 It's my pleasure to introduce the next speaker, Alejandra Rodriguez Verdugo. Alejandra is a professor at the Department of Ecology and Evolutionary Biology at University of California, Uruguay, and she works at the interface between ecology and evolutionary dynamics doing experiment trying to combine the two. And today he's talking about the evolution of microbial interactions in fluctuating environments. So thank you very much, Alejandra, for being with us and feel free to share. Can you see my screen? Excellent. Well, excellent. So thank you so much for the invitation. I'm super excited to be here and to talk about my work that I started in a TTH week when I was a postdoc with Martin Ackerman. And then I'm continuing this work right now in the Department of Ecology and Evolutionary Biology at UC Irvine in California in the U.S. And today I would like to talk about evolution of microbial interaction in fluctuating environment. But first, why do we care about microbes? So microbes are the oldest organisms on earth and they have been able to colonize any possible environment on the planet from the driest places such the Atacama Desert in Chile to the icy lakes of Antarctica. So microbes have been able to thrive and adapt to the harshest condition. And not only they have amazing adaptability, but we know they are very important for life on earth. So many of these microbes underlay the biochemical cycling on elements on earth and then therefore essential for ecosystem functioning. And we know that many of these functions are not performed by microbes in isolation, but they are performed by many microbial species. That means it's really microbial communities that underlay these functions. And now we know also that these microbes are really relevant because for example they can influence climate change by mediating the carbon atmosphere, land exchange, and therefore they can actually feed back into the climate. So it's very important that we understand how these microbial communities respond to change. Also, we know that these microbial communities live in close association with animals and plants and they find their well-being and keep us healthy, but sometimes also cause disease. And here in this image I'm showing a beautiful microbial community that lives in our gut, in the human gut. And this gut microbiota we know is very important for keeping us healthy. And those recent studies that have shown that disrupting these communities with changes of diet or antibiotics can actually lead to chronic disease such as chronic disease or Alzheimer's or anxiety. And even more recent studies can also show that they actually control cognition and behavior. So it's very important that we understand then how disturbing these microbial communities can actually influence or help. And then finally microbes can help us. So they are used in industry and for example they are used for energy production and they are the next generation of biofuel which are clean and renewable. And also now in industry they are engineering whole microbial communities for example to improve the health and the fitness of the host that carries them for example plants. So given the importance for the environment for health and for industry more and more people are trying to understand how these microbial communities work. And although these seems a very simple question and very straightforward question we still don't have a good answer for this and the reason is because microbial communities as you already know are complex systems. So if we look at it they are composed by populations of one single species which are interacting with population of a different species. And then these interaction can actually lead to emergent properties that give certain functionality at the group level. And finally we have to consider that many of these microbial communities live in structured environment and fluctuating environment. So then the environment also is not static and changes. So one way to approach then this complexity is what we can do is to just isolate some of these species and instead of dealing with the whole complexity of thousands of species from nature we can just assemble these few species in what we call synthetic communities and then we can bring them to the lab and study them in the lab. So this is the approach that I'm following now in my research group. And then in my research group at UCI we studied two questions. Overall we are interested in understanding how the species interaction influence evolutionary dynamics and on the other hand we're interested in understanding how changes in individuals can influence evolutionary dynamics. And in general we follow a bottom up approach which probably Alvaro Sanchez talked a lot about this. So basically we use these synthetic communities simplified system and what we do is we try to build quantitative prediction with mathematical modeling that we can then test with experiments. And then these experiments can inform can feed back to inform or quantitative prediction and mathematical models. And for today I would like to talk about one project with look at how the species interaction influence evolutionary dynamics. And this is in the context of the ecology and evolution of pairwise positive pairwise interactions. So this work again was done at TTH Zurich in collaboration with Martin Ackerman. So what are positive pairwise interactions? So positive pairwise interactions are any interaction between two species where either one of the species get a benefit from the interaction or both of the species get a benefit from the interaction. And we know that these ones are very important for ecosystem functioning. So for example we know this kind of association occurring in nature. And for example we can think about this neutralistic association between methane oxidizing archaea and sulphide reducing bacteria. And these ones are very important for deep subsurface ecology and they are really the base of these ecosystems. But for example a more familiar example you might be more familiar. So coral reefs for example is