Dr. Michelle Harwood, Bioinformatics Scientist at Roche, describes key findings from a study on allele-specific expression, uncovering its implications in biomarker discovery, as well as sharing her experience working in the industry.

Amy Li and Nasim Azizi

The possibility of two people carrying the same genetic mutation, where one develops a disease, while the other remains completely healthy, seems impossible – but it is not. Why could that be? For years, scientists emphasized identifying harmful genetic variants, but recent research reveals that simply carrying the mutation does not paint the full picture¹. In fact, what scientists have found is that one version of a mutation in a gene may be expressed more than the other. This phenomenon is known as allele-specific expression (ASE), which acts as a soundboard, with each gene having its own volume setting and adjusting how much a certain mutation is expressed (Figure 1)¹.

But what exactly controls ASE? Does recombination, the rearrangement of genetic material across chromosomes, contribute to ASE? These were the questions that Dr. Michelle Harwood sought to address during her PhD, aiming to improve our understanding of how ASE influences the risk and severity of diseases.

Education and Early Career

 While pursuing her undergraduate degree in Biology at Queen’s University, Dr. Harwood took on a summer research position at the Lougheed lab—a pivotal moment in her journey. There, she developed an appreciation for conservation genomics as she collected environmental DNA samples to survey turtles, preserved different types of snakes to map their diversity across Ontario, and led a pilot study to track ticks near Queen’s University Biological Station. Captivated by the work, she extended her involvement, choosing to conduct her fourth-year thesis in the same lab. For her thesis, she analyzed the population structure of polar bears in the Canadian Arctic, investigating how climate change was impacting them. Additionally, she contributed to developing a new non-invasive method for quantifying DNA from polar bear fecal samples. Her exposure to population genetics sparked a curiosity about its connection to human health and disease, leading her to Dr. Philip Awadalla’s lab for her PhD, where she transitioned from studying wildlife genomics to human genetics.

Unraveling the Link Between Recombination and ASE

Dr. Harwood’s work on ASE began when researchers in Dr. Awadalla’s lab were uncovering clues about how ASE differs between individuals. The lab had access to CARTaGENE, a large population health cohort in Québec, offering a unique opportunity to explore ASE in depth^3,4. In this cohort, Dr. Harwood focused on 884 individuals of multiple ancestries from three major Québec cities, quantifying ASE using advanced sequencing techniques and publicly available reference data for tissue expression (Figure 2)⁵. While these tools allowed her to measure ASE across different genetic variations and tissues, something was still missing. A key piece of the puzzle, recombination, had already been studied in the lab, but no one had yet connected it to ASE. This fueled a question in Dr. Harwood’s mind: “How can we actually intersect ASE and recombination, [given the] interesting insights already demonstrated with recombination and deleterious mutations, [while] ASE was beginning to be explored from the same data?” Determined to find out, she set out to uncover the mechanisms linking these two processes, ultimately revealing new insights into how human genetic variation is shaped.

A major finding in her work was how recombination modulates ASE, offering protection against harmful mutations. Interestingly, this discovery reinforces the findings of a previous paper in the Awadalla lab where Hussin et al.⁶ found that deleterious mutations tend to accumulate in low recombination regions. This is consistent in these regions because the lack of recombination makes it harder for natural selection to eliminate deleterious mutations (Figure 3)⁵. These mutations then stay linked to other genes and ultimately do not get weeded out by natural selection. Digging further, Dr. Harwood then sought the burning question: Why do some people develop disease from a genetic mutation while others remain completely healthy? Her findings suggest that the answer lies in ASE. In regions of high recombination, regulatory variants can act as a protective mechanism by reducing the expression of harmful mutations⁵. This means that if someone inherits a disease-causing mutation along with a protective regulatory variant in these regions, they may stay healthy⁵. However, in low recombination regions, there is less genetic diversity, making protective regulatory variants less common. As a result, harmful mutations in these regions are more likely to be highly expressed, increasing disease risk⁵.  For Dr. Harwood, seeing this theory play out in the data was a pleasant surprise as it strengthens the relationship between recombination and ASE regulation of deleterious mutations.

Transforming Precision Medicine with ASE: What’s Next?

When asked about areas of future exploration in ASE, Dr. Harwood mentioned using ASE in developing biomarkers and integrating them with precision medicine. By analyzing ASE across individuals, researchers may develop more tailored and effective treatments. Dr. Harwood emphasized that this approach is relatively new as traditional methods mainly focus on methylation profiles, single-nucleotide polymorphisms, or total gene expression. However, by integrating ASE and its connection to recombination into these models, more novel and precise insights into disease mechanisms and treatment responses can be provided.

Another potential area of research is the use of ASE in oncology, where identifying allele-specific biomarkers could improve cancer detection and prognosis. Dr. Harwood highlighted that, “not only does [the presence of a] cancer-associated mutation matter, but whether that person has over or under-expression of that mutation may also matter.” Therefore, future therapies could be tailored to not only target genetic mutations, but also to account for how these mutations are expressed in different genomic contexts, leading to more refined and personalized treatment strategies. Furthermore, determining changes in ASE between different cancer mutations can pinpoint differences in treatment response while potentially influencing disease progression.

 While these insights are promising, further research is needed to effectively apply ASE data within clinical settings. Dr. Harwood reflected, “the results that we showed, although are significant statistically, [require] a lot more work to prove in a specific disease context.” This indicates that while the importance of ASE was proven in multiple cohorts, there is still a need for experimental and clinical validation before pharmaceutical companies can consider ASE-based biomarkers in drug development. These key considerations, integral for pharmaceutical companies, are now central to Dr. Harwood’s professional career.

Venturing Into Industry

Dr. Harwood now applies her expertise at Roche, where she works as a Bioinformatics Scientist. Dr. Harwood operates in the Translational Research Bioinformatics group, where she specifically develops bioinformatic assays and workflows that are directly involved in improving human health. One aspect of her work that she enjoys is its direct clinical impact and emphasis on teamwork. Many professionals who have moved from academia to industry share this perspective, as they gain a broader understanding of their work and how different teams collaborate toward a common goal. In an industry setting, team members also have equally vested interests, fostering an environment where everyone is eager to share their insights and consider diverse perspectives. Through these aspects of working in the industry, Dr. Harwood can clearly envision how her projects will be used to improve patients’ lives. An example of this is the development of assays for identifying small numbers of cancer cells remaining after treatment. These assays are designed to detect circulating tumour DNA (ctDNA), a cancer biomarker, in the blood of cancer patients. CtDNA is a booming field in cancer diagnostics with the potential to detect cancer relapse earlier while reducing the need for frequent scans, which can be less sensitive and harmful to the patient. The use of ctDNA as a cancer biomarker is also widely explored in academia, with Wong et al.⁸ discovering that ctDNA can be used

for early cancer detection in Li-Fraumeni syndrome. Hence, seeing this concept cement itself at Roche highlights the crucial role of research and development, as well as commercialization, in bringing diagnostic tools to the bedside. Having experienced both ends of academia and the commercial sector, Dr. Harwood offered unique insights into navigating both worlds.

Lessons from the Frontlines of Genomics

Dr. Harwood recounted several challenges during her PhD and how she overcame them. At the beginning of the project, Dr. Harwood recounted the initial exploratory phase as she struggled to find a direct research question, often going in circles and asking a lot of questions. Rather than getting overwhelmed by the study’s complexities, Dr. Harwood emphasized how important it was to “let the data lead you.” Ultimately, she trusted that the data accurately reflected patient information, allowing her to stay grounded in the evidence. This allowed her to approach the analysis with an open and objective mindset, realizing that the research question became clearer as patterns naturally began to emerge. Along with the ambiguity in the initial exploratory phase, Dr. Harwood struggled to navigate the complications of large patient datasets. Given the substantial amount of time that programming code may take to run, careful thought needs to go into parsing through datasets in an efficient and accurate manner. Although Dr. Harwood faced many hardships during her PhD, she gained valuable lessons along the way.

Reflecting on defining moments in academia, Dr. Harwood recalled gaining a deeper appreciation for her work. While the first two years of her PhD posed to be a huge learning curve, it was not until her third year that she became reinvigorated in her research again. After receiving opportunities to attend talks, presentations, and conferences, she realized that people were not only listening to but also understanding her work. This moment of clarity made her recognize how worthwhile research can be, realizing that “[she] may struggle initially to form the ideas, but eventually proving that they are valid ideas.” These sentiments are not exclusive to her PhD chapter as she also gained knowledge from working in the industry, leaving her with lots of wisdom to share.

Towards the end of the interview, Dr. Harwood detailed essential skills and advice that were instrumental to her success in both academia and industry. In hindsight, Dr. Harwood realized that she benefitted greatly from working at the Ontario Institute for Cancer Research during her PhD. While she did not directly work on oncology projects at the time, she stated that “even just being surrounded by those kinds of projects and conversations on a daily basis in my PhD were very helpful when I’m now working on related projects.” From this experience, Dr. Harwood emphasized the ability to apply what is learned from one context into another as this is a relevant skill in all different career stages. As a result, her exposure to different fields outside of her PhD helped her later in her career at Roche, encouraging students in academia to participate in learning experiences outside of their domain of expertise. Lastly, Dr. Harwood stressed the importance of having a strong appetite to learn and adapt quickly. Echoing advice she received from someone during her PhD, Dr. Harwood expressed how alongside PhD students being expected to become expert learners in their field, they also become excellent problem solvers. Dr. Harwood underscored the essence of problem-solving, emphasizing that the most important skill graduate students develop is the ability to say: “I don’t know the answer, but I know exactly where to go to find the answer.”

References

1. St. Pierre, C. L. et al. Genetic, epigenetic, and environmental mechanisms govern allele-specific gene expression. Genome Res. 32, 1042–1057 (2022).
2. Kukurba, K. R. et al. Allelic Expression of Deleterious Protein-Coding Variants across Human Tissues. PLOS Genet. 10, e1004304 (2014).
3. Dummer, T. J. B. et al. The Canadian Partnership for Tomorrow Project: a pan-Canadian platform for research on chronic disease prevention. CMAJ Can. Med. Assoc. J. 190, E710–E717 (2018).
4. Awadalla, P. et al. Cohort profile of the CARTaGENE study: Quebec’s population-based biobank for public health and personalized genomics. Int. J. Epidemiol. 42, 1285–1299 (2013).
5. Harwood, M. P. et al. Recombination affects allele-specific expression of deleterious variants in human populations. Sci. Adv. 8, eabl3819 (2022).
6. Hussin, J. G. et al. Recombination affects accumulation of damaging and disease-associated mutations in human populations. Nat. Genet. 47, 400–404 (2015).
7. Peñalba, J. V. & Wolf, J. B. W. From molecules to populations: appreciating and estimating recombination rate variation. Nat. Rev. Genet. 21, 476–492 (2020).
8. Wong, D. et al. Cell-free DNA from germline TP53 mutation carriers reflect cancer-like fragmentation patterns. Nat. Commun. 15, 7386 (2024).

Dr. Hannes Röst investigates how multi-omics, the integration of proteomic and metabolomic profiling, can shed light on the development of diabetes and inform personalized medicine decisions in the future.

Priyal Bhavsar, Erika Tvaskis, and Yusra Khan

Imagine a healthcare approach that reaches beyond genetics, providing a real-time snapshot of biological processes by analyzing proteins, metabolites, and cellular interactions. This is the promise of multi-omics, an emerging field that integrates genomics, proteomics, and metabolomics to offer a more comprehensive understanding of health and disease. As Dr. Hannes Röst explains, “the genome provides a blueprint, but we also need to understand the active processes happening in real time.” With the limitations of single-omics approaches becoming increasingly apparent, multi-omics is ready to advance biomedical research and precision medicine.

Dr. Hannes Röst, an Assistant Professor at the University of Toronto and Canada Research Chair in Mass Spectrometry-based Personalized Medicine, is at the forefront of developing new methods to analyze biological data. His research focuses on improving mass spectrometry techniques, specifically in proteomics and metabolomics, to better understand human health and disease. However, what makes his work truly groundbreaking is his approach to integrating multiple layers of biological information through multi-omics methods. While genomics has revolutionized medicine, it only tells part of the story. Dr. Röst acknowledges the need for connecting the dots between different layers of biology to explain how diseases develop and improve diagnoses. He highlights that “we don’t have a very good model for understanding how we go from genotype to phenotype. There are many missing steps in between that we cannot accurately model, and we don’t fully understand.” This is where investigating proteins, metabolites, and other small molecules, for the purpose of multi-omic analysis comes into play.

