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Wilhelm, K.

Publications and source records attributed to Wilhelm, K..

5 recordsLinked to original sources

From nest to hatchling: nidobiome assembly and host selection in oviparous vertebrates without parental care

Early microbial assembly is fundamental for host development and fitness, yet in oviparous vertebrates lacking parental care, the mechanisms by which eggs and neonates acquire their microbiome remain poorly understood. The nidobiome concept predicts that maternal and environmental sources, and host selection, collectively shape early microbial communities. To test this, we conducted a longitudinal study on microbial assembly in the yellow-spotted Amazon river turtle (Podocnemis unifilis). We sampled maternal mucosa, freshly laid outer eggshells, and nest sand before incubation, along with hatchery sand, outer and inner eggshells, and hatchling cloaca after incubation. Using 16S and ITS amplicon sequencing to quantify microbial diversity, we observed that freshly laid outer eggshells shared bacterial diversity with maternal mucosa, whereas nest sand harboured a distinct community, providing evidence of early vertical transmission. After incubation, eggshell microbial communities became more even without a change in richness, while nest sand communities exhibited the opposite pattern, reflecting divergent host-environment assembly dynamics. SourceTracker analyses confirmed contributions from both maternal and environmental sources, yet inner eggshells and hatchling cloaca assembled distinct communities compared with outer eggshells, indicating that host selection is largely deterministic. Interkingdom interactions favoured beneficial microorganisms, suggesting active host filtering throughout development. Even in the presence of fusariosis, a widespread threat in turtle nests, host selection enriched protective taxa, linking microbiome composition to hatching success. These findings demonstrate that maternal seeding, priority effects, and deterministic host selection govern early-life microbiome assembly in oviparous reptiles without parental care, with direct implications for microbe-informed conservation strategies. ImportanceUnderstanding early-life microbial assembly is critical for uncovering the ecological and evolutionary drivers of host-microbiome interactions. This study reveals how microbial communities develop in oviparous vertebrates lacking parental care, highlighting key mechanisms such as vertical transmission, host selection, and environmental modulation that shape early-life microbiomes. Our results demonstrate that maternal microbiota seeding and priority effects play fundamental roles in structuring egg-associated microbial communities, directly influencing host fitness and survival. By identifying deterministic processes that enrich beneficial microorganisms during early development, this research underscores the pivotal role of microbiomes in host resilience and adaptation. These insights carry urgent conservation implications, offering microbe-informed strategies to mitigate emerging threats, such as diseases in vulnerable species like the yellow-spotted Amazon river turtle, and emphasising the broader importance of microbiome stewardship for ecosystem health.

ecology↗

PIEZO1-mediated mechanosensation links aging to bladder dysfunction

Aging is accompanied by profound changes in bladder function, leading to increased urinary frequency and incontinence that impair quality of life in humans and are recapitulated in mouse models. Bladder filling and emptying rely on precise mechanosensory feedback, yet how aging alters this sensory control remains unclear. PIEZO ion channels convert mechanical forces into cellular signals essential for bladder fullness sensation. Using inducible smooth-muscle specific Piezo1 deletion, we find that loss of Piezo1 attenuates aging-related bladder dysfunction. Dietary enrichment with margaric acid, a membrane-active fatty acid previously shown to inhibit PIEZO channels, reduced urinary dysfunction in aged mice. In humans, genotype-phenotype analyses reveal an association between a PIEZO1 gain-of-function variant and early-onset neuromuscular bladder dysfunction. Together, these findings define a smooth-muscle, PIEZO1-mediated mechanosensory basis for aging-related bladder dysfunction and introduce a non-invasive strategy for targeting PIEZO channels in vivo.

physiology↗

Fertilization impacts microbiomes along the grassland trophic chain

Agricultural grasslands are often managed intensively, influencing soil properties and microbial communities. This can result in significant community changes and challenges to health and function at different levels along the trophic chain. This study investigates how fertilization affects microbial communities in grassland ecosystems in multiple connected trophic compartments. Shifts in microbial composition occurred in response to fertilization with effects being host-dependent and changes being more pronounced in belowground compartments. Strong interactions between trophic levels facilitated transmission of bacterial genera from soil and roots to higher trophic levels. Pig slurry- derived microbes were found in all compartments, but their low prevalence suggests an indirect effect of fertilisation, primarily due to changes in nutrient availability. Our findings emphasize the importance of considering both individual compartments and trophic interactions in ecosystems to fully understand the effects of anthropogenic disturbances on environmental and human health.