this association between an animal and an algae. And we know that this actually positive neutralistic association is really the base for these shallow marine ecosystems. So therefore given they're important for the environment more people are trying to understand how stable are these positive interactions. But then one thing one has to consider is that they actually the environment is not static. And for example we can have that there's changes in resources and these changes in resources in the environment can alter a species interaction. So very simplistic what can imagine the interaction between two species as squirrel and a bird that live in an environment with a lot of resources and different kind of resources. So we have that they have a neutral interaction. But then if the condition changes and now we have that actually they are in an environment with limited resources now we have that this positive these neutral interaction change to a negative interaction and now they are in competition. So then we should try to address this question of how stable are positive bird-wise interaction considering that the environment also varies. So to address this question I use the synthetic community composed by these two bacterial species which is acinetobacter and pseudomonas. And these two bacteria are very interesting because they have amazing metabolic capabilities. Also they were isolated from nature from a polluted aquifer in Denmark. So they coexist in nature and what we did was to just isolate them and bring them to the lab. And then the metabolic capabilities that they have is that they can degrade aromatic compounds which are very hard to degrade by many other microbial species or the other species. So they definitely are of interest for bioremediation. And we have that for example we can tune the interaction depending on the resources. So for example if we grow these two bacteria in an environment where we supply them with pencil alcohol as a carbon source and carbon of energy source we have that only these bacteria acinetobacter can use these benzyl alcohol. But then if we grow them in the present of these other bacteria what happens is that acinetobacter can actually oxidize these benzyl alcohol and then it accumulates an intermediate product which is benzoate which passively leaks out in the external environment and then in the external environment pseudomonas can utilize these benzoate. So we have in ecological terms a commensal interaction where pseudomonas get benefited by the present of acinetobacter and acinetobacter is neutral to the present of pseudomonas. So we have this cross feeding interaction. But then now if we change them in a minimal media where we grow them with citrate as the limiting nutrients we have that these two species compete for this citrate. So now we have a negative interaction. So as you can see it's very nice model system because we can then tune interaction depending just on the carbon source. So with this model yeah so first how do we measure interactions in the lab? So the way we do it is we use a batch culture system. So that means we just grow these bacteria enough in a shaking cube with these nutrients and then we wait until they grow 24 hours. So at the beginning they have a lag phase then they have exponential faith and then they saturate the nutrients in the media and then after this whole cycle after 24 hours we transfer one percent of the population into a fresh medium and we continue these cycles for many days. So at the end the kind of results I'm going to be showing is this kind of graph where in the x-axis I have the time in days and in the y-axis I have the cell density which we estimate as colony forming units in agar plates and then in this particular example for example you can see that the blue type is able to sustain this serial dilution regime so we say that it has a stable population over time. Well for example in this particular case the red type even though it's able to grow it's not able to sustain this serial dilution regime and it gets extinct after four days. So the first thing we wanted to see is to do these experiments first in what we do is monoculture so that means we grow them in isolation to see how these bacteria behave and we can see that as an actobacter is very good at using this benzyl alcohol it achieves high cell densities why seromonas is not very good at utilizing this benzyl alcohol. But then when we do these experiments in co-culture so now growing them together we have now a different situation where we have that seromonas achieves two orders of magnitude higher cell densities in the presence of as an actobacter than just alone. And then what happened in oh yeah so also we can visualize actually this kind of interactions using microfluidics and coupling with a microscope. So what I'm going to play now just to visualize this direct cross feeding interaction is I'm representing as an actobacter as this round cells and then seromonas is in green like a road shape. So you can see that actually we played this time last movie we have first the grow of as an actobacter and this growth is followed by this kind of green fluorescence and that's the grow of seromonas. So we really see these sequential growth where we visualize this cross feeding interaction. So now what in the other condition so in the condition of citrate when we do our experiments in monocultures growing them in isolation we have that both species can grow well in monoculture and achieve high densities but now when we put them in core cultures we do see that actually seromonas is a very good competitor and is such good competitor and it utilizes this citrate so well that it actually outcompetes as an actobacter only after five days of growing them together. So we can see this very asymmetric competition situation. So what we wanted to test in this experiment is then how are these I mean can we predict this ecology and evolution of this pairwise interaction. So what we did was to ask two questions. So how stable are these positive pairwise interaction in block trading environment. So