The New Era of Genomics in Health Care Policy

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

Dr. Yvonne Bombard, PhD (she/her) is an interdisciplinary genomics and health services researcher. She is an Associate Professor at the Institute of Health Policy, Management and Evaluation, University of Toronto and director of the Genomics Health Services Research Program at St. Michael’s Hospital.

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 20172,3 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 risks4,5,6.

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 law2,3.  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 feasible6,7. 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 equity6,7

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.

Figure 1. The steps towards making a Genomic deicion AiD about incidental sequencing results. The Genomics ADvISER protocol to follow9,11.

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 testing9,10.  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”.


  1. Government of Canada. (2022). Available at: (Accessed: 10th April 2023)
  2. Standing Senate Committee on Human Rights (41st Parliament … – Sencanada,
  3. Branch, L. S. Consolidated federal laws of canada, Genetic Non-Discrimination Act. Genetic Non-Discrimination Act(2023). Available at: (Accessed: 10th April 2023) 
  1. Bombard, Y. et al. Managing genetic discrimination: Strategies used by individuals found to have the Huntington disease mutation. Clin. Genet. (2007).
  2. 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
  3. “Why We Need a Law to Prevent Genetic Discrimination.” The Globe and mail,
  4. Mighton, Chloe, et al. “From the Patient to the Population: Use of Genomics for Population Screening.” Frontiers in Genetics. (2022)
  5. 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
  6. 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).
  7. 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
  8. 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

Epistasis is more important than models of heritability assume

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

Lorin Crawford, PhD., Principal Researcher at Microsoft Research New England and Associate Professor of Biostatistics at Brown University.

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.

Figure 1: Schematic of Genome-wide Association Studies. The genomes of individuals presenting with a disease, or phenotype, and those without are sequenced. Single nucleotide polymorphisms (SNPs) that are enriched only in those individuals with the disease are classified as disease-associated SNPs and serve as markers for the genomic region in which the causative variant is likely located. Taken from 5.

“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 interactions6, 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.


  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: (Accessed: 12th March 2023)
  5. Genome-wide association studies fact sheet. National Human Genome Research Institute (2020). Available at: (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.

Yeast May Crack the Code on what is Truly “Essential” for Human Life 

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

Dr. Charles Boone, Ph.D., FRSC.

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. 

Figure 2. A genetic map showing interactions between Saccharomyces cerevisiae genes.
This figure shows the interactions between various genes (represented as dots) in the yeast genome. Genes with linked effects/outcomes are connected by lines. Genes that are closer together indicate strongly correlated effects. Colours correspond to the biological processes and organelles in which the genes are involved.

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.    


1.        Donnelly Centre for Cellular and Biomolecular Research – University of Toronto. 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. (2023).

6.        Donnelly Centre for Cellular and Biomolecular Research – University of Toronto. Jason Moffat. (2023).

7.        The Hospital for Sick Children. Jason Moffat. (2022).

Finding hope for the rare: EpiSign’s Promise For The Detection of Rare Genetic Disorders

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

Dr. Bekim Sadikovic is the scientific and clinical director of the Verspeeten Clinical Genome Centre and research chair in clinical genomics and epigenomics at the London Health Sciences Centre (LHSC) in Ontario, Canada. He also serves as the program head of the molecular diagnostics program in the department of pathology and laboratory medicine at LHSC and St. Joseph’s Health Care. Photo provided by Dr. Sadikovic.

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.

Figure 1: The EpiSign DNA methylation profiling pipeline. Defining an episignature begins with methylation and probe-based detection, followed by machine learning algorithms to identify and distinguish episignatures between patients with and without the disease. Adapted from 1.

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.

Figure 2: Screening for epigenomic-related syndromes. Patient DNA methlyation profiles are screened against available episignatures in the EpiSign Knowledge Database. If the patient’s epigenetic signature matches an episignature associated with a disorder in the same genetic region, a more accurate and precise diagnosis can be achieved. Taken from 5.

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.”


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.

Opening a time-capsule of Menkes disease with Dr. Bibudhendra Sarkar

“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.

Dr. Bibudhendra Sarkar, M Pharm, PhD,
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. Photo courtesy of Dr. Bibudhendra Sarkar

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 normalcy8,9.   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 efficacy10,11. 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. 

Figure: Graphical representation of mutations seen in ATP7A gene along with their phenotype (Classical and Atypical) observed. Adapted from12. Figure generated using Biorender.

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.


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.
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).

Addressing the Need for Inclusivity and Collaboration in Lupus Research

Lupus is a highly variable disease with relevant environmental and genetic causality. Dr. Linda Hiraki sits down to discuss the issues this presents from a research perspective, focusing on the lack of genetic diversity in data and the need for scientific collaboration.

