This is our interview with Nadine Ziemert, from the University of Tübingen. This continues our short series of genome mining, the science of examining DNA sequence to try and predict the structures of secondary metabolites and to understand their evolution. It was a fun, wide-ranging conversation, where we talked about our shared experience working with the marine bacteria Salinispora at Scripps Institution of Oceanography, her accidental entry into data analysis, the evolution of secondary metabolite pathways as viewed through metagenome studies, and what more JGI and others can do to enable progress in this area of natural products.
For further reading she has some great reviews in the field, and I’m looking forward to trying out ARTS 2.0, which was published shortly after we talked.
DAN UDWARY: You’re listening to the Department of Energy Joint Genome Institute’s Natural Prodcast, a podcast about natural products and the science and scientists of secondary metabolism.
Welcome back. This will be episode 12 of Natural Prodcast. And it’s our conversation with Nadine Ziemert from the University of Tübingen in Germany. Like me, she’s a genome miner and has been involved with the AntiSMASH and MIBiG projects.
And I found her review in natural product reports on the evolution of genome mining and microbes with Mohammad Alanjary and Tilmann Weber to be a really valuable piece that shaped my thoughts about the history of genome mining and the directions it might take in the future. I highly recommend it. I’ll link to it in the show notes. Which I’ll remind you, you can always find at naturalprodcast.com.
You should know we’ve recorded this a few months ago when we were at the height of the initial spread of the pandemic in Europe. So if there are any kind of dated references to the news, that’s why. But it was a really fun conversation. And I think you’ll enjoy it. So here it is, episode 12 with Nadine Ziemert.
DAN UDWARY: Hey, Alison.
ALISON TAKEMURA: Hey, Dan.
DAN UDWARY: So we’re back in our far corners of the world. So, yeah.
ALISON TAKEMURA: That’s right. Yeah, we’re doing this from home. And my own podcasting setup– and yours, too, Dan.
DAN UDWARY: Yeah, you’re looking very professional.
ALISON TAKEMURA: Thank you. You, too.
DAN UDWARY: Oh, yeah.
ALISON TAKEMURA: My big headphones.
DAN UDWARY: I’m sitting on my bed in Paris, pointing a microphone at my face because I didn’t want to be in the other room where the kids are messing around because it’s too late. They should be asleep already. But we’re not just here to talk about us. We have a great person to talk to tonight. We have Nadine Ziemert from University of Tübingen. And did I pronounce your last name correctly, Nadine?
NADINE ZIEMERT: Yes, perfect. I’ve heard it in all variations, and I think that was perfect.
DAN UDWARY: I mean, I get that, too, so. But so I’m over sensitive to it sometimes. Well, welcome, Nadine.
ALISON TAKEMURA: What is the big picture for you in your lab?
NADINE ZIEMERT: Big picture, I want to understand how secondary metabolites evolve– who makes them, how do they spread, why do they spread in a certain way from one bacteria to another, how do they change then, and why are they really made. I think that’s a question that a lot of people want to answer again from a very different standpoint or from very different angles, right?
Like, the big question is that there are all these molecules around us all the time. We smell them, right? We drink them. I mean, secondary metabolites, natural products, caffeine, all that stuff around us. And we actually don’t know a lot about them by their mate, what their role in nature, how have they been selected for or how do they evolve, all those things. So that’s what I really want to understand.
And then of course, we can use that knowledge to actually use it in drug discovery, right? I mean, at the end, we somehow have the big goal of making that process a little more effective than it is now. Or a little more– yeah, put more rationale into drug discovery by knowing what these things actually do and how they evolve.
ALISON TAKEMURA: So Nadine, what got you interested in studying natural products?
NADINE ZIEMERT: Like almost coincidence, I think.
ALISON TAKEMURA: Oh!
NADINE ZIEMERT: I needed a job during college or university, and I got this great job as a student assistant in the lab of Elke Dittmann, where I did my PhD later. And she happened to work with nature products from cyanobacteria. So that’s how I started.
