Journal Club

3D structures of gut bacteria and the human immune system

When I talk to people about my work I sometimes get the question, “Do you really think that the microbiome has a direct effect on human health?” It’s a completely understandable question – the study-of-the-week which makes it into the news cycle tends to just confirm what we already know about the importance of diet and exercise. Then I come across these beautiful papers that show just how intimately connected we are with our gut bacteria. Here’s a good example, and it even comes with a video.

Ladinsky, M.S., et al. Endocytosis of commensal antigens by intestinal epithelial cells regulates mucosal T cell homeostasis. Science. 363(6431). DOI: 10.1126/science.aat4042.

There are some beautiful illustrations and graphics in this paper which I won’t reproduce here, but which I hope you can access from whichever side of the paywall you are on.

Background: Researchers are continuing to find evidence that the type of bacteria in your gut (if you are a mouse or a human) influences the type of your immune response. If you don’t study the immune system, just remember that the immune system responds in different ways to different kinds of pathogens – viruses are different from bacteria, which are different from parasites, etc. Mounting the correct type of response is essential, and it seems that which bacteria you have in your gut has some influence over the nature of those responses.

The Gist: This study focused on the how of the question, the specific molecular mechanism which would explain this observed relationship between bacteria and the immune system. They used one particular type of bacteria (segmented filamentous bacteria, or “SFB”) and showed that this bacteria gets so close to human cells that bacterial proteins are actually taken up and can be found inside the human cells. In addition, this movement of bacterial proteins inside human cells causes a shift in the type of response mounted by the immune system.

What Caught My Eye: This paper has a video showing a protrusion of a bacterial cell pushing deep into a human cell, complete with a 3D reconstruction of the physical structure using electron tomography. If you can follow the link above and make it to the video, I highly recommend taking a look.

The biggest story for me in the microbiome these days is that there are a number of great researchers who are starting to figure out some of the specific molecular mechanisms by which the microbiome may influence human health. This makes me more and more optimistic and excited that we will see a day where microbiome-based therapeutics make it into the clinic, which could have a profound impact on a broad range of diseases, from inflammatory bowel disease to colorectal cancer and auto-inflammatory disease. It is exciting to be a part of this effort and try to help as we bring that day closer.

Molecules Mediating Microbial Manipulation of Mouse (and Human) Maladies

Sometime in the last ten years I gave up on the idea of truly keeping up with the microbiome field. In graduate school it was more reasonable because I had the luxury of focusing on viruses in the microbiome, but since then my interests have broadened and the size of the field has continued to expand. These days I try to focus on the subset of papers which are telling the story of either gene-level metagenomics, or the specific metabolites which mediate the biological effect of the microbiome on human health. The other day I happened across a paper which did both, and so I thought it might be worth describing it quickly here.

Brown, EM, et al. Bacteroides-Derived Sphingolipids Are Critical for Maintaining Intestinal Homeostasis and Symbiosis. Cell Host & Microbe 2019 25(5) link

As a human, my interest is drawn by stories that confirm my general beliefs about the world, and do so with new specific evidence. Of course this is the fallacy of ascertainment bias, but it’s also an accurate description of why this paper caught my eye.

The larger narrative that I see this paper falling into is the one which says that microbes influence human health largely because they produce a set of specific molecules which interact with human cells. By extension, if you happen to have a set of microbes which cannot produce a certain molecule, then your health will be changed in some way. This narrative is attractive because it implies that if we understand which microbes are making which metabolites (molecules), and how those metabolites act on us, then we can design a therapeutic to improve human health.

Motivating This Study

Jumping into this paper, the authors describe a recently emerging literature (which I was unaware of) on how bacterially-produced sphingolipids have been predicted to influence intestinal inflammation like IBD. Very generally, sphingolipids are a diverse class of molecules that can be found in bacterial cell membranes, but which also can be produced by other organisms, and which also can have a signaling effect on human cells. The gist of the prior evidence going into this paper is that

  • people with IBD have lower levels of different sphingolipids in their stool, and

  • genomic analysis of the microbiome of people with IBD predicts that their bacteria are making less sphingolipids

Of course, those observations don’t go very far on their own, mostly because there are a ton of things that are different in the microbiome of people with IBD, and so it’s hard to point to any one bacteria or molecule from the bunch and say that it is having a causal role, and isn’t just a knock-on effect from some other cause.

