Advancing technologies, new genomic testing techniques, and championing for up-to-date health care policies are the focus of Dr. Yvonne Bombard and her lab. Dr. Bombard’s mission is shaping patient care with the fast-evolving health care tools of Medical Genomics.

Kevin Navarro Hernandez and James Sayre

A major aspect of the Canadian identity is its recognition and protection of historically marginalized groups. The protection of individuals based on gender, ethnicity, and physical ability have long been recognized in Canadian law – more recently joined by the protection of sexual orientation of individuals in 1996¹. These rights are at the heart of protecting individuals from direct discrimination on the bases of traits which are inherent to their personhood. From a biological perspective many of these protected traits– ethnicity, gender, physical ability, sexual orientation – are genetic at their root, but the protection against genetic discrimination was only ratified federally in 2017^2,3 thanks to the initiative of Dr. Bombard.

While observing patients being tested for Huntington’s disease during her PhD, Dr. Bombard witnessed genetic discrimination firsthand. A life-altering neurodegenerative disorder, Huntington’s disease is a rare genetic disorder that damages nerve cells, leading to reduced mobility, cognitive ability, and in some cases psychiatric disorders. One of the patients Dr. Bombard connected with shared how their employer upon learning of their diagnosis began to assign them fewer working hours, discriminately changed their job responsibilities and put the individual under surveillance at work.  The disease had not advanced to warrant such changes.  It was the employer’s knowledge of the diagnosis that prompted these actions making work much more difficult for the patient.

Dr. Bombard recalled: “I remember … phoning a legal scholar that I knew at the University of Toronto and asking for his advice; asking him, is this legal? Does this happen? Does Canada have any protections against this for their patients? And he said, no.”

Seeing there was no protection for those receiving rare disease genetic diagnoses, Dr. Bombard set about to describe a framework of protection for such patients.   Her vision was to develop a system for those found to be at greater risk of being diagnosed with a genetic disorder; a system that allowed them to not only navigate their test results but to also learn what would be considered as discriminatory towards them because of their diagnosis.  Dr. Bombard characterized this as a socio-medical relationship between patients and their employers, showing it to be fraught with difficult decisions, financial considerations, and emotional risks^4,5,6.

Catalyzed and determined by her work with Huntington’s, Dr. Bombard pivoted her doctoral work towards the development of legal protection of individuals on a genetic composition basis, using what she had seen with Huntington’s disease as a model. Working with academic colleagues, legal scholars, and political allies, Dr. Bombard was instrumental in introducing Bill S201 to federal parliament. Bill S201 passed on May 4, 2017, in the federal legislature bringing the Genetic Non-discrimination Act into Canadian law^2,3.  This act protects individuals from being forced to undergo genetic testing, protects the results of previous tests from disclosure to employers and insurance providers, and bolsters Canadian’s right to medical privacy.

Dr. Bombard’s Lab Mission

Dr. Bombard’s work on behalf of patients receiving genetic testing did not end there.  Dr. Bombard and her laboratory are in a constant race with the advances in genomics.  The field of genomics grows daily as technology quickly develops, improving research methods and implementation of treatments for various genetic diseases.

Genomic medicine is expanding from a focus on research and diagnosis to prevention at the population level. As technology progresses and test costs fall, screening pre-symptomatic individuals through public health-based approaches could become feasible^6,7. Recent studies found that restricting screening for patients who meet the family history-based criteria for breast cancer has missed more than 50% of individuals with pathogenic BRCA1 and BRCA2 (BRCA1/2) variants⁸. These findings are suggestive that population screening is the key towards preventative medicine. Being able to identify variants even in communities where there is no family history of rare diseases is bringing health care to everyone and a step towards making it universal.

Considerations for population genomics screening are whether to perform full gene sequencing or targeted variant testing. Other variables to consider are whether to test for novel variants or only known pathogenic ones, as well as performing deletion/duplication analysis in addition to sequence analysis. These kinds of decisions will have an impact on the test cost and cost-effectiveness effect on patients. Some of the most prevalent hereditary conditions that should be considered for population screening include familial hypercholesterolemia (FH), Lynch syndrome (LS), and hereditary breast and ovarian cancer syndrome (HBOC)⁸. These are some of the diseases that the Bombard lab is researching.

Issues with implementing population genomic screening include the optimal testing approach, penetrance of these conditions in the general population, clinical and cost-effectiveness, acceptability, health system capacity to implement such a program, ethical issues such as overdiagnosis, access challenges and equity^6,7. 

At the center of Dr. Bombard’s research, is a strong ethos of patient centric and collaborative care. As an extension of this ethos, part of her current work revolves around educating patients on the fundamentals of genetics. This opens the door to discussion about the implications of genetic testing, and describing how to best communicate results in a way that allows patients to make informed decisions about their care. This research has culminated in the development ofThe Genomics ADvISER (Genomics decision AiD about Incidental Sequencing Results), a platform designed to fill the gaps in contemporary care by offering education resources, exercises to help patients explore their values with regards to testing, and ultimately feel empowered in making their care decisions.

A common question is: What makes genetic testing different from other more common medical testing? Genetic testing can have unexpected and broader implications and responsibilities.  It has the potential to open up avenues of far reaching medical consequences.  Firstly, because genetic information is heritable, passed between parents and children, testing inherently provides information on related individuals. This can expand the implications of the testing beyond the individual for which it is intended.  Furthermore, secondary findings – findings during genetic testing which are not related to the primary purpose of testing – are becoming far more common with increases in the use of Next Generation Sequencing techniques, and whole genome sequencing which return huge amounts of genomic information. “we’re now just inundated, bombarded, if you will,…with a lot of data that we have to sift through”.  This is where the ADvISER platform helps bridge the gaps between this mass of information, physicians who are already stretched thin and patients who may be encountering the world of genomics for the first time, in potentially vulnerable states. Proponents for genomic medicine say that we should be sequencing everyone at birth, and while there is merit to this approach in terms of treatment, there is also subsequent fallout that is generated by secondary findings, implications for related individuals who are uninterested in genetic testing and the larger scale delivery of this information in a comprehensible form.

Dr. Bombard describes how “ ADvISER was actually also built, developed and then tested out of necessity because we were returning secondary findings…where we’re sequencing individuals and returning results”. Because of the untargeted nature of extensive genomic sequencing, like whole genome sequencing, secondary findings are an unavoidable product of genetic screening. Some sequence information can simply be overlooked, but what are physicians and patients supposed to do in cases where testing returns consequential and actionable secondary findings? This is where the ADvISER platform is intended to aid patients in the decision-making process to come to a conclusion which best aligns with their personal ethics around testing, treatment, and how to handle the potential uncertainties that comes with genetic testing^9,10.  Genetic testing has the potential of TMI (too much information).  ADvISER helps doctor, patient and medical system disseminate the results of the genetic testing in order to plot a way forward for patient care.

