Pyura chilensis, a species that exhibits a low larval transport potential but high anthropogenic dispersal. A recent study in this species recorded significant genetic differentiation across a transition zone around 30°S in putatively adaptive SNPs, but not in neutral ones, suggesting an important role of environmental heterogeneity in driving genetic structure. Here, we aim to understand genomic-oceanographic associations in P. chilensis along the Southeastern Pacific coast using two combined seascape genomic approaches. Using 149 individuals from five locations along the HCS, a total of 2,902 SNPs were obtained by Genotyping-By-Sequencing, of which 29–585 were putatively adaptive loci, depending on the method used for detection. In adaptive loci, spatial genetic structure was better correlated with environmental differences along the study area  than to the geographic distance between sites. Additionally, results consistently showed the presence of two groups, located north and south of 30°S, which suggest that local adaptation processes seem to allow the maintenance of genomic differentiation and the spatial genomic structure of the species across the 30°S biogeographic transition zone of the Humboldt Current System, overriding the homogenizing effects of gene flow.The highly heterogeneous Humboldt Current System (HCS) and the 30°S transition zone on the southeast Pacific coast, represent an ideal scenario to test the influence of the environment on the spatial genomic structure in marine near-shore benthic organisms. In this study, we used seascape genomic tools to evaluate the genetic structure of the commercially important ascidian  Dispersal has been often assumed to be an effective mechanism of maintaining connectivity between local populations5. Nonetheless, many species display inconsistent patterns of genetic structure with those predicted from their developmental modes9. For instance, several direct developers are broadly distributed over thousands of kilometers, probably through long-distance dispersal mediated by rafting13, while many broadcast-spawners exhibit high levels of genetic structure at narrower geographical scales14. Currently, it is widely accepted that evolutionary forces, despite dispersal, can shape the genetic structure of a species across its distribution. Such factors include natural selection, phenotype-environmental mismatch and local adaptation, which may override the genetic signatures expected solely on the basis of gene flow17.Most marine taxa, and especially near-shore invertebrates, have free-living larval stages allowing dispersal across heterogeneous environments that lack evident barriers to gene flow23. Many relevant environmental variables are heterogeneous across a species’ distributional range and generate differential local selective pressures that may lead to major differences in global patterns of genetic differentiation. Thus, local adaptation can lead to genetic structure across a species’ range even in the presence of high levels of gene flow25.Understanding how environmental factors influence the contemporary spatial distribution of genomic diversity has become a main goal in evolutionary ecology and biogeography18. A main focus is the study of adaptive genetic structure associated with environmental data, which has been increasingly applied using Next Generation Sequencing (NGS) techniques through the identification of thousands of polymorphic loci scattered across the genomes27. However, very few studies on marine benthic invertebrates have attempted to identify loci that are putatively under selection in association with seascape; most studies have use traditional DNA markers33, and seascape genetic studies have evaluated neutral processes associated with genetic connectivity, niche modeling, as well as the role of physical factors33. Genomic-based studies considering the potential effects of environmental factors in neutral and adaptive genetic variation in marine invertebrates are still scarce37.Landscape genomics combines population genomics and landscape ecology to further understand how biotic and abiotic factors affect the processes associated with local adaptation and population structure38. Such patterns of genetic structure have mainly been associated with a widely reported biogeographical/phylogeographic transition zone at ~ 30°S40. This transition zone has been attributed to historical factors because only species with low dispersal capacity exhibit signals of marked genetic differentiation3. According to Lara et al.41, patterns of differentiation in poorly dispersing taxa are probably maintained through environmental differences north and south of 30°S. Therefore, this environmental discontinuity may also have contemporary influence on the genetic structure in species with low dispersal potential along the HCS. Conversely, in species with high dispersive potential, the signals of genetic differentiation across this transition zone have been completely erased by neutral processes probably associated with the homogenizing effect of gene flow3.The Humboldt Current System (HCS) is an environmentally heterogeneous system and accordingly, a very interesting area to evaluate adaptive genetic structure in marine organisms. This system does not have evident geographic or physical barriers to gene flow along its ~ 2,600 km of linear coastline. Nevertheless, several studies have recorded significant genetic differences between local populations in the areaPyura chilensis Molina 1782 is a benthic intertidal tunicate that is endemic to the southeastern Pacific coast, and represents one of the most conspicuous inhabitants of the HCS42. Like many other tunicates, P. chilensis is considered a poor disperser because it has a very short free-living lecithotrophic larval stage (12–24 h), after which it settles on hard substrate or on the matrix of conspecifics45. Phylogeographic studies using allozymes46, and nucleic acids8 suggest low or even a complete lack of genetic structure across the 30°S transition zone of the HCS. The pattern of genetic homogeneity in P. chilensis contrasts with those recorded in other near-shore species with low dispersive potential along the study area40. The absence of genetic differentiation in P. chilensis has been explained by anthropogenic transport promoting high levels of connectivity47, as recorded in other ascidians worldwide49. Even though the mechanism associated with the anthropogenic dispersal of P. chilensis has not been explicitly evaluated, this tunicate has repeatedly been reported on artisanal boat hulls, buoys and marine installations as an important component of the biofouling communities51. Hence, it is likely that human activities may play an important role in maintaining genetic homogeneity recorded in the species across the HCS. More recently Segovia et al.47 using Single Nucleotide Polymorphisms generated with Genotyping-By-Sequencing (SNP-GBS) evaluated neutral and putatively adaptive genetic structure in the species and recorded contrasting results to those previously detected, particularly across the 30°S transition zone. Consistent with traditional markers46, neutral SNPs loci47 showed absence of genetic structure across the 30° zone. In this neutral loci, 30°S structure appear only forcing a sub-optimal clustering. In contrast, the analyses of putatively selective SNP loci showed strong evidence of genomic structure across the transition zone47. Such results indicate that marine environmental stressors have a selective role shaping patterns of genomic structure along the HCS and override the effect of gene flow that is most likely mediated by anthropogenic transport47.The ascidian P. chilensis across the 30°S transition zone of the HCS through the combined use of two seascape genomic-oceanographic approaches. Through this, it will be possible gain a better understanding of the role of environmental differences in the spatial genomic structure in a species with low intrinsic dispersion potential but high physical transport along the study area.The aim of this study was to characterize both, neutral and putatively adaptive genomic structure in tags were successfully retained yielding, before filtering, 102,816 SNPs in the variant call process using TASSEL. Using restrictive bioinformatic filters described by Segovia et al.46 for P. chilensis , a total of 2,902 SNPs were genotyped. Among these, 67 candidate loci for positive selection and 2,521 as putatively neutral loci were identified using Bayescan analysis. A total of 51 loci (76%) showed strong or very strong evidence of positive selection following the Bayes Factor. Similarly, Out-flank and PCAdapt detected 31 and 97 candidate loci for positive selection, respectively. In consensus, the three different methods detected in common a total of 29 candidate loci for positive selection  across the HCS transition zone. At the same time, we considered those SNPs explicitly defined as putatively neutral loci  by Bayescan. Neutral data were used in two ways. First, to analyze neutral spatial genetic structure and correlate it with environmental variables (RDA) and compare with adaptive genetic structure. Second, to incorporate the neutral genetic signal in the detection of local adaptation candidate loci based on genotype-oceanographic associations.A total of 344,964,130 reads with a mean quality score (QF) of 34.34 were obtained from 149 individuals collected from five localities along the southern Chilean coast from two consecutive years  than neutral loci  and Caleta Pajonales (CP), which was non-significant , we determined the relative contribution of geographical position and environmental variables to the genetic structure of the outlier and neutral genotypes. For this, using the 29 consensus outlier loci, we carried on a multivariate correlation with environmental variables with a Partial Redundancy Analysis . Optimal models were estimated incorporating oceanographic variables as fixed factors and controlling by spatial variables (dbMEMs), which resulted in a highly significant model . The first two axes of the outlier loci model explained 98.4% of the total variance, and the distribution of the loci showed two groups, north and south of 30°S, that were significantly correlated to Sea Surface Temperature (SST), pH, fluorescence, and non-significant for silicate , but with a much lower correlation value (Radj2 = 0.036). This model included SST, Nitrate, Silicate and Chl-a, being statistically significant only in the first three variables , geographic distance, and the Mahalanovic environmental distance  as a co-variate resulted in a highly positive correlation for neutral loci . Interestingly, the same model for outlier loci was not significant for geographic distance .In summary, the RDA analyses showed dissimilar signals for the influence of environmental variables of putatively adaptive and neutral loci, except for the influence of SST. Although the neutral model was significant, the correlation coefficient was low, suggesting that neutral genotypes could be more associated with geographic distance than with environmental differences. Interpretation of these results was validated by the fact that a Partial Mantel Test using standardized matrices of genetic distance (find.clusters function) using the Bayesian Information Criterion (BIC), and to determine the neutral genetic structure. With this, DAPC identified an optimal K of two genetic groups; one north ) and another group south , Ancud (AC)) of 30° transition