Biology subjects

Fuchs, F.

Publications and source records attributed to Fuchs, F..

6 recordsLinked to original sources

bioRxiv2025

Biofilm development of Porphyromonas gingivalis on titanium surfaces in response to 1,4-dihydroxy-2-naphthoic acid - a hybrid in vitro - in silico approach

Colonization of titanium dental implants by the oral pathogen Porphyromonas gingivalis can lead to peri-implant diseases and, ultimately, implant failure. P. gingivalis growth can be stimulated by 1,4-dihydroxy-2-naphthoic acid (DHNA), a menaquinone precursor from various oral bacteria, yet its impact on biofilm formation remains unclear. The aim of the study was to evaluate P. gingivalis growth and metabolic activity over six days in response to DHNA on two titanium grade IV surfaces with different roughness using a hybrid in vitro - in silico approach. P. gingivalis growth was modestly stimulated by DHNA and exhibited an inverse correlation with ammonia concentration in culture medium. Notably, this growth pattern transitioned from an initial linear phase to a later exponential phase, with DHNA-treated biofilms reaching this exponential shift at an earlier stage than untreated controls. Confocal microscopy revealed that DHNA-treated biofilms exhibited surface-dependent growth patterns, with larger biofilm volumes observed on rougher surfaces in later biofilm stages, compared to smoother surfaces. Regardless of surface characteristics, the area occupied by biofilms and the size of the aggregates exhibited a consistent and progressive increase over time and was larger in late DHNA-treated biofilms. The experimental data were used to calibrate a coupled finite element method (FEM)-based model that simulated P. gingivalis biofilm dynamics and nutrient utilization. Summarizing, DHNA moderately stimulated P. gingivalis growth, accelerated its transition to ammonia-independent growth, and promoted an increase in biofilm area and aggregate size. Our coupled approach offers significant potential for advancing in vitro biofilm research. ImportanceResults of our hybrid in vitro - in silico experiments advance the research on P. gingivalis physiology and its DHNA-dependent colonization of implant surfaces. Our findings reveal that DHNA accelerates P. gingivalis growth, induces aggregation and promotes colonization of titanium surfaces. For the first time, DHNA-induced P. gingivalis growth acceleration and an earlier shift away from ammonia dependency were observed fluorometrically, highlighting ammonia assimilation as a promising marker of P. gingivalis physiology during early biofilm expansion. Understanding how growth factors together with surface properties influence P. gingivalis colonization offers a basis for future preventive strategies. Our studys stringent characterization of 3D surface texture parameters is expected to improve reproducibility of biofilm-surface interactions experiments. The findings were validated using a continuum-based in silico model, initiating a hybrid approach where computational models complement in vitro research. Our interdisciplinary approach offers a versatile framework for investigating additional aspects of oral biofilms on titanium.

microbiology↗

bioRxiv2024

Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients.

Over the last decade, Hippo signaling has emerged as a major tumor-suppressing pathway. Its dysregulation is associated with abnormal expression of YAP1 and TEAD-family genes. Recent works have highlighted the role of YAP1/TEAD activity in several cancers and its potential therapeutic implications. Therefore, identifying patients with a dysregulated Hippo pathway is key to enhancing treatment impact. Although recent studies have derived RNAseq-based signatures, there remains a need for a reproducible and cost-effective method to measure the pathway activation. In recent years, deep learning applied to histology slides have emerged as an effective way to predict molecular information from a data modality available in clinical routine. Here, we trained models to predict YAP1/TEAD activity from H&E-stained histology slides in multiple cancers. The robustness of our approach was assessed in seven independent validation cohorts. Finally, we showed that histological markers of disease aggressiveness were associated with dysfunctional Hippo signaling.

bioinformatics↗

bioRxiv2024

Diverse Database and Machine Learning Model to narrow the generalization gap in RNA structure prediction

