Dr. Ling employs large-scale computational analyses of next-generation sequencing data to identify novel disease mechanisms, with the goal of translating these discoveries to the clinic.

Jonathan Ling, Ph.D. is an Assistant Professor of Pathology at The Johns Hopkins School of Medicine who began his journey at Johns Hopkins as an undergraduate student and continued to pursue his education and career at the university. His research combines bioinformatics and genetic engineering to develop therapeutic strategies for age-related neurodegenerative diseases and monogenic disorders. JHTV had the opportunity to discuss Dr. Ling’s research and beyond.

What brought you to Johns Hopkins University?

I came to Hopkins mainly due to the strength of the biomedical engineering (BME) undergraduate program. In this program you’re able to take courses with many BME professors that are not available to the general undergraduate population. BME allowed me to explore many different disciplines simultaneously and the classes were eye-opening. It was an impossibly wide array of subjects and material that we had to cover, but the BME curriculum was essential for helping me understand the type of science that I enjoyed working with.

I believe that the best science involves some kind of knowledge arbitrage and I’ve always enjoyed combining multiple disciplines together. BME taught me how to model complex systems, what assumptions you could and couldn’t make. Then, through my undergrad research, I was introduced to the idea of combining bioinformatics and wet lab experimentation to model human disease.

After graduating, I joined Phil Wong’s lab for my Ph.D. thesis. We were working on exciting neurodegeneration research, a wetlab/drylab hybrid of exactly what I wanted to do: use bioinformatics to study TDP-43, a protein that becomes dysfunctional in brain diseases such as Alzheimer’s, ALS and frontotemporal dementia. I think many of us assume that it is natural to lose our cognitive function as we grow older, but I believe that these deficits can often be traced to pathological changes that aren’t just irreversible changes due to normal aging.

How and why did you choose your subject matter?

I’ve always been interested in cognition and neuroscience in general. The brain is fascinating, and now with AI reproducing incredible facsimiles of human creativity, storytelling, music, etc. But these approximations are still just that, and while AI is impressive, our cognitive functions are fundamental to how we perceive and interact with the world. I think it’s one of the most important things to safeguard in terms of our health, and this is the major driving force behind my desire to work on preventing cognitive decline.

What are you currently working on?

Amyloid beta is the main target for many immunotherapy drugs being developed for Alzheimer’s disease, but their effectiveness is still under scrutiny, despite some promising results. Many people in the general population have massive deposits of amyloid beta but no cognitive decline. We need more biomarkers to predict the rate of cognitive decline in patients with neurodegeneration, as well as the probability that someone will develop neurodegeneration in the general population.

Recently, we developed a novel biomarker assay that is sensitive to the pathological changes that occurs in brain with TDP-43 loss of function. These warning signals can be found before any symptoms are clinically observed and could serve as important biomarkers for early detection of neurodegeneration. We think that our pre-symptomatic biomarker can also be used as a blood test, which would be extremely beneficial for patient enrollment in clinical trials and help validate therapeutic strategies.

Many of the advances that we’re seeing now in the anti-amyloid field were decades in the making, and the same can be said for our work on TDP-43. Years of basic science research laid out the groundwork for our biomarker discovery and we are now seeing the rewards of our efforts.

Given TDP-43 pathology is found in roughly 50% of Alzheimer’s patients, it is possible that our biomarker approach may be able to help stratify patient populations to identify which patients would benefit the most from anti-amyloid or anti-tau strategies. When one starts to consider the risk/benefit relationship of these treatments, biomarkers such as ours could be very helpful in shifting that balance.

Finally, if you can identify appropriate patients earlier, the potential therapeutic benefit could be even greater. One can imagine potentially incorporating a panel of biomarkers into annual cognitive assessments for at-risk patients, with the goal of pinpointing the optimal time to initiate treatment. We’re beginning to understand the complexity of Alzheimer’s and related dementias and I believe that personalized medicine strategies will undoubtedly be used in the future.

What’s next for your research?

We’ve made significant strides in the research and have filed patents on several technologies we’ve developed, including for biomarkers and gene therapeutic strategies. Fight now, we’re primarily focused on how best to move these forward commercially to provide benefit to patients as soon as possible. We’ve been working closely with JHTV, specifically the Technology Development team, to think through how we move it forward, and they have been great in connecting us with pharma and investors, as well as external mentors to help strategize. There is a lot to consider regarding funding opportunities, navigating the legal issues, and putting in place routine things like NDAs or conflict-of-interest agreements, and JHTV has been extremely helpful in supporting us through these. We’re still figuring out exactly what structure we want to have right now; however, there has been a growing interest in our work, and so the time is now; it is just a matter of what makes the most sense for the technology and getting it to patients as quickly as possible.

Beyond what has been discussed, what does success look like to you?

Success would be a gene therapy for ALS that significantly extends survival periods in patients, and an established biomarker panel that works in the blood that would be available from a primary care doctor’s office. It would be incredibly impactful if we could develop a TDP-43 blood test through which you could identify patients and direct them to the best treatment available — but this is really the 10-year vision.

What has Johns Hopkins University meant for your work? 

I think Hopkins is unique in the sense that it is renowned for its medical school, Hopkins also does truly groundbreaking work in many other disciplines including computer science and engineering. Throughout my entire time at Hopkins, there has been a strong culture of collaboration. At every stage of my career, I’ve been able to meet with anyone just by knocking on their door. At Hopkins, there is such a broad level of expertise that is easily within your reach. Building up these connections has been incredibly important for my career and a key factor in my success.