Recombinant viruses delivering the necroptosis mediator MLKL induce a potent antitumor immunity in mice

Vaccinia viruses (VACV) are a novel class of immune-oncolytic therapeutics and their mechanism of action is based both on their capacity to replicate selectively in cancer cells and to elicit danger signals that can boost anti-tumor immunity. We recently reported that the intratumor expression of MLKL, a necroptosis inducing factor, generates a protective anti-tumor immunity. Here, we combined both approaches to test the use of VACV to deliver MLKL into the tumor. We generated VACV vectors expressing MLKL and evaluated the effects of MLKL on antitumor efficacy. In vitro infection of cancer cells with MLKL-expressing vectors led to cell death with necroptotic hallmarks. In syngeneic mouse tumor models, VACV expressing MLKL induced an outstanding antitumor activity, which was associated with a robust immunity directed against neo-epitopes. In conclusion, delivery of MLKL by VACV vectors boosts the intrinsic anti-tumor properties of these viral vectors by promoting in situ immunogenic cell death of infected cancer cells

Longterm survey (7 years) in a population at risk for Lyme borreliosis: what happens to the seropositive individuals?

In 1986, a 26% seroprevalence of IgG- anti-Borrelia burgdorferi antibodies was observed among 950 orienteers and the incidence of new clinical infections was 0.8%. In 1993, a total of 305 seropositive orienteers were reexamined. During that time, 15 cases (4.9%) of definite/probable Lyme disease occurred in this seropositive group (12 skin manifestations and 3 monoarticular joint manifestations). Among the 12 definite cases, 9 showed new clinical infections (7 EM, 1 acrodermatitis chronica atrophicans, 1 arthritis), and 3 were recurrent (2 EM, 1 arthritis). The annual incidence (0.8%) in this seropositive group was identical to the incidence observed among the whole population in 1986. The individual antibody titer decreased slightly but the seroreversion rate was low (7%). Serology was not very helpful in identifying clinical cases and evolutions, and it can be stated, that a positive serology is much more frequent in this risk group than clinical disease

Single domain antibodies targeting neuraminidase protect against an H5N1 influenza virus challenge

Influenza virus neuraminidase (NA) is an interesting target of small-molecule antiviral drugs. We isolated a set of H5N1 NAspecific single-domain antibodies (N1-VHHm) and evaluated their in vitro and in vivo antiviral potential. Two of them inhibited the NA activity and in vitro replication of clade 1 and 2 H5N1 viruses. We then generated bivalent derivatives of N1-VHHm by two methods. First, we made N1-VHHb by genetically joining two N1-VHHm moieties with a flexible linker. Second, bivalent N1-VHH-Fc proteins were obtained by genetic fusion of the N1-VHHm moiety with the crystallizable region of mouse IgG2a (Fc). The in vitro antiviral potency against H5N1 of both bivalent N1-VHHb formats was 30- to 240-fold higher than that of their monovalent counterparts, with 50% inhibitory concentrations in the low nanomolar range. Moreover, single-dose prophylactic treatment with bivalent N1-VHHb or N1-VHH-Fc protected BALB/c mice against a lethal challenge with H5N1 virus, including an oseltamivir-resistant H5N1 variant. Surprisingly, an N1-VHH-Fc fusion without in vitro NA-inhibitory or antiviral activity also protected mice against an H5N1 challenge. Virus escape selection experiments indicated that one amino acid residue close to the catalytic site is required for N1-VHHm binding. We conclude that single-domain antibodies directed against influenza virus NA protect against H5N1 virus infection, and when engineered with a conventional Fc domain, they can do so in the absence of detectable NA-inhibitory activity.Fil: Cardoso, Francisco Miguel. VIB Inflammation Research Center; Bélgica. Ghent University. Department for Biomedical Molecular Biology; BélgicaFil: Ibañez, Lorena Itatí. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Ciencias y Tecnología "Dr. Cesar Milstein"; Argentina. VIB Inflammation Research Center; Bélgica. Ghent University. Department for Biomedical Molecular Biology; BélgicaFil: Van Den Hoecke, Silvie. VIB Inflammation Research Center; Bélgica. Ghent University. Department for Biomedical Molecular Biology; BélgicaFil: De Baets, Sarah. VIB Inflammation Research Center; Bélgica. Ghent University. Department for Biomedical Molecular Biology; BélgicaFil: Smet, Anouk. VIB Inflammation Research Center; Bélgica. Ghent University. Department for Biomedical Molecular Biology; BélgicaFil: Roose, Kenny. VIB Inflammation Research Center; Bélgica. Ghent University. Department for Biomedical Molecular Biology; BélgicaFil: Schepens, Bert. VIB Inflammation Research Center; Bélgica. Ghent University. Department for Biomedical Molecular Biology; BélgicaFil: Descamps, Francis J.. VIB Inflammation Research Center; Bélgica. Ghent University. Department for Biomedical Molecular Biology; BélgicaFil: Fiers, Walter. VIB Inflammation Research Center; Bélgica. Ghent University. Department for Biomedical Molecular Biology; BélgicaFil: Muyldermans, Serge. Structural Biology Research Center; Bélgica. Vrije Universiteit Brussel. Laboratory of Cellular and Molecular Immunology; BélgicaFil: Depicker, Ann. VIB. Department of Plant Systems Biology; Bélgica. Department of Biotechnology and Bioinformatics; BélgicaFil: Saelens, Xavier. VIB Inflammation Research Center; Bélgica. Ghent University. Department for Biomedical Molecular Biology; Bélgic

