Epidemiology of invasive disease in adults in England, 2013-2017.

Extraintestinal pathogenic (ExPEC) causes invasive disease (IED), including bacteraemia and (uro)sepsis, resulting in a high disease burden, especially among older adults. This study describes the epidemiology of IED in England (2013-2017) by combining laboratory surveillance and clinical data. A total of 191 612 IED cases were identified.

AmiA and AliA peptide ligands, found in Klebsiella pneumoniae, are imported into pneumococci and alter the transcriptome.

Klebsiella pneumoniae releases the peptides AKTIKITQTR and FNEMQPIVDRQ, which bind the pneumococcal proteins AmiA and AliA respectively, two substrate-binding proteins of the ABC transporter Ami-AliA/AliB oligopeptide permease. Exposure to these peptides alters pneumococcal phenotypes such as growth. Using a mutant in which a permease domain of the transporter was disrupted, by growth analysis and epifluorescence microscopy, we confirmed peptide uptake via the Ami permease and intracellular location in the pneumococcus.

Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A.

Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the () gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models.

FiTMuSiC: leveraging structural and (co)evolutionary data for protein fitness prediction.

Systematically predicting the effects of mutations on protein fitness is essential for the understanding of genetic diseases. Indeed, predictions complement experimental efforts in analyzing how variants lead to dysfunctional proteins that in turn can cause diseases. Here we present our new fitness predictor, FiTMuSiC, which leverages structural, evolutionary and coevolutionary information.