Macrotyloma geocarpum as an example, we used ecological niche modeling to explore the role of ecology on the current and future distributions of genetic populations of Kersting’s groundnut. Our findings showed that: (1) the models had good predictive power, indicating that M. geocarpum’s distribution was correlated with both climatic and soil layers; (2) identity and similarity tests revealed that the two genetic groups have identical and similar environmental niches; (3) by integrating the genetic information in niche modeling, niches projections show divergence in the response of the species and genetic populations to ongoing climate change. This study highlights the importance of incorporating genetic data into Ecological Niche Modeling (ENM) approaches to obtain a finer information of species’ future distribution, and explores the implications for agricultural adaptation, with a particular focus on identifying priority actions in orphan crops conservation and breeding.Orphan legume crops play an important role in smallholder farmers’ food systems. Though less documented, they have the potential to contribute to adequate nutrition in vulnerable communities. Unfortunately, data are scarce about the potential of those crops to withstand current and future climate variations. Using  Availability of the genetic resources of those crops is still a challenge for many genebanks at national, regional, and international levels. This situation jeopardizes the sustainable utilization of the plant genetic diversity that can be useful for current and future food systems and secured nutrition4. Such genetic resources are important for successful cultivars development and selection of economic and agronomic traits and could confer resilience to evolving climate.Defining how and where plant varieties will adequately respond to environmental variations is a central topic in plant science research. This is more preoccupying for orphan crops that are largely grown in marginal areas and neglected in the mainstream research agendaMacrotyloma geocarpum (Harms) Maréchal and Baudet], a multipurpose staple orphan crop with high nutritional and economic values for smallholder farmers in West Africa9, the need to solve the ecological suitability of the extant genetic resources arose despite the significant achievements made recently on the germplasm collection, conservation, and characterization. Kersting’s groundnut plays an important role in farming sustainability through its ability to fix atmospheric nitrogen in the soil and enhance soil fertility10. Furthermore, It serves in traditional medicine for local populations11. However, the production of Kersting’s groundnut is declining rapidly and the genetic resources were rarely collected and safeguarded for the future generation. In addition, environmental stresses are among the main causes for declining Kersting’s groundnut production from its cultivated areas12. Though Kersting’s groundnut has relatively good adaptation to low-input conditions14, increased frequency of drought, intense precipitations, elevated temperatures, and increased salt and heavy metals in soils will often be accompanied by increased infestation by pests, and pathogens, are expected to limit the plant growth and productivity, and consequently the crop’s yield and production15.In Kersting’s groundnut [16 that limits the extent of its genetic diversity and cultivated zones. Kersting’s groundnut counts six landraces set mostly within three agroclimatic zones; Northern-Guinean (NG), Northern-Sudanian (NS), and Southern-Sudanian (SS) of Benin, Burkina Faso, Ghana, and Togo with the predominance of genetic resources and diversity in the Southern-Sudanian zone12. Overall, the area of cultivation and adaptation of landraces differ among agroclimatic regions. The Black landrace was largely collected in the Northern-Sudanian environmental conditions and was widely preferred, cultivated, and maintained by farmers12. The White landrace was widely grown in the Northern-Guinean transition zone of Benin6 while less cultivated in Burkina Faso12 and absent in other countries of West-Africa. The production of the Brown landrace was specifically limited to the Ghana farming system12.Recent studies revealed a low variation within the species17. To find an adequate preferendum where the species can thrive, it has become crucial to approximate the potential distribution of the crop and its genetic resources.Kersting’s groundnut landraces are the direct results of farmer selection, cultivation, and maintenance over the centuries. This continual adaptation of the crop to smallholders farming conditions could continue to play a role in adapting production to climate change. Also, local adaptation of landraces could vary in their climatic response and requirement and therefore, may spread differentially under evolving environmental conditions18, as Kersting’s groundnut at National and Regional levels. Ecological niche modeling (ENM) can identify the environmental parameters that can impact a species’ distribution and project its potential distribution area onto new environmental surfaces to examine the effect of present or future environmental change20.Unfortunately, with the rapid evolution in climate conditions and the further introduction and adoption of new cash crops with high economic importance, local seed systems alone will likely be insufficient to ensure the endurance of the crop genetic resources and diversity. In these conditions, applying ecological research is required to inform conservation and management decisions to mitigate a species genetic erosion25. The basic modeling framework of species distribution models (SDMs) in general has been criticized on a number of gaps, such as ignoring heterogeneity in population and genetic structure in different parts of a species geographical range26. However, many species are organized into differentiated genetic lineages across their geographical ranges28 and populations differ in their adaptive potential to respond to environmental change29. Studies proved that incorporating molecular data into SDMs represents an important step forward for modeling the effects of climate change on species geographical ranges32.Several statistical and mechanistic techniques proved effective in quantifying niches and spatial distribution of natural and cultivated species33, much uncertainty remains concerning the ability of the crop to withstand the changing climate, suggesting that there is a clear need to comprehensively analyze the response of the crop diversity under new environmental conditions of the coming decades.In the case of Kersting’s groundnut16. Kafoutchoni, et al.34 also assessed the genetic structure of the species through the GBS approach with 217 individuals, and Discriminant analysis of principal components (DAPC) and found eight genetically distinct groups from five origins. In this context, the following questions are of high interest: do the agroclimatic niches of KG vary with climate changes? Would KG genetic groups differ in their ability to respond to present and future climatic scenarios?The present study was undertaken to determine the environmental conditions in which KG will need to be cultivated in the coming decades and to utilize this information to prioritize genetic resource conservation and breeding efforts. Recent molecular studies involving 281 individuals from Benin and Togo identified two major genetic clusters of KG and these two groups were distributed across Southern-Sudanian and Northern-Guinean agroclimatic zonesThis study examines the response of orphan crops to future climates by using genetic information and ecological niche modeling approach (gENMs) using KG as an example. Therefore, we combined KG population genomics data with ecological niche modeling: (1) to analyze the relationship between climate factors and species populations distribution in agroclimatic zones of Burkina Faso, Benin, Ghana, and Togo, and (2) to predict areas that would be suitable for the species and genetic populations under the future scenarios. We hypothesized that: (1) the future climates will impact the distribution of KG, and (2) genetically distinct populations of orphan crops would respond differently to climate change.Despite low levels of diversity , Kersting’s groundnut populations remained genetically well differentiated. Admixture models with a putative number of tested genetic clusters (K) from one to five, showed that the most likely number of inferred members was 2 with ΔK = 101.917 Fig. . The claTable The random distribution of landraces , and significant background test statistic , Northern Sudanian (NS), and Northern-Guinean (NG) Fig. a1. The SAnalyses based on projecting the final models into the future (2055) scenarios revealed varied patterns in KG and genetic populations cultivable areas Fig. , and S7.Important increases were observed in the potential cultivable areas of the Pop1 under future climatic scenarios (> 500% under RCP 4.5 and ≈ 50% under RCP 8.5) Fig. . This trThe MOP results indicated that the calibration environments were dissimilar from the extrapolative conditions in the two future scenarios of 2055 Fig. . Figure By incorporating genetic information in ENM, this study provided evidence that the two genetically distinct groups in Kersting’s groundnut respond differently to climate changes though distributed in similar environmental niches, and suggested implications for sustainable management and breeding perspectives under current and future climate variations.37.We used two approaches to model KG potential environmental niches across agroclimatic zones of four West African countries: a traditional species ENM using species-wide cultivated range, and intraspecific models based on genetic information. We found that decomposing a species into intraspecific genetic groups increases our understanding of the potential distribution of KG, which is consistent with results from previous studies incorporating genetic information into ENM12. Tamini also showed that the different sowing dates and seasons with varied temperatures influenced KG growing cycle by delaying or accelerating the flowering time. To cope with these weather issues, farmers managed their cropping calendar by advancing (in June) or delaying (in August) the date of planting KG. The change in temperature and moisture levels are not only determined by climatic parameters but may also lead to a change in the absorption rate of water, fertilizers, and other minerals in soils, which determine yield output39. Therefore, the consideration of both climate and soil type was essential to better predict KG and genetic groups’ distribution. The physical properties of soil such as bulk density of soil in kg / cubic-meter for 10 cm depth (blt_2), soil texture fraction clay at 10 cm depth (clyppt_d2), and soil texture fraction silt in percent for 10 cm depth (Sltppt_d2) also play a relevant role in KG and populations distribution. Kouelo et al.40 reported that the soil texture and preparation (type of tillage) influenced the crop productivity in Benin. Although applying ENM to intraspecific genetic groups allowed the detection of environmental variables, a multidisciplinary approach involving agronomists and biologists would allow a clear understanding of the weight that each of these components may have on crop growth and development.Our approach allowed to identify six environmental variables correlated with the