not just keeping the environment constant and in particular we ask what happened if we interrupt this positive interaction with periods of competition or negative interaction. And our second question we address is how stable are positive pairwise interaction over evolutionary timescales both in constant and in block trading environment. So for our first question the first thing we did was to build a mathematical model and this was done in collaboration with Clément Villain which is right now at the University of Zurich. And what we did was to build this mathematical model that we parameterized with single species growth measurement. So for this part what we did was to just do grokers essay of these monocultures and then from there we also estimated for example the excretion rate of these benzoate by HPLC. And at the end we end up defining the parameters that are important for our model which are the maximum growth rate in exponential phase, the maximum optic rate which is just the rate of how they saturate the optic rate for high resource concentration. Then we have that another parameter that was very important for model was the health saturation constant which is the resource concentration supporting health maximum optic rate. Then the duration of the lag phase which is how long the bacteria remain in this non-growing state. And finally the excretion conversion rate which is a proxy of how much benzoate is situated in the environment. And then what we did was to just use a set of differential equation in which we model the resources explicitly and then in here I'm showing those equations. So basically we just model the bacterial growth which is dependent on how much of the resources they are using. And then in the second set of differential equation we have the changes in resources over time which is related to the growth of the bacteria. And then with this model well I mean I can spend a little bit more of time. So basically as I mentioned before this basically these dynamics are really related to the maximum growth rate of the bacteria and also is related to the health saturation constant and also to how they are of taking these resources over time. And then finally to the excretion rate of the benzoate. So the first thing we wanted to do with this model is to just validate the previous results that I showed. So basically when we actually use this model we do see that we can recapitulate the behaviors in monoculture in co-cultures in benzil alcohol and also in citrate which is the other competition. So we do see that with this model we can recapitulate and capture these ecological dynamics quite well. So now the question was what dynamics do we observe in fluctuating environment? So for example in this case we define one day fluctuation environment means that one day we simulate that they grow in citrate and then we transfer them in benzil alcohol the next day and again to citrate and that's what we simulate our fluctuating environment. So when we do this we do see that when we have it in one day fluctuation environment the two species can coexist over time and one species is more favor in one condition than the other. But then when we for example start increasing the length that they stay in each of the carbon sources. So for example two-day fluctuating environment means that today they spend in citrates and today it's in benzil alcohol. We do see that we still have species coexistence but hopefully you can start seeing that the more they stay in one condition the amplitude between the two species start getting larger and larger and then when we heat the four and five days fluctuating environment we do have that actually pseudomonas bring as an intobacter to extinction. And this in the model also what we observe is that pseudomonas eventually also end up going to extinction and the reason is because it out-competed the co-parameter that it needed to coexist in this environment. So in these four and five days fluctuating environment we see a complete extinction of the community. So now we wanted to actually see if we can see these results with experiments. So what we did was to just grow the bacteria in co-culture starting with a ratio one to one and we did this in triplicate for six days and with experiments we actually see again these dynamics that we actually predicted with the mathematical model. So we have that in one two and three three days fluctuating environment we have coexistence of these two species with the amplitude between the two getting larger and then for the four and five days fluctuation we have extinction of asthenetobacter. So we do not see extinction of pseudomonas we just see that pseudomonas is actually to persist at very low densities when we have them in benzyl alcohol and eventually with citrate they just recover. So now that we define this part so the conclusion is that past fluctuation maintains species coexistence but for example in an environment that fluctuates slowly what we can see is that we observe coexistence breakdown. So all of these that I've been talking about are ecological dynamics. So this is over six days which is around 70 generation for this bacteria so we assume that evolution is not playing a big role for these ecological dynamics. So the next question we ask is what happened if we repeat this experiment but now in evolutionary timescales and then we were particularly interested about this condition of the cross-fitting interaction. So we wonder how stable is this cross-fitting interaction if we do it over evolutionary timescales. So for example 200 generations and the other condition we were very interested is the one day fluctuating environment. So in this condition we saw coexistence of these two species so we wonder if we can actually have coexistence over evolutionary timescales. So in order to perform this we did a large-scale evolution experiment in these three conditions so keeping just benzyl alcohol as a sole carbon source just keeping citrate and then in our daily fluctuating environment and we did these experiments in monocultures and also