Biology subjects

Kersting, J.

Publications and source records attributed to Kersting, J..

5 recordsLinked to original sources

bioRxiv2025

Network Inference Reveals Distinct Transcriptional Regulation in Barley against Drought and Fusarium Head Blight

We analyzed transcriptional networks in barley under single and combined Fusarium head blight (FHB) and drought stress. We applied complementary Weighted Gene Correlation Network Analysis (WGCNA) to identify stress-associated gene co-expression modules and GENIE3 to infer gene regulatory networks (GRNs). Integration of these frameworks revealed strong overlaps between co-expression modules and GRN clusters, highlighting robust regulatory patterns. Key transcription factors (TFs) were identified based on their weighted node degrees, reflecting their connectivity within the network. Independent analysis of paired transcription factor binding sites in promoter regions further supported predicted regulatory interactions. Notably, WRKY TFs emerged as central regulators of FHB response, consistent with their known roles in defense and secondary metabolite biosynthesis, but did not appear in drought-associated contexts. For bHLH or NAC TFs, individual family members steered FHB or drought responses but not both. Our findings demonstrate the power of combining network inference and motif enrichment to identify candidate TFs controlling stress responses, providing a solid foundation for targeted functional validation.

plant biology↗

bioRxiv2025

Inferring and Evaluating Network Medicine-Based Disease Modules with Nextflow

Most human diseases result from complex molecular interactions of genes and proteins. Various network-based computational methods characterize these mechanisms by expanding seed genes into disease-associated subnetworks, or disease modules. Evaluating these diverse methods is tedious due to unique installation and data preparation requirements. Moreover, the underlying algorithmic strategies differ, making it difficult to determine which of the created modules are most useful or biologically plausible. To address this challenge, we developed an all-in-one Nextflow pipeline that enables automated and reproducible analyses. It handles installation, input preparation, execution, and systematic evaluation of six widely used module detection tools, considering module topology, functional coherence, robustness, and the capacity to recover seeds. In addition, it annotates the resulting disease modules with biological context information, prioritizes potential drug candidates, and generates visualizations and a comprehensive summary report. To showcase the value of our pipeline and offer guidance to potential users, we performed a comprehensive evaluation across 50 different disease-network combinations, revealing substantial variability among the derived disease modules. We show that this variability is driven by differences in modeling approach, input network, and seed composition. While most methods are robust to minor perturbations, they struggle to recover omitted seeds, and none consistently outperforms others, underscoring the need for careful method selection. Our work enables the research community to systematically compare approaches for disease module discovery, promoting reproducible network medicine research. Integrated into the nf-core project (https://nf-co.re/diseasemodulediscovery), it is intended as an extendable, long-term resource for tracking progress in the field.

bioinformatics↗

bioRxiv2024

Lifestyle-specific responses of Trichoderma spp. in mycoparasitic confrontations and implications for biocontrol of Populus x canescens

O_LITrichoderma spp. are gaining popularity in agriculture and forestry due to their multifaceted roles in promoting plant growth through e.g. nutrient translocation, hormone production, induction of plant systemic resistance, but also direct antagonism of other fungi. However, the mycotrophic nature of the genus bears the risk of possible interference with other native plant-beneficial fungi, such as ectomycorrhiza, in the rhizosphere. Such interference could yield unpredictable consequences for the host plants of these ecosystems. C_LIO_LIWe investigated whether Trichoderma spp. can differentiate between beneficial ectomycorrhizal fungi (represented by Laccaria bicolor and Hebeloma cylindrosporum) and pathogenic fungi (represented by Fusarium graminearum and Alternaria alternata) in different confrontation scenarios, including a newly developed olfactometer "race tube"-like system. C_LIO_LIUsing two independent species, T. harzianum, and T. atrobrunneum, with plant-growth-promoting and immune-stimulating properties towards Populus x canescens, our study revealed robustly accelerated growth towards phytopathogens, while showing a contrary response to ectomycorrhizal fungi. Transcriptomic analyses identified distinct genetic programs during interaction corresponding to the lifestyles, emphasizing the expression of mycoparasitism-related genes only in the presence of phytopathogens. C_LIO_LIThe findings reveal a critical mode of fungal community interactions belowground and suggest that Trichoderma spp. can distinguish between fungal partners of different lifestyles already at a distance. C_LI

microbiology↗

bioRxiv2022

DysRegNet: Patient-specific and confounder-aware dysregulated network inference

Gene regulation is frequently altered in diseases in unique and patient-specific ways. Hence, personalized strategies have been proposed to infer patient-specific gene-regulatory networks. However, existing methods do not scale well as they often require recomputing the entire network per sample. Moreover, they do not account for clinically important confounding factors such as age, sex, or treatment history. Finally, a user-friendly implementation for the analysis and interpretation of such net-works is missing. We present DysRegNet, a method for inferring patient-specific regulatory alterations (dysregulations) from bulk gene expression profiles. We compared DysRegNet to SSN, a well-known sample-specific network approach. We demonstrate that both SSN and DysRegNet produce interpretable and biologically meaningful networks across various cancer types. In contrast to SSN, DysRegNet can scale to arbitrary sample numbers and highlights the importance of confounders in network inference, revealing an age-specific bias in gene regulation in breast cancer. DysRegNet is available as a Python package (https://github.com/biomedbigdata/DysRegNet_package), and analysis results for eleven TCGA cancer types are available through an interactive web interface (https://exbio.wzw.tum.de/dysregnet).

bioinformatics↗

bioRxiv2022

Alternative splicing analysis benchmark with DICAST

Alternative splicing is a major contributor to transcriptome and proteome diversity in health and disease. A plethora of tools have been developed for studying alternative splicing in RNA-seq data. Previous benchmarks focused on isoform quantification and mapping. They neglected event detection tools, which arguably provide the most detailed insights into the alternative splicing process. DICAST offers a modular and extensible framework for the analysis of alternative splicing integrating 11 splice-aware mapping and eight event detection tools. We benchmark all tools extensively on simulated as well as whole blood RNA-seq data. STAR and HISAT2 demonstrated the best balance between performance and run time. The performance of event detection tools varies widely with no tool outperforming all others. DICAST allows researchers to employ a consensus approach to consider the most successful tools jointly for robust event detection. Furthermore, we propose the first reporting standard to unify existing formats and to guide future tool development.

bioinformatics↗