Biology subjects

Garssen, J.

Publications and source records attributed to Garssen, J..

10 recordsLinked to original sources

bioRxiv2026

Autologous Immune Cell Assay to Investigate risk of Processed- and Novel Food-Induced type 2 Inflammation in Peanut Allergy

Peanut allergy represents a major food-allergy burden, raising concerns about food processing and novel dietary products. Current diagnostics assess primarily allergic endpoints rather than immune mechanisms initiating and maintaining type 2 inflammation, particularly DC2-mediated Th2 polarization. Here, an in vitro autologous monocyte-derived dendritic cell (moDC)-T cell and B cell assay has been established to study immunomodulatory effects induced by unprocessed (P-D) and processed (P-DH (heated) or P-DHG (heated and glycated)) peanut proteins and emerging foods (protein concentrates or whole biomass), related to ex vivo DC2-T cell reactions. CD14+ monocytes, CD4+ T cells and CD19+ B cells were isolated from six peanut-allergic patients PBMCs. MoDCs generated with IL4/GM-CSF were exposed (48h) to type 2 polarizing cytokine (DC2) mix, or DC2 mix combined with the food samples. Next, DC2s were co-cultured with T cells (5d), followed by B cells incubation with DC2/T cell supernatant and food samples (10d). Supernatants and cells were analyzed for Th1/Th2/Th-regulatory (Treg) cells, IgE and IgG profiles. DC2 induced a strong Th2 phenotype and activity, P-D DC2 further enhanced IL13 secretion and %Tregs. P-DH DC2 favored Th2, whereas P-DHG DC2 increased IFN{gamma}, with neither increasing %Treg. All increased CD40L+CD25+ memory Th2 cells. Wheat, whey and seaweed biomass had little effect, whereas algae DC2 showed distinct immunomodulatory, adjuvant-like activity. In conclusion, this autologous in vitro assay captures peanut-specific and generic Th2 responses and reactivity to food samples, supporting its use as additional tool to assess type 2-driving potential and allergenicity of emerging foods and processing methods in peanut-allergic patients. Clinical trial registrationThe current in vitro study was conducted in accordance with the Declaration of Helsinki and approved by the Medical Ethics Review Committee (METC) of the Erasmus MC (NL79534.078.21 MEC-2021-0905) and registrated at International Clinical Trials Registry Platform (NL-OMON51765). Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=124 SRC="FIGDIR/small/705773v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1f42191org.highwire.dtl.DTLVardef@c7e4baorg.highwire.dtl.DTLVardef@1798547org.highwire.dtl.DTLVardef@11d0d96_HPS_FORMAT_FIGEXP M_FIG C_FIG Immune cell illustrations were adapted (1, 2) with permission; permission conveyed through Copyright Clearance Center, Inc. Capsule summaryThe autologous moDC-T cell-B cell in vitro assay may be used to assess whether processing methods or new foods might have intrinsic capacity to affect type 2 inflammation in peanut-allergic patients.

immunology↗

bioRxiv2023

Maternal allergic sensitization affects the T cell modulatory capacity and molecular cargo of milk derived extracellular vesicles

BackgroundHuman milk extracellular vesicles (EVs) affect various cell types in the gastrointestinal tract, including T cells, and play a role in the development of the newborns immune system by delivering specific molecular cargo to target cells. Although maternal allergic sensitization alters the composition of milk, it is unknown whether this impacts the function of milk EVs. Therefore, we analyzed the T cell modulatory capacity and compared the protein and miRNA cargoes of EVs from milk of allergic and non-allergic mothers. MethodsEVs were isolated from human milk from allergic and non-allergic donors by differential centrifugation, density gradient floatation and size exclusion chromatography. Functional modulation of primary human CD4+ T cells by EVs was assessed in vitro. Proteomic analysis and small RNA sequencing was performed on milk EVs to evaluate protein and miRNA abundance and to identify cellular targets of this EV cargo in relevant T cell signaling pathways. ResultsT cell proliferation, activation and cytokine production were suppressed in the presence of milk EVs. Remarkably, milk EVs from allergic mothers inhibited T cell activation to a lesser extent than EVs from non-allergic mothers. Integrative multi-omics analysis identified EV cargo of which the cellular targets could be linked to differential modulation of T cell activation-associated processes . ConclusionsMilk EVs from non-allergic mothers are stronger inhibitors of T cell activation compared to milk EVs from allergic mothers. This altered functionality might be linked to changes in miRNA and protein cargo that modulate T cell signaling pathways in an integrative manner.

immunology↗

bioRxiv2022

Raw milk kefir: microbiota, bioactive peptides, and immune modulation.

