf2 nuclear localization and transcription of its target genes. These results explain the relationship between HMGB1 and Nrf2 through autophagy.
F I G U R E 7 Nrf2 and p62 co-IP with Keap1 after HMGB1 siRNA transfection. (A) Co-IP of Nrf2 and p62 from NHEM lysates. NHEMs transfected with HMGB1 siRNA or scrambled siRNA were stimulated with H 2 O 2 (0.5 mM) for 12 hours, and whole-cell lysates were immunoprecipitated with antibodies specific for Keap1. IgG was used as a control. The precipitated complexes were separately immunoblotted with Nrf2 or p62-specific antibodies. IP, immunoprecipitation; WB, western blotting. The blots are representative of three experiments with similar results It was reported that locally produced protective molecules such as melatonin also act on NRF2 in protection of melanocytes. 27 Melatonin and its metabolites protect melanocytes from UVB-induced DNA damage and oxidative stress through activation of NRF2dependent pathways; these actions are independent of an effect on the classic membrane melatonin receptors. 28 Melatonin and its derivatives counteract the ultraviolet B radiation-induced damage in human and porcine skin ex vivo. 29 We believe that the study of the relationship between HMGB1 and melatonin is also significant for melanocytes under oxidative stress.
In conclusion, our data showed that oxidative stress may trigger autocrine HMGB1 translocation and release by melanocytes, reducing the expression of Nrf2 and its downstream antioxidant genes to induce melanocyte apoptosis. We think that this is the mechanism by which HMGB1 participates in the pathological process of vitiligo.