So, this is Julian Parkhill. He’s going to be telling us about genomic epidemiology. So, Julian, what is genomic epidemiology? Genomic epidemiology is the process of using whole genome sequences to understand transmission of bacterial pathogens. So, that can be transmission within hospitals or it can be food-borne outbreaks, for example. So is genomics a type of genotyping? So, genomics is the ultimate genotyping. It’s genotyping with full resolution, all the resolution you can get. So, if organisms are identical at the genome level, if they have identical genome sequences, then they are identical. There is no further resolution you can do to separate them. But that means that you have a much, much finer resolution than you do with other genotyping.
And a finer resolution means that you get a better understanding of transmission, you can get a finer understanding of dating and times of transmission. So are there any other advantages of whole genome sequencing over the more traditional methods of molecular typing? So, the main advantage is resolution and accuracy. But the other is interoperability. So, the ability of people around the world to know that you’re working with exactly the same thing. And with a lot of molecular techniques like PFGE, it’s impossible to be certain that the type you’re looking at in America is exactly the same as the type you’re looking at in the UK. With whole genome sequencing, the data is digital, the data is exchangeable.
And you can be absolutely certain that the strain you’re looking at in the UK is exactly the same as the isolate that you’re looking at in America. So, I understand one of the advantages of using the traditional genotyping schemes for molecular epidemiology is that we have established databases and that different labs can compare their results to one another. Can you do that kind of thing when you’re using genome sequencing? Absolutely. You can compare results if you exchange data. And one of the good things about whole genome sequence data is it is digital and it is easily exchangeable, so that the data is easily comparable from one site to another.
The disadvantage, I suppose, is that you don’t have easily human understandable nomenclature. You can’t say this is an– well, you can say, this is an SD 22, but obviously you’re generating data that’s– information that’s much more detailed than that. So, while you can, with traditional typing schemes,
use a human accessible handle that everyone understands: this is an SD 22, this is a PFGE Type 4. It’s much more difficult with genome sequences. Generally, they’re backwards compatible so that you can certainly say, this is an SD 22. People are developing hierarchical schemes that allow you to provide a universal nomenclature. But it is going to be less human understandable. But equally, the ability of the computers to compare that data and to understand and to interpret relationships is much, much finer. So, perhaps that level of nomenclature isn’t quite so important. So, it sounds great. Why isn’t everyone using it yet? And A number of reasons. It’s still relatively expensive.
It takes time and money to build the infrastructure to do this. You need sequencing machines, you need machines for preparing DNA for the right quality, you need people who are trained in doing that, you need people who are trained in interpreting and understanding the data. All of that can be done, you can put in place the infrastructure. You can build the expertise. But it is, at the moment, a little more expensive than a lot of current typing techniques, certainly the molecular techniques like PFGE. Although, it’s probably cheaper than MLST. But building that infrastructure, training those people takes time and money. And running that will also take a certain investment over time.
So, for people to do this, for people to invest in that, for governments to invest in it, it requires a proof that this is a cost-effective thing to do. Especially in hospitals, we have to show that routine sequencing is a cost-effective intervention. And then, I think it will follow. So, is this something that you think will happen? I think it’s inevitable. I think the advantages are so numerous.
Purely in terms of epidemiology, you can generate more accurate data faster than current techniques. That means intervening with outbreaks earlier. It means crucially disproving outbreaks earlier, so you don’t spend a lot of time and effort chasing down outbreaks that aren’t real. When you add in all the additional data that will come longer term, drug resistance prediction, virulence prediction, it becomes quite compelling. And when you think of one of the things that we have to do to address our current problems with antibiotic resistance, is rational antibiotic use and antibiotic stewardship. And that means treating an infection with a drug that you know is going to be effective at that intervention.
And that means knowing what somebody is infected with and knowing what it’s resistant to. And whole genome sequencing is going to be a route to doing that. So, I think it’s inevitable that genome sequencing is going to be used in this way.