I love the lab, I love the topic, and I think what I like the most is just the interdisciplinarity of it. So it wasn’t only chemistry or only biochemistry or genetics. It was also ecology. It was also evolution. It was kind of everything that makes science. And I think that’s why I loved it.
DAN UDWARY: Yeah, I feel a kinship with that answer for sure. That is one of the draws for me of secondary metabolism. We get to do all kinds of wacky things in different areas of science, for sure.
NADINE ZIEMERT: Yeah, so there’s so many. And if you go to natural product conferences, you meet people from all different fields. They all kind of have a different angle, looking at these molecules for different reasons. But there is one common topic.
And if you then start to talk with these people about the same topic, but from so different angles, I think that makes it really exciting because you see how much you can actually learn about it if you look into it from different fields and not just from one angle, right?
So I mean, if you would just be within the chemists or just be within a group of evolutionary ecologists or something, so I think then you always have a certain view on things. And – but if you talk to those people from very different fields, you’ll always get different answers, different angles.
And so when people then start talking about it, you always– so I always, at least for me, have the feeling, oh, yeah, I never thought about it like that. That’s true. So this field is always exciting because of that, I think.
ALISON TAKEMURA: Yeah, what’s an example where that happened, where your view was challenged?
NADINE ZIEMERT: Good question. I don’t remember from my PhD. Too long ago. But I just had an example where we looked at some topic that I’m working with about now. I made a tree of different compounds, and I’d looked at it from an evolutionary standpoint where I really could follow evolution. And I could see a certain time span within these biosynthetic gene clusters. There’s gene fusion going on, and then I could see how the pathway gets streamlined and becomes more effective. That’s how I interpreted it.
And then I talked with my colleague who is a biosynthetic chemist, who was more working from the biosynthesis standpoint. And then I was talking at the same time also with some Australian colleague who’s working on structural biology of these compounds.
And they saw the tree, and I explained to them how I would interpret it. And then he’s like, “Oh, yeah! That makes total sense because as that A domain there changes, then that would happen then!” And like, oh, you see that in my tree? And so it’s just one example of how you can see the same thing and really interpret it very differently.
ALISON TAKEMURA: For that example, was it the case where that colleague saw a potential mechanism for why there would be this streamlining?
NADINE ZIEMERT: Yes, and just how one certain domain would change and then that’s why this domain– another domain also has to change. And that’s why then something becomes more effective or something like that. So he could see really into this– so he could look into the structure, really seeing that.
And I’m always just looking from the genes and pathways and what’s happening there. And he really saw this structure and how that would make the compound faster or something like that. So that was interesting to see.
ALISON TAKEMURA: Yeah, biology is being this engineer that’s constantly refining its designs. But sometimes if you’re looking at it from a zoomed out level or a zoomed in level, you’ll see different things.
NADINE ZIEMERT: Yeah, and I think you need to see all levels, really, right? So I mean, we’re scientists that always– because there is so much we need to know, I think we really focus on one thing. And we have to focus on one thing to really get into that specific, but that sometimes makes you lose the big picture. And I think if you then talk to people from different fields with different views, that really then helps you to get back to, oh, why am I actually studying that question?
DAN UDWARY: We know each other I think– I consider us as something like scientific cousins. So we both went to Scripps [Institution of Oceanography in San Diego] together. And I worked for Brad Moore as a postdoc and you were with Paul Jensen.
NADINE ZIEMERT: And Brad Moore later, too.
DAN UDWARY: And Brad later, yes, sure, right. And yeah, so we worked on the Salinospora genomes. That was a really fun project. We had Brad on and he kind of waxed poetic about the early parts of the project. How did that postdoc where– I know you really dug into the evolution on that system. Did that affect the work that you’re doing now?
NADINE ZIEMERT: Yes, immensely, because I’m– so in my PhD, I was mainly working in the lab, right? I was doing genetics, and I was very much into biosynthesis of these compounds. And I started a little bit about evolution, but not too much. And I kind of thought that’s a little boring, just being on your computer.
