Welcome to the TXG-MAPr app!

TXG‑MAPr is an interactive R Shiny–based bioinformatics platform designed to make toxicogenomic data interpretation intuitive, mechanistic, and test‑system specific. Built on large transcriptomic datasets, TXG‑MAPr organizes high‑dimensional gene expression data into biologically meaningful gene co‑expression network modules using Weighted Gene Co‑expression Network Analysis (WGCNA).

TXG‑MAPr – Mechanism‑Focused Interpretation of Toxicogenomics Data

The platform provides 200–400 functionally annotated gene networks per test system, covering human and rat in vitro and in vivo models (e.g., PHH, HepG2, iPSC-derived HLC, iPSC-derived PTLC, RPTEC/TERT1, rat liver, rat kidney). Each module is enriched for gene ontology terms, pathways, transcription factor activities, and links to cellular response mechanisms, adverse outcome pathway (AOP) key events and toxicological phenotypes, enabling detailed mechanistic interpretation.

TXG‑MAPr allows users to:

• Explore dose‑ and time‑dependent responses of compounds.

• Identify mechanisms of action of compounds (e.g., cyclosporine A, tunicamycin, acetaminophen).

• Visualize module eigengene scores (EGS) that summarize coordinated changes in gene networks.

• Generate EGS for new user‑uploaded datasets to compare external experiments with established toxicogenomic profiles.

• Link module responses to Adverse Outcome Pathway (AOP) key events.

• Support mechanism‑based risk assessment in both research and regulatory contexts.

Developed at Leiden University as part of major European research initiatives, TXG‑MAPr aims to support next‑generation mechanism‑based safety assessment across multiple organ systems.

WGCNA – The Analytical Core of TXG‑MAPr

Weighted Gene Co‑expression Network Analysis (WGCNA) is the central methodology underlying TXG‑MAPr. WGCNA identifies groups of genes that show highly correlated expression patterns across experiments, forming modules that often correspond to biological pathways or cellular stress responses. The resulting modules bridge individual gene‑level changes with emergent, system‑level biological processes. These modules capture well‑known toxicological processes—such as ER stress, oxidative stress, DNA damage responses, immune activation, and mitochondrial dysfunction—and have demonstrated cross‑species relevance in multiple studies.

TXG-MAPr introduces innovation and novelty on six key levels:

1. Test system-specific gene co-expression networks Current standard omics interpretation tools rely on generic databases and are not optimized for the biology of a given model system. TXG-MAPr gene co-expression networks are derived from large, standardized toxicogenomics datasets that are unique for each test system. This reduces data complexity by >95%, ensuring high biological fidelity and correct pathway mapping and interpretation.

2. Curated mechanistic annotation Each gene network (module) includes extensive biological annotation, linking modules to functional processes, stress pathways, cell states, and known toxicological mechanisms. Unlike traditional enrichment tools, TXG-MAPr provides a structured mechanistic interpretation directly relevant to chemical safety and risk assessment.

3. Module level concentration/dose response modelling The tool enables quantitative benchmark concentration analysis at the gene module level. Gene network activity scores act as stable, composite biomarkers, enabling transcriptomic PoDs that are more reproducible and biologically meaningful than single gene benchmarks.

4. Cross species and cross platform robustness Independent studies have demonstrated that TXG-MAPr gene networks preserve biological structure across multiple cell systems and species (e.g., human vs. rat). This makes the tool particularly promising for bridging in vitro–in vivo translation.

5. Regulatory alignment The tool’s reporting format aligns with the expectations of multiple regulatory frameworks, including the OECD Omics Reporting Framework, EFSA’s NAM guidance and NGRA workflows. For example, TXG-MAPr can generate gene network level summaries suitable for regulatory dossiers, making toxicogenomics data more interpretable in the context of classification, read across, or potency assessment.

6. User friendly deployment TXG-MAPr is implemented as a secure, interactive R Shiny application or packaged in Docker containers. This allows for deployment at laboratories, industry sites, CROs, and regulatory institutes with minimal bioinformatics expertise required.

Credits and contacts

Callegaro Giulia^1,# (g.callegaro@lacdr.leidenuniv.nl), Kunnen Steven J.^1,# (s.j.kunnen@lacdr.leidenuniv.nl), van Kessel Hugo W.¹, Sinke Lucy¹, Bruns Imke B.¹, Wijaya Lukas S.¹, van Schaick Mick¹, Danilyuk Tamara Y.¹, Veltman Kirsten H.J.¹, Bahtiri Sibel¹, Tahir Natasha¹, Niemeijer Marije C.¹, Stevens James L.¹, van de Water Bob¹

^# Corresponding authors, Division of Cell Systems and Drug Safety, Leiden Academic Center for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands

¹ Leiden University, Leiden, The Netherlands

This work has received funding from the EU-EFPIA IMI2 Joint Undertaking TransQST project (grant no. 116030) and eTRANSAFE project (grant no. 777365); the EC Horizon2020 projects EU-ToxRisk (grant no. 681002) and RISK-HUNT3R (grant no. 964537), which is part of ASPIS; the Horizon Europe PARC programme (grant no. 101057014); and the VHP4SAFETY project (grant number 1292.19.272, which is part of the NWA research program ‘Research along Routes by Consortia (ORC)’ funded by the Netherlands Organization for Scientific Research (NWO). This work reflects only the authors’ view, and the European Commission and IMI-JU are not responsible for any use that may be made of the information it contains.