zone. Taking into the account that GEA analyses could be corrected with neutral structure, autocorrelation and geographic distance, this clustering was used in subsequent analyses to control for neutral effect in the determination of local adaptation candidate loci to avoid false positives.With neutral loci, we performed a Discriminant Analysis of Principal Components (DAPC) to estimate the number of genetic groups , to detect candidate loci for local adaptation we performed genotype environment association (GEA) analyses which correlate each environmental variable with individual genotypes. With the independent application of three different methods , we detected a range of 315 – 666 putatively adaptive loci that exhibited significant correlation with each of the seven environmental variables. This result was substantially greater than those obtained with outlier detection approaches , while Chl-a and silicate were correlated with fewer loci . A total of 94 local adaptation candidate lociwere identified by all three methods , with 68% of the loci significantly associated with more than one environmental variable. In this context, the most relevant variables were pH (28), phosphate (26) and fluorescence (16), which detected most loci identified by the different methodologies, followed by SST (11) and Nitrate (6). In contrast, two and zero consensus loci, respectively, were detected for Chl-a and Silicate , which represent the genetic variability linked to the spatial structure among sites. The linear regressions performed with the multi-locus clines and the spatial variables (dbMEMs), indicated that the variables that best fit the adaptive genetic structure were SST . From sPCA’s, we extracted the FST, ordination) or based on genotype-environment association (GEA), were suitable and consistent for the search of relevant environmental factors in the adaptive structure of P. chilensis in the study area. The second type of methods (GEA), however, was more specific and allowed identification of a large number of putatively adaptive loci associated with each environmental variable independently.Both types of analysis, i.e. based on outlier detection  showed significant differences in allele frequencies associated with latitude Fig. A–E. The P. chilensis was more influenced by geographic distance between sites than by environmental differences. Conversely, the genomic spatial structure of putatively adaptive loci was more influenced by environmental features. Specifically, adaptive genetic structure of P. chilensis across the HCS was better explained by the contrasting environmental conditions found north and south of the 30°S transition zone than by geographic distance.This study represents the first evaluation of the role of environmental factors in the adaptive processes of benthic species across a biogeographic transition zone (30°S) in the Humboldt Current System (HCS). A main conclusion is that the spatial genetic structure of neutral loci in 47 was the first to suggest an active role of the environment in the maintenance of the 30° transition zone in the HCS at the genomic level. Likely, environmental differences north and south of 30°S, as reported by several studies54, could be shaping the genomic structure of the species. This transition zone has been considered as a historical biogeographical break3 , because only species with low capacity of larval dispersal have a coincident genetic differentiation at the 30°S . In addition to the historic origin of the break, there are ongoing processes that are likely shaping adaptive genetic variation. Moreover, genetic differentiation of neutral markers does not reflect a genetic break at 30°S neither with mtDNA, nuclear traditional markers8 or neutral SNPs in P. chilensis46. This is consistent with several studies in were SNPs markers show finer genetic structure respect to traditional markers, but the general patterns tend to be in touch59. The fact that both, traditional markers  and neutral SNPs (contemporary gene-flow) showed absence of genetic structure across the 30°S transition zone, suggests that anthropogenic transport is an effective agent of long-distance dispersal in the species47.The study of Segovia et al.P. chilensis at 30°S in putatively adaptive loci were Sea Surface Temperature (SST), pH, upwelling-related variables , and photosynthetic activity measured by fluorescence (Flu), chlorophyll a (Chl-a). Association of these variables to genotypes tended to generate two consistent groups –north and south of 30°S – instead of multi-locus gradient clines. In spite of slight latitudinal variations, multi-locus clines showed an important intra-group (north and south of 30°S) degree of cohesion and accordingly, the most plausible scenario is the presence of two independent groups. Environmental differences north and south of the 30°S transition zone41 may constitute key factors in maintaining the genetic differentiation in spite of transport of individuals between local populations47. In the context of our study model, natural selection seems to override the homogenizing effect of gene flow across the 30°S transition zone of the HCS in putatively selected loci and accordingly lead to local adaptation processes.The main environmental factors that explain genetic structure in P. chilensis could be due to the presence of two areas with contrasting differences in terms of upwelling intensity. The first one, between 30°S and 18°S in northern Chile, includes upwelling-favorable winds across the year. The second one, between 30°S and 41°S, exhibits a marked seasonality in upwelling, especially in the austral spring and summer60, which also produces cold water near the coast from 30° southward52. The influence of environmental variables associated with temperature and productivity on the ecological structuring in near-shore marine benthic organisms has been widely reported both in the northeast and southeast Pacific coasts63. These studies suggest that upwelling regimes have an influence on the communities in terms of recruitment rates, biomass, and individual growth of the organisms64. More specifically, the concentration of ecological suppliers of primary productivity (i.e. nutrients) tend to show a variation in their growth rate along with upwelling and that the greatest response occurs when upwelling is intermittent65.In addition to the commonly reported effects of temperature on the genetic structure, the relevance of environmental variables associated with upwelling in maintaining adaptive genetic differentiation across the transition zone of the HCS in P. chilensis, eco-physiological differences have been reported across the study area68 which together with the limited capacity of the species to colonize new areas, despite being a dominant substrate competitor69, suggest an active role of local adaptation. The weak colonizing capacity of P. chilensis may be a consequence of non-selective larval settlement that could be influenced by water flow, coastal configurations, ecological interactions45, and other selective environmental variables. For example, differences in SST have a significant influence on the seasonal and spatial reproductive activity of P. chilensis43. Despite this, it has also been reported that the species may have a wide range of physiological tolerance to temperature70, which could explain its wide geographic distribution range (10°–44°S)42. It is possible that the wide physiological tolerance range may be counteracted by adaptive genetic differentiation along the species distribution. Local adaptation could lead to genotypic differences between adjacent populations inhabiting contrasting environmental conditions. In fact, recently Giles et al.71 and Morales-González et al.72 showed that P. chilensis is significantly structured over fine spatial scales (20–40 km). Consistent with this finding, the genetic diversity of candidate loci for local adaptation varied along the study area. Both, major and minor alleles, were in some cases present as heterozygotes along the study area but, particularly the minor allele, tended to be geographically restricted to the region north of 30°S. This suggests that transport of individuals between regions by anthropogenic vectors could allow the recruitment of individuals in areas with sub-optimal conditions for genetic migrants, as proposed by Haye & Muñoz-Herrera8 and Segovia et al.47.For the particular case of 75. Taking this caveat into account, one locus was consistently and significantly correlated with four environmental variables  and its sequence matched that of a gene that appears to be involved in cilia-mediated processes, including filtration and ventilation. Interestingly, temperature and phytoplankton concentration appear to be important environmental variables that control fundamental processes associated with ciliate-mediated filtration in ascidians77. A positive relationship between filtration rate (cilia activity) and temperature has been described in the ascidian Ciona intestinalis78. Cilia activity also varies with water productivity, with a negative relationship between cilia activity and phytoplankton biomass78. Thus, both changes in productivity and temperature could have a significant effect on the rate of cilia mediated filtration and ventilation in P. chilensis, which could be a key factor in the maintenance of contemporary genetic structure and restriction of certain alleles north and south of 30°S.From all candidate SNPs, several potential genes with known function were significantly associated with one or more environmental variables. However, since GBS loci are random short loci distributed across the genome, gene mapping could be highly influenced by chance, or by hitchhikingP. chilensis may indicate the influence of continental waters, especially river discharges79. The influence of continental waters is also variable in the study zone; river discharges have little influence north of 32°S while between 36°S and 42°S there is a considerable influence of continental waters, particularly during autumn and winter80. The multilocus clines associated with putatively adaptive loci showed similar values in the two sites south of 30°S, which suggests that the allele frequencies varied less than expected on the basis on considerable distance between them (~ 580 km). Such genomic homogeneity in the putatively adaptive genetic structure south of 30°S may be due to the influence of continental waters (glaciers and river discharges) occurring mainly in that area79 combined with the effect of anthropogenic transport that allows dispersion of individuals47.Finally, in addition to the role of upwelling, the influence of the pH, nutrients such as phosphate and photosynthetic activity in the genomic structure of 19. Remote sensing of oceanographic variables can be quantified across relevant spatial scales, and including genomic diversity analyses, allows to better understand how seascape attributes can shape the spatial genetic structure in near-shore marine organisms81. This study represents the first attempt to evaluate the association between environmental factors and the maintenance of a genetic discontinuity across the HCS in a species for which previous studies showed genetic homogeneity at a large spatial scale, suggesting active gene flow along the southeastern Pacific coast. The main results suggest that at the recorded transition zone (30°S), local adaptation processes could allow the maintenance of contemporary genomic differentiation and spatial genetic