BUSCADOR RECOLECTA

Simulated data: For all the studies protein families (DNAK, DDL, TPIS, TRPA, TRXB, SH2). - The folder “*SimulatedANDinferredDATA” includes all simulated (true) protein sequence alignments and inferred with ProtASR under MF and the empirical JTT model (both for joint and marginal ASR) - The folder “*ComputedEnergiesFromData” includes the calculated energies of sequences of every MSA (files *.dat)., Real data: Files of the analysis based on real data are shown in the folder “RealData”, for the studied protein families (DNAK, DDL, TPIS, TRPA, TRXB). MSA and phylogenetic tree are in .fas/.nex formats and Newick format, respectively. Energies are shown in the .dat file and printed on the tree in files *Energies.tre (we recommend open them with FrigTree)., The computational reconstruction of ancestral proteins provides information on past biological events and has practical implications for biomedicine and biotechnology. Currently available tools for ancestral sequence reconstruction (ASR) are often based on empirical amino acid substitution models that assume that all sites evolve at the same rate and under the same process. However, this assumption is frequently violated because protein evolution is highly heterogeneous due to different selective constraints among sites. Here, we present ProtASR, a new evolutionary framework to infer ancestral protein sequences accounting for selection on protein stability. First, ProtASR generates site-specific substitution matrices through the structurally constrained mean-field substitution model (MF), which considers both unfolding and misfolding stability. We previously showed that MF models outperform empirical amino acid substitution models, as well as other structurally constrained substitution models, both in terms of likelihood and correctly inferring amino acid distributions across sites. In the second step, ProtASR adapts a well-established maximum-likelihood (ML) ASR procedure to infer ancestral proteins under MF models. A known bias of ML ASR methods is that they tend to overestimate the stability of ancestral proteins by under-estimating the frequency of deleterious mutations. We compared ProtASR under MF to two empirical substitution models (JTT and CAT), reconstructing the ancestral sequences of simulated proteins. ProtASR yields reconstructed proteins with less biased stabilities, which are significantly closer to those of the simulated proteins. Analysis of extant protein families suggests that folding stability evolves through time across protein families, potentially reflecting neutral fluctuation. Some families exhibit a more constant protein folding stability, while others are more variable. ProtASR is freely available from https://github.com/miguelarenas/protasr and includes detailed documentation and ready-to-use examples. It runs in seconds/minutes depending on protein length and alignment size., Peer reviewed

Table S1: Chemical compositions of the control wines without oenological additives (average values); Table S2: Chemical compositions of the oenological additives used in this study., Peer reviewed

Table S1. Columns used for the optimization of the HILIC × RP method used in the present research. Table S2. Identification proposed for the anthocyanins evaluated in the grape samples during ripening for a 10-week period. Figure S1. C18-RP 1DLC analysis of grape W9 (A) and wine (B). Blue: separation acquired at 280 nm; red: separation acquired at 520 nm. Figure S2. Chromatograms (280 nm) obtained for the HILIC separations obtained from a grape sample after optimization in a Zic-HILIC (A), diol (B) and silica (C) columns. Separation conditions are shown in inserts. Figure S3. Chromatograms (280 nm) obtained for the RP-based separations obtained from a grape sample after optimization in a PFP (A) and C18 (B) columns. Separation conditions are: flow rate 2 ml min-1 (Panel A) or 3 ml min-1 (panel B); elution with water (0.1% formic acid, solvent A) and acetonitrile (solvent B) as mobile phases. Specific gradients were shown in the respective chromatogram. Figure S4. 2D contour plots from the polyphenolic fraction of a grape sample (W10) obtained using different gradients and mobile phases composition in the second dimension, as indicated. Figure S5. 2D plots obtained under the optimum separation conditions for the grape samples of different ripening degree (W1 to W10)., Peer reviewed

[Introduction] Sunflower breeding for resistance to the parasitic plant sunflower broomrape (Orobanche cumana Wallr.) requires the identification of novel resistance genes. In this research, we conducted a genome-wide association study (GWAS) to identify QTLs associated with broomrape resistance., [Methods] The marker-trait associations were examined across a germplasm set composed of 104 sunflower accessions. They were genotyped with a 600k AXIOM® genome-wide array and evaluated for resistance to three populations of the parasite with varying levels of virulence (races EFR, FGV, and GTK) in two environments., [Results and Discussion] The analysis of the genetic structure of the germplasm set revealed the presence of two main groups. The application of optimized treatments based on the general linear model (GLM) and the mixed linear model (MLM) allowed the detection of 14 SNP markers significantly associated with broomrape resistance. The highest number of marker-trait associations were identified on chromosome 3, clustered in two different genomic regions of this chromosome. Other associations were identified on chromosomes 5, 10, 13, and 16. Candidate genes for the main genomic regions associated with broomrape resistance were studied and discussed. Particularly, two significant SNPs on chromosome 3 associated with races EFR and FGV were found at two tightly linked SWEET sugar transporter genes. The results of this study have confirmed the role of some QTL on resistance to sunflower broomrape and have revealed new ones that may play an important role in the development of durable resistance to this parasitic weed in sunflower., Peer reviewed