in co-cultures and then finally we did for each of the conditions four replicates and the whole experiment run for a month which is 200 generations. So the first question so is cross-fitting interaction stable over evolutionary timescales. So here I'm plotting the result of just one of the replicates which is consortium number two so you can see that now in the in the x-axis I have the time now over 30 days and in the y-axis I have again the cell density and then what you can see is that there are actually there's species coexistence over evolutionary timescales and then there's definitely some kind of fluctuations in the cell densities over time so it's not that constant but overall for the four replicates or four consortium we see species coexistence until the end of the experiment. So we concluded that indeed this cross-fitting is stable over evolutionary timescales but now if we actually look at the second condition which is the one-day fluctuating conditions now we do see a different case so for example here again I'm plotting just the result of one of the consortium consortium number two and then here is very interesting because we see that at the beginning of the experiment the two species coexist and maintain certain stable population dynamics but then around day 14 something happened to one of the species in this case to a synetobacter that is start decreasing in cell density until actually it gets extinct and even more interesting at least for an evolutionary biologist is that actually this outcome of extinction of one of the species was only one of two evolutionary outcomes so we do see that this extinction occurred in two of the replicates but in the other two replicates we do see coexistence of these two species until the end of the experiment. So then this is very exciting because this is definitely a different evolutionary outcome so the the one thing we can do with evolution experiment which is very nice is that we can freeze these bacteria at different time points during the experiment and that's exactly what we did so we kept a frozen record of these populations at every seven days so every week we will freeze these whole tubes with the bacteria so what we can do then is to go back in the experiment let's say at day 14 before the extinction of one of the species and then revive this bacteria and see for example if we do a replay evolution experiment to see if we again see the extinction of acenetobacter or not and when we did these replay experiments this time we did it with high replication so we repeat this experiment for each of the consortium we did six replicates so what we do see is that for the two cases where we previously observed extinction of acenetobacter we again observe extinction of these species when we replay the experiment and in the other two cases what we previously saw coexistence we again recapitulate this coexistence when we do this replay experiment so the conclusion is that extinction was a highly deterministic event so it's not random at all so we can definitely replay these dynamics so then the next thing we wanted to know is what is causing this extinction of one of the species and for answering this question what we did too was to isolate some single clones at different time points so at the end of the experiment at day 14 and at day zero so which is the ancestor so we isolated one single clone for each of the replicates of the population and then we did grow curves experiments and also we sequenced the full genomes to try to see if there were any genetic changes that could explain these deterministic dynamics and first I'm going to show the results that we have when we actually do the full genome sequencing the end of the experiment so when we take this clone at the end of the experiment we did the grow curves so again we just estimated the maximum grow rate and the yield and when we do this experiment I mean here I'm just summarizing the results of all the experiments but hopefully you can see that there's a lot of green and the green indicate that there's actually a significant higher growth of the evolved types compared to under ancestral types and these indicate that actually over the course of the evolution experiments the evolved type are definitely growing better than the ancestor so this is already a sign that these strains actually have adapted to these conditions and then when we actually look at the genomes from this single clone so what we did was to sequence the genome of the evolved type and the sequence the genomes of the ancestral types and then we just compare the two genomes and then we call the noble mutations so anything that was not present in the ancestor and then that we observe in the evolved clones and overall we can say that we observe that there's we observe around one to two fixed mutations per clone and when we look at where are these mutations located in the genome we see that these actually mutations are not randomly distributed in the genome but tended to hit certain genes so for example in a synetobacter we observe 15 mutations in these or acetyltransferase gene and then for example in pseudomonas we do see that they accumulate mutation in these three genes which is a sensor protein a flagellar component and transcriptional cyclic DGMP so basically we do observe a high level of parallelism which already indicate that these mutations are most likely adaptive and then also when we look how they are distributed not necessarily in the genome but among conditions we do see that these ones tend to not be specific to one particular condition but they were observed in many of the conditions so for example in the case of a synetobacter we do see these mutations in the condition of the constants benzyl alcohol in monoculture and co-culture but also in the citrate and in the fluctuating environment and is similar in the case of pseudomonas so overall the conclusion is that species show signs of adaptive evolution after a 200 generation but these changes are not specific so it seems to be general adaptation to the culture conditions so now we go