Dr. Bombard’s field of research and job are challenging.  In reply to what is the biggest challenge in policy research, Dr. Bombard said: “being mindful of the fact that there’s a fine line between being a scientist, a science advocate and a policy advocate”. The job comes with many responsibilities not only for her as an advocate, but also the responsibility of developing efficient policies that meet society’s needs. This is an ongoing process with no end in sight. During our interview with Dr. Bombard, she said: “the irony is that the technologies will constantly evolve and what we can bring into our system will have to evolve as well”. This means that even if the system is restructured and redesigned to serve the point in time we live in now, it will have to be reshaped to keep up with technological advances.  The world of genomic policy is rapidly growing and changing.  At the end of our interview, Dr. Bombard left us with this final quote about what her end goal as a pioneer of medical genomics in policy would look like: “I feel like mission complete is when we’ve structured our health care system for our patients via genomics and it is patient centered serving them in the way that they want and need”.

References

1. Government of Canada. Canada.ca (2022). Available at: https://www.canada.ca/en/canadian-heritage/services/rights-lgbti-persons.html. (Accessed: 10th April 2023)
2. Standing Senate Committee on Human Rights (41^st Parliament … – Sencanada, sencanada.ca/en/Content/Sen/committee/412/ridr/11ev-51620-e.
3. Branch, L. S. Consolidated federal laws of canada, Genetic Non-Discrimination Act. Genetic Non-Discrimination Act(2023). Available at: https://laws-lois.justice.gc.ca/eng/acts/G-2.5/page-1.html. (Accessed: 10th April 2023) 

4. Bombard, Y. et al. Managing genetic discrimination: Strategies used by individuals found to have the Huntington disease mutation. Clin. Genet. (2007).
5. Bombard, Yvonne et al. “Perceptions of genetic discrimination among people at risk for Huntington’s disease: a cross sectional survey.” BMJ (Clinical research ed.) (2009). doi:10.1136/bmj.b2175
6. “Why We Need a Law to Prevent Genetic Discrimination.” The Globe and mail, http://www.theglobeandmail.com/amp/opinion/why-we-need-a-law-to-prevent-genetic-discrimination/article31936476/.
7. Mighton, Chloe, et al. “From the Patient to the Population: Use of Genomics for Population Screening.” Frontiers in Genetics. (2022) https://doi.org/10.3389/fgene.2022.893832.
8. Manchanda, R., Loggenberg, K., Sanderson, S., Burnell,M.,Wardle, J., Gessler, S., et al. Population testing for cancer predisposing BRCA1/BRCA2 mutations in the ashkenazi-jewish community: A randomized controlled trial. J. Natl. Cancer Inst. (2015). https://doi:10.1093/jnci/dju379
9. Bombard, Y. et al. Genomics ADvISER: development and usability testing of a decision aid for the selection of incidental sequencing results. Eur. J. Hum. Genet. (2018).
10. Bombard, Yvonne et al. “Effectiveness of the Genomics ADvISER decision aid for the selection of secondary findings from genomic sequencing: a randomized clinical trial.” Genetics in medicine: official journal of the American College of Medical Genetics. (2020). doi:10.1038/s41436-019-0702-z
11. Shickh, Salma et al. “Genetics Adviser: a protocol for a mixed-methods randomised controlled trial evaluating a digital platform for genetics service delivery.” BMJ (Clinical research ed.) (2022). doi:10.1136/bmjopen-2022-060899

Lorin Crawford investigates the role of epistasis in complex trait architecture. His findings emphasize the importance of including diverse populations in genomics research – and the perils of neglecting them.

Nina Anggala and Solomiya Hnatovska

From our first correspondence, Lorin Crawford is effusive, warm, and quick to respond — the antithesis of what you might expect from a rising genetics superstar. We each phone in from the northeast coast, falling snow at our windows. By the end of the conversation, we are left reminiscing about childhoods under the California sun and shared vantages of the hills. Convenient opportunity to complain about the snow aside, charting the geography of his life also allows us to chart the evolution of his career, from an undergraduate in mathematics to a career as a lauded genome scientist.

We begin our discussion with Crawford’s latest venture: Marginal Epistatic Linkage Disequilibrium regression, or MELD¹. A software package three years in the making, MELD is a testament to his crossing of disciplines, an algorithm designed to detect non-additive interactions, or epistasis, in Genome-wide Association Studies (GWAS). The success they’ve found applying MELD to real-world data invites us to redefine our understanding of gene-to-gene interactions and, on a larger scale, confront the very foundations on which we’ve built these models.

The very word is anathema for many biology majors, but the seeds for MELD can inevitably be found in Crawford’s math undergrad, completed at Clark Atlanta University. When asked about his transition to genomics, he throws it all the way back to a summer research experience where his focus was group theory, “a pure math topic about trying to understand how groups of numbers are related to each other in different theoretical ways.” At the final poster presentation, he recalls a judge asking him about the practical applications of his group’s research—to which he replied, there were none.

Though she laughed it off, that singular interaction launched Crawford into deep introspection. In addition to having no practical applicability, he had found the whole experience largely isolating, with students working independently until they compiled their work. He realized that in order to find fulfillment in his work, it needed to have real world impact.

“So that following semester,” he tells us, “I took statistics.”

Statistics is a body of applied mathematics, integral to all the life sciences. Where we cannot ethically or feasibly control an individual’s environment, statistics is how we account for those confounding variables². He recalls two formative experiences in particular: a summer studying prostate cancer incidence rates in Atlanta’s African American population and a “life-changing” Differential Equations class. In the latter, he learned about modeling HIV mutation rates and how the rate of mutation impedes the search for a cure—what some have aptly described as a fraught arms race between virologist and virus.

“Every three days, you basically have a new disease,” he says, “I thought that was super fascinating – and you could study all that just using differential equations.” The contours of the career he wanted for himself were beginning to take form. (Intrusive thoughts of going to business school notwithstanding.)

As an aside, if there was ever any doubt that the way forward for Crawford was statistics, when asked whether he’d rather live through a zombie apocalypse or an alien invasion, he answers, “Man. I’m gonna say a zombie apocalypse because at least I know what I’m up against. An alien invasion sounds tough because there are a lot of unknowns there.” Affinity for known variables aside, Crawford is clearly adaptable enough to handle any (terrestrial) curveballs thrown his way.

Graduating summa cum laude – an achievement he didn’t volunteer himself, but that we dug up on Google – with a Bachelor’s in mathematics, he decided to pursue a doctorate in Statistical Sciences at Duke University, under the supervision of Sayan Mukherjee. Mukherjee in turn introduced him to Kris Wood, whose lab was attempting to model therapeutic resistance in cancer subtypes. Under their joint supervision, Crawford would spend his graduate education going back and forth between the Statistics Department and the wet lab.

