Montpellier Ecology and Evolution Seminars (SEEM). Seminar by Dr. Gwenaël Piganeau (Observatoire océanologique de Banyuls, Banyuls sur mer, France) on ” “Multi-omics” insights into phytoplankton-virus interactions”.
Supported by the Labex CeMEB: http://www.labex-cemeb.org/fr/recherche/seminaires-en-ecologie-et-evolution-de-montpellier-seem
Uh hello wa So people are arriving on the zoom I’m going to wait a few seconds okay hello everybody Welcome to today’s uh SE seminar uh unfortunately due to a strike in oitan train we can’t have gr in M today so the seminar will be uh 100% online uh as every week you can ask your
Questions on the Q&A if there is a problem you can also contact us on the chat but uh it will be much simpler today I assume and now I leave stepanie introduce today’s speaker uh hello so it’s my pleasure to have gel Pano giving us a seminar today
So gel is working uh in Banes at the Marine biodiversity and biotechnology uh unit and she’s working on a range of topics around phytoplankton Evolution and ecology and um gwel um please let us know your last fascinating stories thank you okay thank you very much I’m sorry not to be in
Person there today so I’m going to talk to you about what we have been doing in my group in B you can see in the in the background of this first slide that is two hours and a half away from m in train when the train do uh
Work so um in my group we are we are working on phonic organism and on this slide you can see the chlorop emission on from our planet and this is just to illustrate the ecological importance of this Marine phop planting organism that are responsible for roughly half of the
Photosynthesis on our planet and I use this illustration to just locate a banul Sur on this map so photo photosynthesis is a very important metabolism on our planet and it has been adopted by many lineages so it has been invented by bacteria a long time ago and the S maybe approximatively
One or two billion years ago a narot adopted an ancestral cob bacteria and this gave rise to the green lineage so in my group we are working on many members of these green lineages of the Class M but other eotic lineages have ad have engulfed some photosynthetic bacteria or
A carots and so in a bucklet of seawater you do actually have photosynthetic organism from all of the three of life so you can see each colored Branch here correspond to a photosynthetic organism so um we’re interested in um uh small photosynthetic chariots uh they have a simple cellular
Organization by that I mean they have one single chloroplast one single mitochondria the white dot here actually I hope you can see my uh my mouse the the white dot is a starch Gran uh they are ecologically important especially in coastal area where they can represent up to 80% of the
Biomass uh the strains we have isolated uh are are haids like most green orotic microalgae what is a kind of very good news for for all the genomics and B informatic analysis uh pipelines we use they are relatively easy to grow in the labs they usually
Divide once per day if you give them 12 hour of light and uh 12 hours of night and so they are very interesting models for plant systems biology so over the last 20 years we have optimized a little bit our sampling protocol uh so uh from 8 to five weeks
From sample uh from environmental sampling to identification of a strain so basically the most rapid method uses single cell sorting with a sitomer we have in b directly on the freshly filtered seawater uh and once we’ve got uh the the culture grown from one single cell
We do actually sequence the 18s to to get the taxonomic affiliation and then we can know which species we have so it’s difficult to Target a special species because actually they do live they’re very diverse all over the place within chlorops you usually get either use btic cuse or Micromon from the same
Sample and for all of these strains we do have a completely sequenced genome the natural environment of our microalgae is Infested by viruses and Nigel Grim who a emiritus NS researcher in my group isolated the first ocus virus uh 2006 so these are doubl stranded DNA viruses
Um that are very diverse in the environment as well and um it turns out that double stranded DNA virus um are very abandon in in the sunlet um in the sunlet uh ocean and they tend to infect many other photosynthetic a carots from other lineages so this double Str D various phop
Plankton system it’s phog gentically very diverse in the ocean um if you look at the the phenomics of these these viruses they are part of the giant uh virus family that uh that has this very very big genomes of two meab Bas so you can see
Them here so they they’re not that big they’re infecting the smallest photosynthetic cariot they’re 2 200 kilobase Big so basically the The genome resources we are are working one there are so for the hosts they are hpid small compact genomes on 17 to 21 chromosomes approximately 15 megabase and for the
Viruses it’s a linear sequence of 200 kilobase so today I’m going to present you some some results we obtained in the past about the genomic signatures of the the cell immunity of our algae or more exactly Pico algae because they are so small to viruses in natural population and then I will most
Of my talk will about the will be about the results we are we are we are having now about the um the understanding of the the change in phenotype between susceptible and resistance strains so the first part will be strictly about population genomics while the second part will be about
Experimental Evolution and combining different levels of information we want to to have on our system on the transcriptomes genome the metabolome and the translat so um how do we uh phenotype actually our strengths and our system uh basically we expose a mic culture to some environmental water or