And then I went to Paul by chance. Again, that was one of the lucky coincidences you sometimes have, right? He was looking for a postdoc and just wrote an email to everyone. And I’m like, oh, I’m looking for a postdoc, so that would fit. So I just wrote him an email, and he’s like, yeah, come as soon as possible.
So I ended up in Paul’s lab, and I noticed that this was a perfect fit for what I wanted to do. Also studying evolution and the role of the secondary metabolites–
ALISON TAKEMURA: And this is Paul Jensen in the early 2000s? Late 2000s?
NADINE ZIEMERT: No, yeah, 2010.
ALISON TAKEMURA: 2010, OK. You’re a young faculty.
NADINE ZIEMERT: Yes. Yeah. I mean, I am– yeah. I just got the position like three years, four years– four years ago I think. Yeah. Yeah, Paul Jensen. What was the question again?
DAN UDWARY: I was just asking if the Scripps research …
NADINE ZIEMERT: Oh, yeah, Scripps. Yeah.
DAN UDWARY: How that sort of got you thinking about evolution.
NADINE ZIEMERT: Yeah, so he started, and I started with the computer work, right? Which I had to get into first, and the standing trees. And he had this whole idea of NaPDoS, this computer program which uses phylogeny to predict compounds.
And so I really had to dig into all the different biosynthetic mechanisms and into phylogeny. And I had to look at so many pathways and so many gene clusters. And later, I looked in all these different Salinospora genomes, and that was great that you started that work. So I had some gene classes I could start on. So… but you started with two genomes, right?
DAN UDWARY: Right.
NADINE ZIEMERT: And then we had, like, 120 or something. So I looked at all these 120 genomes, and each of them have– I don’t know– 30 gene clusters or so. So I think I spent two years only looking at pathways the whole day.
No, yeah, and then I think that having all these very, very related genomes, right – on their 16S level, they’re almost identical! But seeing all these different pathways, and that really helped me to get an idea how diverse the secondary metabolites are, even if primary metabolism seems to be very similar.
But how fast they switch, and then you see really– what I thought was really interesting and what I still think about a lot is that I almost never saw little half-pathways or truncated pathways or truncated gene clusters, right? You either see there is a gene cluster, and you can really see there is a gene cluster, or there’s none anymore. So this evolution of really when you don’t need them anymore and when they’re broken, they just get deleted from the genome very fast that I thought–
DAN UDWARY: Yeah, there’s no selective pressure to keep them, right?
NADINE ZIEMERT: Yes, and then how effective this process, which is coincidental, but it’s very effective, really. I think you can really see in all these genomes. That was fascinating, I think.
DAN UDWARY: Yeah, and that was a JGI Community Sequencing Project. You’re right, yeah.
NADINE ZIEMERT: Yes.
DAN UDWARY: I think that was one of the JGI’s early moves into sequencing things for the purpose of looking at the secondary metabolism. I know, and it was Salinospora where we did that.
NADINE ZIEMERT: Yeah, but they were in pretty good shape. It was only Illumina, right?
DAN UDWARY: I think so.
NADINE ZIEMERT: But we still had had only a few contexts, so it worked pretty well.
DAN UDWARY: No, they were very high quality. Yeah, for sure.
NADINE ZIEMERT: Yeah, Dan… it’s a marine actinomycete bacterium. So they look kind of like fungi but also make these mycelia. They kind of look like Streptomyces, as well. They have this typical mycelium. But they only grow in saltwater. So we’re thinking they’re obligate marine. There’s so far no species or no strains known that really grow in freshwater.
But they’re actually pretty orange, and they make spores. And they make a lot of really cool compounds.
ALISON TAKEMURA: And when you say orange, what kinds of orange? And then also what texture do they look like?
NADINE ZIEMERT: I think the texture is kind of like– Dan, do they look like a mold, like a fungi or so?
DAN UDWARY: Not really. They’re usually a little bit waxy. They sort of have this orange-y pigment to them.
NADINE ZIEMERT: They’re really bright orange, most of the time.
DAN UDWARY: Yeah, it’s some kind of carotene kind of thing. I don’t really know.
ALISON TAKEMURA: Almost a bit like a carrot kind of orange.