potential distribution ranges of the species and populations. The max temperature warmest month (bio5), mean annual rainfall (bio12), and rainfall driest month (bio14) were the climatic variables mostly involved in fitting the three models. The ecological weight that precipitations may have in KG’s distribution is also supported by a recent ethnobotanical study where farmers identified drought and high moisture during the reproductive stage as the main factors hindering species production36. Populations of KG are both cultivated in the Northern-Guinean and the Southern-Sudanian zones of Benin, characterized by bimodal  and unimodal  growing seasons, respectively. Although Pop 2 is grown also in the Northern-Sudanian zone, where rainfalls are relatively low , very few number of occurences (7) from this region were used in the model. Therefore, they may not show variations in their environmental niches and adaptation to different climate conditions, as previously reported by Wellenreuther, et al.41 who analyzed the ecological causes of the ranges limits and the coexistence of two congeneric damselflies .We found identical and similar environmental niches for the species and the two genetic populations, suggesting no variation in their environmental niches and adaptation to climate conditions. Our results did not support the view that a given species could be considered as an assemblage of genetic units differing in their spatial distributionM. geocarpum are relatively different in the distribution areas. The species and genetic populations occupied the distribution ranges but do not respond similarly to the environmental conditions. Indeed, KG individuals displayed difference in their performance when evaluated under the same environmental conditions44.In contrast, Maxent’s outputs revealed that climatic niches of the two genetic populations of 46.Our results, found evidence of variability in the species response to future environmental conditions, confirming the assumption that changes in climate will influence the environmental niches of KG species and populatons. This corroborates many other studies which showed the impact of future climatic conditions on crops production, such as cereals, legumes, vegetables31 tested and demonstrated the hypothesis that species locally adapted to current environments are likely to become maladapted in the future. The same trends were reported on other cereals such as maize, wheat, sorghum, and barley which tend to decrease their area of suitability in their centres of origin47. Out of the 361 individuals included in this study, 101 belong to the genetic Pop2, and counts all the landraces grown—diversity based on seed coat color12. According to the conclusion of Vigouroux, et al.48 who described the evolution and adaptation of pearl millet in West Africa, such diversity was supposed to confert adaptation to climate variation. The southern and Northern-Sudanian benefit of the lowest precipitations and are hence, the most prone to drought, widely reported as one of the main factors hindering KG production49. In the context of an inevitable agricultural reduction in these agroclimatic zones, the earlier the mitigation actions are taken, the more successful will be the collection and conservation efforts of KG’s resources.We also combined genetic information to ENM to test the hypothesis that genetic divergent groups would respond differently to environmental change. The projection under future climatic scenarios RCP4.5 and RCP8.5 indicate that the response of Kersting’s groundnut and genetic groups varied significantly across agroclimatic zones. Our results confirmed the idea that different genetic clusters potentially showed adaptive variation to different abiotic conditions within the geographical range of the species. Globally, although Central Benin and Northern Togo are presumed to be a centre of origin for the crop, we observed a loss in suitable areas for the species production. The same trend was observed in other countries were the crop has been grown for thousand of years. Similarly, Ikeda et al.2) in the Northern and Southern-Guinean zones of Ghana, Benin and Togo. With regards to these results, significant headway can be made by creating more favorable policy environments. Two strategies proposed by Ramirez-Cabral, Kumar and Taylor23 can be used to mitigate loss in KG resources and diversity: first, the use of all the resources of Pop2 in regions where the stress of climate conditions become unsuitable for the species production. An example of this is the Southern-Sudanian zone of Central Benin, where the use of KG is more important than other regions. The second option was to address future loss of suitability by shifting Pop2's current cultivation areas to new regions, such as the Northern-Guinean zone of Southern Benin, Ghana and Togo where future climatic conditions are projected to become more cultivable for KG.On the other hand, future environmental conditions remain, even become more favorable to the Pop1 cultivation (with an increase in suitable areas of 588.812% compared to the present day). However, under the severe environmental conditions (RCP8.5), the cultivable area of this genetic Pop1 is predicted to decline from RCP4.5  of the species and has been reported having the best phenotypic performance42. The individuals in this genetic population can serve as a potential source for diversity on which breeding efforts could be based to confer best performance for plant growth and yield to Pop1, mainly composed of the white landrace mostly preferred, grown, and sold by farmers in Benin6. Similarly, the genetic Pop1 can serve as potential parents to confer resilience to changing climates and increase sustainabilityof genetic Pop2. In addition, advancements in molecular plant breeding would be an importance of paramount to increase the genetic gains and make more accurate the breeding process. In the particular case of KG, currently, available partial GBS data34 would allow the analyses of intraspecific genetic clusters based on gene network variation for various important phenotypic traits . This novel approach would provide possibilities to assess the extent to which key functional genes and genetic variation may be threatened under future ecological conditions52. Plant breeders have widely and successfully implemented genome-wide breeding approach for the development of climate-resilient varieties55, through marker-assisted selection and genomic selection. Another approach is to increase variability within the species, particularly in Pop1, clustering mostly white seed coloured individuals, through mutation methods  combined with molecular markers ). Such techniques have been successfully used in breeding of many legume crops to enhance diversity and to develop mutant cultivars57.Undertaking any global initiatives to overcome food insecurity challenges requires an assessment of staple crop productivity, quality, and environmentally suitable areas under climate changeM. geocarpum and the two populations are on different climate change trajectories. The projected distribution maps presented in this paper have been hence, analysed and used to identify strategic measures to manage the impacts of reduced climate cultivability while taking advantage of the opportunities in areas of improved suitability for Kersting’s groundnut cultivation in the future. Our findings could be downscaled to a country level to assist national policymakers in developing strategic control initiatives to prevent the scarcity of this legume.This study assessed the potential impacts of climate variations on environments suitability for Kersting’s groundnut cultivation, and consequently its distribution around four West-African countries. The use of Maxent’ models and genetic information allowed a preliminary understanding of the stress factors influencing the climate suitability of the species and genetic populations under two future scenarios . The overall trend shown by our results indicates an increase in climate suitability for the species cultivation in the Northern-Guinean zone of Southern Benin and Togo. However, an important decrease was predicted in other agroclimatic areas, while genetic Pop1 production areas increase. Our findings illustrate also that projected areas of environments cultivability for Although this study represents the first insight into examining the potential of Kersting’s groundnut as a resilient crop under climate change, several limitations are to be noted to develop the right tools to reduce model uncertainty and make better predictions in future research. First, the global distribution of the species and its wild relatives is still incompletely documented online: collecting more and finer occurrence data, especially in regions where its production was previously reported would greatly help in refining or confirming our results. Second, our models identified rainfall, temperature, and soil variables that contributed significantly to their fits. However, socioeconomic factors such as the local market value of the species and cultural preferences must also be considered in the predictions. Combining also measures of key phenotypic traits and botic stress factors (pests ans diseases associated with KG production) in the modeling process would contribute to improving predictions of the impact of climate change on this legume crop. Third, the non-availability of whole genome-wide data in the case of Kersting’s groundnut limited access to phylogenetic information and identification of key functional genes for various important phenotypic traits. That may provide means to assess the response of key functional genes under evolutionary climate change.Nevertheless, using our approach, we identified species, genetic populations, and cultivable areas for further germplasm collecting to enhance available germplasm and better direct Kersting’s groundnut breeding priorities in the future.http://hub.africabiosciences.org/activities/services) located in Nairobi, Kenya. The quality of the DNA was confirmed by electrophoresis in 0.8% agarose, and the quantification was carried out using UVP BioDoc-It2 Imaging System. All of the samples were diluted to 50 ng/µl for the DArT genotyping platform. Genotyping was performed using the DArT-Seq™ platform at Diversity Arrays Technology58. The quality analysis of the genotypic data was performed using Illumina HiSeq 250059. The SilicoDArt calling algorithms (DArTsoft14) were used to score DArTseq markers into a binary format (presence = 1 and absence = 0) for each sample genomic representation. A total of 2844 SNP markers were obtained and processed in TASSEL v560, for quality check. SNPs were filtered with TASSEL v5 for further analysis using the proportion of missing data < 20%. A total of 2323 SNPs were retained and the Nipals model in kdcompute (https://kdcompute.igss-africa.org) was used for data imputation.For the present work, a total of 361 accessions of KG collected from Benin, Burkina Faso, Ghana and Togo were used. The DNA of each sample was extracted from young leaves of each accession, using the protocol of the Integrated genotyping service and support (IGSS) at the Biosciences Eastern and Central Africa  interactions; and grouping (K) ranging from 1 to 5 in 10 independent runs. The results generated were used as input into the POPHELPER version 2.3.1, an R package62, to determine the most likely genetic clusters based on the Evanno method63 and to