Plutella xylostella) collected from 75 sites spanning six continents to reveal that climate-associated adaptive variation exhibits a roughly latitudinal pattern. By developing an eco-genetic index that combines genetic variation and physiological responses, we predict that most P. xylostella populations have high tolerance to projected future climates. Using genome editing, a key gene, PxCad, emerged from our analysis as functionally temperature responsive. Our results demonstrate that P. xylostella is largely capable of tolerating future climates in most of the world and will remain a global pest beyond 2050. This work improves our understanding of adaptive variation along environmental gradients, and advances pest forecasting by highlighting the genetic basis for local climate adaptation.Understanding the genetic basis of climatic adaptation is essential for predicting species’ responses to climate change. However, intraspecific variation of these responses arising from local adaptation remains ambiguous for most species. Here, we analyze genomic data from diamondback moth ( The diamondback moth is a cosmopolitan pest of significant economic importance. Here the authors analyse globally distributed genomic data to find evidence of climate-associated adaptive variation, and use an ecogenetic index to predict that it will maintain a global pest status under climate change. The ability of pests to successfully adapt to these changes will impact biodiversity, food production, and the economy. Intraspecific variation in tolerance to climate change has been documented for many plant and animal species4. Populations with high adaptive potential are expected to cope with changes in habitat suitability arising from climate change6, but the mechanisms are not well understood. Studying the genetic mechanisms that underpin the adaptation of species to local climate is therefore important to predict both population- and global-level responses to future environmental change and assist in management efforts5.Human-induced climate change, especially gradual changes in temperature and precipitation9. Insects with high fecundity and short generation time can accumulate adaptive alleles rapidly through new mutations and standing variation, and potentially have high capacity to respond to changing environmental conditions12. However, little is known about the extent to which adaptive variation is driven by climate in arthropod species, and how populations differ in their capacity to adapt to climate change.Genetic variation associated with climate variables has been demonstrated in several speciesPlutella xylostella L. (Lepidoptera: Plutellidae). This insect is one of the world’s top 10 arthropod pests13, with a global distribution spanning a remarkably wide range of climates14. We define a new eco-genetic index to examine population-level variation in response to climate change by combining the genetic offset (that quantifies the disruption of gene-environment relationships subject to future climates) with the ecoclimatic index (that describes phenology-based habitat suitability for species persistence). Subsequently, informed by a core dataset of identified nuclear SNPs, we functionally test a temperature-related gene to reveal its role in climatic adaptation in DBM. Our results imply that P. xylostella is largely capable of tolerating future climates in most regions of the world, and its pest status will be maintained beyond 2050.In this study, by combining a newly available genomic resource with climate data, we analyze the relationship between genomic variation and climate variables in the diamondback moth (DBM), 15. To investigate the adaptive genetic variation associated with contemporary climates, we used a subset of samples from regions in which DBM is able to persist year-round with a positive ecoclimatic index (EI > 0)16, where populations are subject to seasonally uninterrupted local selection by climatic factors. After quality filtering, we generated a dataset of 200,055 SNPs across 357 DBM individuals collected from 75 different sites worldwide 18, and Bayenv 219, we conducted a genome-wide scan to test climate associations for the quality-filtered SNPs. A total of 3648 putatively adaptive SNPs were identified by one or more of the three models  sequences of a worldwide sample of 532 DBM individuals collected from 114 locations (sites) in a diverse range of biogeographical regions5. We used generalized dissimilarity modelling (GDM)20 to examine climate-mediated genomic variation among different populations based on 517 SNPs selected among the 3648 SNPs. Of the 12 environmental variables that had a pairwise Pearson’s r less than 0.8, the top four variables included one that is precipitation-related (bio18) and three that are temperature-related   Fig. .4, to investigate which DBM populations might be more vulnerable to future climate change. Populations with higher genetic offset are more vulnerable to climate change and require greater adaptive potential (or genetic variation necessary for adaptation) to the changing environment4. Under greenhouse gas emission scenarios RCP8.5 for 2050, the genetic offset was low for most populations  between current and projected future climate scenarios across year-round persistence regions of DBM worldwide. We observed that the total area of regions with decreasing EI was much larger than that of regions where EI increased under the RCP8.5 scenario  that combined climate data with eco-physiological traitsrio Fig. . DBM pop23. To better reflect the role of population-level genetic variation in DBM’s responses to climate change, we developed a new metric, the “eco-genetic index” (EGI), which combines the genetic offset4 and EI-based prediction of climatic habitat suitability. We assume that regions with increasing EI will remain hospitable to DBM while regions with decreasing EI will be challenged by habitat suitability decline under the RCP8.5 scenario for 2050. Thus, our analysis focused on regions with decreasing EI. Under climate-change scenario RCP8.5, the challenges to most populations  is known for heat and cold stress tolerance in insects25; whilst folding26 and gene expression27 of the nicotinic acetylcholine receptor (Px017786) are temperature-sensitive.To explore the molecular and genetic basis of climate adaptation, we performed a functional analysis of a core dataset of 94 putatively adaptive SNPs that were identified by at least two of three models  identified in the core dataset (94 putatively adaptive SNPs), PxCad in the wild-type G88 strain  strains under favorable temperature (26 °C) and several extreme temperatures . Compared with the WT, survival of the MU was not affected at favorable temperature, but the survival rate declined at extreme temperatures. In the cold-shock experiment  and 32.In this study, we provide evidence of the genetic basis of climate adaptation in DBM, a worldwide pest important to cruciferous crop production and thus the economy. Climate-associated genetic variation in DBM populations was quantified and visualized. A multi-model analysis of Samβada, LFMM, and Bayenv 2 allowed robust identification of climate-associated adaptive loci, reducing false-positivesPhaulacridium vittatum33, Chironomus riparius34, and Ceracris kiangsu35. Further, physiological data can be used to determine the tolerance of DBM to future climate at the species level30. To date, however, there are no site-specific data available for investigation on the physiological variation in different populations of DBM. In this study, we found that most DBM populations might experience little interruption of existing gene-environment associations under projected future climates. This genomic association with climate may elicit region-specific responses to climate change and indicates that DBM is capable of persisting year-round as a pest in most regions of the world beyond 2050 under the RCP8.5 climate change scenario. This is of practical use to future pest management because the majority of DBM populations are shown to be resistant to changing environments under a future climate. Not only will pest management in these regions need to be maintained and strengthened, but also cooler areas where DBM seasonally colonizes are likely to become more vulnerable as temperatures increase, rendering the habitats favorable for permanent residence. For a species with high migratory capacity, like DBM, pest status is likely to increase given high levels of gene flow. DBM needs to be monitored at landscape and regional scales , considering its genetic adaptive capacity and the spatial dynamics of insecticide resistant strains.Our analyses with the nuclear SNPs from geographically distributed samples go further in defining the effects of both temperature- and precipitation-related variables on the climate-associated genetic variation in DBM populations worldwide. This follows a number of recent studies demonstrating the key role of temperature in mediating environment-associated adaptive variation for other insects: PxCad, represented a temperature-sensitive responder to climatic change, thus contributing to the genetic basis of adaptive evolution. PxCad is annotated as encoding cadherin-like proteins37. Classical cadherins are a superfamily of transmembrane proteins involved in regulating cell-cell adhesion, signal transduction and tissue morphogenesis39. In mammals, epithelial cadherin (E-cadherin) is involved in morphogenesis41 whilst in insects, studies of cadherin-like proteins have focused primarily on their involvement in mediating resistance to the biological insecticide, Bt45. Pigott & Ellar46 have also demonstrated the roles of cadherin-like proteins in maintaining structural integrity of midgut epithelial organization.Using RT-qPCR and CRISPR/Cas9 approaches in this species, we verified that a specific gene, 48. Our results show that PxCad expression in female adults was significantly downregulated at high temperature. This is consistent with E-cadherin studies in human lung adenocarcinoma cells49 and in the purple sea urchin Strongylocentrotus purpuratus50. In contrast, cold stress significantly upregulated PxCad expression in both males and females, and the tolerance to cold stress in DBM declined when PxCad was knocked-out. Comparable phenotypes have also been reported in the clam, Ruditapes philippinarum, with cadherin genes acting as responders to cold stress31. Under heat and cold stress, considerable variation of Pxcad expression and survival rates between DBM stains indicates that PxCad is involved in regulating DBM’s response to thermal stress.Thermal stress (heat or cold) generally disrupts cellular homeostasis16 developed a CLIMEX-based algorithm to predict the ecoclimatic index (EI) for the habitat suitability of DBM using climate data and eco-physiological traits. However, the EI-based prediction of habitat suitability is highly generalized without reflecting population-specific levels of genetic tolerance. In this paper, based on our recently-generated genomic data from a worldwide sample15, we have developed a new metric, the eco-genetic index (EGI), which combines the genetic offset (GO)4 and difference in ecoclimatic index (DEI) between current and projected future climate scenarios. This index mechanistically represents the genetic change required for adaptation to a changing environment, and reflects how population-specific genomic data can be incorporated into EI to better predict the habitat suitability of DBM when subjected to climate change, particularly when the population-specific data on physiological tolerance levels are not available. Our results indicate that DEI and GO are functionally complementary. With the EI prediction, we are able to