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Figure 6 in Bacterial diversity in high Andean grassland soils disturbed with Lepidium meyenii crops evaluated by metagenomics

Figure 6. Analysis of principal coordinates of sampling sectors according to the distribution of bacterial families reported at 40% contribution according to SIMPER analysis.

Figure 2 in Bacterial diversity in high Andean grassland soils disturbed with Lepidium meyenii crops evaluated by metagenomics

Figure 2. Good quality DNA samples from bacterial populations divided by sampling time factor in soils with pre-sowing levels (A), Lepidium meyenii hypocotyl development (B) and post-harvest (C).

Figure 7 in Bacterial diversity in high Andean grassland soils disturbed with Lepidium meyenii crops evaluated by metagenomics

Figure 7. Dendogram of bacterial families at 40% contribution according to SIMPER analysis in fields disturbed by Lepidium meyenii culture under the effect of the two factors under study (use pressure and sampling period).

Figure 8 in Bacterial diversity in high Andean grassland soils disturbed with Lepidium meyenii crops evaluated by metagenomics

Figure 8. Analysis of the behavior of the clusters of bacterial families significantly differentiated (p<0.05) according to the SIMPROF analysis at a 40% contribution of the total of families registered in soils under the factors use pressure and sampling period.

Figure 3 in Bacterial diversity in high Andean grassland soils disturbed with Lepidium meyenii crops evaluated by metagenomics

Figure 3. Band migration in bacterial populations according to the V3 - V4 region of the 16S bacterial rRNA genes, for the 12 samples divided by the sampling time factor in soils with pre-sowing levels (A),Lepidium meyenii hypocotyl development (B) and post-harvest (C).

Figure 5 in Bacterial diversity in high Andean grassland soils disturbed with Lepidium meyenii crops evaluated by metagenomics

Figure 5. Non-metric MSD and cluster analysis of sampling sectors divided by use pressure and sampling period factors.

Fig. 4 in Alkaloids from Lepidium meyenii (Maca), structural revision of macaridine and UPLC-MS/MS feature-based molecular networking

Fig. 4. Cluster containing identified imidazole and amidine alkaloids. Nodes were numbered from lowest to highest m/z values. Detailed information on unknown nodes can be found as Supporting Information.

Fig. 8 in Glucosinolate catabolism during postharvest drying determines the ratio of bioactive macamides to deaminated benzenoids in Lepidium meyenii (maca) root flour

Fig. 8. Reaction scheme for the formation of BOH (6) and BCOOH (8) from reactive benzaldehyde through a Cannizzaro type reaction, catalyzed by a benzaldehyde dehydrogenase (based on Wuensch et al., 2013). The proposed scheme would require only one enzymatic activity for both the oxidative and reductive half-reactions.

Fig. 7 in Glucosinolate catabolism during postharvest drying determines the ratio of bioactive macamides to deaminated benzenoids in Lepidium meyenii (maca) root flour

Fig. 7. Proposed scheme for reactions taking place during the maca drying. The sequence is based on the main hydrolytic metabolites observed during the drying process. Step A describes reactions generating the initial hydrolysis products and including BITC (2), BCN (4) and BIOC (7). Step B is the formation of BNH2 (3), the main accumulation product, from BITC (2). Step C describes deamination of BNH2 (3) to generate various deaminated benzenoids. Step D corresponds to the disproportionation reactions leading to pools of BCHO (5), BOH (6) and BCOOH (8) through the action of an aldehyde dehydrogenase. Step E is the condensation of BNH2 (3) with free fatty acids to produce macamides (MAC 9–13) and step F corresponds to esterification or glycosylation of BCOOH (8) as a detoxification mechanism. Numbers correspond to those in Fig. 1. Dark lines show favored reactions according to our results. Compounds in light gray are minor transient products.

Fig. 4. Pearson correlation values for glucosinolate hydrolytic products. Panel A in Glucosinolate catabolism during postharvest drying determines the ratio of bioactive macamides to deaminated benzenoids in Lepidium meyenii (maca) root flour

Fig. 4. Pearson correlation values for glucosinolate hydrolytic products. Panel A shows the correlation of metabolites for the early stage of drying, where reactions are caused by direct damage to the tissue by shredding. Panel B shows the correlation between intermediaries as a result of the late stage tissue dehydration. BCOOR- 2, BCHO-2 and BCOOH-2 shown in panel B correspond to data points in Fig. 5 shown as part of the red solid line while BCOOR (8a), BCHO (5) and BCOOH (8) in panel A correspond to the early stage in the figure shown in solid black lines. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 6 in Glucosinolate catabolism during postharvest drying determines the ratio of bioactive macamides to deaminated benzenoids in Lepidium meyenii (maca) root flour

Fig. 6. Time course for the concentration of ammonium ion in maca tissues during drying. Deamination of benzyl amine and amino acids by amine oxidases are potential sources of ammonia under our short (72 h) drying conditions. Peak values of NH+ are 25 μmol g 1 dry wt.. Total amount of deaminated benzenoids 4 accounts for only 0.75 μmol g 1 dry wt. indicating other major sources of ammonium in the process. Curve adjusted by nonlinear regression (R2 = 0.9472).