This study aims to characterize the microbial and peptidomic composition of raw milk kefir, and to address the potential anti-allergic effects of raw milk kefir using validated research models for food allergy. Raw milk kefir was produced after incubation with a defined freeze-dried starter culture. Kefir was sampled during fermentation at seven-time intervals. For comparison, kefir was also prepared from boiled milk (100 {degrees}C; 5 sec). Peptide compositions were determined for the raw and heated milk, plus kefir end products made from these milks. In a murine food allergy model, the two kefir end products were investigated on their allergy modulating effects. In both kefirs, we identified amplicon sequence variants identical to those in the starter, matching the bacteria Lactococcus lactis, Streptococcus thermophilus, Leuconostoc and the yeast Debaromyces. In raw milk kefir, additional sequence variants of Lactococcus lactis and the yeasts Pichia and Galactomyces could be identified, which were absent in heated milk kefir. Analysis of peptide compositions in kefirs indicated that the number and intensity of peptides drastically increased after fermentation. Heating of milk negatively affected the diversity of the peptide composition in kefir. Only raw milk kefir suppressed the acute allergic skin response to the food allergen ovalbumin in sensitised mice. These effects coincided with differences in the T-cell compartment, with lower percentages of activated Th1 cells and IFNg production after treatment with kefir made from heated milk. The results of this study indicate specific properties of raw milk kefir that may contribute to its additional health benefits.

microbiology↗

bioRxiv2022

Human Milk Oligosaccharide 2'-fucosyllactose inhibits ligand binding to C-type lectin DC-SIGN but not to Langerin

Human milk oligosaccharides (HMOs) and its most abundant component, 2-Fucosyllactose (2-FL), are known to be immunomodulatory. Previously, it was shown that HMOs and 2-FL bind to the C-type lectin receptor DC-SIGN. Here we show, using a ligand-receptor competition assay, that a whole mixture of HMOs from pooled human milk (HMOS) and 2-FL inhibit the binding of the carbohydrate-binding receptor DC-SIGN to its prototypical ligands, fucose and the oligosaccharide Lewis-B, (Leb) in a dose-dependent way. Interestingly, such inhibition by HMOS and 2-FL was not detected for another C-type lectin, Langerin, evolutionary similar to DC-SIGN. The cell-ligand competition assay using DC-SIGN expressing cells confirmed that 2-FL inhibits the binding of DC-SIGN to Leb. Molecular dynamics (MD) simulations show that 2-FL exists in a preorganized bioactive conformation before binding to DC-SIGN and this conformation is retained after binding to DC-SIGN. Leb has more flexible conformations and utilizes two binding modes, which operate one at a time via its two fucoses to bind to DC-SIGN. 2-FL may have a reduced entropic penalty due to its preorganized state compared to Leb, and it has lower binding enthalpy, suggesting better binding to DC-SIGN. Thus, due to the better binding to DC-SIGN, 2-FL may replace Leb from its binding pocket in DC-SIGN. MD simulations also showed that 2-FL does not bind to Langerin. Our studies confirm 2-FL as a specific ligand for DC-SIGN and suggest that 2-FL can replace other DC-SIGN ligands from its binding pocket during ligand-receptor interactions in possible immunomodulatory processes.

biochemistry↗

bioRxiv2022

SARS-CoV-2 Omicron Variant AI-based Primers

As the COVID-19 pandemic continues to affect the world, a new variant of concern, B.1.1.529 (Omicron), has been recently identified by the World Health Organization. At the time of writing, there are still no available primer sets specific to the Omicron variant, and its identification is only possible by using multiple targets, checking for specific failures, amplifying the suspect samples, and sequencing the results. This procedure is considerably time-consuming, in a situation where time might be of the essence. In this paper we use an Artificial Intelligence (AI) technique to identify a candidate primer set for the Omicron variant. The technique, based on Evolutionary Algorithms (EAs), has been already exploited in the recent past to develop primers for the B.1.1.7/Alpha variant, that have later been successfully tested in the lab. Starting from available virus samples, the technique explores the space of all possible subsequences of viral RNA, evaluating them as candidate primers. The criteria used to establish the suitability of a sequence as primer includes its frequency of appearance in samples labeled as Omicron, its absence from samples labeled as other variants, a specific range of melting temperature, and its CG content. The resulting primer set has been validated in silico and proves successful in preliminary laboratory tests. Thus, these results prove further that our technique could be established as a working template for a quick response to the appearance of new SARS-CoV-2 variants.

bioinformatics↗

bioRxiv2021

Design of Specific Primer Sets for the Detection of B.1.1.7, B.1.351 and P.1 SARS-CoV-2 Variants using Deep Learning