Understanding macromolecular structures of proteins and nucleic acids is critical for discerning their functions and biological roles. Advanced techniques--crystallography, NMR, and CryoEM--have facilitated the determination of over 180,000 protein structures, all cataloged in the Protein Data Bank (PDB). This comprehensive repository has been pivotal in developing deep learning algorithms for predicting protein structures directly from sequences. In contrast, RNA structure prediction has lagged, and suffers from a scarcity of structural data. Here, we present the secondary structure models of 1098 pri-miRNAs and 1456 human mRNA regions determined through chemical probing. We develop a novel deep learning architecture, inspired from the Evoformer model of Alphafold and traditional architectures for secondary structure prediction. This new model, eFold, was trained on our newly generated database and over 300,000 secondary structures across multiple sources. We benchmark eFold on two new test sets of long and diverse RNA structures and show that our dataset and new architecture contribute to increasing the prediction performance, compared to similar state-of-the-art methods. All together, our results reveal that merely expanding the database size is insufficient for generalization across families, whereas incorporating a greater diversity and complexity of RNAs structures allows for enhanced model performance.

biochemistry↗

bioRxiv2023

JUMP Cell Painting dataset: morphological impact of 136,000 chemical and genetic perturbations

Image-based profiling has emerged as a powerful technology for various steps in basic biological and pharmaceutical discovery, but the community has lacked a large, public reference set of data from chemical and genetic perturbations. Here we present data generated by the Joint Undertaking for Morphological Profiling (JUMP)-Cell Painting Consortium, a collaboration between 10 pharmaceutical companies, six supporting technology companies, and two non-profit partners. When completed, the dataset will contain images and profiles from the Cell Painting assay for over 116,750 unique compounds, over-expression of 12,602 genes, and knockout of 7,975 genes using CRISPR-Cas9, all in human osteosarcoma cells (U2OS). The dataset is estimated to be 115 TB in size and capturing 1.6 billion cells and their single-cell profiles. File quality control and upload is underway and will be completed over the coming months at the Cell Painting Gallery: https://registry.opendata.aws/cellpainting-gallery. A portal to visualize a subset of the data is available at https://phenaid.ardigen.com/jumpcpexplorer/.

bioinformatics↗

bioRxiv2020

Genomic and phenotypic analysis of COVID-19-associated pulmonary aspergillosis isolates of Aspergillus fumigatus

The ongoing global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the coronavirus disease 2019 (COVID-19) first described from Wuhan, China. A subset of COVID-19 patients has been reported to have acquired secondary infections by microbial pathogens, such as fungal opportunistic pathogens from the genus Aspergillus. To gain insight into COVID-19 associated pulmonary aspergillosis (CAPA), we analyzed the genomes and characterized the phenotypic profiles of four CAPA isolates of Aspergillus fumigatus obtained from patients treated in the area of North Rhine-Westphalia, Germany. By examining the mutational spectrum of single nucleotide polymorphisms, insertion-deletion polymorphisms, and copy number variants among 206 genes known to modulate A. fumigatus virulence, we found that CAPA isolate genomes do not exhibit major differences from the genome of the Af293 reference strain. By examining virulence in an invertebrate moth model, growth in the presence of osmotic, cell wall, and oxidative stressors, and the minimum inhibitory concentration of antifungal drugs, we found that CAPA isolates were generally, but not always, similar to A. fumigatus reference strains Af293 and CEA17. Notably, CAPA isolate D had more putative loss of function mutations in genes known to increase virulence when deleted (e.g., in the FLEA gene, which encodes a lectin recognized by macrophages). Moreover, CAPA isolate D was significantly more virulent than the other three CAPA isolates and the A. fumigatus reference strains tested. These findings expand our understanding of the genomic and phenotypic characteristics of isolates that cause CAPA.

microbiology↗

bioRxiv2020

Melanopsin-dependent direct photic effects are equal to clock-driven effects in shaping the nychthemeral sleep-wake cycle

Nychthemeral sleep-wake cycles (SWc) are known to be generated by the circadian clock in suprachiasmatic nuclei (SCN), entrained to the light-dark cycle. Light also exerts direct acute effects on sleep and waking. However, under longer photic exposure such as the 24-hour day, the precise significance of sustained direct light effects (SDLE) and circuitry involved have been neither clarified nor quantified, as disentangling them from circadian influence is difficult. Recording sleep in mice lacking a circadian pacemaker and/or melanopsin-based phototransduction, we uncovered, contrary to prevailing assumptions, that circadian-driven input shapes only half of SWc, with SDLE being equally important. SDLE were primarily mediated (>80%) through melanopsin, of which half were relayed through SCN, independent of clock function. These findings were used for a model that predicted SWc under simulated jet-lag, and revealed SDLE as a crucial mechanism influencing behavior, and should be considered for circadian/sleep disorder management and societal lighting optimization.

neuroscience↗