Some virus we have previously isolated from Environmental water and then we just observe what’s going on we say that a strain is susceptible when it when it lies and the number of virus particle increases resistance is uh defined by the fact that the microalgae doesn’t care about the virus and we also
Have some uh uh cultures where basically both the mikalia strain and the the virus do increase in in quantity so um when we say that we have a resistance strain we actually mean uh we may mean three different things depending on how we observed resistance
So if we uh looked at citometry data uh we get a very precise estimation of the resistance we have observed the we have we have observed that the the V the number of various particles doesn’t change and we can actually detect whether we have a resistant a residance resistant producer susceptible phenotype
Historically we didn’t do that historically we we just performed a visual color tests uh that we repeated of course and we also looked at microscopy to to see that we we had actually Lis and when we do that basically we we’ve got a r idea whether it’s resistant or susceptible or or
Highly susceptible to the virus and the third thing that also needs to be done is a plague essay basically you put the M algae in some liquid um agar and you uh and and the virus and then you can actually count the number of plague forms and send you get the number of
Infected viruses so this is also very important technique so basically we we use a three techniques but we use the same word and and this can create confusions so this is a an illustration of uh when you want to check the resistance spectrum of one strain you
Into Zar on different viruses this is what you do you do it in duplicate if there are two lices you say oh this strain is susceptible to that virus and where there’s no lies you say oh this strain is resistant to that virus so we have been collecting these viruses so we
Have been doing a cross experiment so this is a simple visual experiment and with this experiment we could Define uh super susceptible strains like the first one s4221 that is lized by most viruses we have isolated and strain a just below you can see that we can hardly find any
Virus that that will lies this strain so you have a high variation of of cell immunity within Osteo cocosto natural population uh we had the the sequences of the host and this is something that we’re continuing to do trying to isolate a big population of these strains and when we sequence
When we first sequence this train so at the beginning we just had 12 we noticed that one chromosome that is uh calls a small outlier chromosome because it has a different GC content and many transposable elements so we had called it small outlayer chromosome due on just these genomic features that this small
Outlayer chromosome had a very uh variable coverage in this trrain so when you do a short read uh basically you you map the short we against the reference genome and this gives you information about the the the presence absence of the DNA okay so when you have a lot of zero coverage region
That is probably due to a deletion and when we looked at the the size of the of this outlier chromosome we noted that uh strains that tended to to be resistant to many viruses we had in the lab tend to have a larger uh outlier chromosome and this made us very
Curious about the the sequence of this outlier chromosome in in natural population and this we can only sort out with long read because of the highly repetitive nature of this chromosome and um and we observed something very interesting it’s that this chromosome so here you have six individuals that have six different
Outlayer chromosomes from 400 to 600 kilobase in length and um uh it’s the this chromosome is hyperv variable um you’ve got few synic regions in in in blue other otherwise you’ve got a lot of rearranged DNA so this is what I colored in yellow here and then you have
Approximately one3 of of DNA that is actually just in one uh chromosome and not in the others so we just did a sequence similarity uh search of this um um strain specific DNA and at that time so maybe we would need to to do that we didn’t find a lot
Of information there was not a lot of of data of of meaningful data in the databases about where this DNA could could come from uh s we had a lot of um DNA that actually comes from uh the the Miko algae but from other chromosomes okay so meaning that this this
DNA has been translocated from other chromosomes into that outlier chromosome and in one case we had a tiny uh 50 nucleotide long fasino virus sequence but just in one stren so um this made us very very curious about uh how cell immunity to viruses uh work in in our system so the take-home
Message of this first part is that uh well we do have a a variable immunity to preso viruses in natural populations of Osteo coccus that one chromosome size increases with antiviral resistance and that this chromosome is hyper variable and that this level of hyper variability can only be generated by Massive rearrangements duplications
Deletions translocations so now it brings me to my to the second part of my talk about uh the evolution of resistance and susceptibility so basically when you infect a mikal with a virus if you wait uh if you are patient enough to wait for several days uh you will
Always uh actually have some cells that will survive and either become resistant or resistant producers so the two differences it’s whether they continue to produce viruses or not in the culture so we’ve got uh we’ve got the mo that actually because we always start our culture from one single cell so we
