Adapting to Mutations: How to Cope with Nonsense

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00:00:12.17 Mutations can be very detrimental.
00:00:14.03 They can cause a wide variety of genetic diseases.
00:00:17.14 Until now, there’s around 6,000 known genetic diseases,
00:00:20.17 with most of them being rare.
00:00:22.17 And being rare, they do not receive much investment
00:00:24.28 from the pharmaceutical industry,
00:00:27.01 which makes the people who suffer from genetic diseases
00:00:30.06 suffer alone.
00:00:32.07 Nowadays, using a simple saliva test,
00:00:35.00 you can get your genome sequenced
00:00:36.24 and get a lot of information about your ancestry,
00:00:40.03 but also your predisposition for particular diseases.
00:00:44.05 Now, let’s imagine a scenario
00:00:46.16 where you get the bad information or frustrating news
00:00:48.06 that you’re predisposed to a specific disease
00:00:50.23 that can cause you blindness,
00:00:53.23 or you’re predisposed with a mutation that can cause you blindness.
00:00:57.20 Now, this can be very frustrating,
00:00:59.12 but what if I told you that despite having this mutation
00:01:01.20 you can have a completely normal sight.
00:01:04.29 Recent studies have given us hope
00:01:07.01 that many individuals do harbor gene mutations
00:01:09.21 in disease-relevant genes,
00:01:11.19 but despite of that,
00:01:13.03 they do not show any obvious symptom
00:01:15.18 or any obvious defect or phenotype.
00:01:17.26 And these individuals were referred to as “genetic superheroes”.
00:01:22.14 So, again, they are healthy individuals,
00:01:25.07 they harbor disease-causing mutations in several genes,
00:01:27.28 and nevertheless they don’t show the symptoms of the disease.
00:01:31.08 So, when I started my graduate studies,
00:01:34.01 I was interested to understand what makes those individuals so special,
00:01:37.08 because if we get to understand
00:01:39.21 what makes them so special,
00:01:41.17 we can help other people who suffer from genetic diseases.
00:01:44.29 My name is Mohamed El-Brolosy,
00:01:46.28 and I am a Junior Fellow of the Harvard Society of Fellows,
00:01:49.00 working at the Whitehead Institute for Biomedical Research,
00:01:51.22 and today I’m going to be telling you about my graduate work,
00:01:54.12 which I did at the Max Planck Institute
00:01:56.20 for Heart and Lung Research
00:01:58.11 at Bad Nauheim, Germany,
00:02:00.27 at the lab of professor Didier Stainier.
00:02:03.17 So, at the lab of professor Didier Stainier,
00:02:06.21 people use zebrafish as a modern organism
00:02:08.17 to study cardiovascular development.
00:02:11.15 And it’s a developmental genetics lab,
00:02:13.08 so people are trying to work on understanding gene function
00:02:15.21 in those processes.
00:02:17.17 And to do so, scientists generate mutations within those genes
00:02:21.19 and see how this affects the development
00:02:24.04 of the cardiovascular system within the zebrafish embryo
00:02:26.27 to infer the function of the gene in those processes.
00:02:30.20 However, this is not always as simple as it sounds.
00:02:33.16 Many generated mutant fish
00:02:35.26 did not actually show any obvious phenotype.
00:02:38.14 So, the scientists would spend years trying to generate a mutant animal
00:02:42.16 for a particular gene
00:02:44.09 and not observe any phenotype or defect,
00:02:46.12 and thereby hinder our ability to understand gene function.
00:02:49.18 This was also observed in a wide variety of modern organisms,
00:02:52.11 not just zebrafish,
00:02:54.01 and this understanding of gene function
00:02:56.17 is actually something very central in developing therapeutics
00:02:59.26 and identifying lead drug targets.
00:03:03.00 So, our inability to understand gene function
00:03:05.04 because of the lack of phenotype
00:03:06.25 is hindering such a process.
00:03:09.17 Not seeing a phenotype made us wonder,
00:03:11.18 could those fish actually be also an example of genetic superheroes?
00:03:17.02 Before I started in the lab,
00:03:18.29 two postdocs were studying an endothelial gene known as egfl7,
00:03:22.29 and this gene is implicated in blood vessel development,
00:03:26.23 and they decided to generate a mutant animal for that gene.
00:03:30.03 And as you can observe in the lower panel of the figure,
