Boehringer Ingelheim (Australia)

A seven-step enantioselective route for the synthesis of benzyl (R)-10-oxo-2-oxa-7-azaspiro[4.5]decane-7-carboxylate (1) with >99% ee is reported on a multi-hundred-gram scale in 42% overall yield. The synthesis features an asymmetric allylic alkylation to establish the quaternary stereocenter on a piperidine ring, followed by an oxidation/reduction and Mitsunobu conditions sequence to construct the tetrahydrofuran ring within the chiral oxa-azaspirocycle.

The eastern paralysis tick of Australia, Ixodes holocyclus, is by far the most important ectoparasite of dogs and cats in eastern Australia. In spite of the development of tick-antisera and the availability of highly effective tick-preventative medications, thousands of dogs and cats present to veterinary clinics and hospitals with signs of tick paralysis each year; about 10% of these dogs and 8% of these cats may die from tick paralysis or are euthanized due to severe signs of tick envenomation. One of the mainstays of optimising patient outcome of dogs and cats with signs of tick paralysis is the prompt removal of the tick. In the present paper, we studied 10,913 attachment-sitesof I. holocyclus arising from 10,311 veterinary consultations of dogs and cats in eastern Australia. This is, to our knowledge, the largest study of attachment-site preference of I. holocyclus on dogs and cats. We found that whereas I. holocyclus is most often found on the head of dogs, on cats, these ticks are most often found on their necks. In addition, we report attachment-site preferences of adult and immature (larval and nymphal) I. holocyclus. We also highlight some unusual and inconspicuous attachment-sitesof I. holocyclus, which may be informative to veterinarians and pet owners alike.

Electron microscopy (EM) and especially three-dimensional (3D) EM techniques have become established methods in structural virology. Investigation of virus-induced alteration of the cellular ultrastructure, such as Zika virus (ZIKV)-induced replication factories or coronavirus replication organelles, demands 3D imaging. Transmission electron microscopy (TEM) tomography is a widely used method, despite its limitation to samples with a thickness of up to 200 nm. Focused ion beam-scanning electron microscopy (SEM)-tomography can produce 3D data of larger volumes with isotropic, albeit typically lower resolution. Alternative techniques, such as block face-scanning electron microscopy (BF-SEM), SEM array tomography, or TEM imaging of serial sections are used for imaging of larger volumes. However, compared to the previously mentioned techniques, these techniques come at the cost of much lower resolution along the Z-axis of the sample. A technique that provides 3D information of samples up to 1 µm thickness with isotropic resolution of a few nanometers is scanning transmission electron microscopy (STEM) tomography. Here, we present a protocol for the preparation of high-pressure frozen, freeze-substituted, and resin-embedded virological specimens and their analysis using STEM tomography. This protocol benefits from the advantages of room temperature imaging while preserving the biological ultrastructure in a near-native state. We show two representative examples for questions that can be answered using STEM tomography. First, we apply the protocol for studying virion morphogenesis of a recombinant vesicular stomatitis virus (VSV). The STEM tomograms offer information on recombinant VSV budding that is otherwise not accessible by 2D imaging. Second, we show correlative light and electron microscopy (CLEM) using Foerster resonance energy transfer (FRET) imaging and STEM tomography (FRET-3D-CLEM) of EF-C peptide nanofibrils. This recently published combination gives new insights into the uptake and disassembly of infection-enhancing peptide nanofibrils, especially profiting from the large volume that is accessible by STEM tomography.

Monoclonal antibodies' Fc N-glycans play a crucial role in their therapeutic efficacy, as they influence effector functions through Fc receptor binding. However, the impact of asymmetrical Fc glyco-pairs is often overlooked in assessing Fc receptor binding and effector functions. This study addresses this gap by generating pure asymmetrical Fc glyco-pairs and evaluating their Fc receptor binding properties, thereby providing a comprehensive understanding of the impact of Fc N-glycans. Utilizing redox pairing and affinity chromatography, homogeneously asymmetrical Fc glyco-pairs were generated, and their interaction properties toward Fcγ receptors IIIa, IIa, IIb, and I were determined by surface plasmon resonance. The results underscore the importance of considering the apparent glycan distribution of Fc N-glycans as glycan pairing was found to individually influence Fc receptor binding. Notably, single afucosylation significantly increased the affinity for FcγRIIIa, while the effect of galactosylation was detectable but less pronounced. Galactosylation, however, played a crucial role in FcγRIIa binding, with asymmetrical galactosylation being sufficient for the whole effect. In contrast, for FcγRIIb, afucosylation was more important, while galactosylation played a minor role. Furthermore, glycosylation-dependent Fc-FcγRI complex stability differences could be resolved, challenging the commonly held belief that this interaction is glycosylation independent.

