Computational tools for inferring transcription factor activity.

Transcription factors (TFs) are essential players in orchestrating the regulatory landscape in cells. Still, their exact modes of action and dependencies on other regulatory aspects remain elusive. Since TFs act cell type-specific and each TF has its own characteristics, untangling their regulatory interactions from an experimental point of view is laborious and convoluted. Thus, there is an ongoing development of computational tools that estimate transcription factor activity (TFA) from a variety of data modalities, either based on a mapping of TFs to their putative target genes or in a genome-wide, gene-unspecific fashion. These tools can help to gain insights into TF regulation and to prioritize candidates for experimental validation. We want to give an overview of available computational tools that estimate TFA, illustrate examples of their application, debate common result validation strategies, and discuss assumptions and concomitant limitations.

The decision for or against mycoparasitic attack by Trichoderma spp. is taken already at a distance in a prey-specific manner and benefits plant-beneficial interactions.

BACKGROUND: The application of plant-beneficial microorganisms as bio-fertilizer and biocontrol agents has gained traction in recent years, as both agriculture and forestry are facing the challenges of poor soils and climate change. Trichoderma 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. So far, it remains unclear, whether Trichoderma is able to differentiate between plant-beneficial and plant-pathogenic fungi during the process of plant colonization. RESULTS: We 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. Using 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. CONCLUSION: The 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. This sheds light on the entangled interactions of fungi in the rhizosphere and emphasizes the potential benefits of using Trichoderma spp. as a biocontrol agent and bio-fertilizer in tree plantations.

Mitochondrial perturbation in the intestine causes microbiota-dependent injury and gene signatures discriminative of inflammatory disease.

Mitochondrial dysfunction is associated with inflammatory bowel diseases (IBDs). To understand how microbial-metabolic circuits contribute to intestinal injury, we disrupt mitochondrial function in the epithelium by deleting the mitochondrial chaperone, heat shock protein 60 (Hsp60Δ/ΔIEC). This metabolic perturbation causes self-resolving tissue injury. Regeneration is disrupted in the absence of the aryl hydrocarbon receptor (Hsp60Δ/ΔIEC;AhR-/-) involved in intestinal homeostasis or inflammatory regulator interleukin (IL)-10 (Hsp60Δ/ΔIEC;Il10-/-), causing IBD-like pathology. Injury is absent in the distal colon of germ-free (GF) Hsp60Δ/ΔIEC mice, highlighting bacterial control of metabolic injury. Colonizing GF Hsp60Δ/ΔIEC mice with the synthetic community OMM12 reveals expansion of metabolically flexible Bacteroides, and B. caecimuris mono-colonization recapitulates the injury. Transcriptional profiling of the metabolically impaired epithelium reveals gene signatures involved in oxidative stress (Ido1, Nos2, Duox2). These signatures are observed in samples from Crohn's disease patients, distinguishing active from inactive inflammation. Thus, mitochondrial perturbation of the epithelium causes microbiota-dependent injury with discriminative inflammatory gene profiles relevant for IBD.

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 analysing alternative splicing integrating eleven 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.