tIn this study we aimed to identify genes that are responsive to pertussis toxin (PTx) and might eventu-ally be used as biological markers in a testing strategy to detect residual PTx in vaccines. By microarrayanalysis we screened six human cell types (bronchial epithelial cell line BEAS-2B, fetal lung fibroblastcell line MRC-5, primary cardiac microvascular endothelial cells, primary pulmonary artery smooth mus-cle cells, hybrid cell line EA.Hy926 of umbilical vein endothelial cells and epithelial cell line A549 andimmature monocyte-derived dendritic cells) for differential gene expression induced by PTx. Imma-ture monocyte-derived dendritic cells (iMoDCs) were the only cells in which PTx induced significantdifferential expression of genes. Results were confirmed using different donors and further extendedby showing specificity for PTx in comparison to Escherichia coli lipopolysaccharide (LPS) and Bordetellapertussis lipo-oligosaccharide (LOS). Statistical analysis indicated 6 genes, namely IFNG, IL2, XCL1, CD69,CSF2 and CXCL10, as significantly upregulated by PTx which was also demonstrated at the protein levelfor genes encoding secreted proteins. IL-2 and IFN- gave the strongest response. The minimal PTx con-centrations that induced production of IL-2 and IFN- in iMoDCs were 12.5 and 25 IU/ml, respectively.High concentrations of LPS slightly induced IFN- but not IL-2, while LOS and detoxified pertussis toxindid not induce production of either cytokine. In conclusion, using microarray analysis we evaluated sixhuman cell lines/types for their responsiveness to PTx and found 6 PTx-responsive genes in iMoDCs ofwhich IL2 is the most promising candidate to be used as a biomarker for the detection of residual PTx.
Manure application can spread antimicrobial resistance (AMR) from manure to soil and surface water. This study evaluated the role of the soil texture on the dynamics of antimicrobial resistance genes (ARGs) in soils and surrounding surface waters. Six dairy farms with distinct soil textures (clay, sand, and peat) were sampled at different time points after the application of manure, and three representative ARGs sul1, erm(B), and tet(W) were quantified with qPCR. Manuring initially increased levels of erm(B) by 1.5 ± 0.5 log copies/kg of soil and tet(W) by 0.8 ± 0.4 log copies/kg across soil textures, after which levels gradually declined. In surface waters from clay environments, regardless of the ARG, the gene levels initially increased by 2.6 ± 1.6 log copies/L, after which levels gradually declined. The gene decay in soils was strongly dependent on the type of ARG (erm(B) < tet(W) < sul1; half-lives of 7, 11, and 75 days, respectively), while in water, the decay was primarily dependent on the soil texture adjacent to the sampled surface water (clay < peat < sand; half-lives of 2, 6, and 10 days, respectively). Finally, recovery of ARG levels was predicted after 29–42 days. The results thus showed that there was not a complete restoration of ARGs in soils between rounds of manure application. In conclusion, this study demonstrates that rather than showing similar dynamics of decay, factors such as the type of ARG and soil texture drive the ARG persistence in the environment.
MULTIFILE
Phylogenetic patterns show the presence or absence of certain genes in a set of full genomes derived from different species. They can also be used to determine sets of genes that occur only in certain evolutionary branches. Previously, we presented a database named PhyloPat which allows the complete Ensembl gene database to be queried using phylogenetic patterns. Here, we describe an updated version of PhyloPat which can be queried by an improved web server. We used a single linkage clustering algorithm to create 241 697 phylogenetic lineages, using all the orthologies provided by Ensembl v49. PhyloPat offers the possibility of querying with binary phylogenetic patterns or regular expressions, or through a phylogenetic tree of the 39 included species. Users can also input a list of Ensembl, EMBL, EntrezGene or HGNC IDs to check which phylogenetic lineage any gene belongs to. A link to the FatiGO web interface has been incorporated in the HTML output. For each gene, the surrounding genes on the chromosome, color coded according to their phylogenetic lineage can be viewed, as well as FASTA files of the peptide sequences of each lineage. Furthermore, lists of omnipresent, polypresent, oligopresent and anticorrelating genes have been included. PhyloPat is freely available at http://www.cmbi.ru.nl/phylopat. © 2008 The Author(s).
