A wealth of transcriptomic data waiting to be explored
Streptococcus pneumoniae remains an important human pathogen, causing diseases ranging from pneumonia to invasive infections such as meningitis and sepsis. Like many bacterial pathogens, it must rapidly adjust its gene expression to survive changing environments, including exposure to antibiotics.
RNA sequencing (RNA-seq) has provided increasingly detailed snapshots of these responses. Yet as more datasets accumulate, a new question emerges: how can we move beyond examining individual genes and extract biological meaning from hundreds of transcriptomic profiles together?
Our interest in this question began with another major human pathogen, Streptococcus pyogenes. In previous work, we applied independent component analysis (ICA) to a large collection of RNA-seq profiles and identified 42 independently modulated gene sets, or “iModulons.” This experience convinced us that iModulon analysis could add a new layer of interpretation to bacterial transcriptomics by revealing coordinated transcriptional programs rather than simply lists of differentially expressed genes.
We then turned our attention to S. pneumoniae TIGR4. A remarkably large collection of RNA-seq data for this widely studied strain had accumulated in public repositories, but we felt that this valuable resource had not yet been fully exploited. Rather than viewing these datasets as the products of separate experiments, we wondered what new biology might emerge if they were analyzed together.
Bringing pathogenic bacteriology and systems biology together
Making this idea a reality required expertise far beyond conventional bacterial pathogenesis research.
This study was made possible through collaboration between the Department of Microbiology at the Graduate School of Dentistry, The University of Osaka, the Palsson Lab in the Department of Bioengineering at the University of California San Diego, and the Nizet Lab in the Department of Pediatrics at the University of California San Diego School of Medicine.
The collaboration brought together complementary expertise in experimental pathogenic bacteriology and systems biology. The computational identification and characterization of iModulons from hundreds of transcriptomic profiles required sophisticated analytical expertise in independent component analysis and bacterial systems biology. The expertise and generous support of the Palsson Lab were therefore fundamental to this study; without their contribution, establishing the iModulon framework for S. pneumoniae TIGR4 would simply not have been possible.
Building on this framework, we worked together to interpret the resulting iModulons in the context of pneumococcal biology, antibiotic stress, and adaptation. The complementary expertise in bacterial pathogenesis from The University of Osaka and the Nizet Lab helped connect these systems-level patterns to biologically meaningful questions.
For us, this collaboration was much more than bringing together different analytical approaches. It demonstrated how the integration of systems biology and pathogenic bacteriology can reveal aspects of a familiar pathogen that neither perspective could readily uncover alone. This study could not have been accomplished without the expertise, commitment, and generous support of our collaborators.
From 718 transcriptomes to biological programs
After quality control, we assembled 718 RNA-seq profiles from S. pneumoniae TIGR4 and applied ICA. We identified 60 iModulons, 30 of which showed significant correspondence with previously characterized regulatory systems.
One informative example was the CiaRH iModulon. CiaRH is a two-component regulatory system implicated in pneumococcal stress responses and antibiotic susceptibility. Remarkably, our analysis recovered a biologically meaningful CiaRH-associated iModulon even though transcriptomes from ciaR or ciaH deletion mutants were not included in the compendium.
More importantly, measuring iModulon activities enabled us to compare coordinated transcriptional programs between antibiotic-sensitive and antibiotic-adapted bacteria. Rather than simply asking how strongly the transcriptome changed, we could ask which regulatory programs were changing.
The dedication behind the discovery
A particularly important story behind this paper is the contribution of two researchers in our laboratory: Ayako Bando, a graduate student, and Toshiki Tabuchi, an undergraduate student.
Both devoted tremendous time and effort to organizing, analyzing, and interpreting this large and complex collection of transcriptomic data. Ayako persistently explored the biological meaning of the iModulons, while Toshiki took on the challenge of working with hundreds of RNA-seq profiles and carefully examining their activity patterns across experimental conditions. Despite being an undergraduate student, Toshiki's persistence and commitment to the analysis were remarkable. His careful data curation and analysis, together with Ayako's work, were essential for turning a large collection of transcriptomes into interpretable biological findings.
Their efforts allowed us to follow how iModulon activities changed under antibiotic stress and ultimately led us to patterns that became central to the paper.
One particularly interesting example emerged from the vancomycin datasets. Although the CiaRH iModulon was activated in both vancomycin-adapted and vancomycin-exposed sensitive TIGR4 strains, looking across the wider iModulon landscape revealed important differences. The PflR iModulon was induced in the sensitive strain, whereas the TreR iModulon showed a distinct activity pattern in the adapted strain.
These findings illustrated exactly what we had hoped iModulon analysis could achieve: revealing which coordinated biological programs change during antibiotic stress and adaptation.
Finding new biology in existing data
We also asked whether the patterns discovered through analysis of public data could be reproduced experimentally. We therefore generated new TIGR4 RNA-seq data under chemically defined conditions and again detected key responses, including vancomycin-responsive activation of the CiaRH iModulon. Comparison with another pneumococcal strain, serotype 19F, provided another important lesson: some responses were shared between strains, while others were strongly strain-dependent.
For us, this highlights a broader message. Public transcriptomic datasets are not simply archives of experiments that have already been completed. When enough high-quality data accumulate, new analytical frameworks can allow us to return to them with entirely different questions.
Our study began with a simple thought: hundreds of S. pneumoniae TIGR4 transcriptomes were already available—could we extract more biological meaning from them?
We hope that the resulting iModulon framework will help researchers explore how pneumococci reorganize their transcriptional programs during antibiotic stress, adaptation, and other environmental challenges. More broadly, this study reminds us that new discoveries can emerge when existing data are viewed from a new perspective—and when researchers with different expertise and at different stages of their careers work together to uncover what has been hidden within them.