No abstract

It is well known that biotransformation processes in the human body are crucial to form potentially bioactive metabolites from particular classes of natural products. However, little research has been conducted concerning the bioavailability of polyphenols, especially in the colon. The gastrointestinal stability and colonic biotransformation of the crude extract of the leaves of Cecropia obtusifolia, rich in flavone C-glycosides, was investigated under in vitro conditions, and the processing and interpretation of results were facilitated by using an automated machine learning model. This investigation revealed that flavone C-glycosides and flavonolignans from C. obtusifolia were stable throughout their passage in the simulated gastrointestinal tract including the colon phase. On the other hand, the colon bacteria extensively metabolized chlorogenic acid, flavonol, and triterpenoid O-glycosides. This investigation revealed that the colonic microbiota has an important role in the biotransformation of some chemical constituents of this extract.

The generation of cartilage from progenitor cells for the purpose of cartilage repair is often hampered by hypertrophic differentiation of the engineered cartilaginous tissue caused by endochondral ossification. Since a healthy cartilage matrix contains high amounts of Aggrecan and COMP, we hypothesized that their supplementation in the biogel used in the generation of subperiosteal cartilage mimics the composition of the cartilage extracellular matrix environment, with beneficial properties for the engineered cartilage. Supplementation of COMP or Aggrecan was studied in vitro during chondrogenic differentiation of rabbit periosteum cells and periosteum-derived chondrocytes. Low melting agarose was supplemented with bovine Aggrecan, human recombinant COMP or vehicle and was injected between the bone and periosteum at the upper medial side of the tibia of New Zealand white rabbits. Generated subperiosteal cartilage tissue was analyzed for weight, GAG and DNA content and ALP activity. Key markers of different phases of endochondral ossification were measured by RT-qPCR. For the in vitro experiments, no significant differences in chondrogenic marker expression were detected following COMP or Aggrecan supplementation, while in vivo favorable chondrogenic marker expression was detected. Gene expression levels of hypertrophic markers as well as ALP activity were significantly decreased in the Aggrecan and COMP supplemented conditions compared to controls. The wet weight and GAG content of the in vivo generated subperiosteal cartilage tissue was not significantly different between groups. Data demonstrate the potential of Aggrecan and COMP to favorably influence the subperiosteal microenvironment for the in vivo generation of cartilage for the optimization of cartilage regenerative approaches.

No abstract

Data analysis for metabolomics is undergoing rapid progress thanks to the proliferation of novel tools and the standardization of existing workflows. As untargeted metabolomics datasets and experiments continue to increase in size and complexity, standardized workflows are often not sufficiently sophisticated. In addition, the ground truth for untargeted metabolomics experiments is intrinsically unknown and the performance of tools is difficult to evaluate. Here, the problem of dynamic multi-class metabolomics experiments was investigated using a simulated dataset with a known ground truth. This simulated dataset was used to evaluate the performance of tinderesting, a new and intuitive tool based on gathering expert knowledge to be used in machine learning. The results were compared to EDGE, a statistical method for time series data. This paper presents three novel outcomes. The first is a way to simulate dynamic metabolomics data with a known ground truth based on ordinary differential equations. This method is made available through the MetaboLouise R package. Second, the EDGE tool, originally developed for genomics data analysis, is highly performant in analyzing dynamic case vs. control metabolomics data. Third, the tinderesting method is introduced to analyse more complex dynamic metabolomics experiments. This tool consists of a Shiny app for collecting expert knowledge, which in turn is used to train a machine learning model to emulate the decision process of the expert. This approach does not replace traditional data analysis workflows for metabolomics, but can provide additional information, improved performance or easier interpretation of results. The advantage is that the tool is agnostic to the complexity of the experiment, and thus is easier to use in advanced setups. All code for the presented analysis, MetaboLouise and tinderesting are freely available.