The Building Blocks of Multi-Omics

Proteomics, the study of proteins, aims to characterize the flow of cellular information through protein pathways and networks¹. By characterizing protein levels and activity, proteomics significantly aids in understanding disease mechanisms for the development of personalized therapeutics^1,2. Dr. Röst explains that usually the correlation between protein expression and [RNA] transcript levels is not perfect, especially in higher eucaryotes. While a gene can be transcribed into RNA, it may not necessarily be translated into a functional protein in significant amounts. This discrepancy can lead to misleading conclusions if researchers rely solely on RNA expression data without considering the actual protein levels. On the other hand, metabolomics refers to the study of metabolism byproducts, which mainly focuses on the analysis of small molecules including sugars, lipids, and amino acids³. Metabolomics provides functional insights into cellular metabolism and dynamic changes in metabolic pathways, complementing proteomics by revealing outcomes of protein activity and environmental responses (Figure 2)⁴.

Multi-omics is the integration of different biological data layers including genomics, proteomics, and metabolomics, to better understand how complex systems function. While genomics shows the genetic blueprint, proteomics and metabolomics show real-time activity of proteins and metabolites in the body. By combining these layers, multi-omics provides a more complete and dynamic view of health and disease than single-omics approaches alone, making it particularly useful for studying disease progression and treatment responses. Within diabetes research, Dr. Röst explains that blood glucose and insulin levels, key metabolic markers, offer far more actionable insights into disease state than genetic risk factors alone. He uses mass spectrometry, a technology allowing for the precise and high-throughput measurement of proteins, metabolites, and other small molecules from biological samples, to explore the molecular signatures of diabetes.

For years genetics has been at the center of biomedical research, but scientists are increasingly realizing its limitations. “If you think about diabetes, it’s useful to know the genomic background and whether somebody is predisposed,” says Dr. Röst, “but what doctors actually care about is the patient’s current control over their blood sugar.” In other words, while genes can tell us about risk, they don’t always reflect real-time health conditions, which is the focus in healthcare.

Uncovering the Path from Gestational Diabetes to Type 2 Diabetes

One of Dr. Röst’s current research interests is unraveling the hidden connections between gestational diabetes mellitus (GDM) and type 2 diabetes mellitus (T2D). During pregnancy, the body naturally becomes more resistant to insulin, the hormone that helps regulate blood sugar (glucose)⁵. In certain cases, the pancreas is unable to produce more insulin to counter this resistance, leading to elevated blood glucose levels and a diagnosis of GDM⁵. For some women, however, this problem does not simply resolve after childbirth. “We now know that about 30% of women will transition to type 2 diabetes after a gestational diabetes pregnancy, but we don’t know which ones,” Dr. Röst explains. Both GDM and T2D are complex, polygenic diseases influenced by both genetic and environmental factors, as well as their interactions^6,7. While genetic predisposition plays a role, it is not fully understood and, on its own, is insufficient for predicting which women post-GDM pregnancy may progress to T2D^6,7. This uncertainty has left a critical gap in identifying who is most at risk and preventing disease progression, urging Dr. Röst to work with methods that “are close to the phenotype and can also disentangle [the effect of environmental] components” (Figure 3).

Seeking early indicators of T2D risk, Dr. Röst and collaborators conducted a case-control study, employing integrated multi-omics to track the biological shifts occurring in the critical postpartum window⁷. By comparing the omic profiles of pair-matched women at 6-9 weeks postpartum (baseline), those who later developed T2D (case) versus those who remained diabetes-free (control), the study identified key molecular signatures of early disease onset (Figure 3). Even at baseline, subtle biological differences had already emerged. Women who eventually developed T2D were significantly more likely to exhibit glycemic irregularities, higher insulin resistance, and lower insulin sensitivity, suggesting that the development of T2D was already in motion. Additionally, untargeted mass spectrometry revealed 21 differentially expressed proteins between cases and controls. These proteins pointed to dysregulation in pathways related to wound healing, blood clotting, and inflammation, further highlighting that the body’s recovery process is imbalanced in those on the path to T2D. Interestingly, the timing of T2D onset appeared to align with different biological patterns: women who transitioned to T2D within the first year of postpartum showed stronger activation of wound-healing and blood clotting pathways, while those who developed T2D later (1-4 years postpartum) exhibited more pronounced inflammatory and metabolic signatures⁷. These early indicators, present at just 6-9 weeks postpartum, could be the key in early identification and research into disease prevention strategies.

Driving Single-Omics & Multi-Omics from Bench Side to Bedside

For complex diseases with a strong environmental component, such as T2D, single layer profiling presents new opportunities for early diagnosis and prevention. Dr. Röst emphasizes that mass spectrometry can identify proteins, metabolites, and other molecules linked to disease risk or progression, such as the 21 proteins differentially expressed in women who later develop T2D. Once these biomarkers are identified, specific antibodies can be developed, enabling the use of enzyme-linked immunosorbent assays (ELISA) as a low-cost and scalable clinical tool. It is “often that the very expensive and thorough approaches that we can do in scientific research are probably not the ones that we can afford to do on a population level”, Dr. Röst notes. Thus, this two-tiered strategy: using single layer profiling for discovery and targeted, cost-effective methods for clinical applications, offers a promising model for translating research into routine care.

Using omics-based methods in research to identify biomarkers for disease prognosis and diagnosis is just the beginning of what multi-omics profiling can uncover. As Dr. Röst notes: “we would want to be in a situation where we can do all high throughput methods all the time on every single patient”, underscoring the potential of a multi-omic approach, where genomic, proteomic, and metabolomic data are obtained from patients to ultimately guide personalized treatments in the future. When Dr. Röst and his team combined data from multiple biological layers for multi-omic analysis of women who transition into T2D, they found that these systems appeared to be interconnected⁷. Some of the same proteins that were linked to inflammation were also tied to changes in fat metabolism, suggesting a feedback loop between the immune system and metabolism. Together, the findings point towards a quiet imbalance persisting in the body after a GDM pregnancy, where residual inflammation and metabolic stress fuel each other to gradually lead to T2D⁷. These insights could help identify women at risk and open new doors for prevention during the postpartum period. Overall, this recent work highlights the importance of studying the proteome and how it interacts with the other molecules in our body. Ultimately, conducting multi-omic analysis on each patient would enable scientists to gain a deeper understanding of the pathways involved in complex diseases.

At a patient level when integrating multi-omics based analyses into routine clinical visits, there is still a long way to go. Despite the strong potential of multi-omics as an approach, it is not widely used in clinical settings because of logistical and financial constraints.

Overcoming the Challenges of Multi-Omics

There are some challenges that arise when integrating the genomic profiles with other omics data. Dr. Röst finds that the lack of robust models and poor understanding of cellular regulation leading to protein and metabolite expression patterns adds to the complexity of conducting research in the multi-omics field. Although multi-omics methods aim to capture the biological complexity of human disease systems, the biological interactions between omic layers are poorly understood and difficult to model. Developing clear computational models relating genetic and transcriptional information to generate multi-omics data requires missing information about regulation on the transcription, translation and protein degradation level. To be able to interpret multi-omics results and develop diagnostic and prognostic models based on unique disease signatures, understanding the omics interactions is crucial.

Cost and reproducibility are also big factors influencing the clinical implementation of multi-omics based methods for disease diagnosis and management. Expensive equipment such as mass-spectrometers, computational resources, and the expertise needed to process and interpret the data, all come at a cost. To bring it to a large-scale diagnostic screening level, Dr. Röst also emphasizes that “a big part that needs to happen in the future is making the method more reproducible”. When considering the development of clinical tests for diseases, it is not always the most economical to conduct whole genome profiling for all patients due to the high error rates, incidental findings, and cost. In Dr. Röst’s lab, he and his team aim to drive down costs and save resources in multi-omics based analysis methods through the development of computational resources.

Shaping the Future of Multi-Omics

With the integration of mass-spectrometry data with other multi-omics data, Dr. Röst believes that “we are very far away from doing a [multi-omics] profile and then handing it back to the patient and expecting them to understand what is going on”. On the research side, proteomics experts like Dr. Röst continue to use our understanding of the connections between genetics, transcripts, proteins and small molecules to derive full picture models of what is at play in each patient. Labs like Dr. Röst’s recognize the importance of dynamic molecular interactions, and its integration with genomic methods for complete multi-omics analyses. Our current understanding of the interactions between biological layers is very limited and complex, requiring a strong foundation to be set by the bench side before it is brought to the bedside for personalized medicine.

The use of proteomics and metabolomics will hopefully become more widespread as more researchers utilize multi-omic methods to guide hypothesis generation. As medicine advances, it is clear that single-omic approaches simply do not provide the full picture. Using multiple approaches together will be the standard of medicine to put together the puzzle pieces of human health.

References

1. Petricoin, E. F., Zoon, K. C., Kohn, E. C., Barrett, J. C. & Liotta, L. A. Clinical proteomics: translating benchside promise into bedside reality. Nature Reviews Drug Discovery 1, 683–695 (2002).
2. Hanash, S. Disease proteomics. Nature 422, 226–232 (2003).
3. Chen, Y., Li, E.-M. & Xu, L.-Y. Guide to Metabolomics Analysis: A Bioinformatics Workflow. Metabolites 12, 357 (2022).
4. Schmidt, D. R. et al. Metabolomics in cancer research and emerging applications in clinical oncology. CA: A Cancer Journal for Clinicians 71, (2021).
5. Sweeting, A., Wong, J., Murphy, H. R. & Ross, G. P. A clinical update on gestational diabetes mellitus. Endocr. Rev. 43, 763–793 (2022).
6. Jääskeläinen, T. & Klemetti, M. M. Genetic risk factors and gene-lifestyle interactions in gestational diabetes. Nutrients 14, 4799. doi: 10.3390/nu14224799 (2022).
7. Van, J. A. D. et al. Postpartum defects in inflammatory response after gestational diabetes precede progression to type 2 diabetes: a nested case-control study within the SWIFT study. Metabolism 149, 155695 (2023).

In the rapidly evolving field of cancer research, Dr. Jason Berman and his research team have pioneered the transplantation of human cancer cells into zebrafish larvae, creating innovative preclinical models to advance drug discovery and personalized treatment.

Sofia Edissi and Weier Fan

Imagine this: you or a loved one has been diagnosed with cancer. Years of chemotherapy, radiation, and other treatments have taken a toll – physically, emotionally, and financially. Yet, despite everything, healthcare professionals are still unable to effectively treat the cancer. For many patients, especially young children, this is a reality. But what if there was a way to develop more precise, personalized treatments – ones tailored to an individual’s unique cancer?

This is a question that Dr. Jason Berman and his team at the Children’s Hospital of Eastern Ontario (CHEO) Research Institute are trying to answer.

After two years of undergraduate education at York University, Dr. Berman moved on to medical school at the University of Toronto, where a second-year lecture on pediatric leukemia sparked his passion for pediatric oncology. This set him on a path that led to a fellowship in Pediatric Hematology/Oncology at Boston Children’s Hospital and post-doctoral training at the Dana-Farber Cancer Institute. During his time there, he witnessed firsthand both the triumphs and the limitations of cancer treatment — many children were being cured, but this was not the reality for all children.

Chemotherapy, one of the first lines of cancer treatment, often comes with severe side effects and lacks precision. It works by killing or stopping the growth of cancer cells; however, it also attacks healthy cells, causing significant collateral damage to the surrounding area. “The analogy I always give is like using a cannon to kill a fly on the wall,” Dr. Berman says in describing the impacts of chemotherapy, “The fly is dead, but [you destroyed] the wall”. This analogy emphasizes the need for developing and testing more targeted therapies for cancer. Seeking a better alternative, Dr. Berman was motivated to pursue research where he could study the biological mechanisms underlying cancer. When reflecting on his transition to research, Dr. Berman explains, “This was around the time that people started to recognize that cancer is a genetic disease – genetic mutations lead to cancer. If we could understand the biology better, then we could understand diseases better, and find better treatments”.

Likewise, atthis time (early 2000s), the scientific community was just beginning to recognize the potential of zebrafish as powerful genetic models for studying human diseases, as about 70% of human protein-coding genes are found in zebrafish¹. This is extremely valuable because if zebrafish have similar genetics to humans, then they likely have similar molecular pathways and can be used to replicate diseases healthcare professionals want to treat in humans². Acknowledging the potential of this model organism to understand cancer biology, Dr. Berman made the transition from clinical practice to research. While working with zebrafish, Dr. Berman recognized the capacity of zebrafish to be used as pre-clinical models to find new targeted therapies for cancer treatment.