Supplementary Table 1. Correlation coefficients between phenolic compounds and methylxanthines, and the radical scavenging, cytoprotective properties, and cellular antioxidant activity in the coffee pulp flour (CPF) and extract (CPE)., Peer reviewed

[Introduction] Sunflower breeding for resistance to the parasitic plant sunflower broomrape (Orobanche cumana Wallr.) requires the identification of novel resistance genes. In this research, we conducted a genome-wide association study (GWAS) to identify QTLs associated with broomrape resistance., [Methods] The marker-trait associations were examined across a germplasm set composed of 104 sunflower accessions. They were genotyped with a 600k AXIOM® genome-wide array and evaluated for resistance to three populations of the parasite with varying levels of virulence (races EFR, FGV, and GTK) in two environments., [Results and Discussion] The analysis of the genetic structure of the germplasm set revealed the presence of two main groups. The application of optimized treatments based on the general linear model (GLM) and the mixed linear model (MLM) allowed the detection of 14 SNP markers significantly associated with broomrape resistance. The highest number of marker-trait associations were identified on chromosome 3, clustered in two different genomic regions of this chromosome. Other associations were identified on chromosomes 5, 10, 13, and 16. Candidate genes for the main genomic regions associated with broomrape resistance were studied and discussed. Particularly, two significant SNPs on chromosome 3 associated with races EFR and FGV were found at two tightly linked SWEET sugar transporter genes. The results of this study have confirmed the role of some QTL on resistance to sunflower broomrape and have revealed new ones that may play an important role in the development of durable resistance to this parasitic weed in sunflower., Peer reviewed

Figure S1: C30 reversed-phase chromatograms of carotenoids at 450 nm obtained from Sweet Mary papaya (Carica papaya L.) peel encapsulated by O/W emulsions in (a) carotenoid-enriched soybean oil (vegetable oil + papaya carotenoid extract (carotenoid μg/g vegetable oil), (b) carotenoid-enriched sunflower oil (vegetable oil + papaya carotenoid extract (μg carotenoid μg/g vegetable oil), (c) O/W soybean emulsion with encapsulated carotenoid extract, (d) O/W sunflower emulsion with encapsulated carotenoid extract, and in the digesta fractions during the in vitro digestion of (e) O/W soybean emulsion and (f) O/W sunflower oil emulsion in the oral phase, (g) O/W soybean emulsion and (h) O/W sunflower oil emulsion in the gastric phase, and (i) O/W soybean emulsion and (j) O/W sunflower oil emulsion in the intestinal phase. Peak identities in Table S1; Figure S2: C30 reversed-phase chromatograms of carotenoids at 450 nm obtained from Sweet Mary papaya (Carica papaya L.) peel encapsulated by O/W emulsions in the micellar fractions during the in vitro digestion of (a) soybean microemulsion and (b) sunflower oil microemulsion in the oral phase, (c) soybean microemulsion and (d) sunflower oil microemulsion in the gastric phase, and (e) soybean microemulsion and (f) sunflower oil microemulsion in the intestinal phase. Peak identities in Table S1; Figure S3: Stability of main papaya carotenoids (μg carotenoids/g emulsion) ((all-E)-β-cryptoxanthin, (all-E)-β-carotene, (all-E)-β-cryptoxanthin laurate, and (all-E)-lycopene) in (a) O/W soybean oil and (b) O/W sunflower oil emulsions before and after in vitro gastrointestinal digestion. Non-digested values refer to the content of each carotenoid in the carotenoid-enriched oil extract before encapsulation by O/W emulsions; Figure S4: Images taken with confocal microscope of (a) soybean and (b) sunflower final emulsions after being processed through high-pressure homogenization (HPH) at 100 MPa for 5 cycles. Scale bars are 5 μm long. Table S1: Percentage (%) fatty acid composition of edible vegetable oils (Dubois et al., 2007) [18]; Table S2: Chromatographic identification a of carotenoids and carotenoid esters from Sweet Mary papaya (Carica papaya L.) peel to formulate soybean oil and sunflower oil carotenoid-enriched emulsions. Table S3: Content of individual carotenoids and carotenoid esters in Sweet Mary papaya (Carica papaya L.) peel extracts., Peer reviewed