determine the regional distribution of DBM throughout the world, which helps us focus on the regions in which DBM persists year-round with a positive ecoclimatic index (EI > 0)16. Using the new metric (EGI), we have identified that some DBM populations in regions of decreasing EI  under future climate can overcome the challenge of habitat suitability decline given lower levels of genetic vulnerability, suggesting that only minor adaptive evolution will be required in situ to keep-up with climate change  may buffer populations against environmental changes, allowing rapid adaptive responses to climate change15. DNA was extracted from each of the 532 individuals using DNeasy Blood and Tissue Kit  following the manufacturer’s protocol, and eluted from the DNeasy Mini spin column in 200 μl TE buffer. Genomic sequencing was performed with Illumina HiSeq 2000 at BGI, Shenzhen, China, to produce 90 bp paired-end reads for every individual. Using custom scripts, raw reads were processed to filter out poor reads with 10 ambiguous “N” bases, >40% low-quality bases, or identical sequences at the two ends and obtain clean reads. The clean reads were mapped onto the DBM reference genome (v2)21 with Stampy (v1.0.27) using default parameters. SNP calling was then performed using the GATK HaplotypeCaller with parameters --emitRefConfidence GVCF --variant_index_type LINEAR --variant_index_parameter 128,000. The 40,107,925 nuclear SNPs generated present one variant on average in every six bp of the reference genome, which is the densest variant map for any organism, including the recently released data on human58 and Arabidopsis thaliana genome sequences59. The SNP dataset is available at https://www.ebi.ac.uk/ena with the accession code PRJEB24034.The foundational resource for this study was a dataset of 40,107,925 nuclear SNPs sequenced from a worldwide sample of 532 DBM individuals collected in 114 different sites based on our previous project16. This was done because in these regions, populations are unlikely to receive perennially unpunctuated selection by local environmental variables and genetic variation cannot be continuously passed on to future generations over years. Specifically, regions that are only seasonally suitable for DBM growth and development  are too harsh to allow survival in low temperature conditions during the winter. Annual recolonization of those regions from areas where DBM can overwinter  has been biologically and genetically confirmed63. No genetic differentiation was found among different geographical populations spanning from overwintering regions to seasonally inhabited regions63. If migration from one habitat overwhelms the other, migration from the source introduces new genetic variation that may prevent local adaptation65. The retained samples included 372 individuals from 78 sampling sites in the year-round persistence regions of DBM across the world (where EI > 0). These samples were collected from different continents, with 13 samples from Africa, 29 from Asia, 5 from Europe, 13 from North America including Hawaii, 12 from South America, and 6 from Oceania 15. Using VCFtools v.0.1.6, we excluded the SNPs with minor allele frequency (MAF) < 5% and missing rate >10%. We then sampled data by examining single SNPs in small, 25 bp DNA window to focus on loci independent of linkage disequilibrium. After quality filtering, a total of 200,055 bi-allelic SNPs across 357 DBM individuals collected from 75 different sites worldwide was retained for further analysis  and represented most of the genomic variation among 532 individuals (Supplementary Data https://www.worldclim.org)66.Nineteen climate variables related to temperature and precipitation  v1.418 and Bayenv 219, were used to identify putatively adaptive loci associated with climate variables. Samβada identifies associations between specific genetic markers and environmental variables by logistic regression17. Simple univariate and multivariate logistic regression models for each climate variable were fitted. A single SNP was considered to be a candidate locus when the log-likelihood ratios (G scores) and/or Wald scores were significant with Bonferroni correction at a 99% confidence level. LFMM is based on population genetics, ecological modelling, and statistical analysis to identify the candidate loci that are highly correlated with environmental variables18. SNPs showing an association with climate variables were identified based on z-scores, which was computed using 10,000 cycles and 5000 sweeps for burn-in. We used the R package LEA to estimate the median z-scores of 5 runs and re-adjusting p-values with FDR correction67. SNPs with median z-scores above the absolute value of 4 and corresponding to P value < 10−5 were considered as significant locus. In Bayenv 2, a covariance matrix based on putatively neutral markers is used as a null model to control for demographic effects when testing relationships between the genetic differentiation and a given environmental variable19. We randomly sampled SNPs at 200 SNP intervals from the SNP dataset. A total of 117,887 SNPs with loose linkage disequilibrium were obtained for developing the covariance matrix, which was estimated with 100,000 iterations. We then assessed the correlations between individual SNP and 19 climate variables at 100,000 Markov chain Monte Carlo (MCMC) for Bayes factor analysis. Five independent runs of the Bayenv program were performed with different random seeds68. The results were presented as Bayes factors (BFs). An averaged log10(BF) value of five runs >1.5 is considered as high support for a model where environmental parameters have significant effects on allele frequencies69. A total of 3648 putative adaptive SNPs were identified by at least one of the three models  to obtain accurate allele frequencies. To reduce the number of bioclimatic variables, we preferentially discarded those with multiple correlated variables GDM Fig. . The maxrld Fig. . The genors Fig. . The genors Fig. .4. The GO represents the extent of mismatch between current and expected future genetic variation based on genotype-environment relationships modelled by GDM analysis73. The projected future climate variables of 2050 and 2080 for four different greenhouse gas scenarios, Representative Concentration Pathways (RCPs), including RCP2.6, RCP4.5, RCP6.0 and RCP8.5 based on the NorESM1-M Global Climate Model (GCM) across the world were retrieved from WorldClim, using ArcGIS 10.2. Those four RCPs represent different gas emission scenarios, reflecting mild (RCP2.6) to extreme (RCP8.5) conditions74. The GO was predicted by predict.gdm function in R package gdm. A metric of GO for each of the gridded climate points was obtained, which implied that the populations with greater genetic offset would be more vulnerable or less tolerant to the future climate change. The resulting GOs were rescaled between 0.1 and 0.976, and then mapped with ArcGIS 10.2 to show the geographical distributions of population-level variation in genetic tolerance to future climate changes  under future climate scenarios, we used the “Genetic offset” (GO) method of Fitzpatrick and Kellerges Fig. .77, was used to predict the habitat suitability for DBM in 2050 under the RCP8.5 scenario, the only one that is available in CliMond (https://www.climond.org/). The CLIMEX model for DBM in Zalucki and Furlong16 was developed based on temperature, moisture, and stress indices. Temperature indices include limiting minimum (DV0) and maximum temperature (DV3), lower (DV1), and upper (DV2) optimal temperature. Moisture indices include minimum (SM0) and maximum (SM3) tolerable soil moisture, lower (SM1) and upper (SM2) optimal soil moisture. Stress indices include cold stress, heat stress, dry stress, wet stress, and hot-wet stress. The values of these parameters for prediction of habitat suitability in our study were taken from Zalucki & Furlong16. We altered one of the parameters in CLIMEX16, changing the hot-wet stress temperature threshold from 30 °C to 32 °C based on a study on the relationship between temperature and developmental rate showing that DBM can survive and develop at temperatures <32 °C78  and Overlay in ArcGIS 10.2 were performed to generate maps showing the predicted distribution of DEI values (with DEI > 0 showing the EI-increased regions and DEI < 0 showing the EI-decreased regions) for each of the gridded climate points across the year-round persistence regions worldwide , which combines the predictions based on genetic offset (GO) with the difference in ecoclimatic index between current and projected future climate scenarios (DEI). EGI allows us to incorporate our population-specific genomic data into EI for better predicting the habitat suitability of DBM subject to climate in 2050 under RCP8.5 scenario based on NorESM1-M GCM. Because DEI and GO, are correlated  operator and the artificial bee colony (ABC) algorithm to optimize the value of alpha and improve the algorithm of EGI. Here, only regions of decreasing EI (DEI < 0) were considered because in these regions, DBM populations will be challenged by habitat suitability decline under the RCP8.5 scenario for 2050. Each gridded point in ArcGIS was described as a vector: Pi = {deii,goi}, where i = 1, 2, …, n, and n is the number of gridded points. Let the EGI and GO of each point be egii and goi, calculated as follows:The ecoclimatic index (EI) values based on CLIMEX simulations are highly generalized and do not reflect evolutionary adaptation and variation of tolerance levels among populations, because of the absence of information on fitness traits. Based on our recently-generated dataset of genome-wide single nucleotide polymorphisms (SNPs) sequenced from a worldwide DBM sample of different locations (sites) across a diverse range of biogeographical regionsi and goi and normalize76 them into .Step 1: Calculate the absolute values of deii and goi with the weighted geometric averaging (WGA) operator80 as:i, i = 1, 2, …, n.Step 2: Combine the normalized deiStep 3: The optimal value of α can be determined with the following steps:Step 3.1: Take the natural logarithm of Eq.  can be expressed as:Hence, the minimized model (M − 1) can be equivalently written as:Based on Eq. , the min82 is used to solve model (M − 2), which is a fractional programming problem where Y is the objective function and α is the independent variable. The parameters are set as: population size = 20, number of iterations = 30, and limit = 5. The convergence curve of the ABC algorithm for solving the above model is plotted in Supplementary Fig. Step 3.3: The Artificial bee colony (ABC) algorithmrio Fig. . It is n83. It was provided by Dr. Antony M. Shelton  to the Institute of Applied Ecology, Fujian Agriculture and Forestry University in 2016. Since then, we maintained this strain on artificial diet without exposure to insecticides at 26 °C that is a favorable temperature to rear and maintain this wild-type strain.The wild-type strain of DBM, Geneva (G88), was used in this assay. This G88 strain was collected from the New York State Agricultural Experiment Station in 1988 and has since been maintained on artificial diet without exposure to insecticides84 and our preliminary experiments: three high-temperature treatments: (1) H1: 40 °C for 30 min, (2) H2: 43 °C for 30 min, (3) H3: 43 °C for 30 min with 24 h of recovery at 26 °C; and six low-temperature treatments: (1) L1: −14 °C for 30 min, (2) L2: −14 °C for 30 min with 24 h of recovery at 26 °C, (3) L3: −17 °C for 30 min, (4) L4: −17 °C for 30 min with 24 h of recovery at 26 °C, (5) L5: −20 °C for 15 min, and (6) L6: −20 °C for 15 min with 24 h recovery at 26 °C  with a pinhole in the side wall to allow air exchange. Before treatments, all vials