Van een studie bewegingswetenschappen, naar onderzoek in overtraining bij sporters en militairen, tot het bestuderen van depressies bij jongeren. Lector Esther Nederhof kwam met een grote omweg terecht bij het onderwerp van haar lectoraat bij Hogeschool Van Hall Larenstein: Gezonde en Duurzame Voeding en Welvaartsziekten. Toch draagt deze multidisciplinaire achtergrond zeker bij aan haar huidige onderzoek, waar juist de gezonde functies van de mens centraal staan.
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Op de HAS Hogeschool wordt al een aantal jaren voer voor vis en schaaldieren op basis van insecten vergeleken met regulier voer op basis van vismeel en/of visolie. Dit onderzoek wordt uitgevoerd in samenwerking met New Generation Nutrition. Resultaten laten zien dat garnalen gevoerd met voer op basis van insecten even goed groeien als bij regulier voer. Tot op heden is onbekend of het voer op basis van insecten gezondheidsrisico’s met zich meebrengt en dergelijk onderzoek komt na het aanstellen van Olga Haenen als lector Gezonde en Duurzame eiwitten in een stroomversnelling.
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This publication by Kathryn Best accompanied the Lector’s inauguration as head of the research group Cross-media, Brand, Reputation & Design Management (CBRD) in January 2011. The book outlines current debates around the Creative Industries, business and design education and the place of ’well being’ in society, the environment and the economy, before focusing in on the place for design thinking in creative and innovation processes, and how this is driving new applied research agendas and initiatives in education and industry.
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
In the context of sustainability, the use of biocatalysis in organic synthesis is increasingly observed as an essential tool towards a modern and ‘green’ chemical industry. However, the lack of a diverse set of commercially available enzymes with a broad selectivity toward industrially-relevant substrates keeps hampering the widespread implementation of biocatalysis. Aminoverse B.V. aims to contribute to this challenge by developing enzymatic screening kits and identifying novel enzyme families with significant potential for biocatalysis. One of the most important, yet notoriously challenging reaction in organic synthesis is site-selective functionalization (e.g. hydroxylation) of inert C-H bonds. Interestingly, Fe(II)/α-ketoglutarate-dependent oxygenases (KGOs) have been found to perform C-H hydroxylation, as well as other oxyfunctionalization, spontaneously in nature. However, as KGOs are not commercially available, or even extensively studied in this context, their potential is not readily accessible to the chemical industry. This project aims to demonstrate the potential of KGOs in biocatalysis. In order to achieve this, the following challenges will be addressed: i) establishing an enzymatic screening methodology to study the activity and selectivity of recombinant KGOs towards industrially relevant substrates, ii) establishing analytical methods to characterize KGO-catalyzed substrate conversion and product formation. Eventually, the proof-of-principle demonstrated during this project will allow Aminoverse B.V. to develop a commercial biocatalysis kit comprised of KGO enzymes with a diverse activity profile, allowing their application in the sustainable production of either commodity, fine or speciality chemicals. The project consortium is composed of: i) Aminoverse B.V, a start-up company dedicated to facilitate chemical partners towards implementing biocatalysis in their chemical processes, and ii) Zuyd University, which will link Aminoverse B.V. with students and (bio)chemical professionals in creating a novel collaboration which will not only stimulate the development of (bio)chemical students, but also the translation of academic knowledge on KGOs towards a feasible biocatalytic application.