The Big Deal Here

The hypothesis in this study is that one particular type of bacteria, Bacteroides are producing sphingolipids which reduce inflammation in the host. The experimental system they used were mice that were born completely germ-free, and which were subsequently colonized with strains of Bacteroides that either did or did not have the genes required to make some particular types of sphingolipids. The really cool thing here was that they were able to knock out the gene for sphingolipid production in one specific species of Bacteroides, and so they could see what the effect was of that particular set of genes, while keeping everything else constant. They found a pretty striking result, which is that inflammation was much lower in the mice which were colonized with the strain which was able to make the sphingolipid.

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To me, narrowing down the biological effect in an experiment to the difference of a single gene is hugely motivating, and really makes me think that this could plausibly have a role in the overall phenomenon of microbiome-associated inflammation.

The authors rightly point out that sphingolipids might not actually be the molecular messenger having an impact on host physiology — there are a lot of other things different in the sphingolipid-deficient bacteria used here, including carbohydrate metabolism and membrane composition, but it’s certainly a good place to keep looking.

Of course the authors did a bunch of other work in this paper to demonstrate that the experimental system was doing what they said, and they also went on to re-analyze the metabolites from human stool and identify specific sphingolipids that may be produced by these Bacteroides species, but I hope that my short summary gives you an idea of what they are getting at.

All About Those Genes

I think it can be difficult for non-microbiologists to appreciate just how much genetic diversity there is among bacteria. Strains which seem quite similar can have vastly different sets of genes (encoding, for example, a giant harpoon used to kill neighboring cells), and strains which seem quite different may in fact be sharing genes through exotic forms of horizontal gene transfer. With all of this complexity, I find it very comforting when scientists are able to conduct experiments which identify specific molecules and specific genes within the microbiome which have an impact on human health. I think we are moving closer to a world where we are able to use our knowledge of the microbiome to improve human health, and I think studies like this are bringing us closer.

Massive unexplored genetic diversity of the human microbiome

When you analyze extremely large datasets, you tend to be guided by your intuition or predictions on how those datasets are composed, or how they will behave. Having studied the microbiome for a while, I would say that my primary rule of thumb for what to expect from any new sample is tons of novel diversity. This week saw the publication of another great paper showing just how true this is.

Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle Resource

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The Approach

If you are new to the microbiome, you may be interested to know that there are basically two approaches to figuring out what microbes (bacteria, viruses, etc.) are in a given sample (e.g. stool). You can either (1) compare all of the DNA in that sample to a reference database of microbial genomes, or (2) try to reassemble the genomes in each sample directly from the DNA.

The thesis of this paper is one that I strongly support: reference databases contain very little of the total genomic content of microbes out there in the world. By extension, they predict that (1) would perform poorly, while (2) will generate a much better representation of what microbes are present.

Testing this idea, the authors analyzed an immense amount of microbiome data (almost 10,000 biological samples!), performing the relatively computationally intensive task of reconstructing genomes (so-called _de novo_ assembly).

The Results

The authors found a lot of things, but the big message is that they were able to reconstruct a *ton* of new genomes from these samples — organisms that had never been sequenced before, and many that don’t really resemble any phyla that we know of. In other words, they found a lot more novel genomic content than even I expected, and I was sure that they would find a lot.

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There’s a lot more content here for microbial genome afficianados, so feel free to dig in on your own (yum yum).

Take Home

When you think about what microbes are present in the microbiome, remember that there are many new microbes that we’ve never seen before. Some of those are new strains of clearly recognizable species (e.g. E. coli with a dozen new genes), but some will be novel organisms that have never been cultured or sequenced by any lab.

If you’re a scientist, keep that in mind when you are working in this area. If you’re a human, take hope and be encouraged by the fact that there is still a massive undiscovered universe within us, full of potential and amazing new things waiting to be discovered.

Niche Theory and the Human Gut Microbiome

Without really having the time to write a full blog post, I want to mention two recent papers that have strongly influenced my understanding of the microbiome.