DAN UDWARY: Yeah, totally.
ALISON TAKEMURA: Yeah. And then I also wanted to ask, it sounded like you had to spend a lot of time in front of the computer, analyzing these genomes. And that didn’t sound like that was initially something that you were excited about doing?
NADINE ZIEMERT: Now, that’s all I do all day. So I noticed that I don’t really miss the lab, anymore. I had fun working in the lab. That was a great thing, too. And I always thought that’s what I wanted. And I can never think about only looking at my computer and not having the excitement about experiments.
And then I actually got into it. I learned programming. I looked at all these things on my computer, and I noticed how much I actually can learn just by really digging into the data and understanding what’s going on there. And now, that’s all I want to do. I don’t want to stand in the lab, anymore, like pipetting stuff together. I get very excited about my experiments on the computer.
ALISON TAKEMURA: Can you describe what an experiment on the computer looks like?
NADINE ZIEMERT: First, usually, it’s a lot of searching the internet, searching literature, and making databases. Because that’s what I noticed is really a problem, so far, that there’s a lot of information out there is not easily searchable or in databases, where you can easily get information. Usually, in a lot of cases, we still have to go through publications to get proteins, and their functions, and all that stuff. And that’s the most laborious work.
But also, it’s the work where you learn the most. Because at the same time, you read a lot about the protein or the genes that you are analyzing. And then we’re building phylogenies a lot, making phylogenetic trees, and then trying to see if the trees make sense, biologically. So if I can see patterns in that certain clades have certain functions, and if I can follow evolution and see whenever a gene switched a function, then I can see that in the tree. And then your clade gets up.
And then if I have an unknown protein, and I want to know the function, and I don’t know it later, then I can predict the function. Because I know it’s created in this specific clade. That’s something that, I think, Dan used a lot, as well, right?
DAN UDWARY: Sure.
NADINE ZIEMERT: In WarpDrive or so?
DAN UDWARY: Yeah. That’s what you have to do to try to figure out what things are. You try to compare them to other things that you know about.
NADINE ZIEMERT: And then you just see if you can see patterns and if you can actually make predictions. And then you get excited if you can make predictions. It’s not always the case. But when it works, then it works pretty well. And then that’s pretty exciting for us.
DAN UDWARY: That is genome mining in a nutshell.
ALISON TAKEMURA: Do you have a favorite genome mining example, like a natural product that you found the mechanism for or the pathway for?
NADINE ZIEMERT: I think my favorite example is still my first example from my PhD. Genome mining wasn’t that well known. People did it, but we were looking for a compound that was well known for a long time, the microviridins. These are cyclic peptides from cyanobacteria. They have a tricyclic structure that’s really unique. And everyone thought because of their structure, and their unique amino acids, and these cross links that they must be made in a certain way by these non-ribosomal peptide synthetases. Because they thought, at that time, that was the only way you make weird peptides.
But then we actually found out that these are made via the ribosome. These are ribosomal peptide synthetases. And at the time, we didn’t have a genome. So we had to go through different ways. It wasn’t a time where you would get, easily, genomes for not a lot of money.
So what we did is we looked at other strains, where the genomes were known. And we found some proteins where we thought, OK, these might be similar. And we then researched and made something called a fosmid library, where you chop up the genome and put pieces into vectors into bacteria. And then you search for – re-search for these biosynthetic enzymes that we think must be similar within the fosmid library. And then we did, still, hybridization. We used probes from the other genome, and we hybridized it at the time with the radioactively labeled probes.
And I never thought it would work, but we actually found fosmids. And we sequenced the whole fosmid at the time with Sanger sequencing, and really going from the ends and sequencing, step by step, the whole fosmid.
DAN UDWARY: How long ago was this?
NADINE ZIEMERT: It wasn’t that long ago, but maybe 2006, 2007, or something like that. And we actually found the whole gene cluster that way. That’s still crazy. And the crazier thing is we have these clusters on the fosmid, and we just put them in E coli and wanted to see if they made it. And they made tons of that!
ALISON TAKEMURA: Wow.