generate the delta K (ΔK) graph and bar graph. The most likely number of K clusters was determined by its higher ΔK value. After these searches, we found a likely number of K = 2.The program Structure 2.3.464 to test for statistically significant differences between the two genetic groups. The pairwise Fst comparisons between the two groups were calculated with 9999 permutations using package ‘hierfstat’ v 0.5–10 in R environment. General patterns of genetic diversity were also evaluated by calculating observed (Ho) versus expected (He) heterozygosity and gene diversity (Hs) within each population. Based on the genetic analyses, we classified the two clusters as distinct genetic populations (Pop1 and Pop2) encompassed within three agroclimatic zones of Burkina Faso, Benin, Ghana, and Togo . We then used these genetically defined populations and their GPS points available, to define populations' location points.The molecular variation (AMOVA) was determined using the R package adegenetM. geocarpum was obtained from the Global Biodiversity Information Facility , an online available database, and the crop self-collected material of the Laboratory of Genetics, Biotechnology, and Seeds Science (GBioS). As the different data sources and a large dataset (> 500 occurrence records) would likely carry elevated geographical or environmental space biases66, the number of records were decreased in Wallace package, an online workspace based on R interface67 using four complementary approaches: (1) we first removed occurrences collected before 1986 to match with environmental layers and soil properties; (2) considerable ambiguity may exist in GBIF data over the identity of the species due to synonymous names . To avoid any confusion arising from this taxonomic ambiguity, we searched through the online databases using the following keywords: Macrotyloma geocarpum, Kerstingiella geocarpa, var. geocarpa, or var. geocarpum; orphan legumes. We then harmonized the GBIF database and discarded the reports on var. tisserantii and Kerstingiella tisserantii; (3) we used spatially filtering occurrences located ≤ 10 km from other occurrences using the spThin68, an R package; finally, (4) we manually checked isolated locations points in Africa (in ArcGIS ver. 10.7.1) and removed occurrences in areas where M. geocarpum is not generally grown.The occurrence data for The defined genetic clusters data with their geographic coordinates were also filtered separately to ensure the real distribution of each population within agroclimatic zones. After data cleaning steps, the final occurrence datasets contained: for the species, a dataset of 53 occurences (which include 17 points retained from GBIF), used for modeling without genetic information. And two data files that comprised 24 locations points for Pop1, and 26 occurences for Pop2, used in subsequent analyses integrating genetic information 69. Current and future variables averaged between the periods 1986–2015 (2000) and 2041–2060 (2055) were downloaded at a 30 arc-s (~ 1 km) spatial resolution. For future climatic conditions, predictions from the Ensemble model69 were used. This model simulates changes based on a set of scenarios. The projections were run under Representative Concentration Pathway (RCP), RCP 4.5, and RCP 8.5 for the 2055 time horizon70. In all RCPs, the climatic conditions are extreme in RCP 8.5 scenarios compared to RCP 4.5. RCP 4.5 projects temperatures to rise above industrial levels by at least 1.5 °C in West Africa, with atmospheric CO2 reaching 500 ppm while in RCP 8.5 projections, temperatures are predicted to rise by 2.8 °C and atmospheric CO2 to be over 550 ppm71. These climate projections were statistically downscaled to match the bioclimatic variables using the delta method72.We used bioclimatic layers combined with soil properties to project current and future niches for the species and each genetic group. A total of 15 bioclimatic variables were downloaded from Africlim online regional climate models (RCMs) data portal  we cropped all variables to encompass the broad geographic regions that define Kersting’s groundnut global distribution.Data related to soil characteristics were available in the World Soil Information (ISRIC) databases (Soil-property-maps-of-Africa-at-250-m-resolution) at 250 m resolution74, Mean annual rainfall (bio12), Rainfall driest month (bio14), Bulk density in kg/cubic-meter for 10 cm depth (blt_d2), soil texture fraction clay at 10 cm depth (clyppt_d2), = Soil texture fraction silt in percent for 10 cm depth (Sltppt_d2) were chosen based on their noncollinearity and contribution to the prediction models and were used in the final models of the species, and with genetic information (Pop1 and Pop2).We discarded highly correlated variables (≥ 0.8) by Pearson´s rank correlation coefficients analysis using the R library ‘ntbox’76 implemented in Maxent ver. 3.4.475. The algorithm has been extensively tested and benchmarked78. Many studies have reported Maxent as one of the highest performing presence-background algorithms79. As the selection of sample points can influence model performance in Maxent80, we restricted the selection of background points using the regularization of 10,000 background points21. Models were trained with data from the present and projected in the future. Three Maxent models were generated with different occurrence datasets:model 1: all accessions location points together without genetic information were used in projecting the entire distribution of KG, distributed in four West African countries  where the distribution range of the species is characterized by three agroclimatic zones including the Northern-Sudanian, Southern-Sudanian, and Northern-Guinean.model 2 and model 3: were developed using occurrences of Pop1 and Pop2, respectively the genetically defined populations. Pop1 location points are covered by the agroclimatic zones of Southern-Sudanian, and Northern-Guinean of Southern and central Benin, and Northern Togo. On the other hand, the occupation range of Pop2 extends over the four countries and the three agroclimatic zones.To calibrate our models, we employed the maximum entropy method76. We Kept the Auto Features box checked Linear, quadratic, product, hinge, and threshold functions of predictor variables were employed, and variable importance was assessed using a jackknife analysis79. For easy interpretation of the results of niche models, we specified the Maxent’ output format to a logistic form.We used a Bootsrap method, by defining 75% of the data for model training and 25% for model testing for 10 iterations81, by specifying 500 iterations with the omission threshold set at five percent. The statistical significance of pROC values was examined using t-test statistic. The AICc was estimated based on the number of parameters and likelihoods of continuous raw outputs, using R library ENMEval v 2.082, following the method described by Warren and Seifert83. The lower is the value of AICc, more accurate is the predictive model84. Models omission rates calculated with the 10th percentile training presence threshold, and the area under the receiver operating characteristic curve (AUC), were also used as secondary criteria for testing performance of each model85. A model is considered as having a good fit when its AUC is closed to one (AUC ≥ 0.75)21. The final models were projected onto future climatic scenarios (RCP4.5. and RCP8.5) of the horizon 2055, using Maxent software with ten bootstrap iterations. Each model was set to thresholds with the 10 percentile training presence values to produce continuous and binary raster maps for current and future scenarios in ArcGIS v. 10.7.1.The three models performance was tested based on the partial receiver operator characteristics (pROC) and the corrected Akaike information criterion (AICc) approaches. We used the outputs from Maxent to perform pROC in R package ‘ENMGadgets’ v 0.1.0.12 the surface occupied by each condition (suitable and unsuitable) using the continuous raw outputs from maxent in ArcGIS v. 10.7.1. We estimated the range size change under scenarios RCP 4.5 and RCP 8.5 of the period 2055 for the species and two genetic populations, following the model used by Hu et al.86:2) under a given future scenario; CA is the suitable areas in current conditions. Negative, null, and positive values represent range lost, stable, and gained, respectively. Furthermore, to visualize the potential changes of suitable areas for Kersting’s groundnut production, we compared current and future distribution ranges of the crop and genetic populations using package “tmap” version 3.3-187 in R. Finally, to determine the zones where each model transfers would require extrapolation under future climatic conditions, we used the mobility-oriented parity (MOP) metric. The MOP analysis was performed with the R library ‘ntbox’ by setting for environmental distances to the nearest 10% of the reference region and 500 runs.Two different levels were, therefore, defined: unsuitable and suitable. Finally, we quantified in km35. Niche equivalency test is used to test out the null hypothesis that two species have identical environmental niches. The niche equivalency test examines the observed niche similarities between the predicting models of two species. The niche background test evaluates the power to detect differences between two ENMs. Compared to the existing tests, these methods improve the accuracy of niche similarity quantifications and corresponding statistical tests35. The Schoener's D statistic is used to determine how similar the occupied niches of two species are based on the original input occurrences. The observed values of D in both the niche identity test and the background similarity test were compared to the mean values of the randomized runs using t-test88. Hence, the environmental niches were considered significantly different if the observed values of niche overlap were less than 95% or 99%  of the overlap values derived from the pseudoreplicates.To understand whether differences between species and genetic populations emerge from true niche divergences, we performed Schoener’s niche equivalency (identity) and similarity test (D), and Warren’s niche background test (I) using the R package ‘humboldt’89 used a kernel density function90 to create a continuous E-space surface in a grid of 100 × 100 cells, estimating the occupied E-space of the focal species or its environment, respectively, using PC values from either the input occurrence localities or study region data.We visualized and analyzed E-space as two axes of a Principal Component Analysis (PCA) of input environmental variables throughout the entire study areas of the species and genetic populations. Broennimann et al.35. The fundamental niche was characterised by truncating species occupied E-space by the available E-space in its environment. The larger the truncated proportion, the greater the risk that the occupied niche does not accurately reflect fundamental niche of the species. Hence, the Potential Niche Truncation Index (PNTI) was estimated to measure the amount of the observed E-space truncated by the available E-space. When the values of PNTI are ranged between 0.15 and 0.3, there is a moderate truncation risk while values > 0.3 explained a high risk of niche truncation due to limited available E-space35.Furthermore, The species fundamental niche from observed niche was analyzed with the packge ‘humboldt’Supplementary Information.