Niche Theory

The ecological concept of the “niche” is something that is discussed quite often in the field of the microbiome, namely that each bacterial species occupies a niche, and any incoming organism trying to use that same niche will be blocked from establishing itself. The mechanisms and physical factors that cause this “niche exclusion” is probably much more clearly described in the ecological study of plants and animals — in the case of the microbiome I have often wondered just what utility or value this concept really had.

That all changed a few weeks ago with a pair of papers from the Elinav group.

The Papers

Personalized Gut Mucosal Colonization Resistance to Empiric Probiotics Is Associated with Unique Host and Microbiome Features

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Quick, Quick Summary

At the risk of oversimplifying, I’ll try to summarize the two biggest points I took home from these papers.

  1. Lowering the abundance and diversity of bacteria in the gut can increase the probability that a new strain of bacteria (from a probiotic) is able to grow and establish itself

  2. The ability of a new bacteria (from a probiotic) to grow and persist in the gut varies widely on a person-by-person basis

Basically, the authors showed quite convincingly that the “niche exclusion” effect does indeed happen in the microbiome, and that the degree of niche exclusion is highly dependent on what microbes are present, as well as a host of other unknown factors.

So many more questions

Like any good study, this raises more questions than it answers. What genetic factors determine whether a new strain of bacteria can grow in the gut? Is it even possible to design a probiotic that can grow in the gut of any human? Are the rules for “niche exclusion” consistent across bacterial species or varied?

As an aside, these studies demonstrate the consistent observation that probiotics generally don’t stick around after you take them. If you have to take a probiotic every day in order to sustain its effect, it’s not a real probiotic.

I invite you to read over these papers and take what you can from them. If I manage to put together a more lengthy or interesting summary, I’ll make sure to post it at some point.

Microbiome Research: Hope over Hype

The story of microbiome research is one of hope and hype, both elevated to an extreme and fraught with controversy. You need look no further than popular blogs and high profile review articles to see this conflict play out. 

As a passionate microbiome researcher, I like to highlight the hope that I see -- the hope that humans will be able to understand and harness the microbiome to improve human health. 

The story of hope I have for you today is a story of the heart. The human heart. Well, heart disease. Reducing heart disease, really.

Setup

When I was in graduate school I saw the most fascinating lecture. It was from Stanley Hazen, a researcher at the Cleveland Clinic, and he was describing an experiment in which it appeared that bacteria in the gut were responsible for converting a normal part of our diet into a molecule that promoted atherosclerosis (heart disease). With a combination of (1) molecular analysis of the blood of humans with heart disease and (2) experiments in mice varying the diet and microbes present in the gut, they showed pretty convincingly that bacteria were converting phosphatidylcholine from food into TMAO, which then promoted heart disease (Wang, et al. 2011 Nature). 

Payoff

Fast forward 7 years, and the microbiome research field has advanced far enough to identify the exact bacterial genes involved in this process. Not only that, they are able to inhibit those specific bacterial enzymes and show in a mouse model that levels of harmful TMAO are pushed down as a result (Roberts, et al. 2018 Nature).

Fig. 5: A microbial choline TMA lyase inhibitor reverses diet-induced changes in cecal microbial community composition associated with plasma TMAO levels, platelet responsiveness, and in vivo thrombosis potential. Schematic of the relationship between human gut commensal choline TMA lyase activity, TMA and TMAO generation, and enhanced platelet responsiveness and thrombosis risk in the host

Fig. 5: A microbial choline TMA lyase inhibitor reverses diet-induced changes in cecal microbial community composition associated with plasma TMAO levels, platelet responsiveness, and in vivo thrombosis potential. Schematic of the relationship between human gut commensal choline TMA lyase activity, TMA and TMAO generation, and enhanced platelet responsiveness and thrombosis risk in the host

Bioinformatics Aside

One aspect of this story that I'll point out for the bioinformatics folks in the audience is that the biological mechanism involved in choline -> TMAO is not a phylogenetically conserved one. It is mediated by a set of genes that are distributed sporadically across the bacterial tree of life. For that reason and others, I am a strong supporter of microbiome analysis tools that enable gene-level comparison between large sets of samples in order to identify mechanisms like these in the future. 

Summary

My hope, my dream, is that the entire human microbiome field is able to eventually follow this path. We observe that the microbiome influences some aspect of human health, we identify the biological mechanism responsible for this effect, and then we demonstrate our knowledge and mastery of this biology to such an extent that we can intentionally manipulate this system and eventually improve human health. 