00:03:33.08 you can appreciate that when they generated mutant animals
00:03:36.08 for those zebrafish embryos,
00:03:37.21 they did not display any blood vessel defects
00:03:41.01 compared to wild type.
00:03:42.17 And this was quite puzzling at the beginning,
00:03:44.11 and they wanted to understand what makes those mutants so special,
00:03:47.15 so they analyzed the expression levels,
00:03:49.06 or the production levels,
00:03:50.21 of several genes across those zebra…
00:03:53.22 mutant zebrafish embryos,
00:03:55.17 and they were able to observe…
00:03:57.18 relative to the wild type… over here, you’re looking at qPCR diagrams,
00:04:01.02 where we’re trying to understand the expression levels
00:04:03.22 of particular genes relative to wild type.
00:04:06.14 They found that there’s an increase in production
00:04:08.23 of a particular family of related genes,
00:04:12.03 of functionally related genes to that egfl7 gene,
00:04:14.14 known as emilins,
00:04:16.28 in the mutants compared to the wild type.
00:04:18.25 And you can appreciate that there’s an increase in production
00:04:21.20 of emilin3a, emilin3b, and emilin2a
00:04:25.04 compared to the wild type.
00:04:28.04 So, this was quite interesting,
00:04:29.13 but it was not known how this response was induced,
00:04:32.15 because they also showed that this response,
00:04:34.04 which was termed later on as transcriptional adaptation,
00:04:37.07 is what drives this functional genetic compensation,
00:04:40.08 and what drives why those egfl7 mutants
00:04:43.07 do not have a phenotype or do not display a phenotype.
00:04:48.26 So, this was the basis of my PhD.
00:04:50.15 I wanted to understand, how is this transcriptional adaptation response induced?
00:04:55.26 And before I go into tackling how did I answer this question,
00:04:59.02 I want to introduce, what are related genes,
00:05:01.27 what are functionally related genes that I’m referring to?
00:05:05.01 So, the easiest example of words to think about
00:05:08.26 is paralogs.
00:05:10.12 So, paralogs are genes that arose from events in evolution
00:05:13.17 known as genome duplication events
00:05:15.15 or gene duplication events.
00:05:16.27 So, what happened is, across the evolution,
00:05:18.24 some genes underwent duplication
00:05:21.02 and thereby they had two copies of the same gene.
00:05:23.23 So, for example, if you think about gene X,
00:05:26.03 you would have gene X-a and gene X-b
00:05:29.05 within the same cell.
00:05:31.09 And both of them initially had the exact same functions,
00:05:34.07 and along the evolution,
00:05:36.13 they might lose or gain new functions,
00:05:38.19 but nevertheless they would maintain a bit of functionality,
00:05:41.27 even if they are expressed in different tissues.
00:05:44.22 You can think of that as the genome or the cell
00:05:48.25 trying to make a backup of a genetic information to the cloud.
00:05:52.05 Looking at paralogs was the easiest to think of
00:05:54.23 because they are the most functionally related genes,
00:05:57.08 and that constituted how I started screening for my question.
00:06:02.16 So, I started by screening a lot of zebrafish mutants
00:06:04.27 that are present in the lab
00:06:06.18 and looking at mutations within paralogous genes.
00:06:10.12 So, I wanted to ask, if I mutate gene X-a,
00:06:13.06 what happens to the expression levels of gene X-b?
00:06:15.16 And I was able, often,
00:06:18.03 to observe that if you have a mutation in gene X-a in one paralog,
00:06:20.26 you get the increased production of gene X-b.
00:06:23.07 For example, you’re looking here at this graph…
00:06:25.23 in the first two bars, you’re looking at wild type versus hbegfa
00:06:30.08 mutant zebrafish embryos,
00:06:31.28 and you can appreciate that the hbegfa mutant embryos…
00:06:35.00 they increase the production levels of the paralog, hbegfb,
00:06:38.19 which is written below it.
00:06:40.19 And then you can also observe in the next two bars…
00:06:44.01 alcama mutants… they increase the production levels of the paralog,
00:06:47.04 alcamb, and so on.
00:06:48.25 And I was able to observe this in
00:06:51.20 at least six more zebrafish models.
00:06:53.16 The same findings were observed in cultured mouse cells.
00:06:56.03 For example, mutant cells for Fermt2