Due to the increasing prevalence of growth-related myopathies and abnormalities in turkey meat, the ability to include meat quality traits in poultry breeding strategies is an issue of key importance. In the present study, genetic parameters for meat quality traits and their correlations with body weight and meat yield were estimated using a population of purebred male turkeys. Information on live body, breast, thigh, and drum weights, breast meat yield, feed conversion ratio, breast lightness (L*), redness (a*), and yellowness (b*), ultimate pH, and white striping (WS) severity score were collected on 11,986 toms from three purebred genetic lines. Heritability and genetic and partial phenotypic correlations were estimated for each trait using an animal model with genetic line, hatch week-year, and age at slaughter included as fixed effects. Heritability of ultimate pH was estimated to be 0.34 ± 0.05 and a range of 0.20 ± 0.02 to 0.23 ± 0.02 for breast meat colour . White striping was also estimated to be moderately heritable at 0.15 ± 0.02. Unfavorable genetic correlations were observed between body weight and meat quality traits as well as white striping, indicating that selection for increased body weight and meat yield may decrease pH and increase the incidence of pale meat with more severe white striping. The results of this analysis provide insight into the effect of current selection strategies on meat quality and emphasize the need to include meat quality traits into future selection indexes for turkeys. With an increasing desire for lean, quality poultry meat products emphasis needs to be placed on improving technological characteristics and functional properties of the meat  . HoweverIn addition to the rise in PSE meat observed in the poultry industry over the past decades, an increase in the incidence of the growth-related myopathy white striping (WS) has been observed in the turkey industry . This myEstimation of genetic parameters of meat quality traits is crucial to evaluate the possibility of genetic selection and more importantly, the magnitude of indirect selection on these traits. Due to the complexity of measuring these traits, research on their genetic parameters in turkeys is limited in comparison to other species and when conducted, generally have small sample sizes . HoweverData were collected on male turkeys from three purebred genetic lines  over 44 weeks between July 2018 and November 2019. There were 11,986 birds included in this study; 2,569 were from line A, 5,299 from line B, and 4,118 from line C. The genetic lines included a dam-line that was selected primarily for body weight and reproductive traits (line A), a dam-line selected mainly for reproductive traits (line B), and a sire-line with selection focused on body weight, meat yield, and feed efficiency (line C). All genetic lines were raised under the same husbandry conditions . ProcessPectoralis major (fillets), Pectoralis minor (tenders), thighs , and drums  were individually weighed. All remaining carcasses were processed separately, and the weight of total breast meat  were measured. All weights were measured in kg. Breast meat yield (BMY) was calculated as a percentage of BW.Body weight (BW) was collected 2 days prior to slaughter (20–24 weeks of age). A real-time automated system was used to record individual feed intake  and feedMeat quality measurements included ultimate pH and color. A pH measurement was taken from the dorsal side of an intact, deboned fillet from the randomly selected, broken down carcasses at 24 h post-mortem . Breast lightness, redness, and yellowness , hatch week-year (58 levels), and age at slaughter  for all models and the addition of score observer (6 levels) for WS; a is a vector of additive genetic effects distributed as A is the numerator relationship matrix including the inbreeding coefficients and G is the additive genetic variance-covariance matrix between traits; e is the vector of residual effects which has a distribution of Eiy is an mi x mi matrix corresponding to the traits that were present for animal i and mi is the number of traits present for animal i; and X and Z are design matrices relating the observations to the fixed and random effects, respectively. Heritability (h2) was estimated as the proportion of phenotypic variance  explained by additive genetic variance estimated from the univariate models. Genetic correlation coefficients (rg) were calculated using the following equation:x and y, respectively. Pearson partial phenotypic correlation coefficients (rp) were calculated using PROC GLM in SAS  to account for the fixed effects included in the genetic (co)variance estimates.Univariate and bivariate linear animal models were used to estimate (co)variance components through restricted maximum likelihood carried out using the BLUPf90 family of programs . The linh2 = 0.44 ± 0.03) and BMY (h2 = 0.41 ± 0.02) were high and within the range of previously published estimates in turkeys, which ranged from 0.23 to 0.45 for BW (h2 = 0.18 ± 0.03) which was slightly higher than previously published estimates in turkeys which ranged from 0.05 to 0.16  and a* (h2 = 0.22 ± 0.02) were similar to the previous estimates reported in turkeys. Estimates for L* and a* have been reported to range between 0.12 to 0.27 and 0.21 to 0.30, respectively (h2 = 0.23 ± 0.02) was higher than previous estimates of 0.15 and 0.14 in turkeys  in the present study was much greater than the previous estimates  were similar in magnitude, showing all three traits to be moderately heritable. The heritability estimates for L*  suggesting the potential for genetic selection against WS in turkeys. This estimate was low compared to reported estimates for broiler chickens which ranged from 0.19 to 0.50 when using a similar 0–3 scoring system , BW and thighs , and BW and drums . These are the three largest muscle groups which make up the largest portion of overall body weight. Moderate, favorable genetic and partial phenotypic correlations were observed between BrW and thighs  and BrW and drums , which is logical as birds with larger and heavier breast muscles require stronger leg muscles to support walking ability. However, since the genetic correlation estimates are not perfect, stronger emphasis placed on selection for breast traits may still lead to an unbalanced development of the leg muscles. Correlations between body and carcass component weights and FCR were all weak (−0.04 < rg < 0.04), except for fillets , suggesting a moderate, unfavorable relationship between fillet weight and FCR in the studied population. This was not the case in previously published studies in turkeys which showed a genetic correlation of BW and FCR ranging from 0.10 to 0.19 (rg < 0.04) with the exception of FCR and L*  which showed a moderate, favorable genetic correlation and FCR and a*  which showed moderate, favorable genetic and phenotypic correlations. This suggests that selection for FCR may affect meat quality, however, this may be due to the connection between FCR and fillet weight and should be investigated further.Additive genetic and partial phenotypic correlation coefficients between the traits are presented in  to 0.19 . All corrg = −0.18 ± 0.08, rp = −0.17) as well as BrW and pHu . The correlation between size of the bird and pHu of the breast muscle has been documented with larger, faster-growing lines showing a tendency to have a faster pH decline and lower pHu  potentially due to the increased chilling time required for larger carcasses resulting in higher carcass temperatures and functional protein degradation. Finally, the relationship between pHu and L* was strong and negative in the present study  similar to previously published genetic correlation estimates in turkeys which ranged from −0.42 to −0.53  and WS and BrW , suggesting that selection for growth will also lead to an increase in WS prevalence if additional traits are not used to balance correlated effects. WS also showed weak to moderate, unfavorable correlations with the studied meat quality traits, the strongest of which was with b* . Moderate genetic correlations were also estimated with pHu  and a* , while a weak genetic correlation was estimated between WS and L* . Varying results have been previously published regarding the relationship between WS and meat quality in poultry. Several studies have reported the relationship between growth and muscle myopathies, including WS, in turkeys and broiler chickens . In the In the present study, we estimated the genetic parameters for white striping severity, breast meat colour, and pHu alongside several body and carcass component weights and FCR in turkeys. We have reported the first estimate of heritability for white striping in turkeys and have added to the body of knowledge surrounding heritability of meat quality. The estimates show potential for future genetic selection for improved meat quality and reduced white striping severity. The estimates also provide insight into the unfavorable effects of selection for growth, meat yield, and efficiency have on meat quality.


GLIS1, LOC101117953, PDGFD and T were in the significant divergent regions between DS and STH–MG. A nonsynonymous point mutation (g.27807636G>T) was found within GLIS1 in STH–MG and resulted in a Pro to Thr substitution. As a pro-adipogenic factor, GLIS1 may play critical roles in the mesodermal cell differentiation during fetal development affecting fat deposition in sheep tails. This study gives a new insight into the genetic basis of species-specific traits of sheep tails.Fat deposition in sheep tails is as a result of a complicated mechanism. Mongolian sheep (MG) and Small Tail Han sheep (STH) are two fat-tailed Chinese indigenous sheep breeds while DairyMeade and East Friesian (DS) are two thin-tailed dairy sheep breeds recently introduced to China. In this study, population genomics analysis was applied to identify candidate genes associated with sheep tails based on an in-depth whole-genome sequencing of MG, STH and DS. The selective signature analysis demonstrated that The online version contains supplementary material available at 10.1007/s11033-021-06468-w. Ovis aries) was one of the first domesticated livestock, providing humans with meat, milk, fur, and wool products. Domestication, natural and artificial selection have resulted in remarkable phenotypic diversity in animal appearance, growth rate, local adaptation, and fertility rate [Sheep (ity rate . China hity rate . These bThe wild ancestors of domestic sheep had thin tails. The fat tails in sheep are perceived to have developed following domestication as an adaptive response to store energy for use during migration and in harsh winter . In chin1 and 1 DairyMeade × F1 F2 sheep), 7 Small Tail Han sheep from 7 different pedigrees, and 9 Mongolian sheep from 9 different pedigrees were collected from different locations in Inner Mongolia Autonomous Region, China, for whole-genome resequencing . All the ear tissues collected were immediately stored in liquid nitrogen. The animal experimental procedures were performed according to the guidelines set by the Ethics Committee of Inner Mongolia University (IMU-sheep-2018-011).Ear tissues of 13 dairy sheep . Next-generation sequence library construction was performed with 3 μg of genomic DNA according to the standard Illumina library preparation protocols and insert size of 350 bp. All libraries were sequenced on an Illumina Hiseq 2500 platform to generate paired-end reads. The resequencing depth ranged from 12.3 × to 35.5 × fold coverage, with an average depth of 18.14 × .http://picard.sourceforge.net). SAMTOOLS [The adaptors and low-quality sequences of raw reads were trimmed and filtered to obtain clean reads using FastQC (version 0.11.7)  and TrimSAMTOOLS  was usedSAMTOOLS . FinallySAMTOOLS  accordin2 (-minMAF 0.05 -hwcutoff 0.001 -Het 0.88 -Miss 0.25) for the pairs of SNPs and to plot the LD curves. To remove the bias introduced by differing sample sizes in different populations, individuals in each population were randomly sampled to maintain a consistent sample size during the calculations . Only SNPs with a minor allele frequency (MAF) greater than 0.05 were considered.Based on the autosomal genetic variants, PLINK v1.9  was usedp-value correction. Only terms with a p-value less than 0.05 were considered significant and listed. Data analysis and visualization were carried out with customized R scripts.Selective sweep signals were identified using the population differentiation index , and there was no genetic exchange .Whole-genome sequencing was carried out at an average depth of 18.14 × coverage (Table S2), on the ear tissues collected from sheep in different regions of Inner Mongolia, China. After rigorously filtering, a total of 25,375,422 high-quality SNPs were obtained for further analysis. Among them, 15,525,859 SNPs were in intergenic regions, while 171,462 SNPs were in exonic regions (Table S3). The genetic relationships between the sheep breeds were explored based on all the genetic variants and fourfold degenerate sites. The phylogenetic tree constructed by the neighbor-joining (NJ) method showed that each breed population had a distinct clade  . Similar genetic affinities were obtained in phylogenetic trees constructed by neighbor-joining  and maximum-likelihood (ML) methods  using fourfold degenerate sites. Principle component analysis (PCA) also uncovered different population structuring among DS, MG and STH, and the PC1 (4.06%) revealed the fat-tailed and thin-tailed sheep variants . ADMIXTURE analysis revealed that the fat-tailed and thin-tailed sheep, belonged to different clades   and a slower decay rate of linkage disequilibrium  and MG group (46 kb)) . These results suggest that indigenous breeds (MG and STH) have a higher genetic diversity, while bottlenecking and/or inbreeding occurred in the two dairy sheep breeds.p-value = 0.00086) and T cell receptor signaling pathway  (Table S6).Tail size was the prominent phenotypic difference between DS and MG/STH. We analyzed the inter/intra-population diversities of the highly significant sweep regions to explore the genetic basis underlying fat deposition in the tail. The population differentiation index mentclass2pt{minimGLIS1 gene  substitution, thus making STH–MG different from DM and other thin tail mammals in this locus . Another genomic region (from 3.825 to 3.90 Mb) on chromosome 15 also exhibited strong selection signatures  between DS and STH–MG , that harbors PDGFD gene, a member of the platelet-derived growth factor family. Other genes related to sheep tail traits, such as T , then it expresses in the anterior region of the forelimb, ventral part of the body and tail at 10.5 dpc and the expression is increased at 11.0 dpc, which is consistent with mesoderm differentiation [GLIS1 was recognized as a novel pro-adipogenic transcription factor. It is highly expressed in bipotent muscle satellite cells. But when overexpressed, increased occupancy of GLIS1 is observed at the promoters of adipogenic genes Adipoq, Cebpa and Ucp1, and drives brown adipogenesis both in vitro and in vivo [GLIS1 role in sheep has not been extensively studied. A SNP in GLIS1 affects the feed efficiency in Dual Purpose and Blackface rams [GLIS1, as a pro-adipogenic factor, plays a key role in mesodermal cell differentiation during fetal development in fat-tailed sheep to initiate differentiation of preadipocytes and fat accumulation.The fat tail phenotype in sheep occurs as a result of multiple genes. With the usage of new genomic materials, this study revealed that there was a recent selective sweep at cription . In mousntiation . In a re in vivo . Howeverace rams , which mLOC101117953 and BMP2  were related to tail-fat deposition [LOC101117953 is a retro-copy of PPP1CC , which is not expressed in adult tissues as it lacks promoter region, and is thus less likely to be the causative gene for the tail phenotypes [PDGFD is a likely causal gene for fat deposition in sheep tail, which promotes proliferation and inhibits differentiation of preadipocyte [PDGFD significantly affect the tail length and width [T, a key regulator of mesoderm formation during early development, was found related to short-tail phenotype in Hulunbuir sheep, a subpopulation of Mongolia sheep [Previous studies reported that dipocyte –33. Two nd width . T, a keia sheep . It may GLIS1 locus. As a novel pro-adipogenic transcription factor, GLIS1 may initiate the accumulation and differentiation of preadipocytes in the tails during fetal development and affect the tail phenotypes in sheep.This study revealed that the ovine genome has recently encountered a selective sweep at GLIS1, LOC101117953, PDGFD and T have encountered a recent selective sweep. A nonsynonymous point mutation (g.27807636G>T) within GLIS1 locus in STH–MG resulted in a Pro to Thr substitution. As a pro-adipogenic factor, GLIS1 may play critical roles in the mesodermal cell differentiation during fetal development and affect fat deposition in sheep tails. This study gives a new insight into the genetic basis of species-specific adaptive traits in sheep and provides a novel opportunity to develop therapies for complex diseases related to fat metabolism.Fat tail in sheep is occurs as a result of multiple genes. This study demonstrated that Supplementary file1 (DOCX 1511 kb)Supplementary file2 (XLSX 98 kb)Below is the link to the electronic supplementary material.