microbiology↗

Moving translational mass spectrometry imaging towards transparent and reproducible data analyses: A case study of an urothelial cancer cohort analyzed in the Galaxy framework

BackgroundMass spectrometry imaging (MSI) derives spatial molecular distribution maps directly from clinical tissue specimens. This allows for spatial characterization of molecular compositions of different tissue types and tumor subtypes, which bears great potential for assisting pathologists with diagnostic decisions or personalized treatments. Unfortunately, progress in translational MSI is often hindered by insufficient quality control and lack of reproducible data analysis. Raw data and analysis scripts are rarely publicly shared. Here, we demonstrate the application of the Galaxy MSI tool set for the reproducible analysis of an urothelial carcinoma dataset. MethodsTryptic peptides were imaged in a cohort of 39 formalin-fixed, paraffin-embedded human urothelial cancer tissue cores with a MALDI-TOF/TOF device. The complete data analysis was performed in a fully transparent and reproducible manner on the European Galaxy Server. Annotations of tumor and stroma were performed by a pathologist and transferred to the MSI data to allow for supervised classifications of tumor vs. stroma tissue areas as well as for muscle-infiltrating and non-muscle invasive urothelial carcinomas. For putative peptide identifications, m/z features were matched to the MSiMass list. ResultsRigorous quality control in combination with careful pre-processing enabled reduction of m/z shifts and intensity batch effects. High classification accuracy was found for both, tumor vs. stroma and muscle-infiltrating vs. non-muscle invasive tumors. Some of the most discriminative m/z features for each condition could be assigned a putative identity: Stromal tissue was characterized by collagen type I peptides and tumor tissue by histone and heat shock protein beta-1 peptides. Intermediate filaments such as cytokeratins and vimentin were discriminative between the tumors with different muscle-infiltration status. To make the study fully reproducible and to advocate the criteria of FAIR (findability, accessibility, interoperability, and reusability) research data, we share the raw data, spectra annotations as well as all Galaxy histories and workflows. Data are available via ProteomeXchange with identifier PXD026459 and Galaxy results via https://github.com/foellmelanie/Bladder_MSI_Manuscript_Galaxy_links. ConclusionHere, we show that translational MSI data analysis in a fully transparent and reproducible manner is possible and we would like to encourage the community to join our efforts.

cancer biology↗

A novel workflow to improve multi-locus genotyping of wildlife species: an experimental set-up with a known model system

AO_SCPCAPBSTRACTC_SCPCAPGenotyping novel complex multigene systems is particularly challenging in non-model organisms. Target primers frequently amplify simultaneously multiple loci leading to high PCR and sequencing artefacts such as chimeras and allele amplification bias. Most next-generation sequencing genotyping pipelines have been validated in non-model systems whereby the real genotype is unknown and the generation of artefacts may be highly repeatable. Further hindering accurate genotyping, the relationship between artefacts and copy number variation (CNV) within a PCR remains poorly described. Here we investigate the latter by experimentally combining multiple known major histocompatibility complex (MHC) haplotypes of a model organism (chicken, Gallus gallus, 43 artificial genotypes with 2-13 alleles per amplicon). In addition to well defined "optimal" primers, we simulated a non-model species situation by designing "naive" primers, with sequence data from closely related Galliform species. We applied a novel open-source genotyping pipeline (ACACIA) to the data, and compared its performance with another, previously published, pipeline. ACACIA yielded very high allele calling accuracy (>98%). Non-chimeric artefacts increased linearly with increasing CNV but chimeric artefacts leveled when amplifying more than 4-6 alleles. As expected, we found heterogeneous amplification efficiency of allelic variants when co-amplifying multiple loci. Using our validated ACACIA pipeline and the example data of this study, we discuss in detail the pitfalls researchers should avoid in order to reliably genotype complex multigene systems. ACACIA and the datasets used in this study are publicly available at GitLab and FigShare (https://gitlab.com/psc_santos/ACACIA and https://figshare.com/projects/ACACIA/66485).

molecular biology↗