Jaarlijks worden in Nederland ongeveer 600.000 mensen ziek door het eten van besmet voedsel. De voedselverwerkende industrie heeft sterke behoefte aan meer grip op het bewaken van de hygiëne in de fabrieken om te voorkomen dat besmette producten in de winkels komen. In het afgeronde RAAK-mkb project “Precision Food Safety” is onderzocht wat de meerwaarde is van de toepassing van Whole Genome Sequencing (WGS) bij het achterhalen van de transmissieroutes van de pathogene bacterie Listeria monocytogenes bij voedselverwerkende bedrijven. Er is een biobank opgebouwd met bijna 600 L. monocytogenes stammen afkomstig van de fabrieksomgeving en producten van vis-, vlees- en groente-verwerkende bedrijven. Deze stammen zijn gesequenced met behulp van Nanopore sequencing. Vervolgens is de verwantschap tussen de stammen bepaald met een in het project ontwikkelde bioinformatica pijplijn. Het project bleek zeer succesvol. In “Advanced Precision in Food Safety ” wordt het onderzoek naar voedselveiligheid verbreed, door L. monocytogenes al aan het begin van de voedselverwerkingsketen (in grondstoffen en ingrediënten) te monitoren. Verder zal de WGS-methodiek worden toegepast op Salmonella enterica en zal de huidige bioinformatica pijplijn worden aangepast om transmissieroutes van dit andere belangrijke voedselpathogeen te achterhalen. Ter verdieping zal het ziekteverwekkende karakter van L. monocytogenes stammen worden bepaald op basis van het serotype en de aanwezigheid van ~60 beschreven virulentiegenen. Daarbij worden gegevens uit verschillende databases, met sequence data van zowel humane als niet humane stammen, met elkaar vergeleken. Zowel in het laboratorium als in de fabrieksomgeving zal het effect van verschillende schoonmaakmiddelen en schoonmaaktechnieken worden onderzocht op het elimineren van L. monocytogenes van oppervlaktes. Tevens wordt onderzocht of shotgun metagenomics analyse kan worden ingezet om voedsel snel en breed op voedselpathogenen te monitoren. Een prototype van een webapplicatie, waarmee bedrijven verkregen resultaten kunnen inzien en aanvullen zal verder worden ontwikkeld en door voedselverwerkende bedrijven worden getest en geïmplementeerd.
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).
Kennisnetwerken in het Nederlandse onderwijsveld zijn door formalisering en schaalvergroting aan een nieuwe fase begonnen. In dit project wordt kennis over de effectiviteit van deze formele netwerken hertaald naar de huidige situatie.Doel Kennisnetwerken in het Nederlandse onderwijsveld richten zich op de ontwikkeling van onderwijsprofessionals en -organisaties. Het zijn geneste systemen met een collectieve verantwoordelijkheid voor organisatie-overstijgende onderwijsvraagstukken. Dit onderzoek brengt voor 12 regionale kennisnetwerken in kaart hoe zij als complex systeem functioneren, wat de opbrengsten zijn en onder welke omstandigheden die zich voordoen. Doel is ontwerprichtlijnen voor optimale, duurzame processen van kennisontwikkeling, -deling en -benutting in regionale kennisnetwerken te formuleren. Het gaat hier om netwerken waarin scholen, kennisinstellingen en bedrijven plaatsnemen en samenwerken aan bijv. een duurzame onderzoekscultuur. Resultaten ontwerprichtlijnen voor optimale, duurzame processen van kennisontwikkeling, -deling en -benutting in regionale kennisnetwerken jaarlijkse reflectiesessies voor de kennisnetwerken leidraden voor de kennisnetwerken en de gehele onderwijspraktijk wetenschappelijke artikelen en bijdragen aan congressen Looptijd 01 juli 2023 - 31 december 2027 Aanpak We zetten verschillende elkaar aanvullende kwalitatieve en kwantitatieve onderzoeksmethoden in zoals sociaal netwerk vragenlijsten, (groeps-)interviews en document/productanalyses ten behoeve van within-case en cross-case analyses. Vanuit lectoraat Werken in Onderwijs voegen wij o.a. expertise toe over sociaal netwerk analyse.