We have a long way to go, but I believe that this is the path that we can follow, the example we can aspire to. I hope that this story gives you hope, and helps cut through the hype. 

Hybrid Approach to Microbiome Research (to Culture, and Not to Culture)

I was rereading a great paper from the Huttenhower group (at Harvard) this week and I was struck by a common theme that it shared with another great paper from the Segre group (at NIH), which I think is a nice little window into how good scientists are approaching the microbiome these days. 

The paper I'm thinking about is Hall, et al. A novel Ruminococcus gnavus clade enriched in inflammatory bowel disease patients (2017) Genome Medicine. The paper is open access so you can feel free to go read it yourself, but my super short summary is: (1) they analyzed the gut microbiome from patients with (and without) IBD and found that a specific clade of Ruminococcus gnavus was enriched in IBD; and then (2) they took the extra step of growing up those bacteria in the lab and sequencing their genomes in order to figure out which specific genes were enriched in IBD. 

The basic result is fantastically interesting – they found enriched genes that were associated with oxidative stress, adhesion, iron acquisition, and mucus utilization, all of which make sense in terms of IBD – but I mostly want to talk about the way they figured this out. Namely, they took a combined approach of (1) analyzing the total DNA from stool samples with culture free genome sequencing, and then (2) they isolated and grew R. gnavus strains in culture from those same stool samples so that they could analyze their genomes.

Fig. 3:  R. gnavus  metagenomic strain phylogeny. 

Fig. 3: R. gnavus metagenomic strain phylogeny. 

Now, if you cast your mind back to the paper on pediatric atopic dermatitis from Drs. Segre and Kong (Byrd, et al. 2017 Science Translational Medicine) you will remember that they took a very similar approach. They did culture-free sequencing of skin samples, while growing Staph strains from those same skin samples in parallel. With the cultures in hand they were able to sequence the genomes of those strains as well as testing for virulence in a mouse model of dermatitis.

So, why do I think this is worth writing a post about? It helps tell the story of how microbiome research has been developing in recent years. At the start, all we could do was describe how different organisms were in higher and lower abundance in different body sites, disease states, etc. Now that the field has progressed, it is becoming clear that the strain-level differences within a given species may be very important to human health and disease. We know that although people may contain a similar set of common bacterial species, the exact strains in their gut (for example) are different between people and usually stick around for months and years. 

With this increased focus on strain-level diversity, we are coming up against the technological challenges of characterizing those differences between people, and how those differences track with health and disease. The two papers I've mentioned here are not the only ones to take this approach (it's also worth mentioning this great paper on urea metabolism in Crohn's disease from UPenn), which was to neatly interweave the complementary sets of information that can gleaned from culture-free whole-genome shotgun sequencing as well as culture-based strain isolation. Both of those techniques are difficult and they require extremely different sets of skills, so it's great to see collaborations come together to make these studies possible.

With such a short post, I've surely left out some important details from these papers, but I hope that the general reflection and point about the development of microbiome research has been of interest. It's certainly going to stay on my mind for the years to come.

Journal Club: Discovering new antibiotics with SLAY

This paper is a bit of a departure for me, but even though it's not a microbiome paper it's still one of the most surprising and wonderful papers that I've seen in the last year, so bear with me.

We're looking at Tucker, et al. "Discovery of Next-Generation Antimicrobials through Bacterial Self-Screening of Surface- Displayed Peptide Libraries" Cell 172:3, 2018. (http://www.cell.com/cell/fulltext/S0092-8674(17)31451-4).

When I first learned about this project, I said, "That's a fun idea, but it won't possibly work," to which the PI responded, "We've already done it." 

The extremely cool idea here was to rapidly discover new antibiotics by quickly screening an immense library of novel peptides. The diagram below lays it out: they created a library of peptides, expressed those peptides on the surface of E. coli, and then used genome sequencing to figure out which of those peptides were killing the bacteria. The overall method is called SLAY, which exceedingly clever.

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While this idea seems simple, there are more than a few parts that I thought would be impossibly difficult. They include (a) building a sufficiently large library of peptides, (b) expressing those peptides on the surface of the bacteria and not inside the cell, and (c) making sure that the peptides were only killing the cells they were tethered to and not any neighbors. 

