Proteomics reveals that quinoa bioester promotes replenishing effects in epidermal tissue

Abstract

The continuous search for natural products that attenuate age-related losses has increasingly gained notice; among them, those applicable for skin care have drawn significant attention. The bioester generated from the Chenopodium quinoa’s oil is a natural-origin ingredient described to produce replenishing skin effects. With this as motivation, we used shotgun proteomics to study the effects of quinoa bioester on human reconstructed epidermis tridimensional cell cultures after 0, 3, 6, 12, 24, and 48 h of exposure. Our experimental setup employed reversed-phase nano-chromatography coupled online with an Orbitrap-XL and PatternLab for proteomics as the data analysis tool. Extracted ion chromatograms were obtained as surrogates for relative peptide quantitation. Our findings spotlight proteins with increased abundance, as compared to the untreated cell culture counterparts at the same timepoints, that were related to preventing premature aging, homeostasis, tissue regeneration, protection against ultraviolet radiation and oxidative damage.

Protective Effect of the Pearl Extract from Pinctada fucata martensii Dunker on UV‐Induced Photoaging in Mice

“…Aging of skin is a complex process, caused by intrinsic and extrinsic factors [27] . Skin aging is classified into extrinsic aging, by environmental exposure, such as UV radiation and intrinsic determined by genetic factors [28] . UV irradiation‐induced skin aging is a heavy extrinsic form of aging, also called photoaging.…”

“…[27] Skin aging is classified into extrinsic aging, by environmental exposure, such as UV radiation and intrinsic determined by genetic factors. [28] UV irradiation-induced skin aging is a heavy extrinsic form of aging, also called photoaging. Photoaging causes premature skin aging, which leads to macroscopic and microscopic skin changes.…”

Simple, efficient and thorough shotgun proteomic analysis with PatternLab V

“…The PatternLab for proteomics software has been tested on millions of spectra by various groups and has addressed a broad range of biological questions by identifying, quantitating and helping interpretation in several types of proteomic experiments, including labeled and label-free proteomics. The applicability of previous versions of PatternLab is very broad; some examples of works that resorted to our software for helping to interpret proteomic data are on (a) uncovering the role of the BAF complex heterogeneity in maintaining the transcriptional network of pluripotency in mouse embryonic stem cells 30 , (b) highlighting exosomes as extracellular mediators promoting tumor progression 31 , (c) unveiling mechanisms of Z-ring assembly and regulation in bacteria 32 , (d) describing how methylation aids in bacterial adhesion and host cell invasion 33 and (e) investigating the effects of quinoa bioester in epidermal tissue 34 .…”

“…For example, say that a given protein produced a spectral count of 1 per biological replicate (total of three) in biological condition A, and counts of 4, 5, 4 for the replicates in biological condition B; these data would produce a 4.3-fold change with P < 0.05; nevertheless, this P value might be artifactual given that the quantitation values of condition A could be a result of, say, dynamic exclusion. For experiments that comprise more than two biological conditions, such as a time-course experiment, the TrendQuest module comes in handy 34 . TrendQuest will group proteins according to their expression profiles; it is expected that proteins with similar trends sbelong, for example, to the same metabolic pathway 23 .…”

“…PROTOCOL UPDATE • 'Node ID' is the number of the cluster • 'Count' is the number of proteins in the cluster • 'Consensus' is the profile obtained by averaging the quantitation profiles of the proteins in the cluster • 'Color' is a user-selected color to represent the cluster in the plot 61 (Optional) In the 'Secondary Labels' tab, the user can label biological conditions to generate clusters according to labeling. An example of use for this feature is illustrated by Camillo-Andrade and collaborators 34 ; the authors performed a time-course experiment on a cell culture exposed to quinoa bioester and analyzed results alongside those from an unexposed cell culture. 'Secondary Labels' were used to treat the two sets of results independently, even though they share timepoints.…”