The advantages of being small : Glycosyltransferases in many dimensions and glycolipid synthesis in Mycoplasma Pneumoniae

The synthesis and breakdown of sugars is one of the most important functions in Nature. Consequently, sugar structures are used both as energy storage and as building blocks to stabilise and protect the cell. The formation of these structures is performed by glycosyltransferases (GT), an enzyme group structurally conserved within all kingdoms. Until now, only two different folds have been discovered by crystallisation of GTs, i.e. GT-A and GT-B. A third fold family has however been proposed by fold predictions. In this thesis, a multivariate data analysis was successfully used in classifying and predicting both fold and reaction mechanism (inverting or retaining) of GTs. This method was also used to obtain information about the separating parameters for the reaction mechanism classification. This information could be traced back to the amino acid sequence. The method could as well be used to analyse and identify the properties of membrane binding regions of GTs, and subsequently distinguish soluble from membrane-associated enzymes. Most functionally characterised enzymes only use one substrate, synthesising one product. Mycoplasma pneumoniae, a common human pathogen with a small genome has only three proposed GTs. The bacterium was, however expected to have a greater number of GTs, due to its ability to make both glycolipids and capsule. Here we have determined the function of one of these enzymes, MPN483 and discovered its ability to both use different acceptors, and make elongated glycolipids with up to three galactose residues, with both DAG and ceramide as the base. Many of the synthesised glycolipids were also found to be immunogenic, hence showing their biological importance. The properties of lipids are known to be important for the function of a biological membrane. We have here shown that not only the charge but also the shape of the lipids are important for several protein mediated membrane processes in Echerichia coli, such as the function of the LacY

An artificial intelligence-powered, patient-centric digital tool for self-management of chronic pain : a prospective, multicenter clinical trial

Objective: To investigate how a behavioral health, artificial intelligence (AI)-powered, digital self-management tool affects the daily functions in adults with chronic back and neck pain. Design: Eligible subjects were enrolled in a 12-week prospective, multicenter, single-arm, open-label study and instructed to use the digital coach daily. Primary outcome was a change in Patient-Reported Outcomes Measurement Information Systems (PROMIS) scores for pain interference. Secondary outcomes were changes in PROMIS physical function, anxiety, depression, pain intensity scores and pain catastrophizing scale (PCS) scores. Methods: Subjects logged daily activities, using PainDrainerTM, and data analyzed by the AI engine. Questionnaire and web-based data were collected at 6 and 12 weeks and compared to subjects' baseline. Results: Subjects completed the 6- (n = 41) and 12-week (n = 34) questionnaires. A statistically significant Minimal Important Difference (MID) for pain interference was demonstrated in 57.5% of the subjects. Similarly, MID for physical function was demonstrated in 72.5% of the subjects. A pre- to post-intervention improvement in depression score was also statistically significant, observed in 100% of subjects, as was the improvement in anxiety scores, evident in 81.3% of the subjects. PCS mean scores was also significantly decreased at 12 weeks. Conclusion: Chronic pain self-management, using an AI-powered, digital coach anchored in behavioral health principles significantly improved subjects' pain interference, physical function, depression, anxiety, and pain catastrophizing over the 12-week study period