The metabolic domestication syndrome of budding yeast

Cellular metabolism evolves through changes in the structure and quantitative states of metabolic networks. Here, we explore the evolutionary dynamics of metabolic states by focusing on the collection of metabolite levels, the metabolome, which captures key aspects of cellular physiology. Using a phylogenetic framework, we profiled metabolites in 27 populations of nine budding yeast species, providing a graduated view of metabolic variation across multiple evolutionary time scales. Metabolite levels evolve more rapidly and independently of changes in the metabolic network's structure, providing complementary information to enzyme repertoire. Although metabolome variation accumulates mainly gradually over time, it is profoundly affected by domestication. We found pervasive signatures of convergent evolution in the metabolomes of independently domesticated clades of Saccharomyces cerevisiae. Such recurring metabolite differences between wild and domesticated populations affect a substantial part of the metabolome, including rewiring of the TCA cycle and several amino acids that influence aroma production, likely reflecting adaptation to human niches. Overall, our work reveals previously unrecognized diversity in central metabolism and the pervasive influence of human-driven selection on metabolite levels in yeasts.

Lipidomic Analysis of Geopropolis of Brazilian Stingless Bees by LC-HRMS

Stingless bees (Meliponini) represent over than 500 species, found in tropical and sub-tropical regions of the world. They produce geopropolis, a resinous natural product containing bioactive compounds, which is commonly used in folk medicine. In the current study, LC-HRMS and bioinformatic tools were used to carry out for the first time the lipidomic analysis of geopropolis from indigenous Brazilian stingless bees. As a result, 61 compounds of several lipid classes were identified with elevated degree of confidence. Then, we demonstrated that lipids in geopropolis are not restricted to waxes and fatty acids; but fatty amides and amines, phenolic lipids, resorcinols, retinoids, abietanoids, diterpenoids, pentacyclic triterpenoids, prostaglandins, retinoids, and steroids were found. In addition, multivariate analysis, based on the lipidomic profile of extracts, reinforces the assumption that the species of stingless bees, as well as the geographical origin are relevant factors to affect geopropolis composition once that the lipidic profile allowed the discrimination of geopropolis in groups related to the geographical origin, bee specie or bee genus. The lipidic profile also suggest a selective forage habits of T. angustula, which seems to collect resins from more specific vegetal sources regardless geographic origin, while other stingless bees, such as M. marginata and M. quadrifasciata, are less selective and may adapt to collect resins from a wider variety of plants.

Photoacoustic spectroscopy applied to the direct detection of bioactive compounds in Agaricus brasiliensis mycelium

This paper describes the application of the photoacoustic spectroscopic (PAS) for detection of bioactive compounds in Agaricus brasiliensis mycelium. The mycelium was cultivated by solid-state fermentation and by submerged fermentation. Vegetal residues from food industry were used as substrates for fermentation: apple pomace (Malus domestica), wheat (Triticum aestivum), peel and pomace of pineapple (Ananas comosus), malt (Hordeum vulgare) and grape pomace (Vitis vinifera). Dry and ground samples of biomass were directly put into the PA cell. The optical absorption spectra indicated the existence of three main absorption bands: one around 280 nm related to phytosterols (ergosterol), phenolic acids, flavonoids and aromatic amino acids, another at 340 nm, due to phenolic and flavonoid compounds, and the third one at around 550 nm associated with anthocyanins and anthocyanidins. A correlation between the PA signal and the total phenolic content was satisfactory, as well as for the analyzed spectrum region (270 nm up to 1000 nm), using multivariate methods. Our results indicated that PA technique may be considered as an analytical tool to quickly detect bioactive compounds in mushrooms without the need of sample pretreatment.