To explore novel cancer therapeutics, the gold standard for preclinical testing involves implanting human cancer cells into mice². This process, called xenotransplantation, is invaluable in cancer therapeutic research because it allows scientists to test drugs on human tumour cells in living organisms^2,3. However, mouse xenograft models come with significant drawbacks, hindering the translation of cancer research breakthroughs into clinical benefits. Most notably, the xenograft process is slow to establish and stabilize in mice, making it unsuitable for studying aggressive cancers, where clinical decisions around treatment need to be made more rapidly. Additionally, mouse xenografts require injecting a substantial number of human cells (0.5-1 million), which is a particular concern when using precious patient samples ^2,3

On the other hand, zebrafish offer several unique advantages over mice, including (1) they lack a fully developed adaptive immune system until about a month of age, eliminating the need for immunosuppression to prevent graft rejection; (2) only 50-300 human cells per larva are required for engraftment; (3) their small size makes them easy to handle in the lab; (4) water soluble drugs can be added to the water, allowing zebrafish larvae to absorb them without requiring drug injections; and (5) zebrafish are transparent for the first portion of their life, allowing for real-time visualization of fluorescently labelled human cancer cells to assess cell proliferation and migration² (Figure 1). All these advantages help shorten the experimental time in xenotransplantation – taking as little as 7 days to get results.

To determine whether zebrafish xenografts were a viable alternative over mouse xenografts, Dr. Berman collaborated with one of his colleagues at Dalhousie University to design a proof-of-concept study⁴. Using two different leukemia cell lines, each known to respond to one drug but not the other, they performed xenotransplantation by injecting these cell lines into zebrafish larvae. By tracking fluorescently labelled leukemic cells, they observed cell growth in larvae, as expected in cancer patients. After replicating the tumour properties in zebrafish larvae, they tested targeted drugs with known responses to treat these tumours. When the zebrafish were exposed to the appropriate drug that targets the specific molecular alteration in the cancer cell line, the number of leukemia cells in the zebrafish would decrease, but when exposed to the incorrect targeted drug, the leukemic cells were unaffected. This suggested that cell-line derived xenografts (CDXs) in zebrafish can be used to observe a response that is similar to what would occur in a mouse or patient.

With the success of this initial proof-of-concept study assessing CDXs in zebrafish, Dr. Berman moved forward with another pivotal study assessing patient-derived xenografts (PDXs) in zebrafish⁵. To do so, his lab injected two patient samples of T-cell acute leukemia (T-ALL) cells into different zebrafish and found these cells to grow similarly to what they observed in the CDXs. At this time, the molecular pathways in T-ALL were already well understood, and several targeted drugs had been developed for treatment. With this knowledge, three clinically used drugs for T-ALL, each with a different molecular target, were tested on these PDXs. They observed that only one drug, a Notch inhibitor, was effective for treating the first patient’s tumour cells in the zebrafish PDX model, while none of the drugs were effective for the second patient’s tumour cells. Notch inhibitors target the Notch signalling pathway, an important cell communication pathway that is frequently hyperactive in cancer⁶. Based on this evidence, they sequenced key genes involved in the Notch signalling pathway and were able to molecularly identify a novel genetic variant involved in promoting this specific patient’s cancer⁵. This is pivotal because for cancer treatment, Dr. Berman explains, “what’s more important than [identifying] the mutation is the actual response [to the drug]”. Dr. Berman recounts this study as a defining moment in his career as it demonstrated the applications of zebrafish to be used for precision medicine in cancer treatment. By identifying which treatment a specific patient is most likely to respond well to and which to avoid, his research has the potential to revolutionize cancer treatment. This is especially true for patients with aggressive and hard-to-treat cancers as it provides meaningful guidance in shaping their treatment decisions.

Currently, Dr. Berman and his research team at CHEO are leveraging the advantages of zebrafish to advance care for pediatric patients with cancer. Through the xenotransplantation branch of Dr. Berman’s research program, the Berman Lab proposes two distinct and novel approaches to ameliorate cancer treatment. Firstly, using CDXs, his research is helping to identify which targeted treatments should be prioritized for pre-clinical testing of novel therapeutics in mouse models. Dr. Berman emphasizes, “We are not trying to say the zebrafish replaces the mouse. The mouse is still going to be an animal model much closer to humans”, which is important for assessing drug dosage and potential side effects. However, zebrafish could act as a vital intermediary in the pre-clinical testing pipeline because they are much less expensive and less time-consuming than mouse models. Additionally, the Berman Lab uses PDXs, which their lab likes to call “patient avatars,” to personalize care for pediatric patients with aggressive and hard-to-treat cancers. The process from creating a PDX in zebrafish to testing drugs on the tumour can be completed by his lab in as little as 7-10 days. This turnover is remarkably important because it allows the results to get back to the physician in a clinically actionable timeframe to inform patient treatment decisions. This type of precision medicine is not possible with PDXs in mice because the process typically takes several months. “You don’t have that time with cancer, especially the aggressive types that need a rapid response to treatment,” Dr. Berman stresses.

At this time, Dr. Berman’s lab is the only one in Canada using larval zebrafish xenografts to identify targeted treatments for cancer patients, making them a desirable collaborator in many research projects. One such initiative is Precision Oncology For Young peopLE (PROFYLE), a pan-Canadian effort providing children and young adults with hard-to-treat cancers an additional avenue for finding effective therapies. PROFYLE is currently part of the national CIHR-funded Advancing Childhood Cancer Experience, Science & Survivorship (ACCESS) program, which unites researchers, hospitals, and industry partners across Canada to leverage precision medicine, genomic sequencing, and advanced data analysis to create personalized treatment plans for those with relapsed, refractory, or rare cancers.

As his work continues to gain more recognition from both physicians and researchers, through projects such as PROFYLE/ACCESS, Dr. Berman shared that the biggest challenge he continues to face is shifting the scientific paradigm. “The world still sees mice as the gold standard,” he says. He recalled presenting his research at a zebrafish conference back in 2017 or 2018, where he faced strong pushback from senior colleagues who questioned whether clinical response data from zebrafish could truly inform patient care. Recently, however, Dr. Berman and his team completed a study with their Australian colleagues that directly addressed these concerns. The study compared pediatric patient drug response data from both mouse PDXs (from the Australian team) and zebrafish PDXs (from Dr. Berman’s lab) in each pediatric patient. The results showed a strong correlation between the two animal models and most importantly, to the patient responses. “In fact, the fish was even better than the mouse,” Dr. Berman notes. In three cases, tumours that failed to grow in the mouse model, successfully grew in zebrafish. This highlights the importance of zebrafish PDXs in patient treatment, as relying solely on mouse PDXs for these patients would have deprived them of critical treatment guidance that zebrafish PDXs could have provided. Despite this compelling evidence, Dr. Berman shared that the study is still under review for publication, as it has been faced with pushback from journals, reflecting the ongoing challenge of changing long-standing scientific perspectives. However, he hopes that the publication of this paper will strengthen confidence in zebrafish models for translational research.

When asked about what other future endeavours are in store for this project, Dr. Berman excitedly expressed their work on creating humanized zebrafish. He explained that they had previously created a humanized zebrafish model expressing specific human cytokines, which are chemical messengers of the immune system, to try and create a more human-like environment for acute myeloid leukemia cancer cells. Their next step is to establish a humanized zebrafish model for lymphoid cancers, striving to develop patient avatars that more closely reflect the patient and their clinical responses.

Since the early 2000s, zebrafish have transformed medical research, advancing our understanding of disease mechanisms and treatments. Dr. Berman’s work in pioneering xenotransplantation in zebrafish has placed him at the forefront of patient-centred precision medicine in oncology. Dr. Berman strives to remind others that “there is a person at the end of every sample,” a philosophy he tries to carry out in his research and teachings. Even after more than two decades, Dr. Berman continues to revolutionize the pediatric oncology field with the belief that “there is still more work to be done. We can still do better.”  

References

1. Howe, K., Clark, M., Torroja, C. et al. The zebrafish reference genome sequence and its relationship to the human genome. Nature 496, 498–503 (2013).
2. Wertman, J., Veinotte, C. J., Dellaire, G. & Berman, J. N. The Zebrafish Xenograft Platform: Evolution of a Novel Cancer Model and Preclinical Screening Tool. in Cancer and Zebrafish (ed. Langenau, D. M.) vol. 916 289–314 (Springer International Publishing, Cham, 2016).
3. Chen, X., Li, Y., Yao, T. & Jia, R. Benefits of Zebrafish Xenograft Models in Cancer Research. Front. Cell Dev. Biol. 9, 616551 (2021).
4. Corkery, D. P., Dellaire, G. & Berman, J. N. Leukaemia xenotransplantation in zebrafish – chemotherapy response assay in vivo. Br J Haematol 153, 786–789 (2011).
5. Bentley, V. L. et al. Focused chemical genomics using zebrafish xenotransplantation as a pre-clinical therapeutic platform for T-cell acute lymphoblastic leukemia. Haematologica 100, 70–76 (2015).
6. Shi, Q., Xue, C., Zeng, Y. et al. Notch signaling pathway in cancer: from mechanistic insights to targeted therapies. Sig Transduct Target Ther 9, 128 (2024).

Dr. Philipp Maass’s laboratory has developed a groundbreaking interdisciplinary approach for determining the spatial organization of the genome.

Mahek Khatri, Rohan Khan, and Nithya Gopalakrishnan

The human genome is essential to life, yet only a small portion of DNA encodes for proteins. A majority of the genome is actually non-coding, playing a role in regulating the expression of proteins, dictating when genes are “turned” on or off. From transcription to gene-regulatory elements like enhancers and chromosomal interactions, the non-coding genome is complex and can have significant implications on the protein expression of our cells. Understanding these mechanisms plays a key role in uncovering the impact of the genome on our daily lives.

At the forefront of this research is Dr. Philipp Maass, a senior scientist in the Genetic and Genome Biology Program at the SickKids Research Institute. His research at SickKids is focused on functional regions of the non-coding genome, such as chromosomal interactions and long non-coding RNAs (lncRNAs), and their impact on development and disease mechanisms. In particular, he has led the discovery and understanding of inter-chromosomal contacts (ICCs) – trans interactions between different chromosomes that were once thought to be rare or insignificant¹.

From Mendelian Genetics to Computational Biology: An Intercontinental Journey

Dr. Maass found his passion for genetics early on in life, long before he came to be a leading researcher at SickKids studying 3D genome organization. It was in high school that he became fascinated by the intricacies of gene regulation, from enhancer elements to chromatin looping. He recalls asking fundamental questions: “How [is genome] organization maintained? How are genes regulated by [a] three-dimensional architecture? How is chromatin organized, and how are the genes transcribed?” These questions about the three-dimensional structure of the nucleus sparked a curiosity that would shape his scientific career.

Dr. Maass’s passion for genetics and genome organization led him to the Max Delbrück Center for Molecular Medicine in Berlin. During his time in Berlin, genomic research was focused on understanding disease mechanisms. Researchers were looking at Mendelian disorders with specific disease mechanisms in families. However, Dr. Maass found himself drawn to broader questions about gene regulation. His scientific curiosity soon pushed him to explore further and answer broader questions about how large-scale genome organization influences biological function.

This quest led him to Harvard University, an experience that broadened his scientific perspective. “At Harvard it’s 24/7 science… you go for a beer in the evening… and it’s a ball of science,” he recalls. The research at Harvard was aimed at gaining an understanding of basic processes like gene and genome regulation, and lncRNA biology, all of which perfectly aligned with Dr. Maass’ interests.

Here, he was introduced to interdisciplinary approaches to address biology. Specifically, he learnt to implement bioinformatics, machine learning, and statistical approaches to do genetic and genomic research. This computational knowledge became a cornerstone of his recent publications, allowing him to analyze genome structure in previously unattainable ways.

Dr. Maass’s journey from Europe to North America, from classical genetics to cutting-edge computational biology, has played a significant role in shaping his scientific outlook. He emphasizes that, “for science, it’s really important that you do interdisciplinary or multidisciplinary science [and] get an understanding of what other people are doing.” By having such global exposure to research and understanding of what other scientists in the community are working on, it can allow us to gain a deeper understanding of our field.

Challenging Genome Organization Models

During his academic journey, many scientists had dismissed the idea that chromosomes could physically interact. Instead, the first models of genome organization suggested that each chromosome occupied its own space within the nucleus². But Dr. Maass was determined to investigate genome architecture further. He was drawn to the challenge of understanding chromosome organization, gene regulation and how these transcriptional programs dictate crucial biological processes such as development and tissue maintenance.

A pivotal moment in Dr. Maass’s research came during his PhD, when he found evidence of ICCs, a discovery that challenged assumptions in the field³. He describes that he “came across one amazing example where [he] found a contact between different chromosomes.”. At the time, however, many geneticists were skeptical. “They did not believe that chromosomes were interacting,” he explains. Despite this skepticism, Dr. Maass’s findings suggested that ICCs exist³. “This ‘chromosomal kissing,’ which people are now starting to accept, is really happening in a non-random manner,” he notes. Since then, it has been found that these interactions play a critical role in gene regulation and disease mechanisms, reinforcing the idea that our genome is far more deterministically organized than previously believed^1,4,5. To assess disease mechanisms and ICCs, we need a strong understanding of human DNA topology – the spatial organization and three-dimensional structure of the genome¹.

