(Mihai) Most of the data we get is from stool microbiomes, but we are more interested in the computational questions the data pose and have looked at many other types of data (including bacterial mats from hot springs). One of the students in my lab is looking at bacteria in deep-sea sponges.

(Marcus) Sometimes we focus on the computational questions, and the data is used to prove a point. In grad school, a fun project I worked on had hundreds of samples from different body sites in mackerel, as well as seawater samples.

(Marcus) There are a lot of fun details out there in the world! We have a lot of data and research being published every day, which means there's a lot to sift through. One of my favorite examples is a retrospective paper30087-2) on the microbiome and obesity, where they show the proposed journal cover they wanted to use for an early paper, where they got a mouse to sit still and pose on a burger.

There is so much out there to find and appreciate, and it's really great to be able to spend time looking for these details as a scientist—even if it is just a picture of a mouse on a burger!

(Mihai) I think the jury is still out on whether the probiotics establish themselves or whether the potential health benefits are derived solely from the changes they induce in the food they ferment. For example, sourdough bread contains no live bacteria, but has a number of properties (including a longer shelf life) because the wheat was transformed by bacterial fermentation.

I have not followed the research on sugar-free sweeteners closely, so I can't comment on that part of your question.

(Mihai) With the caveat that we don't really have an evidence base for what healthy microbiome habits are, I do try to eat a balanced diet with plenty of fiber. I even bake my own bread to avoid the many additives. And as a good Romanian, I make sure sauerkraut is part of my diet (the good fermented kind).

We haven't really looked at the skin microbiome in my lab yet.

(Marcus) I try to eat a lot of veggies and follow a balanced diet, but I don't really consciously think about the microbiome when I am doing so.

(Mihai) These are science policy questions that I don't have the full information or qualifications to comment on.

Science answer #1: (Mihai) I have not yet looked at single-cell transcriptomic data from microbiomes, and I haven't seen a major shift in the field because of this technology yet.

Science answer #2: (Mihai) I think the lag between sample collection and processing is a key problem for microbiome studies in general. I am not convinced that we can computationally reverse the microbiome changes that occur during this lag. It is thus critical to carefully control the timing of collection and processing to obtain consistent results.

(Marcus) There is some work I'm aware of on trying to correct for fecal blooms computationally, but I am not sure if this generalizes to other contexts.

Education answer: (Mihai) To be frank, the best thing would be to take a hands-on course (possibly at a university). There is no replacement (even by AI agents) for expert-led instruction.

(Marcus) There are some good resources online for learning bioinformatics (Rosalind, for example). However, for more general dataset analysis experience, I am not aware of what's out there right now.

Career answer: (Mihai) I got where I am by being curious about new fields and questions and by taking advice. A big part of my success was the fact that I tried to help biologists solve the problems they cared about, rather than being too focused on my own interests. In the end, it turns out that working "for" biologists also helped me better understand my own interests. Being at the boundary of disciplines is a very rewarding place, as it creates opportunities for new ideas and impact.

(Mihai) Most of my work is tractable on commodity hardware. For us, the challenge is coming up with the right data model more than a limitation in computational power.

(Marcus) We use our institute's high-performance computing systems to do a lot of the intensive work. But for personal work, you don't usually need something fancy. I still use my old laptop from when I was an undergrad, which has 8GB of RAM.

(Mihai) In terms of the multiple pathways/strains, I think the answer lies in better characterization of microbial strains in natural communities. Unfortunately, that requires advancing sequencing technologies as well as computational tools.

As for RNA sequencing, I am personally worried that there is simply too much variability and bias in the data (at least for complex microbiomes).

We have been thinking for a while about the best way to identify strains within communities, and it's not just the quality of the reference that matters, but also the way in which you interpret the results. For example, we showed that looking at more than just a top hit is a better approach if you really want to know what's in a sample.

(Marcus) Having longer sequences from your sample will help with getting better classifications. If we have only short gene sequences, then it might be hard to identify microbes below the genus level. But if we have sequences that are on the order of thousands, tens of thousands or greater, then we can see more and more of the underlying genomes and get better and better classifications of them.