with DBM were placed into the incubator at 26 °C. A group of thirty vials  was frozen in liquid nitrogen and used as controls. Additional groups of thirty vials  were exposed to each of nine distinct temperature treatments. These treatments were defined from a previous study on lethal temperature limits of DBM85. qRT-PCR was conducted with 2 µL cDNA, 0.4 µL of each primer, 0.15 µL CXR Reference Dye, 7.05 µL DEPC water and 10 µL SYBR Green Supermix  in a 20 µL total reaction mixture using QuantStudioTM 6 Flex real-time PCR system . PCR conditions were set as follows: 10 min at 95 °C followed by 40 cycles of 15 s at 95 °C, 30 s at 60 °C and then 15 s at 95 °C, 1 min at 60 °C, 15 s at 95 °C for a melt curve. The relative expression level of PxCad in each treatment was normalized to the abundance of samples under control temperature, using the 2−ΔΔCt method86. In the present study, we focused on the comparison of difference in gene expression between each of the temperature treatments and the control rather than the comparison between different non-control temperature treatments. Therefore, we used independent t-tests to perform in R to establish differences in gene expression between each temperature treatment and the control.Total RNA was extracted using Eastep® Super Total RNA Extraction kit , following the manufacturer’s protocol. A NanoDrop Spectrophotometer  and 2% agarose gel electrophoresis were used to determine the quality and quantity of RNA. cDNA was synthesized with 1 μg of total RNA using GoScriptTM Reverse Transcription System . RT-qPCR was carried out using the target gene-specific primer , which was designed by Oligo 7 and the reference gene primer for normalization  according to the previous reportsPxCad. We selected two sgRNA target sites, 5’-AGGGAGATGTCATCATTGCA-3’ and 5’-ACTCGTCCATGTAGATGTTG-3’, in PxCad exon 3 according to the principle of 5’-N20NGG−3’ (with the PAM sequence underlined). To obtain the templates for in vitro transcription of the two sgRNAs, we performed PCR with two primer pairs .Fresh eggs from G88 moths laid within 15–20 min were injected with a mixture of Cas9 mRNA (380 ng/μl) and sgRNAs (100 ng/μl) using an IM 300 Microinjector . Microinjected eggs were then incubated at 26 °C and allowed to develop to adult (G0). Virgin G0 adults were crossed with the wild-type G88 strain in single pairs to produce the G1 progeny. After oviposition, we sacrificed G0 adults and extracted the genomic DNA using the Tissue DNA Kit . gDNA was then used to amplify the fragment containing the sgRNA target sites using PCR with the Phanta Max Super-Fidelity DNA Polymerase  and a primer pair  proteins from the G88 strain and knockout strain using the differential magnesium precipitation method88. Total extracted BBMV proteins (30 μg) were separated on 7.5% SDS-PAGE, and the regions (~120–250 kDa) predicted to contain PxCad proteins were excised from the gel stained with Coomassie blue. These gel slices were subjected to tryptic digestion and nano-LC-MS/MS analysis at Huada Protein Research Center .To verify the absence of PxCad protein in the DBM strains used in this assay were wild-type (G88 or WT) and mutant (G88-Cad or MU) adults. Female or male individuals emerging within 24 h were placed into a 1.5 ml clear plastic vial (4.0 cm height) with a pinhole in the side wall. Twenty vials with females and twenty with males were then put into a plastic container .Four plastic containers with DBM adults were placed into a climate incubator  where DBM adults were exposed to one of the following heat shock treatments: 40 °C, 41 °C, 42 °C, 43 °C, and 44 °C, for 2 h. After each heat shock treatment, the plastic containers were removed from the climate incubator and placed in a temperature-controlled room at 26 °C for 24 h, after which survival was recorded.A similar procedure was used for cold stress, with four plastic containers of DBM adults being placed into a freezer. Three temperature treatments were used: −14 °C, −17 °C, and −20 °C. When the temperature was set at −14 °C, the adults were exposed for periods of 150, 210, 270, and 330 min. At −17 °C, durations were 40, 60, 80, 100, and 120 min. At −20 °C, durations were 20, 25, 30 and 35 min. After cold treatments, adults were transferred to a temperature-controlled room at 26 °C for 24 h, after which survival was recorded.p/(1−p)] = aijk + bijkt where p is the probability of survival, i is the temperature index , j is the strain index (WT or MU), k is sex , and t is exposure duration (min) used as a continuous predictor. The responses were complex, with significant interactions to the third order. Therefore, to allow clearer interpretation of the results, a separate analysis was performed for each temperature using the simpler model log[p/(1 − p)] = ajk + bjkt.Logistic regression models were fitted to the survival data to test behavioral responses to different temperatures. Results of the cold-exposure experiment were analyzed first using the model: log[p/(1 − p)] = ajk + bjkT where j is the strain (WT or MU), k is sex , and T is temperature (°C) used as a continuous predictor.The results of the heat-shock experiment were analyzed using the model log[Further information on research design is available in the Nature Research Supplementary informationPeer Review FileDescription of Additional Supplementary FilesSupplementary data 1–5Reporting summary


Drosophila encodes global motion patterns. Whereas the mouse retina encodes four types of optic flow, the fly visual system encodes six. This matches the larger number of degrees of freedom and the increased complexity of translational and rotational motion patterns during flight. The four uniformly tuned T4/T5 subtypes described previously represent a local subset of the population. Thus, a population code for global motion patterns appears to be a general coding principle of visual systems that matches local motion responses to modes of the animal’s movement.Self-motion generates visual patterns on the eye that are important for navigation. These optic flow patterns are encoded by the population of local direction–selective cells in the mouse retina, whereas in flies, local direction–selective T4/T5 cells are thought to be uniformly tuned. How complex global motion patterns can be computed downstream is unclear. We show that the population of T4/T5 cells in  Local direction–selective cells encode six types of global motion patterns, facilitating the computation of fly self-motion. Animals that use the visual system to navigate through their environment need to detect and compute global motion patterns elicited on the eye. These optic flow patterns are generated by locomotion, such as during walking, riding, or flying, where different types of behavior will elicit different optic flow patterns. In the mouse retina, this optic flow generated by self-motion is represented by the population of local motion-sensitive retinal ganglion cells  or OFF (T5) contrast signals. In contrast to the local direction–selective ganglion cells of the mouse retina, T4/T5 neurons are thought to be uniformly tuned throughout the visual field, representing the four cardinal directions: upward, downward, front-to-back, and back-to-front motions .To understand how the T4/T5 neurons contribute to downstream optic flow fields, it is necessary to have a detailed map of T4/T5 direction tuning across retinotopic space. We used in vivo two-photon calcium imaging to record motion responses from large populations of T4/T5 neurons in individual flies. We imaged GCaMP6f in T4/T5 axon terminals in the lobula plate. In each fly, we recorded responses to perspective-corrected visual stimuli, comprising ON and OFF edges moving in eight directions at three different fly orientations relative to the screen. In addition, we obtained signals in each fly in different planes along the dorsoventral axis of the lobula plate. This allowed us to record T4/T5 responses across the dorsofrontal visual field, covering ~100° in azimuth and ~50° in elevation (fig. S1A). To allow comparability of data between flies, we report the anatomical position of the recording relative to T4/T5 cell bodies as an internal reference . When juR2 = 0.96, layer D: R2 = 0.96). In comparison, fits by one Gaussian for layers A and B were worse , and these layers were fit better by a sum of two Gaussians . In layers A and B, an unsupervised statistical model based on the minimum message length principle [SNOB;  and rotation  for a fixed distance of the visual space to the fly  extends its dendrites solely in layer A of the lobula plate, and its receptive field resembles the subtype A.I global motion pattern [Drosophila attp5/R59E08-LexAattP40, lexAop-GCaMP6f-p10su(Hw)attp5w were recorded 3 to 5 days after eclosion at room temperature (20°C). attP40R59E08-LexA and su(Hw)attp5lexAop-GCaMP6f-p10 were obtained from the Bloomington Drosophila Stock Center , recombined, and crossed into a +w background. The attP40R59E08-LexA line shows full coverage of T4/T5 neurons, as tested by full overlap with the expression pattern of the commonly used attP2R42F06-Gal4 T4/T5 driver line (n = 10 flies). The cuticle on the right eye, fat bodies, and tracheae were removed using breakable razor blades and forceps. To ensure constant nutrients and calcium supply, flies were perfused with a carboxygenated saline containing 103 mM NaCl, 3 mM KCl, 5 mM N-tris(hydroxymethyl)methyl-2-aminoethanesulfonic acid (TES), 1 mM NaH2PO4, 4 mM MgCl2, 1.5 mM CaCl2, 10 mM trehalose, 10 mM glucose, 7 mM sucrose, and 26 mM NaHCO3 (pH 7.3). To record calcium activity, a two-photon microscope , equipped with a 25×/1.1 objective , was used. For excitation of GCaMP6f, the excitation laser (Spectra-Physics InSight DS+) was tuned to a wavelength of 920 nm with <20 mW of laser power measured at the objective. Emitted light was filtered through an SP680 short-pass filter, a 560 lpxr dichroic filter, and a 525/70 emission filter and detected by photomultiplier tubes set to a gain of 855 V. Imaging frames were acquired at a frame rate of ~15 to 20 Hz and 5× optical zoom, corresponding to a pixel size of ~0.5 μm using PrairieView software. Each fly was recorded in at least three to five different focal planes (z-depth). We determined z-depth position relative to cell bodies and started the first recording at a z-depth of 30 to 35 μm from there (movie S1). A total of 3537 cells in 14 flies, with 479/926 layer A, 252/662 layer B, 365/220 layer C, and 280/353 layer D T4/T5 cells, were recorded. Planes were then imaged every 15 μm  was used on the left side of the brain and the thorax. Using this mounting method, head angle between flies differed by ~0.5°, measured by the alignment of the pseudopupil between the two eyes . In total, we thus stimulated an area of the visual field ranging from −34° to 134° in azimuth and −17° to 36° at the closest point of the screen to the fly in elevation (fig. S1A). Note that results are just plotted in a range between −23° and 120° in azimuth, as no neuronal responses were measured to the stimulus beyond that visual area. Stimuli were filtered through a 482/18 bandpass filter (Semrock) and ND1.0 neutral density filter (Thorlabs) and projected using a LightCrafter 4500 DLP  with a frame rate of 100 Hz and synchronized with the recording of the microscope as described previously  or Python 2.7. Motion artifacts were corrected using Sequential Image Alignment SIMA, applying an extended hidden Markov model (s = 1.5) and then threshold-selected by Otsu’s method (DSI) and contrast selectivity index (CSI) for each pixel as followsPDmax and NDmax denote the maximal response into the preferred direction (PD) and null direction (ND) and PCmax and NCmax denote the maximum responses for the preferred