Try to reduce a level of variability in our cultures uh this motherline will generate daughters that will become resistant somehow so let’s look into one of these cases so this is another ocus Medan species we were interested in for evolutionary regions evolution reasons you know it’s it’s branching at the base
Of the ostage so we looked into its genome and when we obtained The genome assembly we uh we observed that there was a complete preso viruses uh in our genome data sets and surpris when we got back into the culture of this stream actually the virus was still being
Produced and what you can see on this graph is that um depending on how you measure the virus by citometry it’s empty triangles and the full triangles they are the the bip PL forming unit that gives you an estimation of the number of infected viruses you do have
Actually two orders of magnitude of difference in the estimation of these various particles we didn’t dig into that what we took home from this is that the the virus was still being produced uh in this population and it it was linear increasing well it was increasing together with the
M uh so this was a stable uh what we call a stable um a coexistence h this is just a picture of the Genome of the virus to convince you that it’s a proper virus and so we were um thinking about how how how is this how does this working how has it
The these cells producing the virus so we looked at it at using electron microscopy and basically 99.5% of the cells they they look fine there’s no virus in the cells in the cytoplasma they look fine but a few proportion of them uh seem to be infected so we imagin
We imagined basically that uh there was a there are two states for the same cell corresponding to these two phenotypes that actually there was a switch in in in this phenotype happening in the culture so we collaborated with theori who uh came up with uh parameters and equations and basically they predicted
The equilibrium various to mical um proportion as a function of these parameters and what is really interesting as a parameter is this rate of switch between the two phenotypes because this we can try to assess experimentally and see if if it makes sense and since the second thing we’re
Interested in is basically the molecular mechanism generating this switch between phenotypes uh okay so basically what we think might happen in our cultures is that the the the cells they do switch phenotypes at a certain rate the susceptible strains they divide a little bit more rapid rapidly and the resistant one and so
They are prevalent when there’s no virus when the virus arrives all susceptible cells are lied and basically the produce the production of um viruses is a consequence of the switch from resistance to susceptibility over time so let’s look now into the the the the mechanism involved in this susceptible well first resistant to
Susceptible switch so if you take a resistant culture that has no virus meaning that when you add a virus you do not increase the number of viruses so that is what we call a resistant cell you can actually isolate and single cells and then you will grow these cells
And then you will expose them again to the virus so we did that more than 200 times and basically what you will observe is that in for this trrain in this condition at this temperature in this media we had 20% of of of susceptible strains meaning that we observe this switch between resistant
And susceptible cells so we looked at the the genomes of these resistant and susceptible cells and at that time we did pul field gel electroforesis um The genome is so small in our ararot that you can actually migrate it on a gel and then you can actually see the chromosomes and then
You can use uh probes to check where this or this chromosome here so what you can see on on this slide is that actually it’s actually the small outlayer chromosome that changed in size between resistant and susceptible cells and so we decided to sequence uh this resistant and this
Susceptible uh cell using a l again long read Because you you can’t resolve this outlier chromosome with short read and so we can activiz a 60 kilobase deletion on the sock that is associated to the loss of resistance because the sock is full of repeats it’s not because you delete 60
Kilobases that you delete all the genes basically the only uh genes that are just in this region are the the six genes the seven genes that are here so these are the perative genes that are involved in the resistant phenotype uh two hypothetical proteins one transposon I do not think is
Involved in um in in the loss of resistance and then uh to uh two enzymes that actually change may change sugars okay so it’s not very clear how this resistant to susceptible switch uh is organized so the the conclusion of this uh resistant to susceptible uh switch is still very
Incomplete but we are uh working on it so uh we do know how to isolate susceptible strain from resistant Lines by single cell actually single cell um isolation uh we do have just the genomic signatures for now so it’s a deletion of of part of the S chromosome but we plan
To do transcriptomics metabolomics and translatomics in 2025 so this was the the the the the resistant to susceptible switch and now I’m going to go into the the reverse switch from susceptible to resistance where we have much more data because we were uh somehow more interested in the evolution of
Resistance than the reverse so basically you take susceptible strains you infect them with viruses uh so you need um a lot of viruses you need to amplify from from your strain and then you are going to infect uh some some some of these cultures you put in different