NADINE ZIEMERT: They just made it. That was good luck. I mean, I’m still proud of the strategy, how we found the gene cluster. But had they then actually made the compound and a lot of that, that was pure luck. And I think, at the time, I couldn’t appreciate or didn’t appreciate how lucky that was. Because I know how hard it is, actually, at the end to get the compound expressed in a heterologous host strain.
DAN UDWARY: Yeah, sure. That’s very similar to the story Eric Schmidt told us about his RiPP endeavors.
NADINE ZIEMERT: Yeah. Actually, his patellamide, I think, was one of the first compounds that they noticed that is made ribosomally and not via these non-ribosomal peptide synthetases. And that’s why we had the idea that the microviridins also could be completely different compounds. But that got at the idea that if there’s no non-ribosomal peptide synthetase, that makes me think maybe their ribosomal. So that got us thinking because of Eric Schmidt’s work.
DAN UDWARY: Yeah, definitely. So we had you on the schedule to talk to us way back, when there was going to be a JGI user meeting. That didn’t quite happen, this year. But do you want to tell us a little bit about what you would have talked about?
NADINE ZIEMERT: I’m still not sure what I would have talked about.
DAN UDWARY: You’re one of those talk writers then?
NADINE ZIEMERT: Yeah. No, I got invited, and I thought that the JGI meeting would be the best meeting to actually start talking about the stuff we do now. Because we get more into the meta genome mining. We started to build the tools for natural product researchers that easily can identify secondary metabolite pathways in metagenomes, which is still hard to do.
We are mining a lot of environments to see where are environments that have the most potential to actually make secondary metabolites.
ALISON TAKEMURA: What are some of these environments?
NADINE ZIEMERT: So we started with soil. But we also have, in collaboration with other people, lake sediments. We have microbiomes from different organisms, including the human microbiome. We just really want to look into different environments to see, can we find patterns of things that we can say, OK, these secondary metabolites are always there, others are there, there’s overlap. There’s something that’s everywhere, stuff like that.
And of course, it’s hard to make a full story out of that yet. So we have some small, little stories, all the time. We have compared three very different soils in the area around Tübingen that is almost next to each other, to see if it makes a difference– if I really have to go to the Bahamas or to the tropics to get cool secondary metabolites, or if it’s not enough if I just go in 3 very different soils outside my yard or so.
Also, what we wanted to look at is, does it make a difference if I go today and tomorrow? Can I find different stuff? So really answering some of these bigger questions. But I don’t know how much I would have talked about that, because at the talk, you always want to make a full story. And so far, I have a lot of smaller stories that I still have to wrap up into one big story. That’s why I wasn’t really sure what exactly I would’ve talked about at the JGI meeting.
DAN UDWARY: What’s your gut feeling about that sampling? Do you find different things on different days, a few inches apart?
NADINE ZIEMERT: Yeah. Very, very different things. That’s what I always expected. Because we know from soil that communities change very much, that whenever it rains, then some certain bacteria, certain taxa come up that might be there, but not as abundant. So as the community changes and also the secondary metabolites will change, that was my hypothesis. And that is really what we can see.
But what I was wondering is if you have a certain community, there’s usually just one or two members within that community that might make a secondary metabolite or that makes a secondary metabolite with strong antibiotic activity. So you would think that if there’s just one member in a big community that makes a strong antibiotic, that this has a huge impact on the whole community.
So maybe the secondary metabolites within a community are kind of stable even if the community itself changes a lot. Because you have to be kind of resistant against that one antibiotic that is there. Otherwise, you cannot grow in that area. And stuff like that is really what we wanted to look at or what we want to get into and see if we can see these things.
Also, it seems like not only in nature products, but in science, there is a lot of things that people know just because they always knew. So like you say secondary metabolites are only in the upper part of the soil, because there’s no secondary metabolites from anaerobic bacteria– something we know now is actually not true and a lot of stuff like that.
And also, what people say is you have to go to diverse environments to get more diverse secondary metabolites and stuff like that. Which we actually don’t necessarily see. So it doesn’t seem to be a correlation between biodiversity in taxa and biodiversity in secondary metabolites. So stuff like that, I actually want to put numbers on and see if these notions are true or if people made that up at some point.