Development and evaluation of uncertainty quantifying machine learning models to predict piperacillin plasma concentrations in critically ill patients

Background: Beta-lactam antimicrobial concentrations are frequently suboptimal in critically ill patients. Population pharmacokinetic (PopPK) modeling is the golden standard to predict drug concentrations. However, currently available PopPK models often lack predictive accuracy, making them less suited to guide dosing regimen adaptations. Furthermore, many currently developed models for clinical applications often lack uncertainty quantification. We, therefore, aimed to develop machine learning (ML) models for the prediction of piperacillin plasma concentrations while also providing uncertainty quantification with the aim of clinical practice. Methods: Blood samples for piperacillin analysis were prospectively collected from critically ill patients receiving continuous infusion of piperacillin/tazobactam. Interpretable ML models for the prediction of piperacillin concentrations were designed using CatBoost and Gaussian processes. Distribution-based Uncertainty Quantification was added to the CatBoost model using a proposed Quantile Ensemble method, useable for any model optimizing a quantile function. These models are subsequently evaluated using the distribution coverage error, a proposed interpretable uncertainty quantification calibration metric. Development and internal evaluation of the ML models were performed on the Ghent University Hospital database (752 piperacillin concentrations from 282 patients). Ensuing, ML models were compared with a published PopPK model on a database from the University Medical Centre of Groningen where a different dosing regimen is used (46 piperacillin concentrations from 15 patients.). Results: The best performing model was the Catboost model with an RMSE and R-2 of 31.94-0.64 and 33.53-0.60 for internal evaluation with and without previous concentration. Furthermore, the results prove the added value of the proposed Quantile Ensemble model in providing clinically useful individualized uncertainty predictions and show the limits of homoscedastic methods like Gaussian Processes in clinical applications. Conclusions: Our results show that ML models can consistently estimate piperacillin concentrations with acceptable and high predictive accuracy when identical dosing regimens as in the training data are used while providing highly relevant uncertainty predictions. However, generalization capabilities to other dosing schemes are limited. Notwithstanding, incorporating ML models in therapeutic drug monitoring programs seems definitely promising and the current work provides a basis for validating the model in clinical practice

Involvement of the Choroid Plexus in the Pathogenesis of Niemann-Pick Disease Type C

Niemann-Pick type C (NPC) disease, sometimes called childhood Alzheimer's, is a rare neurovisceral lipid storage disease with progressive neurodegeneration leading to premature death. The disease is caused by loss-of-function mutations in the Npc1 or Npc2 gene which both result into lipid accumulation in the late endosomes and lysosomes. Since the disease presents with a broad heterogenous clinical spectrum, the involved disease mechanisms are still incompletely understood and this hampers finding an effective treatment. As NPC patients, who carry NPC1 mutations, have shown to share several pathological features with Alzheimer's disease (AD) and we and others have previously shown that AD is associated with a dysfunctionality of the blood-cerebrospinal fluid (CSF) barrier located at choroid plexus, we investigated the functionality of this latter barrier in NPC1 pathology. Using NPC1-/- mice, we show that despite an increase in inflammatory gene expression in choroid plexus epithelial (CPE) cells, the blood-CSF barrier integrity is not dramatically affected. Interestingly, we did observe a massive increase in autophagosomes in CPE cells and enlarged extracellular vesicles (EVs) in CSF upon NPC1 pathology. Additionally, we revealed that these EVs exert toxic effects on brain tissue, in vitro as well as in vivo. Moreover, we observed that EVs derived from the supernatant of NPC1-/- choroid plexus explants are able to induce typical brain pathology characteristics of NPC1-/-, more specifically microgliosis and astrogliosis. Taken together, our data reveal for the first time that the choroid plexus and CSF EVs might play a role in the brain-related pathogenesis of NPC1

Impact of index hopping and bias towards the reference allele on accuracy of genotype calls from low-coverage sequencing

Abstract Background Inherent sources of error and bias that affect the quality of sequence data include index hopping and bias towards the reference allele. The impact of these artefacts is likely greater for low-coverage data than for high-coverage data because low-coverage data has scant information and many standard tools for processing sequence data were designed for high-coverage data. With the proliferation of cost-effective low-coverage sequencing, there is a need to understand the impact of these errors and bias on resulting genotype calls from low-coverage sequencing. Results We used a dataset of 26 pigs sequenced both at 2× with multiplexing and at 30× without multiplexing to show that index hopping and bias towards the reference allele due to alignment had little impact on genotype calls. However, pruning of alternative haplotypes supported by a number of reads below a predefined threshold, which is a default and desired step of some variant callers for removing potential sequencing errors in high-coverage data, introduced an unexpected bias towards the reference allele when applied to low-coverage sequence data. This bias reduced best-guess genotype concordance of low-coverage sequence data by 19.0 absolute percentage points. Conclusions We propose a simple pipeline to correct the preferential bias towards the reference allele that can occur during variant discovery and we recommend that users of low-coverage sequence data be wary of unexpected biases that may be produced by bioinformatic tools that were designed for high-coverage sequence data