Flasks and you will observe the liis with these viruses and uh um you you you have a control experiment of course where you don’t add viruses and since the first thing we did is what to do a comparative transcriptomics between the susceptible lines and the resistant lines that had evolved from the susceptible
Lines and what we found here was uh well differ differently expressed genes uh and many of these genes they were located on the outlier chromosome uh and the expression on the out outl chromosome was actually clustered there are some regions that were on and some regions that were
Off uh the function of these upregulated and down down regulated genes uh they are of of different types but many of these genes are actually unknown so the hypothesis we have it’s again it turns around the role of glycotransferases that may change uh the uh the sugars on on membran proteins
Or me transfer that may may be involved maybe in epigenomic modification changing the transcription of um um of the of the Genome of the mikal so um s we wanted to look into the genomic signatures uh because we realized that this uh chromosome was actually hyper variable
And and um that we we needed to characterize this hyper variability so we did the same kind of experiment again where uh going from um a susceptible strain we evolved uh resistance and then we uh sequenced the genome with nanopore so at that time long weed sequencing was still a little
Bit expensive so we couldn’t do it for 40 uh independent strains we just did it for five so actually the mother line has its uh the coverage of its s chromosome uh represented here so it’s a 600 kilobase so chromosome the black uh color represents a deletion as compared to the
Reference genome so this is the the the the reference genome is a resistance strain so mazine is one of the susceptible Des of this um Moine that has lost a part of this sock this is what you can see here so then uh the daughters of these mine they evolved independently five times
Resistance and this is uh the coverage of the outlier chromosomes we obtain so on this particular couple you can see differences between the resistant and the susceptible train with very well more copies of this part of the chromosome is a resistance Trend however it seems to be an exception in all the
Other uh evolutions of resistance the um coverage of the outlier chromosome seems to be very similar however what we noted with this experiment is that uh the the outlier chromosome is very variable within our population independently of the evolution of resistance and so this led us to con conclude that
The the changes in size of the sock are actually not linked to the acquisition of resistance or the vious infection it’s a spontaneous phenomenon in our culture so this is just a coverage analysis so it’s not very precise we are looking into the long weed assemblies to
Get to get the T to T chromosomes uh to characterize exactly the nature of the outlier chromosomes in um um in in this data set okay and um then we also wanted to um to look at the the metabolomic signatures the initial idea was that this would help us to understand
Uh the function of the unknown genes we have in the genome in general those that are differentially expressed between resistant and susceptible lines and all these genes on the out chromosome we have no idea of their function so we did um a monitoring of the metabolome and
The transcriptome over the life of ocus it’s 24 day 24 hours sorry and as a photosynthetic organism you can imagine the transcriptome is very different between the light and the in the night so we took four transcriptomes uh four transcriptome and two metabolomes so the metabolomic analysis was performed by
Chemists and uh this is what they found so they extracted depending on their mass and on their ch the compounds in the susceptible and in the resistant cells and since I annotated these compounds and uh for genom it was interesting to see that they also have a
Lot of unknown annotations as we have for genome so compounds they have the mass they have the charge they have the Spectra but actually they have no idea of the structure of these compounds however what you can see from their analysis is that um you have actually much more compounds that are
Over abandoned insusceptible as compared to resistant cells and then I give you two example of susceptible biomarkers that are lipids so are the two molecules on the top and this camid is one of the metabolize that is more prevalent in the resistance in the resistance
Cells and what you can also see here is that there’s no marker of resistance uh in the night what about the transcriptomic signatures so this is a classical differential gene expression analysis along the the four time point so we’ve got in total seven more than well 800 differently expressed genes
That are is overe expressed in the susceptible or resistant cells and so of course we wanted to integrate these two kinds of information to to understand uh the resistant and susceptible phenotypes so when we uh tried the top bottom approach looking at the differently expressed uh Gene in our
Data set they were not at all involved in uh the production of these metabolites no link so we tried the bottom up approach we looked at the genes that are involved in the synthesis of this metabolites and then we went back to the trans cryptomic data to check whether the genes that are
Involved and and then indeed it’s it’s a chain of Gene that is involved in the production of this metabolite whether uh its transcription is changed between susceptible and resistance strain and then we found a signal so uh we can Define congruence between the gene expression and a metabolite when the