I won't go through the entire paper, but I will say that the authors ended up doing quite a bit of work to convince the readers that they actually discovered new antimicrobial peptides, and that they weren't observing some artifact. At the end of the day it seems pretty irrefutable to me that they were able to use this entirely novel approach in order to identify a few new antibiotic candidates, which typically takes hundreds of millions of dollars and decades of work. 

In short, it looks like smart people are doing good work, even outside of the microbiome field. I'll definitely be keeping an eye on these authors to see what they come up with next!

Journal Club: Metagenomic binning and association of plasmids with bacterial host genomes using DNA methylation

Amid the holiday rush last month, I was gratified to see a publication describing a new computational method that had been on my mind. 

The paper is "Metagenomic binning and association of plasmids with bacterial host genomes using DNA methylation", published in Nature Biotechnology on December 11, 2017. 

The Concept

Bacteria do something funny to their DNA that you may not have heard of. It's called covalent DNA modification, and it often consists of adding a small methyl group to the DNA molecule itself. This added methyl group (or hydroxyl, or hydroxymethyl, etc.) doesn't interfere with the normal operation of DNA (making RNA, and more DNA). Instead it serves as a marker that differentiates self-DNA from invading DNA (such as from a phage, plasmid, etc.).

For example, one species may methylate the motif ACCGAG (at the bolded base), while another might methylate CTGCAG. If the first species encounters a ACCGAG motif that lacks the appropriate methyl, it treats it as invading DNA and chews it up.

The ongoing arms race of mobile DNA elements has helped maintain a diversity of DNA modifications, such that many different species of bacteria have a unique profile. 

The interesting trick here is that we now have a way to read out the methylation patterns in DNA in addition to the sequence. This is most notably available via PacBio sequencing, which typically will generate a smaller number of much longer sequence fragments than other genome sequencing methods. Bringing it all together, the authors of this paper were able to use the methylation patterns from PacBio sequencing to much more accurately reconstruct the microbes present in a set of environmental samples (such as the human microbiome). 

The Data

The approach of the authors was to first assemble the PacBio sequences, and then bin those larger genome fragments together based on a common epigenetic signature. 

Figure 2c shows the binning of genome fragments in a sample containing a known mixture of bacteria.

Figure 2c shows the binning of genome fragments in a sample containing a known mixture of bacteria.

Figure 2e shows the binning of genome fragments in a gut microbiome sample containing a mixture of unknown bacteria (and viruses, fungi, etc.).

Figure 2e shows the binning of genome fragments in a gut microbiome sample containing a mixture of unknown bacteria (and viruses, fungi, etc.).

In general, this seems like a very interesting approach. After my read of the paper, it appears that the bacteria present in the microbiome contain a distinct enough set of epigenetic patterns to enable the deconvolution of many different species and strains. I look forward to seeing how this method stacks up against other binning approaches in the future. 

Final note

For those of you interested in phage and mobile genetic elements, I wanted to point out that the authors also explore a topic that has been studied somewhat by others – the linkage of phage to their hosts via epigenetic signatures. The idea here is that it can be computationally difficult to match a phage genome or plasmid with its host. One experimental method that accomplishes this is Hi-C, which takes advantage of the physical proximity of the host genome within an intact cell. In contrast, the PacBio method does not require intact cells, and can be used to link phage or plasmids with their host based on a shared epigenetic signature. 

My hope is that this type of data starts to become more widely available. There are clearly a number of computational tools that need to be refined in order to get full use of all this information, but it does seem to hold a good deal of promise. 

Journal Club: A role for bacterial urease in gut dysbiosis and Crohn’s disease

It's nice when you see a new paper describing the type of science that you find the most exciting and relevant, and doubly so when it's written by a group of people that you know and respect. Full disclosure: this paper is from the group that I did my graduate work with at the University of Pennsylvania, and there's quite a lot to talk about. 

Ni, J. et al. (2017) ‘A role for bacterial urease in gut dysbiosis and Crohn’s disease’, Science Translational Medicine, 9(416), p. eaah6888. doi: 10.1126/scitranslmed.aah6888.

