Improving food quality using advanced microbial profiling

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Transcript

Hello everybody and thank you for tuning in to this video on how you can improve food quality using advanced microbial profiling which I'm now going to call AMP for the rest of this video.

OK, so who am I? My name is Greg Jones and I am a microbiologist here at Campden BRI and my job on a daily basis is to look at how microbes grow in food, whether or not that is from the point of view of spoilage or if it's from the point of view of pathogenic organisms.

Today we're going to focus on the spoilage side of things. Now, when I do my job, day-to-day all I'm essentially doing is making a series of choices and introducing a series of biases into the analysis. I make choices by looking at various different groups of microorganisms and I choose those organisms based on my experience with food and what I think should be in there and, once I've made those choices, I introduce further biases by choosing which agar I'm going to be growing my organisms on, which media I'm going to be growing them in. Then I introduce another bias by growing those organisms at a certain temperature and a further bias by choosing what time I'm going to be keeping those organisms in my incubator.

All these biases essentially mean that I'm only looking at a subset of the population of my product. There could well be organisms there which we have basically missed because we weren't expecting them to be there. To get around this we can use a culture independent method called AMP - I've just got a little schematic here of how it works.

So, essentially what we do is we take our food product and we then extract microbial DNA. Once we have extracted that DNA, we can amplify a marker gene within it and, once we have that gene amplified, we can put all those sequences onto a sequencer. Once we've sequenced all those different sequences, we can use some software to compare those sequences against the database of known sequences and what we essentially get is a list of names and numbers.

So, what do we do with that list of names and numbers? Well let's have a look first at the sort of data we get from a culture-based analysis. Our culture-based results would look something like this. If I was looking at this product, I would have chosen to look for Pseudomonas, Enterobacteriaceae and lactic acid bacteria and with that particular soil analysis you get the results that come out like this. So we've got 3x106 colony forming units per gram and for Pseudomonas, we've got 4x103 colony forming units per gram Enterobacteriaceae, we've got 2x104 colony forming units per gram lactic acid bacteria and that's the sort of information that one expects from a culture based analysis.

With AMP we get more information. So here we have the sort of results we get with our sequence-based results. We've got species of Pseudomonas so there's our Pseudomonas counts but here we've split those down into species and we get a number of reads associated with those particular species. What we also find is that we get organisms which we are not expecting. So here we didn't even look for anything like Brochothrix but we have it. We have organisms which are related to Pseudomonas, we've got a Chromobacterium violaceum here and we always find organisms which we are not expecting in the AMP analysis which just goes to show that beforehand we were biasing our approach quite a lot.

I need to stop being a microbiologist and turn into an ecologist instead because instead of looking at numbers of certain bugs, now I’m thinking of them as basically monocultures. I need to start thinking about them as populations as consortium of different organisms and I'm going to illustrate that here with my diagram of some fish and my creepy crawlies here. I can say “okay well I've got some fish I've got some creepy crawlies” if I was to analyse these two populations and find this sort of a situation like that, but all of a sudden if my creepy crawlies had a fish in them I would be able to tell that there had been some communication between those two populations. I also know the direction of that communication I can say that a fish of one of these population has moved into this population here so with AMP you can start to do that for food you can start to say, “okay well why am I looking at a population I'm not expecting to see in this particular product? Where has that come from?”. We can also use this for spoilage investigations as well. Here we have a diagram based on some work we did for a client. In this particular case we had some chicken. Basically, a chicken breast in a modified atmosphere packaging. The breast was spoiling and going blue. There were some blue patches on the surface of that chicken breast and the first thought was “are these blue patches maybe dyes? Has there been some leaching of the dye from the packaging into the product?”. Our chemists confirmed that that was not the case so we got hold of the product and ran a spoilage investigation using AMP. We found a normal profile and we found a spoiled profile and we found a different profile to what we were expecting so here that is as a big green blob on my graph.

When we came to look at what the big green blob was then we found that it was an organism called Arthrobacter which is an obligate aerobe so it requires the presence of oxygen in order to be able to grow. It is noted for its production of blue pigments which told us two things: one, it was likely to be the causative agent of the spoilage we saw because it produces pigments, and two it shouldn't have been there in the first place because it only grows in the presence of oxygen and these packs were supposed to be oxygen free. So, when the client got that information back they could run an investigation on their packaging line and they did find a fault with the packaging.

It's not just spoilage investigations we can use AMP four, we can look at factory hygiene so we can look at whether or not a clean area is in fact a clean area by looking at the population of microbes there compared to other areas of the factory. We can look at fermented products, we have a team looking at fermented products and the microbial composition of those products and these are exciting at the moment because there is a lot of interest on fermented products, fermented vegetables, kombucha, kimchi's, all that kind of stuff, and we can also use it to look at authenticity as well. Imagine we have two types of product maybe we're looking at two types of Italian hard cheese here and we have our parmesan section up here and we have Grana Padano up here and we have parmesan down here and we are looking at a sample which is claiming to be parmesan but actually turns out to be Grana Padano we can look at trying to place those populations in relation to each other and to see whether or not a product is as authentic as far as people are claiming.

That's really just a short Whistle stop tour through what we can do with AMP so if you have any questions then there's contact details surrounding this video no doubt and please do get in touch and give us a shout because it's a fascinating area and I'd love you to use it.

Thank you very much