“The way I learned about genomics really was being in [Wood’s] group,” he tells us. “It was kind of the equivalent of, I would say, living in a different country. And then you have to learn the language of that country, right? The best way to do this is to actually embed yourself in that culture.”

Although not a genomicist by trade yet, Crawford was uniquely suited for this project. The same principles govern therapeutic resistance in cancer and HIV: the disease evolves, and researchers must find a way to track these mutations and respond accordingly. “It presented me a way to approach that problem from a purely statistical modeling perspective,” he says. “His lab didn’t have anyone computational, so it really gave me the opportunity to grow in that space.”

He would go on to present his thesis on the use of machine learning in statistical genetics and cancer genomics, topics that would fuel the beginnings of MELD when he arrived in Providence – where he now holds an Associate Professorship at Brown University and works as a Principal Researcher with Microsoft Research New England.

Crawford soon turned his eye to the goliath of big data statistics: GWAS. GWAS involves sequencing the genomes of a large sample of people, in an attempt to find statistically significant correlations between variants present in that population and a phenotype of interest (Fig. 1)³. This technique is most useful when studying relatively common traits, as innocuous as height or as widespread as diabetes. In these cases, you expect to see modest contributions from many variants, culminating in your phenotype of interest. An additive model of trait architecture, which describes the genetic underpinning of a phenotype, sums these contributions⁴.

“There’s a theory that additive effects are primarily the types of effects that drive the variation across traits,” Crawford tells us. “There are a lot of different reasons for this line of thinking … but the simple answer is, you would imagine that humans are going to take the easiest path towards evolution.”

The fault in the additive theory is not that it is theoretically unsound, but that it’s been treated as universal even though these inferences have only ever been validated in an exclusive subset of people – “The databases that we study are heavily skewed towards one type of person,” Crawford tells us. “Over 90% of the samples in some of these databases only represent roughly 19% of the human population” – and despite evidence for significant contributions from non-additive interactions^{6, 7}. In a non-additive interaction, two or more loci modulate each other’s expression to produce a phenotype; for example, inheriting the allele for baldness would supersede the effect of alleles for hair colour. This is what we refer to as epistasis, wherein the effect of a mutation depends on the genetic background in which it is present⁸.

“If I changed my viewpoint on different groups of individuals, I might see different types of effects start to arise as being important,” Crawford says. “Computationally speaking, we’ve bought so much into this linear additive theory, we don’t even build models anymore to investigate the nonlinear portions.”

The fruits of the Crawford lab’s labor came once they applied MELD, which had been trained and tested on simulated data, on the UK and Japan BioBanks. By including non-additive effects in the MELD algorithm, they were better able to quantify the variation and heritability of 23 out of 25 complex traits they studied, such as height and cholesterol levels¹. These results challenge the long-standing belief that epistatic interactions are negligible and provide evidence that epistasis is much more widespread than previously thought.

Crawford also notes that their findings are ultimately “aligned with long standing theories.” Rather than a reinvention of the wheel, his group patches fault lines in the spokes by adding to the assumptions baked into previous models. He hopes to carry this philosophy into future research projects, which he says will center around two major focal points: redefining diversity as a descriptor that exists along many different axes – “not just along the lines of ancestry, which can sometimes be conflated with race and ethnicity” – and encouraging the use of machine learning to tackle increasingly large, -omics level data sets.

“The biggest impact I hope that we have,” Crawford says, “is this notion of thinking about what it means to have our understanding of trait architecture be for all people.” His research aims are integral to achieving equality in precision medicine, which will remain out of reach until the field reaches a more nuanced understanding of diversity and identity.

Realizing this goal will require larger, more diverse human datasets (this is where machine learning comes into play), though Crawford urges caution, citing the pervasiveness of helicopter science. Helicopter science is a manifestation of predatory inclusion: an ongoing approach to collecting genomic data, wherein researchers descend on lower income communities without forming meaningful collaborations⁹. Comprehensive datasets benefit everyone, but the inclusion of an individual’s genome doesn’t guarantee them the resources or infrastructure to access the knowledge their information yields. Emphasizing the need for an ethical, human-oriented approach to genomics research, Crawford sees the future of genomics, and indeed all data science, in interdisciplinary collaborations across math, genetics, and social sciences.

When it comes to research in underrepresented communities, Crawford envisions “an almost complete marriage” of research groups: between specialists coming from the outside and experts within those populations, with the goal of building infrastructures to support their study and its aftermath. These are all hallmarks of community building initiatives, which Crawford points out is difficult to do with only geneticists. “You work across these lines where it looks like we don’t have anything in common,” he says. “And so, each person is bringing a unique perspective to the field.”

Crawford is, without doubt, a brilliant mind, able to tackle some of the biggest questions in genetics, but he is made even more admirable by his humility and pathos. He emphasizes that his work is rewarding, because it is inherently collaborative: MELD was initially an idea he dreamed up with Sohini Ramachandran when he first arrived at Brown and was brought to fruition in collaboration with the Ramachandran lab.

“I’ve been really fortunate to work with people who are insanely smart,” he says, “who like to ask really hard questions, are super humble and don’t take themselves too seriously. I’m always the least knowledgeable person in the room.”

Whether he’s deserving of that superlative or not, Crawford has been inducted as a member of The Root’s 100 Most Influential African Americans, was featured in Forbes’ 30 Under 30, and is the recipient of numerous prestigious fellowships, including the David & Lucile Packard Foundation Fellowship for Science and Engineering. We ask him which of his achievements he is most proud of and he answers, without hesitation, “I graduated four people last year. Those are my first four students that I graduated.”

The joy he finds in pedagogy and collaboration belies an even deeper inclination – to work at the bleeding edge without leaving anyone behind. Crawford embodies a future wherein scientific inquiry lights the way forward for those who are most vulnerable. A throughline in his academic career, since that fateful summer, has been a desire for his research to have real word application; MELD presents a more complete understanding of complex human genetics and in doing so lays the groundwork for the development of precision medicine for all.

But Crawford doesn’t ordain himself a revolutionary.  “I got into this because I loved the notion of having autonomy,” he says. “And that’s always kind of been my North Star, being in a space where I get to define what’s cool.” 

Cool, after all, is the very highest accolade any scientist can hope to achieve – we humbly submit that, for a guy from Chino Hills, Lorin Crawford is pretty cool.

