Q&A with Xuhang Li and HeFei Zhang

Dr. HeFei Zhang is a postdoctoral fellow and Dr. Xuhang Li is a recently graduated student in Marian Walhout’s lab.

 

Tell us a little bit about yourself. Where are you from? What motivated you to become a scientist and what led you to your current position.

Xuhang Li: Sure! My name is Xuhang Li, but since “Xu” can be tricky to pronounce, I go by Hang. I’m from Xiangyang, an ancient city in central China, about a three-hour drive from Wuhan—a city I’m sure most people have heard of. 🤣

I was drawn to life sciences as a kid - like maybe 12 years old – not for any particular reason, it just felt so interesting to know how life works. This pure curiosity led me to join my high school Biology Olympiad club, where I received my first formal training in biology and earned me direct admission to college as a biology major.

When you’re deeply interested in learning something, the excitement of obtaining new knowledge comes naturally. My curiosity pushed me to join a research lab as an undergraduate, where I started as a research assistant during my very first year. That was the true beginning of my scientific journey.

My journey at UMass Chan has been full of serendipity. When I first came here for graduate school, I expected to work on RNA. My first rotation was in Phil Zamore’s lab, and by the end of it, I asked Phil if I could stay. He told me, “The door is open, but you should explore.” I feel fortunate that I took his words seriously and decided to explore some labs to “just have fun”. I had always been curious about how biology could be described with math and theory but never had the chance to dive into it. That’s when I came across Marian’s lab. I read Safak’s 2016 Cell Systems paper, was completely blown away, and immediately emailed Marian about rotating in her lab. That’s how the magic started.

HeFei Zhang: I was born and raised in a small town in China, where we were taught from an early age to admire the role of scientists in society as a noble and a highly respected career. This mindset deeply influenced me and shaped my aspiration to pursue science as my future path. During college, I developed a profound fascination and a strong passion for biology. I was fascinated by the complexity of biological systems and how they couldn’t be fully explained by the fundamental principles of physics and chemistry alone. This curiosity led me to pursue a PhD in biology at Fudan University.  While I gained a strong foundation in molecular biology during my doctoral training, I noticed that the field was still heavily shaped by methodologies borrowed from physics and chemistry, focusing on single molecules to explain life processes. However, I was drawn to the idea that life is best understood at a systems and holistic level, where interactions and networks come together to create biological functions. This perspective inspired me to join Dr. Marian Walhout’s lab to further my training in systems biology, where I could explore these ideas and develop my expertise in this interdisciplinary field.

Why did you start working on the project(s) reported in your two recent papers? What first drew you to the question(s)?

Fei: I am particularly interested in understanding metabolism at a network level, and my expertise lies in molecular biology. When I joined the lab, I discovered that there were no established experimental approaches for conducting systems-level studies on metabolic networks. Fortunately, I had the opportunity to collaborate with Xuhang, who is an expert in metabolic network modeling. During our discussions, we brainstormed the idea of systematically perturbing each metabolic gene and using RNA-seq to analyze how the metabolic network transcriptionally responds to these perturbations. This approach would allow us to address a fundamental question in metabolism: how the network adapts at a transcriptional level to metabolic changes.

What’s interesting is that this idea didn’t come from strict reasoning or logic—it emerged organically during our discussions. It was a collaborative effort that combined my molecular biology background with Xuhang’s modeling expertise, highlighting the power of interdisciplinary teamwork in driving innovative research.

Hang: As I mentioned, my journey has been full of unexpected turns. Early in graduate school, I did a lot of computational modeling - ranging from pure mathematical modeling of metabolism to large-scale data integration. But my main project at the time didn’t pan out.

That project made me realize that modeling biology purely from first principles was still too premature. However, when we combined modeling with experimental data integration, it worked remarkably well. Since my background was in experimental biology, I started thinking about generating our own data. That’s when Fei joined the lab, and we immediately clicked. We decided to perform large-scale perturbations followed by RNA sequencing.

Initially, our goal was to use this data to study genetic interactions in metabolism—stemming from my failed synthetic lethality project. But as we progressed, we realized this work was revealing a systems-level view of metabolic rewiring. Eventually, it even provided insights into metabolic wiring itself. Although we didn’t start with the intention of solving the wiring and rewiring problem, it was a question I had been thinking about for a long time in metabolic modeling. Once we saw the connection, we formalized it as a full-fledged project.

When you first proposed to perturb every metabolic gene in C. elegans followed by transcriptome analysis to Marian, your mentor, what was her reaction?

Hang: Another fun fact on the journey is that I clearly remember Marian was highly skeptical throughout the early journey but remarkably openminded. Her favorite sentence was:  “I disagree but I am happy to be proven wrong”. And I remember the time we showed her the proof-of-concept data where she said: “you know what – I am all in!”.

