#microtwjc- What's living in your bathroom.. Weeee

And so dawns another week of the microbiology twitter journal club. This week we're discuss public toilets. WEEEEEEE!

Because the greatest concern when anyone goes to the restroom is the bacteria. Yes, you can't see them, but you know they're there.... watching...waiting...plotting.

But who are these mysterious bacteria? Can the 6 layers of two-ply really protect you as you sit straining atop your cushioned throne? This paper probably won't answer that.

This paper was an attempt to work out the identities of the bacteria in the restrooms, and where they come from.

But there is a problem. You can't just smudge a petri dish across the surface of everything and expect stuff to grow it. And for many years, most conventional microbiology techniques relied on getting living bacteria to the lab. In recent years we've realised that most bacteria don't survive the journey, so we don't know much about them. So we've turned to techniques that don't require living bacteria to work.

These techniques rely on detecting DNA. DNA is a relatively stable compound, and can stick around long after that bacteria that had it died out. It's like in CSI when they get DNA evidence from the crime scene to identify the criminal. Except the crime scene here is the toilet, and the criminals are bacteria.

So what did they do?

They looked in two buildings of the boulder campus in the University of Colorado, and sampled six girls restrooms, and six boys restrooms for bacteria. They took sterile swabs and wiped them over the main points of contact between the users and the wash room, and took samples of tap water.

They  then extracted the genomic DNA from all of their samples.

So how do you identify a bacteria just by it's genome ? Well, there is this gene for 16s rRNA. What is this? this is the sequence for the ribosome, which all you biobuffs will probably know is essential to protein synthesis. So this sequence is found in all bacterial species, but the genetic code differs between species. So we can class each bacteria into a known species based on their specific 16s rRNA code.

"What about bacteria that don't have that sequence? " Shut up voice of cynical doubt in my head, if that were possible, 16s rRNA typing would surely have detected it.

So lets begin !

Figure 1

So what is this figure showing?

This figure shows the 19 most abundant bacterial species found in total, and how those species were distributed across different surfaces within the toilet. 

The also sampled lots of human volunteers (all who presumably who worked in the building) as well, so that you can compare the bacterial species. 

A) This picture shows all of the data from all of the volunteers and all of the toilets combined. They didn't note down the genders of their volunteers, so we can't tell if there is any bias in the bacterial communities samples based on genders. Luckily this won't be a problem if all the data is lumped together, and stick your fingers in your ears and scream doodoodalallala then none of this will pose any problems. I mean, its not as if in any point in the paper they suggest that gender has any effect on the microbiota. 

Anyway, back to the actual figure. It shows the proportions of bacteria found from each sampled surface. You may notice that none of these graphs go to a hundred percent, but that's due to all the bacterial communities not shown, because they are rare and variable. So the floor has the highest number of these variable bacteria, because it's graph is the shortest.

With this graph, you can compare the species found on a bacterial surface, such as a toilet handle, and compare it to the bacteria found in faeces, and you can say for certain that we get out faecal bacteria from rubbing our smelly bits on flush handles.

B) Here, they show the same data as above, but this time broken down by boys toilets and girls toilets, and we can marvel at how guys have very few lactobacilli, which actually isn't surprising since lactobacilli are commonly found in the female genital tract, and have been posited as being an extra line of defence against infection. 

So anyway, what this data certainly points to is that toilet seats and flush handles generally harbour bacteria that are commonly found in the gut suggesting that these surfaces are more likely to be contaminated with faeces. Yes Shit Sherlock !

Figure 2.

This is a PCoA plot. "What on earth is that and why did you just spit at me?" you may ask. 

"Why, it's a principle coordinates analysis plot, an because I always try to say acronyms phonetically" I reply. 

"Yes, but what is it?"

"Complicated"

Alright , I'll try to give a very very simplified explanation of this. Each dot represents a data point i.e. they sampled 12 toilets seats and flush handles, hence each data point represents the bacterial community for say each toilet seat sample, or every toilet floor sampled. But what do they represent from each bacterial community? 

Well, they represent the the variation in the bacterial communities in two dimensions. Using magic (or Sufficiently Advanced Statistics,.) the variation can be classified into two components, PCO1 and PCO2. Essentially, the more each bacterial community has in common, the closer together they'll be on this graph. 

So actually it suggests that in one particular toilet had a very similar community to the toilet floor. They say that people may be operating toilets with their feet.Either someone dropped a swab on the floor and told no-one. I know, that's crazy, lets go with the feet explanation !

Who cares about any of that when we can bask in the glow of...

Figure 3

This figure essentially breaks down the data from the previous sections and displayed it in a user friendly way. Each of these pictures shows an average toilet (for those two buildings that were tested). The light blue indicates low bacterial contamination, and dark blue means high. There are no intermediates. 

A) This picture shows the distribution of skin associated bacteria, which were found to be common on all surfaces, but mostly on places like door handles and the soap dispenser. Wait, they don't get any hand dryers or toilet paper? 

B) This picture shows the distribution of gut associated bacteria, which is mostly on the toilet seat and the flush handle. Which is a freaking miracle ! Considering that they must be wiping themselves out with their fingers, it's amazing that this entire stall isn't covered in desperate deep blue handprints.

C) This picture shows the distribution of the soil bacteria within the restroom, and it is mostly on the floor. Because shoes.

And now it's time for the final figure:

Figure 4

Here they ask themselves whether they can define the relationships of each surface to the source of the bacteria, using SourceTracker analysis. So in the previous few figures we looked at a couple of surfaces and then said to ourselves that these communities look similar. But Sourcetracker allows us to go one step further.

So you basically give it details about the numbers of species of bacteria in your urine. Then you set it to analyse the microbial communities in a stall, and it will tell you to proportion of those that are similar to the ones found in your urine from each surface.

So they took samples from faeces, to represent the gut. They took samples from the mouths of volunteers to represent bacteria in the mouth. They took urine samples to represent the bacteria in the urine.The samples of tap water were use to represent bacteria which may come from the tap.A variety of soil samples were sampled to represent the soil. Still with me?

 So the surfaces of the face and hands, and hair were use to represent skin  "skin". Oh, and the skin on the surface of genitalia were also lumped in with the rest. Seems legit.

So this source tracker analysis found that actually the skin is the greatest contributor of microbial flora in the toilets, and that faecal flora are ineed more common on toilet seats, and the flush handles.

So what does this all mean? Is it actually useful?

That's up to you. This was a basic look-see type of paper. They had a machine, and they had to go and use it for something, and if they got a publication out of it, then brilliant. Other studies tend to go into more detail , with actually trying to identify these species, like where any of the staphylococci are community MRSA's or whether there were any other pathogens hanging around. It really didn't go into much detail at all. 

Honestly, the conclusions I can draw from this paper are no different from say this paper written in the mide 70s. Sure, they've got new technology,but the conclusions to be drawn are essentially the same. 

In fact they can argue that this is yet more confirmation that their techniques work, because they prove what we already know.

I'm so glad I spent my time reading this. That was time well spent

Rule of 6ix has an alternate take on this paper that is worth reading