Tim van Opijnen

The van Opijnen Lab works at the intersection of microbial systems biology, functional genomics, and computational modeling. Our central focus is understanding how bacterial pathogens survive the combined assault of host immunity and antibiotic treatment and translating those insights into actionable strategies against pathogenic (antibiotic-resistant) infections.

The Core Challenge. Bacterial pathogens survive through two intertwined strategies: evading the host immune system and withstanding antibiotic treatment. Neither can be understood in isolation. Pathogens that successfully dodge complement, opsonization, or cellular immunity buy time to develop tolerance and resistance, while bacteria that persist through antibiotic treatment exploit the same cell states to evade immune clearance. Cracking treatment failure therefore requires a simultaneous, systems-level understanding of both fronts. This dual challenge sits at the heart of everything the lab does, from the tools we build to the targets we pursue.

Building the Bacterial CODEX. The lab develops and deploys high-throughput genomic tools that link genotype to phenotype across hundreds of conditions. We pioneered Tn-Seq, a massively parallel sequencing approach for quantifying genome-wide fitness, and have since built a family of increasingly sophisticated tools: droplet Tn-Seq for single-cell phenotyping, CRISPRi-TnSeq for mapping interactions between essential and non-essential genes, and dual CRISPRi for genome-wide genetic interaction networks. Integrating fitness, transcriptional, and interaction data across these platforms, we have constructed the bacterial CODEX: a dynamic, mineable, systems-level representation of pathogen survival that revealed, for instance, that gene essentiality is not fixed but context-dependent, with direct consequences for drug and vaccine target selection.

Decrypting Immune Evasion. To map how pathogens interface with host immunity, we developed Hii-TnSeq (Host Immune Interaction Tn-Seq), which couples transposon mutagenesis with flow cytometry and cell sorting to quantify physical interactions between host immune factors and the bacterial surface, genome-wide. Using this tool, we identified a new previously missed critical complement evasion factor in S. pneumoniae, a surface scaffold that recruits complement regulators to inactivate the opsonin C3b. Antibody-based blocking of this factor re-sensitizes bacteria to complement killing and, in animal models, prevents host-to-host transmission. We are expanding this approach across multiple complement factors, cellular effectors and species.

AI-Powered Prediction and Drug Potentiation. The ultimate goal is to move from mapping to predicting. We are developing a machine learning framework that integrates fitness and transcriptomic data to forecast drug efficacy and design potentiation strategies. A key insight driving this work is transcriptional entropy: lethal antibiotic stress decouples gene expression from fitness, pushing bacteria into a state of measurable regulatory collapse that precedes death and represents a targetable vulnerability.

Training Opportunities. Students in the lab gain experience across functional genomics, single-cell sequencing, microfluidics, Tn-Seq/CRISPR-based screening, computational biology, machine learning, and in vivo infection models. The lab is collaborative, internationally connected, and has projects spanning early tool development through translational application.