Biology subjects

Repsilber, D.

Publications and source records attributed to Repsilber, D..

6 recordsLinked to original sources

bioRxiv2025

Effects of incrementally increased plant-based protein intake on gut microbiota and inflammatory-metabolic biomarkers in healthy adults

Shifting to a plant-based diet naturally alters protein source choices. In many countries, protein from yellow pea is widely used as main ingredient in meat alternatives. Still, its biological effects, especially regarding gastrointestinal health, remain incompletely understood. The aim of our study was to investigate how a weekly increase in the intake of a well-characterized pea protein isolate affects surrogate markers of health, fecal short-chain fatty acids and gut microbiota composition in healthy individuals. Male and female adults (N=29) participated in this exploratory intervention study. A 4-week pre-intervention period for questionnaires and fecal samples collection was followed by a 4-week supplementation. Participants consumed isolated pea protein in weekly increasing amounts, starting from 0.25 g/kg body mass/day in week 5 to 1.00 g/kg body mass/day in week 8. Questionnaire data, fecal samples as well as fasting blood and 24-h urine samples were collected weekly. Data from biological samples and questionnaires confirmed a healthy study population and compliance. Fecal calprotectin levels significantly increased only in a subset of participants, which was also accompanied by higher fecal water cytotoxicity in vitro. Short-chain fatty acids mainly rose in those subjects with stable calprotectin levels. Relative abundances of Limosilactobacillus frumenti, Odoribacter splanchnicus and Lactobacillus crispatus increased significantly in the total population during the intervention while the relative abundance of Bifidobacterium longum and Bifidobacterium catenulatum decreased. Our results indicate that an increased intake of pea protein isolate affects the growth of certain beneficial bacteria strains and differentially influences markers related to gut inflammation in healthy individuals.

biochemistry↗

bioRxiv2024

Transcriptional modulation unique to vulnerable motor neurons predict ALS across species and SOD gene mutations

Amyotrophic lateral sclerosis (ALS) is characterized by the progressive loss of motor neurons that innervate skeletal muscles. However, certain motor neuron groups including ocular motor neurons, are relatively resilient. To reveal key drivers of resilience versus vulnerability in ALS, we investigate the transcriptional dynamics of four distinct motor neuron populations in SOD1G93A ALS mice using LCM-seq and single molecule fluorescent in situ hybridization. We find that resilient ocular motor neurons regulate few genes in response to disease. Instead, they exhibit high baseline gene expression of neuroprotective factors including En1, Pvalb, Cd63 and Gal, some of which vulnerable motor neurons upregulate during disease. Vulnerable motor neuron groups upregulate both detrimental and regenerative responses to ALS and share pathway activation, indicating that breakdown occurs through similar mechanisms across vulnerable neurons, albeit with distinct timing. Meta-analysis across four rodent mutant SOD1 motor neuron transcriptome datasets identify a shared vulnerability code of 39 genes including e.g Atf4, Nupr1, Ddit3 and Penk, involved in apoptosis, as well as a proregenerative and anti-apoptotic signature consisting of Atf3, Vgf, Ina, Sprr1a, Fgf21, Gap43, Adcyap1, and Mt1. Machine learning using genes upregulated in SOD1G93A spinal motor neuron predicts disease in human stem cell-derived SOD1E100G motor neurons, and shows that dysregulation of VGF, INA, PENK and NTS are strong disease-predictors across species and SOD1 mutations. Our study reveals motor neuron population-specific gene expression and temporal disease-induced regulation that together provide a basis to explain ALS selective vulnerability and resilience and that can be used to predict disease.

neuroscience↗

bioRxiv2023

Mathematical models disentangle the role of IL-10 feedbacks in human monocytes upon proinflammatory activation

Inflammation is one of the vital mechanisms through which the immune system responds to harmful stimuli. During inflammation, pro and anti-inflammatory cytokines interplay to orchestrate fine-tuned, dynamic immune responses. The cytokine interplay governs switches in the inflammatory response and dictates the propagation of inflammation. Molecular pathways underlying the interplay are complex, and time-resolved monitoring of mediators and cytokines is necessary as a basis to study them in detail. Our understanding can be advanced by in silico models which enable to analyze the system of interactions and their dynamical interplay in detail. We, therefore, used a mathematical modeling approach to study the interplay between prominent pro and anti-inflammatory cytokines with a focus on Tumor Necrosis Factor (TNF) and Interleukin 10 (IL-10) in lipopolysaccharide (LPS)-primed primary human monocytes. Relevant time-resolved data were generated by experimentally adding or blocking IL-10 at different time points. The model was successfully trained and could predict independent validation data and was further used to perform in silico experiments to disentangle the role of IL-10 feedbacks in acute inflammation. We used the insight to obtain a reduced predictive model including only the necessary IL-10-mediated feedbacks. Finally, the validated reduced model was used to predict early IL-10 - TNF switches in the inflammatory response. Overall, we gained detailed insights into fine-tuning of inflammatory responses in human monocytes and present a model for further use in studying the complex and dynamic process of cytokine-regulated acute inflammation.

systems biology↗

bioRxiv2020

Serum proteomic profiling at diagnosis predicts clinical course, and need for intensification of treatment in inflammatory bowel disease.

