Insights from our Insides, a Q&A with Dan Knights

Insights from our Insides, a Q&A with Dan Knights

Microbiomes are rapidly evolving, and this computational biologist is out to debug the mystery of why.

Colleen Smith

We are what we eat but there’s also a host of microbes living in our guts that help us make the most of all that food. Computational Biologist, Dan Knights investigates the dynamic and rapidly evolving relationship between humans and the bugs living within.

Just like macro-organisms, bacteria have specific environmental needs — and in the case of gut bugs, that environment is the gastrointestinal tract. Central to the concept of the ‘microbiome’ is that a symbiotic relationship exists between this community of microbes and their human hosts. Yet when conditions change, so do microbiomes, and a lot is changing in our world right now.

Knights’ work focuses on understanding what makes a microbiome healthy, and what drives it towards illness. Read more about how he melds biology with bytes in the Q&A below.

Since microbiomes can contain so many different species at once, how do you collect meaningful information about them?

“The main way that we study microbiomes is to grind up their DNA and sequence it. If you try to grow the bugs, you get an enormous bias towards bugs that grow easily in the lab. For a long time, people thought one of the main gut bugs was E. coli. It turns out E. coli just grows really well in a petri dish. E. coli is in most people, but in a healthy individual, it only takes up about one tenth of a percent. We’ve found that instead, if you grind up the DNA and sequence it, you can capture all of the diversity that’s there.”

As a computational biologist, how does computer science affect your work?

“We spend about half of our time designing experiments, carrying out experiments, and analyzing the data. The other half of the time is spent developing new algorithms. The most exciting parts are when we’re developing new tools to support the experiments we’re running, so we really get synergy between the two disciplines of Biology and Computer Science.”

How do computer algorithms aid your research?

“The first way in which we use algorithms is to go from the raw DNA to understanding which bugs they come from, what they might be doing, or which types of genes they are. After we’ve done that raw interpretation, there’s another set of algorithms that we use to interpret the biological significance of the bugs we find. We want to know which are the good bugs and which are the bad ones, and we want to build a new test that will tell you how healthy your microbiome is, based on the species present within it.”

What does a healthy microbiome look like?

“A healthy gut microbiome has hundreds or thousands of different species living in it, and that’s actually more healthy than one that has 50 or 100 species. The diversity makes our data sets high dimensional, but there’s also high variation in bugs between healthy people, in a certain person over time, or between people with a given disease.

It’s not something like blood pressure, where you have a simple ratio. Instead, there are hundreds of numbers, so it presents a very interesting computational problem. What we’re doing primarily is trying to enable precision medicine with the microbiome. This means being able to tell as precisely as possible which species and which strains are in a person, and then to be able to classify them as being healthy or unhealthy. If a person’s microbiome is unhealthy, we say that it is in a state of dysbiosis.”

Does dysbiosis happen on a case-by-case basis, or is something broader occurring?

“Something broader is happening with modernization. We have cross-sectional data where we can see that people who are in a developing country tend to have significantly more diverse microbiota than, say, people living in the USA. If you can find very rural peoples living in developing countries, their guts are even more diverse. We don’t know what benefits those missing bugs are conferring, but we do have epidemic levels of obesity, diabetes, Crohn’s disease, colitis, asthma, and allergies — all of which are linked to a shift in your gut bugs, and all of which are also linked to increased exposure to antibiotics in kids.

It’s likely that we’re missing some of the bugs that we evolved to have. Which exact ones are crucial, we don’t know, but that’s one of the things that we’re studying in our primate microbiome project.”

What patterns have you found through these projects?

“We found that when we have samples from wild monkeys, two different species of monkey will have completely different gut microbiota. But as soon as they move into captivity, they lose most of their native bugs and acquire modern human bugs.

It seems as though there is an axis of dysbiosis. If you start with wild monkeys and continue along to captive monkeys, the next group you get to is non-westernized humans, and then all the way at the end, the farthest ones from the wild, are Americans. And that’s not where you want to be. Our studies of recent immigrants in Minnesota have confirmed this pattern.”

How could biotechnology help those suffering from dysbiosis?

“One way is to give someone new bugs — i.e. a probiotic — although what people typically think of as probiotics is a very limited set of bugs. You can find thousands of probiotics on Amazon, but if you look at the ingredients, it’s the same small set of strains over and over. We’re talking about a much broader set, so that would include bugs like Faecalibacterium prausnitzii. That’s a good bug. Everybody who’s healthy has it. Every time we study a disease, it’s depleted in people who have the disease or are at risk, and yet you can’t buy it.
It’s not a probiotic yet, but that’s an example of a bug we could potentially use as a therapeutic.

Another way is to give someone prebiotics, which are food specifically for your bugs rather than the bugs themselves. You could supplement with a particular chemical that you know encourages certain types of bugs to grow.

Or, there could be targeted antibiotics that would protect certain members of the microbiome without being as comprehensive or broadly destructive.”

Why is this dysbiosis happening, and how can computational biologists contribute to solutions?

