A partial success

In 2010, Poliseno & co published some results on the regulation of a gene by a transcript from a pseudogene. Now, Kerwin & co have published a replication study, the protocol for which came out in 2015 (Khan et al). An editor summarises it like this in an accompanying commentary (Calin 2020):

The partial success of a study to reproduce experiments that linked pseudogenes and cancer proves that understanding RNA networks is more complicated than expected.

I guess he means ”partial success” in the sense that they partially succeeded in performing the replication experiments they wanted. These experiments did not reproduce the gene regulation results from 2010.

Seen from the outside — I have no insight in what is going on here or who the people involved are — something is not working here. If it takes five years from paper to replication effort, and then another five years to replication study accompanied by an editorial commentary that subtly undermines it, we can’t expect replication studies to update the literature, can we?


What’s the moral of the story, according to Calin?

What are the take-home messages from this Replication Study? One is the importance of fruitful communication between the laboratory that did the initial experiments and the lab trying to repeat them. The lack of such communication – which should extend to the exchange of protocols and reagents – was the reason why the experiments involving microRNAs could not be reproduced. The original paper did not give catalogue numbers for these reagents, so the wrong microRNA reagents were used in the Replication Study. The introduction of reporting standards at many journals means that this is less likely to be an issue for more recent papers.

There is something right and something wrong about this. On the one hand, talking to your colleagues in the field obviously makes life easier. We would like researchers to put all pertinent information in writing, and we would like there to be good communication channels in cases where the information turns out not to be what the reader needed. On the other hand, we don’t want science to be esoteric. We would like experiments to be reproducible without the special artifact or secret sauce. If nothing else, because the people’s time and willingness to provide tech support for their old papers might be limited. Of course, this is hard, in a world where the reproducibility of an experiment might depend on the length of digestion (Hines et al 2014) or that little plastic thingamajig you need for the washing step.

Another take-home message is that it is finally time for the research community to make raw data obtained with quantitative real-time PCR openly available for papers that rely on such data. This would be of great benefit to any group exploring the expression of the same gene/pseudogene/non-coding RNA in the same cell line or tissue type.

This is true. You know how doctored, or just poor, Western blots are a notorious issue in the literature? I don’t think that’s because Western blot as a technique is exceptionally bad, but because there is a culture of showing the raw data (the gel), so people can notice problems. However, even if I’m all for showing real-time PCR amplification curves (as well as melting curves, standard curves, and the actual batch and plate information from the runs), I doubt that it’s going to be possible to trouble-shoot PCR retrospectively from those curves. Maybe sometimes one would be able to spot a PCR that looks iffy, but beyond that, I’m not sure what we would learn. PCR issues are likely to have to do with subtle things like primer design, reaction conditions and handling that can only really be tackled in the lab.

The world is messy, alright

Both the commentary and the replication study (Kerwin et al 2020) are cautious when presenting their results. I think it reads as if the authors themselves either don’t truly believe their failure to replicate or are bending over backwards to acknowledge everything that could have gone wrong.

The original study reported that overexpression of PTEN 3’UTR increased PTENP1 levels in DU145 cells (Figure 4A), whereas the Replication Study reports that it does not. …

However, the original study and the Replication Study both found that overexpression of PTEN 3’UTR led to a statistically significant decrease in the proliferation of DU145 cells compared to controls.

In the original study Poliseno et al. reported that two microRNAs – miR-19b and miR-20a – suppress the transcription of both PTEN and PTENP1 in DU145 prostate cancer cells (Figure 1D), and that the depletion of PTEN or PTENP1 led to a statistically significant reduction in the corresponding pseudogene or gene (Figure 2G). Neither of these effects were seen in the Replication Study. There are many possible explanations for this. For example, although both studies used DU145 prostate cancer cells, they did not come from the same batch, so there could be significant genetic differences between them: see Andor et al. (2020) for more on cell lines acquiring mutations during cell cultures. Furthermore, one of the techniques used in both studies – quantitative real-time PCR – depends strongly on the reagents and operating procedures used in the experiments. Indeed, there are no widely accepted standard operating procedures for this technique, despite over a decade of efforts to establish such procedures (Willems et al., 2008; Schwarzenbach et al., 2015).

That is both commentary and replication study seem to subscribe to a view of the world where biology is so rich and complex that both might be right, conditional on unobserved moderating variables. This is true, but it throws us into a discussion of generalisability. If a result only holds in some genotypes of DU145 prostate cancer cells, which might very well be the case, does it generalise enough to be useful for cancer research?

Power underwhelming

There is another possible view of the world, though … Indeed, biology rich and complicated, but in the absence of accurate estimates, we don’t know which of all these potential moderating variables actually do anything. First order, before we start imagining scenarios that might explain the discrepancy, is to get a really good estimate of it. How do we do that? It’s hard, but how about starting with a cell size greater than N = 5?

