I worked on a project recently looking at tissue-specific nuclease expression. I made this interactive heatmap from the enormous GTEX dataset that looks at just nuclease gene expression (in TPM) across more than 50 tissues in the human body. It’s fun to play around with the interactive plot. This is the way data should be presented in 2017. I used the Plotly Python API for the chart.
Unfortunately, Plotly is now nearly $400/year if you want to use it for anything more than a few charts and there is no free option to keep sensitive research data private. There should be an exception for academic research, but there isn’t as far as I know.
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:
Expression vs. fitness changes for various strains of S. pneumoniae during nutrient depletion (left) and antibiotic stress (right).
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:
Changes in gene expression and fitness in the Trp biosynthesis pathway. The dashed line is the branch point into Trp synthesis.
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.
According to a new paper, basically, no. Actually that is an oversimplification, but the authors find that quality trimming of RNA-Seq reads results in skewed gene expression estimates for up to 10% of genes. Furthermore, the authors claim that:
“Finally, an analysis of paired RNA-seq/microarray data sets suggests that no or modest trimming results in the most biologically accurate gene expression estimates.”
First, the authors show how aggressive trimming affects mappability in Figure 2:
Influence of quality-based trimming on mappability of reads.
You can see that as the threshold becomes more severe (approaching 40), the number of RNA-Seq reads remaining drops off considerably, and the overall % mappability increases. Overall, you’d think this would be a good thing, but it leads to problems as shown in Figure 4 of the paper:
Isoform and gene expression levels after trimming.
Here you can see in (a) how increasingly aggressive trimming thresholds lead to increased differential expression estimates between untrimmed and trimmed data (red dots). Section (b) and (c) also show that the number of biased isoforms and genes, respectively, increases dramatically as one approaches the Q40 threshold.
One way to correct this bias is to introduce length filtering on the quality-trimmed RNA-Seq reads. In Figure 5, the authors show that this can recover much of the bias in gene expression estimates:
Isoform and gene expression levels after length-filtering.
Now in (b-d) it is clear that as the length filter increases to 36, the number of biased expression estimates goes rapidly down. There seems to be a sweet spot around L20, where you get the maximum decrease in bias while keeping as many reads as possible.
Taken together, the authors suggest that aggressive trimming can strongly bias gene expression estimates through the incorrect alignment of short reads that result from quality trimming. A secondary length filter step can mitigate some of the damage. In the end, the use of trimming depends on your project type and goals. If you have tons of reads, some modest trimming and length filtering may not be too destructive. Similarly, if your data are initially of low quality, trimming may be necessary to recover low-quality reads. However, you should be restrained in your trimming and look at the resulting length distributions if possible before deciding on quality thresholds for your project.