back to now that we characterize these evolved types so then we go back to this idea of trying to see what happened at day 14 which is just before the extinction of one of the species and when we go back to that time and we do the full genome sequences I'm summarizing in this table what we observe so pseudomonas even after 92 generations of evolution already have 2 to 1 fixed mutation in the genome and again this kind of mutation we observe them in monoculture suggesting that general adaptation to the culture condition but I think what's really interesting is that in the case of a synetobacter we do see that in the two cases where it went extinct we do not see mutation at this time point of 92 generation where in this consortium number three and number four we do see that this synetobacter has two or one mutation so our current hypothesis that we are trying to test is that mutations in a synetobacter rescue it somehow from this extinction and in particular our hypothesis is the following so we think that pseudomonas accumulate mutations so it's rapidly evolved and these mutations actually confer higher growth to these pseudomonas and these exert an indirect effect on a synetobacter which either has to it goes extinct or it has to keep up with this higher growth of pseudomonas higher competition and then it can actually it has adaptive mutation then it can coexist with pseudomonas and this hypothesis is again sustained by this observation that pseudomonas actually has higher growth after 92 generation so we think is really the mutation that is causing this higher growth and also the second line of evidence to support our hypothesis is that we went back to our mathematical model and what we did was to input these growth that we observe after this evolution and when we do input these growth into the model so for example in the case of the ancestor competing versus the evolved types of pseudomonas with this higher growth we do see that pseudomonas can actually but just growing better at compete a synetobacter only after 20 days but actually 20 days is quite different for 14 days so we believe that is not only indirect effects but they might be actually direct competition they might be some antagonism or some yeah some kind of type six secretion system that pseudomonas might be antagonizing a synetobacter in addition to using more resources so to conclude this part we see that positive per wise interaction are stable in a constant environment that's the cross feeding environment and we do see that there's two outcome outcomes in a fluctuating environment so we either see coexistence or extinction of one of the species and I don't have to actually give this take home message to this audience because you're very well aware of this but our conclusion is that the environmental context matters so it really matters who is your neighbor and who is evolving next to you in terms of species and then in that sense we should move forward into not only studying experimental evolution with single species but try to do this evolution experiments in a community context considering interaction with different species and this concludes my talk I left ample time for questions and I just want to thank the people involved in this project in Switzerland so my postdoc advisor Martin Ackermann, Clemorevulin and Jean Claude who helped so much with the sequence also the whole lab in Switzerland and the funding which mostly came from EMBO and from the adaptation to a changing environment from ETH Zurich and thank you very much for your attention. Thank you very much for the nice talk so we have time for questions so if you have any please use the raisin tool or type it in the chat and while we are waiting I can ask one question since we have a lot of time so have you tried to do a co-culture experiment where you put the pseudomonas evolved in or the acinetobacter evolved in a co-culture that coexisted with the pseudomonas that actually outcompeted the acinetobacter so sort of too well probably technically it's difficult but to isolate the evolved strains in different experiments and pair them together across different experiments. Yeah no absolutely that's that great one so we did like a preliminary experiment with only one replicate and unfortunately then the pandemic hit and we couldn't continue this experiment because this is definitely the way we can test this hypothesis so evolving these clones so what I can say is from our preliminary experiments which I have here so this is evolving like competing these clones at generation 92 and actually from these pairwise competitions of the evolved clones we do not see this extinction of acinetobacter as you can see consortium one and two which is the one that acinetobacter is supposed to go extinct actually we do not see this but this was with one replicates and we definitely have to repeat these experiments and also the conditions were not fully optimized to replicate the condition from the experiments but definitely we need to be trying to play with these evolved clones versus ancestors and these kind of replicate transplants kind of ideas to try to test this hypothesis so far it seemed like it's really at the population level and not at the clone level that we do see these behaviors so we don't know exactly what's going on if we didn't isolate the proper clone or if it's something that is really characteristic to a whole population with whole like genetic diversity in some sense there is one question from Flavia oh yes can you hear me oh yes okay so a very nice talk congratulations um i'm interested to understand if you can measure somehow if these bacterias that you work with if they differ somehow in their mutation rate or how they evolve yeah no that's a very great question so we definitely tested the the mutation rate so they have very similar mutation rates these two species so we thought for some reason that pseudomonas may evolve faster because that's kind of a common belief in the literature but they are actually very similar in terms of mutation rate and in many other ways they they