Biology subjects

Peeters, T.

Publications and source records attributed to Peeters, T..

4 recordsLinked to original sources

bioRxiv2025

Development and characterisation of pNarsenic: a naringenin-inducible biosensor for arsenic in Escherichia coli

Whole-cell biosensors detecting the heavy metal arsenic have been widely studied for their potential in environmental monitoring. And while inducible biosensors have been shown to be an effective tool to tune the operational range, a thoroughly characterised inducible biosensor is currently lacking. Here, we present an Escherichia coli biosensor for arsenic in which the transcription factor gene arsR is inducible by naringenin, a plant-derived secondary metabolite. Increasing naringenin concentrations reduced the basal output while increasing both the dynamic range and sensing threshold of the biosensor dose-response curves, but the operational ranges appeared constrained by a fixed upper limit. Comparison with a previously published phenomenological model revealed good overall agreement between experimental data and model predictions, except for the behaviour of the maximum output and threshold. This work expands the biosensor toolbox with a profoundly characterised arsenic biosensor and raises a potential practical limit to dose-response curve engineering by tuning transcription factor expression alone. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/678503v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@c15051org.highwire.dtl.DTLVardef@180214forg.highwire.dtl.DTLVardef@10afa84org.highwire.dtl.DTLVardef@1c51bba_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2024

AI-driven Discovery of Morphomolecular Signatures in Toxicology

Early identification of drug toxicity is essential yet challenging in drug development. At the preclinical stage, toxicity is assessed with histopathological examination of tissue sections from animal models to detect morphological lesions. To complement this analysis, toxicogenomics is increasingly employed to understand the mechanism of action of the compound and ultimately identify lesion-specific safety biomarkers for which in vitro assays can be designed. However, existing works that aim to identify morphological correlates of expression changes rely on qualitative or semi-quantitative morphological characterization and remain limited in scale or morphological diversity. Artificial intelligence (AI) offers a promising approach for quantitatively modeling this relationship at an unprecedented scale. Here, we introduce GEESE, an AI model designed to impute morphomolecular signatures in toxicology data. Our model was trained to predict 1,536 gene targets on a cohort of 8,231 hematoxylin and eosin-stained liver sections from Rattus norvegicus across 127 preclinical toxicity studies. The model, evaluated on 2,002 tissue sections from 29 held-out studies, can yield pseudo-spatially resolved gene expression maps, which we correlate with six key drug-induced liver injuries (DILI). From the resulting 25 million lesion-expression pairs, we established quantitative relations between up and downregulated genes and lesions. Validation of these signatures against toxicogenomic databases, pathway enrichment analyses, and human hepatocyte cell lines asserted their relevance. Overall, our study introduces new methods for characterizing toxicity at an unprecedented scale and granularity, paving the way for AI-driven discovery of toxicity biomarkers. Live demo: https://mahmoodlab.github.io/tox-discovery-ui/

bioinformatics↗

bioRxiv2023

Dose-Response Profiling in Heterologous Gene Expression: Insights from Proteome Fraction Analysis

Many studies characterize transcription factors and other regulatory elements to control the expression of heterologous genes in recombinant systems. However, most lack a formal approach to analyse the parameters and context-specific variations of these regulatory components. This study addresses this gap by establishing formal and convenient methods for characterising regulatory circuits. We model the bacterial cell as a collection of a small number of proteome fractions. Then, we derive the proteome fraction over time and obtain a general theorem describing its change as a function of its expression fraction, which represents a specific portion of the total biosynthesis flux of the cell. Formal deduction reveals that when the proteome fraction reaches a maximum, it becomes equivalent to its expression fraction. This equation enables the reliable measurement of the expression fraction through direct protein quantification. In addition, experimental data demonstrate a linear correlation between protein production rate and specific growth rate over a significant time period. This suggests a constant expression fraction within this window. The expression fractions estimated from the slopes of these intervals and those obtained from maximum protein amount points can both be independently fitted to a Hill function. In the case of an IPTG biosensor, in five cellular contexts, expression fractions determined by the maximum method and the slope method produced similar dose-response parameters. Additionally, by analysing two more biosensors, for mercury and cumate detection, we demonstrate that the slope method can be effectively applied to various systems, generating reliable Hill function parameters.

synthetic biology↗

bioRxiv2021

ANP32A represses Wnt signaling across tissues tissues thereby protecting against joint and heart disease

Wnt signaling is key to diverse homeostatic and pathological processes. This cascade is hyper-activated in osteoarthritis, the most common joint disease. Yet, fundamental aspects of Wnt signaling remain undiscovered. Here, we report that ANP32A negatively regulates Wnt signaling across tissues. In cartilage, loss of Anp32a triggered Wnt hyper-activation. Mechanistically, ANP32A directly interacted with Wnt pathway components and inhibited Wnt target genes via histone acetylation masking. Wnt antagonist treatment reduced severity of osteoarthritis in Anp32a-deficient mice preventing osteophyte formation, contrasting with cartilage-protective effects of ANP32A on oxidative stress. Hence, dual therapy targeting Wnt signaling and oxidative stress in Anp32a-deficient mice ameliorated more osteoarthritis features than individual treatments. Anp32a loss also resulted in Wnt hyper-activation in the heart with cardiac hypertrophy, and in the hippocampus, shedding light on mechanisms for reported links between ANP32A and Alzheimers disease. Collectively, this work reveals that ANP32A is a translationally relevant repressor of Wnt signaling, impacting homeostasis and disease across tissues.

cell biology↗