00:06:58.23 increased the expression levels of the paralog, Fermt1;
00:07:02.20 Actb increased the expression levels of the paralog, Actg1;
00:07:06.07 Actg1 mutants increased the expression levels of Actg2;
00:07:09.04 and so on.
00:07:11.00 So, how are those mutants generated?
00:07:13.21 How do we analyze them?
00:07:14.29 How do we generate them?
00:07:17.00 So, most of those mutants… in zebrafish at least,
00:07:19.01 they’re generated by the CRISPR system, the CRISPR/Cas9 system,
00:07:22.01 and those…
00:07:23.29 we target the CRISPR system to a particular gene
00:07:26.17 and it leads to changes that can lead to
00:07:30.09 what is known as a premature stop codon within an RNA.
00:07:34.05 So, when you have the mutation,
00:07:35.26 it will be transcribed into an RNA,
00:07:38.07 and the RNA normally has a start codon and a stop codon
00:07:41.20 through which the ribosome translates.
00:07:44.04 And if you have a mutation that leads to a premature stop codon,
00:07:46.28 this is known as a nonsense mutation.
00:07:50.22 So, what happens at nonsense mutations is
00:07:53.19 instead of the ribosome translating from the start, as in green,
00:07:58.11 and ending at the stop, which is red over here,
00:07:59.25 it will stop at the premature stop codon, which is yellow.
00:08:03.04 And instead of producing a complete functional protein,
00:08:05.25 it may produce a truncated protein
00:08:09.09 that can be toxic to the cell.
00:08:12.05 So, to avoid production of toxic proteins
00:08:14.11 that are toxic to the cell…
00:08:15.17 or, sorry, truncated proteins that are toxic to the cell,
00:08:17.19 the cell has evolved a machinery
00:08:19.25 that is known as the RNA surveillance machinery.
00:08:22.09 You can think of it as a surveillance camera.
00:08:25.03 Whenever a ribosome hits a premature stop codon,
00:08:28.15 it signals to this RNA surveillance machinery
00:08:31.28 and recruits RNA decay enzymes
00:08:35.08 that are responsible for degrading this RNA
00:08:38.10 to prevent it from producing the truncated protein.
00:08:41.23 So, what happens after stopping at the premature stop codon…
00:08:45.07 it degrades it and leads to the production of those fragments,
00:08:47.24 or completely degrades the RNA.
00:08:50.17 And this was something that we observed in all of the mutants
00:08:53.03 that we were studying.
00:08:54.11 So, over here, for example,
00:08:56.01 you’re looking at the alcama mutants,
00:08:57.12 but right now you’re not looking at the paralogous gene expression levels
00:08:59.29 but rather the mutant gene expression levels.
00:09:02.28 And you can appreciate that in the alcama mutants…
00:09:05.21 in the second bar in blue,
00:09:07.22 compared to wild type in red,
00:09:09.08 you can appreciate that there’s a decrease in the production levels
00:09:11.25 or expression levels of alcama itself,
00:09:14.15 compared to the wild type,
00:09:16.08 suggesting that there’s an active degradation happening in that case.
00:09:19.14 And this was observed across all of the mutants
00:09:21.14 that we were studying.
00:09:24.08 So, what made us interested in this response
00:09:26.19 is that we were studying two different mutants
00:09:28.25 for the same gene,
00:09:30.07 that behaved differently in terms of inducing
00:09:33.20 this transcriptional adaptation response.
00:09:36.20 So, the gene is known as vinculin a,
00:09:38.18 or referred to as vcla,
00:09:40.19 and we were able to see that one mutant
00:09:44.00 — which is referred to here as mutant 1, in the second bar —
00:09:46.19 increases the expression levels of its paralog, vinculin b,
00:09:50.17 while the second mutant, mutant 2,
00:09:53.05 did not increase the production levels of the paralog, vinculin b.
00:09:57.01 And this was quite puzzling at the beginning,
00:09:59.17 until we came across this correlation between
00:10:02.00 the upregulation levels of the paralog and the degrees of decay of the mutant gene.
00:10:06.20 So, in the second half of the graph,
00:10:08.22 you’re looking now at the expression levels of the mutant gene itself,
00:10:11.23 vinculin a itself,
00:10:13.02 and you can appreciate that the mutant 1,
00:10:15.04 which upregulated or increased the expression level
00:10:18.21 of vinculin b, the paralog,
00:10:20.24 had RNA decay.
00:10:22.08 So, you can appreciate that…
