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Common Questions: Differences in Testing Technology: mRNA vs DNA

Guy Daniels
Onegevity

Check out the YouTube presentation or read below!


Slide 1:

Welcome. My name is Guy, and I'm the Head of Medical Education at Onegevity. You may be familiar with my ongoing webinars series and the microbiome, and if you're not out, I'd like to direct you to the Onegevity Journal page on our website. This short presentation will cover one of the most common questions we get--- that of how our method of microbial analysis differs from that of other companies in our space. So let's get started.


Slide 2: 

We'll use the words from a third party to begin this process. Here's a paper published in 2015 out of the United Kingdom. It's not important that you can read all the words, I'm just showing you the reference. 


Slide 3: 

Here you can see a portion of table one from within their paper. It shows the comparisons between the older 16S technology and ours: shotgun metagenomics. I will start by saying that 16S technology has gotten us a long way over the past decade-plus in helping us understand the microbiome; it too uses DNA. These authors refer to 16S as fast and cheap, and as you can see in their limitations column, the simple translation is that it has accuracy and identification issues. If you look in the characteristic column for us, shotgun metagenomics, you see terms like total genes, high coverage, no bias, and novel genes. When you look at the limitations column for us, shotgun metagenomics, you see terms like expensive, complicated, and time-consuming. So which sounds better to you?

Let me try to illustrate this more simply.


Slide 4:

Instead of using bacteria, I decided to use something we can all relate to-- us. We’re classified by various characteristics and we fall under the kingdom of animals. We have a spinal cord and we are in a class of mammals, but there are still many mammals within our class. We’re further broken down into primates, but that still leaves chimpanzees, gorillas, etc.

Speaker 1: (01:56)

For the subclassifications tell us we belong to the man-shaped family, the genus, homo meaning man along with Neanderthals and other recently discovered. Lastly, we're within this species sapiens to distinguish ourselves from the others who have since gone extinct. But with bacteria, we can go a step further into the strain. So from a human standpoint, it's like identifying those among us by gender, eye, hair and skin color, and height and other such features. So we’re very specific to distinguish between the 7 billion of us. So keeping this in mind, let's look at a couple of papers to compare apples to apples.


Slide 5:

I've chosen to research papers from 2019 both in regard to the autoimmune condition Ankylosing Spondylitis (an inflammatory arthritis condition affecting the spine and large joints). This paper uses 16S technology and let's take a look at typical 16S findings.


Slide 6: 

I know their data here, looks intimidating, but just bear with me. All they are telling us is which groups of bacteria were high or low in this autoimmune condition. Some of the report is down to the species level, that's all of us – homo sapiens, but if you look at the three highlighted spots they could also be put on the class bacilli, which is huge in bacteria-- like with us, that's like saying mammals. They also report on the order lactobacillus,

and there are many members within here too some good and some bad, and this is akin to saying primates for us. I think you get the idea. They do report on families and genera but in the bacteria world, although helpful, many of these taxa are mixed with good and bad actors. Again, 16S technology has gotten us a long way, but like all technologies, something better comes along eventually and that's what we'll see here as part of our apple to apple comparison.


Slide 7:

This too is a research paper from 2019 in the autoimmune condition Ankylosing Spondylitis and this one uses shotgun metagenomics. I know there's a lot here, but all of this text on the left side tells us which strain within a species, within its respective genus, was high or low in the collective microbiome of these subjects. So again, to use the human analogy, if we're asking which homo sapiens are caucasian over six feet tall, blonde-haired, blue-eyed, and are female, 16S technology may tell us primates are hominids. That's pretty vague.

Shotgun metagenomics will point out precisely who among the 7 billion of us meet those criteria.


Slide 8:

To take our technology a step further, we can look into their genetic potential from the same paper. We're looking at the genetic capacity of the microbiome. It tells us what things the autoimmune microbiome does and makes different from the healthy controls. One of the key findings here is that the microbiome from the Ankylosing Spondylitis subjects was making too much LPS or endotoxin. As the name implies, this is not good news. If you're familiar with my webinars, you'll know that LPS comes from various bacteria, generally speaking, ones that don't behave all that well. If we again use our human analogy, this is like identifying characteristics like age, IQ, fertility, and speed from among our caucasian over six foot blonde-haired, blue-eyed females out of the 7 billion of us.


Slide 9:

So now we return to our initial paper, the review out of the U.K., and here we again see their comments on our technology, shotgun metagenomics, and below that, you can see their commentary on another commercial technology meta-transcriptomics. That's a fancy way to say they're looking at mRNA. In their comments, they tell us two important drawbacks. One that there are no reference databases and it's a transient picture of a complex community. The advantage is that you can look at gene expression. In other words which bugs are happiest. I'll explain all this in a moment, but first, let's quickly revisit what mRNA is.


Slide 10:

We all know what DNA is. That's our gene pool, so to speak. mRNA is the messenger. It takes a blueprint from the DNA over to the manufacturing facility to produce proteins in a specific sequence. In other words, if you measure mRNA, you're taking a look at, in this case, which bacteria are the most physiologically active at that moment in time. These are the ones that are the happiest under the current circumstances. They are happily thriving by being physiologically active in making more of themselves. That's all well and good in theory, but let's take a look at the GI-tract.


Slide 11:

Each part of the GI tract has its own unique microbiome. On the left of this slide, I've listed the top reasons why each section of the gut can harbor different bacteria according to what they can tolerate. So when we collect a fecal sample, it comes from the last place of residence, which is at the bottom of this figure-- the rectum. But the environment of the rectum is very different from the rest of the GI tract, in particular, the distal ileum and the proximal colon where most of the important activities take place. So if you're looking to see which bacteria are happiest in the rectum, then measure mRNA, but that's not going to give you a picture of who's happiest, where it counts. Unless you're concerned about colorectal cancer. Then I can see how this would be a more ideal analysis. Although still, this methodology doesn't have the best reference databases which have been built up over the past two decades.


Slide 12:

In other words, you can't be certain what the data means. Let's look at this using our same example. Left to our own devices with no technology, no comforts, not even clothes, which climate here representing different parts of the GI-tract would we as homo sapiens be happiest be able to survive and thrive? One that provides us with a climate we can endure and food and water. It's the same for bacteria. Only we never give them any thought.

mRNA tells us who's happiest in the rectum, but that's really not the location we're concerned about, and we don't have the historical data to tell us it's meaningful anyway. Over two decades of DNA, technology has gotten us a long way to understanding the microbiome. We don't know everything, but we know enough to make a dramatic improvement in people's lives.