contrast (PC) and the nonpreferred or inverse contrast (NC). We excluded all pixels that did not exceed the CSI threshold of 0.2 to obtain clean T4 or T5 responses. For the final clustering, we used the quantified DSI and CSI parameters and the timing of the response to the PD. On the basis of these parameters, the Euclidean distance between each pair of pixels was calculated and average-linkage agglomerative hierarchical clustering was performed. We further evaluated the optimal distance threshold that yielded most clusters of the appropriate size between 1.25 and 6.25 μm2. All resulting clusters that fell outside this range were excluded from further analysis. Cluster locations were saved and matched with subsequent recordings of the same cells to other stimulus types.For the extraction of single T4 or T5 axon terminals, we made use of their contrast- and direction-selective responses to ON and OFF edges moving into four directions. First, the aligned images were averaged across time, and the average image intensity was Gaussian-filtered  is the response to angle θk. The direction of the vector Ldir denotes the tuning angle of the cell, and the normalized length of the vector is related to the circular variance and thus represents the selectivity of the cell.For To extract receptive-field centers, we used a back-propagation algorithm to map the receptive fields of T4 and T5 cells and to locate the center of the receptive fields  color-coded by their directional preference were generated in MATLAB. Images containing data from different All statistics were done in MATLAB using Circular Statistics Toolbox . The data were fitted using 10-fold cross-validation (CV), dividing the data into 10 random subsets. In each fold, nine subsets were used for training and the remaining subset was used for testing the model fit. For each CV fold, the same training data were fit 10 times starting from 10 different random conditions, and the best fit was stored and used to calculate the performance on the test set. The same training and testing data were used for all models, resulting in repeated measures of the test performance across models. Statistical testing was done on the 10 test-performance values obtained per model. A one-tailed nonparametric Wilcoxon signed-rank test was used to determine whether the performance of each of the self-motion models was higher than the performance of the uniform model, for tests pooling all subtypes  and test


However, the causal links between this large-scale, low-frequency instability, and the time–space evolution of magma storage, crystal fractionation/accumulation, lithospheric assimilation, and partial melting remains unclear. This paper aims at tracking time variations and links between lithospheric, crustal and surface processes before and after a major flank collapse (Monte Amarelo collapse ca. 70 ka) of Fogo volcano, Cape Verde Islands, by analysing the chemical composition  and age-controlled stratigraphy (K–Ar and Ar–Ar dating) of lavas along vertical sections . The high-resolution sampling allows detecting original variations of composition at different time-scales: (1) a 60 kyrs-long period of increase of magma differentiation before the collapse; (2) a 10 kyrs-long episode of reorganization of magma storage and evacuation of residual magmas (enriched in incompatible elements) after the collapse; and (3) a delayed impact at the lithospheric scale ~ 50 kyrs after the collapse (increasing EM1-like materiel assimilation).Mass-wasting of ocean island volcanoes is a well-documented phenomenon. Massive flank collapses may imply tens to hundreds of km The influence of external parameters such as climate and sea-level variations has been debated12, but large-scale flank instability of ocean island volcanoes seems closely linked to their volcanic and intrusive history. Indeed, mechanisms of feedback between gravitational instability, structural discontinuities and the intrusive system have been appraised19. Most of these studies focused on edifice-scale processes, without considering deeper (lithospheric) processes. Numerical simulations20 indicate that a massive flank collapse may induce pressure changes at depth down to magma storages in the uppermost mantle (~ 20–40 km), thus accounting for observed variations in magma composition after the collapse26.Among the largest landslides on Earth (tens to hundreds of km3) at ~ 60–70 ka32. The walls of the horseshoe-shaped collapse caldera  is combined with an innovative bottom-up approach . We focus on the last 160 kyrs, with a major flank collapse  and flank collapse41. The caldera floor is filled by post-caldera volcanism, including the growth of the Pico do Fogo central cone and historical lavas. The volcanic history of Fogo Island is poorly documented, although different stages of growth separated by major unconformities were distinguished38. Uplifted lavas of the seamount stage are exposed in valleys of the western part of the island together with carbonatite dated 5.1 to 3.2 Ma43. The early subaerial stage (lower shield), exposed in the northern valleys and in the lower part of the caldera walls, is not chronologically well constrained, with only one lava flow dated 212 ± 20 ka (K–Ar) at the base of the caldera wall31. The lavas produced during this stage range from nephelinite and melilitite, to phonolite. Eroded remnants of the lower shield are overlapped by an upper shield, the Monte Amarelo edifice32, thus forming a major erosional unconformity that is clearly visible on the walls of the caldera  3He exposure ages of pre-collapse (~ 123 ka) and early post-collapse (~ 62 ka) lava flows on Fogo27; (b) 3He exposure ages of megaclasts transported by the collapse-generated tsunami on Santiago Island (65–84 ka29), (c) and K–Ar ages of pre-collapse (~ 60 ka) and post-collapse (43 ka) lava flows of the caldera walls31.The eastern flank of the Monte Amarelo edifice was destroyed by a massive flank failure leading to the formation of the horseshoe-shaped caldera40Ar/39Ar ages give a more precise age of ~ 68 ka for the Monte Amarelo collapse , and 20 ± 2 ka . The oldest post-collapse lava flow exposed along the eastern coast is 35 ± 4 ka old (Table 3/ka (~ 200 km3 of lavas produced between 160 and 68 ka), which is relatively high compared to growth rates calculated for the Canary Islands48. Post-collapse volcanism represents a volume of 74 km3 filling the collapse scar at a lower rate of 1.1 km3/ka (< 68 ka).Our K–Ar and pse Fig. . Indeed,pse Fig. , is overka Table . A post-ka Table , that isld Table . Using a49. Lavas of the southern islands are slightly more alkaline than those of the northern islands34. Intrusive and extrusive carbonatites found on São Vicente, Maio, Santiago, Fogo and Brava are interpreted as resulting or deriving from the partial melting of a carbonated oceanic crust53. Mixing models accounting for the spatial variability of the isotopic signature (Sr–Nd–Pb–He–Os) of the Cape Verde Islands have been proposed59. Isotopic and trace element compositions of the lavas of the southern islands suggest a mixture between a moderate HIMU-like (High µ = high 238U/204Pb) end-member and an EM1-like (Enriched Mantle 1) end-member during magma differentiation at shallow depth56. The HIMU component is commonly explained by the recycling of a Proterozoic oceanic crust by the mantle plume, while the origin of the enriched component has been debated (lithospheric57 or asthenospheric58). The existence of a third component similar in composition to the Depleted MORB Mantle (DMM) was proposed57, but in a very small proportion in the southern islands. Some Cape Verde basalts have also experienced shallow-level contamination by oceanic crustal material 34, especially in São Nicolau Island, but this interaction is negligible (possibly non-existent) for Fogo lavas57. The overall isotopic (Sr–Nd–Pb) composition of the 48 lava samples collected in Fogo  do not fully overlap with post-collapse lavas  minor variations in the primitive melt composition, and (b) fractionation/accumulation of crystals, mainly pyroxene and olivine. This was previously demonstrated61, and it is also confirmed by the negative correlation between incompatible elements and MgO  preceding the Monte Amarelo collapse  trend  K2O lavas on the southern flanks. Major element composition somehow varies geographically, with lavas being more differentiated near the paleo-centre of the Monte Amarelo edifice . Because the major element composition of the primary magmas is almost independent of the source mantle componentspse Fig. . Lavas bice Fig. .Figure 256. The HIMU component is characterised by a relatively high 206Pb/204Pb but low 87Sr/86Sr, and the EM1 component has low 206Pb/204Pb and high 87Sr/86Sr. The Pb–Sr–Nd isotopic composition of the lavas displays a significant temporal evolution during the last 160 kyrs . Near-collapse lavas are characterised by higher 143Nd/144Nd and low 87Sr/86Sr ratios compared to other pre-collapse lavas  share some similarities with the latest pre-collapse lavas in terms of differentiation Fig. A and iso2O/MgO for late pre-collapse (< 100 ka) and late post-collapse lavas  are characterized by a degree of differentiation that is similar to early (> 100 ka) pre-collapse lavas, i.e. significantly less differentiated than near-collapse lavas Fig. A. This i87Sr/86Sr ratio increased slightly faster than it was decreasing before the collapse  until volcanic activity moved to the South, thus building a new shield (Cumbre Vieja). The central edifice of Tenerife provides another example of such correlation between volcano growth, storage conditions, eruptive processes shifting from effusive to explosive, and flank instability of an ocean island65.Timescales of astenospheric, lithospheric, and volcanic processes are different, but their respective histories are not disconnected, and the Fogo example provides an original example of interactions between the source and storage of the magmas, and a major volcano flank collapse. In the case of Fogo volcano, major element composition of lavas evidence a progressive magma differentiation due to increasing crystal fractionation during the 60 kyrs preceding the Monte Amarelo flank collapse. This temporal evolution of magma storage in the lithospheric mantle is coeval with the development of shallower reservoirs, including a plutonic complex inside the volcanic edifice itself, and recurrent explosive activity forming ignimbrites. The increasing load of the growing volcano may have promoted magma stagnation, as previously proposed for Teno volcano, Canary IslandsHowever, the unique dataset presented here for Fogo volcano highlights two different scales of consequences of the flank collapse on lava composition. The delayed response to the unloading effect of the collapse takes ~ 50 kyrs at the lithospheric scale , compared to ~ 10 kyrs at the depth of the main magma storage and crystal fractionation. The shallow and short-term impact (~ 10 kyrs) is evidenced by the composition of early post-collapse lavas (68–59 ka) that are incompatible element-rich. The collapse-induced unloading upset magma storage conditions in the lithospheric mantle (~ 20–30 km) and favoured the release of highly fractionated magma. Major and trace element composition of lavas generated during the last 60 kyrs indicates a degree of differentiation that is similar to that of early (> 100 ka) pre-collapse lavas.19, with early post-collapse lavas (68–59 ka) being located along the head of a large failure plane corresponding to the present-day caldera rim  or indirectly (considering their stratigraphic position compared to the dated samples). Six stratigraphic sections were extensively sampled: (1) the northern caldera wall and the Montinho