Many mathematicians have a rich internal world of mental imagery. Using elementary mathematical skills, this study probes the mathematical imagination's sensorimotor foundations. Mental imagery is perturbed using body position: having the head and vestibular system in different positions with respect to gravity. No two mathematicians described the same imagery. Eight out of 11 habitually visualize, one uses sensorimotor imagery, and two do not habitually used mental imagery. Imagery was both intentional and partly autonomous. For example, coordinate planes rotated, drifted, wobbled, or slid down from vertical to horizontal. Parabolae slid into place or, on one side, a parabola arm reached upward in gravity. The sensorimotor foundation of imagery was evidenced in several ways. The imagery was placed with respect to the body. Further, the imagery had a variety of relationships to the body, such as the body being the coordinate system or the coordinate system being placed in front of the eyes for easy viewing by the mind's eye. The mind's eye, mind's arm, and awareness almost always obeyed the geometry of the real eye and arm. The imagery and body behaved as a dyad, so that the imagery moved or placed itself for the convenience of the mind's eye or arm, which in turn moved to follow the imagery. With eyes closed, participants created a peripersonal imagery space, along with the peripersonal space of the unseen environment. Although mathematics is fundamentally abstract, imagery was sometimes concrete or used a concrete substrate or was placed to avoid being inside concrete objects, such as furniture. Mathematicians varied in the numbers of components of mental imagery and the ways they interacted. The autonomy of the imagery was sometimes of mathematical interest, suggesting that the interaction of imagery habits and autonomy can be a source of mathematical creativity. Learning to read graphs in algebra, students learn to recognize and draw coordinate axes, one right-left, one vertical. They learn to judge the relationship between a curve and coordinate axes  were recruited from four institutions and a wide variety of specialties, including algebra, topology, computation, and mathematics education. Eight were mathematics professors, from assistant to full professor, and 3 were senior graduate assistants who teach mathematics at the university level. Each signed a statement of informed consent. The procedures were approved by the Portland State University Institutional Review Board. Participants were assigned arbitrary numbers and are specified P1-P11. Also, they are assigned a nickname for ease of reading, for example P8 Straight-ahead.Participants were asked to produce mental imagery with closed eyes:Task 0: Imagine a coordinate planey = x2Task 1: Graph x = y2Task 2: Graph y = 1/xTask 3: Graph y = x(x2 − 1)Task 4: Graph Task 5: Imagine a cylinder and wrap a spiral around itTask 6: Imagine a cone and wrap a spiral around itThese items are not rote images, but mathematical structures that can be instantiated in many ways in imagery.The entire series was introduced by a request to arrange a place and time where the participant could lie down undisturbed, in keeping with the typical solitary nature of mathematical work. Each task was introduced by a short paragraph asking questions intended to evoke further description, which it did. In Tasks 3,4, participants were asked to follow the graph from negative to positive. Full instructions are found in the All participants except P1, P9, and P10 performed the whole series of tasks in one session. P1 and P10 followed the written instructions and did the study in two sessions in 1 day, P1 following T0-T3 in the morning and the rest in the late afternoon and P10 performing T0 in the late afternoon and the rest in the late evening. P9, in person, was tired after T3; we resumed the experiment the following week.Figure 8. A trial is specified by participant number Px, task number Tx, and body position, for example P3T4L.Participants were asked to perform each task in various body positions: lying on their backs (B), right sides (R), left sides (L), face down (D), or sitting (S) , 2. SomeEight of the 11 participants performed the experiment at their own convenience. Of these eight, P6 drew diagrams and made notes around them, P7 made an audio recording as the imagery developed, and the other six  wrote brief but rather expressive notes immediately after each body position. Three  requested in-person experimental sessions, and their descriptions, including gestures, were recorded in notes by the experimenter. Although the same task instructions were read in person, the verbal and gestural descriptions led to more freedom being taken with the instructions and to some illuminating side comments that are reported in the Results.Perhaps the most important factor in the recording of the data was that the participants, overall, were surprised and amused at the imagery. Especially the verbal descriptions expressed the deviations of the mental imagery from textbook drawings and participants' everyday use of imagery in their work, in their usual body positions.The overwhelming variety of responses was gradually reduced by comparing participants' responses to identify commonalities and differences. This is the method of constant comparison. As more specific comparative questions arose, the original data were queried again for further detail.Only P6 supplied drawings. All other imagery described by participants was drawn by the experimenter from verbal and gestural descriptions, as in y-axis is typically “up” on a graph, but where is up? In imagination, mathematicians could choose any direction as up. Relationships such as the direction of up were assessed from the mathematicians' descriptions of the imagery. Mathematicians positioned imagery in peripersonal space and described axes as parallel to the gravitational upward direction, up with respect to the body, or a mixture. They also described directions and positions with respect to architectural features, such as floors, walls, and ceilings.The y-axis in my mind” (P6T3B) were interpreted as movement of attention or awareness.Participants described geometrical relationships, which allowed inference of sensory and sensorimotor relationships. For example, visual imagery was usually placed where it could be seen with the eyes, had they been open. There was considerable explicit variation, for example, following a spiral with “awareness” when it was behind a cylinder or cone and could not be “seen” with the mind's eye. Attention or awareness is often studied in connection with eye movements and intentional following of images . Each experience is part of a longer story, particular to each individual.No two mathematicians had the same experiences of imagery overall. For example, y axis, which is parallel to the body axis, gradually stabilizing over 5 min, after the parabola is added. P2 Rotator's plane is rotated around the head toward the left, the arm of the parabola reaches upward in gravity, and the parabola moves in the plane, its vertex not fixed at the origin.As shown in The straightforward, body-parallel view of P3 Active, P8 Straight-ahead, P10 Architect, and P11 Precise are similar, at different distances. P10's and P2 Rotator's are “in the plane of the eyes.”P5 In-plane's parabola falls toward the floor. P5 and P8 Straight-ahead do not habitually use mental imagery, visual or otherwise. Although the lack of visualization surprises many mathematicians, it is known in the literature. For example, John von Neumann apparently did not habitually visualize Ulam, .P6 Learner's plane is halfway between gravitational and body upright. Something blocks full view of P7 Vivid's parabola, but allows a scan along it.P9 Feeling does not use visual but rather somatosensory imagery. P9's plane is a segment of a sphere, all in reach of the mind's arm. Of the 11 mathematicians, 8 used visual imagery, 1 somatosensory imagery, and 2 used no mental imagery habitually. When mental imagery was used, it was primarily of the spatial rather than object type , horizontal with respect to gravity (h), or body-parallel (b) . GravitaIn S, body axis and gravity are parallel. Almost all participants' planes were on or in front of the face, body-parallel and gravitationally upright . The excy-axis body-parallel by less participants: D - 6; R - 3 (and partly by P7); L - 5.In D, R, and L, also with body axis orthogonal to gravity, placement was more variable. The coordinate plane was placed in front of the face with the y-axis was always in the sagittal plane (mid-plane of the body). P9 Feeling's plane was rotated in pitch above the head, not one of the three standard positions . P7 Vivid's plane was rotated farther in pitch to gravitationally vertical. P4 Multiple's plane was behind the head and horizontal.There was a major difference in the ways placement and orientation varied in D vs. R,L. In D, the Planes were horizontal twice in R and four times in L . In R wiThe plane was in a rolled position for five participants  in R and L and never in D. In R, P7 Vivid used the dramatic compromise of “switching back and forth” between gravitationally vertical and horizontal or body-parallel “sort of like what happens in an optical illusion” . HoweverOne reason a rolled plane may be preferred in R and L is that when the head tilts, the eyes tort (rotate about the pupillary axis)  . CoordinIn addition to orientation, imagery is related to the body in something like attachment , 2. Thisy-axis being the body axis, the origin the head, and the x-axis right to left. In Task 1, P5 used eye movements to draw parabolae , it was rooted to architecture—ceiling, wall, or floor—in 13 cases, including D for all tasks . P3 ActiP9 lacked P10's and P7's clear Euclidean relationships among architecture, the body, and gravity. Because P9 Feeling's imagery was somatosensory, the body both sensed and served as spatial reference. In T0B, P9 explained: “I don't see pictures in my head. It's more a kinaesthetic sense, like something I can reach out and feel.” However, as discussed in 3.1, P9 had a strong sense of the interactive creation of imagery. For example, when asked for more details about the parabola in T1B, P9 said: “It's there to be used, but you haven't asked me to engage with it yet.” In T0R, the plane was parallel to the ground near the left shoulder, like standing by a chalkboard “so I could engage without getting in someone else's way.” When reaching for a coordinate plane with the mind's arm, the plane tended to be a segment of a sphere . In contAs P9 wrapped the spiral around the cone in T5R, it became “more definite.” Before that, even though mathematically it was a cone: “I don't know whether it's skinny and tall or short and squat,” and “I don't know whether it makes spatial sense.” The active manipulation of wrapping the spiral cooperated with the settling of the proportions of the cone. Manipulation also made a difference for P4 Multiple, who visualized the generic coordinate plane as a “bunch” representing an equivalence class. While the coordinate planes typically faced the body, P4 repeatedly said their position was intentional, for example in T0L: “I'm willing them to move with me. They could be anyplace.” In T3, participants were asked to follow the graph from negative to positive x. P4 started in B by graphing point by point. “I'm graphing on the main one. The other ones follow.” In the next position, R: “Now it's constructed, so it can't be unconstructed. I'm just following it with my eyes.” In R, L, D, and S, the graphs turned with the body, and the mind's eyes followed the graph on the “main one” in front. “If I can't see it, I can't manipulate it, right?” P4 was asked to repeat position D, leaving the graphs above, that is, behind the body. “I can take the generic one in front of my face and manipulate it. Then the ones in back change. Or! (Laughter) Face down, I imagine my body turned over, so I can manipulate the graph in front of me.”As discussed in 3.2.1, coordinate planes were typically in front of the face or body and body-parallel, but sometimes roll-rotated in R or L or pitch-rotated in D. In a sense, the plane is attached to the body or, equivalently, made available for manipulation, as expressed by P4.In four cases for P3 Active, the coordinate plane was attached both to the eyes and to a wall or horizontal surface . In T2S,The experimental tasks—imagining, following a graph , wrapping a spiral —resemble many mathematicians' everyday work. They image and manipulate imagery Sfard, . StudentMovement of the mind's eye, of attention, of the mind's arm, and even movements of the “self” in engaging with imagery conform mostly to the geometry of normal vision and arm movements. The world of imagery is in many ways a simulacrum of the sensorimotor world Farah, . In otheWe have seen in the previous sections that the imagery is typically in front of the body, where the eyes and hands operate best. Further evidence for visual geometry comes from examples of what participants could “see.” Even though their eyes were closed, the ability to “see” the internal imagery with the mind's eye typically followed the geometry of eyes-open vision. P11 Precise uses visualization habitually in mathematical work, placing imagery at an angle to make it more “visible”; P11 did the same in wrapping cylinders and cones with spirals. Other participants described spirals as “invisible” or “disappearing” because they were behind a cylinder or cone . P2 Rotator made all cylinders and some cones  transparent to “see” to wrap the spiral as it passed behind. In T6S, the cone was placed with its tip at the bridge of the nose and the axis at various angles from there, evidently so that the spiral could be “seen” . P3 ActiAnother option, when the spiral went behind, was to view it with “awareness.” P1 Fluid, who followed this strategy throughout T5-6, remarked about T6L “I followed the spiral with my sight in front and awareness in back.” Awareness or attention, in this spatial sense, is closely related to eye movements .In Tasks 3,4, participants were asked to follow the graph from negative to positive values of x. In most cases the graph was in front of the face, as was the coordinate plane in Task 0 3.2.1; , 2. As dInstead of the mind's eye, P9 Feeling used the mind's hand and arm. In T0L, the coordinate plane was parallel to the ground at couch level, “so I can write on it with my right hand.” Face down in T0D, the “plane wanted to be above my head” but compromised at 45°, by the forehead . P9 got Both wrapping the spiral and following or graphing a curve involve engagement with imagery, as a mathematician would do when manipulating imagery in thinking. In most cases, the imagery was in front of the body where it could be viewed and often where the hands can reach . This arNot only do the mind's eye, attention, and mind's arm move in peripersonal space like the real eye and arm, but also the imagery accommodates sensorimotor geometry, against or without the conscious intention of the imager. The appearance of imagery parallel to the body  allowed Instead, P5 In-Plane started out with the body being the coordinate frame, so that eye movements were in the coordinate plane 3.2.2; . In T1B,The direction of gravity changes sensorimotor convenience, even for the mind's eye and arm. In R,L, P6 Learner always saw the coordinates, cylinder, or cone at a diagonal between body upright and gravitational upright. Such a diagonal was not uncommon  and may y-axis was face-parallel, so the primary direction of mind's-eye movements would have been gravitationally vertical, as discussed in 3.2.1. By sliding, P1's imagery solved the problem “where is up?” in R and L without discomfort to the mind's eye.P1 Fluid's imagery slid, which avoided the need for the mind's eye to move along the axes . In T0R:x-axis didn't stay in my frontal plane but rather tipped back and forth relative to gravity, i.e., toward and away from my right and left sides” , there was an additional motion: the “t sides” , 5D. A p . When asked in T5D what the spiral was made of, P9 said, “I don't know. It feels like asking What color is square? or What color is 2?” Concreteness was seen as an intrusion, often amusing. For example, in T5S, P1 Fluid said, “The spiral/cylinder situation was the same as in [D] except that the spiral went around about three times and was greenish (“yellowish-green”?) and fuzzier. (I'm laughing now.)” Besides describing mathematical objects as concrete objects, participants treated them as if concrete, described them acting as if concrete, and gave imagery concrete substrates .In many cases, participants looked to see where the imagery was and what it was doing, tacitly giving it autonomy , visualized ice-cream cones in a holder in T6. P6 Learner's imagery placed itself autonomously; in R,L, it placed itself on a diagonal that sometimes caused tension (3.2.1). Overall, the concrete aspects and autonomy gave the mathematicians' imagery exuberance. For example, P11 Precise's graphs in T2-4D were deep in the earth and rainbow-colored on a dark background.Five participants' imagery had color , and P1 Even abstract mathematical imagery was treated as if it were concrete. In T0D, 3 participants'  coordinate planes were rotated over their heads or behind, so that they did not occupy the same space as the supporting furniture, even though that placement put them out of normal visual range. With eyes closed, mental imagery occupies peripersonal space. At the same time, peripersonal space is filled with concrete objects, such as furniture, one knows are there but can't see or feel. Eight participants immersed mental imagery within the mental awareness of furniture in T0; three  avoided overlap. In the other tasks, the three avoided conflict in some way: by placement, by tension, or (P9) by changing from virtual arm movements to movements of attention or of the eyes (3.2.3). Requests to follow a graph  or wrap a spiral  caused P4 a conflict in D between avoiding imagery/furniture conflict and the need to have at least one example of the imagery in view, either in front or to the side, for manipulation (3.2.2).y-axis was between body- and gravity-parallel. In addition, the graph came out “skewed as if asymptoting to the y axis,” that is, asymptoting to gravitational vertical, like a plant growing upward. The plane itself is skewed toward the gravitational vertical, and the graph is separately and further affected by the gravity. Gravity affecting imagery shows imagery behaving like a concrete object, with mass, or autonomously, reacting to gravity. P2 also had an arm of a parabola reach up in gravity, P5's parabola fell down  in early tasks. In T1B,L,D, the plane is identified with the body, and the parabola is separable from the plane . In T2B,Tasks 3 ,4 ask participants to follow the graph negative to positive—a request to separate elements. In T3, P4 Multiple realized that the graph had to be in front of the body to follow or “manipulate” it (3.2.2). The manipulation became more challenging when participants were requested to wrap a spiral around a cylinder (T5) or cone (T6). In T5R, P4 commented: “They're hard to wrap unless they're in front and parallel to my body. ... If I leave them [gravity-parallel rather than body-parallel], it's as if I have to think around a corner.” Only in T6 did P4's imagery become susceptible to gravity (3.2.2).Somewhat like P7's parabola gliding into place , P10 ArcP5 In-plane, who doesn't habitually use internal imagery, strikes most of the themes in the relationship between cylinder or cone and spiral components. P5 found in T5B that the cylinder and spiral “don't mix ... They didn't feel related to each other.” Next, in R: “I can wrap the spiral around it easily this time. It starts at the bottom and wraps up. ... the cylinder then continued on for much longer than the original assumed length making it feel infinitely long.” In R, the wrapping starts as P5's action, but then becomes autonomous and continues, perhaps to infinity. Then in L: “The cylinder is rotating away from me. ... I cannot make the spiral go up the cylinder since they go in the same direction at the same time.” D is easy, and S is difficult.P9 Feeling's spiral also kept spinning at the top in T5B,R. In S, the wrapping was “loose.”In T6B,S, P5's difficulty was in making the spiral “tighten up to match the shape.” R, L, and D were easy.P3 Active also had difficulties wrapping spirals in T6 (3.3). In L: “Cone evaporates midway,” spiral “sproings off, gets twice as large as base before it reaches the height where it would have been at the top of the cone.” In S: “Cone won't stay the same size! ... It turns into a cylinder.”P2 Rotator's spirals tended to distort in T6. Cones were near the nose and sometimes inside the head, like the cylinders in T5 , 8 In B:The narrowing of the cone in P2T6R is arguably an expression of the relationship between cone and spiral, rather than a distortion of that relationship. In T6B, P9's “string [spiral] went slow, then really fast,” expressing the change in distance and the cone “swept,” curving in at the top.As a continuation of P7 Vivid's infinity theme in T5 3.3, , 9, in TP3 Active's spirals in T5 expressed both difficulty and relationship