DAN UDWARY: What do you think it’ll take to get there?
NADINE ZIEMERT: A lot of groups working together, having a lot of samples, sequencing a lot of environments, varied with a lot of metadata. I think that’s important. Really, also, having the same protocols– I think, still looking into standards– how do you say, standardization?
DAN UDWARY: Sure.
NADINE ZIEMERT: Of stuff like that you really can compare different samples from different labs, for example. Because we noticed whenever we analyzed samples from different labs, there already is a huge difference, which is just the batch effect. Because they use different extraction methods and stuff like that. So we have a lot of data out there, but it’s hard to really compare them and to really answer questions that we have, specifically, with data that are public.
DAN UDWARY: So, an episode that’ll be just before yours, we had Roger Linnington on to talk about NP Atlas. And we got talking about what it would take to expand on the existing technology. And a lot of that was around chemical identification technology, mass spec, NMR.
And we talked a little bit about what it would take to start connecting genomes and gene clusters to molecules. And we didn’t have any, necessarily, home-run ideas on how to do that tomorrow, because it does take some improvements on things. So from your perspective on genome mining, what do you think it would take to improve on the data side of things and sequencing analysis side of things to really start to be able to identify cryptic gene clusters and what their products are?
NADINE ZIEMERT: I think there’s a lot of aspects. Like you say about the data side, I think it started great with databases, like MIBiG, which is a huge help for us, right now. If I just remember when I developed NaPDoS, I really had to go again in the literature and take out all these pathways by myself and look at what they do. Now, you have this database already there. That might not be perfect, but this is a great start.
Databases like, I think, JGI’s ABC also has a nice overview of all these different gene clusters. That helps. I think on the data side, people started with that. Now, it would be nice if, actually, everyone would also submit their data to databases like that, so the developers don’t have to keep their time in keeping these databases up. I think that would be important.
Also, funding for stuff like that, I think, would need to be there. Because usually, I get funding for new tools, but really to keep up tools and make them work for a long time for the community – I think that is still problematic. That would lead to–
DAN UDWARY: Yeah, that’s always a problem in academic software development.
NADINE ZIEMERT: Yeah. And I think, otherwise, a lot of problems that we notice as well is, for example, taxonomy. It’s always wrong. So we need a standard in that. Because if strains are related, the chance that they make similar compounds are higher. We know that. But if something is called Amycolatopsis, [maybe] it is a Streptomyces. Stuff like that. Or even if the strains are called three different things over the course of time, that really can make it hard to follow certain databases. So that makes it hard.
The same with compounds, if something is called very different– although it’s practically the same compound but with a methyl group on it or something. But it has a very different name– that also makes it hard for us to really collect data there. So somehow, databases like Natural Product Atlas, or MIBiG, I think, are a good start. But now, we really need to move on with depositing data in a certain way and also have a standard for genome taxonomy. I think GTDB also started that. But I think taxonomy and genomic data are still kind of a mess.
DAN UDWARY: Yeah, sequence analysis is hard.
NADINE ZIEMERT: Yeah, because some people use the NTBI taxonomy, others the GTDB taxonomy, and so it would be nice to have a standard. Otherwise, I think a lot of time is just spent searching for data manually or having wrong data, wrong names with things. I think it could be better. That is a challenge.
And otherwise, I don’t know. I think the hardest part, actually, is not on the data side, but it’s still really the high throughput of getting these clusters activated. So most of them are silent. Most of them are not produced under laboratory conditions.
And really, it would be nice if there was just a certain molecular switch that you could put on to get them all grown. But it seems like there’s nothing like that, at least no universal switch, where you can switch on everything. So I think what we have to do more is really figure out more about regulation and when they’re really produced. Then we can more easily connect molecules to genes.
DAN UDWARY: Yeah, that’s a good point. We can do a lot with prediction, and we can go very high throughput with that when we throw enough CPUs at it. But at the end of the day, you still want to prove things. You want molecules in a tube at the end of the day. And so that’s the really hard part.