Fei: When Marian first heard about this idea, she thought it was crazy, but she also found it exciting—provided we could overcome the technical challenges. She supported us in taking incremental steps to demonstrate that the idea was feasible. Additionally, she connected us with Manuel, an expert in RNA-seq, to collaborate on the project.

In 3-4 sentences, can you summarize the key findings of your work?

Hang: We developed Worm Perturb-Seq, a massively parallel RNAi and RNA-seq technology in C. elegans, allowing us to systematically perturb every metabolic gene in the animal and record the transcriptional responses. Using this dataset, we uncovered the systems-level design principles of metabolic rewiring. Almost like an unexpected gift, we also realized that integrating our data with metabolic network models could reveal how metabolism is wired in the first place—leading to surprising insights, such as the use of dietary RNA as a carbon source. Together, these findings establish a new paradigm for studying metabolism through the lens of genomics, enabling a systems-level understanding of metabolic dynamics.

Fei: Our work introduced a next-generation functional genomics approach called Worm Perturb-Seq, which enables systematic analysis of transcriptional responses to metabolic perturbations. Using this method, we uncovered a design principle for transcriptional metabolic rewiring, termed the Compensation/Repression model. Additionally, the molecular phenotypes generated by Worm Perturb-Seq significantly enhanced our ability to infer metabolic flux at a systems level, revealing unconventional insights into C. elegans metabolism, particularly in central carbon and energy metabolism.

What was the most exciting moment for you, or was there a particular result that surprised you while working on this project?

Fei: We used the Worm Perturb-Seq data to generate testable predictions about the metabolic network wiring in adult C. elegans. The most exciting part was seeing the majority of these predictions validated through isotope tracing experiments! Those results indicated our concept of using molecular phenotypes to predict metabolic network wiring at systems level is really reliable!

Hang: For me, it was when I saw the colored bar graphs illustrating the Compensation-Repression (CR) model. At the time, I was sick in bed with a COVID fever, but as I analyzed the data, I suddenly saw the complex and diverse rewiring interactions fall into a strikingly organized pattern. I knew we had something significant.

I immediately sent the figure to Fei—it was a pure “Eureka!” moment. Honestly, that discovery worked better than ibuprofen.

Collaboration was important for this project, both between the two of you and with other labs. Can you talk about the collaborations and why they were essential?

Fei: Different people excel in different areas, and having partners with skills that complement one another creates a balanced and effective team. For example, I specialize in wet lab experiments, while Xuhang is an expert in computational, or "dry," experiments. Our projects required the seamless integration of both experimental and computational approaches, making close collaboration indispensable for making rapid and meaningful progress.

Hang: This project was extremely interdisciplinary, spanning experimental genomics, bioinformatics, metabolic modeling, and isotope tracing analysis. Beyond that, the sheer scope of work was massive—imagine generating and analyzing 4,000 RNA-seq profiles. This simply couldn’t have been done by a single person.

My collaboration with Fei was particularly unique—we had perfectly complementary skill sets and mindsets, and we operated almost like a single unit. We actively discussed every detail, from experiments to computational analyses, which led us to make the right decisions time and time again. This, along with collaborations with other labs, created a synergy that not only made solving this big question possible but also allowed us to do it at an incredible speed.

In your opinion, what are the most pressing questions in the field right now?

Fei: There are many pressing questions in the field, but I am particularly passionate about exploring tissue-specific metabolism and tissue-tissue coordination through metabolic interactions. While our current work has established a paradigm for understanding metabolism at the cellular and whole-animal levels, it lacks tissue-level resolution. Yet, animals maintain whole-body homeostasis through tissue-specific metabolic functions and inter-tissue communication.

Hang: Broadly speaking, one of the biggest challenges is understanding metabolism in action. We know a lot about the static aspects—what the enzymes are and what reactions they catalyze. But we still know very little about how these reactions and enzymes are dynamically used in vivo, how metabolism adapts to different biological contexts, and how homeostasis is maintained.

Essentially, this is the metabolic wiring and rewiring question. The field is now waiting to explore this question in greater breadth and depth—across different tissues, organisms, and also in humans.

Do you have any advice for young scientists, from undergraduates to postdocs?

Hang: Follow your heart and enjoy the process of doing science.

Fei: Think freely and boundlessly, but act pragmatically and grounded.

What do you like to do outside of work?

Hang: Watching TV with my wife.

Fei: Being together with my family.

And finally, what’s next for you?

Fei: I would like to find a PI position in China and continue my research journal on systems biology.

Hang: Hopefully, I can find an early independence opportunity to start my own lab, bring together a group of young scientists who love science as much as I do, and continue having fun exploring metabolism!