BackgroundSuccess in personalised medicine in complex disease is critically dependent on biomarker discovery. We profiled serum proteins using a novel proximity extension assay (PEA) to identify diagnostic and prognostic biomarkers in inflammatory bowel disease (IBD). MethodsWe conducted a prospective case-control study in an inception cohort of 552 patients (328 IBD, 224 non-IBD), profiling proteins recruited across 6 centres. Treatment escalation was characterised by the need for biological agents or surgery after initial disease remission. Nested leave-one-out cross validation was used to examine the performance of diagnostic and prognostic proteins. ResultsA total of 66 serum proteins differentiated IBD from symptomatic non-IBD controls including Matrix Metalloproteinase-12 (Holm adjusted p=4.1x10-23) and Oncostatin-M (OSM, p=3.7x10-16). Nine of these proteins associate with cis- germline variation (59 independent SNPs). Fifteen proteins, all members of TNF independent pathways including interleukin-1 and OSM predicted escalation, over a median follow-up of 518 (IQR 224-756) days. Nested cross-validation of the entire data set allows characterisation of 5-protein-models (96% comprising five core proteins ITGAV, EpCAM, IL18, SLAMF7, and IL8) which define a high-risk subgroup in IBD (HR 3.90, 95% CI: 2.43-6.26), or allows distinct 2, and 3 protein models for UC and CD respectively. ConclusionWe have characterised a simple oligo-protein panel that has the potential to identify IBD from symptomatic controls and predicts the evolution of disease over time. The technology could be suitable as a point of care testing in defining risk. Further prospective work is required to characterise the utility of the approach.

immunology↗

bioRxiv2020

The Atlas of Inflammation-Resolution (AIR)

Acute inflammation is a protective reaction by the immune system in response to invading pathogens or tissue damage. Ideally, the response should be localized, self-limited, and returning to homeostasis. If not resolved, acute inflammation can result in organ pathologies leading to chronic inflammatory phenotypes. Acute inflammation and inflammation resolution are complex coordinated processes, involving a number of cell types, interacting in space and time. The biomolecular complexity and the fact that several biomedical fields are involved, make a multi and interdisciplinary approach necessary. This Atlas of Inflammation Resolution (AIR) is a web-based resource capturing the state-of-the-art in acute inflammation and inflammation resolution research. The AIR provides an interface for users to search thousands of interactions, arranged in inter-connected multi-layers of process diagrams, covering a wide range of clinically relevant phenotypes. The AIR serves as an open access knowledgebase, including a gateway to numerous public databases. It is furthermore possible for the user to map experimental data onto the molecular interaction maps of the AIR, providing the basis for bioinformatics analyses and systems biology approaches. By mapping experimental data onto the Atlas, it can be used to elucidate drug action as well as molecular mechanisms underlying different disease phenotypes. For the visualization and exploration of information, the AIR uses the Minerva platform, which is a well-established tool for the presentation of disease maps. The molecular details of the AIR are encoded using international standards. The Atlas of Inflammation Resolution was created as a freely accessible resource, supporting research and education in the fields of acute inflammation and inflammation resolution. The AIR connects research communities, facilitates clinical decision making, and supports research scientists in the formulation and validation of hypotheses.

immunology↗

bioRxiv2018

Capturing context-specific regulation in molecular interaction networks

MotivationGene expression changes over time in response to perturbations. These changes are coordinated into functional modules via regulatory interactions. The genes within a functional module are expected to be differentially expressed in a manner coherent with their regulatory network. This perspective presents a promising approach to increase power to detect differential signals as well as for describing regulated modules from a mechanistic point of view.\n\nResultsWe present an effective procedure for identifying differentially activated subnetworks in molecular interaction networks. Differential gene expression coherent with the regulatory nature of the network is identified. Sequentially controlling error on genes and links results in more efficient inference. By focusing on local inference, our method is ignorant of the global topology, and as a result equally effective on exponential and scale-free networks. We apply our procedure both to systematically simulated data, comparing its performance to alternative methods, and to the transcription regulatory network in the context of particle-induced pulmonary inflammation, recapitulating and proposing additional candidates to some previously obtained results.\n\nContactstephen.rush@oru.se, dirk.repsilber@oru.se

systems biology↗