NADINE ZIEMERT: Yeah, that’s what we think. And so far, for us, that’s, right now, the bottleneck. We have a lot of cool clusters, and I think a lot of really exciting chemistry, you would think. But now, we just have to find it. So then we tried to heterologously express them, but that takes a lot of time to really get them to be made.
DAN UDWARY: Sure.
ALISON TAKEMURA: Something that came to mind when you were talking about sample standardization, I heard from another scientist that I interviewed recently about how she was leading this team effort to sequence microbiomes of rivers around the world and that JGI was being very helpful in being able to sequence them and standardize that sample processing. Do you see that as a way that JGI could help with natural products? Or is there another way that you think JGI would be helpful in this endeavor?
NADINE ZIEMERT: Yeah, I think that would not only be helpful for natural products, but I think for any kind of science. I think that’s for anything that you want to analyze, if you want to really have big questions if there’s changes within different environments or if there’s something different, even in primary metabolism or in certain snips of viruses or something. I don’t know. Then I think making sure that the samples are processed in the same way and really comparable, and that the metadata are there– at least for people like me that also analyze a lot of samples from public databases, I think that would be really useful and helpful for everyone.
And I think if JGI can for sure help there. I think it needs government institutions like that. Because who else would be able to do that?
DAN UDWARY: I think one of the things JG will be able to do in the next little while is to really start thinking about how to create the kind of community to help with genome mining that I think we need, that you were talking about. We do need the community to be able to come together.
And in the past, we’ve had these monolithic databases, NCBI and IMG, that have a lot of data and really useful data, really useful tools, but are often very static. And biosynthesis is kind of a different animal in that we’ve got hundreds of thousands of biosynthetic gene clusters that don’t have a known function. We need some way to get the community engaged to actually start exploring and figuring out what these things are. So that’s what’s on my mind, lately.
NADINE ZIEMERT: That’s perfect. I think no database would really fit in that. That miner information, someone worked with a gene cluster in some certain organism, and then just having some information like, oh, we notice it’s transcribed under this condition or something. We never found the compound, but if we found that about that gene cluster, it would be helpful. So we wouldn’t start from scratch every time we look at a certain gene cluster. So you could get some information about it, at least, even if there’s not a paper coming out, because you don’t think it’s worth publishing. But that would be great.
DAN UDWARY: Sure. Having some mechanism to capture all that kind of information, it’s not out there, right now. And it probably needs to be.
ALISON TAKEMURA: Yes.
DAN UDWARY: Nadine, it was really lovely talking to you. I think this has been a great conversation. Thanks so much for joining us.
NADINE ZIEMERT: Thank you for having me.
DAN UDWARY: I’m Dan Udwary, and you’ve been listening to Natural Prodcast, a podcast produced by the US Department of Energy Joint Genome Institute, a DoE Office of Science user facility, located at Lawrence Berkeley National Lab. You can find links to transcripts, more information on this episode, and our other episodes at NaturalProdcast.com. Special thanks, as always, to my co-host, Alison Takemura.
If you like Alison, and you want to hear more science from her, check out her podcast, Genome Insider. She talks to lots of great scientists outside of secondary metabolism. And if you like what we’re doing here, you’ll probably enjoy Genome Insider, too. So check it out.
My intro and outro music are by Jazzar. Please, help spread the word by leaving a review of Natural Prodcast on Apple Podcasts, Google, Spotify, or wherever you got the podcast. If you have a question or want to give us feedback, tweet us at JGI or to me at Dan Udwary. That’s D-A-N U-D-W-A-R-Y.
If you want to record and send us a question that we might play on air, email us at JGI-comms, that’s JGI dash C-O-M-M-S @LBL.gov. And because we’re a user facility, if you’re interested in partnering with us, we want to hear from you. We have projects in genome sequencing, DNA synthesis, transcriptomics, metabolomics, and natural products in plants, fungi, and microorganisms.
If you want to collaborate, let us know. Find out more at JGI.doe.gov/user-programs. Thanks, and see you next time.