Human DNA is condensed into 23 pairs of chromosomes inside the nucleus. These chromosomes will interact at specific contact sites – ICCs (Figure 1)¹. These interactions form a part of the 3D genome, following consistent patterns, and contributing to genetic functions such as genome organization and gene regulation⁶. For example, certain ICCs organize active genes in regions of the nucleus where transcriptional activity is high, making it easier for the cell to produce proteins from these genes^1,5. Although they may have significant importance, detecting and studying chromosomal interactions has been difficult due to analytical and computational challenges.

Identifying ICCs Using Machine Learning

Hi-C (High-throughput Chromosome Conformation Capture) is a method in which DNA fragments, that are physically close in three-dimensional space, are sequenced together^6,7. This, alongside imaging of chromosomes in individual cells, can reveal how chromosomes are spatially organized within the nucleus and the interactions between them^1,6. When reflecting on his research, Dr. Maass again stresses the essentiality of interdisciplinary approaches, especially within systems biology. “In terms of imaging, seeing is believing,” He explained. “The good thing about imaging is you go from cell to cell [to see variability, although] it is a laborious approach.” At the University of Toronto Dr. Maass’s team has been utilizing an interdisciplinary approach between computational and biological methods to analyze the entire Spatially Interacting Genomic Architecture (Signature) and to generate a new data science pipeline. This Signature algorithm applied machine learning to analyze Hi-C datasets (Figure 1) and ultimately identified over 40,000 ICCs across 53 different human cell types, highlighting that they are not rare or random and play a key role in genome topology⁶.

From analyzing 62 different Hi-C datasets with Signature, Dr. Maass and his team showcased that while some ICCs are consistent across different cell types, there are others which are cell-specific⁶. Beyond this, ICCs are commonly enriched in gene-dense regions with high gene expression and transcription factor binding⁶. This further indicates that ICCs are not just structural features but are also functional and may aid in defining a cell’s identity. For example, in neuronal cells, these contacts may cluster genes responsible for specialized neuron cell functions into regulatory hubs, influencing their expression. However, these same ICCs may not exist in muscle cells where those genes are not essential. This suggests that the non-coding genome is important in cell differentiation, and that ICCs help orchestrate cell-type-specific transcriptional programs, ensuring that genes interact in ways that support each cell’s specialized role⁶.

Another important finding from Signature was the confirmation of Rabl’s configuration in human cells. Rabl’s configuration is a structural feature where telomeres and centromeres of chromosomes arrange such that they cluster at opposite ends of a nucleus (proposed by Carl Rabl around 1895)⁶. This configuration has been observed in yeast, plants, and other mammals, but until now it was unclear whether this occurred in the human genome⁶. From Signature analysis, it was demonstrated that ICCs align with this spatial organization, suggesting that genome organization in humans follows principles that have been conserved across species⁶. This conservation lends credence to the theory of ICCs being important in genomic regulation.

The ability of Signature to analyze ICCs is groundbreaking within the field, but its development was not always seamless. The most significant challenge to visualizing ICCs in a three-dimensional genome topology came in the form of a lack of available computing power. Analyzing every possible combination of ICCs required both a strong computing cluster and an abundance of time, a frustrating caveat to being able to process the copious amount of Hi-C data the models were trained on. Dr. Maass recalls that early iterations of Signature required over “400 hours for one job [and it would] crash because [the servers ran] out of memory”. Workflow and computation optimization had to be refined to efficiently process this large-scale data.

Then came the breakthrough. Dr. Maass recollects the day when Signature was able to successfully identify ICCs. “I remember the day when my postdoc, Daniella, was showing me on her screen just a plotted result… It showed that Signature can determine these trans-contacts.” Although this plot never made it into a publication, it was the first indication that Signature was working, proving that this machine learning approach had potential. The seeds of Signature had finally sprouted, slowly growing into today’s robust and scalable version.

The Future of Chromosomal Contacts and Implications for Genetics and Medicine

Despite some initial hurdles, Signature’s publication signifies a host of new possibilities for clinical genomic interpretation. Dr. Maass emphasizes that understanding genome organization in healthy cells is a necessary step in recognizing the basis for clinical phenotypes for a wide variety of conditions, from a ‘simple’ trisomy to a cancer genome. In the future, he hopes to see the application of spatial genome topology for therapeutic targets in disease treatment or improved prognosis. A potential future direction could be assessing cancer genetics, with genomic targets of interest being oncogenes such as MYC or TP53^8,9. Signature has the potential to redefine our understanding of the genomic basis of several conditions including but not limited to cancer, and it remains to be seen how exactly the program can aid in diagnostics and therapeutics.

In terms of future directions, Dr. Maass highlights three ongoing projects, ranging from domestic to international partnerships. The first is oligo-painting as a means of imaging the genomic spatial gradient with Dr. Eric Joyce’s lab at the University of Pennsylvania. He then mentions Dr. Ana Pombo’s lab at the Max Delbrück Centre for Molecular Medicine in Berlin, to characterize genomic organization in brain disorders. Integrating genome topology studies into the construction of a clinical phenotype for conditions is a thrilling future direction for genomics. Lastly, Dr. Maass was sure to call attention to Dr. Artem Babaian, from University of Toronto’s Donnelly Centre, who is a leading expert in using ultra massive cloud computing to demonstrate the utility of technological advancements within the field to compute high-dimensional datasets of 3D genome topology. Across all three of these partnerships, it is abundantly clear that Signature’s potential is multifactorial across diverse contexts.

In addition to developing Signature, Dr. Maass is engaged with leading his lab and teaching. He is fueled by his passion for science and collaboration with his team, pictured below. Reflecting on his favourite part of research and lab work, he left us with some advice for future scientists. “I think it’s great to validate some ideas that other people had more than 100 years ago, like when we talk about the Rabl configuration,” he explained. This love for validation clearly culminated in Signature, and he is deliberate to highlight his excitement about how advancements in technology can help confirm these long-standing structural ideas. The new generation of scientists in medical genomics are imperative to furthering this work and should be supported. “When you’re interested in pursuing a scientific academic career, let me tell you, it’s not easy. But if you really want to do it, then do it,” Dr. Maass encourages. “At the end of the day, research is about finding new things and validating them. That’s the fun part.” With such passionate researchers within the field working in tandem with ever-evolving technological capabilities and computational tools, the future of medical genomics is looking bright.

References

1.         Maass, P. G., Barutcu, A. R. & Rinn, J. L. Interchromosomal interactions: A genomic love story of kissing chromosomes. J. Cell Biol. 218, 27–38 (2019).

2.         Cremer, T. & Cremer, M. Chromosome Territories. Cold Spring Harb. Perspect. Biol. 2, a003889 (2010).

3.         Maass, P. G. et al. A misplaced lncRNA causes brachydactyly in humans. J. Clin. Invest. 122, 3990–4002 (2012).

4.         McStay, B. Nucleolar organizer regions: genomic ‘dark matter’ requiring illumination. Genes Dev. 30, 1598–1610 (2016).

5.         Lomvardas, S. et al. Interchromosomal interactions and olfactory receptor choice. Cell 126, 403–413 (2006).

6.         Mokhtaridoost, M. et al. Inter-chromosomal contacts demarcate genome topology along a spatial gradient. Nat. Commun. 15, 9813 (2024).

7.         Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).

8.         Marei, H. E. et al. p53 signaling in cancer progression and therapy. Cancer Cell Int. 21, 703 (2021).

9.         Ahmadi, S. E., Rahimi, S., Zarandi, B., Chegeni, R. & Safa, M. MYC: a multipurpose oncogene with prognostic and therapeutic implications in blood malignancies. J. Hematol. Oncol.J Hematol Oncol 14, 121 (2021).

Dr. Michael Brudno, PhD, explores how artificial intelligence and the open-source software PhenoTips are transforming clinical genomics, addressing challenges in variant interpretation, advancing patient care, and ultimately helping to resolve a patient’s diagnostic odyssey.

Kayla Krolikowski and Anushka Deshmukh

Throughout the past couple decades, challenges have persisted in understanding health informatics and addressing systemic issues in health deployment. Artificial intelligence (AI) has the potential to enhance variant identification and advance personalized medicine¹. As AI continues to play a growing role in computational genomics, how will this influence the future direction of the field? Also, can software programs help streamline genomic data analysis in clinical settings, making it more effective in improving patient care?

In conversation with Dr. Michael Brudno, Chief Data Scientist at University Health Network (UHN), and Professor in the Department of Computer Science at the University of Toronto, we explored these questions and more. Dr. Brudno shared his unconventional journey beginning with his passion in computer science to groundbreaking contributions to software, including PhenoTips, also touching on how AI is set to reshape the field of genomic medicine.

Dr. Brudno never imagined he would end up in genomics. In fact, biology was the last subject he wanted to pursue. Reflecting on his high school biology class, he recalls only truly enjoying Punnett squares and chromosomal crossovers. However, during the latter part of his undergraduate degree, he became intrigued by conserved sequences in the genome between species and its functional significance. Approaching the subject from a computer science perspective, he focused on improving the efficiency of sequence comparison tools: how to score them, how to optimize algorithms, and how to make computers perform these tasks faster. This interest led to his PhD work in comparative genomics. Today, he is not only passionate about research within the field of computational biology but also mentoring individuals who embark on their own journeys in the field.

PhenoTips: Development, Features, Challenges

With support from Dr. Brudno, Dr. Marta Girdea, a former postdoctoral researcher, was able to develop the idea for PhenoTips. PhenoTips is an open-source software designed to analyse phenotypic information for patients with genetic disorders while also seamlessly integrating into a clinician’s workflow². The motivation came from an initial machine learning project focused on identifying genetic mutations that lead to specific clinical phenotypes³. In 2012, when they approached the Molecular Diagnostic Laboratory at the Hospital for Sick Children for access to data, they were provided with a well-organized Excel file. However, when they asked for the patient phenotype information, they received unstructured clinical notes filled with typos, inconsistent abbreviations, and free-form text unsuitable for automated analysis². Recognizing this challenge, Dr. Girdea took matters into her own hands and developed a software to standardize and streamline the process.

PhenoTips provides clinicians with an intuitive interface for entering patient data, utilizing the Human Phenotype Ontology vocabulary to capture phenotypic details alongside, demographic information, medical history and other relative clinical data (Figure 1)². Once recorded, the software suggests Online Mendelian Inheritance in Man (OMIM) diseases that closely matches a patient’s phenotypic profile, while also ranking unselected phenotypic features by relevance². This is valuable, as even experienced clinical geneticists may overlook key symptoms, leading to misdiagnosis. Although the benefits of using this software extend beyond reducing the workload of clinicians and molecular diagnostic laboratories. As Dr. Brudno describes, PhenoTips can be expanded into other hospital departments such as cardiology and neurology. As many patients with genetic conditions do not first see a geneticist, but see a cardiologist as an example, making it crucial to integrate genetic analyses into other specialties. This expansion could help clinicians identify genetic conditions earlier, enabling patients to receive the right care at the right time no matter which department they visit first.

Currently, PhenoTips is being implemented on a hospital-by-hospital basis. In the future, it could be expanded into a coordinated system that allows for data sharing between hospitals, clinics and other healthcare institutions. However, implementing this informatic system in hospitals introduces significant challenges. One major hurdle is the decision-making process within hospital Information and Technology Services (IT) Departments, which have their own set of priorities. Dr. Brudno explains that IT departments must handle a range of requests, such as fixing critical imaging system issues for radiologists or updating outpatient monitoring software. These needs are often prioritized over the implementation of software programs, including PhenoTips.Dr. Brudno notes, “Getting the right person to push your request up the queue is one of the biggest challenges.”

Another challenge is securing funding. Clinicians, who want to use data integration tools like PhenoTips, are not necessarily the personnel ultimately making the decision, leading to a disconnect between those who benefit from the software and those responsible for funding it⁴. Dr. Brudno provides an example, “Let’s say it costs the hospital $1 million a year to implement, but it saves a ton of unnecessary visits. The hospital spends $1 million implementing it. The result? They see fewer patients. That means they get less money from the Ministry of Health for seeing those patients.”  In addition, financial disconnect is also seen between publicly and privately funded healthcare, which is one of the main reasons PhenoTips has struggled to break into the United States market. “In Canada, people ask, ‘Is this better?’ In US, they ask, ‘Does this make money?’ That changes how you pitch things,” Dr. Brudno says. Unlike Canada’s publicly funded health system, where clinical outcomes and service efficiency determine coverage, the US healthcare market relies on insurance providers, who prioritize financial considerations in reimbursement decisions⁴.By overcoming such challenges, PhenoTips has the potential to continue helping patients facing a diagnostic odyssey.  