post-collapse lava flow on the caldera rim , and 2 ignimbrite samples were collected along the caldera walls and coastal cliffs Table  and S3. rim Fig. ; (2) therim Fig. ; (3) therim Fig. ; (4) the66. Alteration may cause gain and/or loss of K and Ar and as a consequence may lead to under or overestimated K–Ar ages67. To decide if a sample should be used for dating, it is crucial to estimate its degree of alteration. Indeed, after macroscopic and microscopic observations many pre-collapse samples collected along the walls of the caldera were discarded due to their high degree of alteration. The loss-on-ignition values (L.O.I.) range from 0 to 0.4% . Groundmass splits (1.0–2.5 g) of samples were wrapped into 99.5% copper foil packets, loaded in the sample holder, which had been turbo-molecular pumped for about 20 h. During the last two hours of that stage, the molybdemium (Mo) crucible was degassed at about 1500 °C until the pressure decreased to 10–9 Torr. The sample was then dropped into the Mo crucible and molten at full power of the induction furnace. During the melting stage (i.e. 20 min), the extracted gas was adsorbed by an active charcoal finger at liquid nitrogen temperature. Next to the melting, the gas was released by heating the charcoal at 110 °C and purified via the mutual action of a titanium sublimation pump and a SAES 10 GP-MK3 Zr–Al getter operated at 400 °C. This first step of gas clean-up generally lasted 40 min and was followed by three consecutive exposures of five minutes each of the gas to SAES 10 GP-MK3 Zr–Al getters also operated at 400 °C. The gas was then adsorbed 5 min by a second active charcoal maintained at liquid nitrogen temperature. After adsorption, this sector is isolated. The gas, released at room temperature, was finally cleaned up by a SAES APGP-10 Zr–Al getter operated at 400 °C during another 5 min and introduced into a 180°, 6 cm radius mass spectrometer, equipped with a double Faraday collector. Isotopic analysis was performed on total 40Ar contents ranging between 0.5 and 2.5 × 10–11 mol. One or multiple manometrically-calibrated doses of atmospheric argon were used to convert beam intensities into atomic abundances and to monitor the atmospheric correction. The manometric calibration is based on periodic, replicate determinations of the international dating standard HD-B1 (24.21 ± 0.32 Ma71). Uncertainties for the K and Ar data reported in Table 40Ar , and 40Ar* determinations. All uncertainties on the ages are given at 2σ.Splits of purified groundmass separates from 4 pre-collapse  and 6 post-collapse samples  were prepared following the procedure described by4% Table . They ar40Ar/39Ar age determination on sample Fo-01 . Neutron fluence (J) was monitored by co-irradiation of Acs crystals74 placed in three pits encircling each sample. The J value for each sample was determined from 6 single crystal laser fusion analyses of Acs. Corresponding J values were calculated using an age of 1.1840 ± 0.0007 Ma75.In addition to K–Ar dating and to better constrain the age of the collapse, we have achieved a 01 Table , previou76. BHVO-1 standard was also analysed during the analytical session. For trace elements analysis  at LMV. The BHVO-2 standard was measured every five samples, and the reproducibility was determined with BCR-2 and CMS standards57, acid-digested with HF-HNO3, and passed through the chromatography procedure of 78. Strontium, Nd and Pb analytical blanks were all negligible with respect to sample contents. Strontium isotopic measurements were performed on a Finnigan Triton thermo-ionization mass spectrometer (TIMS), Pb measurements on a Thermo Fisher Neptune Plus Multiple Collector—Inductively Coupled Plasma—Mass Spectrometer (MC–ICP–MS), and Nd measurements used both instrumentations  and 143Nd/144Nd = 0.512104 ± 6 . Lead mass discrimination was corrected by standard bracketing of samples with NBS-981 standard using values of 79 recalculated to 208Pb/206Pb = 2.167780. Average NBS-981 deviations from drifts observed during the different day-sessions of analysis were in the ranges 35–120, 45–190 and 65–255 ppm for 206Pb/204Pb, 206Pb/204Pb and 206Pb/204Pb ratios. This corresponds to maximum errors of ± 0.002, 0.003 and 0.010, respectively.One hundred milligrams of bulk-rock powder were leached with hot 6 M-HCl (75 °C) during 3 h following the protocol described in ns Table . StrontiSupplementary Figure S1.Supplementary Figure S2.Supplementary Figure S3.Supplementary Figure S4.Supplementary Figure S5.Supplementary Figure S6.Supplementary Figure S7.Supplementary Figure S8.Supplementary Figure S9.Supplementary Information.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.


In Italy, Chianti Classico identifies a territory located in the heart of Tuscany that was once known as Chianti. From the pedological point of view, the entire DOCG (Denomination of controlled and guaranteed origin) has some common features but also shows many specific features related to certain small areas that give rise to the presence of many “terroirs”. Due to the intertwining created by the alternation of valleys and hills and the different characteristics of the territory, factors such as altitude and exposure play a very important role in the vegetative and productive expression of grapes. Some production areas were identified within the appellation where it is argued that the terroir and the grapes are quite distinct from those of other surrounding areas, albeit within the Chianti Classico appellation. On the basis of this information and considering that no data are available in the literature, the present study proposed an innovative multidisciplinary approach  that was capable of carrying out an objective evaluation of the various sub-areas investigated, using Sangiovese grapes as the variety in question. This research took into account the climatic results and the different pedological characteristics, evaluating the evolutionary phenomena that were linked to the ripening of the grapes in each phase of its formation. In the world when people talk about the famous wine regions , they alViticulture is, therefore, one of the few activities that everywhere, in distant and more recent times, have engaged humans, who have stubbornly made even the most inhospitable and hostile areas productive, creating at the same time, especially in Italy, the most superb landscapes, so much so that the strong visual impact of these growing areas has now become synonymous with the high quality and cultural values of wine ,6,7.In Italy, the Chianti area is located between Florence and Siena. It is mostly known for the production of high-quality red wines, which are distinguished with a PDO label at the European level  and DOCGFrom the pedological point of view, the whole DOCG has some common traits but also shows many specificities that are linked to certain small areas that give rise to the presence of many “terroirs”, and therefore different wines. To date, the terroir concept is not easily defined and remains one of the most debated issues in the world of wine because of the large variety of interacting natural and human factors, on which there is not always agreement .Indeed, it was observed that the elemental composition of grapes and wines depends on several factors, including the soil characteristics, type of grape, area of production and environmental conditions, allowing for definitions in terms of a representative “fingerprint”, which is especially important for quality wines produced in specific regions ,14,15.In the Chianti Classico area, the alternation of valleys and hills and the different characteristics of the territory factors, such as altitude and exposure, all play very important roles in the vegetative and productive expression of the grape ,16,17.Among all the Italian grape varieties, Sangiovese is one of the most important cultivars from the economic point of view, as it is widely cultivated throughout the country to produce both DOCG/DOC wines and IGT wines, while at the European level, it ranks tenth ,19. At lA recent study conducted on the influence of the soil on the quality of Sangiovese grapes in the Chianti Classico area showed a relationship between the sugar content and nature of the grapes and soil composition, in particular, the organic and clay content .Through various interviews that were carried out at the beginning of 2019 with various producers of the appellation, some production areas were identified within the appellation where it is argued that the terroir and the grapes are quite distinct from those of other surrounding areas, albeit within the Chianti Classico appellation. On the basis of this information and considering that no data are available in the literature, the present study proposed an innovative multidisciplinary approach  that was capable of carrying out an objective evaluation of the various sub-areas investigated. The research took into account the climatic results and the different pedological characteristics, evaluating the evolutionary phenomena that were linked to the ripening of the grapes in each phase of its formation.V. vinifera) (The study was conducted in 2019 on nine Sangiovese vineyards (inifera) . The expOidium and Peronospora diseases during the season. As for the control of moths, the method of sexual confusion was used in all vineyards. The management of the vineyards made it possible to obtain healthy plants overall, and plants were rejected in the case of slight attacks or damaged clusters, paying close attention to the selection of the samples to be used.The vineyards were all managed organically with only manual passage to remove any shoots grown from the old wood. To contain the development of the canopy in all the vineyards, two mechanical trimmings were carried out; moreover, light defoliation on the shaded side of the row was also carried out to allow for good aeration of the grapes. Treatments based on copper and sulphur hydroxide or tribasic copper and sulphur sulphate were necessary to manage the development of MeteoSense 2.0 (NetSens) meteorological control units located near the vineyards that were selected for the study were used to monitor the weather conditions of each vineyard. These weather stations were equipped with several sensors that allowed for the instantaneous detection of air temperature, humidity, leaf wetting and the intensity and amount of rain. The data recorded in the field by the stations were automatically sent to a LiveData cloud portal, which allows for access from any device, e.g., computer or cell phone. The data that are recorded in the cloud allow for the formation of a historical database of great importance that can be used for the evaluation of the meteorological trend of the different years and areas.For this study, the weather stations were initially used for the evaluation of weather conditions on the day of grape sampling, which was necessary for the evaluation of the maturity of the vineyard. This was done in order to ensure fairly similar sampling conditions and thus avoid a situation where the analyses were not comparable. Subsequently, the meteorological archive of the system was used to evaluate the different climatic trends of each area with particular reference to August to October, which are those months of greatest interest as they coincide with the period of major importance of the plant. In order to compare all nine vineyards, the following parameters were selected: average temperature, average excursion, maximum of the maximum temperature and minimum temperature, rainfall and relative humidity.The Chianti Classico region covers almost 72,000 hectares between Florence and Siena. The territory can be assimilated to a rectangular region, bounded by the Chianti Mountains