Daniel is a postdoctoral scholar in Mike Snyder’s Lab on Stanford campus, working to discover personalized molecular fingerprints across omes, opening up new avenues to define health and disease phenotypes and predict personalized disease trajectories. He received his Ph.D. from Max Planck Institute of Biochemistry/ LMU in 2015, and earned his M.S. at Technical University of Munich in 2010.
Can you tell us a bit about yourself, personally and professionally?
Science makes me a great chef, but not because I follow the recipes… more to that later. I am Dan, a postdoc in Mike Snyder’s Lab. I received my Bachelor and Master’s degree in Molecular Biotechnology from the Technical University of Munich (TUM) in Germany. While I was conducting my master studies, I also worked on a startup in which we developed multi-enzyme complexes from extremophile bacteria to degrade biomass efficiently for use in biotech.
After my studies at the TUM, I joined Matthias Mann at the Max Planck Institute of Biochemistry for my PhD. In the Mann lab, I developed proteomics strategies to investigate neurodegenerative diseases. Since I am a biochemist by training, I started my PhD with some wet lab work but after the first year, I began to operate various mass spectrometers and ended up with complex proteomics datasets. This was a great opportunity to learn and establish data analysis strategies in the field of neuroproteomics. Since then, I love to do both and the best percentage for me is 20% experimentalist, 80% data scientist.
How did you end up here? What first got you interested in genetics and science?
As long as I can think back, I have been curious. There is hardly anything more mind-blowing than thinking about the scale of the universe, or the complexity of life. If you talk to my grandparents, they will tell you about a 5-year old boy who tells everyone that he is going to be an astronaut or scientist (I am not implying that an astronaut cannot be a scientist, but I did not have such a nuanced view on job opportunities back then).
Science provides us with a unique perk that allows seeing so much more of the world. If you look at the night sky, you see hundreds of stars. Isn’t it fascinating that the photons hitting your retina started their journey millions of years ago, which means you look back into the past on the very same scale? These photons excite molecules in your Rod cells, starting a signal transduction cascade giving you the feeling of wonder and excitement in the first place. On top of that, some other molecular processes make you think about that or type these words… For me, that experience is addictive and I am glad that we live in a time and socioeconomic context where we have access to the tools to explore the beautiful complexity of nature.
Beyond that, I quickly realized that reason and science are the best tools to fix problems and will be key to making the world a better place. There is a nice quote from Neil deGrasse Tyson that brings it to the point:
“Any time scientists disagree, it’s because we have insufficient data. Then we can agree on what kind of data to get; we get the data; and the data solves the problem. Either I’m right, or you’re right, or we’re both wrong. And we move on. That kind of conflict resolution does not exist in politics or religion.”
Can you tell us about your current research and what you hope to achieve with it?
An asteroid is about to hit Earth. Why is it useful to know that 10 years ahead, instead of one day before it happens? First, knowing it 10 years in advance gives us more time to do something about it. Second, while we would need to massively interfere with its trajectory a day before it impacts, it only takes a gentle poke 10 years earlier to change its path by an iota of a degree to prevent a disaster.
I am convinced the same holds true in biomedicine. Think about devastating chronic diseases, such as diabetes or Alzheimer’s: When a patient shows symptoms, the damage is done. We cannot cure it. What if we could identify and understand molecular deteriorations 10 years earlier and a minor, personalized intervention is sufficient to prevent the diseases from happening?
This is my research mission. I develop biochemical, mass spectrometry, and computational strategies to characterize thousands of molecules from small amounts of biological samples (e.g. blood) to discover and characterize molecular alterations to predict disease trajectories at an early stage. I have been working for 7 years in proteomics and when I joined Stanford, I wanted to expand my omics expertise with another molecular layer. That is why I am now responsible for the lipidomics in the lab and I explore the function of lipids in human physiology and their role in diseases.
Lipids are a complex, yet largely unexplored, molecular family with thousands of individual species. A growing body of evidence suggests that lipids are involved in a variety of physiological processes beyond energy metabolism. This includes key roles as molecular precursors for hormones, structural elements, transporters, or signaling molecules. Alarmingly, more than 70 % of adults show abnormal lipid metabolism (dyslipidemia), which results in an altered lipid concentration in the blood and is associated with chronic metabolic conditions, including diabetes and cardiovascular diseases.
When I started in the Snyder lab a year ago, I established a targeted lipidomics pipeline. I spent the first months optimizing biochemical protocols and the mass spectrometer to extract, identify, and reliably quantify lipids from bio specimens. Now, we can measure close to 1000 lipids across 100 samples in less than 2 days.