Artificial Intelligence: Advantages and Challenges

Moving on from PhenoTips, Dr. Brudno focuses on other projects that continue to utilize his expertise in computer science. Dr. Brudno explains that current genetic testing relies heavily on variant analysts, who must manually sift through variants to determine which might be relevant to a patient’s condition. This process is time-consuming and prone to natural human error. AI has the potential to automate this process, through prioritizing clinically significant variants, reducing the time it takes to interpret genetic data and improving the diagnostic odyssey of patients⁵.

However, scepticism remains regarding the implementation of AI in clinical workflows.  Dr. Brudno address this doubt by acknowledging that people indirectly ask, “‘Is it really safe? Does it really work?’” These are valid concerns, as any new technology must be assessed for reliability, accuracy, and potential unintended consequences. To ensure successful implementation, future efforts should focus on minimizing risks related to system malfunctions, and misinterpretations of results by developing robust safeguards⁶. An example of a safeguard is the need for specialized training for physicians and genetic counselors to help them accurately interpret AI-driven recommendations and integrate them into patient care, preventing misinterpretations⁵. Additionally, ethical and privacy considerations surrounding data-driven AI must be carefully addressed, which can all assist to alleviate the scepticism surrounding its use in clinical workflows⁶.

Another fear is that AI could replace human expertise. Dr. Brudno believes AI should be viewed as a tool for medical professionals, not a replacement. “AI isn’t replacing doctors; it’s helping them,” he states. “Just like autopilot doesn’t replace pilots.” Similarly, AI in genomics is not meant to replace human oversight but rather to serve as an assistant, alleviating workload and improving efficiency while ensuring that experts remain in control of critical decision-making.  Rather than eliminating jobs, AI will shift the focus of geneticists from routine variant analysis to having more time to solve complex cases, continuing to improve patients’ diagnostic odyssey⁵.  With this, AI will not only improve variant interpretation but also redefine how genetic information is used in patient care (Figure 2).

Dr. Brudno envisions a future where AI takes on routine variant analysis entirely, stating “I hope that in 30 years, we won’t be looking at individual variants anymore. AI will handle that, and geneticists will focus on treatment and patient care.” The rapid progress in genomics and AI suggests that personalized medicine is no longer a distant dream but a tangible reality in the making.

Future Directions and Applications in the Field

In the coming decades, genomic medicine is expected to become a seamless part of clinical care⁷. With the continued implementation of tools like PhenoTips in hospitals and the increasing role of AI in variant analysis, identifying genetic conditions will become faster, and more clinically actionable. These advancements would mark a shift from reactive medicine to a proactive, data-driven approach, seamlessly incorporating genomic data into clinical decision-making.

One vision for the future of genomic medicine is the ability to use a device that can analyse a DNA sample and seamlessly integrate the results into a patient’s medical records. This record would provide clinicians with precise recommendations, including diagnostic tests, personalized screening schedules, potential drug interactions, and optimized medication dosages based on genetic metabolism⁵. This integration would allow clinicians to act on genetic data in real time, enhancing efficiency and patient outcomes.

These applications hold promise for patients and families affected by rare diseases. Patients undergoing this odyssey often do not grow up to lead conventional lives. They face multiple hardships, years of medical uncertainty, and systemic gaps in care. Yet, as Dr. Brudno emphasizes, their stories and genomes carry insights that can reshape our understanding of human genetics and the field of healthcare. At a recent meeting in Ottawa focused on rare disease data infrastructure, he was moved by a father’s reflection of having attended more funerals than birthday parties. The father emphasized that while his child may not lead a typical life, their story and genomic insights could meaningfully contribute to medical science.  For researchers like Dr. Brudno, this perspective reinforces the urgency for tools like PhenoTips.  The findings that can be attributed from research on patients with rare disease, help contribute to pieces of a larger puzzle, each one helping to advance care for future generations.

The implementation of these tools, alongside future technologies, will transform the diagnostic process, significantly improving the quality of life for these patients, their families, and giving hope for future families navigating similar journeys. This impact, as Dr. Brudno noted, is what makes his work so rewarding. “I worked with scientists who were solving new problems—how to share data globally without compromising patient confidentiality (a computer science problem), or how to help doctors record clinical data efficiently (also a computer science problem). And seeing how that impacts patients’ lives? That was huge.”

As this field of computational genomics and biomedical science continue to evolve, new challenges will inevitably arise and create barriers in further expanding utility. Whether it is automating variant analysis, integrating software like Phenotips into mainstream clinical care, or designing next-generation algorithms, the future of medicine will be shaped by those who can navigate both biology and data. The stakes are especially high for patients with rare diseases. This is why it is essential for scientists, clinicians, policymakers, and so on to ensure these tools are developed and employed responsibly. The integration of new tools will not just reshape medicine, it will reshape careers, offering professionals new opportunities to enhance patient care. This is why, as Dr. Brudno emphasizes, the ability to learn and to adapt is more crucial than any single piece of knowledge. For students and scientists entering the field, the challenge is not just to master today’s tools, but to help build the ones we’ll need tomorrow.

Dr. Brudno leaves students with a key piece of advice:

“The world is changing; your job will evolve. The most important thing isn’t what you learn today, but your ability to keep learning.”

References

1. Vilhekar, R. S., Rawekar, A., Vilhekar, R. S. & Rawekar, A. Artificial Intelligence in Genetics. Cureus 16, (2024).

2. Girdea, M. et al. PhenoTips: Patient Phenotyping Software for Clinical and Research Use. Hum. Mutat. 34, 1057–1065 (2013).

3. PhenoTips’ Core Genomic Health Record – PhenoTips. https://phenotips.com/core-genomic-health-record/ (2023).

4. Bombard, Y., Ginsburg, G. S., Sturm, A. C., Zhou, A. Y. & Lemke, A. A. Digital health-enabled genomics: Opportunities and challenges. Am. J. Hum. Genet. 109, 1190–1198 (2022).

5. Duong, D. & Solomon, B. D. Artificial intelligence in clinical genetics. Eur. J. Hum. Genet. 33, 281–288 (2025).

6. Jiang, L. et al. Opportunities and challenges of artificial intelligence in the medical field: current application, emerging problems, and problem-solving strategies. J. Int. Med. Res. 49, 03000605211000157 (2021).

7. Esplin, E. D., Oei, L. & Snyder, M. P. Personalized sequencing and the future of medicine: discovery, diagnosis and defeat of disease. Pharmacogenomics 15, 1771–1790 (2014).

Antimicrobial resistance remains the biggest challenge of designing drug treatments for human fungal diseases. Dr. Christian Landry’s efforts in cataloging mutations within Candida albicans has uncovered answers regarding azole treatments and drug targeting.

Lathursha Kalaranjan and Putri Ramadani

Fungal diseases affecting humans can range from superficial infections to life-threatening illnesses, possibly associated with fatal outcomes. With a limited option of treatments, the rise of antimicrobial resistance poses a detrimental challenge against drug development and patient care. Dr. Christian Landry, Professor of Biology and Biochemistry at Université Laval, has taken on the task of defining resistance-inducing mutations that have, thus far, affected drug efficacy in patients of fungal infections. His commitment to the exploration of antimicrobial drug resistance has informed mutational landscapes, public health strategies, and novel research approaches.

Dr. Christian Landry, Professor of Biology and Biochemistry at Université Laval. Image taken by Sandrine Gilbert from Université Laval.

Early Life, Educational Journey, and Emerging Endeavours

Coming from a small village in the Gaspé Peninsula, Dr. Landry always knew he had an interest in the sciences; though it was not until Cégep (pre-university training) when he discovered his specialized curiosity for biology. Upon completing his undergraduate degree in biology, he pursued a graduate degree in population genetics. He then completed his PhD in evolutionary genetics at Harvard University, which led him down the path to genetics and genomics research. After his graduate studies, Dr. Landry gained a faculty position at Université Laval, where he later started the Landry Lab in 2009.

The Landry Lab emphasized the cross-collaboration of Dr. Landry’s various research interests, including evolutionary genetics, population genetics, biochemistry, and bioinformatics. For the past sixteen years, this lab has specifically explored cutting-edge approaches to understand mutations and cell networks, allowing for a deeper insight into the associative phenotypes. This proved to be a commendable feat, as Dr. Landry was honoured with the 2024 Canada Gairdner Momentum Award for his efforts in developing novel approaches in combining synthetic biology, experimental evolution, and systems biology to understand gene function through the lens of human health. This award provided a sense of recognition and motivation: “We’re always questioning whether what we do is good enough [or] the right thing. So when you get an award like this, they look at what you’ve done and say, well, this is important.”

Exploration of Fungal Pathogens

Dr. Landry’s commitment to studying fungal diseases and antibiotic resistance stemmed from the pressing need to address fungal resistance. His research initially focused on model organisms and the genomic analysis of yeasts, including Saccharomyces cerevisiae (baker’s yeast). As he dove deeper into the realm of evolutionary biology, he acknowledged significant gaps between fundamental research and the understanding of antifungal resistance evolution. He noticed a unique opportunity to apply tools and techniques he had developed— for instance, creating libraries of mutant proteins to assess the impact of mutations on genes— to overcome tangible challenges. On top of that, as the Canada Research Chair in cellular systems and synthetic biology, he obtained a new network of collaborators interested in the same topics. A few years later, he led a group of researchers to establish a training program on the evolution of fungal pathogens called EvoFunPath. Through these applications, Dr. Landry has portrayed his determination to fill the gap between fundamental and applied research in fungal resistance.

Two primary research questions currently guiding his lab efforts are the evolution of antibiotic resistance and gene duplication. Initially, they concentrated on studying how particular mutations in fungi can confer drug resistance and the rate at which resistance can develop based on the number of mutations present. The main component of this research evaluates the fitness cost of these changes and their effect on the organism’s survival in the absence of the medicine¹. The second focus area is gene duplication, a basic evolutionary mechanism that turns one gene into two, resulting in specialized roles over time¹. In terms of antifungal research, gene duplication looks into the accumulation of mutations over time which could lead to different functions. The group hopes to gain a deeper understanding of microbial evolution and its implications for basic biology to inform treatment techniques for humans.

Fungal Mutation Indexing: Shifting the Focus Onto C. Albicans

Although there are several fungi that plague human health, one in particular stands out due to its role in an array of diseases: Candida albicans. C. albicans is a polymorphic fungus found in the digestive tract and along mucosal surfaces¹. Overgrowth of this species can result in common infections defined by their location in the body, including thrush (oral candidiasis) and yeast infection (vaginal candidiasis)². In dire cases, C. albicans can result in life-threatening illnesses with fatal outcomes in immunocompromised individuals— with mortality rates of up to 70%¹. One such illness includes invasive candidiasis whereby C. albicans impairs bloodstreams, which can result in fatal infections or conditions like sepsis^2,3.

Figure 1. a) Uninhibited ergosterol biosynthesis pathway. When the pathway is uninhibited, the Erg11 gene produces a demethylated form of lanosterol. The Erg3 gene desaturates this demethylated lanosterol to produce 14α-methylsterol. This is enzymatically processed before producing the final ergosterol product. Ergosterol synthesis results in an uninhibited fungal cell membrane. b) Azole inhibition of ergosterol biosynthesis pathway. Azoles target Erg11, thereby inhibiting the biosynthesis pathway for ergosterol. As a result, Erg3 produces a toxic sterol which accumulates in the fungal cell membrane. The stressed fungal cell membrane exhibits increased permeability which induces cell apoptosis, or cell death of the fungal cells. Figure made using BioRender and adapted from Shapiro, Robbins, and Cowen (2011)⁴.

Candidiasis is commonly treated through antifungal drugs, with the most common class being azoles. Azoles function by inhibiting Erg11, an enzyme involved in the biosynthesis pathway of ergosterol, which is a key component of fungal cell membranes (Figure 1)^1,4. This inhibition results in reduced ergosterol, which increases the permeability of fungal cell membrane and induces cell death^1,4. Mutations that alter protein function could impair binding between this drug and its target, resulting in resistance¹. This suggests the need to profile mutations, in order to provide a comprehensive understanding of the relationship between mutations and potential resistance factors.

Current literature has only identified a fraction of mutations, which highlights the need to expand this catalog. Identifying mutations in regions of interest could allow for a better understanding of molecular mechanisms and help inform drug selection or design. Considering this gap, the Landry Lab took on the task of cataloging mutations found near ERG11 in C. albicans that may confer resistance to medical azoles (Figure 1)¹. In doing so, more than 25,000 phenotypes associated with 3,830 amino acid variants of C. albicans were logged. Across the six azoles, 33% of the mutations conferred resistance in at least one azole, of which 88% were cross-resistant— defined as the resistance to several forms of azoles (Figure 1)¹. These results shed light on the genetic basis of resistance and its potential cross-resistance of C. albicans, suggesting that this species may be resistant to several medical azoles. “It’s important to know so we can manage the drugs differently. When we discover resistance mutations, we know what to do next.” says Dr. Landry, who proposes that the comprehension of the molecular landscape can inform effective treatment choices.