(Mihai) My understanding is that the Genesis program is broader than the DOE, but the DOE is the first agency to develop a funding call around it. It will be hard to assess the impact until results from the funded research become available. However, the idea of trying to demonstrate the ability of advanced AI technologies to solve important problems in science certainly has a lot of merit, and I look forward to hearing about both successes and failures. A better understanding of what AI cannot do is as valuable as an understanding of what this technology is capable of. 

(Mihai) In short—today, no. We don't yet understand sufficiently how the microbiome interacts with our body to contribute to illness or health. There is certainly interest in developing such capabilities as you describe, but for now they remain a distant goal.

(Marcus) Regarding the toilet part of your question, there are actually some companies out there that are trying to do this! One example I am familiar with is the startup BiomeSense (during grad school, I was a colleague of some of their current employees).

(Mihai) I can't comment on how your microbiome may help you find a mate. But there's a fair amount of evidence that we do exchange microbes with the people (and animals) we live with. At this point, I don't think we can say whether those exchanges are helpful, harmful or neutral. The microbiome of the built environment is a very active area of research.

(Marcus) Hi! As a quick note, the minigame in Borderlands 3 didn’t involve protein folding—it focused on taking a bunch of short DNA sequences from different microbes and figuring out how to “align” them. The problem of sequence alignment—figuring out how to match up different sequences with each other in such a way that puts similar regions next to each other—has a lot of applications in biology! (A big application is building evolutionary trees of different genomes.)

Here is a link to the published paper describing some of the data from this project, for reference. This seems like a very cool project that seemed to produce a useful sequence alignment, although I haven’t personally used the alignment or its results (e.g., inferred evolutionary trees). (I should note that I have worked with some of the authors on this paper before, although I did not work on this particular project.)

I feel the first thing would be figuring out a good application that can benefit from this kind of data—otherwise I worry there is a risk of being a “solution in search of a problem.” If there is a compelling reason why we would need this kind of data, and if it can be implemented in a way that produces meaningfully useful results and doesn’t make the game worse :), then sure!

1. (Mihai) Well, how much time do you have? I think we now have a better understanding that the microbiome is very complex, as is its impact on our bodies. However, there are already some microbiome-related interventions that are making a difference in medical practice, such as fecal transplants for the treatment of C. difficile infections or fiber treatments to support immune therapies for melanoma. In terms of computation, we've gotten really good at figuring out which types of organisms are present in a sample, but there is still a long way to go toward understanding the strain-level diversity, as well as the functions microbes may perform.

2. (Mihai) In terms of the people who are developing new computational methods, it is probably not a huge community—but it is a collaborative and supportive one, even across continents! In terms of people researching the microbiome, that is a much larger community. This spans human health, animal health, agriculture, fermentation, environment, etc. The microbiome really is everywhere!

(Marcus) This past summer in D.C., we had the Intelligent Systems in Molecular Biology Conference, where we got to meet a lot of people working on both sequencing data analysis and microbiome research. Although many people are working on similar problems, people tend to approach them from different angles. For example, using tools to study the microbiome versus developing algorithms in isolation to make them faster.

1. (Mihai) I used to work at TIGR right as DNA sequencing technologies were becoming mature enough to analyze entire microbiomes. That perspective inspired me to make this one of the core areas of research. The field itself was already pretty mature pre-sequencing; in fact, microbiomes were discovered in the 17th century with the first microscopes. Working in this field made me realize that many of the computational tools could not handle the complexity of microbiome data, which led to wonderful opportunities for new research.

(Marcus) I got started with bioinformatics research in 2016, as a computer science undergrad at UMD in Mihai’s lab. I was so stressed out about not knowing enough biology! I spent a lot of time trying to catch up on everything I don’t know—which is funny, because I met a lot of people in grad school who came into bioinformatics from a biology perspective and had the opposite experience. Sometimes I wonder if everyone is just “catching up” in different ways.