References

1. Darnell, G., Smith, S. P., Udwin, D., Ramachandran, S. & Crawford, L. Partitioning tagged non-additive genetic effects in summary statistics provides evidence of pervasive epistasis in complex traits. bioRxiv (2022). doi:10.1101/2022.07.21.501001
2. Willcox, W. F. The founder of Statistics. Revue de l’Institut International de Statistique / Review of the International Statistical Institute 5, 321 (1938).
3. Uffelmann, E. et al. Genome-wide association studies. Nature Reviews Methods Primers 1 (2021).
4. Understanding genetics: A district of columbia guide for patients and Health Professionals. National Center for Biotechnology Information (2010). Available at: https://pubmed.ncbi.nlm.nih.gov/23586106/. (Accessed: 12th March 2023)
5. Genome-wide association studies fact sheet. National Human Genome Research Institute (2020). Available at: https://www.genome.gov/about-genomics/fact-sheets/Genome-Wide-Association-Studies-Fact-Sheet. (Accessed: 12th March 2023)
6. Visscher, P. M. & Goddard, M. E. From R.A. Fisher’s 1918 paper to GWAS a century later. Genetics 211, 1125–1130 (2019).
7. Zuk, O., Hechter, E., Sunyaev, S. R. & Lander, E. S. The mystery of missing heritability: Genetic interactions create phantom heritability. Proceedings of the National Academy of Sciences 109, 1193–1198 (2012).
8. Gros, P.-A., Le Nagard Hervé & Tenaillon, O. The evolution of epistasis and its links with genetic robustness, complexity and drift in a phenotypic model of adaptation. Genetics 182, 277–293 (2009).
9. Adame, F. Meaningful collaborations can end ‘helicopter research’. Nature (2021) doi:10.1038/d41586-021-01795-1.

Dr. Charles Boone already rocked the world of yeast genomics in 2018 with the yeast gene interaction map. His work foretells that a gene interaction map for humans may be possible within the year. 

Thomas Barbazuk, Lise Cinq-Mars, and Pooja Kiran Ravi

A little over 20 years ago, two yeast biologists came up with an idea that to most would sound crazy. Dr. Charles Boone and his colleague, Dr. Brenda Andrews wanted to find out which genes were essential to the survival of yeast by systematically knocking out genes in sets of two. Considering that yeast has about 6000 genes, anyone would assume this would be an incredibly lengthy project. Nearly two decades later, Dr. Charles Boone and Dr. Brenda Andrews in collaboration with Dr. Chad Meyers published a gene interaction map of yeast (Figure 1), setting off a storm in the field of genetics.  

A principal investigator and Professor at the University of Toronto’s Donnelly Center for Cellular and Biomolecular Research, Dr. Charles Boone is a monolithic figure within yeast genomics. He completed his Bachelor of Science in Chemistry and Math at Queens University. He subsequently went to McGill University to obtain a Ph.D. in Biology and the University of Oregon where he was a research fellow at the Institute of Molecular Biology. Having been involved in the yeast genetics community for three decades, Dr. Boone has adapted with the ever-changing field of genetics, constantly pursuing a deeper understanding of the fundamentals of life. Yeast provides spectacular insights into cellular processes and gene interactions, whilst remaining a much more tractable organism to work with in a laboratory setting than others. The beauty of studying yeast is that much of it is scalable to human genetics and can be used as a practical model to study human cellular interactions. Eleven Nobel laureates have come from breakthrough discoveries in cancer and cell function by using yeast as a model organism, validating yeast as one of the most thoroughly explored model organisms in the world.  Together with Dr. Brenda Andrews (Professor and Principal Investigator at the University of Toronto and the Donnelly Center respectively¹), Dr Boone has mapped yeast genetic interactions on an unprecedented scale by using their very own automated approach to mapping called Synthetic Genetic Array (SGA) analysis². The method has allowed for the analysis of millions of mutant yeast strains, successfully teasing out gene to gene interactions and their corresponding biological processes to explore the fundamentals of life. 

The yeast genetic interaction network is very informative in addition to being colorful and beautiful (Figure 1). It helps us to understand the complexity of the cell by organizing genes in a categorical manner. It also shows us the genes in the yeast DNA that are similar to humans which could indicate those that are required for survival. The advantage of studying these genes in yeast, in addition to learning about a new organism, is that we can recognize approximately a thousand that are essential to cell function in humans​³​. Any changes to them or a combination of changes can result in cell death​³. Furthermore, the DNA regions are categorized into modules with similar functions which have ways of communicating with each other and any changes to this communication can result in the overall disruption of each cell. For example, the region that controls response for misfolded protein in the endoplasmic reticulum is a large segment of DNA that involves many genes​⁴​.  Introducing different combinations of mutations into yeast DNA shows us that what causes cell death in yeast can affect cells in all organisms. This is the basic principle behind Synthetic genetic array analysis (SGA) technology that is used extensively in Dr. Boone’s lab. 

While some of the immediate implications of such findings are in human medicine, we could also see maps where we understand where our different traits come from such as intelligence, behavior, etc. Dr. Boone noted that “Human genetics is … fundamental to our understanding of human health … and since we don’t know how to interpret it, I think this is the [era] of Human Genetics… That translates into personal medicine. So, I think the most obvious thing is medically related. But … what goes along with Human Genetics is all kinds of stuff [like] behavior, … intelligence, there’s going to be all kinds of things about humans that we will probably be able to quantify.” 

When Dr. Boone entered the field of yeast genetics, the yeast genome was not yet sequenced but there were known regions of genes that helped propel forward an age of genetic experiments with studies in conservation of function. Once the sequence of yeast genome was revealed, it was easier to design microarrays that studied functions since a similar experiment in humans would be much more difficult to design and understand.  

Dr. Boone showed concern that the world is moving its eyes away from Yeast research because “There’s this bias against model organisms in general, even though they’ve basically fueled a lot of biology. And so, it’s harder to get a model Organism grant.” Dr. Boone feels “The biggest problem facing in genetics today is that we don’t understand the general rules yet.” He believes “the genotype phenotype problem for an individual is what’s going to … allow us to figure out personalized medicine.” Given that this is quite complex and that variation in each person’s genome only adds to the complexity, he wants to build a human cell map (much like the one he built for yeast) that would fundamentally change our ability to interpret human genomes.  

“No one’s made a human cell map, so we should be able to do that.” 

This would show us the connections between our 20,000 known genes and help physicians determine how variation in an individual’s genome (genotype) could translate into an altered manifestation of a trait (phenotype).

Another setback for the field is that “the big leaders have all retired… they were like superstar hero humans that span[ed] that transition from phage to yeast and they created this community.” The pioneers of the field were also very helpful in fostering community spirit even while competing against each other. He also mentioned that it was also easy to collaborate and work together in the yeast labs. Dr. Boone and other labs at the Donnelly Centre for Cellular and Biomolecular Research now carry on the tradition of nourishing teamwork.  It also shows when he is proud that his lab is “open concept” which allows for easy collaboration with other researchers – Dr. Tim Hughes and Dr. Brenda Andrews. 