Implementations: Informing Treatment Options and Public Health Strategies

Understanding the molecular landscape within genes of interest can inform drug selection. The Landry Lab identified various resistance mutations in association to particular forms of azoles¹. Specific genes harboring detrimental substitutions can impair the binding between targets in C. albicans and certain azoles¹. In such situations, it would be preferable to use alternative azoles, instead of the ineffective forms. Issues arise in cases of cross-resistance, a common phenomenon between azoles¹. However, there are some drug-specific mutations that are highly informative and can be used to implement this selection of alternative azoles¹. “It would be a little bit like precision medicine but with microbes”, by which fungal genome sequences can inform drug selection to accommodate mutations for effective treatment options.

Although these findings do inform the molecular pathways specific to azoles, it is important to consider the role that the environment plays in selective pressure of mutations. In terms of human health, environmental factors influence the risk and severity of infections, with Dr. Landry emphasizing that “what we’re exposed to determines what we’re affected by.” The most prominent example of environmental factors is warmer climates, in which numerous fungi adapt and thrive⁵. Rising temperatures, seen with climate change, may enable more fungal species to thrive and, hypothetically, infect humans⁶. Other considerations include social and medical factors that predispose individuals to fungal infections. Certain social conditions such as closely working with soils could place an individual within physical proximity to environmental conditions where fungi thrive⁶. Medically, immuno-suppressing treatments or transplants could place individuals at higher risk. Dr. Landry explains that “we see more and more fungal infections, just because of the simple fact that there’s more people susceptible to infections”. These environmental, social, and medical factors can pose risks against differing groups of people. Considering these factors differ between individuals, the next step in treating at-risk individuals or populations is through the development of public health strategies.

There is an urgency to integrate fungal pathogen findings into public health strategies. As pointed out by Dr. Landry, the globally increasing population combined with a surge in immunocompromised individuals is causing more people to become vulnerable to fungal infections. Approximately 1 in 565,000 people face a Candida bloodstream infection or invasive candidiasis annually⁷. This suggests that antifungal drug development and public health strategies must evolve at a rapid pace in order to address this challenge. One promising approach is to employ genomic sequencing to identify drug resistance-inducing mutations within certain fungi, allowing healthcare providers to tailor treatments more effectively⁸. Furthermore, understanding mutations causing these resistances could facilitate the improvement of existing drugs to solve these challenges. This strategy may be significant to control fungal infections and prevent outbreaks, thereby enhancing the readiness of public health systems to address infectious diseases.

“One thing that’s interesting, or frightening, is that this type of species that infects people is changing with time.” It is clear that fungal pathogen research is more relevant than ever, especially given genomic aspects and the rise in mutational resistance that affect our ability to treat fungal pathogens. This emphasizes the need to conduct research on ecological and environmental factors characterizing fungi species and their activity to effectively design personalized medication for fungal diseases.

The Future of Antifungal Research

Considering the array of factors that play a role in antibiotic resistance within fungal infections, interdisciplinary approaches to combine researchers from various backgrounds— from microbial ecology to personalized medicine— could result in novel approaches and ground-breaking findings. “Sometimes the solutions are right in between disciplines; they are not one or the other. And if people don’t talk, they’re missing something that could be important.” This interdisciplinary approach, as used by the Landry Lab, brings together scientists from slightly different backgrounds and allows for fresh perspectives: “If you want to do something original in science, you need to see things a bit differently. And one way to see things differently is to mix people together who have different visions.”

“When I started talking to people, there seemed to be a lot of things we didn’t know about the evolution of antifungal resistance” claims Dr. Landry, who has since played a crucial role in fungal pathogen research. After years of embracing this interdisciplinary approach, the Landry Lab has mastered this approach to address various research topics within this field. Through his research into C. albicans, his lab took on an important role in cataloging variants associated with altered reception to azole. Despite the widespread use of azole drugs for candidiasis treatment, the rise in harmful mutations among fungal pathogens presents particular challenges that compromise its efficiency. Taking a step back, this phenomenon has been seen in several antifungal drugs used to treat various infections plaguing human health. This rise in drug resistance along with invasive infections suggests the importance of exploring antifungal treatment discoveries and public health strategies— which is an, otherwise, race against time.

References

1. Bédard, C. et al. Most azole resistance mutations in the Candida albicans drug target confer cross-resistance without intrinsic fitness cost. Nat. Microbiol. 9, 3025–3040 (2024).

2. Basharat, Z. et al. Inferring Therapeutic Targets in Candida albicans and Possible Inhibition through Natural Products: A Binding and Physiological Based Pharmacokinetics Snapshot. Life 12, (2022).

3. Duggan, S., Leonhardt, I., Hunniger, K. & Kurzai, O. Host response to Candida albicans bloodstream infection and sepsis. Virulence 6, 316–326 (2015).

4. Shapiro, R. S., Robbins, N. & Cowen, L. E. Regulatory Circuitry Governing Fungal Development, Drug Resistance, and Disease. Microbiol. Mol. Biol. Rev. MMBR 75, 213–267 (2011).

5. Leach, M. D. & Cowen, L. E. Surviving the Heat of the Moment: A Fungal Pathogens Perspective. PLOS Pathog. 9, e1003163 (2013).

6. Williams, S. L., Toda, M., Chiller, T., Brunkard, J. M. & Litvintseva, A. P. Effects of climate change on fungal infections. PLOS Pathog. 20, e1012219 (2024).

7. Denning, D. W. Global incidence and mortality of severe fungal disease. Lancet Infect. Dis. 24, e428–e438 (2024).

8. Alastruey-Izquierdo, A. & Martín-Galiano, A. J. The challenges of the genome-based identification of antifungal resistance in the clinical routine. Front. Microbiol. 14, (2023).

Dr. Seema Mital’s efforts in cardiac precision medicine aim to revolutionize cardiac care by leveraging advanced genomic technologies and computational tools to uncover the underlying basis of congenital heart disease (CHD). Dr. Mital’s work bridges discovery to implementation, harnessing the power of genomics to transform CHD care.

Olivia Tesolin and Michelle Spivak

In the era of precision medicine, genomic tools are critical in revolutionizing our understanding of human health and disease. This is especially true for the study of congenital heart disease (CHD), one of the most common birth anomalies, affecting 1 in 100 live births¹. CHD encompasses heritable cardiac malformations or defects present at birth, but the genomic etiology is known in less than 30% of cases with multi-organ involvement and less than 10% of isolated CHD cases². Subtypes of CHD are classified based on the underlying anatomy and physiology³. Currently, individuals diagnosed with CHD often require surgical correction in early infancy and in some complex cases, a cardiac transplant is inevitable. Despite many advancements in clinical care, CHD severely lacks therapeutic strategies, emphasizing the need for further research across the scope of this disease.

Dr. Seema Mital is uncovering the genomic basis of CHD in her role as a Staff Cardiologist & the Head of Cardiovascular Research at SickKids and Scientific Co-Lead of the Ted Rogers Centre for Heart Research. Through leading international collaborative initiatives such as the PeRsOnalized genomics for CongEnital hEart Disease (PROCEED) network, and through the development of novel predictive models, such as a heart-specific splice model, Dr. Mital and her colleagues are transforming CHD research and care.

Understanding the genomic basis of CHD is a complicated but important endeavour in paving the way for advancements in precision medicine. This approach moves beyond a one-size-fits-all strategy to provide tailored care, enabling more accurate diagnosis, targeted therapies, and improving patient outcomes. The PROCEED network⁴ is an international, large-scale collaboration between patients and their families, researchers, and clinicians that uses whole genome sequencing (WGS) and multi-omics to decipher the genomic basis of CHD and genotype-phenotype associations⁴. WGS enables the identification of genetic causes that routine clinical tests, such as gene panels and exome sequencing, may not capture¹. This uncovers not only new disease-causing genes but also novel genetic mechanisms that may contribute to the development and progression of disease, leading to more accurate diagnosis and prediction of outcomes⁴. Since its inception, the PROCEED collaboration has already led to a four-fold increase in CHD diagnosis⁴. Dr. Mital illustrates that,

 “Through genome sequencing, we started with exploring the tip of the iceberg, looking mostly at coding regions of the genome in several hundred CHD genes. But there is a lot in the non-coding and regulatory regions of the genome that affect gene expression that is yielding new discoveries that can explain the missing genetic basis of CHD”.

The non-coding regions of the genome, also referred to as the “dark matter” of the genome, can harbour disruptions to regulatory or functional regions. One such example is disruptions to splice sites which can lead to abnormal protein processing and may confer CHD risk. While canonical splice-disrupting variants may be detected through targeted genetic testing, non-canonical variants outside of these regions can disrupt splicing by creating or removing exon boundaries but are difficult to predict and identify by conventional methods. To determine if a variant influences splicing, Dr. Mital and her team used RNA sequencing of heart muscle tissue to observe the transcripts produced from genes, revealing whether splicing occurs as expected or if aberrant splicing is present due to a genetic variant. Machine learning algorithms and computational tools, such as SpliceAI, were used to predict which variants are likely to disrupt splicing. However, these tools lack heart-specific accuracy and miss variants in non-coding regions, leaving a gap in identifying CHD-related splice variants.

Given that the effects of non-canonical splice-disrupting variants are largely unknown, a significant gap exists for patients presenting with these variants. To help address this, Dr. Mital and her research team developed and validated a heart-specific model for predicting splice-disrupting variants in CHD. By leveraging existing artificial intelligence (AI) tools, they were able to train a novel model to identify splice variants linked to CHD that out-performed SpliceAI. Using WGS and RNA sequencing data from myocardial tissue in the heart from patients affected by two common subtypes of CHD, Dr. Mital and her team were able to identify splice-disrupting variants in at least 10% of these cases (Figure 1)².

This novel model could pave the way for future personalized therapeutic interventions that can treat the underlying genetic cause of CHD, as opposed to simply managing the symptoms. By identifying these variants, it becomes possible to explore targeted therapeutics that modulate these splice-disrupting variants to restore the expected gene function and avoid CHD-related complications. As explained by Dr. Mital,

“Splicing is an area that can be amenable to targeted therapy. Splice modulators have emerged as potential therapeutic options for other medical conditions. The high burden of splice disrupting variants in CHD suggests that splicing targeted therapies may become important in the future for CHD as well.”

The integration of this variant prediction tool, as well as other genomic tools, into clinical practice, can advance diagnosis, offer families more precise reproductive and prenatal counseling, and personalize care. Dr. Mital also highlights the applicability of this new model in other fields, emphasizing that clinicians and researchers are now able to use this heart-specific model or can even modify and apply this approach to their own diseases and tissues of interest. AI tools have considerable advantages and strengths over conventional analytical methods as they can examine complex, multidimensional, and multimodal data. While these initiatives are leading to accelerated genomic discoveries, Dr. Mital cautions:

“The Achilles’ heel of all of this is implementation … how do you go from discovery to implementing the findings?”

Considerations for the Implementation of CHD Discoveries

Navigating the pathway from genomic discovery to clinical implementation requires careful planning and considerations to ensure long-term success, as illustrated by Figure 2. Dr. Mital specifies,

“Even once you implement something, how do you ensure that it is used in a way that is sustained?”

Although clinical practice guidelines may be created, if they are not readily accessible and actionable for healthcare providers, then their clinical impact is limited. Standardized pathways for incorporating genomic data into electronic health records and decision-making processes are needed to translate these findings into patient care. Another hurdle to overcome is the complexity of data sharing; for example, ethical and legal frameworks prevent the sharing of patient genetic data across institutions which slows down validation and adoption of new discoveries and innovations into a clinical setting. Policies that outline responsible data-sharing methods while protecting patient privacy have the potential to allow for large, collaborative research efforts across institutional or even national boundaries.

Additionally, there is limited incentivization for pharmaceutical companies to develop pediatric-targeted therapies. Seeing as CHD is a lifelong condition with symptoms presenting in childhood, there is a need for therapeutics designed specifically for pediatric populations. However, due to regulatory hurdles and limited funding, pediatric drug development is often overlooked by pharmaceutical companies. Dr. Mital has emphasized the need to liberalize the indications for clinical trials to allow for more flexibility and accelerated development timeframes for targeted therapeutics intended for children with rare disorders. The encouragement of these clinical trials needs to happen at many levels, including research ethics boards and regulatory agencies. By incentivizing and streamlining pediatric clinical trials, targeted therapeutics can reach patients faster, potentially altering the course of the disease, preventing complications, and improving long-term outcomes.