Table S1: Phenolic compounds identified in yarrow samples by using HPLC-ESI-QTOF-MS., Peer reviewed

Table S1: Analysis of Variance of extraction yield variable for response surface modeling showing linear, quadratic and interaction relations, and coefficient for model prediction; Table S2: Analysis of Variance of TPC variable for response surface modeling showing linear, quadratic and interaction relations, and coefficient for model prediction; Table S3: Analysis of Variance of ROS variable for response surface modeling showing linear, quadratic and interaction relations, and coefficient for model prediction; Table S4: Analysis of Variance of AChE variable for response surface modeling showing linear, quadratic and interaction relations, and coefficient for model prediction; Table S5: Annotated compounds and their total compound contribution (%) in pracaxi SFE extract after HPLC-CSH-Q-TOF MS/MS ESI (−) analysis; Table S6: Annotated compounds and their total compound contribution (%) in pracaxi SFE extract after HPLC-CSH-Q-TOF MS/MS ESI (+) analysis; Table S7: Annotated compounds and their total compound contribution (%) in pracaxi SFE extract after GC-Q-TOF MS analysis; Table S8: PLE conditions, desirability and predicted response values at the optimum conditions predicted by the model, and experimental response values for the selected optimum conditions; Table S9: Tentative identified compounds in pracaxi nuts PLE extracts after HPLC-C18-Q-TOF MS/MS ESI (+/−) analyses; Figure S1: Estimated response surfaces for each response variable, and their corresponding Standardized Pareto charts: (A) Extraction yield (%); (B) TPC (mg GAE/mL); (C) IC50 ROS (µg/mL); (D) IC50 AChE (µg/mL); Figure S2: Desirability response surface to optimize response variables: (A) including extraction yield as response variable; (B) excluding extraction yield as response variable; Figure S3: PCA score plots of pracaxi nuts PLE extracts data obtained by: (A) HPLC-C18-Q-TOF MS/MS ESI (+); (B) HPLC-C18-Q-TOF MS/MS ESI (−); Figure S4: PLS-DA score plots of pracaxi nuts PLE extracts data obtained by: (A) HPLC-C18-Q-TOF MS/MS ESI (+); (B) HPLC-C18-Q-TOF MS/MS ESI (−)., Peer reviewed

Supplementary Figure 1. The effect of PCA on the frequency content of the different FP generators. The values represent the mean power and standard error (n = 5) normalized to the control (ACSF, black dots) averaged in 15 min epochs after each treatment. PCA produced a significant increase in the population values for 10 of the 25 comparisons (Student t-test). Significant changes were concentrated in the L-M, PP and GCsom generators, and in the mid-high frequency bands (theta, alpha, beta). Therefore, focusing on the frequency content of each FP generator was more effective in discriminating the effects of PCA than when estimated over wideband power., Supplementary Figure 2. Re-analysis of the data for the Schaffer and the L-M generators of a representative animal (see Figure 5). The time course of the FP generators was filter-split into two sets of high and low frequency bands (10 Hz cut-off frequency) to avoid omitting small fast waves that ride on top of slower ones. This effect can be appreciated in the small number of high frequency waves in the LM (fast waves are generally smaller). PCA had similar effects as on the wideband signal, yet some differences were seen. Note PCA reduced the waves detected in the lower voltage thresholds (0.5δ and 1δ) but it increased those in the wideband analysis of Figure 5 at any threshold., Supplementary Table 1. Pearson correlation coefficients (r) between the parameters of the density distributions of the voltages for each FP generator and the power of theta for each treatment. Distribution parameters for each generator were normalized in the population (n = 6 animals) and pooled. Each epoch was segmented in 12-13 time-windows. , mean; δ, standard deviation; k, kurtosis; Sk, skewness., Supplementary Table 2. Statistical analysis comparing the parameters of the density distributions of the voltages for each FP generator (left column) after DMSO and PCA delivery (U-Mann Whitney test). Significant p values are marked in red and the alpha is 0.05 in all cases: x, mean; δ, standard deviation; k, kurtosis; Sk, skewness., Peer reviewed