 With the interdisciplinary nature of the life sciences now, collaboration is a crucial component to success as a researcher. A paradigm shift has occurred within scientific research in the last thirty years towards massive collaborative projects; whereas independent work used to be hailed and applauded as the gold standard. To quote Dr. Boone, “The perfect researcher was formerly one who conducted all facets of the experiment and analysis on their own and the ‘single author’ paper was supposedly the best paper”. With improvements in communication technology and increases in scientific understanding, contemporary science encourages individuals to specialize in specific areas to foster a deeper understanding in their respective fields. Now, experts exist and communicate together regularly, as it remains a necessity due to the multi-faceted nature of the life sciences.  

“Working in systems biology today usually means you need, biologists, computational scientists, and likely engineers, so you’re forced into collaboration, right? And but that’s the most fun part because then you get to do three times as much.” 

In the context of genomics, there is a need for computational scientists as well as wet-bench scientists, not to mention the multitude of very expensive laboratory tools that are not typically found in every research laboratory within a university. Multi-institutional projects are commonplace as scientists each focus on their respective areas to address biological questions with more robust publications. Dr. Boone emphasizes the importance of collaboration in modern science, especially in the context of his research lab. He heavily emphasized that strong collaboration with Dr. Brenda Andrews has been a fundamental part of his laboratory’s success. Dr. Boone also mentioned crucial and regular collaboration with Dr. Chad Meyers (Professor and Co-Director of Graduate Studies for Bioinformatics and Computational Biology at the University of Minnesota⁵) as well as Dr. Jason Moffat (Professor and Principal Investigator at the University of Toronto and the Donnelly Center respectively⁶ & Program Head of Genetics and Genome Biology at the Hospital for Sick Children⁷). Dr. Chad Meyer’s research focuses on integrating complex genomic data to make inferences about biological networks. Dr. Jason Moffat is currently collaborating with the Boone lab on a project that involves mapping human cancer cell interactions by using the Boone labs yeast genetic interaction network as the framework. The Moffat lab harnesses CRISPR gene editing technology to deliberately induce isolated genetic changes in human cells, allowing for the observation of cellular relationships. The course of this experiment will help to explore the disparity between genotype and phenotype in human cancers, allowing for further insights into cancer biology that are fundamental in the development of novel treatment strategies. Dr. Boone emphasized that successful collaboration is largely reliant of finding like-minded individuals with a shared drive for discovery. Dr. Boone stated that trust on all levels is fundamental in successful collaboration such that everyone gets credit for their work. He alluded to the importance of a team-based approach and hinted that successful collaboration with a drive for discovery is what the Donnelly Center is all about.  

“It’s about making sure everyone gets credit and feels like they are part of the discovery while working together to solve a problem as a team.” 

Dr. Boone continues his collaborations in Japan where he is the Leader of the Molecular Ligand Target Research Team at the RIKEN Centre for Sustainable Resource Science. Dr Boone admitted that he truly doesn’t have a lot of freetime since he enjoys his research so much. But in his spare time, he enjoys canoeing & fishing all around the world, including the Arctic, and traveling to Japan to continue his research at the RIKEN Institute. 

Dr. Boone hopes to pull some more focus back to model organisms, such as Yeast, as he believes they will be instrumental in producing a much needed “toolkit” for interpretation of the human genome. Additionally, he and his team are currently studying how the background effects of a genetic network may give answers to the different levels of penetrance in human disease. In 2020, Dr. Boone was named the inaugural Banting & Best Distinguished Scholar which recognizes top researchers at the Temerty Faculty of Medicine who are having life-changing impact through their discoveries. As we wait for the time when Dr. Boone is ready to publish a human cell map, illustrating the complex interactions between all our genes, we can only hope the rest of the world will recognize how this yeast biologist will have changed our lives. Providing a map for the most complex instructions to life and how we function will become the basis of all personalized medicine and undoubtably change the field of medicine forever.    

 References

1.        Donnelly Centre for Cellular and Biomolecular Research – University of Toronto. Brenda Andrews. https://thedonnellycentre.utoronto.ca/faculty/brenda-andrews (2023).

2.        Hin, A., Tong, Y. & Boone, C. Synthetic Genetic Array (SGA) Analysis in Saccharomyces cerevisiae Running head: Synthetic Genetic Array (SGA) Analysis. Yeast Protocols, Second Edition. Methods in Molecular Biology 313, 171- 192 (The Humana Press Inc., Totowa, NJ, U. S. A.) (2005).

3.        Segal, E. et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. 34, 166-176 (2003).

4.        Kincaid, M. M. & Cooper, A. A. Misfolded Proteins Traffic from the Endoplasmic Reticulum (ER) Due to ER Export Signals. Mol. Biol. Cell/ 18, 455–463 (2007).

5.        Department of Computer Science & Engineering – University of Minnesota. Chad L. Myers. https://cse.umn.edu/cs/chad-myers (2023).

6.        Donnelly Centre for Cellular and Biomolecular Research – University of Toronto. Jason Moffat. https://thedonnellycentre.utoronto.ca/faculty/jason-moffat (2023).

7.        The Hospital for Sick Children. Jason Moffat. https://www.sickkids.ca/en/staff/m/jason-moffat/ (2022).

EpiSign establishes itself as a promising first tier test for diagnosing rare disorders through the unique features of epigenetic signatures in combination with the immense computational power of machine learning algorithms.

Ilham Abbasi, Sean Williams, and Mailoan Panchalingam

Rare diseases make up a significant health concern, both in Canada and globally, reaching a total estimated prevalence of 3.5% to 5.9% worldwide¹. This vast and heterogeneous group of disorders often have poor prognoses, leading to chronic illness, decline in function, disability, and early death. Although genetic testing can provide answers for 25–35% of undiagnosed rare disease patients², many are left with inconclusive results and unresolved questions. This results in a long diagnostic odyssey of doctor visits and medical testing, accompanied by an increasing financial burden. Fortunately, with advances in genomics and technology, the field of epigenomics has brought about several breakthroughs in diagnosing rare diseases¹.

When we think about genetics, we often focus on the physical blueprint of the DNA code. However, there are also complex chemical changes that occur on the DNA sequence to influence gene expression – these are known as epigenetic modifications³. Epigenetic modifications take place without any alterations to the raw DNA sequence itself, and can be influenced by environment, diet, and lifestyle choices⁴. One of the most prominent epigenetic mechanisms is DNA methylation, which involves adding a methyl group to specific sites in the DNA sequence, known as CpG sites. DNA methylation is critical for many biological processes, including development, ageing, and illness³. Not suprisingly, a number of diseases have been associated with abnormal DNA methylation patterns in the epigenome, including cancers and neurodevelopmental disorders. These reproducible  DNA methylation patterns, also known as episignatures, can be used as biomarkers for the early identification and diagnosis of various diseases¹.