“Everyone’s trying to put the pieces together right now, because it’s all one giant system. You can’t really study human endocrinology without considering the bugs, and you can’t study how the bugs are affecting the human without endocrinology and immunology, so research teams are converging, and projects are getting bigger, more multidisciplinary, and more reliant on computation.

It took decades to build a functioning model of a complete cell, in a very simple organism. To do that for multicellular organisms and bacterial communities is still quite far beyond reach. The golden apple would be to have a full computational model of everything in the human body. Everything you have to do in between where we are now and that point of
the comprehensive model is fair game for computational biology.

Some people are modeling low level physical processes between interacting molecules. Others are a level up from that, measuring the activity of enzymes and how different expression levels change in response to environmental conditions. Then there are people studying communities of cells and cell-cell heterogeneity, and so on up the line. All of those problems  require computational biology.”

Life? There’s a Map for That. Q&A with Chad Myers

Life? There’s a Map for That. Q&A with Chad Myers

How computational approaches are carving out new ways of understanding biological networks.

Colleen Smith

Computational Biologist Chad Myers applies his expertise in leading edge techniques in computer science to the latest genome-scale technologies in order to understand the genetic architecture of life.

Despite an overabundance of genomic data, researchers remain stumped by many basic questions about how genes interact with each other and  the environment to create such astonishingly diverse organisms — and to present such perplexing and difficult obstacles to curing disease states.

Myers’ lab tackles these fundamental challenges by developing new algorithms and computational tools to both map genetic interactions and understand what those interactions mean in model organisms like yeast and worms all the way across the tree of life to humans.

How do you define a genetic interaction?

“If you make mutations in two genes at the same time, and there is an interesting effect on the organism that you couldn’t predict, that’s called a genetic interaction (GI).
It’s like you’re flipping switches in the organism in a precise way, turning off certain components one by one.

The interesting thing is that flipping most of those switches off individually doesn’t have an effect. It’s been 15 years since researchers first knocked out every single one of yeast’s 6,000 genes. They found that yeast only needs 20% of those genes to grow.”

Does that mean most of yeasts’ genes are redundant?

“There are relatively few genes that are redundant in sequence, but they’re functionally redundant. By flipping combinations of switches, you start to see that you need one of two systems, but you don’t need both of them. Our lab has been heavily involved in mapping those types of interactions.”

What types of questions can you answer by mapping out GIs?

“There’s been a decade and a half of mapping projects in different contexts, and this yeast project is one of those. Everyone decided, since we can measure this on a large scale, let’s just measure it, and understand how much noise is in our measurements, with the expectation that it should be valuable. It was kind of like, build it, and they will come.”

After almost ten years, we’ve now measured interactions for about 15 million combinations of genes in yeast, but what exactly does all this data tell us? Well, we’ve learned quite a bit and much of it generalizes to other species.

For example, even though there are only ~1000 ways to kill a yeast cell by deleting single genes, there are ~100,000 ways to kill a yeast cell by introducing combinations of two mutations. This has major implications beyond yeast because it reveals how complex genetic networks are, and helps to put a measure on the expected complexity of these networks.

By mapping GIs on the scale we have in yeast, we now understand many of the rules so that we can map them more efficiently in other species. For instance, genes that tend to have large numbers of GIs also tend to be enriched for certain functional roles, evolve more slowly, and be more conserved across the tree of life.”

What’s the end goal?

“Computational techniques play big roles in the modern era of data-driven biology. First, they are very important in simply collecting reliable data, which often involves a number of computational steps to process and transform the data. After that, ultimately, what we want to do is squeeze some kind of information with predictive value out of the collected data, where again, computational methods are critical. For example, the goal of techniques like machine learning and data mining is to take a huge amount of data and distill it down
to the important nuggets.”

What types of data are computational biologists trying to distill right now?

“The biggest developments in data are driven by genome sequencing technologies, which have been applied in hundreds of ways to measure various aspects of molecular biology in recent years. There are already several thousand species that have their complete genomes sequenced. In humans, for example, there are probably at least a half million genome sequence profiles from a variety of different normal or diseased tissues, many of them public. Despite having all of this data, we still don’t know what half of the genes do, even when they function normally.”

The new way to measure gene expression quantitatively is RNAseq, a sequencing based technique that provides a snapshot of which genes are expressed in which tissues. That data is also huge, with thousands of different expression profiles available for humans or any model organisms, including plants, like maize. This data is really powerful if you want to understand how genes drive the development of tissues, or which genes differentiate between this phenotype or that phenotype.

A lot of questions in biology are related to which genes work together to accomplish things, or which genes are involved in common processes that support life. So, a type of data my lab cares a lot about are network data that can tell us which genes are related. Gene expression information, especially when it’s collected across a variety of different tissues or individuals can be a valuable source for extracting relationships among genes.”

How do you visualize network data?

“Picture a number of nodes, with lines between them. The nodes are genes, and an edge means that when I delete these two genes in combination, it kills the cell.