Overcoming these barriers will require coordination between researchers, clinicians, and policymakers. To bridge this gap between genomic discoveries and real-world clinical applications, the implementation of patient-centered approaches and engagement, data and code sharing, and health technology assessments need to be prioritized.

Future Directions and Upcoming Initiatives

One major initiative driving this pathway is the Canadian Precision Health Initiative (CPHI), funded by Genome Canada⁵. This is a large initiative bringing together government, industry, and researchers to deliver health innovations and to support the generation of a national genomic dataset of 100,000 genomes that will be made available by the Pan-Canadian Genome Library. National collaboration and the use of emerging sequencing technologies, including long-read sequencing, will enable the detection of complex genetic variants, improving diagnosis and treatment strategies for rare and complex disorders.

A significant component of this new initiative is the Comprehensive Sequencing for Childhood LifeLong Disorders (PCHSeq) project, which aims to sequence 10,000 genomes to enable faster diagnosis, improve disease management, and identify novel therapeutics for pediatric patients with both common and rare and complex childhood disorders⁶. The PCHSeq project will include health technology assessments, which will be performed to assess patient and community perspectives and guide the implementation of genome sequencing as a clinical diagnostic test. Another critical aspect of this program is the generation and integration of genome sequencing data from pediatric cohorts. Access to such information will encourage new discoveries and lead to a greater understanding of the genetic basis of childhood disorders. Dr. Mital highlights the importance of data sharing, noting that when all genomic data is made available in a single genomic library, investigators from Canada and elsewhere can leverage it to identify meaningful insights and potential advancements in precision medicine. Standardized data-sharing frameworks will accelerate research, foster collaboration, and accelerate the translation of genomic insights into clinical applications.

Dr. Seema Mital’s groundbreaking work in cardiac genomics is paving the way for a future where CHD care is guided by genomic insights and AI-driven innovations. By uncovering the genetic basis of CHD, developing novel predictive models, and fostering large-scale collaborations like the PROCEED network, Dr. Mital is transforming how CHD is diagnosed, managed, and potentially treated. Furthermore, national large-scale genomic initiatives such as CPHI, and PCHSeq are laying the groundwork for a future where every patient can benefit from precision care.

References

1. Sun, R., Liu, M., Lu, L., Zheng, Y. & Zhang, P. Congenital heart disease: causes, diagnosis, symptoms, and treatments. Cell Biochem. Biophys. 72, 857–860 (2015).
2. Lesurf, R. et al. A validated heart-specific model for splice-disrupting variants in childhood heart disease. Genome Med. 16, 119 (2024).
3. Micheletti, A. Congenital heart disease classification, epidemiology, diagnosis, treatment, and outcome. In: Flocco, S., Lillo, A., Dellafiore, F., Goossens, E. (eds) Congenital Heart Disease. Springer, Cham (2019).
4. The Heart Centre Biobank. PROCEED Project. Available at: https://theheartcentrebiobank.com/proceed/ (Accessed March 9, 2025).
5. Genome Canada. Canadian Precision Health Initiative. Genome Canada (2025). Available at: https://genomecanada.ca/challenge-areas/canadian-precision-health-initiative/ (Accessed: 25 March 2025).
6. Precision Child Health: Comprehensive Sequencing for Childhood Life-Long Disorders. Genome Canada. Available at: https://genomecanada.ca/project/precision-child-health-comprehensive-sequencing-for-childhood-life-long-disorders/ (Accessed March 9, 2025).

Through years of experience and expertise, Dr. Christian Marshall and his team work relentlessly to bring cutting-edge technologies to the clinical diagnostic space to improve overall patient care by achieving diagnosis and making precision medicine possible, specifically in neurodevelopmental and rare diseases.

Isabella Vessio, Yael Kvint, and Quratulain Zulfiqar Ali

“Sometimes you don’t really choose them [a specialty]. It sort of chooses you, and I guess that’s what happened [with me],” says Dr. Christian Marshall, Director of the Molecular Laboratory in the Division of Genome Diagnostics at The Hospital for Sick Children (SickKids) when asked about why he chose to specialize in genomics, with a focus on identifying genetic causes of neurodevelopmental and rare disorders.

After completing his PhD, Dr. Marshall sought a postdoctoral position and was drawn to Toronto, where he joined SickKids at Dr. Stephen Scherer’s lab in 2005. In this position, he became involved in identifying structural variation in the human genome when microarray technology began to gain traction. While his work initially focused on autism spectrum disorders (ASD), his expertise in copy number variation analysis quickly expanded to other neurodevelopmental disorders such as epilepsy, schizophrenia, and attention deficit hyperactivity disorder. With the advancement of sequencing technology, his work evolved to use genome sequencing to allow a much higher resolution look into the human genome to find answers to the cause of these diseases.

The Role of Pre-Genome Sequencing Technologies in Neurodevelopmental and Rare Disorder Diagnostics

During his postdoctoral career, Dr. Marshall worked extensively with microarray technology, which was particularly exciting when he first started. Microarrays detect relative DNA copy-number changes by measuring the extent to which a sample anneals to a set of matching DNA probes. His work led to novel discoveries of large structural variations, specifically copy number variations (CNVs)—deletions and duplications—associated with ASD risk. This ultimately helped establish microarrays as a key diagnostic tool in ASD, replacing karyotyping —an older method that allows for the detection of large structural variation by visualizing each chromosome, but at a lower resolution¹.

As microarray resolution improved, allowing for the detection of more copy number variations, Dr. Marshall began collaborating with other researchers. While employing this technology, Dr. Marshall discovered that many CNVs linked to one condition were also implicated in others, highlighting significant genetic overlap across neurodevelopmental disorders. Reflecting on this new discovery, Dr. Marshall shared, “We started looking at neurodevelopmental disorders as a whole, using copy number variation to pinpoint genes that we thought were interesting.”     

While microarrays provided valuable insights by uncovering shared genetic links, their resolution posed a significant limitation, as some causative genetic variations were too small to be detected. Closer to 2010, Dr. Marshall started utilizing exome sequencing (ES) and shifted his focus to improving diagnostics for rare genetic disorders through collaboration with the Care4Rare consortium. This consortium includes 21 academic centres working together to establish an international collaborative network for the rapid and accurate diagnosis of Canadian patients with rare diseases.  ES involves capturing and sequencing the protein-coding regions of the genome on a base-level resolution, allowing for the accurate detection of sequence-level variation. This technological advancement was especially useful for rare disorders, where novel and unique mutations can be detected on a genome-wide level, aiding in accurate diagnosis and providing insights into disease pathogenesis. Analysis in pediatric ASD using ES allowed a diagnostic yield to increase from around 9%, when using only microarrays, to ~16% when using both microarrays and ES². This improvement in diagnostic yield was seen as a significant step forward, highlighting the importance of advancing and integrating genomic technologies in rare disorder diagnostics.

Bench to Bedside: Implementation of Genome Sequencing in Clinical Diagnostics

“10 years back, we did not imagine we would be doing clinical genome sequencing [in Canada], and now we have sequenced thousands of families, while providing diagnoses for hundreds.”

 The evolution of genomic technology did not stop at ES, as researchers started to notice a growing gap between research and its implementation in clinical diagnostics. Recognizing the need to close this gap, Dr. Marshall took on a translational genomics role as associate director at the Centre for Genetic Medicine at SickKids in 2012, stating, “The idea was for me to sit halfway in between the research group and the diagnostic laboratory.” Between 2014 to 2018, he completed advanced specialty training in clinical molecular genetics, became certified as a diplomate of the American Board of Medical Genetics and Genomics, and a fellow of the Canadian College of Medical Geneticists and the American College of Medical Genetics. During this time, genome sequencing (GS) technology — an innovation that, as the name suggests, allows for the sequencing of a human genome — became more affordable (Figure 1). As such, GS provides a more complete picture of the human genome, allowing for the identification of both coding and non-coding genetic variation, not just the ones in coding regions.

Dr. Marshall and his colleagues recognized the potential of GS as a clinical test and tested its utility compared to standard-of-care testing, which often includes many tests conducted consecutively, leading to a longer diagnostic odyssey (Figure 2). Expanding access to WGS showed significant improvements by increasing diagnostic yield up to four-fold compared to conventional testing³. However, overall access to GS in the clinic has been limited to individuals who fit certain stringent criteria, largely due to its high cost. Dr. Marshall anticipates this will change soon, with eligibility expanding to allow any interested individuals to have their entire genome sequenced.

Despite these advancements, establishing improved patient diagnoses remains an ongoing challenge. For example, for certain disorders such as ASD, the overall diagnostic rate still hovers at around 20%. For rare disorders overall, Dr. Marshall estimates this number is around 30%, with only approximately one-third of individuals with rare disorders receiving a diagnosis after GS. This illustrates that while sequencing technologies have evolved to provide more data, we are still far from completely understanding the complexity of genomic variation that causes disease. Dr. Marshall mentioned this as one of the surprising revelations throughout his career, noting that “We just don’t know very much still [about the genome] and it’s really hard to interpret”. He emphasized that advancing pipeline development and accelerating data interpretation are crucial for early diagnosis, distinguishing overlapping phenotypes, and guiding targeted interventions. To maximize the value of sequenced genomes, newer bioinformatics pipelines are needed to investigate repeat-expansion disorders and mitochondrial genomes. As multi-omic data complexity increases, integrating ancillary tests like RNA sequencing and developing analytic tools are essential for more accurate disease insights, moving closer to precision medicine.

Long-Read Sequencing as the Future of Diagnostic Genomics

Dr. Marshall expressed his opinion on the future of genomics, emphasizing that one of the most groundbreaking technological advancements in genomics is long-read sequencing (LRS). When asked about the most exciting developments in the field, he confidently highlighted this next-generation technology. “I do actually think that the days of short-read sequencing are relatively numbered”. Instead, LRS would be the technology of the future. The two major players, Pacific Biosciences single molecule real-time (SMRT) and Oxford Nanopore, enable DNA fragments to be left in their original state and provide real-time sequencing information^6,7. Dr. Marshall has implemented both long-read technologies within his work. As GS advances, its potential for widespread global use becomes more evident.

This leads to the question: What makes long-read sequencing so powerful that Dr. Marshall claims it is an ‘all-in-one test,’ capable of revolutionizing genomic analysis?  Sequence alignment can be viewed as putting together a puzzle piece. When a patient’s genome is sequenced, the output is fragments of DNA with varying lengths, like puzzle pieces that must be aligned with a reference genome (Figure 3). With short-read sequencing (SRS) techniques, however, the fragments are so short that not all the puzzle pieces fit together properly. Therefore, the unmapped sequences and puzzle pieces are often discarded, potentially missing important genomic information. Due to the longer fragment pieces generated by LRS technology, a de novo genome assembly can be created instead of mapping it to a reference genome, providing a more comprehensive view of genomic variation. In Dr. Marshall’s words, “… there’s probably interesting stuff that’s happening there that we just don’t really know about and a lot of it will just come with long reads”.

Long-read sequencing also offers a unique opportunity to serve as a versatile technique with direct clinical applications, particularly during critical times. During the height of the COVID-19 pandemic, he collaborated with microbiology laboratories and Oxford Nanopore to sequence SARS-CoV-2, contributing to the development of COVID-19 rapid tests. He reflected on that time, “So it’s a good lesson in learning to use a technology and then just applying it to something else.” This is a powerful example of how adaptable sequencing technology is and its use across other disciplines to develop and integrate essential diagnostic methods.

Given these advantages, it is no surprise that LRS can resolve complex structural variants, sequence-level variants, mitochondrial, and even viral variants without requiring additional processing steps⁴. Therefore, Dr. Marshall highlights that this technology could be beneficial to the SickKids genome testing pipeline with a potential of expanding the diagnostic yield. The promise of LRS also lies in its ability to detect real-time DNA methylation status, providing a greater understanding of imprinting disorders and the epigenome. Thus, LRS proposes many advantages and speaks to its ability as an ‘all-in-one-test’.

While excitement in the field is growing, limitations must be considered before fully jumping ship and abandoning short-read technologies. A major issue with LRS is its high cost and reduced accuracy, particularly in complex regions, which can hinder its ability to detect pathogenic variants or assemble genomes^6,8. LRS also generates a large volume of data, which, as Dr. Marshall points out, we still cannot fully interpret, making it too early to be used on a population-based scale. Consequently, more variant analysts or data interpretation tools are needed to help this technology reach its potential and be adopted for clinical testing.

Finally, when asked about his advice for aspiring professionals in the genomic field, Dr. Marshall suggests, “to try to keep up with what’s happening (in the genomics field). It’s evolving extremely quickly”. He also humbly refers to a quote by Bill Gates that he keeps close: “We often completely overestimate what we can get done in a year, but we also tend to underestimate what we can get done in five years”. Reflecting on this quote, he reinstates that progress happens faster than we realize. Five years ago, clinical GS was just beginning in Canada—now, thousands of genomes have been sequenced. When progress feels slow, it is important to step back and embrace the bigger picture. Just like careers, plans may not unfold as expected, but time moves swiftly, and growth often happens in ways we only recognize when we look back.