As the COVID-19 pandemic continues, new SARS-CoV-2 variants with potentially dangerous features have been identified by the scientific community. Variant B.1.1.7 lineage clade GR from Global Initiative on Sharing All Influenza Data (GISAID) was first detected in the UK, and it appears to possess an increased transmissibility. At the same time, South African authorities reported variant B.1.351, that shares several mutations with B.1.1.7, and might also present high transmissibility. Earlier this year, a variant labelled P.1 with 17 non-synonymous mutations was detected in Brazil. Recently the World Health Organization has raised concern for the variants B.1.617.2 mainly detected in India but now exported worldwide. It is paramount to rapidly develop specific molecular tests to uniquely identify new variants. Using a completely automated pipeline built around deep learning and evolutionary algorithms techniques, we designed primer sets specific to variants B.1.1.7, B.1.351, P.1 and respectively. Starting from sequences openly available in the GISAID repository, our pipeline was able to deliver the primer sets for each variant. In-silico tests show that the sequences in the primer sets present high accuracy and are based on 2 mutations or more. In addition, we present an analysis of key mutations for SARS-CoV-2 variants. Finally, we tested the designed primers for B.1.1.7 using RT-PCR. The presented methodology can be exploited to swiftly obtain primer sets for each new variant, that can later be a part of a multiplexed approach for the initial diagnosis of COVID-19 patients.

bioinformatics↗

bioRxiv2020

Design of Specific Primer Set for Detection of B.1.1.7 SARS-CoV-2 Variant using Deep Learning

The SARS-CoV-2 variant B.1.1.7 lineage, also known as clade GR from Global Initiative on Sharing All Influenza Data (GISAID), Nextstrain clade 20B, or Variant Under Investigation in December 2020 (VUI - 202012/01), appears to have an increased transmissability in comparison to other variants. Thus, to contain and study this variant of the SARS-CoV-2 virus, it is necessary to develop a specific molecular test to uniquely identify it. Using a completely automated pipeline involving deep learning techniques, we designed a primer set which is specific to SARS-CoV-2 variant B.1.1.7 with >99% accuracy, starting from 8,923 sequences from GISAID. The resulting primer set is in the region of the synonymous mutation C16176T in the ORF1ab gene, using the canonical sequence of the variant B.1.1.7 as a reference. Further in-silico testing shows that the primer sets sequences do not appear in different viruses, using 20,571 virus samples from the National Center for Biotechnology Information (NCBI), nor in other coronaviruses, using 487 samples from National Genomics Data Center (NGDC). In conclusion, the presented primer set can be exploited as part of a multiplexed approach in the initial diagnosis of Covid-19 patients, or used as a second step of diagnosis in cases already positive to Covid-19, to identify individuals carrying the B.1.1.7 variant.

bioinformatics↗

bioRxiv2020

A combined microphysiological-computational omics approach in dietary protein evaluation

The ever-growing world population puts pressure on food security. To tackle this, waste stream proteins and novel protein sources need to be evaluated for nutritional value, which requires information on digesta peptide composition in comparison to established protein sources and coupling to biological parameters. Here, we present a novel combined experimental and computational approach comparing seventeen protein sources with cows whey protein (WPC) as benchmark. In vitro digestion was followed by proteomics analysis and statistical model clustering based on Bayesian Information Criterion. Next, we incorporated functional protein data after evaluating the effects of eighteen protein digests on intestinal barrier integrity, viability, brush border enzyme activity and immune parameters using a bioengineered intestine. Our data show that a holistic approach allows evaluating a dietary proteins potential for delivery of bioactive peptides, where protein source (animal, plant or novel source-derived) does not seem to be the driving force for clustering.

bioengineering↗

bioRxiv2020

Human milk extracellular vesicles target nodes in interconnected signalling pathways that enhance oral epithelial barrier function and dampen immune responses

Maternal milk is natures first functional food. It plays a crucial role in the development of the infants gastrointestinal (GI) tract and the immune system. Extracellular vesicles (EVs) are a heterogeneous population of lipid bilayer enclosed vesicles released by cells for intercellular communication and are a component of milk. Recently, we discovered that human milk EVs contain a unique proteome compared to other milk components. Here, we show that physiological concentrations of milk EVs support epithelial barrier function by increasing cell migration via the p38 MAPK pathway. Additionally, milk EVs inhibit agonist-induced activation of endosomal Toll like receptors TLR3 and TLR9. Furthermore, milk EVs directly inhibit activation of CD4+ T cells by temporarily suppressing T cell activation without inducing tolerance. We show that milk EV proteins target key hotspots of signalling networks that can modulate cellular processes in various cell types of the GI tract.

immunology↗

bioRxiv2020

Accurate Identification of SARS-CoV-2 from Viral Genome Sequences using Deep Learning

In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from available repositories, separating the genome of different virus strains from the Coronavirus family with considerable accuracy. The networks behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are first validated on samples from other repositories, and proven able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets on existing datasets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n=6 previously tested positive), delivering a sensibility similar to routine diagnostic methods, and 100% specificity. In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from NGDC, separating the genome of different virus strains from the Coronavirus family with accuracy 98.73%. The networks behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from NCBI and GISAID, and proven able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n=6 previously tested positive), delivering a sensibility similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics.

bioinformatics↗