Biology subjects

Wijaya, L. S.

Publications and source records attributed to Wijaya, L. S..

4 recordsLinked to original sources

bioRxiv2026

Analytical Choices Drive Toxicogenomic Potency Estimates: A Systematic Evaluation of Transcriptomic Points of Departure

Omics technologies are increasingly integrated into next-generation risk assessment, yet quantitative toxicogenomics outcomes remain highly dependent on analytical choices, motivating a systematic evaluation of how bioinformatics workflows influence hazard characterization and transcriptomic Points of Departure (tPOD). Here, we applied five independent transcriptomics pipelines to a shared dataset of RPTEC-TERT1 kidney cells exposed to cisplatin across multiple concentrations and timepoints, comparing effects of pre-processing, benchmark concentration modeling, and pathway-based interpretation strategies. Across workflows, substantial variability was observed in gene-level benchmark concentrations (BMCs), primarily driven by differences in normalization, filtering, and especially the modeling software used. Despite this variability, convergence increased at later timepoints as transcriptional responses strengthened, with 24 h consistently identified as the most sensitive timepoint at the gene level. Aggregation of gene-level BMCs into pathway-based metrics reduced variability but did not eliminate it, with pathway definition emerging as a major determinant of sensitivity estimates. Notably, distinct pathway resources showed minimal gene overlap, and smaller, biologically coherent gene sets (e.g., co-expression modules and biomarker panels) produced lower and less dispersed BMCs compared with broader pathway annotations. Furthermore, direct modeling of pathway activity scores yielded systematically different sensitivity estimates relative to median-based aggregation, with method-dependent conservativeness influenced by pathway coverage and response strength. Overall, our findings demonstrate that both analytical workflow design and pathway selection critically shape toxicogenomic-derived potency estimates, highlighting the need for harmonized, transparent methodologies to enable robust application of transcriptomics in chemical safety assessment and regulatory decision-making.

bioinformatics↗

bioRxiv2025

Data-driven dynamic modelling identifies polyploidisation as key process in cell cycle progression upon DNA damage

Chemotherapeutic agents often cause DNA damage in order to kill fast-dividing cancer cells or disrupt their proliferation. Therefore, understanding the interplay between DNA damage and cell cycle progression is highly relevant for understanding cancer cell behaviour. An important regulator is transcription factor p53, primarily known for its function to maintain genomic stability, regulate transient and permanent cell cycle arrest and apoptosis. Activated p53 transcriptionally regulates the expression of many proteins, among which are MDM2, p21 and BTG2. MDM2 functions as a direct inhibitor of p53 by targeting it for ubiquitination. The proteins p21 and BTG2 are known for their regulatory function in G1 and G2 cell cycle arrest. Using HepG2-FUCCI cells, we showed that exposure to cisplatin or etoposide caused a temporary G2 arrest. To study the link between protein expression and cell cycle arrest, we developed a mathematical model in which we integrated a previously established model for the protein expression dynamics of p53, MDM2, p21 and BTG2 with a cell cycle model. This allowed us to determine the importance of p21 and BTG2 in their stimulation of G1 and G2 cell cycle arrest. We found that the protein dynamics could predict the G2 cell cycle arrest in exposed cells, but only in combination with endoreplication, i.e., the alternation of S and G phases without mitosis, resulting in polyploid cells. Our model predicted that the majority of cells endoreplicate upon exposure to high concentrations of cisplatin and most concentrations of etoposide, which we validated with additional time-lapse imaging data in which we could track individual cells. In conclusion, polyploidisation is a generic response of HepG2 cells after treatment with DNA-damaging compounds.

systems biology↗

bioRxiv2023

Utilizing gene co-expression networks with the rat kidney TXG-MAPr tool to enhance safety assessment, biomarker identification and human translation

Toxicogenomic data represent a valuable source of biological information at molecular and cellular level to understand unanticipated organ toxicities. Weighted gene co-expression networks analysis can reduce the complexity of gene-level transcriptomic data to a set of biological response-networks useful for providing insights into mechanisms of drug-induced adverse outcomes. In this study, we have built co-regulated gene networks (modules) from the TG-GATEs and DrugMatrix rat kidney datasets consisting of time- and dose-response data for 180 compounds, including nephrotoxicants. Data from the 347 modules were incorporated into the rat kidney TXG-MAPr web tool, a user-friendly interface that enables visualization and analysis of module perturbations, quantified by a module eigengene score (EGS) for each treatment condition. Several modules annotated for cellular stress, renal injury and inflammation were statistically associated with concurrent renal pathologies, including modules that contain both well-known and novel renal biomarker genes. In addition, many rat kidney modules contain well annotated, robust gene networks that are preserved across transcriptome datasets, suggesting that these biological networks translate to other (drug-induced) kidney injury cases. Moreover, preservation analysis of human kidney transcriptomic data provided a quantitative metric to assess the likelihood that rat kidney modules, and the associated biological interpretation, translate from non-clinical species to human. In conclusion, the rat kidney TXG-MAPr enables uploading and analysis of kidney gene expression data in the context of rat kidney co-expression networks, which could identify possible safety liabilities and/or mechanisms that can lead to adversity for chemical or drug candidates. Translational StatementGene co-expression networks (modules) were generated using rat kidney toxicogenomics data, which reduced data complexity and retained quantitative mechanisms to enhance safety assessment. Several stress, injury and inflammation modules were statistically associated with renal pathologies, useful for biomarker identification. Moreover, many rat kidney modules contained well-annotated, robust gene-networks that were preserved in human patients transcriptome data after renal transplantation, suggesting that these biological networks translate to human relevant kidney-injury. So, the rat kidney TXG-MAPr tool enables transcriptome analysis in the context of kidney co-expression networks, which could identify chemical-induced safety liabilities and/or mechanisms leading to adversity, relevant for human risk-assessment.

pharmacology and toxicology↗

bioRxiv2023

Spatio-temporal transcriptomic analysis reveals distinct nephrotoxicity, DNA damage and regeneration response after cisplatin

Nephrotoxicity caused by drug or chemical exposure involves different mechanisms and nephron segments as well as a complex temporal integration of injury and repair responses. Distinct cellular transcriptional programs regulate the time-dependent tissue injury and regeneration responses. Whole kidney transcriptome analysis cannot dissect the complex the nephron segment spatio- temporal injury and regeneration responses. Here, we used laser capture microdissection of formalin- fixed paraffin embedded sections followed by whole genome targeted RNA-sequencing-TempO-Seq and co-expression gene-network (module) analysis to determine the spatial-temporal responses in rat kidney glomeruli (GM), cortical proximal tubules (CPT) and outer-medulla proximal tubules (OMPT) comparison with whole kidney, after a single dose of the nephrotoxicant cisplatin. We demonstrate that cisplatin induced early onset of DNA damage in both CPT and OMPT, but not GM. Sustained DNA damage response was strongest in OMPT coinciding with OMPT specific inflammatory signaling, actin cytoskeletal remodeling and increased glycolytic metabolism coincident with suppression of mitochondrial activity. Later responses reflected regeneration-related cell cycle pathway activation and ribosomal biogenesis in the injured OMPT regions. Activation of modules containing kidney injury biomarkers was strongest in the OMPT, with OMPT Clu expression best correlating with urinary clusterin biomarker measurements compared the correlation of Kim1. Our findings also showed that whole kidney responses were less sensitive than OMPT. In conclusion, our LCM-TempO-Seq method reveals a detailed spatial mechanistic understanding of renal injury/regeneration after nephrotoxicant exposure and identifies the most representative mechanism-based nephron segment specific renal injury biomarkers.

pharmacology and toxicology↗