that constitute its eastern border .The soil skeleton consists mainly of two stones type: Galestro and Alberese. The first is a rock that is very typical of the Apennines and consists of hard clayey schist but is very easy to separate. With the addition of water, this stone tends to dissolve and become mud that regains its hardness once it is returned to dryness . AlberesThe Chianti Classico territory can be divided into five areas  21,24,2,224,25.Area A, located on the east side of the DOCG, has an average altitude of 450 m a.s.l. In these areas on sandstone rock (Macigno del Chianti), the abundant soil skeleton consists mainly of Galestro, with the exception of the Radda area, where it is possible to identify some points containing Alberese (mostly west of Radda). In addition to the abundant content of the soil skeleton, 20–25% clay is present: this mixture guarantees a good drainage capacity and, at the same time, also allows for good water reserves. The higher areas of this region have soils that tend to be poorer and less deep, making them capable of delaying the vegetative development and ripening of the grapes. Near the town of Gaiole, especially on the eastern side, the pH of the soils is more prone to neutrality.Area B is located in the heart of Chianti Classico, where the hillsides drop (about 350 m a.s.l.) while moving towards the west. The soils in this area are mainly calcareous-clayey, with a moderately alkaline soil pH. Furthermore, in the area of Panzano, it is possible to find Pietraforte as the main soil skeleton component (brown sandstone made of carbonate cement inside and very appreciated for its resistance).Area C corresponds to the municipality of San Casciano and is therefore located in the northern area of the Chianti Classico. The altitudes continue to decrease (on average 290 m a.s.l.); therefore, the temperatures are slightly higher and the soil tends to be cooler. In these localities, the soils are based on marine deposits with a prevalence of sand. Consequently, on the surface of the soils, it is possible to observe a skeleton that is based on siliceous pebbles (round white stones), which contribute to the fairly early ripening of the grapes.Area D, located to the south, corresponds to the territories of Castelnuovo Berardenga. Here the soils vary from sandstone rocks , predominantly sandy marine deposits , predominantly clayey marine deposits (particularly on the left side), and mainly calcareous-clayey rocks . Being in the southernmost part of the Chianti Classico, Sangiovese tends to be produced more abundantly and matures earlier here than in the other areas.Finally, area E, located on the south/west border, consists of deep soils on ancient lake deposits. The lower altitudes (about 230 m a.s.l.) imply a greater presence of clay in the soil and a smaller presence of a mainly tuffaceous skeleton.The sampling of the grapes began in August 2019. In order to identify the degree of ripeness of the vineyard, 60 plants per vineyard were selected and marked out of 8 rows used as “indicator plants”, which were sampled about every 6–8 days until the harvest day. Since these plants had to be representative of the chosen vineyard, it was considered important not to select individuals placed at the edge of the vineyard, they had to be at least 5 m from each other, have a vigour that was in line with the surrounding plants and be pathologically healthy.The grapes were sampled by taking 100 random berries from both the individual vines and from various sides and parts of the bunch. Once packed and labelled, they were brought to the laboratory for routine analysis to assess the ripening trend, which was essential to identify the ideal technological ripening point and therefore also the harvest date for each of the nine areas.All chemical determinations that were necessary for the characterisation of grapes (sugar content (hexoses g/L), titratable acidity (tartaric acid g/L), pH, L-malic acid (g/L), tartaric acid (g/L) and potassium (mg/L)) were performed following the OIV methods . In partIn order to compare the nine vineyards, the program Root Data Analysis Framework based on C++ language was used for the data processing ,28. The The climatic parameters are represented in v/v). Besides these conditions, high-altitude grapes tend to have a more pronounced and attractive acidity compared to those in the plain [Another very relevant variable for the ripening of the grapes is the altitude of the vineyard, with the main advantage being that vineyards on slopes at a higher altitude receive more solar radiation and benefit from cool temperatures, particularly at night, thus slowing down the grape-ripening process, which in turn increases the production of flavour compounds in the skin. At the same time, the sugars ripen and the acidity decreases: this behaviour tends to happen slowly, thus the grapes are often harvested with fully ripe flavours before the sugar levels have risen to values that would make the wine very alcoholic  in order to allow for the comparison of the different sub-areas.The trends of the main parameters that contributed to identifying the technological maturity of the grapes during the 2019 vintage were evaluated. The grapes were harvested when the parameters that characterised their technological maturity  reached values that are appropriate for the type of wine to be produced. The analytical data were evaluated together with the trends of the lines that best correlate with the evolutionary curve of the parameter, in this case, the equation used was:t = −40 days) of the grapes from the different vineyards: the grapes with the lowest sugar content at the beginning of the sampling (San Casciano and Greve) recorded the highest sugar content at harvest time. Instead, the grapes with the highest sugar content at the start of the sampling (Gaiole and Radda) had the lowest sugar content at the harvest date, excluding Lamole. An exception was the case of Lamole, which, despite having started with high sugar content, did not follow the evolutionary effect of the other areas since it started and remained in the high category. The intermediate zones, on the other hand, had accumulated a small sugar content, thus exhibiting a low final content (Panzano and Tavarnelle) or managed to load a little more while remaining in the intermediate category (Castellina and Castelnuovo Berardenga).A similar approach, taking into account the fact that technological maturity provides for a decrease in titratable acidity (unlike what happens for sugars), was followed for the evaluation of the titratable acidity, malic acid and pH of the grapes. In order to confirm the existence of an inverse correlation between the evolution of the sugar content compared to the acid evolution, from the evaluation of the parameters of the straight lines that correlated the analytical data obtained in the period preceding the harvest, similarities and differences emerged regarding the metabolic behaviour of the grapes from the different vineyards.Indeed, as seen in Since the analysis of the titratable acidity evaluated a set of components, it was interesting to investigate the evolution of malic acid in grapes . Malic aAs is clearly readable, San Casciano was the area with the highest quantity of malic acid, while Radda and Castelnuovo Berardenga were the areas with the lowest value. This was in line with their titratable acidity; indeed, malic acid was less stable than tartaric acid, making it easier to consume it for breathing or other metabolic pathways. In the presence of a large quantity of malic acid , a large titratable acidity value is normally expected. Similarly, the smaller the amount of malic acid , the smaller the titratable acidity value.In accordance with what was observed for titratable acidity and malic acid, the pH of the grapes  remainedWith regard to tartaric acid, for all the tests examined, the values remained almost constant over time, hence no data are reported.Potassium is another parameter that is taken into consideration. Chianti Classico is a territory famous for its strong vocation for the production of wine. The nine areas considered in this work are naturally considered to be different from each other and were selected to study whether the differences between the regions that are considered evident can be explicitly found in the quality of the grapes.From the climatic analysis carried out, it emerged that there were rather important thermal differences between the nine selected areas, both in terms of the average temperature and the excursions and precipitation. The areas of Radda and Lamole had lower temperatures and excursions due to the different altitudes of the vineyards (Lamole 546 m a.s.l. and Radda 449 m a.s.l.). San Casciano and Castelnuovo Berardenga instead stood out for their high average temperatures, albeit with different temperature ranges (low for San Casciano and high for Castelnuovo Berardenga). Furthermore, the rainfall in the different areas of the Chianti Classico varied according to their position, resulting in a different water state of the plants during the ripening period of the grapes.All grapes showed similar ripening trends of the different analytical parameters, although with different dynamics. For example, sugars during the sampling period increased for all areas, as expected. A novelty that this work showed for this parameter was in terms of the dynamics of the areas located at the extremes of sugar content: in fact, the areas that at the beginning of the sampling had a high sugar content (Gaiole and Radda) showed a low growth rate and finished the ripening with a lower sugar content than the other areas; in contrast, those areas that started at the beginning of the sampling period with very low sugar content (San Casciano and Greve) showed a fast increase in the growth rate, resulting in grapes with the highest sugar content at harvest. The only anomalous case, however noteworthy, was the one of Lamole, which even though it started the sampling with high sugar content, at harvest, it was found to be the grape with the highest sugar content of all areas, therefore establishing a good accumulation rate.Furthermore, for the titratable acidity, a phenomenon similar to that previously explained occurred, even if the general trend was of decreasing acidity during the ripening period, as expected. Here the vineyards that showed very strong dynamics (San Casciano and Panzano) had a strong drop at harvest, while the grapes that started with low total acidity (Castelnuovo Berardenga and Radda) showed good acidity at harvest when all nine areas were compared.Regarding the malic acid, it is known that its content and its course during the grape-ripening period depend very much on the climatic characteristics of the area where the plant grows; thus, looking at the climatic data, it is easy to understand the reason why there was a greater reduction in San Casciano than in Radda and Gaiole, which had smaller decreases.The potassium parameter was the one that was most affected by the influence of the climate–soil interaction on grape ripeness. While more clayey soils slowed down its degradation, in contrast, cooler climates and higher rainfall intensified the loss.In conclusion, this study represents the first step in the conceptualisation of the viticultural aspects of the area considered. The idea was to clarify which factors characterised particular production areas and, consequently, which practical aspects were linked to them in order to create a predictive model that can be used by winemakers for their productions. Furthermore, the division into sub-areas could be an easy solution to decrease the competitiveness of small producers and strengthen the local economy.


