Haysmcguire9726

MMseqs2 taxonomy is a new tool to assign taxonomic labels to metagenomic contigs. It extracts all possible protein fragments from each contig, quickly retains those that can contribute to taxonomic annotation, assigns them with robust labels and determines the contig's taxonomic identity by weighted voting. Its fragment extraction step is suitable for the analysis of all domains of life. MMseqs2 taxonomy is 2-18x faster than state-of-the-art tools and also contains new modules for creating and manipulating taxonomic reference databases as well as reporting and visualizing taxonomic assignments.

MMseqs2 taxonomy is part of the MMseqs2 free open-source software package available for Linux, macOS and Windows at https//mmseqs.com.

Supplementary data is available at Bioinformatics online.

Supplementary data is available at Bioinformatics online.Aromatic L-amino acid decarboxylase (AADC) deficiency is a complex inherited neurological disorder of monoamine synthesis which results in dopamine and serotonin deficiency. The majority of affected individuals have variable, though often severe cognitive and motor delay, with a complex movement disorder and high risk of premature mortality. For most, standard pharmacological treatment provides only limited clinical benefit. Promising gene therapy approaches are emerging, though may not be either suitable or easily accessible for all patients. In order to better characterize the underlying disease pathophysiology and guide precision therapies, we generated a patient-derived midbrain dopaminergic (mDA) neuronal model of AADC deficiency from induced pluripotent stem cells (iPSCs). The neuronal model recapitulates key disease features, including absent AADC enzyme activity and dysregulated dopamine metabolism. We observed developmental defects affecting synaptic maturation and neuronal electrical properties, which were improved by lentiviral gene therapy. Bioinformatic and biochemical analyses on recombinant AADC predicted that the activity of one variant could be improved by L-3,4-dihydroxyphenylalanine (L-DOPA) administration; this hypothesis was corroborated in the patient-derived neuronal model, where L-DOPA treatment leads to amelioration of dopamine metabolites. find more Our study has shown that patient-derived disease modelling provides further insight into the neurodevelopmental sequelae of AADC deficiency, as well as a robust platform to investigate and develop personalised therapeutic approaches.The α1,6-fucosyltransferase, FUT8, is the sole enzyme catalyzing the core-fucosylation of N-glycoproteins in mammalian systems. Previous studies using free N-glycans as acceptor substrates indicated that a terminal β1,2-GlcNAc moiety on the Man-α1,3-Man arm of N-glycan substrates is required for efficient FUT8-catalyzed core-fucosylation. In contrast, we recently demonstrated that, in a proper protein context, FUT8 could also fucosylate Man5GlcNAc2 without a GlcNAc at the non-reducing end. We describe here a further study of the substrate specificity of FUT8 using a range of N-glycans containing different aglycones. We found that FUT8 could fucosylate most of high-mannose and complex-type N-glycans, including highly branched N-glycans from chicken ovalbumin, when the aglycone moiety is modified with a 9-fluorenylmethyloxycarbonyl (Fmoc) moiety or in a suitable peptide/protein context, even if they lack the terminal GlcNAc moiety on the Man-α1,3-Man arm. FUT8 could also fucosylate paucimannose structures when they are on glycoprotein substrates. Such core-fucosylated paucimannosylation is a prominent feature of lysosomal proteins of human neutrophils and several types of cancers. We also found that sialylation of N-glycans significantly reduced their activity as a substrate of FUT8. Kinetic analysis demonstrated that Fmoc aglycone modification could either improve the turnover rate or decrease the KM value depending on the nature of the substrates, thus significantly enhancing the overall efficiency of FUT8 catalyzed fucosylation. Our results indicate that an appropriate aglycone context of N-glycans could significantly broaden the acceptor substrate specificity of FUT8 beyond what has previously been thought.Accurate and individualized prediction of response to therapies is central to precision medicine. However, due to the generally complex and multifaceted nature of clinical drug response, realizing this vision is highly challenging, requiring integrating different data types from the same individual into one prediction model. We use the anti-epileptic drug brivaracetam as a case study and combine a hybrid data-/knowledge-driven feature extraction with machine learning to systematically integrate clinical and genetic data from a clinical discovery dataset (n = 235 patients). As such, we construct a model that successfully predicts clinical drug response (AUC = 0.76) and show that even with limited sample size, integrating high-dimensional genetics data with clinical data can inform drug response prediction. After a further validation on data collected from an independently conducted clinical study (AUC = 0.75), we extensively explore our model to gain insights into the determinants of drug response, and identify various clinical and genetic characteristics predisposing to poor response. Finally, we assess the potential impact of our model on clinical trial design and demonstrate that, by enriching for probable responders, significant reductions in clinical study sizes may be achieved. To our knowledge, our model represents the first retrospectively validated machine learning model linking drug mechanism of action and the genetic, clinical and demographic background in epilepsy patients to clinical drug response. Hence, it provides a blueprint for how machine learning-based multimodal data integration can act as a driver in achieving the goals of precision medicine in fields such as neurology.

Anti-vascular endothelial growth factor (VEGF) agents may provide a prophylactic effect in high-risk eyes with intermediate dry age-related macular degeneration (AMD) against conversion to exudative AMD (eAMD), lowering the risk of vision loss.

To evaluate intravitreal aflibercept injection (IAI) as prophylaxis against the conversion to eAMD in high-risk eyes at 24 months.

This single-masked, sham-controlled, randomized clinical trial performed at 4 US clinical sites enrolled patients with intermediate AMD in 1 eye (study eye), defined as presence of more than 10 medium drusen (≥63 to <125 μm), at least 1 large druse (≥125 μm), and/or retinal pigmentary changes, and eAMD in the fellow eye. Patients were treated from June 23, 2015, to March 13, 2019.

Intravitreal aflibercept injection (2 mg) or sham quarterly injection for 24 months (11 randomization).

The primary end point was the proportion of patients with conversion to eAMD at month 24 characterized by development of choroidal neovascularization, as assessed by leakage on fluorescein angiography and fluid on spectral-domain optical coherence tomography by an independent masked reading center.