Building a model for microbiome research

Before talking about what the authors did, I want to describe how they did it. They started (in a previous study) with a cohort of patients being treated for a common disease with no known cure (Crohn's) and looked for differences in the genomic content of their microbiome compared to healthy control subjects. That initial analysis indicated that one particular metabolic pathway was enriched in patients with the disease. In this study they followed up on that finding by analyzing the metabolites produced by those microbes. The combination of those two orthogonal analyses (genomic DNA and fecal metabolites) provided some suggestion that microbes in the gut were producing one particular chemical which may have a role in disease. To test that hypothesis they moved into a mouse model where they could test that hypothesis with controlled experiments, not only adding and removing microbes but also tracking the passage of metabolites via isotopic labelling. That model system provided crucial data that supported the findings from the human clinical samples – that a specific bacterial enzyme may have a role in human disease. After this study, I can only imagine that the next step would be to move back into humans and see if that target can be used to generate a useful therapeutic. 

The microbiome field is relatively new, and I believe that its first batch of groundbreaking therapeutics is just over the horizon. I wouldn't be surprised if the most influential studies in this field follow a trajectory that is similar to what I describe above: generating hypotheses in humans, testing them in animal models, and moving back to human to test and deliver therapeutics. 

Ok, enough predictions, let's get into the paper.

The Microbiome – Who's Who and What's What

Fig. 2. Associations between bacterial taxa abundance ascertained by fecal shotgun metagenomic sequencing and the fecal metabolome in healthy pediatric subjects and those with Crohn’s disease.

Fig. 2. Associations between bacterial taxa abundance ascertained by fecal shotgun metagenomic sequencing and the fecal metabolome in healthy pediatric subjects and those with Crohn’s disease.

The figure above shows the association between Who's in the microbiome (bacterial genera, vertical axis) and What's (metabolites, horizontal axis) in the microbiome. The point here is that certain microbes are associated with certain metabolites, with a big focus on the amino acids that are being produced. The prior set of experiments suggested that the microbes in Crohn's patients had an increased capacity for producing amino acids, while this figure (and Figure 1) goes further to show that there is a subset of microbes associated with higher actual levels of amino acids in those subjects.

Tracking metabolism in the microbiome

Here's a deceptively simple figure describing a powerful finding. 

Fig. 3. In vivo heavy isotope assays using 15N-labeled urea to determine the effect of bacterial urease on nitrogen flux in the murine gut microbiota.

Fig. 3. In vivo heavy isotope assays using 15N-labeled urea to determine the effect of bacterial urease on nitrogen flux in the murine gut microbiota.

 

This experiment uses radiolabelled urea to measure the production of lysine by microbes in the gut. The central question here is what is producing the extra amino acids in Crohn's disease? In this experiment the authors added radiolabelled urea so that they could track how much lysine was being produced from that urea. Crucially, they found that adding either antibiotics or a defined set of microbes (called "ASF") reduced the amount of lysine that was produced from that urea, which supports the hypothesis that microbes in the gut are directly metabolizing urea. 

Tying it all together

Fig. 6. Effect of E. coli urease on colitis in a T cell adoptive transfer mouse model of colitis

Fig. 6. Effect of E. coli urease on colitis in a T cell adoptive transfer mouse model of colitis

I've skipped over a lot of interesting control experiments so that I could get to the grand finale. Everything I've told you up until now has established that (a) Crohn's patients have higher levels of certain amino acids in their stool, (b) those high amino acid levels are associated with particular bacteria, and (c) bacteria in the gut are able to produce amino acids from urea. To bring it all together the authors went to a mouse model of colitis to see whether adding or removing a single gene would have an effect on disease. They found that E. coli with urease (an enzyme that metabolizes urea) caused significantly more disease than E. coli without urease. This brings it all back to the action of a single gene on a model of human disease, which is the classic goal of reductionist molecular biology, but the gene of interest is encoded by the human microbiome. 

I think that's pretty cool. 

From here it's easy to imagine that people might be interested in designing a drug that targets microbial urease in order to reduce human disease, although that's got to be a pretty difficult task and I have no idea how diverse bacterial urease enzymes are. 

Bringing it back to my first point, this seems like the best case example of how to advance our understanding of the human microbiome with the goal of treating human disease. I hope to see many more like it in the years to come.

