Biology subjects

Hermann, L.

Publications and source records attributed to Hermann, L..

3 recordsLinked to original sources

bioRxiv2025

Unveiling the temporal impact: Exploring dynamic changes in the paediatric solid tumour immune microenvironment through time

The composition of the tumour immune microenvironment (TIME) influences tumour evolution and responsiveness to immunotherapy. While longitudinal changes in TIME have been well-characterized in adult cancers, its dynamics in childhood cancers remain poorly documented, limiting our ability to predict treatment responses and tailor immunotherapeutic strategies. This study aimed to evaluate the plasticity of TIME in paediatric solid tumours, investigate its longitudinal evolution, and identify time-dependent immune alterations. Transcriptomic data from longitudinal samples of 27 paediatric patients (<21 years old) with relapsed or refractory solid tumours were analysed, encompassing 70 timepoints: 16 diagnoses and 54 successive relapses. TIME plasticity was assessed using gene expression clustering and immune cell infiltration enumeration. Patient-adjusted longitudinal analyses were performed using generalised linear mixed models (glmmSeq), adjusted for age and sex. Temporal associations of immune changes were further explored using dynamic regression models. Thirteen patients exhibited significant changes in their TIME profile, indicating high TIME plasticity. Over time, the TIME shifted toward a tolerogenic and immunosuppressive state, characterised by decreased activity in immune pathways (e.g., T cell receptor signalling) and enrichment of tolerogenic (e.g., macrophage differentiation) and oncogenic pathways (e.g., IL6-JAK-STAT3). The core enrichment of upregulated pathways contained key immunosuppressive factors: immune checkpoints (CTLA-4), tumour-associated macrophage activators (CSF1/CSF1R), T-regulatory cell activators (TGFB1), and immunosuppressive genes (IL10RA). This study provides evidence that the TIME in paediatric solid tumours is plastic and remodels towards immune depletion and tolerogenicity. This evolution may underlie treatment resistance and disease progression, underscoring the need for TIME-informed therapeutic approaches in paediatric oncology. Significance StatementThis article demonstrates the plasticity of the tumour immune micro-environment (TIME) of paediatric solid tumours throughout disease evolution. Longitudinal transcriptomic analyses of 70 tumour samples from 27 patients showed a progressive remodelling towards tolerogenicity and immune depletion. Key immunosuppressive factors, including immune checkpoints and tumour-associated macrophages, were identified as potential contributors to immune escape. These findings support the relevance of longitudinal immune monitoring in paediatric oncology and may inform future strategies for immunotherapeutic interventions.

cancer biology↗

bioRxiv2024

Beware of Data Leakage from Protein LLM Pretraining

Pretrained protein language models are becoming increasingly popular as a backbone for protein property inference tasks such as structure prediction or function annotation, accelerating biological research. However, related research oftentimes does not consider the effects of data leakage from pretraining on the actual downstream task, resulting in potentially unrealistic performance estimates. Reported generalization might not necessarily be reproducible for proteins highly dissimilar from the pretraining set. In this work, we measure the effects of data leakage from protein language model pretraining in the domain of protein thermostability prediction. Specifically, we compare two different dataset split strategies: a pretraining-aware split, designed to avoid similarity between pretraining data and the held-out test sets, and a commonly-used naive split, relying on clustering the training data for a downstream task without taking the pretraining data into account. Our experiments suggest that data leakage from language model pretraining shows consistent effects on melting point prediction across all experiments, distorting the measured performance. The source code and our dataset splits are available at https://github.com/tfiedlerdev/pretraining-aware-hotprot.

bioinformatics↗

bioRxiv2024

Multiple levels of transcriptional regulation control glycolate metabolism in Paracoccus denitrificans

The hydroxyacid glycolate is a highly abundant carbon source in the environment. Glycolate is produced by unicellular photosynthetic organisms and excreted at petagram scales to the environment, where it serves as growth substrate for heterotrophic bacteria. In microbial metabolism, glycolate is first oxidized to glyoxylate by the enzyme glycolate oxidase. The recently described {beta}-hydroxyaspartate cycle (BHAC) subsequently mediates the carbon-neutral assimilation of glyoxylate into central metabolism in ubiquitous Alpha- and Gammaproteobacteria. While the reaction sequence of the BHAC was elucidated in Paracoccus denitrificans, little is known about the regulation of glycolate and glyoxylate assimilation in this relevant alphaproteobacterial model organism. Here, we show that regulation of glycolate metabolism in P. denitrificans is surprisingly complex, involving two regulators, the IclR-type transcription factor BhcR that acts as an activator for the BHAC gene cluster, as well as the GntR-type transcriptional regulator GlcR, a previously unidentified repressor that controls the production of glycolate oxidase. Furthermore, an additional layer of regulation is exerted at the global level, which involves the transcriptional regulator CceR that controls the switch between glycolysis and gluconeogenesis in P. denitrificans. Together, these regulators control glycolate metabolism in P. denitrificans, allowing the organism to assimilate glycolate together with other carbon substrates in a simultaneous fashion, rather than sequentially. Our results show that the metabolic network of Alphaproteobacteria shows a high degree of flexibility to react to the availability of multiple substrates in the environment.

microbiology↗