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The mechanism of the chlorination reaction of SyrB2, a representative α-ketoglutarate dependent halogenase, was studied with computational methods. First, a macromolecular model of the Michaelis complex was constructed using molecular docking procedures. Based on this structure, a smaller model comprising the first- and some of the second-shell residues of iron and a model substrate was constructed and used in DFT investigations on the reaction mechanism. Computed relative energies and Mössbauer isomer shifts as well as quadrupole splittings indicate that the two oxoferryl species observed experimentally are two stereoisomers resulting from an exchange of the coordination sites occupied by the oxo and chloro ligands. In principle both Fe(IV)═O species are reactive and decay to Fe(III)Cl (OH)/carbon radical intermediates via C-H bond cleavage. In the final rebound step, which is very fast and thus precluding equilibration between the two forms of the radical intermediate, the ligand (oxo or chloro) placed closest to the carbon radical (trans to His235) is transferred to the carbon. For the native substrate (L-Thr) the lowest barrier for C-H cleavage was found for an isomer of the oxoferryl species favoring chlorination in the rebound step. CASPT2 calculations for the spin state splittings in the oxoferryl species support the conclusion that once the Fe(IV)═O intermediate is formed, the reaction proceeds on the quintet potential energy surface.

The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing issues. To address these, we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics. We demonstrate its capabilities in a meta-analysis of fecal metagenomic studies (10,803 samples). When naively transferred across studies, ML models lost accuracy and disease specificity, which could however be resolved by a novel training set augmentation strategy. This reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de.

BackgroundRecent evidence suggests a role for the microbiome in pancreatic ductal adenocarcinoma (PDAC) aetiology and progression.ObjectiveTo explore the faecal and salivary microbiota as potential diagnostic biomarkers.MethodsWe applied shotgun metagenomic and 16S rRNA amplicon sequencing to samples from a Spanish case–control study (n=136), including 57 cases, 50 controls, and 29 patients with chronic pancreatitis in the discovery phase, and from a German case–control study (n=76), in the validation phase.ResultsFaecal metagenomic classifiers performed much better than saliva-based classifiers and identified patients with PDAC with an accuracy of up to 0.84 area under the receiver operating characteristic curve (AUROC) based on a set of 27 microbial species, with consistent accuracy across early and late disease stages. Performance further improved to up to 0.94 AUROC when we combined our microbiome-based predictions with serum levels of carbohydrate antigen (CA) 19–9, the only current non-invasive, Food and Drug Administration approved, low specificity PDAC diagnostic biomarker. Furthermore, a microbiota-based classification model confined to PDAC-enriched species was highly disease-specific when validated against 25 publicly available metagenomic study populations for various health conditions (n=5792). Both microbiome-based models had a high prediction accuracy on a German validation population (n=76). Several faecal PDAC marker species were detectable in pancreatic tumour and non-tumour tissue using 16S rRNA sequencing and fluorescence in situ hybridisation.ConclusionTaken together, our results indicate that non-invasive, robust and specific faecal microbiota-based screening for the early detection of PDAC is feasible.