Since then, my focus has been to develop and employ computational strategies to interpret alterations I observe in the lipidome; for instance, comparing healthy and pre-diabetic people performing an exhaustive cardiopulmonary exercise. To this end, I integrate: i) known biological functions of individual lipid molecules; ii) specific biochemical properties (including the degree of saturation, fatty acid chain length etc.); and iii) information on how these lipids connect to other molecules, such as processing enzymes.
All post images and figures provided by Daniel Hornburg.
Ultimately, we aim to identify not only early biomarkers but also to further our molecular understanding of disease mechanisms by being able to follow disease trajectories over time, on a personalized level.
Briefly, what’s the coolest thing about your work?
I think it is pretty cool to monitor thousands of molecules from a small drop of blood. In particular, I like our inverted research approach. Instead of starting with a narrow hypothesis, I observe complex molecular changes through mass spectrometry and interpret these alterations, using tailored computational strategies. Based on that data, I formulate a falsifiable hypothesis that I can test in close collaboration with a great team of scientists from various disciplines. Another cool thing is the coding itself. I can code from anywhere. I recommend the combination of laptop, hot coffee, and ocean view. 🙂
Were there people (or one person) in particular to whom you would attribute your professional success?
That starts with my family that never got tired of supporting curiosity and exploration. Beyond that, I have been blessed to work with great scientists like Matthias Mann, Felix Meissner, Jurgen Cox, Dieter Langosch, Harald Luksch and, of course, Mike Snyder. Moreover, throughout my career, I worked with friends at the same career stage. All these interactions contributed, and are still contributing, to my personal and professional development and I wouldn’t be where I am without them.
Can you speak a bit to the role you see CEHG playing on Stanford campus?
I am a passionate advocate of interdisciplinary and collaborative research for two reasons. First, it is undoubtedly more fun and helps you get through all the ups and downs. Second, having many brains working on a problem makes it more likely to come up with a solution.
I think research will increasingly depend on collaborative efforts. For instance, if you want to establish a molecular landscape of health this cannot be done without a collaborative interdisciplinary team. Thus, fostering a collaborative scientific network is one key ingredient for success. That said, I think we have to rethink some of the core policies in academia in order to provide the optimal environment to motivate flourishing collaborations across large teams. This is one of the major challenges academia faces, which is why I have expressed my opinion on authorship policies in a blog and correspondence.
What advice would you offer to other grad students or postdocs who are considering pursuing a similar educational and career path as you?
If it is applicable to your field of research, I recommend becoming proficient at both, biochemistry/biology and data analysis. An in-depth biochemical understanding is key to devising and interpreting lab experiments. In turn, knowing how to deal with the data helps you in designing the right experiment in the first place and efficiently analyzing and disseminating your results. Moreover, more often than not, experiments fail and we don’t even know why since the underlying variables of the biological system are underdetermined. This will be frustrating from time to time. It can be very refreshing to work on bioinformatics. Coding is more deterministic and each hour you invest, you learn and/or progress.
Finally, always remember what you like and what you don’t like in science (and life). You might not be able to make a change right now, but the time can come when it is your responsibility to remember and be that great mentor or the devoted voice to advocate for improving our (scientific) community.
What are your future plans? Where do you see yourself professionally in the next 5 or 10 years?
In the long run, I woud like to lead an interdisciplinary and collaborative team that expands our understanding of the biomolecular world and makes a difference. Whether this is in academia, a start-up or industry depends on how things are developing. I keep an open mind and explore opportunities across these three fields. Conceptually, I love academic science and teaching, but success in academia involves many random variables such as: Are you lucky with getting positive results in a timely manner that will result in papers with the “right” author position? I‘ll reflect on my opportunities and select for inspiring environments, cool people and exciting topics. For me, that is the best way to be happy and successful and there are usually more opportunities than there is time.
Tell us what you do when you aren’t working on research and why. Do you have hobbies? Special talents? Other passions besides science?
I love doing sports like jujitsu and biking. This is a great way to free my mind. I also like cooking. The “special talent” in that context is that I can endure hunger for hours. This is important: It motivates me to get started and gives enough time to craft a delicious meal. Perhaps delayed gratification is something you acquire during a scientific career.
Besides that, I am a passionate advocate for science and reason. I am convinced that science provides the best toolset to learn from the past, understand the present and shape the future and, thus, is useful beyond academic research.
the CEHG blog 
Great article Daniel! Written beautifully, as always!