Fig. 3 in Glucosinolate catabolism during postharvest drying determines the ratio of bioactive macamides to deaminated benzenoids in Lepidium meyenii (maca) root flour

Fig. 3. Tissue concentrations during oven drying for benzyl glucosinolate (BGL) and its metabolites. Drying maca tissue was extracted in solvent and the levels of benzyl glucosinolate, its primary hydrolytic products (BITC, BCN) and two products that accumulate in the flour, benzyl amine (BNH2) and macamides (MAC) were analyzed. RW represents residual humidity. Hydrolytic and final accumulation product patterns match those previously reported (Esparza et al., 2015), although for this study, a 72 h drying period and 35 ◦ C constant temperature were employed. Values are expressed in molar fraction of initial glucosinolate concentration, where 1 = 36 ± 5 μmol g 1 dry wt. (N = 6). Nonlinear regression coefficients for the compounds were: R2 = 0.941 (BGL, 1), R2 = 0.971 (BITC, 2), R2 = 0.777 (BCN, 4), R2 = 0.955 (BNH, 3), R2 = 0.985 (MAC, 9–13) and R2 = 0.993 (RW).

Fig. 2 in Glucosinolate catabolism during postharvest drying determines the ratio of bioactive macamides to deaminated benzenoids in Lepidium meyenii (maca) root flour

Fig. 2. Time course of major VOCs from glucosinolate hydrolysis measured by headspace SPME-GC-MS. Values are expressed as percentage of average peak total ion current (TIC) for each compound. Compounds, from top left are: benzyl isothiocyanate (BITC, 2), benzyl nitrile (BCN, 4), benzaldehyde (BCHO, 5), benzyl alcohol (BOH, 6), benzyl isocyanate (BIOC, 7), and average relative humidity (RH) inside the chamber (N = 9). Peak TIC values (x 107) for the compounds were: 238.42 (2, 100%); 2.88 (4, 1.2%); 34.9 (5,14.6%); 28.17 (6, 11.8%) and 3.75 (7, 1.6%). Values in parenthesis after the compound number denote the relative intensity of the peak in relation to the isothiocyanate peak. RH = relative humidity in the headspace.

Fig. 5 in Glucosinolate catabolism during postharvest drying determines the ratio of bioactive macamides to deaminated benzenoids in Lepidium meyenii (maca) root flour

Fig. 5. Time course profiles for transient and final deaminated benzenoid products in drying maca tissues. Kinetic profiles show two stages, early "fast" kinetics (solid black lines) that take place in the initial 12 h, corresponding to damage to the tissues done by shredding to reduce size for drying and a second "slow" stage (solid red lines) that shows the dehydration of the tissues in the later part of the drying process. Data points corresponding to the initial and the late phases have been subjected to nonlinear regression separately. The nonlinear regression coefficients for the metabolites were for BCHO (5): R2 = 0.9962 (early phase), R2 = 0.9880 (late phase), for BOH (6): R2 = 0.9999 (early), R2 = 0.9522 (late), BCOOH (8): R2 = 0.9999 (early), R2 = 0.9526 (late), BCOOR (8a): R2 = 0.9677. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov32/100

Effects of Lepidium Meyenii Walp on Sexual Function in Postmenopausal Women

ClinicalTrials.gov study NCT02624648. IPD Sharing: Not stated. Countries: 1. Publications: 26.

Figure 4 in Bacterial diversity in high Andean grassland soils disturbed with Lepidium meyenii crops evaluated by metagenomics

Figure 4. Diagram of the processes for the metagenomic analysis of soils I. DNA metagenomic extraction, II. Polymerase chain reaction, III. Genomic sequencing of bacterial DNA (Modified from Hernández et al., 2010; Yang et al., 2018).

Fig. 5 in Alkaloids from Lepidium meyenii (Maca), structural revision of macaridine and UPLC-MS/MS feature-based molecular networking

Fig. 5. Results of three channel MRM scans using UPLC conditions for the TQD system.

Fig. 1 in Alkaloids from Lepidium meyenii (Maca), structural revision of macaridine and UPLC-MS/MS feature-based molecular networking

Fig. 1. Structure of isolated imidazole, amidine and β-carboline alkaloids.

Fig. 3 in Alkaloids from Lepidium meyenii (Maca), structural revision of macaridine and UPLC-MS/MS feature-based molecular networking

Fig. 3. Structures of the proposed 'macaridine' (left) and macapyrrolin C (right).

Fig. 2. Key COSY and HMBC correlations observed for lepidiline E in Alkaloids from Lepidium meyenii (Maca), structural revision of macaridine and UPLC-MS/MS feature-based molecular networking

Fig. 2. Key COSY and HMBC correlations observed for lepidiline E (1) F (2) and G (3).