structure in P. chilensis. This species is an example of how seascape heterogeneity across a biogeographic transition zone may override the effects of gene flow and lead to local adaptative processes. Even in the presence of high gene flow, probably mediated by anthropogenic transport between and within regions, P. chilensis appears to be sensitive to environmental heterogeneity, especially for those variables that could influence fundamental processes like feeding, filtration, recruitment and growth. Accordingly, putatively adaptive loci showed strong genetic structure associated mainly with environmental differences north and south of 30°S biogeographic transition zone.Signatures of selection and local adaptation can now be evaluated in populations across entire genomes or genome sampling using population differentiation approaches (i.e. outliers) or in association with environmental variables to test the influence of biotic and abiotic factors in the spatial genomic structure. These evaluations give insights into contemporary processes, and may explain how environmental factors influence selective and neutral genomic diversity within and among populationsThe environmental variables used in this study may be a first step in identifying the selective factors that maintain the current genetic structure north and south of 30°S in other benthic marine species from the southeastern Pacific coast.47 were re-analyzed adding 89 individuals obtained from the same sampling sites, the year after the original sampling (2014–2015). From the original dataset, to avoid bias in the candidate loci detection, the location of Los Molinos (~ 39°S) was excluded due to the presence of a highly differentiated lineage47. In total, thirty individuals of Pyura chilensis were collected from each of five localities between 26°S and 42°S from two consecutive years (2014–2015) in the Southeast Pacific Coast  following the manufacturer’s instructions. The DNA was sequenced using the restriction site-associated DNA sequencing method Genotyping-by-Sequencing82 using the ApeKI restriction enzyme. This enzyme was used following the optimization of Segovia et al.47 for the species, which was chosen due to the wide genome distribution of flanking region and avoiding of repetitive zones83. Libraries were prepared in a HiSeq2000  platform and the resulted reads (100pb) were visualized and analyzed in FastQC version 0.10.1 for quality checks. Demultiplexing and SNP calling filters were done following Segovia et al.47 using the Universal Network-Enabled Analysis Kit pipeline (UNEAK84) from the TASSEL platform (www.maizegenetics.net) using a minimum call rate (mnC) of 85% and a minor allele frequency (mAF) of 0.04 excluding also those loci in Hardy–Weinberg disequilibrium in at least 60% of the sampled sites after a False Discovery Rate (FDR) with a q-value of 0.05.Raw data from Segovia et al.mpling 204–2015. FP. chilensis using seven relevant oceanographic variables for each locality  and from the Bio-Oracle database , and 2) Genotype-environmental (oceanographic) association analysis.We analyzed the effect of environmental factors on the genetic structure of ty Table . Environ87 analyses were performed with a pr_odds of 1,000 with 100,000 iterations and a burn-in of 10,000 steps. Results were corrected using a FDR of 0.05 with the logarithm of the q values. The candidate loci for positive selection considered were those with strong or very strong evidence of selection according to Jeffrey’s criterion88 based on the values of the Bayes Factor (bf > 10).For outlier detection, three independent methods were used: 1) Bayescan 2.17789 analyses were performed using a neutral FST distribution, eliminating loci with extreme FST values in both tails, and using a LeftTrimFraction and RightTrimFraction of 0.05, with a minimum heterozygosity of 0.1. The outliers were identified in the 5 sampling sites using an interval of q values of 0.05. Finally, 3) PCAdapt90 were done by first using a PCA with the number of groups (K) equal to the number of populations studied (5) to define the optimal value of K. Distribution of p values was corrected using a FDR with q-values of 0.05, finally obtaining a list of outlier loci that were candidates for selected loci (See Appendix 2 and Table S2 for detailed methods). Multiple approximations were used to reduce the probability of false positives in the final dataset following Lotterhos & Whitlock91 and Rellstab et al.18.2) Out-flankUsing the information of the data obtained with outlier detection using population differentiation approaches , we determined the relative contribution of geographical position and each considered environmental variable to the genetic structure of outlier and neutral genotypes.P. chilensis was estimated with the geographic coordinates of the sampling sites using distance vectors based on distance-based Moran’s eigenvector maps, dbMEMs93. Following this, dbMEMs were analyzed in Redundancy Analyses (RDA) in the R package vegan94 using standardized environmental variables and the genotypes associated with the neutral and positive selection candidate loci as response variables. Prior to the analysis, genotype data were standardized by removing the broad scale trend using the decostand function with the Hellinger’s method in vegan. With this, we carried out a Partial RDA controlling for the effect of space (dbMEMs). Optimal model in vegan were determined using ordistep and ordiR2step functions and the significances were evaluated for each fixed factor through a marginal ANOVA test . Finally, we calculated the pairwise FST matrices for both datasets using Arlequin 3.5.2.295 using 10,000 permutations. With this, we estimated the relation between genetic, geographic and environmental distances using standardized values with a Partial Mantel test in ecodist package in R. This test was carried on using genetic distance (i.e. FST) and geographical distance between localities, using an environmental distance matrix as covariate. The environmental distance matrix was calculated using Mahalanovic distances of the standardized oceanographic variables  in the R package StatMatch. Final matrix was constructed with the vegdist function of vegan in R, using a Euclidean method.The spatial genomic structure of 18 we used both, multiple approximations and the signal of previously determined neutral loci as control97. For this, the find.clusters function of a Discriminant Analysis of Principal Components (DAPC) of putatively neutral SNPs was performed first in adegenet98 package of R 3.22. This was done to determine the number of genetic groups/clusters using neutral loci with a Bayesian Information Criteria (BIC). Once the neutral structure was determined, we used three approximations: (1) LFMM v 1.4 (Latent Factors of Mixed Models)99, which were executed in the LEA package in R 3.22 using the number of latent factors based on the results of the DAPC . 200,000 iterations were done after a burn-in of the first 20,000 steps. Five independent runs were done for each analysis to estimate the different parameters, as recommended by Frichot et al99, and the mean of the z-scores was used for the final results. The significance of the values associated with each SNP was calculated using α = 0.01 corrected with a FDR.In parallel, we also carried out GEA approaches, that explicitly incorporated the environmental variables to determine candidate loci for local adaptation. In order to avoid false positives due to neutral processes and/or spatial autocorrelation100 we evaluate the correlation between SNPs and environmental factors testing whether the model that includes the environmental variable  fits the data better than a null model . To estimate this matrix, we performed five independent runs using 100,000 iterations. Then the entire set of SNPs was tested individually with the environmental standardized variables using 80,000 MCMC iterations. To identify candidate SNPs with evidence of positive selection for each environmental variable we used Bayes factor > 10 according to Jeffrey’s criterion88.(2) With Bayenv2101 we tested for Genotype-Environment associations considering neutral structure based both, on the number of groups present (optimum K determined by DAPC) and the spatial autocorrelation using the information of the geographic coordinates of the studied sites. We used a multiple logistic regression in which the p values were calculated from regressions of all possible genetic-environmental associations according to a Wald Score Test. The results were compared to a χ2 distribution with one degree of freedom. The loci with q-values less than 0.05, corrected with an FDR, were considered to have significant associations with a specific environmental variable.Finally, with (3) SAMβADAlagged scores associated with the first two principal components for each site. The lagged scores were then used to transform the genetic variation of the candidate SNPs into multilocus geographic clines. To evaluate if the variation of the candidate SNPs correlated best to environmental or spatial variables, the multi-locus clines were used to perform linear regressions with both groups of variables using dbMEM vectors. For statistical reasons, only those sets including more than five loci were retained.Once all consensus candidate SNP loci for local adaptation were obtained, i.e. for each environmental variable and each of the three methods , spatial structure of loci was analyzed independently using spatial Principal Components Analyses (sPCA), in adegenet. These analyses were performed to infer if the variation in the candidate SNPs was significantly associated with each environmental variable above that expected based only on the geographic proximity of sites. For this, we extracted the tags of detected putatively adaptive loci for each environmental variable were extracted to identify potential genes with known functions available in the SWISS-PROT database102 and in GenBank (https://www.ncbi.nlm.nih.gov/genbank/). In the case of Sea Surface Temperature (SST), we used tag sequences of those loci that were significantly correlated in at least two of the three genotype-oceanographic association approaches. SNPs were filtered by e-values preserving only matches greater than 1 × 10−4 and a minimum of 75% homology. The tag sequences associated with candidate loci were used as query sequences for BLAST searches executed in the database of annotated genes from SWISS-PROT.As an exploratory analysis, Using positive matches for Gene Ontology, we used a General Lineal Model (GLM) with binomial error to correlate the genotypes of each candidate locus SNP and the latitude using R environment.https://bitbucket.org/NicoSegoviaC/seascape_pyurachilensis/src/master/.Raw SNP-GBS data (.vcf file) for each sample and environmental variable were deposited in the Bitbucket database: Supplementary file1