Journal Club: Strains, functions and dynamics in the expanded Human Microbiome Project

I must have been really busy these last few weeks to have gone so long without posting about this paper. For anyone interested in the microbiome this is a hugely important paper to read (link). If you want a more comprehensive summary, you can find a few in the popular press

At the risk of rambling on and on, I want to talk about a few things from this paper that really caught my eye. Let's start with Figure 1a.

Figure 1a: Personalization, niche association, and reference genome coverage in strain-level metagenomic profiles. a, Mean phylogenetic divergences17 between strains of species with sufficient coverage at each targeted body site (minimum 2 strain pairs)

Figure 1a: Personalization, niche association, and reference genome coverage in strain-level metagenomic profiles. a, Mean phylogenetic divergences17 between strains of species with sufficient coverage at each targeted body site (minimum 2 strain pairs)

The statistic being plotted is the "mean distance of strains," meaning that they computed the exact genome sequence of each of the strains of all of the dominant organisms in every sample(!) and then calculated how different the strains within each species were between different samples. That process is (in my opinion) a difficult task to pull off well, and I find myself in the position once again of being very much in awe of the Huttenhower group for their skill and hard work. Ok, now how about the biology? This figure tells us that people harbor strains of microbes in their microbiome that are distinct from other people's strains, and that those strains stick around over time to some degree. Not only that, but the degree to which those strains stick around varies by body site, with the stool (and gut) likely having the most persistent set of strains. 

Ok, so now we've covered the fact that people have different strains of the same microbial species in their microbiomes, so let's go into a bit more depth with more of figure 1.

Figure 1, continued. b, Individuals tended to retain personalized strains, as visualized by a principal coordinates analysis (PCoA) plot for Actinomyces sp. oral taxon 448, in which lines connect samples from the same individual. d, PCoA showing niche association of Haemophilus parainfluenzae, showing subspecies specialization to three different body sites. e, PCoA for Eubacterium siraeum. 

Figure 1, continued. b, Individuals tended to retain personalized strains, as visualized by a principal coordinates analysis (PCoA) plot for Actinomyces sp. oral taxon 448, in which lines connect samples from the same individual. d, PCoA showing niche association of Haemophilus parainfluenzae, showing subspecies specialization to three different body sites. e, PCoA for Eubacterium siraeum. 

Let's break this out:

  • Figure 1b: The exact strain of (one of the) bacteria in your dental plaque sticks around from day to day, even though people are (presumably) brushing their teeth!
  • Figure 1d: A bacterial species that is found all over the body, H. parainfluenzae, is genetically distinct (to some degree) by body site. Most intriguingly, it is only partially distinct by body site, which raises all kinds of questions about its evolutionary history.
  • Figure 1e: An organism that we call a single species (E. siraeum) seems to form three completely distinct genetic groupings. Note that the horizontal axis accounts for ~50% of the total genetic variation. That's huge. Is it a single species? Is it three? What is a "species"? Does it matter?

Reflections:

I don't want to try to sum up this paper with a single take-home message. I think this is a paper to read and reread and think about. However there are a few aspects of the methods used that I want to point out for those who may not think about this type of analysis very often. The first is that the authors defined a single strain for each sample (using StrainPhlAn). Do we think that there is only one strain of each species present at a single time? How could we test that hypothesis or even deal with a sample containing multiple, closely related strains? The next is that their most in-depth characterization of strain differences hinged on comparing the samples to known reference genomes. What about the variation that has never been captured in a reference genome? How would we even approach that data?

Lastly I'll say that I really think this work is important because I think that strain level variability in the microbiome is a crucial factor in human health and disease. This paper provides strong evidence that strain level variation is extensive, and the authors have provided powerful tools for characterizing that variation. The next question is, how do we apply this type of data in a way that uncovers the biological mechanisms underlying human health? In other words, how do we use microbiome profiling to generate some experimentally testable hypotheses? It seems so clear to me that this avenue of research is going to uncover important biological mechanisms of the human microbiome, but I can also see that we're going to need some creative, collaborative problem solving in order to realize this system's full potential. 

 

UPDATE:

On October 30, 2017, the Pollard Lab posted a preprint in which they specifically tackle the challenge of analyzing multiple strains per species in a given sample. You can read the preprint here. There's a lot of detail there that really deserves its own blog post, so stay tuned.