Serious complications in gene-expression studies with stress perturbation: An example of UV-exposed p53-mutant mouse embryonic fibroblasts

Allele-specific wild-type TP53 expression in the unaffected carrier parent of children with Li-Fraumeni syndrome

Li-Fraumeni syndrome (LFS) is an autosomal dominant disorder where an oncogenic TP53 germline mutation is passed from parent to child. Tumor protein p53 is a key tumor suppressor regulating cell cycle arrest in response to DNA damage. Paradoxically, some mutant TP53 carriers remain unaffected, while their children develop cancer within the first few years of life. To address this paradox, response to UV stress was compared in dermal fibroblasts (dFb) from an affected LFS patient vs. their unaffected carrier parent. UV induction of CDKN1A/p21, a regulatory target of p53, in LFS patient dFb was significantly reduced compared to the unaffected parent. UV exposure also induced significantly greater p53[Ser15]-phosphorylation in LFS patient dFb, a reported property of some mutant p53 variants. Taken together, these results suggested that unaffected parental dFb may express an increased proportion of wild-type vs. mutant p53. Indeed, a significantly increased ratio of wild-type to mutant TP53 allele-specific expression in the unaffected parent dFb was confirmed by RT-PCR-RFLP and RNA-seq analysis. Hence, allele-specific expression of wild-type TP53 may allow an unaffected parent to mount a response to genotoxic stress more characteristic of homozygous wild-type TP53 individuals than their affected offspring, providing protection from the oncogenesis associated with LFS.

Valuable lessons-learned in transcriptomics experimentation

We have collected several valuable lessons that will help improve transcriptomics experimentation. These lessons relate to experiment design, execution, and analysis. The cautions, but also the pointers, may help biologists avoid common pitfalls in transcriptomics experimentation and achieve better results with their transcriptome studies.

Confounding Factors in the Transcriptome Analysis of an In-Vivo Exposure Experiment

Confounding factors

In transcriptomics experimentation, confounding factors frequently exist alongside the intended experimental factors and can severely influence the outcome of a transcriptome analysis. Confounding factors are regularly discussed in methodological literature, but their actual, practical impact on the outcome and interpretation of transcriptomics experiments is, to our knowledge, not documented. For instance, in-vivo experimental factors; like Individual, Sample-Composition and Time-of-Day are potentially formidable confounding factors. To study these confounding factors, we designed an extensive in-vivo transcriptome experiment (n = 264) with UVR exposure of murine skin containing six consecutive samples from each individual mouse (n = 64).

Analysis Approach

Evaluation of the confounding factors: Sample-Composition, Time-of-Day, Handling-Stress, and Individual-Mouse resulted in the identification of many genes that were affected by them. These genes sometimes showed over 30-fold expression differences. The most prominent confounding factor was Sample-Composition caused by mouse-dependent skin composition differences, sampling variation and/or influx/efflux of mobile cells. Although we can only evaluate these effects for known cell type specifically expressed genes in our complex heterogeneous samples, it is clear that the observed variations also affect the cumulative expression levels of many other non-cell-type-specific genes.

ANOVA

ANOVA analysis can only attempt to neutralize the effects of the well-defined confounding factors, such as Individual-Mouse, on the experimental factors UV-Dose and Recovery-Time. Also, by definition, ANOVA only yields reproducible gene-expression differences, but we found that these differences were very small compared to the fold changes induced by the confounding factors, questioning the biological relevance of these ANOVA-detected differences. Furthermore, it turned out that many of the differentially expressed genes found by ANOVA were also present in the gene clusters associated with the confounding factors.

Conclusion

Hence our overall conclusion is that confounding factors have a major impact on the outcome of in-vivo transcriptomics experiments. Thus the set-up, analysis, and interpretation of such experiments should be approached with the utmost prudence.

A range finding protocol to support design for transcriptomics experimentation: examples of in-vitro and in-vivo murine UV exposure

In transcriptomics research, design for experimentation by carefully considering biological, technological, practical and statistical aspects is very important, because the experimental design space is essentially limitless. Usually, the ranges of variable biological parameters of the design space are based on common practices and in turn on phenotypic endpoints. However, specific sub-cellular processes might only be partially reflected by phenotypic endpoints or outside the associated parameter range. Here, we provide a generic protocol for range finding in design for transcriptomics experimentation based on small-scale gene-expression experiments to help in the search for the right location in the design space by analyzing the activity of already known genes of relevant molecular mechanisms. Two examples illustrate the applicability: in-vitro UV-C exposure of mouse embryonic fibroblasts and in-vivo UV-B exposure of mouse skin. Our pragmatic approach is based on: framing a specific biological question and associated gene-set, performing a wide-ranged experiment without replication, eliminating potentially non-relevant genes, and determining the experimental ‘sweet spot’ by gene-set enrichment plus dose-response correlation analysis. Examination of many cellular processes that are related to UV response, such as DNA repair and cell-cycle arrest, revealed that basically each cellular (sub-) process is active at its own specific spot(s) in the experimental design space. Hence, the use of range finding, based on an affordable protocol like this, enables researchers to conveniently identify the ‘sweet spot’ for their cellular process of interest in an experimental design space and might have far-reaching implications for experimental standardization.