A crucial tool to unravel this mystery was the use of the de.NBI service DESeq2 (https://www.denbi.de/services/282-deseq2-r-bioconductor-package-for-differential-gene-expression-analysis). DESeq2 is a bioinformatic tool for differential gene expression analysis. This allowed us to identify a multigene locus that plays a critical role in the major oxidative stress defense pathway of F. nucleatum.
We uncovered a critical role in this process of this multigene locus encoding a single, fused methionine sulfoxide reductase (MsrAB), a two-component signal transduction system (ModRS), and thioredoxin (Trx)- and cytochrome c (CcdA)-like proteins, which are induced when fusobacterial cells are exposed to hydrogen peroxide. Differential gene expression analysis with DESeq2 revealed that the response regulator ModR regulates a large regulon that includes trx, ccdA, and many metabolic genes. This multigene locus represents a major oxidative stress defense pathway that protects fusobacteria against oxidative damage in immune cells.
The results of this study have important implications for the development of new strategies for preventing and treating diseases associated with this bacterium, such as periodontitis, adverse pregnancy outcomes, and colorectal cancer.
DESeq2 is freely available as an R package from Bioconductor. This makes it easily accessible to researchers. The tool requires read counts for each gene and condition as input. These read counts are typically obtained from high-throughput sequencing experiments, such as RNA-Seq, by using a read counting software.
DESeq2 is accompanied by thorough and well-documented user guides and tutorials. This comprehensive documentation helps researchers in understanding the tool's functionalities, underlying statistical models, and how to interpret the results correctly. It serves as a valuable resource for both novice and experienced users.
One of the notable strengths of DESeq2 is its ability to identify potential issues or irregularities in the input data. If something seems incorrect or problematic, the tool can automatically adjust its parameters, if possible, to mitigate the impact of outliers or other anomalies. This self-adjusting capability increases the reliability and robustness of the analysis.
The best way to become familiar with DESeq2 is to try it out on your own data. Start by installing the package and working through some example datasets provided in the documentation. This hands-on approach will give you a practical understanding of the tool's capabilities and how it can be applied to your research. If you encounter difficulties or have specific questions about DESeq2, don't hesitate to reach out to the Bioconductor community for assistance.