The registered report contains power calculations, which is commendable. As far as I can see, it does not describe how they arrived at the assumed effect sizes. Power estimates for a study design depend on the assumed effect sizes. Small studies tend to exaggerate effect sizes (because, if an estimate is small the difference can’t be significant). This means that taking the estimates as staring effect sizes might leave you with a design that is still unable to detect a true effect of reasonable size.

I don’t know what effect sizes one should expect in these kinds of experiments, but my intuition would be that even if you think that you can get good power with a handful of samples per cell, can’t you please run a couple more? We are all limited by resources and time, but if you’re running something like a qPCR, the cost per sample must be much smaller than the cost for doing one run of the experiment in the first place. It’s really not as simple as adding one row on a plate, but almost.


Calin, George A. ”Reproducibility in Cancer Biology: Pseudogenes, RNAs and new reproducibility norms.” eLife 9 (2020): e56397.

Hines, William C., et al. ”Sorting out the FACS: a devil in the details.” Cell reports 6.5 (2014): 779-781.

Kerwin, John, and Israr Khan. ”Replication Study: A coding-independent function of gene and pseudogene mRNAs regulates tumour biology.” eLife 9 (2020): e51019.

Khan, Israr, et al. ”Registered report: a coding-independent function of gene and pseudogene mRNAs regulates tumour biology.” Elife 4 (2015): e08245.

Poliseno, Laura, et al. ”A coding-independent function of gene and pseudogene mRNAs regulates tumour biology.” Nature 465.7301 (2010): 1033-1038.

European Society for Evolutionary Biology congress, Groningen, 2017

The European Society for Evolutionary Biology meeting this year took place August 20–25 in Groningen, Netherlands. As usual, the meeting was great, with lots of good talks and posters. I was also happy to meet colleagues, including people from Linköping who I’ve missed a lot since moving.

Here are some of my subjective highlights:

There were several interesting talks in the recombination symposium, spanning from theory to molecular biology and from within-population variation to phylogenetic distances. For example: Irene Tiemann-Boege talked about recombination hotspot evolution from the molecular perspective with mutation bias and GC-biased gene conversion (Arbeithuber & al 2015), while Franciso Úbeda de Torres presented a population genetic model model of recombination hotspots. I would need to pore over the paper to understand what was going on and if the model solves the hotspot paradox (as the title said), and how it is different from his previous model (Úbeda & Wilkins 2011).

There were also talks about young sex chromosomes. Alison Wright talked about recombination suppression on the evolving guppy sex chromosomes (Wright & al 2017), and Bengt Hansson about the autosome–sex chromosome fusion in Sylvioidea birds (Pala & al 2012).

Piter Bijma gave two (!) talks on social genetic effects. That is when your trait value depends not just on your genotype, but on the genotype on others around you, a situation that is probably not at all uncommon. After all, animals often live in groups, and plants have to stay put where they are. One can model this, which leads to a slightly whacky quantitative genetics where heritable variance can be greater than the trait variance, and where the individual and social effects can cancel each other out and prevent response to selection.

I first heard about this at ICQG in Edinburgh a few years ago (if memory serves, it was Bruce Walsh presenting Bijma’s slides?), but have only made a couple of fairly idle and unsuccessful attempts to understand it since. I got the feeling that social genetic effects should have some bearing on debates about kin selection versus multilevel selection, but I’m not sure how it all fits together. It is nice that it comes with a way to estimate effects (given that we know which individuals are in groups together and their relatedness), and there are some compelling case studies (Wade & al 2010). On the other hand, separating social genetic effects from other social effects must be tricky; for example, early social environment effects can look like indirect genetic effects (Canario, Lundeheim & Bijma 2017).

Philipp Gienapp talked about using realised relatedness (i.e. genomic relationships a.k.a. throw all the markers into the model and let partial pooling sort them out) to estimate quantitative genetic parameters in the wild. There is a lot of relevant information in the animal breeding and human genetics literature, but applying these things in the wild comes with challenges that deserves some new research to sort things out. Evolutionary genetics, similar to human genetics, is more interested in parameter estimation than prediction of phenotypes or breeding values. On the other hand, human genetics methods often work on GWAS summary statistics. In this way, evolutionary genetics is probably more similar to breeding. Also, the relatedness structure of the the populations may matter. Evolution happens in all kinds of populations, large and small, structured and well-mixed. Therefore, evolutionary geneticists may work with populations that are different from those in breeding and human genetics.

For example, someone asked about estimating genetic correlations with genomic relationships. There are certainly animal breeding and human genetics papers about realised relatedness and genetic correlation (Jia & Jannik 2012, Visscher & al 2014 etc), because of course, breeders need to deal a lot with correlated traits and human geneticists really like finding genetic correlations between different GWAS traits.

