Biology subjects

Rickert, D.

Publications and source records attributed to Rickert, D..

4 recordsLinked to original sources

bioRxiv2024

Thermosensory neurons control genetic inheritance through regulation of germline transposons

Transposable elements (TEs) can alter genome structure through transposition, and their activity is therefore tightly restricted by small RNA-mediated and chromatin-based silencing mechanisms. In C. elegans, elevated temperature can induce TE expression, raising the question of whether thermosensory neurons influence TE regulation. Here, we investigated the role of the AFD thermosensory neurons in TE regulation using multiple models of AFD dysfunction that altered TE expression; Mirage transposase induction emerged as the most reproducible phenotype across all AFD-dysfunctional strains. In the AFD triple mutant strain (PY9248), we observed strong Tc1 transposase expression, and genome-wide Tc1-enriched de novo insertions over generations. However, CRISPR reconstruction of the genotype in the N2 background did not reproduce the strong Tc1 phenotype, indicating that Tc1 activation and mutagenesis in PY9248 are background-associated rather than solely caused by loss of gcy-8, gcy-18, gcy-23 function. These findings support a model in which AFD neuron dysfunction reproducibly alters TE expression, particularly of Mirage, while heritable Tc1-mediated mutagenesis requires an additional, currently uncharacterized factor present in the AFD triple mutant background.

genetics↗

bioRxiv2024

Defining the cardiac fibroblast secretome in the healthy and infarcted mouse heart

Cardiac fibroblasts (CF) are key players after myocardial infarction (MI), but their signaling is only incompletely understood. Here we report a first secretome atlas of CF in control (cCF) and post-MI hearts (miCF), combining a rapid cell isolation technique with SILAC and click chemistry. In CF, numerous paracrine factors involved in immune homeostasis were identified. Comparing secretome, transcriptome (SLAMseq), and cellular proteome disclosed protein turnover. In miCF at day 5 post-MI, significantly upregulated proteins included SLIT2, FN1, and CRLF1 in mouse and human samples. Comparing the miCF secretome at day 3 and 5 post-MI showed the dynamic nature of protein secretion. Specific in-vivo labeling of miCF proteins via biotin ligase TurboID using the POSTN promotor mirrored the in-vitro data. In summary, we have identified numerous paracrine factors specifically secreted from CF in mice and humans. This secretome atlas may lead to new biomarkers and/or therapeutic targets for the activated CF.

physiology↗

bioRxiv2023

Tick extracellular vesicles impair epidermal homeostasis through immune-epithelial networks during hematophagy

Wound healing has been extensively studied through the lens of inflammatory disorders and cancer, but limited attention has been given to hematophagy and arthropod-borne diseases. Hematophagous ectoparasites, including ticks, subvert the wound healing response to maintain prolonged attachment and facilitate blood-feeding. Here, we unveil a strategy by which extracellular vesicles (EVs) ensure blood-feeding and arthropod survival in three medically relevant tick species. Through single cell RNA sequencing and murine genetics, we demonstrate that wildtype animals infested with EV-deficient Ixodes scapularis display a unique epidermal sub-population with a mesenchymal-like transcriptional program and an overrepresentation of pathways connected to wound healing. Furthermore, tick EVs inhibit proliferation and diminish the capacity of wound closure in keratinocytes. This occurrence was linked to phosphoinositide 3-kinase activity, keratinocyte growth factor 1 (KGF-1) and transforming growth factor {beta} (TGF-{beta}) levels. Collectively, we uncovered a strategy employed by a blood-feeding arthropod that disrupts the circuitry in cutaneous wound healing, contributing to ectoparasite fitness.

microbiology↗

bioRxiv2017

pyABC: distributed, likelihood-free inference

Likelihood-free methods are often required for inference in systems biology. While Approximate Bayesian Computation (ABC) provides a theoretical solution, its practical application has often been challenging due to its high computational demands. To scale likelihood-free inference to computationally demanding stochastic models we developed pyABC: a distributed and scalable ABC-Sequential Monte Carlo (ABC-SMC) framework. It implements computation-minimizing and scalable, runtime-minimizing parallelization strategies for multi-core and distributed environments scaling to thousands of cores. The framework is accessible to non-expert users and also enables advanced users to experiment with and to custom implement many options of ABC-SMC schemes, such as acceptance threshold schedules, transition kernels and distance functions without alteration of pyABCs source code. pyABC includes a web interface to visualize ongoing and 1nished ABC-SMC runs and exposes an API for data querying and post-processing.\n\nAvailability and ImplementationpyABC is written in Python 3 and is released under the GPLv3 license. The source code is hosted on https://github.com/neuralyzer/pyabc and the documentation on http://pyabc.readthedocs.io. It can be installed from the Python Package Index (PyPI).

bioinformatics↗