00:10:24.14 in blue, compared to wild type,
00:10:26.27 you appreciate that there’s almost around 80% decay,
00:10:29.03 or decrease in expression, of vinculin a,
00:10:31.23 while this is missing in mutant 2
00:10:34.11 that did not increase the production levels of the paralog, vinculin b.
00:10:39.06 So, finding that, we thought,
00:10:41.15 could it be that the process of mutant mRNA degradation
00:10:44.00 is what triggers this transcriptional adaptation response?
00:10:48.07 And to answer that,
00:10:49.20 we asked ourselves another question:
00:10:51.02 what if we generate mutant alleles, or mutant animals,
00:10:53.26 that fail to transcribe a particular gene or our gene of interest?
00:10:58.13 So, in that case, we would still have a mutant,
00:11:00.14 because the gene is not producing an RNA
00:11:03.11 and thereby not producing the protein,
00:11:05.12 but at the same time,
00:11:07.02 we are also not having any RNA decay happening,
00:11:09.14 because we don’t have an RNA being produced.
00:11:12.01 So, how did we do that?
00:11:13.23 We did that by blocking the transcription of a particular gene.
00:11:17.03 And there are multiple ways to do that which we took,
00:11:19.18 but the simplest of which is just
00:11:22.11 deleting the entire gene locus.
00:11:25.04 So, we would use the CRISPR/Cas9 system
00:11:28.00 and kick out the gene from the genome,
00:11:30.02 and thereby prevent the production of an RNA.
00:11:32.20 You can think of that as like a cooking book,
00:11:34.16 which has multiple recipes,
00:11:36.13 and the recipes being the genes.
00:11:38.03 So, if you tear out a number of pages for a specific recipe,
00:11:41.22 you can no longer produce this specific meal,
00:11:44.20 because you don’t have the code for it.
00:11:46.29 So, we call these mutants RNA-less alleles,
00:11:49.15 because they don’t produce the RNA,
00:11:51.26 and we next tested whether those RNA-less alleles
00:11:54.07 would fail to induce this transcriptional adaptation response.
00:11:58.10 And we were able to indeed observe that our hypothesis was true.
00:12:01.14 So, if we generate RNA-less alleles,
00:12:04.26 they do not induce this transcriptional adaptation response.
00:12:09.03 For example, over here you’re looking at
00:12:12.06 one of the RNA-less alleles that we generated
00:12:14.21 for a gene known as hbegfa.
00:12:16.19 So, we deleted the entire full gene locus…
00:12:19.20 so, a full locus deletion for that gene,
00:12:21.24 which does not produce the hbegfa mRNA,
00:12:24.22 as you can observe in the first two bars,
00:12:28.01 where you can see that the mutants
00:12:30.01 do not have any production levels of the hbegfa gene.
00:12:32.28 And at the same time, those mutants
00:12:35.15 did not increase the production levels of the paralog, hbegfb,
00:12:38.22 which you can observe in the second half of the graph,
00:12:42.27 unlike the mutants that displayed RNA decay.
00:12:47.09 And we observed this across a lot of RNA-less mutants
00:12:49.29 that we generated and proved that,
00:12:52.04 if you generate RNA-less mutants,
00:12:54.04 you do not have a transcriptional adaptation response
00:12:56.17 being induced.
00:12:58.23 Even more interestingly, we were able to observe stronger phenotypes,
00:13:02.16 or stronger defects,
00:13:04.04 when we had mutant alleles that do….
00:13:06.14 like those RNA-less mutant alleles.
00:13:08.23 So, going back to what I started my talk with,
00:13:11.16 talking about those egfl7 mutants that previous colleagues in the lab worked on,
00:13:15.29 where they generated the mutant alleles
00:13:18.11 that displayed RNA decay for egfl7
00:13:20.10 and found that those mutants displayed RNA decay
00:13:23.12 but displayed no obvious phenotype compared to wild type,
00:13:26.15 in terms of blood vessel development,
00:13:30.11 as you can appreciate in this image.
00:13:32.04 However, once I generated a full locus deletion,
00:13:34.11 an RNA-less mutant for that gene,
00:13:37.05 I was able to observe those strong vascular defects,
00:13:39.20 or strong defects in blood vessel development,
00:13:41.28 that were not appearing in the allele or the mutant
00:13:46.16 displaying RNA decay,
00:13:48.15 suggesting that generation of RNA-less alleles
00:13:51.05 can unmask hidden phenotypes