For GPC, all exploratory analysis methods tested revealed (a) a low to very low coefficient of determination (R2≤ 0.21), (b) a relatively low overall prediction error (RMSE: 0.45–0.49%), compared to results from other literature studies, and (c) that the spectral exploratory analysis approach is slightly better than the multi-temporal approaches, with early season NDSI of 700 with 574 nm and late season NDSI of 707 with 523 nm as the best indicators. Using residual maps from the regression analyses of NDSIs and GPC, we visualized GPC within-field variability and showed that up to 75% of the field area could be mapped with relatively good predictability , therefore showing the potential of remote sensing imagery to capture the within-field variation of GPC under conventional agricultural practices.This study evaluates the potential of high resolution hyperspectral airborne imagery to capture within-field variability of durum wheat grain yield (GY) and grain protein content (GPC) in two commercial fields in the Yaqui Valley (northwestern Mexico). Through a weekly/biweekly airborne flight campaign, we acquired 10 mosaics with a micro-hyperspectral Vis-NIR imaging sensor ranging from 400–850 nanometres (nm). Just before harvest, 114 georeferenced grain samples were obtained manually. Using spectral exploratory analysis, we calculated narrow-band physiological spectral indices—normalized difference spectral index (NDSI) and ratio spectral index (RSI)—from every single hyperspectral mosaic using complete two by two combinations of wavelengths. We applied two methods for the multi-temporal hyperspectral exploratory analysis: (a) Temporal Principal Component Analysis (tPCA) on wavelengths across all images and (b) the integration of vegetation indices over time based on area under the curve (AUC) calculations. For GY, the best R It is also one of the most important crops in Mexico, grown on more than 723,559 ha in 2016, with average yields of 5.3 Mg ha−1 [Wheat (Accurate and timely assessment of within-field GY and grain protein content (GPC) variations over the crop cycle generates different degrees of potential for N and water management/availability as indicated by Reference  and RefeThe design of fertilizer application regimes should combine different factors  with a view to optimize both wheat GY and GPC . For exaThe highest N uptake begins around jointing and reaches a plateau at heading. N uptake, N accumulation and the further partitioning of N within the plant are all important processes that determine GY and GPC ,23. N upa+b) [Crop nitrogen status has been widely reported to be linked to canopy reflectance because of its relation to chlorophyll content  ,26,27. RThe second approach, which is our focus, seeks to establish empirical relationships between plant biophysical variables and vegetation indices (VIs). VIs are essentially a way of combining multispectral observations in a single metric, aiming to minimize the effect of external factors on spectral data and to derive canopy characteristics , whereasA large number of VIs have been developed to minimize those external effects to better convey information about vegetation canopy ,30. IndiChlorophyll related VIs have also been developed due its relation to crop N, as previously mentioned. Combinations of VIs, such as Modified Chlorophyll Absorption in Reflectance Index (MCARI) divided by Optimized Soil-Adjusted Vegetation Index (OSAVI), have shown good results for describing leaf chlorophyll concentration . Also, mThere is spatial variability of GY and GPC within a crop field due to soil spatial variability, micro-climate conditions and crop management ,33–35. CThe majority of those studies are based on multispectral broad-band signals on experimental designs or at the regional scale. There is usually wider range variation of the response variable at the regional scale than at the field scale . In seveNew methods that use hyperspectral remote sensing promote further spectral exploration of the signal, allowing the calculation of several other narrow-band VIs, suggested as potentially useful for precision agriculture ,53. BesiThe present study analyses the respective contribution of the spectral, narrow-band physiological vegetative indices and temporal dimensions of the hyperspectral signal, to the assessment of the within-field spatial variability of GY and GPC in two commercial wheat fields. The feasibility of this study relies on a multi-temporal and spectral exploratory analysis.22.1 ◦C. The soils in this region are coarse sandy-clay, mixed with montmorillonitic clay. The two major soil types in the Yaqui Valley are clayey and alluvial soils at a 3:2 ratio [This study was carried out in a wheat field in the Yaqui Valley near Ciudad Obregón (Sonora), in northwestern Mexico (27°23′43.83”N and 109°55″0.90”W), during the 2014 wheat crop cycle. It consisted of two furrow irrigated 43-ha blocks  . The blo:2 ratio .A weekly/biweekly flight campaign took place starting from stem elongation  until jGround control points (GCPs) were made using 3 × 3 m long × 1 m thick crosses of polyethylene sheet coated with aluminum foil fixed with duct tape and were placed around the study sites  and georThe micro-hyperspectral instrument was radiometrically calibrated in the laboratory using derived coefficients with a calibrated uniform light source  at four levels of illumination and six integration times. Hyperspectral imagery was atmospherically corrected using the total incoming irradiance at 1 nm intervals simulated with the SMARTS model developed by the National Renewable Energy Laboratory, US Department of Energy ,63. Thera) map [a range variation. The decision for different number of points in each block was due to budget constraints and to have at least a reasonable number of points (100) in one of the fields to capture its spatial variability and enable mapping using variography [2 frame centered on the point geocoordinates, where all wheat plants were harvested, threshed and GY measured. A grain sub-sample was taken for laboratory quality analysis, where GPC (%) and moisture content (%) were determined by NIR spectroscopy  calibrated according to official AACC standard methods 39–10 and 46–11A [Manual grain sampling  took plaa) map , to coveiography . Each sad 46–11A . The GY n = 114). The extraction was done by taking the average of a 3 × 3-pixel window (9 m2) around each sampling point to account for minor shift of the images during orthomosaic processing. The resulting averaged spectral data set was used for the following statistical and remote sensing analyses regarding GY and GPC.Spectral binning was performed on each mosaic into 7.5 nm FWHM  to decrease noise effects, resulting in 62 wavelengths. From those, the 751, 759, 766, and 773 nm wavelengths were removed due to oxygen absorption by the sensor. Finally, 58 wavelengths were used for subsequent analyses. Spectral information was extracted from each image using the point sampling location from both blocks (2.22) using global block kriging, fitting the best global variogram according to the spatial variability of the data in VESPER [The GY and GPC data from Block B were interpolated onto a 3-m grid  of the response variables without generating an overcrowded and unintelligible graph.Two exploratory analysis approaches were selected for data analysis of the acquired hyperspectral imagery time series: (a) a spectral approach; and (b) a multi-temporal approach. The spectral exploratory analysis approach is based on a complete two by two combination of all available wavelengths into NDSI and RSI spectral indices (SI). For the multi-temporal exploratory analysis approach, two methods were tested: temporal principal component analysis (tPCA) and the integration of VIs over time using an area under the curve (AUC) approach.2.3For the spectral exploratory analysis, we applied two types of generic formulas to generate SIs. Using complete two by two combinations of spectral wavelengths, the ratio spectral index (RSI) and the normalized difference spectral index (NDSI) were calculated. The RSI (Equation (1)) and NDSI (Equation (2)) are defined as:Ri and Rj are the reflectance for wavelengths i and j, respectively. Representations of RSI and NDSI are the PSSRa and NDVI, whereby both indices use NIR and R wavelengths. Spectral indices based on the complete two by two combinations of the hyperspectral signal were generated by applying the RSI and NDSI formulas, similar to studies by [2) describing the relationships of these SIs with GY and GPC were generated. These maps provide inclusive information on the optimum pair of wavelengths to assess both response variables all along the crop cycle [where udies by –59. Subsop cycle ,55.2.42.4.1The relatively high temporal resolution flight campaign  allowed for a multi-temporal approach to analyze the importance of the mosaics and wavelengths all along the crop cycle and at specific phenological stages (crop growth stages). For this purpose, a standardized principal component analysis (PCA) was applied to each individual wavelength across the 10 images, with 10 principal components (PCs) recorded for each wavelength. The first two PCs were considered in a Pearson’s correlation analysis with GY and GPC. The eigenvector and eigenvalue matrices were assessed to determine which image and wavelength across the crop cycle carried the highest proportion of variance within the dataset.2.4.2For further data exploration, four specific VIs for each response variable were chosen according to their potential to deliver structural and chlorophyll information . Also, tThe VIs’ temporal profiles based on the highest, median and lowest GY and GPC points within the field were plotted. Based on this information, the area under the curve (AUC) using the trapezoidal method  was calculated for each selected VI on each sampling point observation, aiming to integrate its temporal information across the crop cycle for different crop growth stages .p < 0.05.The AUC for each VI and crop growth stage were calculated and the area regressed against GY and GPC using linear regression . Reg33.1Both GY and GPC showed a distribution with mean and median values close to each other. This reflects a broadly symmetrical  distribution although with a high value of kurtosis for GPC . The GPC−1 and 11 to almost 15%, respectively. The threshold of 12.5% for GPC was chosen based on the farmer’s recommendation to differentiate between low and high wheat quality based on premium quality and better market prices. Although the maximum value of 14.96% for GPC may be considered an outlier, after a few analyses (data not shown), we decided to keep the data point. There was no further evidence this value was an error of measurement. GPCs of around 14–15% within farmers’ fields and on a regional scale are reported throughout the literature [Through the mapping exercise , it is pterature ,80. The p-value = 0.49) when we used all data points from the entire field. However, local spatial variation in the correlation between GY and GPC obtained through the moving window approach showed correlation coefficients varying from −0.76 to +0.61. Just two negative coefficients were significant at 5% probability (−0.76 and −0.69) and one positive (0.61) at 10% probability among the other 97 coefficients , which may be a proxy for plant available soil water storage capacity , was quy  are shown in Figures 4 and 5, which are the results of the regression analyses of the NDSI indices versus GY and GPC, respectively.Since the results of the regression analysis using RSI and NDSI were very similar, only NDSI results are shown here. The contour maps of the coefficients of determination . The relationship between crop yield and VIs has been widely studied in the literature. Many authors have reported good correlations between the yield of major commodity crops such as wheat, maize, and cotton based on multispectral broadband data imagery and mostly using combinations of NIR, R and green (G) spectral regions [2 varied from 0.22 to 0.32 . The best results across these images were with combinations between broad parts of the NIR region  and