Are differentially expressed (DE) genes also phenotypically important?
A new paper in Cell Reports utilizes RNA-seq and Tn-seq (the “tn” in tn-seq stands for transposon) to map the transcriptional and fitness changes in bacterial gene networks in response to stressors, like nutrient depletion and antibiotics.
The transcriptional response measures changes in gene expression as measured by RNA-seq. The fitness or phenotypic response describes the importance of each gene to the response. This is measured by a different assay, Tn-seq, which takes advantage of transposon insertion to selectively inactivate genes in the bacterial genome. Those genes that are depleted in the stressor condition are determined to be “high fitness” (owing to the fact that the bacteria without those genes died under stress).
First, before even considering DE genes, they found that there is no correlation between a gene’s transcriptional abundance (not fold change) and it’s fitness. While most high-fitness genes were also high abundance, many more high-abundance genes were not high-fitness. Thus, there is no useful relationship between a gene’s abundance and fitness.
Superficially, however, one might expect that genes that show large changes in abundance (i.e., large DE) in response to stressors would also be critical for the phenotypic expression of the bacteria’s stress response. That is, those genes with high differential expression would confer high fitness on the cell.
Testing the DE / high fitness relationship
As it turns out, little is actually known about this, and in this paper, Opijnen, et. al., set out to test the idea to determine if in fact high DE genes are also high fitness.
The researchers looked at comparing differential expression in response to a reduced nutrient environment (a type of minimal media) and an antibiotic stress versus high-fitness genes. They found no correlation:
You can see from the figure that high fitness genes (those on the far left of the x-axis), are not correlated to high DE genes. There are no genes in the upper-left quadrant of either plot, showing that there is no correlation between fitness and high DE in response to either nutrient or antibiotic stress.
Gene networks co-localize high DE and high fitness genes
Even though the authors found no correlation between DE and fitness changes for individual genes, they took the next step and constructed a metabolic gene network for the S. pneumoniae bacteria. Mapping the DE and fitness changes onto this network revealed a key finding: the high DE genes co-localize in pathways with the high fitness genes. That is, a biochemical pathway might have some members that are high DE, and others that are high fitness. An example of this is the shikimate pathway shown below:
The first half of the pathway consists of six genes with significant fitness changes (red boxes) in a row. The next seven genes, from the Trp branchpoint (blue dashed line) are not high-fitness, but do show high DE expression, with four reaching statistical significance. It is not really understood why this happens, but the authors theorize that having the bottom half of the pathway under transcriptional control allows the bacterial to control flux into Trp synthesis and other AA sub-pathways while always maintaining a stable supply of the starting point intermediates (the product of SP1374) through reversible, end product-regulated biosynthesis.
Transcriptomic data should not be used as a surrogate for functional importance
The authors point out that the reliance on trancriptonal abundance changes as markers for functional importance in bacteria, particularly in drug discovery efforts, may be misguided and need to be revisited in light of this and other studies. They also point on that the response to an “orderly” stressor (like nutrient depletion) for which the bacterium is evolved, is likely to be much more clearly defined on a network basis. While the response to a disorderly stressor (a novel antibiotic, for example) may provoke a disorderly transcriptional and fitnress response that can’t easily be interpreted from network analysis. This has important implications for the design of next-generation antibiotics.