What’s interesting is that in most cases where that happens, it happens within a larger structure, where there are multiple interactions crossing the two pathways. It’s actually a very highly structured bundle of genes.

It’s hard to look for individual edges because computationally, it’s difficult or impossible to interpret these data at the level of the individual gene pair, but when you integrate the data with our existing knowledge of pathways, the structure emerges. We’ve designed algorithms to find these local structures in the data.”

Does this only work in yeast?

“No, this is not yeast specific — we’ve applied this computational approach in
the context of human Genome-Wide Association Studies and found really interesting results.

For example, you might look at two patients and think they have nothing in common because they have mutations in totally different sets of genes. But if you take a step back and say, wait a second, these different mutations are actually affecting the same two pathways, now you can group together patients that otherwise look different. It’s a heterogeneity problem. There are a lot of different paths to the same exact phenotype.”

Is that only true of disease states?

“A fundamental principle is that biological systems are modular. Very rarely do genes work in isolation. They work as part of larger sets of genes that together accomplish something.”

So, these techniques are enabling you to address deeply cross-cutting questions?

“Yes, we can solve one problem here, but by doing so we might also solve a thousand problems like it in different contexts. We might think about maize today, and how we might improve our ability to feed the world, and then tomorrow think about how to treat cancer. A lot of the same concepts are being used and reapplied across diverse biological settings where the computational approach is the bridge.”

Linda Kinkel, Professor of Plant Pathology, joins BTI

Linda Kinkel, Professor of Plant Pathology, joins BTI

Linda Kinkel’s research focuses on the ecology of microbial communities in native prairie and agricultural soils. Kinkel’s work on the ecology and evolutionary biology of streptomycetes and other antibiotic producing bacteria has potential applications in the management of soil-borne plant pathogens.  Her current research, supported by MnDRIVE, will examine the impact of microbial inoculants and carbon inputs on disease suppression and plant productivity in Minnesota’s potato crop.  Learn more about Linda’s research.
The 3rd Dimension

The 3rd Dimension

BTI Researchers test 3-D printing technology to scale up—and down

Since it first appeared on the market in 1984, 3-D printing technology, also known as stereolithography, has been used to create everything from robotic aircraft to artificial limbs. The technology caught the attention of the media this summer, when reports surfaced that a Canadian man fired 14 shots from a rifle manufactured on a 3-D printer using a design downloaded from the Internet.

The cost of the technology is becoming more affordable—desktop units now range from $250-$2500—and the printers are finding their way into artist studios, research labs, and in some cities, the local Fedex/Kinkos copy shop.

In 2012, BTI members Brett Barney (BTI/Department of Bioproducts and Biosystems Engineering) and Igor Libourel (BTI/Department of Plant Biology) approached BTI director Mike Sadowsky for funds to purchase a MakerBot Replicator 2—a low-cost 3-D printer about the size of bread box. Both saw the potential to advance research and training goals but each had a novel approach to experimenting with the technology.

Barney immediately saw the potential of the technology in the classroom and has created brightly colored, hand-painted models to help explain cell metabolism and metabolic pathways to his students.

Beginning with three dimensional images of proteins from the Protein Data Bank, a repository of structural images for large biological molecules, he uses a variety of

3-D rendering tools to refine his models and build the scaffolding required to support the structure as the printer lays down layer upon layer of ASB thermoplastic, similar that found in Lego® building blocks. After cutting away the scaffolding and the excess material, the molecular models are painted, polished and ready for display.

The initial models took up to 15 hours to print, with several additional hours of detailed work for clean up and finish. With practice, he was able to reduce the printing and clean up cycle to a couple of hours. Once the models are complete, Barney posts the plans, with photographs and annotations, on Makerbot’s open-source repository called Thingiverse. Published under the name MoleculeMaker, Barney’s model of the FeMo Cofactor is available for download by members of the online community.

Barney hopes to print a full model of a photosynthetic reaction center, but also builds partial models to highlight unique characteristics of the molecules he studies. In fact, his lab has used the models to help predict sites for mutagenesis studies in wax ester synthases—enzymes important in the effort to produce biodiesel from a complex biomass such as cellulose. Barney recently published the
results in the Journal of Applied and Environmental Microbiology.

For more about Barney’s 3-D Models see: 

Rapid prototyping and development of miniature bioflow reactors

With an eye toward understanding environmental changes brought about by global warming, the Libourel Lab studies metabolic features of Ostreococcus, a picoalgae genus common in the world’s ocean. Investigating the relationship between metabolic adaptation and climate models, the lab relies on bioflow reactors to manipulate and monitor the organism’s response to evolutionary pressure.

After modifying bench scale reactors with customized hardware and software, Libourel and his students realized they could achieve the same results, using fewer resources, by scaling down. But each bioreactor requires a custom enclosure, which can cost as much as $500 to produce, increasing cost and slowing development. Using the Replicator 3-D printer, Libourel hopes to construct and modify the enclosures which house the circuits, pumps, and fans required to run the reactors. In addition to the cost saving, the rapid prototyping and development will allow the lab focus on what’s happening inside the dish instead of the box.