Speaking of population structure, Fst scans are still all the rage. There was a lot of discussion about trying to find regions of the genome that stand out as more differentiated in closely related populations (”genomic islands of speciation/divergence/differentiation”), and as less differentiated in mostly separated populations (introgression, possibly adaptive). But it’s not just Fst outliers. It’s encouraging to see different kinds of quantitative and population genomic methods applied in the same systems. On the hybrid and introgression side of things, Leslie Turner (Turner & Harr 2014) and Jun Kitano (Ravinet & al 2017) gave interesting talks on mice and sticklebacks, respectively. Danièle Filiaut showed an super impressive integrative GWAS and selection mapping study of local adaptation in Swedish Arabidopsis thaliana (Kedaffrec & al 2016).

Susan Johnston spoke about recombination mapping in Soay sheep and Rum deer (Johnston & al 2016, 2017). Given how few large long term genetic studies like this there are, it’s marvelous to be see the same kind of analysis in two parallel systems. Jason Munshi-South gave what seemed like a fascinating talk about rodent evolution in New York City (Harris & Munshi-South 2017). Unfortunately, too many other people thought so too, and I mostly failed to eavesdrop form the corridor.

Finally, Nina Wedell gave a wonderful presidential address about Evolution in the 21th century. ”Because I can. I’m the president now.” Yes!

The talk was about threats to evolutionary biology, examples of it’s usefulness and a series of calls to action. I liked the part about celebrating science much more than the common call to explain science to people. You know, like you hear at seminars and the march for science: We need to ”get out there” (where?) and ”explain what we’re doing” (to whom?). Because if it is true that science and scientists are being questioned, then scientists should speak in a way that works even if they’re not starting by default from a position of authority. Scientists need not just explain the science, but justify why the science is worth listening to in the first place.

”As your current president, I encourage you to celebrate evolution!”

I think this is precisely right, and it made me so happy. Of course, it leaves questions like ”What does that mean?”, ”How do we do it?”, but as a two word slogan, I think it is perfect.

Celebration aligns with sound rhetorical strategy in two ways. First, explanation is fine when someone asks for it, or is otherwise already disposed to listen to an explanation. But otherwise, it is more important to awaken interest and a positive state of mind before laying out the facts. (I can’t claim to be any kind of rhetorics expert. But see Rhetoric: for Herennius, Book I, V-VII for ancient wisdom on the topic.) By the way, I’m sure this is what people who are good at science communication actually do. Second, celebration means concentrating on the excitement and wonder, and the good things science can do. In that way, it prevents the trap of listing all the bad things that will happen if Trumpists, creationists and anti-vaccine activists get their way.

Nina Wedell also gave examples of the usefulness of evolution: biomimicry, directed evolution of enzymes, the power of evolutionary algorithms, plant and animal breeding, and prevention of resistance to herbicides and antibiotics. These are all good, worthy things, but also quite a limited subset of evolutionary biology? Maybe this idea is that evolutionary biology should be a basic science supporting applications like these. In line with that, she brought up how serendipitous useful things can come from studying strange diverse organisms and figuring out how they do things. The example in talk was the CRISPR–Cas system. Similar stories apply to a other proteins used as biomedical and biotechnology tools, such as Taq polymerase and Green fluorescent protein.

I have to question a remark about reproducibility, though. The list of threats included ”critique of the scientific method” and concerns over reproducibility, as if this was something that came from outside of science. I may have misunderstood. It was a very brief comment. But if problems with reproducibility are a threat to science, and I think they can be, then it’s not just a problem of image but a problem with how scientists perform, analyse, and report their science.

Evolutionary biology hasn’t been in the reproducibility crisis news the same way as psychology or behavioural genetics, but I don’t know if that is because of better quality, or just that no one has looked that carefully for the problems. There are certainly contradictory results here too, and the same overly flexible data analysis and selective reporting practices that cause problems elsewhere must be common in evolution too. I can think of some reasons why evolutionary biology may be better off. Parts of the field default to analysing data with multilevel or mixed models. Mixed models are not perfect, but they help with some multiple testing problems by fitting and partially pooling a lot of coefficients in the same model. Also, studies that use classical model organisms may be able to get a lot of replication, low variance, and large sample sizes in a way that is impossible for example with human experiments.

So I don’t know if there is a desperate need for large initiatives for replication of key results, preregistration of studies, and improvement of data analysis practice in evolution; there may or there may not. But wouldn’t it still be wonderful if we had them?

Bingo! I don’t have a ton of photos from Groningen, but here is my conference bingo card. Note what conspicuously isn’t filled in: the poster sessions took place in nice big room, and were not that loud. In retrospect, I probably didn’t go to enough of the non-genetic inheritance talks, and I should’ve put Fisher 1930 instead of 1918.