A microfluidic high-resolution NMR flow probe based on a novel stripline detector chip is demonstrated. This tool is invaluable for the in situ monitoring of reactions performed in microreactors. As an example, the acetylation of benzyl alcohol with acetyl chloride was monitored. Because of the uncompromised (sub-Hz) resolution, this probe holds great promise for metabolomics studies, as shown by an analysis of 600 nL of human cerebrospinal fluid.

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Hepatitis B virus (HBV) replication is initiated by binding of its reverse transcriptase (P) to the apical stem-loop (AL) and primer loop (PL) of epsilon, a highly conserved RNA element at the 5′-end of the RNA pregenome. Mutation studies on duck/heron and human in vitro systems have shown similarities but also differences between their P–epsilon interaction. Here, NMR and UV thermodynamic data on AL (and PL) from these three species are presented. The stabilities of the duck and heron ALs were found to be similar, and much lower than that of human. NMR data show that this low stability stems from an 11-nt internal bulge destabilizing the stem of heron AL. In duck, although structured at low temperature, this region also forms a weak point as its imino resonances broaden to disappearance between 30 and 35°C well below the overall AL melting temperature. Surprisingly, the duck- and heron ALs were both found to be capped by a stable well-structured UGUU tetraloop. All avian ALs are expected to adhere to this because of their conserved sequence. Duck PL is stable and structured and, in view of sequence similarities, the same is expected for heron - and human PL.

Cytochrome P450 BM3 from Bacillus megaterium is a monooxygenase with great potential for biotechnological applications. In this paper, we present engineered drug-metabolizing P450 BM3 mutants as a novel tool for regioselective hydroxylation of steroids at position 16β. In particular, we show that by replacing alanine at position 82 with a tryptophan in P450 BM3 mutants M01 and M11, the selectivity toward 16β-hydroxylation for both testosterone and norethisterone was strongly increased. The A82W mutation led to a ≤42-fold increase in V(max) for 16β-hydroxylation of these steroids. Moreover, this mutation improves the coupling efficiency of the enzyme, which might be explained by a more efficient exclusion of water from the active site. The substrate affinity for testosterone increased at least 9-fold in M11 with tryptophan at position 82. A change in the orientation of testosterone in the M11 A82W mutant as compared to the orientation in M11 was observed by T(1) paramagnetic relaxation nuclear magnetic resonance. Testosterone is oriented in M11 with both the A- and D-ring protons closest to the heme iron. Substituting alanine at position 82 with tryptophan results in increased A-ring proton-iron distances, consistent with the relative decrease in the level of A-ring hydroxylation at position 2β.

BackgroundIn the last decade data fusion has become widespread in the field of metabolomics. Linear data fusion is performed most commonly. However, many data display non-linear parameter dependences. The linear methods are bound to fail in such situations. We used proton Nuclear Magnetic Resonance and Gas Chromatography-Mass Spectrometry, two well established techniques, to generate metabolic profiles of Cerebrospinal fluid of Multiple Sclerosis (MScl) individuals. These datasets represent non-linearly separable groups. Thus, to extract relevant information and to combine them a special framework for data fusion is required.MethodologyThe main aim is to demonstrate a novel approach for data fusion for classification; the approach is applied to metabolomics datasets coming from patients suffering from MScl at a different stage of the disease. The approach involves data fusion in kernel space and consists of four main steps. The first one is to extract the significant information per data source using Support Vector Machine Recursive Feature Elimination. This method allows one to select a set of relevant variables. In the next step the optimized kernel matrices are merged by linear combination. In step 3 the merged datasets are analyzed with a classification technique, namely Kernel Partial Least Square Discriminant Analysis. In the final step, the variables in kernel space are visualized and their significance established.ConclusionsWe find that fusion in kernel space allows for efficient and reliable discrimination of classes (MScl and early stage). This data fusion approach achieves better class prediction accuracy than analysis of individual datasets and the commonly used mid-level fusion. The prediction accuracy on an independent test set (8 samples) reaches 100%. Additionally, the classification model obtained on fused kernels is simpler in terms of complexity, i.e. just one latent variable was sufficient. Finally, visualization of variables importance in kernel space was achieved.