narrow bands of the RE  region. In these regions, reflectance is linked with biomass, which has been shown to be related to GY [Rj) combined with NIR obtained slightly lower adjustments than NIR and RE combinations with peaks at NIR (Ri) and blue (B)/G regions (Rj). Similar results were highlighted in the studies by [Using the R regions –40,89,90ed to GY –93. Thesudies by ,94.2, G/R regions (Ri) combined with B (Rj) showed consistent relationships throughout the three images acquired from booting to anthesis. The correlations between GY and the combinations within the visible spectrum are supported by the fact that chlorophyll (Ca+b) has two peaks of light absorption in the B and R regions [As well as revealing combinations between wavelengths in the NIR, RE and visible spectral regions, relationships between combinations solely within the visible spectrum were also detected. Although with a lower R regions . Also, c regions .2≤ 0.21) obtained in the results.GPC is a function of the conversion of plant nitrogen content into protein, so it is expected that plant nitrogen concentration estimated through remote sensing techniques may be able to partially explain GPC variation . A few s2-contour maps , and 707 to 840 nm on Ri and 486 to 530 nm on Rj after physiological maturity (7 May). All dates show slight R2 differences within those regions of the spectrum. The highest R2 within the images from 27 February  (R2: 0.20) and 7 May  (R2: 0.21) were obtained with narrow band combinations of the RE and G regions . At booting, plants are increasing N uptake, and physiological maturity is the end of grain filling period, where N is redistributed from photosynthetic tissues to form GPC [Through the Rour maps , it is aform GPC . Althougform GPC , may be 2-contour maps) proved to be a simple and robust method for exploring hyperspectral data. Here, combining NIR and RE spectral wavelengths into a normalized formula was shown to be more suitable for assessing GY and combinations of RE and G for assessing GPC. For the assessment of GY and GPC, better results were achieved with images acquired during anthesis (GY) and around booting and just after physiological maturity (GPC).In summary, the complete two by two combinations of spectral wavelengths , wheat plants are at approximately 40% of maximum growth and N uptake increases considerably. Meanwhile, the heading stage (GS55) comes just before the plant reaches its maximum green area index, which influences canopy reflectance and N uptake and reveals the importance of both stages in crop development [For PC1 , all imaelopment .In contrast to PC1, ≤ r ≤ −0.3) correlations across almost all wavelengths from the B to the R region, except in the G region. Moderately significant correlation coefficients (around +0.5) were found mainly for the NIR wavelengths (≤ r ≤ +0.2) across almost all wavelengths, except very weak correlations (±0.2 < r ≤±0.3) with some RE and NIR wavelengths  using some RE and all NIR wavelengths, with the 729 nm wavelength from PC1 having the highest coefficient (−0.25) and its surrounding wavelengths  slightly different coefficients. Images from 14 and 27 February and 11 March had the highest loadings . Both PCs have in common that no correlations existed across all wavelengths from the V to the R region . The temporal profiles for the lowest, median and highest GY and GPC within-field values are shown in For GY, all VI temporal profiles revealed that the curves of highest, median and lowest GY followed a very similar course over the crop season with (a) increasing VI values during early growth stages , (b) peaks at booting  and anthesis , (c) a slight depression at heading , and d) a strong descending response starting from late milk  until grain hard . Furthermore, the highest GY values always showed higher VI values and vice versa, which is expected due to their plant structural information and its correlation to biomass and, consequently, to GY –93.In contrast to GY, the VI temporal profiles for GPC show a different pattern over time for each VI. With exception of the median GPC curve, the highest and lowest GPC curves followed a similar course, whereby—starting from pre-booting crop stage GS41; 27 February)—the lowest GPC values are always related to higher VI values. The low range variation of GPC (refer to Section 3.1) may be the reason of the ‘noisy’ effect in the VI response of the median GPC. The peaks vary: For PSNDc and PSSRc at anthesis , for MCARI/OSAVI at physiological maturity , and for TCARI at booting  and late milk . The VIs MCARI/OSAVI and TCARI indicate a higher potential for GPC estimation at late season, PSNDc and PSSRc at mid-season. Pigment-related VIs have shown to be negatively related to chlorophyll at specific crop stages [ February2 resulting from fitting values between AUCs from selected VI profiles with GY and GPC.To understand the prediction potential of multi-temporal hyperspectral image analysis for GY and GPC, the relationships between the AUC of selected VI temporal profiles and the values of both GY and GPC were statistically evaluated at different crop stages  and very weak for GPC (R2≤ 0.1).Overall, independently of the crop stages (AUC1 to AUC8), the relationships across all VI temporal profiles were weak for GY (R2 were found for AUC1 using EVI and OSAVI (R2: 0.32), followed by AUC4 , AUC7 , and AUC3 . This means that the prediction potential improved slightly (by just 4–6%) using 10 hyperspectral mosaics over the whole crop cycle (AUC1) compared to using 2 mosaics between heading and anthesis (AUC4) or 3 mosaics between grain filling and physiological maturity (AUC7) or booting and anthesis (AUC3). As such, this may not justify making such an investment for highly temporal time series of images.However, when focusing on GY, the best R2: 0.1; TCARI, MCARI/OSAVI), AUC2  and AUC3 . This indicates that the most interesting period is from early to midseason, respectively from steam elongation (GS31) to anthesis (GS65). This is also the period when most N uptake (about 60%) takes place and the period of major photosynthetic capacity [The best results for GPC were obtained with the temporal VI profiles of TCARI and MCARI/OSAVI for the following AUCs: AUC5  of GY variability in wheat production districts in Indian states. Similarly, Xue et al. [Other authors have made use of remote sensing data acquired throughout the season to develop spectral growth profiles based on VIs. Using NDVI as an indicator, Dubey et al.  found the et al.  analyzed2≤ 0.32) and very weak with GPC (R2≤ 0.1), whereby the most suitable VIs and the best temporal window for GPC assessment were identified.In summary, multi—temporal spectral exploratory analysis showed its potential for identifying the optimal image timing across the crop cycle and reflectance wavelengths. Temporal PCA revealed that B, R and NIR spectral information at booting and heading crop stages carried the highest variance of the dataset, followed by late season images across almost all wavelengths, except NIR. Moreover, this approach showed the smooth behavior of the spectrum across the crop cycle, where peaks of correlation coefficients occurred at B, R and NIR spectral regions on PC1 for GY, with similar inverted behavior for GPC. Independently of the crop stages, the AUC approach based on VI temporal profiles demonstrated for all profiles weak relationships with GY  and a 707 and 523 nm combination from the 7 May image . In the following figure, both are presented because the 27 February image provides information around the beginning of booting (early season), which is a suitable temporal window for crop management intervention targeting GPC correction. The 7 May image, which comes after physiological maturity , offers potential for selective harvesting strategies.For the spectral exploratory analysis approach, the highest correlations with GPC were achieved using NDSI of a 700 and 574 nm combination from the 27 February image  to anthesis (GS65), revealed the highest relationships with GPC  was selected for further presentation.Within the multi—temporal exploratory analysis approach, the tested methods were tPCA and AUC of selected VIs. For the tPCA method, the 729 nm wavelength within PC1 obtained the highest correlation with GPC. The linear regression adjustment, although statistically significant at 2), (b) a general relatively high overall prediction error (RMSE) considering the low range variability of GPC for this field, and (c) that the spectral exploratory analysis approach surpasses the multi—temporal approach.The comparison of relationships between GPC and the best fit of each exploratory analysis methods revealed (a) a general low to very low coefficient of determination (R2: 0.64). Although the overall prediction error of our study (RMSE: 0.45–0.49%) was relatively high for the conditions in the study area, the selected methods seem to be relatively more precise when compared to other proximal and remote sensing studies  fields and/or regions with naturally higher GPC variation ,45,46 anstudies  wheat grain quality areas within the field, both estimated maps show a very similar size and distribution of class zones, whereby higher GPC areas were estimated based on the late—season image. The late—season GPC estimation map reflects a more realistic situation of GPC at harvest time (as illustrated by the kriged GPC map based on in—situ measured data).For the detection of lower (<12.5%) and higher  indicated an overall low prediction potential, but without any specific information regarding the within—field variability. In contrast, the residual maps of Block B  narrow—band physiological spectral indices , (b) temporal principal component analysis , and (c) the integration of VIs over time —made it possible to identify the most valuable reflectance wavelengths matching wheat physiology as well as to detect the optimal time window across the crop cycle for image acquisition based on the most relevant crop stages for grain protein prediction. Moreover, this study revealed a generally low to very low coefficient of determination, whereby the spectral exploratory analysis approach surpasses the multi—temporal approaches. Considering the low range of GPC at the study site, the overall prediction error for GPC estimation is relatively high. However, compared to other proximal and remote sensing—based GPC assessment studies, the RMSE values from this study seem to be relatively low. Moreover, residual maps derived during the regression analysis of NDSIs and GPC proved to be good for within—field prediction assessment and that, in our study, an area of ~75% of the total field could be mapped with relatively good prediction.For future studies, this study delivers valuable information on the use of remote sensing data for the assessment of within—field GY and GPC variability aiming N management and selective harvesting: (a) detecting the most important crop stage for image acquisition and (b) identifying the best reflectance wavelengths along the crop cycle. New studies targeting N management for grain protein should be conducted, specifically using long—term experiments with different varieties and locations and applying different amounts of N at different times to generate calibrations with RS data. Furthermore, investigating GPC variation over different scales using different proximal and remote sensing platforms and corresponding spectro—radiometers  may enable transferring GPC prediction models from the field—scale to the regional scale.


