A carbohydrate-active enzyme (CAZy) profile links successful metabolic specialization of Prevotella to its abundance in gut microbiota

Gut microbiota participates in diverse metabolic and homeostatic functions related to health and well-being. Its composition varies between individuals, and depends on factors related to host and microbial communities, which need to adapt to utilize various nutrients present in gut environment. We profiled fecal microbiota in 63 healthy adult individuals using metaproteomics, and focused on microbial CAZy (carbohydrate-active) enzymes involved in glycan foraging. We identified two distinct CAZy profiles, one with many Bacteroides-derived CAZy in more than one-third of subjects (n = 25), and it associated with high abundance of Bacteroides in most subjects. In a smaller subset of donors (n = 8) with dietary parameters similar to others, microbiota showed intense expression of Prevotella-derived CAZy including exo-beta-(1,4)-xylanase, xylan-1,4-beta-xylosidase, alpha-L-arabinofuranosidase and several other CAZy belonging to glycosyl hydrolase families involved in digestion of complex plant-derived polysaccharides. This associated invariably with high abundance of Prevotella in gut microbiota, while in subjects with lower abundance of Prevotella, microbiota showed no Prevotella-derived CAZy. Identification of Bacteroides- and Prevotella-derived CAZy in microbiota proteome and their association with differences in microbiota composition are in evidence of individual variation in metabolic specialization of gut microbes affecting their colonizing competence

Variation of Absorption Angstrom Exponent in Aerosols From Different Emission Sources

The absorption Angstrom exponent (AAE) describes the spectral dependence of light absorption by aerosols. AAE is typically used to differentiate between different aerosol types for example., black carbon, brown carbon, and dust particles. In this study, the variation of AAE was investigated mainly in fresh aerosol emissions from different fuel and combustion types, including emissions from ships, buses, coal-fired power plants, and residential wood burning. The results were assembled to provide a compendium of AAE values from different emission sources. A dual-spot aethalometer (AE33) was used in all measurements to obtain the light absorption coefficients at seven wavelengths (370-950 nm). AAE(470/950) varied greatly between the different emission sources, ranging from -0.2 +/- 0.7 to 3.0 +/- 0.8. The correlation between the AAE(470/950) and AAE(370-950) results was good (R-2 = 0.95) and the mean bias error between these was 0.02. In the ship engine exhaust emissions, the highest AAE(470/950) values (up to 2.0 +/- 0.1) were observed when high sulfur content heavy fuel oil was used, whereas low sulfur content fuels had the lowest AAE(470/950) (0.9-1.1). In the diesel bus exhaust emissions, AAE(470/950) increased in the order of acceleration (0.8 +/- 0.1), deceleration (1.1 +/- 0.1), and steady driving (1.2 +/- 0.1). In the coal-fired power plant emissions, the variation of AAE(470/950) was substantial (from -0.1 +/- 2.1 to 0.9 +/- 1.6) due to the differences in the fuels and flue gas cleaning conditions. Fresh wood-burning derived aerosols had AAE(470/950) from 1.1 +/- 0.1 (modern masonry heater) to 1.4 +/- 0.1 (pellet boiler), lower than typically associated with wood burning, while the burn cycle phase affected AAE variation.Peer reviewe

Functional Proteomics

Data-independent acquisition (DIA) mode of mass spectrometry, such as the SWATH-MS technology, enables accurate and consistent measurement of proteins, which is crucial for comparative proteomics studies. However, there is lack of free and easy to implement data analysis protocols that can handle the different data processing steps from raw spectrum files to peptide intensity matrix and its downstream analysis. Here, we provide a data analysis protocol, named diatools, covering all these steps from spectral library building to differential expression analysis of DIA proteomics data. The data analysis tools used in this protocol are open source and the protocol is distributed at Docker Hub as a complete software environment that supports Linux, Windows, and macOS operating systems.</p