At the forefront of epigenomics is Dr. Bekim Sadikovic, the founder of EpiSign. Dr. Sadikovic’s passion for the field began during his Ph.D. studies at the London Health Sciences Centre (LHSC) and Western University, where he studied the epigenomics of cancer. He chose to work in this trailblazing field because he “felt strongly that genomics and epigenomics and big data are going to make an important impact in the diagnostic field”. Following his postdoctoral studies and clinical training, he joined LHSC as a laboratory director, where a majority of his time has been devoted to translational research in genomics and epigenomic diagnostic biomarkers. As the program head of the molecular diagnostics program, Dr. Sadikovic oversees the implementation of molecular diagnostics with large genomic databases into clinical care, which has opened up new avenues for patient diagnostics, treatments, and disease prevention.

The defining moment when Dr. Sadikovic knew epigenomics could transform patient diagnostics and clinical care occurred over a decade ago. While analyzing data for a research study, he noticed that two patients with the same genetic disorder had strikingly similar genomic DNA methylation patterns. This discovery sparked a question for Dr. Sadikovic – could epigenomic patterns be used to diagnose new patients with the same disorder? Fast forward to today, we commonly know these unique but reproducible patterns as “episignatures”. This was only the beginning of many “aha” moments in Dr. Sadikovic’s career that eventually led to the inception of EpiSign – the first clinically validated test that can analyze whole genome methylation signatures for the diagnosis of rare genetic disorders. Not only has EpiSign impacted the field of epigenomics, but it has impacted the healthcare system on a global scale:

“We became the first lab in the world to actually use genome-wide methylation data, that are built on artificial intelligence algorithms, to officially start diagnosing patients, which was a big deal.”

EpiSign primarily relies on machine learning AI algorithms that use DNA methylation patterns as biomarkers to diagnose patients with rare genetic conditions (Fig. 1)^1. The computational process of defining an episignature occurs through two steps. First, is identifying which CpG sites in patients with a particular condition are differentially methylated compared to healthy individuals. This is known as probe selection, and it requires large patient cohorts and a vast amount of genomic data to ensure that the analysis is accurate and predictive. Once the differentially methylated sites have been determined and statistically validated, machine learning algorithms are used to distinguish between the methylation patterns that come from rare disease patients and healthy individuals. This is used to develop a mathematical model called a classifier, which can be used to diagnose patients with a particular disorder based on their DNA methylation profile.

In the clinic, a rare disease patient’s journey with epigenetic testing starts when they have undergone genetic testing and receive unambiguous results known as variants of uncertain significance (VUSs). These VUSs make it difficult to narrow down diagnosis for a single genetic condition, since their impact on disease is unclear. But, as Dr. Sadikovic puts it, “it’s not because these patients don’t have genetic conditions. It’s very likely that many of them do. It’s because our ability to understand the human genome is still very limited”. To resolve these ambiguous findings, a patient may be referred to Pathology and Laboratory Medicine at LHSC for EpiSign testing. In other cases, a patient is directly referred for EpiSign testing as a first-tier test because they are suspected to have clinical features associated with an epigenomic-related syndrome.

The DNA methylation profiles of these patients are then screened against known episignatures housed in the EpiSign Knowledge Database – the world’s largest data methylation profile database for rare disorders, developed by Dr. Sadikovic and his research group (Fig. 2)⁵. If a VUS occurs in a genomic region which has been epigenetically mapped, the patient’s DNA methylation profile can be compared to defined episignatures in this region that are known to be associated with a particular condition. For patients with no genetic testing results, their DNA methylation profile is typically screened against all syndromes in the EpiSign Knowledge Database. Not only can this reclassify inconclusive and negative genetic testing results in 20 – 30% of cases, but it can rule out more than 70 epigenomic-related syndromes as well⁵. However, as Dr. Sadikovic points out, a negative EpiSign test result does not necessarily mean that a patient does not have a genetic condition, or that the VUS is not associated with the disease:

 “We know we haven’t mapped all episignatures. We don’t know what’s not there…there may be variants that produce an alternate type of methylation signature that we just haven’t mapped.”

To date, EpiSign has mapped out episignatures for over 90 genetic conditions and 96 different genes, and this number only continues to grow. Currently, Dr. Sadikovic’s group is mapping out episignatures for over 500 different conditons in a collaborative effort with over 200 institutions across 30 countries – a major impact on healthcare both nationally and globally. When asked about what the future holds for EpiSign, Dr. Sadikovic explains how the development of novel technologies will improve genetic testing within the healthcare system. His lab is working with multiple research groups and industry partners to create technology that will bridge the gap between genomic variant detection and epigenetic signatures simultaneously in a single test. This will ultimately benefit both patients and the healthcare system in two ways – firstly, patients can gain a significant amount of information from a single test, reducing the amount of genetic testing required by a patient. Secondly, the cost burden for both patients and the healthcare system will be minimized by decreasing the need for further genetic testing among patients who initially received inconclusive results.

Dr. Sadikovic further elaborates on the implications that episignatures and patient DNA methylation profiles will have on the development of drug therapies. He believes that the identification of common episignatures within DNA methylation profiles of patients with rare disorders may one day be exploited to develop more effective drug therapeutics. Conventional personalized genomic medicine sees a single drug developed to treat a single patient with a single variant, whereas through modifying epigenetic patterns, a broader spectrum of patients may be able to benefit from targeted drugs. Dr. Sadikovic then goes on to describe the enormous cost and health benefit of developing drug therapeutics that are based on DNA methylation profiles:

“Development processes can cost 10s to 100s of $1,000,000 for a single drug that sometimes impacts a single patient. So, if we’re able to start identifying and unifying things in patients with rare disorders, being disruptive epigenomic regions, we might want to start thinking about how can we develop potential drugs to change those epigenetic patterns.”

EpiSign has shown that epigenetic signatures serve as a strong source for a first-tier diagnostic test for rare genetic disorders and will likely play a greater role in clinical care, beyond diagnostics. For example, we may see episignatures being used in the near future to predict prognostic outcomes and treatment interventions for patients with rare disorders. This demonstrates a pressing need for more research and innovation in epigenomic testing and technology, which is further exemplified by a fitting final statement by Dr. Sadikovic as he mentions his dreams and goals for the future EpiSign and epigenomics as a whole:

“My goal, and I guess my ambition and my dream, is to see epigenomic profiling for every patient with a suspected hereditary genetic condition.”