Red blood cells (RBCs) are susceptible to sustained free radical damage during circulation, while the changes of antioxidant capacity and regulatory mechanism of RBCs under different oxygen gradients remain unclear. Here, we investigated the changes of oxidative damage and antioxidant capacity of RBCs in different oxygen gradients and identified the underlying mechanisms using an in vitro model of the hypoxanthine/xanthine oxidase (HX/XO) system. In the present study, we reported that the hypoxic RBCs showed much higher oxidative stress injury and lower antioxidant capacity compared with normoxic RBCs. In addition, we found that the disturbance of the recycling process, but not de novo synthesis of glutathione (GSH), accounted for the significantly decreased antioxidant capacity of hypoxic RBCs compared to normoxic RBCs. We further elucidated the underlying molecular mechanism by which oxidative phosphorylation of Band 3 blocked the hexose monophosphate pathway (HMP) and decreased NADPH production aggravating the dysfunction of GSH synthesis in hypoxic RBCs under oxidative conditions. Red blood cells (RBCs) are susceptible to oxidative injury because of high concentrations of molecular oxygen, membrane polyunsaturated fatty acids, and heme-bound and free iron atoms . ChangesRecently, studies report that hypoxia exposure induces free radical oxidative damage in various tissues , 7. Unde2-responsive variations in flux through the Embden-Meyerhof pathway (EMP) or the hexose monophosphate pathway (HMP) [in vitro and in vivo decreased purine oxidation and enhanced purine salvage reactions in human and mouse RBCs [The activation and synthesis of most antioxidant enzymes need a constant supply of energy which reduce power; these include ATP, NADPH, and NADH, which are mostly products of glucose metabolism in RBCs . Accumulay (HMP) , 14. Theay (HMP) . Howeveray (HMP) –18. Oxygay (HMP) , 20. Hypuse RBCs . Furtheruse RBCs –24. Receuse RBCs –27. Hypouse RBCs . Therefoin vitro metabolic marker of RBC injury [in vitro model of the hypoxanthine/xanthine oxidase (HX/XO) system was used to detect the change of oxidative damage and antioxidant capacity of RBCs under both normoxic and hypoxic environments. Furthermore, we assessed the influence of oxygen gradients on the balance of the recycling process and de novo synthesis of GSH in RBCs and underline the regulation of the glucose metabolism pathway between HMP and EMP. In addition, we also studied the effects of oxidation and phosphorylation of Band 3 on glucose metabolism and antioxidant capacity in RBCs under an in vitro oxidation environment.Based on these observations, we hypothesized that the antioxidant capacity could be associated with the alterations in the oxygen gradients of RBCs. Hypoxanthine is an C injury . To testAll chemicals and reagents were purchased from Sigma-Aldrich . Antibodies to GCLc (ab240379), GCLm (ab126704), Band 3 (ab108414), GAPDH (ab9485), and phosphotyrosine (ab179530) were obtained from Abcam . Electrophoresis equipment and related supplies were obtained from Bio-Rad (USA). The spectrophotometer was purchased from Thermo Fisher Scientific (USA).n = 6), and RBCs from each donor were isolated and washed three times in isotonic Hepes buffer, then resuspended to a hematocrit of 50% in Krebs buffer containing 2 g/l glucose (pH 7.4) for further experiments. All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Binzhou Medical University (permit number: 2019-0004).Fresh blood was collected by venipuncture from healthy human volunteers  containing 1 mM ethylene glycol tetraacetic acid (EGTA), 1 mM sodium orthovanadate, and 1 mM phenylmethylsulfonyl fluoride (lysis buffer) and incubated at 4°C for 10 min. RBC ghosts were washed thrice by centrifugation  to obtain “white” ghosts.2, N2, and CO2 tanks. Washed RBCs were exposed for 25 min to either oxygenating conditions , yielding >97% HbSO2, or to deoxygenating conditions , yielding <10% HbSO2.For manipulation of Hb conformation, washed RBCs (20-25 ml) were injected into a rotating glass tonometer , allowing controlled RBC oxygenation or deoxygenation, as well as sample extraction without atmospheric exposure as described previously . The tonA hypoxanthine/xanthine oxidase (HX/XO) system was used to generate a steady, gradable production of superoxide as described previously . In brieμM, 15 min, 37°C), a selective inhibitor of glucose-6-phosphate dehydrogenase (G6PD), to inhibit glucose utilization through HMP [μM, 15 min, 37°C), a selective inhibitor of glyceraldehyde-3-phosphate dehydrogenase (G3PD), to inhibit glucose utilization through EMP [Washed RBCs were incubated with dehydroepiandrosterone (DHEA)  of the RBC membrane was quantified according to the method of Yamaguchi et al. . SH-radiThe RBCs were fixed for 24 h in a 2% solution of glutaraldehyde. After that, the preparations were washed in phosphate buffer for 30 min, and then the material was dehydrated in a rising series of ethyl alcohol concentrations . Each sample was washed for 15 min in an appropriate concentration, with the material remaining in pure ethyl alcohol for 30 min. Next, the RBCs were dried for 12 h at room temperature and coated with gold using a sputter coater . The material's ultrastructure was analyzed using a scanning microscope (Zeiss EVO LS15) with an SE1 detector, under high vacuum and accelerating voltage (acceleration voltage (EHT) = 10 kV). Individual forms of RBCs were ascribed morphological indices according to the Bessis scale .μM), and the cells were incubated at 37°C for 30 min. After incubation, the cells were washed and analyzed by FCM.The intracellular production of ROS was assessed by 2′,7′-dichlorofluorescein diacetate (H2DCF-DA) . The RBCs were collected and washed with PBS solution, H2DCF-DA was added . Red cell MetHb levels were determined as described by Kohn et al. [Blood was centrifuged at 2,000 rpm at 4°C to remove plasma, passed through cotton to eliminate white cells, and washed three times with choline wash solution  and enzymes (hexokinase and glucose-6-phosphate dehydrogenase) required for the enzymatic reaction to occur. The amount of NADH produced, which is proportional to the amount of ATP within the sample, was measured spectrophotometrically.ATP concentrations were determined as we reported previously . RBC sam+ and permit measurement of the remaining NADPH. A detection reagent was added, and NADP(H) was monitored by absorbance at 450 nm for a period of 3 hours and quantified using standards [Total NADP(H) and NADPH were measured using a colorimetric assay kit . Hemoglobin and other proteins with MW greater than 30 kDa were removed from RBC lysates using centrifugal filters with a 30 kDa threshold, and filtrates were heated to 60°C for 30 minutes per the manufacturer's protocol to decompose NADPtandards .μl of plasma. The absorbance was read at 593 nm at an interval of 30 sec for 4 min. An aqueous solution of a known Fe2+ concentration in the range of 100-1,000 μmol/l was used for calibration. Using the regression equation, the FRAP value (μmol Fe(II) per liter) of the plasma was calculated.The ferric-reducing ability of plasma (FRAP) values were determined following the method of Benzie and Strain . In brieRBCs were lysed with ice-cold 5 mM sodium phosphate buffer (pH 8.0) and vortexed. The RBC membrane was removed by centrifuging at 20,000 g for 20 min at 4°C. The supernatant was saved and used to measure cytosolic G6PD activity by a G6PD assay kit  , and cytThe values of GSH, total free glutathione (TFG), and GSH/GSSG ratio were determined using a method described previously . In brieThe procedure for measuring L-cysteine influx was essentially the same as described earlier . A totalμg of membrane proteins on a 5-15% linear acrylamide gradient gel (10 μg protein/lane) according to Laemmli for protein staining by colloidal Coomassie Blue.Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) was conducted by heating the samples for 8 min at 100°C and loading 10 Western blot analyses were performed as previously described . In shorRBCs were fixed with 4% paraformaldehyde and 0.05% glutaraldehyde in PBS and permeabilized in the same solution containing 0.05% Triton X-100. After being blocked with 3% BSA and 0.1% Tween 20 in PBS to block nonspecific protein binding, cells were treated with primary antibodies diluted in 10 mg/ml PBS for 1 h at room temperature. The sources of all antibodies were as mentioned above. Cells were washed at 3 min intervals thrice with gentle shaking, and then incubated for 1 h with secondary antibodies at 1 : 700 dilution in PBS, and washed thrice in PBS. Fluorescence was imaged using an Olympus IX71 microscope with a 63/1.25 oil immersion objective and equipped with a CCD camera .The extracellular acidification rate (ECAR) was measured in 500,000 purified RBCs per well using the Seahorse XF96 Extracellular Flux Analyzer  as previous described . After hP < 0.05 was considered statistically significant.The experimental data were expressed as mean ± SEM. The statistical analysis was performed by one-way ANOVA followed by Tukey's test for multiple comparisons using the SPSS software . A value of in vitro experiments, we tested the relationship among oxygen gradients and free thiol radicals in RBC membranes following graded oxidative stress (45 min exposure). As shown in In a series of in vitro oxidation , we detected the degree of oxidative damage of RBCs under hypoxia or normoxia conditions. As shown in Figures Under the same condition of Scanning electron microscopy showed that the flattened biconcave disc morphology of RBCs was damaged and dysmorphic RBCs were present  after HX/XO exposure . In addiin vitro oxidation . As shown in In order to determine the reason for the increased oxidative damage of RBCs in the hypoxia group, we analyzed the antioxidant capacity of RBCs in the hypoxia and normoxia groups under To determine the reason for the change of the antioxidant capacity of RBCs in different oxygen gradients, we analyzed the activities of the main enzymatic and nonenzymatic antioxidants in RBCs. As shown in Figures In the aspect of nonenzymatic antioxidants, we found that HX/XO exposure induced significantly decreased GSH and TFG levels and GSH/GSSG ratio in both hypoxic and normoxic RBCs Figures . In addiWe further explored the changes of GSH synthesis-related enzyme activities in RBCs under different oxygen gradients. As shown in Figures Similarly, we found that the activities of GPX and GR decreased significantly in both the hypoxia and normoxia groups after HX/XO exposure Figures . By contATP and NADPH provided