00:13:53.28 and thereby allow us to better understand gene function —
00:13:56.26 and as I mentioned, something that is very central
00:13:59.04 in thinking about therapeutics.
00:14:02.15 So, a summary of what I’ve told you so far…
00:14:04.27 we have found that mutant zebrafish and mouse cell lines
00:14:07.23 do increase the expression levels of related genes,
00:14:11.17 and we found that there is a correlation between this induction
00:14:14.04 of the transcriptional adaptation response
00:14:16.15 and thereby genetic compensation and RNA decay.
00:14:20.19 And if we’re… one were to generate mutant alleles
00:14:22.20 that fail to transcribe a gene,
00:14:24.26 or fail to produce an RNA of a gene
00:14:27.06 and thereby not have decay…
00:14:28.19 those alleles do not have a transcriptional adaptation response
00:14:31.02 and display stronger phenotypes than alleles that display decay,
00:14:35.29 allowing us to better understand gene function.
00:14:39.05 So, at this stage, we’re at this model
00:14:42.25 that we have mutations that lead to defective transcripts,
00:14:45.03 potentially harboring a premature stop codon,
00:14:47.05 in yellow in this diagram,
00:14:48.19 and lead to degradation.
00:14:51.03 But we didn’t really understand this link between degradation
00:14:54.13 and increasing the production level of a paralog.
00:14:57.25 And this was the question that came up next:
00:14:59.25 how is this happening?
00:15:01.01 How do we go from degradation
00:15:02.13 to this increase in production?
00:15:03.27 And in the past decade,
00:15:05.13 a number of studies have shown that RNA degradation and RNA synthesis
00:15:09.04 are coupled processes,
00:15:11.04 where they have shown that, following degradation,
00:15:16.07 the cell would want to equilibrate its production levels of a particular gene
00:15:19.21 and thereby signal back to increase the production of the gene
00:15:22.07 that undergoes decay.
00:15:23.28 You can think of it as,
00:15:25.21 what do you feel after training or after a workout?
00:15:29.16 You feel hungry, because your body depleted its sugar,
00:15:31.15 and thereby it wants to replenish its sugar levels,
00:15:34.17 and thereby you feel hungry to eat more.
00:15:36.18 This is the same concept.
00:15:38.06 So, you have degradation of a particular gene,
00:15:40.00 so your body knows that it needs to maintain
00:15:42.06 specific production levels of that gene,
00:15:44.08 so it signals back to increase the production of that gene.
00:15:48.08 So, we tried to think of this model
00:15:50.07 and if it could explain what we observed in this transcriptional adaptation machinery.
00:15:53.26 And we tried to think of a way,
00:15:56.00 similar to a guide RNA and a Cas9,
00:15:57.22 where those RNA decay intermediates
00:16:00.19 can act as guides to specific proteins
00:16:02.26 — let’s say the decay factors or the decay enzymes themselves —
00:16:06.08 and bring them back to specific loci of those paralogs
00:16:11.02 and to help them increase the expression levels of those paralogs.
00:16:16.00 And how does this happen?
00:16:17.13 We thought of a way through what is known
00:16:19.16 as homology-mediated base pairing.
00:16:22.10 And this happens, actually…
00:16:23.28 you can think of it as a magnet,
00:16:25.16 where different poles will attract and similar poles will repel.
00:16:30.04 So, for example, if we think about a gene
00:16:32.26 that has a sequence of CTAG,
00:16:35.24 it will only be able to bind to an RNA molecule
00:16:39.08 that has the complementary sequence, GAUC.
00:16:43.04 But if you have an RNA molecule
00:16:45.21 that has a different sequence that is not complementary,
00:16:48.02 it will not be able to bind.
00:16:49.24 So, we thought that if this is indeed what’s happening
00:16:53.21 — that there is an increasing expression of those paralogs
00:16:56.11 because of homology-mediated base pairing —
00:16:59.22 then one should not only expect only those one or two paralogs
00:17:03.00 we’re looking at to be increased in expression,
00:17:04.29 but potentially other genes
00:17:07.16 that exhibit sequence similarity or share sequence similarity
00:17:10.09 with the mutant gene’s mRNA to be upregulated
00:17:13.12 in an un… in a more of, like, a non-specific manner to this paralog,
