Biology subjects

Weidinger, S.

Publications and source records attributed to Weidinger, S..

4 recordsLinked to original sources

bioRxiv2026

MycorrhizaFinder: an efficient machine learning tool to quantify endomycorrhizal colonisation of real-world roots

AbstractO_ST_ABSBackground and aimsC_ST_ABSRoot colonisation by endomycorrhizal fungi has long been one of the most widely used metrics in mycorrhizal studies. However, due to the significant time required to assess colonisation using traditional microscope techniques, studies of colonisation at large scales are impractical. AI-powered approaches may increase output and facilitate ecosystem assessments. MethodsWe trained an AI-powered tool (MycorrhizaFinder) on field roots from diverse grasslands and heathlands hosting common Northern European plants with a range of arbuscular (AM) and ericoid mycorrhizal (ErM) fungal structures, and dark septate endophytes (DSE), also common in field-sourced roots. We incorporated a user-customized confidence threshold to encourage the user to engage with inevitable morphological ambiguities, in e.g. ErM and DSE. A Macro F1 statistic was used to assess the tools development. ResultsWe provide a sample workflow from root processing and microscope slide scanning to semi-automated model training and performance evaluation. Without human supervision, our automated baseline Macro F1 is 66% for arbuscular and at 57% for ericoid mycorrhizal colonisation assessment. ConclusionMycorrhizaFinder is user friendly, requires no programming skills and offers flexibility for advanced agronomists or ecologists who wish to train the tool using their own labelled mycorrhizal root datasets, including images acquired from different instruments or staining protocols. This adaptability allows users to customize the model for specific ecosystems or experimental designs. Leveraged with molecular identification and/or functional assessment of fungi, MycorrhizaFinder could support scalable and repeatable monitoring across ecosystems to assess mycorrhizal status and track land-use changes over time.

ecology↗

bioRxiv2025

The inflammatory skin disease map (ISD map): an interactive computational resource focused on psoriasis and atopic dermatitis molecular mechanisms

BackgroundInflammatory skin diseases (ISD), including atopic dermatitis (AD) and psoriasis (PsO), emerge from a complex network of inter- and intracellular biochemical interactions under the influence of genetic and environmental factors. The complexity of ISD mechanisms hinders translation of research findings into effective treatments and may explain the low remission rates despite the availability of modern targeted therapies. ObjectiveTo model AD- and PsO-associated mechanisms as networks of context-specific molecular interactions, the so-called ISD map, and to check the usefulness of this map as a graphically guided review of AD and PsO mechanisms and as a mechanistic hypothesis-generating platform. MethodsThe ISD map was built by assembling mechanistically resolved causal interactions obtained from relevant biomedical literature via manual curation. ResultsWe demonstrate that the ISD map (https://imi-biomap.elixir-luxembourg.org/) serves as an interactive, graphical review of AD and PsO molecular mechanisms and as a mechanistic hypothesis-generating platform. By analysing the map structure itself or the map integrated with genetics and functional genomics data, we could generate the following mechanistic hypotheses: (i) AD poor response to dupilumab is associated with a potential upregulation of IFNG, IL22, TSLP, IL-17A and IL25 signalling pathways in keratinocytes and/or single nucleotide polymorphisms (SNPs) in genes encoding regulators of IFNG expression in Th1 cells and (ii) PsO resistance to cytokine-induced apoptosis is associated with SNPs in IFNG signalling genes regulating SOCS1 in keratinocytes. Finally, the IL4/IL13 pathway in the AD submap of the ISD map was converted into a probabilistic Boolean model to simulate the effects of IFNG in sensory perception of itching after treatment with dupilumab. Our findings suggest that inhibiting both IFNG and IL4R may improve the therapeutic management of itching. ConclusionThe ISD map provides a significant interactive, computationally accessible resource of molecular knowledge on AD and PsO that can be used to graphically review known AD and PsO mechanisms and generate mechanistic hypotheses.

bioinformatics↗

bioRxiv2023

Tryptophan wasting and disease activity as a systems phenomenon in inflammation - an analysis across 13 chronic inflammatory diseases.