by the EMP and HMP pathways of RBCs are required for the de novo synthesis and recycling regeneration of GSH . TherefoOn the other hand, the activity of G6PD, the rate-limiting enzyme of HMP, also decreased significantly in both the hypoxia and normoxia groups after HX/XO exposure . HoweverAs a result, we detected significantly reduced levels of ATP and NADPH in RBCs of both the hypoxia and normoxia groups after HX/XO exposure Figures . CompareThe generation of NADPH (HMP) or ATP, NADH, and 2,3-DPG (EMP) shifts with the RBC oxygen content because of the competition between key EMP enzymes and deoxy-Hb for binding to the cytoplasmic domain of the Band 3 membrane protein. To examine whether Band 3 is involved in the abnormal GSH synthesis induced by hypoxia, we detected the oxidative crosslinking and tyrosine phosphorylation (p-Tyr) of Band 3 in hypoxic and normoxic RBCs after HX/XO exposure . Band 3 immunoblotting is shown in To verify the hypothesis, we locked the Hb conformation in the R state with 100% carbon monoxide (CO) to block the release of the EMP enzyme from Band 3 in hypoxic RBCs. As shown in Figures in vitro oxidation conditions, we found the dysfunction of GSH synthesis in both de novo synthesis and the recycling process in hypoxic RBCs. In addition, we provide evidence that the main reason for the significantly decreased GSH content and antioxidant capacity of hypoxic RBCs compared with the normoxia group is the disturbance of the recycling process but not the de novo synthesis of GSH. Further investigation reflects an underlying molecular mechanism by which oxidative phosphorylation of Band 3 blocked the HMP pathway and decreased NADPH production aggravating the dysfunction of GSH synthesis in hypoxic RBCs under oxidative conditions.The research we have done suggests a decreased antioxidant capacity and an increased risk of oxidative damage in hypoxic RBCs. Under in vitro oxidation conditions. In addition, the hypoxia group showed more serious oxidative stress injury compared with the normoxia group. This is in line with the previous research report that significantly increased oxidative stress of RBCs in the human body under a high-altitude hypoxic environment [RBCs are permanently in danger of oxidative stress across their lifetime due to the high cellular concentration of oxygen and Hb , 50. It ironment .in vitro oxidation conditions. But on the contrary, the antioxidant capacity of RBCs was significantly decreased accompanied by the increase of MDA and MetHb levels in RBCs under in vitro oxidation conditions. This may be due to the continuous production of oxidation products after the antioxidant capacity reaches the upper limit, which in turn destroys the antioxidant metabolism of RBCs. Interestingly, we also found that the antioxidant capacity of RBCs in the hypoxia group was significantly lower than that in the normoxia group. And this result was in agreement with previous research that hypoxia limits the antioxidant capacity in RBCs [in vitro oxidation conditions. In the present study, we found significantly decreased GSH and TFG levels and GSH/GSSG ratio in RBCs of both the hypoxia and normoxia groups under in vitro oxidation conditions, and the decrease of the GSH level and GSH/GSSG ratio was more significant in the hypoxia group. On the contrary, there was no significant difference in TFG level between the hypoxia and normoxia groups after HX/XO exposure. These results indicated that although both the recycling process and de novo synthesis of GSH in RBCs were damaged by oxidative stress, the recycling process is more susceptible to dysfunction in a hypoxic environment.It is well known that RBCs possess enzymatic and nonenzymatic antioxidants, mainly composed of CAT, SOD, and GSH, to remove free radicals produced inside and outside cells , 56. In in vitro oxidation conditions. These findings verify our previous speculation that compared with the de novo synthesis, the recycling process of GSH is more vulnerable to oxidative stress injury in a hypoxic environment.RBC GSH content is largely determined by intracellular production through de novo synthesis by glutamic acid, cysteine, and glycine, a process catalyzed by GCL and glutathione synthetase (GSS), as well as through the recycling regeneration between GSH and GSSG, a process catalyzed by GPX and GR . In addiin vitro oxidation conditions, we found significantly increased levels of GSH and TFG and GSH/GSSG ratio in hypoxic RBCs pretreated with the GAPDH inhibitor KA to block glucose metabolism via EMP. On the contrary, blocking glucose metabolism via HMP with the G6PD inhibitor DHEA significantly decreased the levels of GSH and TFG and GSH/GSSG ratio in normoxic RBCs with HX/XO exposure. Our results in this study confirmed these previous findings that glucose metabolism in RBCs is characterized by O2-responsive variations in flux through EMP or HMP [Previous studies have shown that both the L-cysteine transport and the de novo synthesis of GSH catalyzed by GCL in RBCs requires the EMP pathway to provide ATP , 59. On P or HMP . And theDeoxygenated RBCs are known to undergo reversible phosphorylation of Band 3 and improve the binding with increased deoxy-Hb, which inhibit the binding of GAPDH with Band 3 and improve the glucose metabolism flux through EMP –62. WhenIn summary, we presented evidence that the antioxidant capacity of RBCs is reduced and more vulnerable to oxidative damage under hypoxia conditions. The disturbance of the recycling process, but not de novo synthesis of GSH, accounted for the decreased antioxidant capacity in hypoxic RBCs. In addition, the transition from the HMP pathway to the EMP pathway of glucose metabolism, which was interfered with by the oxidative phosphorylation of Band 3, is also involved in the regulation of the antioxidant capacity of RBCs under different oxygen gradients.


Fenugreek as a self-pollinated plant is ideal for genome-wide association mapping where traits can be marked by their association with natural mutations. However, fenugreek is poorly investigated at the genomic level due to the lack of information regarding its genome. To fill this gap, we genotyped a collection of 112 genotypes with 153,881 SNPs using double digest restriction site-associated DNA sequencing. We used 38,142 polymorphic SNPs to prove the suitability of the population for association mapping. One significant SNP was associated with both seed length and seed width, and another SNP was associated with seed color. Due to the lack of a comprehensive genetic map, it is neither possible to align the newly developed markers to chromosomes nor to predict the underlying genes. Therefore, systematic targeting of those markers to homologous genomes of other legumes can overcome those problems. A BLAST search using the genomic fenugreek sequence flanking the identified SNPs showed high homology with several members of the Trifolieae tribe indicating the potential of translational approaches to improving our understanding of the fenugreek genome. Using such a comprehensively-genotyped fenugreek population is the first step towards identifying genes underlying complex traits and to underpin fenugreek marker-assisted breeding programs. Trigonella foenum-graecum L.) is a small-seeded annual dicotyledonous legume that belongs to the family Leguminosae (Fabaceae). The genus Trigonella belongs to the Trifolieae tribe and the Trigonellinae subtribe that includes other several genera such as Trifolium, Melilotus, and Medicago [Fenugreek  is a small-seeded annual dicotyledonous legume that belongs to the family Leguminosae (Fabaceae). The genus Trigonella belongs to the Trifolieae tribe and the Trigonellinae subtribe that includes other several genera such as Trifolium, Melilotus, and Medicago [Fenugreek  or genes is by using the progeny of selected crosses such as recombinant inbred lines, backcrosses, or double haploid populations. However, this approach suffers from the limited variation existing in the parents and the time required to create such populations. Therefore, the use of the alternative approach known as association mapping (AM), relying on linkage disequilibrium (LD) between polymorphic molecular markers and the causal variants in a large number of individuals, has become commonplace. The advantage of AM over traditional QTL mapping is the use of already-existing naturally evolved and adapted genotypes with wider genetic variation, eliminating the need to generate new mapping populations. This criterion makes neat use of the historical recombination accumulated over hundreds or thousands of generations in a large number of diverse genotypes. To detect such crossing-overs, those genotypes need to be densely genotyped to obtain high statistical mapping resolution and to identify single nucleotide polymorphisms (SNP) associated with the examined trait ,16. To aMedicago truncatula (barrel clover) and Trifolium repens L. (white clover) [Trifolium pretense and Trifolium medium (zigzag clover) [Very little information is available about the fenugreek genome. Most available studies focused on identifying genes involved in the biosynthesis of diosgenin using de novo transcriptome sequencing  and next clover) , and bet clover) . To our knowledge, no previous study has examined the population structure and association mapping or homology analysis using fenugreek germplasm genotyped with a large number of SNPs. Therefore, we used a local collection of 112 genotypes collected from different locations in Egypt and genotyped at high density using the ddRAD-seq technique. To our knowledge, there are no certified accessions collected in the Egyptian gene bank. Therefore, we have collected seeds of 112 fenugreek genotypes directly from local farmers in all governorates that produce fenugreek . The larwww.zymoresearch.com). DNA concentration was measured using NanoDrop . One hundred micrograms of DNA per sample in 96 well plates were digested in 1× NEB Cut Smart Buffer with EcoRI and MboI (NEB) at 37 °C for 4 h. Following a 20 min 80 °C enzyme inactivation, samples were held at 12 °C until ligation with T4 DNA Ligase (NEB) and adapters containing 1 of 48 unique barcodes and Illumina-compatible P5 sequences coupled to an EcoRI overhang and Illumina-compatible P7 sequences coupled to the MboI overhang. Plates were incubated 8 h at 16 °C and heat inactivated at 80 °C for 20 min. Samples were then pooled in three pools of 40, 38 and 34 samples respectively and mixed with EDTA and ethanol precipitated. Pellets were re-suspended in EB, purified with PCR Purification columns  and further cleaned up with one volume