[MUSIC] Hello everyone, my name is Marcus Johansson and I'm a PhD student at a Natural Food Institute here at the Technical University of Denmark and today I'm going to talk with you about mobile genetic elements, and how we can predict. Today's lecture we're going to have a brief discussion of the different types of mobile elements. We're also going to have a look at the two mobile element finder and its output, but we might as well begin with asking ourselves two question why are we interested in more mobile elements. To begin with for me that also track question is because they are tremendous important for bacterial evolution as they can recruit and disseminate new jeans across the population. You all aware of horizontal gene transfer and it's important for bacteria as it gives the bacteria debility to have a much more diverse genome than it would normally have as its only reproduces clonally. As you know, there are free principle methods for horizontally gene transfer. We have transduction where the DNA can be encapsulated in a bacterial phage that's being transmitted, we have transformation that allows the bacteria to take up DNA from the extracellular environment. And finally, we have conjugation that's mainly facilitated by mobile elements such as plasmids and certain transposons. In this case, we have two general types of plasmids, conjugatory plasmids and mobilizable plasmids. Conjugatory plasmids carries all the machinery for performing conjugation, so while mobilizable elements require to be co-mobilized when other elements congregate, as they only have a limit, they don't have the full set of genes. Mobile elements are highly diverse. They divided into different types, dependance on their properties of the element and their genetic layout. Here we can see general anatomy of several different types of mobile elements, ranging from simple mobile elements like might and insertion sequences to very complex and large elements like cognitive transposons, as we can also see, some of the types share similar characteristics. Again, insertion sequences is essentially transposes gene bounded by invert, repeats unit transposons. Many of them also has a transpose. A similar transpose, since also bounded by inverter repeats as well. However, even within a type of mobile element, there is considerable diversity as well. Again, the insertion sequence. Transposes gene exist in many different version that utilizes different chemistry to transpose and transposes by different types of methods. Another example of this of this diversity are the conjugative transposons, that has all the functional units, all the genes that conduct a similar function clustered together, meaning that the order of the genes frequently change and these functional units can even be switched out for another functional units from another conjugative transpose on as well. Perhaps it's done better to take a step back and start thinking about the broader picture, where we focus more on what function they can provide and how the interplay with one another. So in this case we have two boxes. The top box here contains mobile elements that transposes within the cell, while the bottom box contains mobile elements that transposes between cells within each box. I have grouped mobile elements based on a function they can perform, for instance insertion sequences and might while they cannot carry accessory genes and transpose the genes around, they can still modulated gene expression. I divine in activating to genes or by up or down regulation nearby genes. We have integrals, for instance that can recruit in harbor a lot of different genes as cassette regions. We have gene transporters, like composite transposons, unit transposons that carry these accessory genes and move them around. Put into DNA, and moving between cells, we have these conjugative or mobilizable elements that we have previously mentioned. But this is just not enough because mobile elements we need to think about how do they interplay with one another as well. For instance, while integrals, cannot transpose on its own, it's very good at recruiting hormone genes if it is located on a uni-transposon which it frequently are. Then suddenly that unit transposing and can't move the integrand around within the bacterial cell and possibly move genes from a chromosome to a plasmid that endoplasmic can lead disseminated through the population. So it's really the interplay of mobile elements that's important. Additionally, small elements like insertion sequences frequently exist in multiple copies in the bacterial genome. That's important, because, among other things, that act as promotion for homologous recombination and DNA exchange in addition. Insertion sequences has the ability to form composite transposons units. If there are two encircle insertion sequences of similar type that are in close proximity to one another. So by doing that they can also transpose genes via different method in a different a different way. In summary, mobile genetic elements are very important as they mobilized DNA for outer bacterial population. They are important because they can promote gene rearrangements and they also important because they as I said disseminate the genes, they can enable still bacteria to have access to a large diversity of genes and traits at a relatively low fitness cost compared to if the bacteria would had to maintain a large genome that contained all them genes. Now it's distributed in the population instead. With great powers comes great needs for regular regulation because this is potentially very destructive for the bacteria itself, as rapid genome rearrangements could be very deleterious. Therefore, the transposition of mobile elements is tightly regulated and often coupled to the S response systems. It's generally