00:17:17.28 but rather in a more gene sequence similarity-specific manner.
00:17:23.07 So, to test this hypothesis we wanted to ask,
00:17:27.00 okay, how many genes that share sequence similarity
00:17:29.23 are upregulated in the alleles
00:17:32.03 or in the mutants that display RNA decay
00:17:34.10 compared to alleles that do not display RNA decay?
00:17:38.22 And how many of the genes that do not exhibit sequence similarity
00:17:42.02 are upregulated?
00:17:43.15 So, to test that, we took advantage of a method
00:17:45.22 known as RNA sequencing,
00:17:47.10 and RNA sequencing is an approach
00:17:49.15 that allows us to test the transcript levels,
00:17:51.16 or the mRNA levels,
00:17:53.01 of all genes within the genome.
00:17:56.01 And we did that, and we binned the genes into two different bins
00:17:59.06 — either genes that exhibit sequence similarity with the mutant gene’s mRNA,
00:18:03.29 or genes that do not exhibit sequence similarity
00:18:06.20 with the mutant gene’s mRNA —
00:18:08.04 and asked, what’s the relative upregulation level
00:18:11.00 between those two different bins?
00:18:14.22 And interestingly, we were able to observe that in the…
00:18:19.00 there was indeed an enrichment of genes
00:18:20.27 exhibiting sequence similarity to be upregulated.
00:18:23.27 So, in this graph over here, you’re looking at, in red,
00:18:27.06 the genes that do exhibit sequence similarity,
00:18:29.16 and you can appreciate that at least 50-60% of genes exhibiting sequence similarity
00:18:35.09 are being upregulated in the mutant mouse cell lines
00:18:38.14 that display decay,
00:18:39.27 compared to a maximum of 10-20%
00:18:42.29 when looking at genes that do not exhibit sequence similarity.
00:18:47.02 And quite interestingly, most of those genes
00:18:49.11 that exhibit sequence similarity, those red ones,
00:18:51.06 were not upregulated in the RNA-less alleles,
00:18:54.07 or the mutants that do not transcribe the gene
00:18:57.11 and thereby do not have a transcriptional adaptation response.
00:19:01.13 So, this and other experiments made us build a model
00:19:05.00 where we think what’s happening is following a mutation that leads to a defective transcript
00:19:11.02 — let’s say having a premature stop codon,
00:19:12.27 a nonsense mutation —
00:19:15.22 that leads to degradation, those decay intermediates can go back to the nucleus
00:19:19.28 and promote the expression levels of paralogs,
00:19:22.11 and thereby lead to a transcriptional adaptation response,
00:19:24.23 and if it increases the production levels
00:19:27.09 of a closely functionally related gene
00:19:29.25 it can lead to a genetic compensation response,
00:19:31.26 and thereby prevent any defect or phenotype development
00:19:35.16 within the animal.
00:19:37.20 So, this work has been published,
00:19:39.04 where we show that the RNA surveillance machinery
00:19:42.02 is not only important to prevent the production
00:19:44.11 of truncated proteins that can be toxic to the cell,
00:19:47.21 but it also helps in buffering against mutations
00:19:51.11 to prevent the development of defects
00:19:53.10 through loss of the gene function.
00:19:56.17 We have also provided guidelines for the scientific community
00:19:59.23 on how to better design mutant animals
00:20:03.18 to better understand gene function.
00:20:05.08 So, through generation of RNA-less alleles,
00:20:07.23 as I’ve shown you,
00:20:10.01 we were able to unmask hidden phenotypes and uncover them,
00:20:12.24 and thereby be able to better understand gene function,
00:20:15.23 something that should be very helpful for the scientific community,
00:20:18.14 and it has helped our lab to better understand
00:20:21.07 or unmask a lot of hidden phenotypes.
00:20:24.00 So, before I conclude my talk,
00:20:25.17 I want to say a couple of words on implications of this machinery
00:20:28.21 on human genetics.
00:20:30.20 Now, it might be surprising to see Chewbacca
00:20:33.12 when talking about human genetics,
00:20:35.13 but the reason for this connection is the actor who portrayed Chewbacca,
00:20:38.01 Peter Mayhew.
00:20:39.19 Peter Mayhew was characterized by a specific genetic disease
00:20:43.05 known as Marfan syndrome,
00:20:44.29 which… one of its famous characteristics
00:20:46.17 is actually very long limbs,