References

1. Haghshenas, S., Bhai, P., Aref-Eshghi, E. & Sadikovic, B. Diagnostic Utility of Genome-Wide DNA Methylation Analysis in Mendelian Neurodevelopmental Disorders. International Journal of Molecular Sciences 2020, Vol. 21, Page 9303 21, 9303 (2020).

2. Marwaha, S., Knowles, J. W. & Ashley, E. A. A guide for the diagnosis of rare and undiagnosed disease: beyond the exome. Genome Medicine 2022 14:1 14, 1–22 (2022).

3. Jin, B., Li, Y. & Robertson, K. D. DNA Methylation. Genes Cancer 2, 607–617 (2011).

4. Gibney, E. R. & Nolan, C. M. Epigenetics and gene expression. Heredity 105, 4–13 (2010).

5. LHSC Epigenetics. https://episign.lhsc.on.ca/index.html.

“By the age of three, the child who would have succumbed to Menkes disease was now climbing through stairs and developing normally like other children.”

Syed A K Shifat Ahmed, Nowrin Aman, and Kamalika Bhandari Deka

In the present era, the cost of sequencing an entire genome of an individual is around $1000 with a turnaround time of one week¹. By contrast, in 2003, creating a reference genome for the human genome project took $3 billion and 15 years². With advancement in sequencing tools and improved understanding of human genomics we can now quickly investigate any novel disease or gene- but this was not the case even until the 1990s.  In a time when molecular based diagnosis and treatment were not well established, it required deeper understanding of disease biochemistry and innovative treatment solutions to tackle rare genetic diseases. Dr Bibudhendra Sarkar is one such pioneer scientist who made a novel discovery to treat patients with a rare genetic condition causing neurodegeneration, known as the Menkes Disease.

In 1937 two Australian veterinary scientists first recognized that sheep grazed in copper-deficient pastures stumbled³. Later in the 1960s, researchers approached the Australia Wool Research Laboratories to investigate possible links between copper deficient diet and defective hair formation in sheep³, although any conclusive links remained unsubstantiated then⁴. Two years later five male infants from English Irish heritage were diagnosed with a progressive neurodegenerative disorder and clinical manifestations that included epileptic seizures, developmental regression, and unusual thin “kinky” hair⁴. The disease was called Menkes, a rare inherited disease named after the pediatric neurologist Dr. John Hans Menkes, who first described it in 1962⁴. Later in 1972-73, Dr. David M Danks in a series of landmark publications connected the mystery of “kinky” hairs in sheep and Menkes patients to copper deficiency⁵.

Currently a Senior Scientist Emeritus at the Research Institute of the Hospital for Sick Children (SickKids) and Professor Emeritus, Department of Biochemistry at the University of Toronto, Dr. Bibudhendra Sarkar recalled that research at that time was limited.  Although the Research Institute in SickKids was established in 1954, the only research that was conducted was from a case study perspective. Sarkar was a biochemist with a strong background in biophysics and mathematics, but he had no clinical training. Despite that, he was hired at SickKids and that made him the first basic scientist at the hospital in 1964. It all started with an invitation for a talk from Dr. Andrew Sass-Kortsak, who met Sarkar at a meeting in Chicago. Sass-Kortsak was a physician in genetic metabolic diseases at the Hospital for Sick Children. He specialized in Wilson disease which causes toxic accumulation of copper in vital organs ⁶.

This was Sarkar’s first visit to Canada, and he fondly recalls the talk as spontaneously attended by medical doctors, academicians and students who were greatly enthused by the presentation of his research. The success of the talk was such that Sarkar was offered a Staff Scientist position in the Genetic Metabolic Research Program. Decades later when reflecting what motivated him to leave the US and take the offer at SickKids, an uncommon move in those days for a basic scientist, Sarkar says “I remember after the talk that day, I visited the hospital ward with Sass-Kortsak and seeing the faces of the sick children I felt a human connection that I could never feel in the boundaries of my laboratory and among the inanimate experimental tools.”

In research, collaboration is very important and great scientific ideas are often conceived through discussions with experts. Although not medically trained, Sarkar learned about medical sciences from attending regular clinical rounds and frequent conversations with Sass-Kortsak. It was during one such discussion with Sass-Kortsak about Wilson disease, the idea of copper removal therapy was mentioned. Wilson disease is a genetic disease that leads to overaccumulation of copper in vital organs like the brain, kidney, and liver and a straight-forward therapeutic approach was to remove excess copper⁶. The discussion then moved on to blood in general. According to Sass-Kortsak, the concentration of copper in blood was due to copper bound mainly to proteins and a small amount remained freely suspended in blood to which the biochemist Sarkar disagreed. He knew from his inorganic biochemistry background that copper had a high affinity for nitrogen, and it was unlikely for copper to remain free when there are so many other nitrogenous substances in blood. To prove his hypothesis, he experimented with his own blood and after a thorough investigation he was able to demonstrate it being bound to an amino acid histidine in blood. He recalled, “I could not believe my own discovery, therefore I looked for the same in blood samples of residents staff too.” They happily gave him their blood samples because in return he would measure their iron levels. He is grateful for the help from the staff members which tremendously helped to confirm his findings. This seminal discovery of isolation of copper-histidine in human blood was published in 1966⁷ and it acted as a catalyst for the development of copper-histidine therapy for a genetic disease called Menkes Disease.

The first Menkes patient to receive copper-histidine therapy

Menkes disease is a neurodegenerative disease resulting in early death by the age of three years, but contrary to Wilson disease, it is characterized by copper deficiency³. In 1972 during a scientific conference in Paris, Sarkar met the eminent pediatric neurologist Dr. David M Danks. His discovery on the unique association of Menkes patients and sheep in relation to copper deficiency intrigued Sarkar which led him to explore the possibility of copper imbalance treatments.

In 1976, the first Menkes case was diagnosed at SickKids. The patient was prematurely born, and the blood copper levels were dropping alarmingly. Considering Sarkar’s experience in copper, Sass-Kortsak asked for his advice in this case. Based on his extensive research on copper exchange kinetics with albumin in blood and copper-histidine function as biological transporter, Sarkar suggested administration of copper-histidine for this Menkes patient. “Since I knew copper will not be absorbed from the gut if given orally in Menkes disease and with  intravenous administration was not feasible in this case ;  therefore, the only option was to inject copper-histidine subcutaneously”, Sarkar explained. 