only lifted during stressful scenarios or in the presence of single stranded DNA. That's also is a signal for a recent conjugation events. In addition, transcription of new genes that are associated with mobile elements or near mobile elements is also influenced by different host factors. For instance, we. See that in some cases the transcription virulence factors up regulated during affection because they are associated with mobile elements. How do we go about predicting mobile elements in our group and I have been developing a tool called mobile elements Finder that takes assemble sequences, context, scaffolds and complete genomes as input and tries to detect mobile elements based on sequence similarity to already known elements. It, the tool comes with a database consisting of about 4400 mobile elements. The reason why takes assembled sequence data is because when it comes to mobile elements, we not just interest in their presence. Absence of elements, but also the context of the elements, what genes they are, for instance near and therefore we chose to design to assemble sequences. A caveat is that we should take care and really verify the quality of the assemblies as large and complex and mobile elements could be broken down over several contexts. Otherwise the tool exists in two versions. One web server version that has a graphical user interface and that additionally annotate a mass virulence factors and plasmids using rest Finder, virulence Finder, and plasmid finer tools. It is assigned to for being more user friendly and discoverable. The local version is an installable Python package that can be installed via pip from pie pie. It is more flexible, but it require but less intuitive and it allows instead for doing batch analysis of many samples. How does this tool work? Mobile element Finder works by taking to assemble sequences as input and via blast annotate in a line modem, mobile elements, two doses sequences via blast there resulting HSP high scoring segment pairs or sub alignments. Or then group together and this is because when do to the flexibility of mobile elements and there is quite likely that we not only get full perfect alignment students, high mobile element, but that we also get partial alignments that could be due to mobile for instance, or mobile element that normally carries gene A in the database in the sample instead carries another gene. Therefore it, we wouldn't find it when we used align it to the database. We can also see structural variations where for instance, to several rigs and regions within the mobile elements had just been switched around and have a different order, so that's why we try to group these sub alignments together so the sublime that are from the same mobile elements and we do the prediction on those. We also take care to remove overlapping sub alignments from different mobile elements. This is because some of these elements carry genes or regions that are very similar to one another, so you would see potentially many overlapping alignments from different elements. And the final prediction is made on using threshold against alignment quality metrics like alignment cover. Sequence identity and the level of truncation of the mobile elements. So we can assure that we have pretty much the entire mobile elements and we have conserved inverted repeats, for instance. Additionally, the tool is able to detect putative composite transposons. And it does that by trying to find occasions where I have two insertion sequences of the same type, the same insertion sequences in close proximity to one another. The default threshold is 50,000 nucleotides. And it ensures that one of them must be full conservative, fully functional, and there are the needs to at least have a conserved inverted repeat region. When interpreting the results, you should take care when including these, and do additional research on them. Just because they exhibit these properties doesn't mean that they are actually able to form these composite transposons, as it's really up to the chemistry and a type of insertion sequence. Let's have a look at the output. When you have uploaded in our sample two mobile element find end result and analysis has been finished, you will get this output view here. And the output view consists of two separate elements. This is a top element, this called the result index page. And it's basically just a summary of the results, where you can see the number of mobile elements that was predicted for each contig. You also get a notation of if we found a plasmid replica on the same contig, and if you toggle it on, which AMs and virulence factor that was also predicted on this contig. The tool also allows you to set custom filtering criteria by expanding the customized filters button. Here we can arrow the search. So we only include the mobile elements were interested in. We can also set custom quality filtering criteria, and also toggle on special cases that is being shown regardless of its quality. Like if we want to make sure that we don't have mobile elements that could potentially span outside the contig. If we click on one of these links, so we'll go to one of the contigs, we will see a result view like this. This is the contig overview. And here we have first the contig, and that contig is all the elements that were predicted on the contig. For instance, in this case, we have a plasmid replicon, resistancin and two insertion sequences. If one of these mobile elements were to carry a gene, those genes would be nested under the empty entry as well. It would to try to have some more logical consistency. We can also expand to show the alignments. We can download the displayed result either as a CSV file and also the mobile elements sequences. I hope you found this lecture interesting. And thank you. [MUSIC]