to the cylinder. In R: “Spiral hits closed loop attractor, and just spirals asymptotically to it. Poor spiral, never making it to the top of the cylinder.” In L: “...doesn't spiral in a geodesic. As it goes up, it drops down quite a bit before going back up.” In D: “Spiral starts at the top of my head, hits an attractor around my forehead or eyes, asymptotically spirals out. Try starting at feet. Shoots up in a few spirals, again, non-geodesics with significant drop-downs on the back-side of the spiral necessitating steep upsurges on the front side.” In S, it is drawn on a blackboard: “Spirals up nicely, except that the cylinder has become a cone! As such, spirals tighter and tighter at the vertex. I force it open, and all is forgiven as the spiral concludes its upward journey.”The attractors and non-geodesics of P3's spirals actively expressed a mathematical idea, rather than offering sensorimotor convenience. So did P7 Vivid's infinity theme, P9 Feeling's sweeping of the cone shape as the spiral is added (3.4), and P3's cylinder turning into a cone. All participants described imagery that expressed mathematical ideas not directly requested, some more exuberantly than others.Fixed expressions of mathematical ideas included P4 Multiple's equivalence classes of visualizations (3.1) and P11 Precise's preference to work in three dimensions (3.2.1). P6 Learner expressed the same preference more mildly. The three-dimensionality of the peripersonal space in which the imagery was created was expressed in many ways, including P5's parabola falling down T1-2, , 3.2.2 aOne is tempted to think of three-dimensional imagery space as a Euclidean space, as in classical physics. However, neurally it is unlikely to be so, since sensation depends on disparate senses and on processes at multiple levels of neural organization. For example, P10 Architect in T5D first imaged the cylinder as a two-dimensional drawing on the floor. “However, as I force myself to think three-dimensionally, the cylinder rises from the ground before me, so that I see its circular face approaching.” P10 has two simultaneous perspectives: “I am imagining the spiral as if I were looking at it from an angle, even though my original picture is of the cylinder directly beneath me.”Similarly, using somatosensory imagery, P9 Feeling had two perspectives in T5B (3.2.2). Then in T5R, P9's cone lacked a fixed geometry: “I don't know whether it's skinny and tall or short and squat” and “I don't know whether it makes spatial sense.” Mathematically, it was still a cone, just not specified spatially. It became more definite as the spiral was wrapped about it. Similarly, P1 Fluid's cone widened 3.3,  T6L.Just as a cone and a circle have a specific shape however tall or squat the cone or however large or small the circle, a parabola has a precise shape, no matter how broad or skinny it is (using P9 Feeling's descriptors). P7 Vivid's parabola could glide into place on the coordinate grid 3.4;  because y = 1/x in Task 3 consists of an hyperbola in the third and first quadrants , is easy to factor into x(x-1)(x+1), giving the zero-crossings –1, 0, and 1 . Two matP3 Active responded to the up-and-down shape of the cubic, saying in T4B: “the whole graph emerges, then starts dancing up and down, oscillating fairly wildly.” Although P3 Active is the clearest about oscillatory movement of the cubic graph, three other participants mentioned something similar: P7 Vivid mentioned “choppy” and said axes became irrelevant, P9 Feeling said the graph wiggled and came separate from the axes, and P10 Architect in T4B said “Here I finally feel movement! I do a little bump/bounce up/down with my head as I follow the graph from left to right.”The sensorimotor foundation of the mathematical imagination is not obscure, but easily accessible, as demonstrated in this experiment. First, in every body position, the imagery is placed and oriented with respect to the body, with variations depending on body position (3.2.1). In particular, the imagery in R,L tends to be tilted in the roll direction, whereas imagery in D tends to shift in pitch. Second, the imagery has particular relationships to the body (3.2.2). For example, the body can be the coordinate system (P5) or the imagery can be on the eyelids (P2 and P10). Third, the mind's eye and arm, along with the awareness, usually has the same geometry as the real eye and arm (3.2.3). Imagery usually has to be in front of the face to be seen and especially to be manipulated. Fourth, the imagery moves to accommodate the body (3.2.4). For example, after some struggles, P5 In-plane's imagery autonomously drew itself and placed itself in front of the eyes.This sensorimotor nature of mathematicians' imagery relates it to non-mathematical imagery studied by manipulating the vestibular sense . Throughout, the imagery placed itself and moved autonomously, with wide individual variation. There was a varying relationship to the concrete (3.3), which raises questions. What is the nature of a parabola that falls in gravity P5,  or that These Alice-In-Wonderland imagery phenomena are endemic to mathematics, as discussed by Núñez in his delightful essay on the metaphorical motions of numbers Núñez, . Núñez sSriraman  documentAnother way to use imagery for creativity is as an expressive medium, to communicate with others Sfard, . CommuniAnother is as a supple medium in which to express possibilities to oneself. Use of imagery as a medium would lead to such imagery skills as demonstrated by several of the participants, for example, the ability to coordinate multiple imagery components.The present study is about the sensorimotor underpinnings of the mathematical imagination, not about the sensorimotor underpinnings of mathematical structures. For example, the concept of the integers—the numbers both positive and negative—may arise from right-left body symmetry and its related neurobiology and mental habits  for the publication of any potentially identifiable images or data included in this article.GM thought of the experiment, gathered and analyzed the data, made the drawings, and wrote the manuscript.The Publication Fee was supported by Gerhard Magnus, Phoebe Warren, Wendy Ellison, Elizabeth Robbins, Nancy Church, Laura Smolkin, David Summers, Sarah Morgan, Larkin Plaeger-McCollum, Hariharan G. K., and an anonymous donor.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The purity of chlorophylls plays one of the key role for the production of chlorophyllides. We have designed a facile method for chlorophyll purification by twice solvent extraction. Twice extraction causes the loss of chlorophylls, but the purity of total chlorophylls can be enhanced 182%. Then, the purified chlorophylls can be converted to relatively pure chlorophyllides facilely. The results show that higher purity of chlorophyllides could be obtained when purified chlorophylls (ethanol-hexane extract) was used as starting materials than that of crude chlorophylls (ethanol-only extract). In biocompatibility test, the results showed that the prepared chlorophyllides can be applied as biomaterials. When the prepared chlorophyllides were applied to anticancer tests, they were active both in MCF7 and MDA-MB-231 (multidrug resistant breast cancer cells) cell lines. In addition, the results suggested that the prepared chlorophyllides could be a potential candidate of combination therapy with doxorubicin to breast cancers. Chlorophyllides have been used for diverse applications. For example, the antigrowth ability of Staphylococcus aureus [Staphylococcus aureus [Escherichia coli, Serratia marcescens, Bacillus thuringiensis and Saccharomyces cerevisiae [Chlorophyllides were first named by Willstätter and Stoll in 1911 . Chloroprevisiae  and glutrevisiae , and as N, N’-dimethylformamide [et al. dephytylated chlorophylls to produce chlorophyllides using chlorophyllase isolated from the leaves of Ficus macrocarpa [Chlamydomonas reinhardtii and Brassica oleracea for the production of chlorophyllides [Chlorophyllides are unstable under normal conditions. Therefore, considerable research has been devoted to the synthesis of chlorophyllides. The major methods for chlorophyllides preparation are conversion of the protochlorophyllide by enzymes, separation from natural products, and hydrolysis from chlorophyll by enzymes. Several studies have shown that chlorophyllides can be interconverted from protochlorophyllide by protochlorophyllide reductase . In addiormamide , diethylormamide , or hexaormamide  to extraormamide . Many stcrocarpa . We havehyllides , 21.Previous studies into the synthesis and extraction of chlorophyllides are well established, however, the aforementioned methods have limitations in purity, normality, or stability. For example, our previous study could manufacture crude chlorophyllides, but their impurities limit their applications . In the n-hexane, n-butanol, and methanol were purchased from Seedchem Company Pty. Ltd. , Acros Organics , and Aencore Chemical Pty. Ltd. , respectively. Doxorubicin, 3--2,5-diphenyl tetrazolium bromide (MTT), potassium hydroxide, sodium phosphate, Triton™ X-100, and chlorophyll a/b standards were purchased from Sigma-Aldrich, Inc. . Sweet potato leaves were purchased from a local market in Kaohsiung, Taiwan. Fibroblast cells (NIH/3T3) and human breast cancer cell lines (MCF7 and MDA-MB-231) were purchased from Bioresource Collection and Research Center . Dulbecco’s modified Eagle’s medium (DMEM) and fetal bovine serum (FBS) were obtained from Invitrogen .Acetone, diethyl ether, ethanol, ethyl acetate, and petroleum ether were purchased from Echo Chemical Co., Ltd. (Taiwan). n-hexane, ethyl acetate, acetone, n-butanol, ethanol and methanol. After 48 h, each extract was centrifuged at 1500 ×g for 5 min, and the chlorophylls extracts were obtained.Chlorophyll was extracted using a method based on our previous studies , 23. Alln-hexane in the dark. The double extract of chlorophylls was centrifuged at 1500 ×g for 5 min, and purified chlorophylls from ethanol-hexane extracts were obtained.The ethanol extract of the chlorophylls was sequentially extracted using a/b, the extracts were passed through a 0.22-μm filter, and the filtrates were analyzed using UV-Vis spectrophotometer . The absorbance was measured at 649 and 665 nm, which are the major absorption peaks of chlorophyll a and b, respectively. The chlorophyll a/b contents of the extracts were calculated using previously reported equations [To measure the concentrations of chlorophyll g for 10 min, and then the organic and aqueous phases were obtained. After the organic phase was removed, the chlorophyllides could be obtained