Bioinformatics Institute

Lung cancer is the leading cause of cancer-related death and has the second-highest incidence worldwide. For patients with advanced EGFR-mutated non-small cell lung cancer, EGFR tyrosine kinase inhibitors (EGFR-TKIs) are the preferred treatment option; however, acquired resistance to TKIs is inevitable. Gefitinib and osimertinib, the first-generation and third-generation EGFR-TKI, have shown promising results in patients with EGFR-mutated lung cancer in clinical treatment. Here, we identified that pyruvate dehydrogenase kinase 1 (PDK1) was up-regulated in gefitinib- and osimertinib-resistant cell lines, and PDK1 knockdown rendered cells more sensitive to TKI treatment. PDK1 expression levels were significantly increased in lung, colon, liver, and breast cancer tissues compared with those in normal tissues. Histone demethylase KDM3A was also induced in TKI-resistant cell lines, and demethylated histone H3 lysine 9 to facilitate PDK1 expression to regulate TKI resistance. Further study demonstrated that METTL16 promoted the m6A modification of PDK1 mRNA, and the m6A reader IGF2BP1 directly recognized and enhanced PDK1 mRNA stability. Interestingly, KDM3A also induced METTL16 expression. Moreover, PDK1 inhibitor JX06 rendered cancer cells more sensitive to gefitinib treatment in vivo, and JX06 and gefitinib combination treatments have a synergic effect to inhibit tumor growth. In conclusion, the KDM3A/METTL16/PDK1 axis plays an important role in cancer development and TKI resistance, which may offer new prognostic biomarkers and therapeutic targets for TKI resistance in the future.

Web-based molecular graphics have transformed the interactive visualization of molecular data, leveraging modern web technologies that provide GPU acceleration, optimized JavaScript engines, and seamless access across devices without additional software installation. We present the graphics engine at the core of the Mol* toolkit, a high-performance, open-source framework that is widely adopted in academia and industry, including by the Protein Data Bank, UniProt, EMDB, and AlphaFold DB. The engine combines a comprehensive styling system with a suite of optimized rendering primitives, including real-time surface generation, to deliver both flexibility and visual fidelity. Efficient handling of large-scale molecular scenes is achieved through level-of-detail management, GPU instancing, spatial acceleration structures, and frustum/occlusion culling. A screen-space global illumination model provides scalable, high-quality lighting, while integrated AR/VR support enables immersive molecular exploration. Together, these capabilities enable engaging, real-time, high-fidelity visualization of molecular systems, across a wide range of scales, from single atoms to billion-atom mesoscale assemblies, demonstrating the strengths of a bespoke web-native rendering engine for molecular graphics, available at https://molstar.org.

ABSTRACT DNA methylation is a critical epigenetic modification that regulates gene expression, maintains genome stability, and influences cellular function during development and disease. Accurate analysis of DNA methylation often requires amplification to generate sufficient material, yet preserving the original epigenetic information during this process is challenging because standard amplification methods can disrupt methylation patterns. To address this, we developed a one-pot strategy that combines helicase-dependent amplification (HDA) with DNA methyltransferase 1 (DNMT1)-mediated methylation, enabling simultaneous DNA amplification and preservation of native methylation marks. A key challenge is that HDA is optimized at 65 °C, whereas DNMT1 is unstable at elevated temperatures. We overcame this by establishing a unified buffer and isothermal reaction at 42 °C that supports both enzymatic activities. Under these conditions, HDA achieved robust amplification (∼5 Ct), while DNMT1 faithfully methylated the newly synthesized DNA, as confirmed by methylation-sensitive restriction enzyme quantitative PCR (MSRE-qPCR), with methylation levels proportional to the input template. This one-pot workflow demonstrates the feasibility of concurrent amplification and methylation, providing a foundation for scalable, accurate, and methylation-preserving DNA analyses for epigenetic and clinical applications. GRAPHICAL ABSTRACT

Allergic reactions to fish pose complex food safety challenges, driven by species diversity and underexplored intraspecies variability. This study comparatively examines allergen profiles in Malabar red snappers (Lutjanus malabaricus, n=39) across body sizes, anatomical regions and production origins using SDS-PAGE, immunoblotting and quantitative mass spectrometry. Protein profiles varied greatly by fish size and muscle region, but not by origin. Smaller fish contained higher levels of major allergen parvalbumin and creatine kinase, while larger fish exhibited elevated levels of heat-labile allergens enolase, aldolase, and glyceraldehyde-3-phosphate dehydrogenase. Parvalbumin levels were highest in head, followed by belly, dorsal, and tail. Greatest variation was observed for the three heat-stable allergens - parvalbumin, tropomyosin, and collagen. Minimal origin-dependent differences affected 2 of 11 registered fish allergens. We established an integrated proteomics workflow for systematic allergenicity assessments to uncover intraspecies variability and provide foundational knowledge for understanding intraspecies variability to improve food safety strategies.