tend to be similar species so they have the same substitution rate for example so okay okay yeah i was i was curious because of that maybe i don't know if there is any other bacteria that you can select and make an experiment with these different rates of mutation or i don't know just uh yeah no that's that's a good question yeah that would be super exciting to start tuning uh like this uh mutation rate and to start seeing like how like by like yeah tuning the mutation rate in either like uh very fast evolving versus slow evolving like how in all dynamics and that's definitely possible and that's definitely something i would like to do in the future okay i'll look for it sounds good great we have time for more questions if any uh hi i have a question sorry that's my video thank you very much for your very interesting talk i had one question i wanted to ask about the plot when you've done longer time like when you didn't look anymore like when you wanted to look at the evolution so you took longer time i think up to 28 days maybe i didn't get it uh from the talk like did you keep the subculturing time like at every day or yes yes it was okay yes yes so we didn't adjust anything um based on how they started evolving so we didn't adjust that so we just for uh for now for simplicity we just kept that content to 24 hour cycles uh regardless of the art if they are first evolving faster and like they are like growing faster and saturating faster like we we ignore that part for simplicity for for now yeah and in the case like at some point in this kind of experiment when you see the uh the the abundance decreasing of course because they are going towards extinction did you like is it possible to adjust the the amount that you subculture because of course if you subculture you're going to take a smaller amount of cells and that can kind of a bit bias like the the direction towards extinction um yeah yeah actually that's a very interesting point because we we did the experiment starting at different ratio so starting either at very high density because we wanted to make sure it was not just so stochasticity in the terms of just ecological stochasticity so actually we repeated this experiment not in a ratio one to one but starting with a lot of acinetobacter and even starting with a lot of acinetobacter compared to pseudomonas we still see the same increase to extinction so it seems definitely not this this other kind of stochasticity perfect yeah and if I can ask like I don't this is a further curiosity because I really enjoy this topic um like are you thinking or is it interest at all like to test coflutuations like uh more than one parameter fluctuating because in this case you apply fluctuation in the nutrient supply but like would it be of any interest to to more than then apply fluctuation let's say the temperature or another like increasing the idea to reagent of the environment oh absolutely so one of the experiments that yeah we're very excited is like to somehow try to add a baotic stress to this so for example yeah temperature is a great uh it's a great like kind of suggestion and something we thought about it and then try to tune a little bit more again what would happen if we favor more one than the other in this particular case for example what would happen if we favor the the one that is not doing so great like acinetobacter so yeah definitely in a way I feel like this is just the start of uh setting up the system and playing with it and now is the fun part because we can actually start testing a lot of uh ecological and evolutionary theories and tuning stuff and actually this system is nice because we can definitely tune interaction evolutionary rates and many things we can also add more species if this yes yes thank you very much thank you great there is another question from Aditya hello thank you for the talk so I was wondering um so I guess you showed here that when you have evolution in the case of a externally imposed fluctuating environment it destabilizes the coexistence so we saw some we saw some talks earlier having to do the bacterial phage interactions in this case the fluctuations are internally driven by the by the dynamics of the different strength and in that case I think we saw something like that evolution can can change the dynamics qualitatively by by changing the direction of the of the cycles between bacteria and phage in in in phase space but I I guess like I'm not sure if I have a specific question but I would just be interested in like whether you can comment on the difference between externally driven and internally driven uh fluctuations and whether these are having whether you think these might have qualitatively different effects on on coexistence or not yeah no that that's a great great point so actually I mean reviewing literature I feel like this uh interesting uh fluctuating dynamics is mostly given by this interaction that either predation or um or like you know like where you have like more like a plus minus interaction or you can have a plus plus interaction but in this particular case when you have just competition or commensalism it's kind of interesting not to observe these internal fluctuations it seems like it's really like this is more pro it's more likely to happen with certain kind of interactions than with other kind of interactions so in this particular case um yeah we do not see anything of that sort but yeah but do you think that so do you think that exogenous versus endogenous interactions or like would they qualitatively have different effects on on coexistence is I guess yeah I don't know like it's hard to I haven't think about it to be honest with you but I think that's a really great thing to think about it's definitely like it gives me like some something to think about I don't know how to end but it's a great point it's really a great point but what intuition thank you okay great if there is any more question please type it in the chat already I don't see anyone okay so thanks a lot Ali again for these very nice talk thank you very much for answering both