of AMPure XP beads. One to three µg DNA was subjected to Pippin Prep size selection on a 2% dye-free agarose gel with internal size markers aiming for 350–500 bp inserts. Recovered samples were cleaned with AMPure XP beads and subjected to a pre-selection PCR (PreCR) in which a biotinylated forward primer and unique indexed reverse primers were used to amplify and tag desired DNA fragments. PCR products were cleaned up with Qiagen PCR purification columns and 1X AMPure XP beads as before. DNA fragments, with biotin at the 5′ ends, only were selected using Dynabeads M-270 Streptavidin coupled magnetic beads (ThermoFisher). Briefly, 50 µL of beads were mixed with up to 2000 ng of each pool and incubated for 20 min at RT. Bead/DNA complexes were captured and washed several times, then resuspended in 50 µL 1× SSC and heated at 98 °C for 5 min then placed on a magnet and supernatant removed as soon as possible. This elution was repeated, and the final supernatants were cleaned up with Qiagen PCR columns. The eluted ssDNA was quantified and diluted to 1 ng µL−1 with EB. A final PCR was performed on 10 ng of input DNA using P5 and P7 primers with only 8 cycles. Final PCR products were purified with 1× AMPure XP beads, quantified and assessed for quality on a Fragment Analyzer System . The samples were sequenced at the Texas A&M AgriLife Genomics and Bioinformatics Services on one lane of Illumina NovaSeq 6000 using a S4 XP sequencing kit. The raw sequencing reads are available through the NCBI BioProject number PRJNA648770, and the NCBI BioSample SAMN15647967. Genomic DNA was extracted from the seeds of 112 Fenugreek genotypes using Quick-DNA plant/seed Miniprep kit . Seed length and width were measured for every single seed and the mean value of the 15 seeds was used for association mapping. ImageJ generated three values of red (R), green (G), and blue (B) colors for each genotype that were used in the following equation [Seeds were cleaned and placed on a white opaque sheet with a rigid sparse to guarantee to have single separated seeds. For each genotype, 15 homogeneous, in shape and color, and healthy seeds were selected and separated then represented in one picture with a resolution of 4632*2608 (width*height). A digital camera was fixed at 25 cm height and JPEG images with a resolution of 96 dpi were taken and analyzed using ImageJ software  to search for sequences showing high homology to the contig sequences on which the significant SNPs associated with these traits were located. The identified top hit sequences were subsequently subjected to six-frame translation followed by a search for existing, conceptual, Open Reading Frames (ORFs). To predict the putative function of the genes represented by these ORFs, we scanned the corresponding protein sequences for conserved domains using the Simple Modular Architecture Research Tool (SMART) (http://smart.embl-heidelberg.de/). To investigate whether the identified genes, in which the SNPs are located, are transcribed, we collected public, raw, fenugreek RNAseq data from the Sequence Read Archive (SRA) at NCBI (https://www.ncbi.nlm.nih.gov/sra) and used it to construct either a genotype-specific (from one SRR file representing a single fenugreek accession) or a consensus (from multiple SRR files representing multiple fenugreek accessions) de novo transcriptome assembly. For that, we made use of the software package Trinity in combination with Trimmomatic for filtering and trimming the reads and then used local BLAST  to search for mRNA transcripts matching our contigs.To map possible candidate genes associated with seed length, width, and color, we used the online NCBI BLASTn tool and the non-redundant (NR) database . A total of 83,532 unique contigs were assembled and further used as reference for mapping the reads. Among these contigs, FreeBayes reported >2M raw variations that were further filtered to minimize the calling of false SNPs due to sequencing error, paralogs, or artifacts from library preparation. The raw variant call file (VCF) was filtered using vcftools v0.1.15 [The 38,142 generated SNPs of the 112 tested fenugreek genotypes were used as an input for the fastSTRUCTURE software , to builA high level of variation was observed among genotypes for seed length, with a heritability of 0.53, and seed width, with a heritability of 0.51 . MinimumP) association detection threshold was set to 6.5 and 5.9 based on Bonferroni correction, and FDR of α = 0.05, respectively. As shown by the Manhattan plots (P) values of 6.71 and 8.36, respectively (SNP-based association mapping was performed using a mixed linear model (MLM) excluding rare alleles with minor allele frequency (MAF) < 5%. The MLM included population structure (Q), and kinship matrix (K) to avoid spurious associations. The −log 10 = 6.88, FDR of α = 0.002, and explained 38% of the seed width variation. In total, the two SNPs explained 84% of the seed width variation. For seed color, the SNP dDocent_Contig_84790_24 was associated at −log10(P) = 6.32, FDR of α = 0.016, and explained 28% of the seed color. The sequences of these three ddRAD markers are provided in This SNP explained 39 and 46% of the seed length and width variation, respectively. The second significant SNP, dDocent_Contig_39741_151, was associated only with seed width with −log10 values of 6.7 and 8.4, and FDR of α = 0.007 and 0.000, respectively. However, using this very strict threshold would result in no significant SNPs associated with seed color. Earlier studies [P-value. Therefore, we checked the SNPs with −log10(P) values less than 6.5 and with FDR < 0.05. One SNP marker, dDocent_Contig_84790_24, was found to be associated with seed color at −log10(P) = 6.32. Considering the low FDR of this SNP, α = 0.019, and the high explained variance, 28%, of the observed variation, altogether, we believe this association can be considered as a true association. However, so far, no data is available which allows the alignment of the newly discovered markers in fenugreek to a comprehensive consensus map that covers its eight chromosomes [As large seeds are expected to emerge more rapidly, to have greater seedling survival rate, and stress tolerance, we have chosen to study seed length and width. The high level of variation together with the relatively high heritability recorded for both traits suggested the suitability of our collection for association mapping studies and to effectively map the associated SNPs that can be further used in the fenugreek marker-assisted breeding programs. We employed MLM including population structure (Q), and kinship (K) matrix to avoid spurious associations. The two commonly used multiple comparison methods to select for the significant threshold level in association mapping studies are Bonferroni correction  and fals studies ,48,49 haomosomes ,51. TherTrifolium pretense and Trifolium medium genomes were reported to have more than 30 and 40%, respectively, of retrotransposable elements in their genome [A more in-depth analysis of the identified trait-associating SNPs shows that two SNPs are located in contigs containing sequences that are conserved among several species from the Trifolieae tribe. The close taxonomic relationship implied by this sequences conservation raises the prospect that the same genomic regions harboring the SNPs might be controlling the same traits among the species that belong to this tribe, permitting a translational approach to gene function discovery. However, further studies are required to confirm this hypothesis. Our finding that two SNPs associated with seed size (length and width) and seed color are both localized in TE genes could be explained by the fact that other species from the Trifolieae tribe, in contrast to other legumes, have genomes that are rich in TEs. For example, r genome ,52, and r genome ,53,54 anM. truncatula and which was found to affect seed size [Trifolium pretense (PNX92211.1) which, similar to our SNP-related ORF, contains additional TE-related domains downstream of the Znf-C2HC domain. This strengthens the notion that this SNP could be in a gene that has a direct effect on seed size. For the other identified SNPs, associated with only seed width or seed color, it is however difficult to conclude whether the corresponding ORFs are affecting the respective trait. These results highlight the possible role of M. truncatula as well as the Trifolieae tribe as a proxy for gene content and order in the fenugreek genome. This is similar to previous studies that reported synteny between M. truncatula and legumes such as white clover [Due to the existence of linkage disequilibrium, we are aware that it is not likely that the identified SNPs are indicating the exact genes underlying a certain trait and that they could rather be merely an association with the trait. However, it is still possible that once a SNP is located in an ORF of a gene, investigating the putative function of this gene and its functional domains can help accepting or rejecting the hypothesis regarding the direct involvement of this gene in the studied trait. In the current scenario, gene modeling analysis and the RNAseq expression data indicate that all the associating SNPs were localized in transcribed genes. In the case of the SNP associated with seed length and width, dDocent_Contig_466_145, it is possible that the identified Znf-C2HC domain, downstream of the GAG domain of the TE is playing a role in gene transcription and thus affecting seed size. This would be similar to what has been reported for the ZnF-C2H2 domain encoding gene that was identified in eed size . Our fure clover , red cloe clover , birdsfoe clover ,58, comme clover , and fabe clover . Syntenye clover .M. truncatula and the Trifolieae tribe to improve our understanding to the fenugreek genome. Using such a well-genotyped collection to investigate more complex traits is the first step towards identifying the underlying genes. Further evaluating this population under diverse environmental conditions can help to dissect genotype by environment interactions and to improve fenugreek marker-assisted breeding programs.Genotyping an Egyptian collection of 112 fenugreek genotypes with 38,142 SNPs using the ddRAD sequencing enabled us to investigate the genetic diversity and the population structure of our collection. Our results revealed that the population is divided into two main and 5 sub-populations. We used seed length, width and color to prove the suitability of this population for association mapping studies and we found three trait-associated SNPs. Our results indicated the possible role of