Phylogenetic analyses of genome sequences from infectious pathogens reveal essential information regarding their evolution and transmission, as seen during the coronavirus disease 2019 pandemic. Recently developed pandemic-scale phylogenetic inference methods reduce the computational demand of phylogenetic reconstruction from genomic epidemiological datasets, allowing the analysis of millions of closely related genomes. However, widespread homoplasies, due to recurrent mutations and sequence errors, cause phylogenetic uncertainty and biases. We present algorithms and models to substantially improve the computational performance and accuracy of pandemic-scale phylogenetics. In particular, we account for, and identify, mutation rate variation and recurrent sequence errors. We reconstruct a reliable and public sequence alignment and phylogenetic tree of >2 million severe acute respiratory syndrome coronavirus 2 genomes encapsulating the evolutionary history and global spread of the virus up to February 2023.

Ivermectin is lethal to Anopheles mosquitoes making it a possible malaria control intervention. The primary mode of action of ivermectin occurs when it binds to the glutamate-gated chloride channel (GluCl), allowing for continuous flow of chloride leading to flaccid paralysis and death of the mosquito. In Caenorhabditis elegans, ivermectin is thought to open the GluCl channel when the M2-M3 loop forms Van der Waals bonds with the first sugar ring and aglycone structure of ivermectin. Here we investigate in Anopheles dirus and Anopheles minimus the mosquito-lethal effect of ivermectin (both sugar rings), monosaccharide (one sugar ring), and aglycone (no sugar rings) demonstrating full, partial, and no effect, respectively. The Anopheles GluCl protein sequences were determined and used to a create 3-D structural docking models. The docking models identified new binding interactions with a hydrogen bond forming between the second sugar ring hydroxyl group (4″-OH) and THR304 of the Anopheles GluCl M2-M3 loop. This hydrogen bond is possible due to a single substitution in the M2-M3 loop from C. elegans ILE273 to Anopheles THR304. The work presented here improves our understanding of Anopheles GluCl-ivermectin interactions as well as how ivermectin resistance could arise in the future.

Microorganisms are ubiquitous in nature. They inhabit soil, water, air, and even the human body. They are fundamental to life on Earth, driving processes such as nutrient cycling, waste degradation, and the production of bioactive compounds. Despite their importance, the majority of microbes remain poorly understood. To study these uncultured organisms, modern microbiology relies on metagenomics, a technique that enables the analysis of all genetic material present in an environmental sample and does not require growing microorganisms in the lab. This approach provides insight into which organisms are present and the potential functions they perform. Conventional methods for analyzing metagenomes often struggle to reconstruct and identify microorganisms in microbial communities, as they either disregard large portions of the genetic material or produce noisy results. In this thesis, I address part of these limitations by introducing a tool called the Read Annotation Tool (RAT), which integrates information across multiple levels of genome reconstruction. By combining these data sources and prioritizing them based on reliability, RAT provides an accurate and comprehensive representation of microbial community composition. Using this approach, I studied a groundwater bioremediation system in Utrecht, located at a former industrial site contaminated with hydrocarbons and other pollutants. Groundwater was sampled along a pipeline connecting the contaminated site to a treatment plant, and metagenomic analysis was used to reconstruct microbial genomes. The results showed a shift from anaerobic, sulfur-oxidizing bacteria in the park to aerobic, pollutant-degrading bacteria in the treatment plant. Genes involved in breaking down aromatic hydrocarbons were more abundant in the oxygen-rich treatment environment compared to the park. These findings show how oxygen availability and long-term operation shape microbial communities in engineered bioremediation systems and identify key microorganisms involved in the clean-up of polluted groundwater. Metagenomics can also be used to identify viruses, e.g. phages. Phages are the most abundant biological entities on the planet and play a central role in regulating microbial populations and facilitating gene exchange. However, their extreme genetic diversity and rapid evolution make them difficult to study. For this research, I analyzed DNA phages across 47,726 public metagenomes spanning 107 distinct ecosystems. I identified 39 million potential viral DNA sequences. Most sequences belonged to Caudoviricetes, while a substantial fraction remained unidentified, representing uncharacterized viral diversity and potentailly other genetic elements. Biome-level comparisons showed clear ecological patterns: human-associated phages were more frequently shared across environments, whereas marine and other environmental phages were more specific. This work provides an overview of phage diversity and distribution across global ecosystems and contributes a new, large dataset to explore the global DNA phageome. Finally, this thesis explores the emerging concept of phage bioaugmentation, where phages have the potential to enhance the degradation of pollutants in soil. Bioremediation often struggles with slow rates and environmental challenges, but phages may help overcome some of these limitations by delivering beneficial genes to native bacteria. These genes can improve host survival and pollutant breakdown, potentially transforming soil clean-up strategies. I review how soil complexity and microbial ecology affect pollutant bioavailability, outline evidence for phages carrying bioremediation-relevant AMGs in contaminated environments, and propose a framework for using phages in bioaugmentation. While many challenges remain, including understanding gene expression and ensuring ecological safety, phage bioaugmentation represents a promising next step toward more efficient, sustainable, and targeted soil remediation. Together, the studies presented in this thesis explore how the knowledge gained from metagenomics and improving bioinformatic methods can be used to better understand microbial communities. The research further shows how learning more about phages and their interaction with microorganisms in e.g. polluted environments could be used in the future to develop new techniques for environmental restoration.