Lactobacillus plantarum (L75), Weissella cibaria (W64), and their combination (LW64 + 75). The physicochemical parameters , total phenols, and carotenoid contents of the smoothies fermented for 48 h and stored for 7 days at 4°C were compared with the unfermented (control) smoothies. Results indicated that LAB fermentation reduced the pH from 3.56 to 2.50 after 48 h (day 2) compared with the non-fermented smoothie at day 2 (pH 3.37). LAB strain L75 significantly reduced the TSS content of the smoothies to 13.06°Bx after 2 days of fermentation. Smoothies fermented by L75 showed overall acceptability after 7 days of storage compared with the non-fermented puree on day 0. The LW64 + 75 significantly reduced the color change (ΔE), which was similar to the control. L75 increased the phenolic content, and W64 enhanced the total carotenoid content of the smoothies after 2 days of fermentation compared with other treatments. The use of an in vitro model simulating gastrointestinal (GI) digestion showed that fermentation with L75 improved the total phenol recovery by 65.96% during the intestinal phase compared with the control. The dialysis phase mimicked an epithelial barrier, and 53.58% of the recovered free soluble are bioavailable from the L75 fermented smoothies compared with the control. The antioxidant capacity of dialyzable fraction of the L75 fermented smoothie was significantly higher than that of the control and smoothies fermented with W64 or LW64 + 75.In this study, popularly consumed traditional chayote leaves and locally produced pineapple fruit were used to develop a fermented smoothie using lactic acid bacteria (LAB) strains:  Several authors have reported the beneficial roles of fruits and vegetables in preventing and managing chronic diseases, such as coronary heart diseases, stroke, obesity, diabetes, and cancer –3. The WFruit and vegetable products are valuable sources of fibers, antioxidants, and essential fatty acids . Fruit aLactic acid fermentation is one of the most economical, oldest, and natural methods of food processing and preservation that keeps or enhances the efficiency and quality of foods while improving the organoleptic qualities and nutritional properties of the product . The impSechium edule) are one of the Réunioneses' favorite vegetables, which are locally naturalized (Ananas comosus cv. Queen Victoria) is an exotic fruit popularly grown in the Réunion Island and with recognized sensory value; a 100 g portion of the fruit contains protein (0.5 g), sugar (9.9 g), Ca (13.00 mg), K (109 mg), Fe (0.29 mg), Na (1 mg), fiber (1.4 g), total carbohydrate (13 g), and total fat (0.1 g)  . Howeverin vivo gastrointestinal (GI) digestion. The in vitro GI digestion is a reference tool to study the bioaccessibility of the dietary polyphenols , and the bioaccessibility of phenolic compounds of the fermented smoothie after an d-glucuronide (pNPG), and voglibose for enzymatic experiments were also purchased from Sigma-Aldrich . The LAB cultures Weissella cibaria 64 (W64) and Lactobacillus plantarum 75 (L75) were obtained from the Microbiology Laboratory of QualiSud, Université de La Réunion, France  and Condalab . Reagents were obtained from Sigma-Aldrich  and VWR chemicals . Type VI-B porcine pancreatic α-amylase, type I α-glucosidase from baker's yeast, starch, 4-nitrophenyl-β-, France .A. comosus) and chayote leaves (S. edule) were purchased from a local market in Réunion Island, France. Chayote leaves, free from dirt and damage caused by pests or decay, were selected, washed with tap water, and rinsed with distilled water. The leaves were dried on a paper towel, sliced into two halves, and homogenized with a domestic blender. Ripe pineapple fruits, free from signs of damage or decay, were peeled, cut using a knife, blended for 3 min, and then bottled in sterile glass containers. For 1.8 L of pineapple juice, 270 g of homogenized chayote leaves were added, and the mixture was blended to obtain a smoothie. The mixture was pasteurized at 82°C for 10 min using a water bath and then cooled to room temperature for 2 h.Pineapples (W. cibaria (W64) and L. plantarum (L75), were obtained from the Microbiology Laboratory of QualiSud, Université de La Réunion, France (8 CFU/ml) at 660 nm wavelength, and 1% inoculum was inoculated into the smoothies (W64 + L75) was developed at equal ratio . The smoothies were incubated at 37°C for 48 h and then stored for 7 days at 4°C. Thereafter, the fermented smoothies were stored at −20°C until analysis. The un-inoculated smoothie served as a control. The fermentation was performed in triplicate.The LAB strains used in this study, , France . The str, France . The resmoothies . Each stThe microbial load and LAB count of the samples were evaluated at the beginning, end of fermentation, and storage through the bacteria count using the pour plating techniques . BrieflyL*, a*, and b* color coordinates where L* designates lightness, a* is the color component from red to green, and b* represents the component from yellow to blue (E) was calculated according to Managa et al.  to assess the effect of fermentation on the color. Measurements were made using  to blue . Total ca et al. , in whicThe pH meter  measured the pH of the fermented and non-fermented smoothies (10 ml). The measurement of the total soluble content of samples occurred before and after fermentation and storage, using the ATAGO PAL-3 pocket refractometer . The refractive index was recorded and converted to °Bx.Samples obtained at 0, 2, and 7 days were evaluated for total carotenoid content. Briefly, 1.5 g of samples was homogenized with 5 ml of extracting solvent  and centrifuged at 3,000 × g for 5 min at 5°C. Then, 1 ml of hexane was added to the supernatant, and the absorbance was measured at 450 nm using a UV-180 Shimadzu spectrophotometer . External calibration with a β-carotene standard solution was used, and total carotenoid content was expressed as mg/100 g dry weight (DW).The organoleptic properties of the smoothies were evaluated using the qualitative descriptive analysis method as previously described by Oliveira et al.  with sli2CO3 was added to each well, and the mixture was incubated for 1 h in the dark. The absorbance was read at 760 nm using a microplate reader . Gallic acid was used as a standard, and the results were expressed as gallic acid equivalents (GAE) in mg/g DW.The Folin–Ciocalteu assay was used to measure the total polyphenol content of the fermented and non-fermented smoothies . Briefly3, followed by 15 μl of the homogenized puree samples. The absorbance was read at 593 nm with a spectrophotometer . The reducing antioxidant power was reported as Trolox, expressed in μmol Trolox Equivalent Antioxidant Capacity (TEAC)/100 g DW.Total antioxidant scavenging activity was determined using the method described by Llorach et al. . A 0.2 gTo evaluate the concentration of antioxidant compounds released and available for absorption from the smoothies, the simulated GI digestion method was adopted according to the Infogest nature protocols . All smo3 (0.5 M) sealed with clips were completely immersed into the GI digested immediately after digestion. The samples with dialysis bags were then incubated for 45 min at 37°C under agitation. Aliquots were collected for later analysis at the end of the incubation time.The intestinal phase was then divided into two successive steps: agitation step and dialysis process with tubing cellulose membrane (MWCO 10 kDa) as a simplified model of the epithelial barrier. Gastric phase sample was mixed with simulated intestinal fluid (SIF) to get an 80 ml of final volume after addition of pancreatin (100 U/ml) and bile salt (10 mM) and pH adjustment at 7. This mixture was then incubated for 2 h at 37°C. After the intestinal phase, aliquots were collected for later analysis. Dialysis bags filled with 5.5 ml NaCl (0.9%) and 5.5 ml NaHCOp < 0.0001). The mean scores from sensory analysis were calculated, and the significant differences were evaluated by ANOVA, followed by post-hoc using Duncan Multiple Range test (p ≤ 0.05).The conducting of the experiment was in a completely randomized design, with three replicates per treatment and repeated twice. The software XLSTAT  was used for all statistical analyses. Data were subjected to one-way analysis of variance (ANOVA), and significant effects of factors were detected with a Fisher's test (LW64 + 75 at 2 days (L. plantarum fermented tomato juice (W64. Similar observations with a lesser decrease of pH in W64 were reported in pineapple juice (LW64 + 75) had the lowest pH value (2.50); this is probably due to the induced fermentation with LAB starters that could have reduced the pH during the first 2 days of fermentation, and thereafter the increased pH during storage might be due to the onset of metabolic activities of colonizing bacteria or fungi that utilize lactic acid as a carbon source, thus releasing metabolites that could alter the acidity of foods  decreased during lactic acid fermentation compared with the control (W64 showed the highest TSS (14.8°Bx) after 2 days of fermentation and were not significantly different to the control at the start of fermentation, whereas the L75 and the combination (LW64 + 75) significantly (p < 0.05) reduced the TSS of the smoothie to 13.1 and 13.7°Bx, respectively, after 2 days. A decline in TSS content during fermentation is due to the utilization of sugars in smoothies by LAB strains for metabolism, cellular growth, and bioconversion into lactic acid  showed the lowest TSS content after storage. This suggests a continuous fermentation in the fermented smoothies and the non-inoculated sample resulting in the use of sugars by fermenting and colonizing cultures, respectively, at 4°C; hence, lactic acid fermentation of pineapple–chayote smoothies can still take place at cold temperature. The TSS decrease in the smoothie fermented with LW64 + 