Chronic inflammatory diseases (CID) are systems disorders affecting various organs including the intestine, joint and skin. The essential amino acid tryptophan (Trp) is not only used for protein synthesis but can also be catabolized to various bioactive derivatives that are important for cellular energy metabolism and immune regulation. Increased Trp catabolism via the kynurenine pathway is seen across individual CID entities1-5. Here, we assessed the levels of Trp and tryptophan derivatives across 13 CID to investigate the extent and nature of Trp wasting as a systems phenomenon in CID. We found reduced serum Trp levels across the majority of CID and a prevailing negative relationship between Trp and systemic inflammatory marker C-reactive protein (CRP). Increases in the kynurenine-to-Trp ratio (Kyn:Trp) indicate that the kynurenine pathway is a major route for CID-related Trp wasting. However, the extent of Trp depletion and its relationship with disease activity varies by disease, indicating potential differences in Trp metabolism. In addition, we find that amino acid catabolism in chronic inflammation is specific to tryptophan wasting, whereas other proteinogenic amino acids are not affected. Hence, our results suggest that increased Trp catabolism is a common metabolic occurrence in CID that may directly affect systemic immunity. Grant supportThis work was supported by the DFG Cluster of Excellence 1261 "Precision medicine in chronic inflammation" (KA, SSchr, PR, BH, SWa), the BMBF (e:Med Juniorverbund "Try-IBD" 01ZX1915A and 01ZX2215, the e:Med Network iTREAT 01ZX2202A, and GUIDE-IBD 031L0188A), DFG RU5042 (PR, KA), and Innovative Medicines Initiative 2 Joint Undertakings ("Taxonomy, Treatments, Targets and Remission", No. 831434, "ImmUniverse", grant agreement No. 853995, "BIOMAP", grant agreement No. 821511).

immunology↗

bioRxiv2021

The integration of large-scale public data and network analysis uncovers molecular characteristics of psoriasis

In recent years, a growing interest in the characterization of the molecular basis of psoriasis has been observed. However, despite the availability of a large amount of molecular data, many pathogenic mechanisms of psoriasis are still poorly understood. In this study, we performed an integrated analysis of 23 public transcriptomic datasets encompassing both lesional and uninvolved skin samples from psoriasis patients. We defined comprehensive gene co-expression network models of psoriatic lesions and uninvolved skin. Moreover, we collected, curated and exploited a wide range of functional information from multiple public sources in order to systematically annotate the inferred networks. The integrated transcriptomics analysis of public datasets shed light on a number of genes which are frequently deregulated in the psoriatic lesion compared with the unaffected skin in a large number of studies. In particular, CRABP2, LCN2, S100A12 and PDZK1IP1 were found to be deregulated in all of the datasets analyzed. Furthermore, the analysis of co-expression networks highlights genes showing aberrant patterns of connectivity in the lesional network as compared to the network inferred from unaffected skin samples. For instance, we identified co-expression patterns of SERPINB4, KYNU and S100A12 as being the most affected by the disease. Network analysis allowed us to identify YPEL1 and HUS1 as plausible, previously unknown, actors in the expression of the psoriasis phenotype. In addition, by exploiting topological properties of the network models, we highlighted a set of 250 non-deregulated genes, 223 of which have never been associated with the disease before, including CACNA1A, HADH, ATP5MC1 and CBARP among others. Finally, we characterized specific communities of co-expressed genes sustaining relevant molecular functions and specific immune cell types expression signatures playing a role in the psoriasis lesion. Overall, integrating experimental driven results with curated functional information from public repositories represents an efficient approach to empower knowledge generation about psoriasis and may be applicable to other complex diseases.

systems biology↗