The human reproductive tract is essential for species perpetuation and overall health. Its development involves complex processes of sex specification, tissue patterning and morphogenesis, the disruption of which can cause lifelong issues, including infertility1–5. Here we present an extensive single-cell and spatial multi-omic atlas of the human reproductive tract during prenatal development to provide insights beyond those that are possible with smaller-scale, organ-focused studies. We describe potential regulators of sexual dimorphism in reproductive organs and pinpoint previously unknown genes involved in Müllerian duct emergence and regression and urethral canalization of the penis. By combining histological features with gene expression and chromatin accessibility data, we define transcription factors and signalling events potentially involved in the regionalization of the Müllerian and Wolffian ducts. We also refine how the HOX code is established in distinct reproductive organs and reveal that the expression of thoracic HOX genes is increased in the rostral mesenchyme of the fallopian tube and epididymis. Our findings further indicate that epithelial regionalization of the fallopian tube and epididymis, which probably contribute to sperm maturation and capacitation, is established during development. By contrast, later events are necessary for regionalization of the uterocervical canal epithelium. Finally, on the basis of single-cell data and fetal-derived organoids, we show that the fetal uterine epithelium is vulnerable to oestrogen-mimicking endocrine disruptors. By mapping sex-specific reproductive tract regionalization and differentiation at the cellular level, our study provides valuable insights into causes and potential treatments of developmental reproductive disorders.

MotivationBulk RNA sequencing (RNA-seq) captures tissue-level transcriptomes that reflect tumor-intrinsic programs and microenvironmental signals, while single-cell RNA-seq (scRNA-seq) enables analysis of cellular heterogeneity. Pseudo-bulk (PB) RNA-seq, generated by aggregating scRNA-seq profiles, has become a standard surrogate for bulk in benchmarking deconvolution, differential expression (DE), and synthetic data generation. However, it remains unclear whether PB faithfully represents true bulk transcriptomes.MethodsWe introduce a gene-centric Jensen–Shannon Divergence (JSD) framework, a model-free, information-theoretic approach to quantify PB–bulk differences at single-gene resolution using matched breast cancer datasets. Genes were stratified into low-JSD ‘stable proxies’ and high-JSD ‘divergent drivers,’ followed by functional enrichment and validation in scRNA-seq clusters.Results and ConclusionHigh-JSD genes (≈25–30% unique) drive systematic PB–bulk divergence and introduce spurious correlations, largely overlooked by standard DE or PCA analytics. Bulk-specific divergent genes are enriched for stromal and immune pathways, reflecting tumor microenvironmental signals. In contrast, PB-specific divergent genes highlighted cell-autonomous processes, including metabolism and transcriptional regulation in endothelial, T-cell, and myeloid cells. Low-JSD genes provide stable cross-platform signals, improving gene-level similarity, batch correction, and alignment. This framework disentangles modality-specific biases in cancer transcriptomics and identifies robust gene subsets for reliable bulk–single-cell integration.