75 represents 18% of the initial TSS value. The decrease in TSS after fermentation and storage supports the previous assertion in this study that lactic acid fermentations proceed in the fermented smoothies after cold storage. The decrease in TSS suggests the utilization of soluble sugars by LAB in the fermented smoothies, thus corroborating the report on the decrease in TSS of L. plantarum fermented cashew–apple juice after 72 h of fermentation  are important quality indicators that relate to sweetness , often r control . Howevertic acid . After 7entation  and in sentation . Most LAentation . From a entation .LW64 + 75) fermented smoothies showed a significantly higher L* (luminosity) and b* values, whereas L75 showed the highest a* value after 2 days of fermentation. The higher redness to greenness color attributes in the W65 fermented smoothie could be due to the steering activity of fermenting LAB causing enzymatic oxidation during the fermentation process. However, the period of fermentation significantly (p < 0.0001) influenced the color parameter values for all smoothies, especially in samples fermented with W64 , and was significantly comparable to the non-fermented smoothies. The W64 fermented smoothie (5.60) had the highest ΔE. According to Wang et al. (E ≥ 2) is regarded as a significant color change in samples. Similarly, the color difference significantly (p < 0.0001) increased with prolonged storage compared with the control samples at day 7 (L. plantarum) that blocks the enzymatic degradation in the fermented smoothies. L. plantarum has been identified as a strong homolactic fermenter of food substrates  at 2 days of fermentation. The fermented smoothies (W64 and LW64 + 75) at 2 days of fermentation were not significantly different at p ≤ 0.05 and also in the non-fermented samples at 0, 2, and after 7 days of storage. The lower values obtained for the fermented smoothies could be due to impaired lightness in the smoothies due to the addition of chayote leaves. The color score of samples were adjudged to be lower after storage of the smoothies. There was no significant difference in the flavor and consistency attributes of both fermented and non-fermented smoothies at 0, 2, and after storage for 7 days. However, the consistency in smoothies was adjudged as highly acceptable, whereas the flavor of the smoothies fermented for 2 days had an acceptable flavor compared with other samples. The perception of acid taste in the samples was scored the highest in LW64 + 75 (4.67) followed by L75 (4.33) at day 2 of fermentation and was significantly different to the non-fermented smoothies at day 0. The high perception of acid taste might be related to the high acid production by L75. L. plantarum is known to produce organic acids, with lactic acid being the major one during its biochemical degradation of sugar substrates  in LW64 + 75 to high sweetness (4.67) in non-fermented samples at day 0. The sweetness perception reduced with fermentation and storage. The sample L75 fermented smoothie after 2 days had the highest overall acceptability (3.67) compared with other smoothies and was significantly different to the non-fermented smoothies (p ≤ 0.05) at 0, 2, and after 7 days of storage.The produced smoothies were evaluated for their organoleptic characteristics before and after fermentation and storage for 7 days. bstrates ; hence, 6 CFU/ml and were the highest in L75 (60 × 106 CFU/ml) but not significantly different to W64 at day 2 of fermentation  and L. plantarum NCIMB 8826 in cranberry juice  and after 7 days (2 days of fermentation at 37°C plus 5 days of storage at 4°C) for the LAB strains, its combination, and control. From the data obtained, the yeast, mold, and aerobic bacterial population (not shown) remained below the detection level for all samples, and there were no significant differences in the treatments, thus meeting the safety of the product for consumption. The LAB counts in the fermented and non-fermented smoothies ranged from 0.01 to 60 × 10entation . Howevert juices . Hence, e et al.  had earlry juice . The chary juice .L75 for 2 days showed higher concentration of TPC than the smoothies fermented with W64 or LW64 + 75. LAB reportedly hold the ability to convert food matrixes into functional moieties  in the non-fermented smoothie was the highest on day 2 compared with the fermented smoothies and the control samples at day 2 or 7 . Howevermoieties . The biomoieties , based ooteinase , which al-γ-glutamyl-S-sinapyl-l-cysteine, S-sinapylglutathione, S-sinapyl-l-cysteine, furanones, glycosides, and p-coumaric acids. The TPC reduced significantly (p < 0.0001) in the fermented and non-fermented smoothies after storage (day 7). At day 7 after storage, the smoothies fermented with L75, W64, and LW64 + 75 showed 12–16% reduction in the TPCs compared with the control (day 0). The non-fermented smoothie at day 7 after storage exhibited a 9% decrease in TPC compared with day 0. The decrease in TPC of the fermented smoothie is in agreement with the report by Hashemi et al.  being the most abundant phenolics . Similari et al.  in L. plin fruit , 59. Conin fruit , 61. Thein fruit , 62.W64 fermented smoothie was lower than those of the L75, LW64 + 75, and control (L. plantarum fermented sweet lemon juice (The non-fermented smoothie (day 0) showed the highest total carotenoid contents. The effect of lactic acid fermentation on the carotenoids is highly dependent on the matrix, carotenoid composition, and LAB strain, but in general, the total carotenoid content remains stable . A gradu control . The redon juice . After 7on juice , since up < 0.0001) compared with their respective undigested sample; however, the non-fermented smoothies showed a non-significant difference (p > 0.0001). Moreover, the TPC in the intestinal phase was higher than that in the respective undigested samples and gastric digesta for all fermented and non-fermented smoothies, whereas the smoothies fermented by L75 showed the highest concentration at 609.17 mg/100 g, followed by LW64 + 75 at 521.86 mg/100 g. A significant declining trend was observed in TPC at the dialysis phase compared with the undigested samples, gastric, and intestinal digesta. The bioaccessible TPCs after GI digestion at the dialysis phase were 77.15, 67.14, 59.32, and 52.23% in the smoothies fermented by L75, LW64 + 75, W64, and non-fermented smoothies, respectively, in terms of the percentage recovery relative to the TPC in the respective undigested samples.in vitro digestion of edible leaves, Olax zeylanica (“mella”), Centella asiatica (“gotukola”), and Sesbania grandiflora (“kathurumurunga”) showed higher levels of total polyphenol content in the intestinal phase than in the gastric digesta (Moringa oleifera leaves (L75 compared with those fermented with W64 (LW75 + 64), control (non-fermented smoothies), and the undigested smoothies, indicating the effect of LAB strain on smoothie phenolic contents.A shift from the acid gastric condition to mild alkaline at the intestinal phase was expected to reduce the levels of bioaccessible total phenols as previously described in fruit juices , 66. How digesta . The obs digesta , or to t digesta . Further digesta , as theya leaves . The amoin vitro digestion of grape marc freeze-dried powder in the presence of probiotics. The increase in total phenols in the gastric and intestinal phases can be attributed to the conversion of simple phenolic compounds due to the glycosylation or decarboxylation or the depolymerization of high-molecular-weight phenolic compounds , followed by LW64 + 75 (3.66 μmol TEAC/100 g). At the same time, the dialyzable digesta of the smoothies fermented by L75 showed the highest antioxidant capacity (1.94 μmol TEAC/100 g) compared with undigested samples and the dialyzable digesta of the smoothies fermented by W64 and LW64 + 75.Antioxidant capacity (FRAP activity) in smoothies at the gastric phase was significantly higher than that in the fermented and non-fermented smoothies with respect to their original unfermented samples . AlthougL75 in the gastric and intestinal digesta, indicating positive correlations between total phenolics and total antioxidant capacity  crossing a cellulose membrane  and thus be potentially available for further uptake. However, further investigations on phenolic and carotenoid fractions with Caco-2 cellular models must be performed to confirm the uptake of phenolic and carotenoid components. Based on the finding of this study, the fermentation with L75 can be recommended to local food manufacturers in Réunion Island to improve the functional benefits and accessibilities of total phenols in chayote leaves and pineapple smoothies. Consumer's overall acceptability for marketing is higher for the smoothie composed of pineapple and chayote leaves fermented by L75 after 7 days of storage at 4°C than for the non-fermented sample at day 0. The metabolomics profile characterization of the fermented smoothies will be necessary to gain further knowledge.This study demonstrated that fermentation of a smoothie composed of pineapple and chayote leaves with The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.in vitro digestion. The research collaborator and co-supervisor provided the editorial support. CG was responsible for in vitro digestion analysis, methodology, and training. DS conceptualized the research, supervised MM, and improved the article further. All authors contributed to the article and approved the submitted version.MM performed the experiment, generated the data, and wrote the first draft of this manuscript. SA validated the data, improved the write up of physicochemical, and sensory parameters related to fermentations. FR was responsible for fermentation, conceptualized the research, and visualized and validated the data for physiochemical properties responsible for The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.