References

1. Marshall, C. R. et al. Structural variation of chromosomes in Autism Spectrum Disorder. Am. J. Hum. Genet. 82, 477–488 (2008).
2. Tammimies, K. et al. Molecular diagnostic yield of chromosomal microarray analysis and whole-exome sequencing in children with Autism Spectrum Disorder. JAMA 314, 895 (2015).
3. Stavropoulos, D. J. et al. Whole-genome sequencing expands diagnostic utility and improves clinical management in paediatric medicine. NPJ Genom. Med. 1, 15012 (2016).
4. Thompson and Thompson. Principles of clinical cytogenetics and genome analysis. in Genet. Med. (2015).
5. Trost, B., Loureiro, L. O. & Scherer, S. W. Discovery of genomic variation across a generation. Hum. Mol. Genet. 30, R174–R186 (2021).
6. Warburton, P. E. & Sebra, R. P. Long-Read DNA sequencing: Recent advances and remaining challenges. Annu. Rev. Genomics Hum. Genet. 24, 109–132 (2023).
7. Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016).
8. Satam, H. et al. Next-Generation sequencing technology: Current trends and advancements. Biology (Basel) 12, (2023).

Dr. Hal Dietz, M.D., highlights how patient-centered care allowed for breakthroughs in the mechanistic underpinnings of Marfan Syndrome, and is the best route for the future of its treatment.

Connie Fierro, Michelle Mariaprabhu, and Paula Zachcial

When it comes to our individual health, unanswered questions are typically unfavourable. As humans, we fundamentally desire the knowledge of what is affecting our well-being to best supplement the longevity of our health. But what happens when you are met with the unknown? What happens when a physician is also in the dark? What pathways can you take when your symptoms do not align with the most common presentation of a diagnosis? Questions like these are ones that healthcare teams encounter every day. Most importantly, these questions are the driving force behind scientific research and the ones that Dr. Dietz encountered in his clinical practice that ultimately sparked the preliminary breakthroughs in Marfan Syndrome and the discovery of Loeys-Dietz Syndrome (LDS).

Over a journey spanning 35 years, Dr. Harry (Hal) Dietz has been trying to understand the genetic interplay behind Marfan Syndrome¹. Marfan syndrome, a rare genetic age-related connective tissue disorder, is characterized by heart complications, namely an asymptomatic enlargement of the aortic root, due to mutations in a single connective tissue gene: fibrillin-1 (FBN1) ^2,3. With the heart complications associated with this disorder, there arises a challenging prognosis for patients, conferring a higher risk of vascular rupture ³. This shattering reality inspired Dr. Dietz to devote his academic career to advancing the treatment of this disease through his work as a clinician and researcher. This is a privilege that he attributes to a process he terms “sanctioned daydreaming”: “...we get to just come up with ideas by synthesizing, using data that’s been previously published but come up with our own ideas and then have the luxury of being able to go to work and test your ideas the next day.”

The Journey Through Marfan Syndrome to Loeys-Dietz Syndrome

While initially pursuing a fellowship in pediatric cardiology, Dr. Dietz was introduced to the groundbreaking research at John Hopkins for patients with Marfan syndrome. By integrating himself into this world, he encountered many affected families and became passionate about transforming patient care. However, this was not always a linear process. Due to the disorder’s specific targeting of connective tissues, Marfan syndrome was originally perceived to be a structural defect. Dr. Dietz shared that it was a conversation with a patient’s father that revolutionized his thinking and sparked his investigation into restructuring the pathogenesis of Marfan syndrome. He reflects, “…he looked at me and said ‘Well, if this is all caused by weakness of the tissues, why do the bones get long? How does weakness of the tissues influence the growth of the bones? And for that matter, why are there low-fat stores in Marfan Syndrome? How does a weak tissue cause fat to not form?’ And it was kind of a startling question. It said right away that we don’t have all the answers, and there is something missing, and that missing piece of the puzzle might be really important.” Dr. Dietz noted that this inspired him to make the courageous decision to take a step back from clinical care and revisit the mechanism of the disease to provide answers for his patients.

Backtracking and investigating the fundamental concepts led to Dr. Dietz’s most seminal works in identifying Marfan variants in the FBN1 gene^2,4. Further research into these mutations concluded that dysregulation of the transforming growth factor-beta (TGF-β) signalling pathway contributes to the pathogenesis of the disease. Through his work and the work of many others, it is now known the fibrillin-1 protein binds to and regulates TGF-β and that this is relevant to many manifestations of Marfan syndrome including the life-threatening vascular complications^2,5.

While this journey appears smooth, it is important to note that Dr. Dietz frequently encountered inexplicable situations. Two decades ago, he recognized patients who had physical features that were not traditionally associated with Marfan syndrome. Originally thought to be a subtype of the disease, Dr. Dietz identified some marked differences. This included widely-spaced eyes, a split in the uvula (bifid uvula), and premature skull bone fusion, among other features. Most importantly, these individuals commonly had a strong predisposition for aneurysms throughout the body that tended to rupture at smaller dimensions and younger ages, when compared to Marfan syndrome. Strikingly, there were also no pathogenic variants in the FBN1 gene, effectively ruling out classic Marfan syndrome. In fact, they found heterozygous mutations in the TGF-β receptors 1 and 2 genes (TGFBR1 and TGFBR2)^6. This breakthrough led to the discovery of a new genetic disorder, LDS.

This breakthrough was not the only moment where Dr. Dietz had to revisit and adjust his expectations throughout his journey.  When searching for a treatment for both LDS and Marfan, many in the field, including Dr. Dietz and his team, hypothesized that lowering blood pressure would reduce the risk of aortic rupture. Building on previous studies examining beta blockers on suppressing aortic aneurysm growth, Dr.Dietz analyzed the use of calcium channel blockers, another class of blood pressure lowering drug. These trials unexpectedly showed accelerated aortic growth and rupture. Dr. Dietz regards this experience as an opportunity, not a failure, exclaiming,“Oh, my God, that’s startling. That didn’t work. Why didn’t it work? And what can we learn from that?”  Additional work in mouse models highlighted the potential of a different class of medications called angiotensin-receptor blockers. Drugs in this class, such as losartan, both lower blood pressure and can regulate the activity of the TGF-β pathway. When tried in mice with Marfan syndrome, losartan completely normalized aortic growth and stabilized its wall structure^7,8. Transitioning into clinical trials, it is now confirmed that both angiotensin receptor blockers and beta blockers suppress abnormal aortic growth and can reduce the need for aortic surgery and the risk of aortic tear in patients with Marfan syndrome. Dr. Dietz attributes many of these advances to initial failures, suggesting ultimately, both successes and failures drive scientific discovery, reinforcing the need to always ask: Why?  

Individual Fit: The Future of Treatment  

Dr. Dietz believes the future of Marfan syndrome and LDS treatment lies within the field of precision medicine – an approach to medicine that tailor’s treatment to an individual’s genetic makeup. He stresses, “I think we’re in an era now where we’re no longer asking what is best for the average patient with this disease. Instead, we’re asking what’s going to work best for this person sitting in front of me right now?” He stresses, however, that as ideal as this concept may sound, it holds its challenges for LDS and Marfan syndrome alike.

Despite advancements in the treatment space, disease severity cannot yet be fully predicted based on genetic variants alone. While the knowledge of the gene is known, there lies a challenge in clinical presentation variability. Individuals with Marfan’s and LDS have significant allelic heterogeneity, where patients and families harbour unique mutations, further complicating the prediction of disease severity. However, the increasing availability and usage of genotypic data through large consortiums is providing progress in this field. Dr. Dietz highlights that this pooling of data has established and predicted the severity of LDS in certain cases.

Some patients carry a specific variant in the TGFBR2 gene, which is known to be the most common recurrent variant causal of LDS, and the most severe. Through the pooling of genotypic data, Dr. Dietz and his team observed that carrying this genetic alteration is predictably aggressive, where there are complications beginning in various parts of the aortic branches in early childhood, raising the likelihood of vascular rupture. According to Dr. Dietz, the question becomes how we can implement precision medicine to patients who harbour this and other particularly aggressive variants. Early surgical intervention is clearly essential. However, this technique poses its own set of important considerations: should the initial surgery only repair the part of the aorta that is already abnormal, or should it anticipate and address other vascular regions that are destined to develop problems? This unpredictability in LDS reinforces the importance of leveraging international cooperation and data sharing as well as the need to further develop precision medicine in the clinical context.

Dr. Dietz notes that techniques one thought impossible just five years ago are now achievable, and rapid advancements in molecular therapy instill growing optimism for him. Although in vitro correction of variants in the aortic cells of patients with Marfan syndrome is already possible with gene editing techniques, a major roadblock exists in the translatability of this technology to all the cells in a living and growing individual. Despite this challenge, the shift from molecular therapy being a distant possibility to its current position on the verge of integration poses great hope for Dr. Dietz. Looking ahead, he anticipates that advancements in these therapies will allow for a bigger impact across many age groups, and in particular states “…I don’t think there’s going to be a single window of opportunity to make a difference. I think that opportunity will extend throughout the lifespan.”

This wide window of opportunity also provides hope for assessing environmental contributions to disease as well as pharmacogenomics, a field studying how genetics influences drug response. Dr. Dietz believes that these fields will have their biggest impact in elucidating genetic variants and environmental factors that can protect people with Marfan syndrome – so-called protective modifiers.  This knowledge can hopefully be leveraged in the development of future therapies. He hopes that pharmacogenomics can eventually inform personalized treatments by targeting existing therapies to drug responders while simultaneously researching why some patients do not respond to the drug.  

Science is Driven by Passion and Constant Learning

Ultimately, Dr. Dietz can be described as a clinician-scientist who finds reward by fuelling his passions, by translating basic science into clinical solutions. Since his introduction in the field, he emphasizes that the key concept of his work revolves around his patients, quoting “Every experiment I do has a name and a face and a story associated with it.” The decades of research were complemented with the highs of scientific discovery, but also paired with the lows – of uncertainty, doubt, and failures. However, Dr. Dietz does not regret these moments, rather he views them as inevitable in science, “If you’re doing bold science, if you’re really trying to push the envelope, there are going to be lows. You’re going to be wrong many times.”  While this proved true throughout his journey, the failures led to questions, revelation, and ultimately advance.  

While an ideal end goal would be the cure for Marfan syndrome and LDS, Dr. Dietz highlights that his success would be attributed to doing the best he can, for as long as he can and for the patients he focuses his work around. He stresses the significance of creating a space for supporting young researchers to continue making groundbreaking discoveries. When asking about what advice Dr. Dietz would like to provide the next generation of scientists hoping to make an impact in the field of genetic research and translational medicine, he underlined that the driving factor is passion, “It seems sort of simple, but my answer is always to reflect deeply, know yourself and do what you love.” He also encourages his students to identify and embrace their sources of joy that will sustain them through the inevitable lows in a scientific career, be it the process of discovery, new knowledge, or impact for patients. Although the fundamental question of why hasn’t been answered yet, Dr. Dietz’s passion to understand these diseases has provided some answers to his patients, who were previously left in the dark. This is a sentiment that he hopes will extend not only to all future scientists but will lead to breakthroughs for multiple rare diseases.

Bibliography

1.              Dr. Hal C. Dietz III, MD – Baltimore, MD – Medical Genetics, Pediatric Genetics – Schedule an Appointment. https://profiles.hopkinsmedicine.org/provider/hal-c-dietz-iii/2708017.

2.              Milewicz, D. M. et al. Marfan syndrome. Nat. Rev. Dis. Primer 7, 1–24 (2021).

3.              McKusick, V. A. Introductory Speech for Hal Dietz. Am. J. Hum. Genet. 81, 660–661 (2007).

4.              Dietz, H. C. et al. Marfan syndrome caused by a recurrent de novo missense mutation in the fibrillin gene. Nature 352, 337–339 (1991).

5.         Neptune, E. R. et al. Dysregulation of TGF-beta activation contributes to pathogenesis in Marfan syndrome. Nat. Genet. 33, 407–411 (2003).

6.         Loeys, B. L. et al. Aneurysm Syndromes Caused by Mutations in the TGF-β Receptor. N. Engl. J. Med. 355, 788–798 (2006).

7.         Habashi, J. P. et al. Losartan, an AT1 antagonist, prevents aortic aneurysm in a mouse model of Marfan syndrome. Science 312, 117–121 (2006).

8.         Brooke, B. S. et al. Angiotensin II Blockade and Aortic-Root Dilation in Marfan’s Syndrome. N. Engl. J. Med. 358, 2787–2795 (2008).