Abundance of a metabolite uh is uh related to gene expression variation and for these three metabolites on the on the left on the right sorry of my slide there’s a clear congruence of the gene expression of the different genes that are involved in the production of this metabolite and the
Biomaker metabolite so this is quite reassuring uh however uh indeed when you look at the differently expressed genes you wouldn’t find these genes so this means that there’s a decoupling in the signal between the transcription change that appear and the metabolomic change we can detect an interesting result we got is
That some of the metabolomic results enable that to annotate some genes uh in our genome but not a lot okay so this uh brings me to the last uh layer of information we have we are going to deploy to uh try to understand the resistant and susceptible phenotype
Uh this is what I call translatomics so um it’s a translation actually of of mangen RNA so uh we are interested in the duplication and rearrangement rate in our organism and basically when you look at the DNA copy number for example of a chromosome or large chromosome chromosomal region and you estimate the
Transcription of the gene on this chromosome uh the transcription generally scales with a DNA copy number okay so this is true in our pical it is true in most cariot so this is not a big surprise this raises the question how does the cell cope with this imbalance of transcripts following a
Duplication to assess this we did we we extracted uh the rnas that are bounded to the ribosomes the polysomes so to do that you Ultra refug youra your data and they bounded to uh the the ribosome they are heavier and then you can sequence this fraction and by comparing the RNA
That abounded to the ribosomes whereas the total RNA you can estimate how many of the meagen RNA are translated okay are actually transformed into proteins and what we could see here very clearly is that there’s uh the the translation weate of the genes on duplicated chromosome 4 they nearly half
Translated as compared to the genes that are on single copy chromosomes meaning that you have DOS compensation of protein synthesis at the level of translation somehow the cell sense there’s too much mesogen RNA and they fix less ribosomes onto it and this motivated us to uh also check uh translation weights especially
As we’re interested in large scale duplications uh in the phenotypic change because the this transatomic data will provide a more precise estimation of protein abundance as compared as transcription rates okay so uh what is the take-home and a message of all this so we do have uh acquired we have acquired
Independently uh different signatures of the susceptible to resistance um switch at a transcriptomic level so uh targeting genes on the small outlier chromosome that are highly influenced by this Chang in phenotype on the genomic level uh we understood that basically uh some of these transcriptional changes are the simple consequence of large structural
Rearrangement of the sock like delions and duplication the metabolomic protocols we developed they enabled us to uh see that the resistance cells actually have less biomarkers especially lipids and susceptible cell uh meaning that maybe resistance is just um being more simple uh on on the outside membran less is more for resistance to
Viruses and as for the translatorx approach uh we think it’s important to do that uh to to get a more accurate estimation of of protein abundance in these cells so all these results they were obtained on different experiments uh because uh this is research you never
Get the money to do everything at once and you also learn as you progress so the transcript for to do transcriptomics we had 40 um 40 lines and you need it in triplicates and when we did genomics it was very expensive so we could just do it uh for five
Events uh metabolomic is extremely expensive in biomass the chemist they want huge amounts of culture they want five replicates so we just did it for one susceptible to resistant uh change and uh and so we compiled all this preliminary result to get funding to do it all at
Once to make uh all these different level of information to get them from the same experimental Evolution so basically this is the the the project so it’s 13 days of experiment you infect your cultures with a virus you will observe lies and S resistant and again this cycle you will prep
For transcriptomics genomics metabolomics and translatomics you will also do electron microscopy you will try to to fix uh the samples to observe the dynamic of the infected cells uh across time uh this requires a lot of biomass much more than we used to do so we had
To uh change the way we culture our uh culture so here you can see the experiment we started just before the Christmas holiday so you can see you can see the lies strains and not we tried bubbles we tried uh uh without bubbles and to our surprise um uh basically the resistance
Instead of evolving um after three days evolved after 10 days and the whole experiment instead of um of being made in in in 50 days lasted one month uh we don’t know exactly why it is it could be because of the temperature uh because we we did it at 15 degrees
And all our previous experiments we did at 20 degrees so it’s an unexpected interesting effect of temperature on the evolution of resistance in our system so now uh we are doing the experiment again at 20° it’s still in progress uh we hope it’s going to be less Long than the
Previous one and this has actually the pilot experiment we need to do to organize a big experiment because to do the experiment you need six train people uh to to do all the the protocols at once so that’s it uh I need to thank all my colleagues from Banes uh in in bold