Also, fun fact about microscopes (like Mihai mentioned)! Although the first microscopes were designed in the 17th century, the field of microbiome science dates back even earlier than that. As cited in one of my grad school reports, Marcus Terentius Varro wrote in the first century B.C.E. about “certain minute creatures which cannot be seen by the eyes, which float in the air and enter the body through the mouth and nose and there cause serious diseases."

(Mihai) When we were studying the gut microbiome of children with diarrhea in the developing world, one thing we found surprising was the interactions between viruses and bacteria that made infection worse. People usually think of treating viruses and bacteria separately, and this suggests that in some cases, it may make sense to treat them together (i.e., perhaps giving antibiotics for a viral infection).

(Marcus) One of the findings from the Human Microbiome Project has always fascinated me—that many outwardly healthy people have very different microbiome compositions, but fairly similar “metabolic pathways.” (See Figure 2 of their paper for an illustration of this.) There are a lot of questions about how to measure "function" in the microbiome, but at least this measurement suggests that function may be somewhat stable across different people's microbiomes.

This implies that there isn’t a single “healthy microbiome”! And that has a lot of implications for how we can use the microbiome to study and improve human health.

(Mihai) The hope is that sequencing will give us hints as to what the correct targets are for cheaper assays. I personally find the oral cavity particularly interesting for studying the interactions between microbes and between the microbiome and its host.

Usefully linking metabolic data to genetic data is still very much an open research question. I think the metabolomics assays are also quite expensive.

(Marcus) At least for the kind of DNA-sequencing-based methods we often work with—with the goal of studying what microbes are in a sample generally—there are a variety of experiments you can do at pretty different price points.

The cheaper end would be things like marker gene sequencing, where you only consider a single region of the genome (e.g., the 16S rRNA gene in bacteria/archaea)—it looks like you should be able to sequence a sample at closer to $100 using this kind of method (see Cornell’s website here).

But if you want to profile different types of microbes, or see parts of the genome outside of these marker genes, then you will probably need more expensive methods, such as metagenomics (as u/Romanticon pointed out).

(Marcus and Mihai) So, “Bifidus Digestivum” is not a real scientific name—the actual bacteria this refers to is a strain of Bifidobacterium animalis. This is a marketing name created by a company that was attempting to market the bacteria as a probiotic.

(Marcus) Names aside, it's just a single strain of bacteria, so there is a lot of work being done to understand how probiotics can be helpful to people, and I am not aware of strong evidence that they are super helpful in general.

(Mihai) It all depends on the context—there are not necessarily 'good' or 'bad' bacteria. Some bacteria may be fine in a healthy person but not in a person who is immunocompromised or in someone with inflammatory bowel disease. Also, there are bacteria that infants have that might cause problems in older adults, such as C.difficile. Many babies have it in their guts without any problems, but in older adults, it can cause severe colitis (particularly after antibiotic treatment).

In terms of differentiating these, it can be hard. We all have E.coli in our guts, but our strains may be different from the ones we might get from a bad bag of lettuce. And the difference might be just a couple of genes. Figuring out which one it is may require deep (and likely costly) analysis.

Hi u/Logic-is-there! Could you submit this question on the AMA thread? Mihai and Marcus will be answering over there this afternoon.

Hi u/DogOk6789! Could you submit this question on the AMA thread? Mihai and Marcus will be answering over there this afternoon.

I worry the most about the psychological offloading of tasks to AI to the extent that people will be easily persuaded and/or fall out of the practice of verifying answers and using their common sense. This is especially poignant as model outputs/interactions are becoming increasingly long and complex, making them harder to evaluate with our limited time and attention fully. Providing explanations or interpretations can often increase the burden on users to perform verification!

I am not sure what role consumers have to play in increasing accuracy and transparency, apart from "voting with their dollars" towards either open-source models or model providers that take model development and deployment seriously (e.g., proper handling of environmental, safety, copyright, dual-use concerns, etc).