in the aqueous phase. The solvents of chlorophyllides were removed by evaporation under reduced pressure at 40°C on a rotary evaporator . The concentrated chlorophyllides were processed by lyophilization, weighed, and stored at -80°C for further experiments. Chlorophyllides from ethanol-hexane extracts (purified chlorophylls) were named as purified chlorophyllides.Recombinant chlorophyllase was produced using a method based on our previous studies . All prog for 10 min at 4°C. 3 uL of supernatant was used for analysis. UPLC elution conditions: The elution solvent system comprised 5 mM ammonium formate (A) and acetonitrile (B) at a flow rate of 0.3 mL/min. Samples were eluted using a linear gradient condition: start, 95% A; 2 min, 95% A; 2.5 min, 0 A; 5 min, 40% A; 8 min, 20% A; 10 min, 20% A; 10.5 min, 5% A; 15.5 min, 5% A; 25.5 min, zero A; 30.5 min, zero A; 31 min, 95% A; 33 min, 95% A. Mass spectrometric analyses were performed on a Waters VION LC Q-TOF equipped with an electrospray ionization (ESI) source in positive ion mode. Mass spectrometry conditions: the scanning range was m/z 50–1000. The capillary voltage was 1.5 kV, the low collision energy was 6 eV, cone voltage was 20 V, and the higher collision energy was 20–60 eV. The desolvation gas temperature was 500°C. The desolvation gas flow rate was 1000 L/h. Leucine was the lock mass, which was used to correct the mass error. Spectral data analysis and quantification were performed using Unifi software . The chlorophyllides and pheophorbide a standards were purchased from DHI lab products .The identification of chlorophyllides was performed by ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry  . Before 2.NIH/3T3, MCF7 and MDA-MB-231 cells were cultured in DMEM supplemented with 10% FBS. The cells were maintained at 37°C under a humidified atmosphere of 5% CO4 cells/well) were stimulated with different doses of chlorophyllides . After treatment for 24 h, supernatants were removed from the wells and then 1% of 20 μL MTT solution was added to each well. The plates were incubated for 4 h at 37°C and the optical density was determined at 595 nm using a multi-well Multiskan spectrophotometer . All measurements made in the 96-well plates were performed using five technical replicates. Further statistical analysis was performed by one way ANOVA using SigmaPlot Version 14.0.Cell viability was examined by MTT assay following a previously described procedure . Cells  was determined using ComboSyn software  where a CI < 1 implied synergism, a CI = 1 implied additive interaction, and a CI > 1 indicated antagonism .n-hexane, ethyl acetate, dichloromethane, butanol, methanol, petroleum ether, chloroform, and water exhibited anticancer activities [Table 1 shows that neither petroleum ether nor n-hexane could easily extract chlorophylls. The extraction yields of chlorophylls in different solvents in descending order were as follows: ethanol > methanol > acetone > ethyl acetate > n-butanol > diethyl ether. The ethanol solvent showed the highest extraction yield of both chlorophyll a and chlorophyll b. The chlorophyll a extraction yield of ethanol was 93% higher than that of diethyl ether. The chlorophyll b extraction yield of ethanol was 143% higher than that of diethyl ether. We concluded that ethanol was the optimal solvent for chlorophyll extraction. Similar results were reported by Suzuki et al. [et al. [Previous researchers have reported that solvents and their polarity determine the profile of the compounds extracted from plants –29. Extrtivities . In this [et al. . General [et al. . In thisa extraction yield from double solvent extraction (e.g. ethanol-hexane extraction) was around 9.8% lower than that of ethanol-only extraction. The chlorophyll b extraction yield from ethanol-hexane extraction was 33.41% lower than that from ethanol-only extraction. The total chlorophylls from ethanol-hexane extraction were decreased 17.03% compared with ethanol-only extraction. Double extraction caused the loss of both chlorophyll a and chlorophyll b, but the purity of chlorophyll a and chlorophyll b was enhanced by 146.56% and 35.49%, respectively.To exclude the hydrophilic compounds and obtain purer chlorophylls, a second step solvent extraction was employed. We propose that the additional hexane extraction could removes the undesired water-soluble compounds from the crude chlorophylls extract. The chlorophyll Fig 1). The products were different when two different starting materials were used . When crude chlorophylls (ethanol-only extract) were used as starting materials, at least ten peaks of UPLC were present, as shown in Fig 1A. When purified chlorophylls (ethanol-hexane extract) were used as starting materials, three main peaks could be detected, as shown in Fig 1B. Compared to standards, peaks 1, 2, and 3 were referred to chlorophyllide b, chlorophyllide a and pheophorbide a, respectively [Fig 2 shows the comparison of the three hydrolyzed products from two type of starting materials. The results showed that higher chlorophyllide a (143.5%) was obtained when purified chlorophylls (ethanol-hexane extract) were used as starting materials than the crude chlorophylls (ethanol-only extract). Lower chlorophyllide b (16.8%) was obtained from purified chlorophylls. Moreover, the total chlorophyllides from purified chlorophylls were increased about 14.55%. A trend toward increased level was observed in both purified chlorophylls and purified chlorophyllides. Therefore, purified chlorophylls were therefore a better starting material for the manufacture of chlorophyllides.Chlorophyllides can be prepared from crude chlorophylls (ethanol-only extract) or purified chlorophylls (ethanol-hexane extract). After chlorophyll was hydrolyzed by recombinant chlorophyllase, the chlorophyllides could be obtain in the aqueous phase. UPLC-Q-TOF/MS was used to analyze the purity of the chlorophyllides . NIH/3T3 cells were cultured with the purifed chlorophyllides (0–200 μg/mL) for 24 h. The cell viability of purified chlorophyllides was approximately 90%–95%. There is no statistical difference between the control group (chlorophyllides absent). Therefore, the purified chlorophyllides could be used as biomaterials.Cytotoxic effects could provide useful and crucial information about materials used in biomedical research. The purifed chlorophyllides were obtained from ethanol-hexane-extracted chlorophylls. To increase the applicability of the purified chlorophyllides as a biomaterial, we studied their biocompatibility using MTT assays . In Fig 4A, the chlorophyllides are shown to be active in MCF7 cell lines. The cell viabilities of chlorophyllides in MCF7 cell lines were decreased in a dose-dependent manner (50–200 μg/mL).The purified chlorophyllides were used to test the cytotoxic effects in breast cancer cell lines. Breast cancer is the second most common cause of cancer-related mortality in women . One of Fig 4B. Cell viabilities of the chlorophyllides exhibited strong dose-response curves. For example, the cell viabilities with chlorophyllides at 200 μg/mL were 47.56%, indicating that the purified chlorophyllides could effectively inhibit the cell growth of MDA-MB-231. These results suggest that the MDA-MB-231 cell lines were more sensitive to chlorophyllides than the MCF7 cell lines. In addition, IC50 of 188.91 μg/mL was obtained using the purified chlorophyllides in MDA-MB-231 cell lines.To further examine the cytotoxic effects of the purified chlorophyllides in breast cancer with multiple drug resistance, MDA-MB-231 cell lines were used. The purified chlorophyllides significantly decreased the viability of MDA-MB-231 cell lines, as shown in Fig 4B. The cell viabilities resulting from the combination therapy with 0.625 μg/mL doxorubicin and chlorophyllides (0–200 μg/mL) were 49–65%. The results demonstrated that chlorophyllides could enhance the chemosensitivity of breast cancer cells to doxorubicin.Doxorubicin is currently one of the most effective chemotherapeutic drugs for breast cancer treatment . HoweverTable 2). Combinations of chlorophyllides from 12.5 to 100 μg/mL and doxorubicin at 0.625 μg/mL exhibited a CI of 0.07109–0.60723, thus showing synergistic interactions in MDA-MB-231 cells. The best synergism rate (CI = 0.07109) was observed with doxorubicin at a dose of 0.625 μg/mL combined with purified chlorophyllide at dose of 12.5 μg/mL. Using chlorophyllides at dose of 200 μg/mL with doxorubicin at 0.625 μg/mL, the CI was above 1, indicating antagonism. Combinations of chlorophyllides at 100 μg/mL and doxorubicin from 0.625 to 20 μg/mL produced a CI of 0.24743–0.49972, thus showing synergistic interactions. The results indicate that the synergistic effects were stronger when the doxorubicin and chlorophyllides were combined at relatively low concentrations. The dose of chlorophyllides at 200 μg/mL with doxorubicin at 0.625 μg/mL suggests that combination with chlorophyllides could decrease the dosage of doxorubicin. The detailed mechanism of chlorophyllides in combination therapy with doxorubicin in breast cancer will be studied in the future. We therefore demonstrated that purified chlorophyllides could be a potential candidate for combination therapy to breast cancers with multiple drug resistance.Drug–drug combinations are convenient models of additivity and provide valuable insights into the significance of synergistic or antagonistic interactions . To ascea and chlorophyllides increased by 43.5% and 14.55%, respectively. The MTT assays, the purified chlorophyllides were effective against both MCF7 and MDA-MB-231 cells. In addition, the results illustrated that the combination of chlorophyllides and doxorubicin showed synergistic cytotoxic effects. We have demonstrated that purified chlorophyllides could be a potential candidate for combination therapy in multiple drug resistant breast cancers.A new method for the production of chlorophyllides using double extractions was successfully developed. For the medicinal applications of chlorophyllides, ethanol and hexane are generally recognized as safe solvents for use. Purified chlorophyllides were obtained in the aqueous phase when the purified chlorophylls were hydrolyzed with chlorophyllase. The results revealed that the purity of chlorophyllide S1 FigMTT assay of (A) chlorophyllides in MCF7 cell lines, (B) chlorophyllides in MDA-MB-231 cell lines, (C) the combination of doxorubicin (0.625 μg/mL) and chlorophyllides in the MDA-MB-231 cell lines. The statistical method used in this article is one way ANOVA using SigmaPlot Version 14.0.(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S1 Graph abstract(TIF)Click here for additional data file.