Machine learning and deep learning are powerful tools for analyzing electronic health records (EHRs) in healthcare research. Although family health history has been recognized as a major predictor for a wide spectrum of diseases, research has so far adopted a limited view of family relations, essentially treating patients as independent samples in the analysis. To address this gap, we present ALIGATEHR, which models inferred family relations in a graph attention network augmented with an attention-based medical ontology representation, thus accounting for the complex influence of genetics, shared environmental exposures, and disease dependencies. Taking disease risk prediction as a use case, we demonstrate that explicitly modeling family relations significantly improves predictions across the disease spectrum. We then show how ALIGATEHR's attention mechanism, which links patients' disease risk to their relatives' clinical profiles, successfully captures genetic aspects of diseases using longitudinal EHR diagnosis data. Finally, we use ALIGATEHR to successfully distinguish the 2 main inflammatory bowel disease subtypes with highly shared risk factors and symptoms (Crohn's disease and ulcerative colitis). Overall, our results highlight that family relations should not be overlooked in EHR research and illustrate ALIGATEHR's great potential for enhancing patient representation learning for predictive and interpretable modeling of EHRs.

The rising prevalence of liver diseases related to obesity and excessive use of alcohol is fuelling an increasing demand for accurate biomarkers aimed at community screening, diagnosis of steatohepatitis and significant fibrosis, monitoring, prognostication and prediction of treatment efficacy. Breakthroughs in omics methodologies and the power of bioinformatics have created an excellent opportunity to apply technological advances to clinical needs, for instance in the development of precision biomarkers for personalised medicine. Via omics technologies, biological processes from the genes to circulating protein, as well as the microbiome - including bacteria, viruses and fungi, can be investigated on an axis. However, there are important barriers to omics-based biomarker discovery and validation, including the use of semi-quantitative measurements from untargeted platforms, which may exhibit high analytical, inter- and intra-individual variance. Standardising methods and the need to validate them across diverse populations presents a challenge, partly due to disease complexity and the dynamic nature of biomarker expression at different disease stages. Lack of validity causes lost opportunities when studies fail to provide the knowledge needed for regulatory approvals, all of which contributes to a delayed translation of these discoveries into clinical practice. While no omics-based biomarkers have matured to clinical implementation, the extent of data generated has enabled the hypothesis-free discovery of a plethora of candidate biomarkers that warrant further validation. To explore the many opportunities of omics technologies, hepatologists need detailed knowledge of commonalities and differences between the various omics layers, and both the barriers to and advantages of these approaches.

Abstract Precision medicine requires accurate identification of clinically relevant patient subgroups. Electronic health records provide major opportunities for leveraging machine learning approaches to uncover novel patient subgroups. However, many existing approaches fail to adequately capture complex interactions between diagnosis trajectories and disease-relevant risk events, leading to subgroups that can still display great heterogeneity in event risk and underlying molecular mechanisms. To address this challenge, we implemented VaDeSC-EHR, a transformer-based variational autoencoder for clustering longitudinal survival data as extracted from electronic health records. We show that VaDeSC-EHR outperforms baseline methods on both synthetic and real-world benchmark datasets with known ground-truth cluster labels. In an application to Crohn’s disease, VaDeSC-EHR successfully identifies four distinct subgroups with divergent diagnosis trajectories and risk profiles, revealing clinically and genetically relevant factors in Crohn’s disease. Our results show that VaDeSC-EHR can be a powerful tool for discovering novel patient subgroups in the development of precision medicine approaches.