Realizing the complexity of the situation, Sass-Kortsak presented this novel treatment option to the Clinical Investigation Unit (CIU), now known as the Research Ethics Board. The CIU took into consideration that since copper-histidine is a biological component already present in human blood, it was less likely to get rejected and would only require strict dose monitoring. With no other option available, the committee approved the copper-histidine formulation as an experimental therapy for Menkes disease. With consent of the patient’s family, the copper-histidine formulation was subcutaneously injected. The dosage was controlled and strictly monitored for safety. The experimental drug worked in bringing the copper level to normalcy^8,9.   Sarkar ecstatically recalls “By the age of three, the child who would have succumbed to Menkes disease was now climbing through stairs and developing normally like other children”. This clinical intervention happened at a time when patenting was not common and funding for rare diseases was difficult to obtain, therefore research on diseases like Menkes was challenging, Sarkar recalls. Furthermore it was  difficult to diagnose rare genetic diseases because of lack of genetic testing conducted on those days, making the treatment more difficult.

Another Menkes patient was administered the same formulation soon after birth to normalise the copper level. It was later found through genetic studies that both the patients had a lethal form of Menkes gene defect. It was soon understood that the timing of the dose administration and the nature of genetic mutation played a critical role in determining the treatment efficacy^10,11. Following the publication of Menkes treatment, Sarkar received requests for the drug formulation from different parts of the world. The formulation of copper-histidine was shared free of cost and soon this innovative therapy was being clinically applied to Menkes patients around the world.

The discovery of the Menkes disease gene 

Initially, Menkes disease was diagnosed by biochemical analysis based on intracellular accumulation of copper supported by clinical manifestations of the disease. Things changed with isolation of the Menkes disease gene in 1993¹¹.  Sarkar says “previously we missed a lot of Menkes patients because of our lack of knowledge on rare diseases and also due to limitations in early diagnosis but with isolation of the gene ATP7A that codes for copper transporting ATPase, prenatal screening became possible”. The gene identification and improvement in genetics tools allowed doctors to offer treatments right from birth that helped in management of diseases symptoms. While sharing his views on genetic testing, Sarkar recalled an experience from his visit to Japan in early 1990s, he said “I saw two brothers both of whom had Menkes disease, but the elder brother, around 5 years of age looked weak and  had to be carried around while his younger brother who was prenatally tested with Menkes gene defect and given copper-histidine treatment from birth was seen running and jumping around just like any 3-year-old.” Hence Sarkar expressed early genetic testing can lead to timely treatment that can improve the quality of life in Menkes patients. 

While the discovery of the Menkes gene was helping in early detection of cases, it had to be supported by effective treatments. Sarkar decided to do a long-term follow-up study on the clinical course of four Menkes patients from Canada, Switzerland and Australia who were treated with copper-histidine from early infancy. The findings showed favourable effects on the neurological symptoms and concluded the treatment was effective if administered early in life¹³. Sarkar says, “Copper-histidine is not a cure for Menkes disease but like most treatments for genetic diseases, it helps to ameliorate the sufferings of patients and provide them with a comparatively longer lives”.

Spirit of collaboration

Sarkar briefly mentioned how the outlook on collaboration had changed over the years. During his time, scientists mostly worked in isolation but now with the emphasis on translational research, there is a lot of collaboration among peers from different departments. Sarkar says “When I first joined SickKids, one of the first things I wanted to introduce was “bench-to-bedside” research, now commonly called as translational research and the Peter Gilgan Centre for Research and Learning  (PGCRL) tower at SickKids is a result of that spirit”.  He proudly mentions SickKids as the birthplace of Cystic Fibrosis (CF) gene discovery. “SickKids is very powerful, not just because of the CF gene, but so many other genes were discovered at SickKids. The CF gene discovery is the pinnacle of our success.”

Sarkar mentioned an aspect he gained during his time at SickKids, his relationship with Menkes patients. He talked about his attachment with patients following years of consultations and follow-ups. He got emotional talking about a particular Menkes Disease patient who died of a severe urinary tract infection. Sarkar felt devastated but found solace when patient’s mother reminded him for his timely therapy, which in turn gave the parents a few additional years of happiness with their son. He recalled the patient’s resilience and a quote from him to his mother where he said, “ You know what mom? None of us get made quite right, until we get to heaven.” Sarkar presented the quote and dedicated his talk to this patient in an International Meeting in Brazil in 1998. He highlights the perils of such rare disease patients and the need to find an early therapy .

It has been over 20 years since Sarkar retired. But he still goes to SickKids where he has his office. He meets people there and answers their questions on Menkes and Wilson diseases’. But what he still loves the most is the stories he collects when he goes to meet the children and their parents in the wards. Sarkar believes humility is the ultimate character of a great scientist. He came from a period when the human genome project was still in its infancy and his stories highlight even in times of adversity the collaborative spirit in science can lead to make miracles.

References:

1.        Dondorp, W. J. & de Wert, G. M. W. R. The ‘thousand-dollar genome’: an ethical exploration. European Journal of Human Genetics 21, S6–S26 (2013).

2.        Human Genome Project Information Archive 1990–2003. Accessed March 9, 2023

3.        Prasad, A. N. & Ojha, R. Menkes disease: what a multidisciplinary approach can do.
J Multidiscip Healthc 9, 371–385 (2016).

4.        Menkes, J. H., Alter, M., Steigleder, G. K., Weakley, D. R. & Sung, J. H. A sex-linked recessive disorder with retardation of growth, peculiar hair, and focal cerebral and cerebellar degeneration. Pediatrics 29, 764–79 (1962).

5.        Danks, D. M., Campbell, P. E., Stevens, B. J., Mayne, V. & Cartwright, E. Menkes’s kinky hair syndrome. An inherited defect in copper absorption with widespread effects. Pediatrics 50, 188–201 (1972).

6.        Wilson disease – About the Disease – Genetic and Rare Diseases Information Center. https://rarediseases.info.nih.gov/diseases/7893/wilson-disease.
Accessed March 12, 2023

7.        B.Sarkar & T.Kruck. Copper-amino acid complexes in human serum. Academic Press, New York 183–196 (1966).

8.        Sherwood, G., Sarkar, B. & Kortsak, A. S. Copper histidinate therapy in Menkes’ disease: prevention of progressive neurodegeneration. J Inherit Metab Dis 12 Suppl 2, 393–6 (1989).

9.        Sarkar, B., Lingertat-Walsh, K. & Clarke, J. T. Copper-histidine therapy for Menkes disease. J Pediatr 123, 828–30 (1993).

10.      Tümer, Z. et al. Early copper-histidine treatment for Menkes disease. Nat Genet 12, 11–3 (1996).

11.      Chelly, J. et al. Isolation of a candidate gene for Menkes disease that encodes a potential heavy metal binding protein. Nat Genet 3, 14–9 (1993).

12.      Møller, L. B., Mogensen, M. & Horn, N. Molecular diagnosis of Menkes disease: genotype-phenotype correlation. Biochimie 91, 1273–7 (2009).

13.      Christodoulou, J. et al. Early treatment of Menkes disease with parenteral copper-histidine: long-term follow-up of four treated patients. Am J Med Genet 76, 154–64 (1998).