All the the colleagues who are actually doing this crazy experiment uh and send our collaborators uh on molecular biology and theoretical biology from the University of Pino uh DJ who’s a chemist on board and also our colleagues from the citometry platform who are helping us a lot on following uh these biological
Systems and I’d like to thank you for your attention and I’m welcoming your question thank you I’m going to clap my hand for everybody thank you very much you should pre-record it it’s nice to actually people can raise their arm but I think they will do it to require the
Microphone uh they can also ask question so Stephanie has a question already St you are a penal so you can talk whenever you want um thank you for your talk so one quite interesting picture is this uh instability or this um liability of of the S chromosome is it only this
Chromosome or is it more feature of the or Genome of the of the algae and then um is it linked to this highly repeated regions that can uh be used as a recombination point and so that’s why you have so many rearrangements in this chromosome so yes we we check cover and
We a higher weight of rearrangement of um rearrangement duplications um uh repetitive regions are magnets for these kinds of events um so yes I think the fact that there is a high amount of rep repetitive English it’s it stimulates uh these events combination events okay alen has a question I open
Your microphone Al so you can ask it directly hi gu very interesting talk thank you and uh I was wondering about the do compensation you characterized towards the end of the talk do you have any idea how it’s regulated at the translation stage yeah that’s a a very intriguing
Finding we have so it seems that there’s a chromosome regulation of translation so this we know how it works in bacteria in aarat it’s quite surprising because the transcription uh you know the translation is is not at the same place and the transcription uh we don’t know uh I
Can’t imagine that um I I can’t these mesna that I in excess they must be tagged um we think that they are tagged with smaller po a lengths so how does a cell sense the success of mesen RNA uh to tag them and to reduce a
Translation uh I don’t know but I am very interested in this question actually there are people working on onil who did observe some translational regulation in sexual chromosomes in dropil some time ago but there was no other uh well I haven’t seen any other well it’s not true there was another
Publication about it but it well it exists inil I I can tell you but indeed we we we don’t know how it um how it how how the cell sense this and how the cells deal with it and I think it’s a very interesting fundamental cell biology question thank
You h okay we have another question by yanis mikis yanis you can talk if you want uh we cannot hear you yanice I don’t know why because your microphone is open I don’t know what happens but J what okay sorry my my speaker stopped functioning um so uh hello G so my
Question was that um I have the impression that the observational uh data suggest the correlation between genome size and resistance while the experimental Evolution data suggest an absence of correlation so I was wondering whether this impression is correct and if inde is correct whether there is a way to reconcile the
Two yeah so I do agree with you uh so what I showed is that what we what we first observed is that when a strain is um is not infected by a lot of virus it tends to have a longer chromosome and what we’re doing in the
Lab is that we are evolving resistance from susceptible strains okay so so not too many viruses so it’s not necessarily contradictory if you imagine that resistance to each virus necessitates a little bit more information that’s not exactly what we’re doing in the lab we’re just evolving resistance to one uh
Strain um I do doubt well I I do agree with you that I I am I am not sure this relationship will hold uh with with a larger data set but it could if if resistance to each virus you know need some need some extra extra something uh
And so to be resistant to many many viruses somehow it it helps to have a larger diversity on this outl chromosome but I’m indeed I’m not sure this result will hold as uh our data set increases I hope it was this your question yeah yeah thank you
Okay we have another question by silv G silv you can talk yes hello uh so I had a question about the virus so and about um so here you talk mostly about one type of resistance but I was wondering whether actually there was some viability in the resistance that you can
Select for and if the virus can if you have evidence that the virus can evolve to different types of resistance so that there would be room for coolu later on do you have some evidence for that so actually you have David Dei and Sher who who are trying to
Uh investigate this at the moment with a master student uh so um I don’t have any results to to tell you but indeed uh it’s it’s a very interesting uh a very interesting thing to look at the evolution of the virus over time thanks okay another question by inas
Bravo you can open your mic and ask your questions we cannot hear you Ino I don’t know why your microphone is a b you hear me now yes good think so hi B we can wake up so you’re not here I have um because I would have many questions
If I could so I will have only one I make only only only one and a half so it’s so yeah coming back to the to the gene dosage which is extremely interesting so what you have shown is that there is a correlation between the
Between so DNA and amount of RNA and but the the the my point is um is this true for all genes encoded in the chromosome because there are like around um I there probably hundreds of genes in each chromosome so this um differential dosage is true for all genes in the