00:20:48.17 which is what Chewbacca is famous for.
00:20:51.04 And this Marfan syndrome is caused by mutations
00:20:54.01 within a gene known as fibrillin 1.
00:20:56.24 And such kind of a disease or syndrome
00:21:00.14 can range in severity from having aortic aneurysm
00:21:04.07 at a very early stage of your life and thereby dying
00:21:07.11 to very mild forms of the disease.
00:21:09.07 And an old study actually tried to understand
00:21:11.23 what makes the difference between having
00:21:14.07 a very severe form of the disease
00:21:15.20 and a mild form one,
00:21:17.01 and they were able to see that mutations that lead to strong decay levels
00:21:21.03 of the fibrillin 1 mutant mRNA
00:21:23.17 actually are the ones that lead to the mildest form of the disease.
00:21:28.22 And this went along with other studies that found that
00:21:31.27 mutations that are less likely to cause RNA decay
00:21:34.18 are more common in affected individuals
00:21:37.00 than those that lead to RNA decay.
00:21:40.04 The current understanding is that if you don’t have decay,
00:21:43.04 you might be under the risk of producing a truncated toxic protein
00:21:47.10 that can lead to the development of the disease.
00:21:49.08 But what we want to investigate in the future,
00:21:53.09 or what we’re aiming to look at right now,
00:21:55.04 is whether when you have decay
00:21:57.18 you have an induction of a transcriptional adaptation-driven compensatory response
00:22:01.20 by increasing the expression level,
00:22:03.19 for example, of a paralog
00:22:05.04 and thereby contributing to your milder form of the disease,
00:22:09.04 or contributing to you being healthy, as those genetic superheroes
00:22:13.12 that we talked about at the beginning.
00:22:15.22 So, the bigger hope from this machinery
00:22:18.03 is to try and find a way where we can convert everyone
00:22:20.21 to a genetic superhero.
00:22:22.16 And the question now is, could we imagine future therapeutic applications
00:22:25.15 for transcriptional adaptation?
00:22:27.22 Could we imagine ways where we can enhance
00:22:30.22 the degradation of a particular mRNA
00:22:32.08 and thereby funnel back
00:22:35.15 into this transcriptional adaptation machinery
00:22:37.15 to increase the production levels of particular paralogs
00:22:39.22 or functionally related genes
00:22:41.22 that can alleviate the symptoms?
00:22:44.00 Or can we think of ways, for example,
00:22:45.18 where we can enhance this translocation
00:22:47.20 of those RNA decay intermediates to the nucleus,
00:22:49.24 or other ways that can enhance the machinery in general,
00:22:53.02 that would require us first to better understand this machinery
00:22:56.15 in a more detailed way,
00:22:58.02 which is something that we’re aiming to do in the coming years.
00:23:02.16 Thank you so much for watching this video.

This Talk

Speaker: Mohamed El-Brolosy

Audience:

Student
Educators of Adv. Undergrad / Grad
Researcher
Educators

Recorded: April 2021

Talk Overview

Some people harbor deleterious mutations in disease-relevant genes, yet they are completely healthy. How are scientists trying to understand these so-called ‘genetic superheros’? In this Share Your Research talk, Dr. Mohamed El-Brolosy describes his thesis research in the model organism zebrafish. Dr. El-Brolosy found that genetic compensation to deleterious mutations relies on degradation of the mutant mRNA, which triggers upregulation of compensatory genes in a homology-dependent manner. These findings can inform strategies to better understand and treat genetic diseases, many of which have limited therapeutic options.

Speaker Bio

Mohamed El-Brolosy

Mohamed El-Brolosy researches genetic compensation and how organisms adapt to mutations, and has authored numerous highly cited papers on the subject. He performed his thesis research with Dr. Didier Stainier at the Max Planck Institute for Heart & Lung Research. He is currently a junior fellow at the Harvard society of fellows. Follow him on… Continue Reading

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Brittany Anderton (iBiology): Producer
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Jiefei Yuan (iBiology): Editor
Chris George (iBiology): Graphics
Maggie Hubbard (iBiology): Graphics

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