Chromosome and then if so could it also be as it has been also I only know about human cells can this can this be that the RNA is not properly exported from the nucleos as it happens in 80 rich uh genes or that it is retained in P bodies
So that you can have the MRNA there but it’s not available for translation so we it will never be enriched in the your polysomes um so the question is how um so how common how Global is this trend for all genes in a chromosome and whether the subcellular location may be
The key for being U present in polysomes or not okay so thank you for your first question indeed you have as in trom and Etc you do have genes that are invariant you double the DNA copy you do not increase the number of RNA because there’s a specific regulation of the um
Trans they are do they do gen variant genes so we do have these genes okay but they are minitary they are minorit and so when you look at the average transcription uh because that’s what we did we were presented the result per chromosome or per chromosomic region you
Do expect a double F but we did also in the paper detail the dosage in variant genes that do exist for you know multimeric domain that have their special transcription Factor so they they have their own regulation so this is the answer to the first question then um um I don’t know
If there are PE bodies uh in in OC cus actually they’re very small cell so it’s very difficult to uh look at Imaging within the cell we’re just trying to start to do that to locate the RNA of the virus inside the cell doing fluis
And protein so I don’t know I I don’t know how to to do that but uh yeah indeed the yeah the these airn they could also be um physically close somehow and um and regulated that way um it would be nice to discuss that with you um I don’t know what’s going
On great no no thanks a lot and then if I so niola may I have the time for one question yeah so then the question is that coming to your metabolomics which I find fascinating right is that so one of the things you said so you have spoken
Essentially or mostly of of signatures that that had to do with some lipids or modified lipids or with the with chlorophile or pigments and then but one of the of the signatures in the in your differentially expressed genes were glycosyl transfer Aces so which which makes sense for the interaction between
Virus and host so do you have any metabolomic signature for differentially expressed sugars and or gly so any glyd that could be could could could explain this differential sensitivity we we don’t have that yet uh we don’t have that we have some amine differences but not sugar differences
And the chemist D told me that the extraction method he used may may not see these kind of glucose changes so we would need to when you do metabolomic you never get the complete metabolites you always get a fraction depending on how you extracted the the comp the the
Thing so I think we we may need to diversify the the extraction method of the metabol to to get sugar information but indeed we um we are just at the beginning of the metabolomic uh characterization thank you very much Silva has another question it say yeah just a quick one it’s about
Again the virus I was wondering whether you because you showed some variation to during I mean between the night and the day I was wondering about the life cycle of the virus does do you have any evidence of the similar sensitivity to the to light and daylight for the life cycle of the
Virus uh um so what we did we we looked at the virus Decay um and we are interested in in keeping our viruses so we know that the decay of our viruses in the fridge in the dark at four degrees is is very very
High uh so we we don’t try to kill our viruses because we work so hard to to to produce them so uh to do experiments so I I don’t well if you hit them you will kill them I I don’t know we didn’t um look into uh destroying our
Viruses what we know however is that temperature seems to have a massive a massive uh and this has been published by David Dei before if you change the temperature you will change a lot of things about the the the violence whether and even you may change
The you may make a strain resistant to a virus if you change the temperature and what I meant is that um maybe during the daylight the the cells are actually I mean because they are Pho photosynthetic they are are changing their physiology and maybe this could
Affect the the growth of the virus and if some resistant cell try to shut off shut down the the photosynthesis maybe the the virus could react to that and maybe that would be a a way to resist I thought that was kind of what some of the results you showed were pointing to
But I was wondering whether that was the case okay now you don’t have a shutdown of photosynthesis uh in the resistant cells okay they are still and actually what happens is that the the virus will go into the cell and the liis occurs at night okay so the viruses is maintaining
The F when it infects cell is maintaining the the energy storage the photosynthetic thing and it’s only once once uh the during the night that it will finish its job and and Li it cell but uh yeah we we may um we may imagine that resistance cell are somehow um
Shutting down um at least um de what goes on on their external meman but they do not sound Pho photosynthesis okay thanks okay I don’t see any more question so maybe Stephanie you can have the last word okay well thanks a lot gel for not for coming was not possible but um yeah
Yeah thanks a lot for your presentation and uh we’ll try to organize something for for you to come soon okay thank you very much for these very nice uh questions and indeed I’m looking forward to uh to coming to mon in person this year okay thank you everybody bye-bye bye bye