Brooke Coe, Vivian Hong, and Kajeetha Sarvananthan

The events that occur in our childhood have a stark ability to shape our adult lives. Such is the case with Dr. Linda Hiraki, a Clinician-Scientist with The Hospital for Sick Children (SickKids), who was a teenager when her sister was diagnosed with a life-altering chronic illness, Systemic Lupus Erythematosus (SLE or lupus). Dr. Hiraki was in medical school when she became impassioned to understand the complex disease that “had such a big impact on [her] sister’s life and [her] whole family.” This connection is what drove Dr. Hiraki to the top of her field as an expert in pediatric lupus. Recalling the events of her past, Dr. Hiraki acknowledges the importance of making a connection between one’s personal and professional life, stating “it [is] what gives our work meaning… And I think it [is] important to feel like our work is meaningful.” She now finds herself at the forefront of pediatric SLE research, working to understand the complex genetic differences underpinning lupus development, to improve physical and mental health experiences for patients, and to address the lack of ethnic diversity in data.

Dr. Linda Hiraki. Clinician-Scientist with The Hospital for Sick Children. Photo courtesy of Dr. Linda Hiraki.

What is Lupus?

Lupus is an autoimmune disease that causes inflammation that leads to tissue damage throughout the body, affecting many organ systems1. Lupus can affect children, adolescents, and adults, with 20% of patients diagnosed in childhood2. While akin to adult SLE, pediatric SLE (also known as childhood-onset SLE or cSLE) commonly results in more severe presentations and higher levels of organ involvement2. In particular, the kidneys or the brain are commonly affected with inflammation that may cause damage, leading to life-threatening complications1.

The presentation of lupus is highly heterogeneous with Dr. Hiraki sharing that it is also known as “the disease of a thousand faces,” meaning that both its clinical manifestations and causality are highly variable. Lupus development and progression is impacted by numerous environmental factors, including hormones, UV exposure, and infection. There is also evidence that genetics play a strong role in lupus predisposition3. Over 180 lupus susceptibility genes have been identified to date, primarily in European populations4. Many associated genes are involved in regulating immune response3. For most people, lupus is a consequence of interactions between genetic, environmental, and epigenetic factors3,4. This results in a wide range of clinical manifestations called lupus as some patients have life-threatening disease while for others, lupus is comparatively mild. However, Dr. Hiraki iterates that although “we call them all the same thing, practically, they [are] very different!”

Despite the variability in presentation, the interplay of lupus risk factors results in the dysregulation of the body’s immune system. This is primarily due to the overproduction of self-attacking antibodies, called autoantibodies, that target proteins on normal, healthy cells as if they were foreign pathogens1,5. This causes a positive feedback loop of inflammation where sustained attacks from autoantibodies creates inflammation that further dysregulates autoantibody activities, creating more inflammation, and eventually tissue and organ damage (figure 1)1,5. The involvement of other immune cell populations and inflammatory intermediaries, such as cytokines, further perpetuates the cycle and severity of inflammation5. Such mediators are commonly linked to lupus in gene association studies, demonstrating the complex and multifactorial nature of the disease5.

Figure 1. Pathogenesis of Immune Dysregulation in Systemic Lupus Erythematosus. A complex interaction of genetics, environment, sex, and ethnicity can lead to immune dysregulation in lupus patients. In the innate immune response pathway, there is defective phagocytosis by macrophages leading to an accumulation of apoptotic cells. This ineffective clearance contributes to the inflammatory response and the continuous release of self-antigens. The antigen presenting cells (APC) activate T cells, which triggers the secretion of pro-inflammatory cytokines and drives the progression of adaptive immune response. These cytokines then help mediate B cell production of autoantibodies. Immune complexes are formed when the autoantibodies bind to the self-antigens, triggering further inflammatory response and positive feedback. The deposition of the immune complexes in multiple organs can lead to tissue damage and eventually organ failure. Figure adapted from5.

Using her background in genetic epidemiology, Dr. Linda Hiraki studies genetic changes associated with different SLE signs, symptoms, and complications in children and adults. At its core, she hopes her work aids our understanding of the underlying biology of SLE, hoping to gain insight into “not so much who gets lupus, but what their lupus looks like – the genetics of different manifestations of the disease.” Dr. Hiraki is currently coordinating large international studies to identify associations between common or low-frequency genetic variants and disease traits. To accomplish these goals, Dr. Hiraki acknowledges two central themes: representation and collaboration.

Issues of Representation in Lupus Research

During our discussion with Dr. Hiraki, the need for broad and representative genetic research in lupus was a recurring theme. Genetic medicine in general, is historically over-representative of white, European populations, leaving other ethnic populations greatly under-represented. This is problematic due to an unaccounted for, yet well recognized, prevalence of lupus in non-European populations6. Ethnicity and geography play a significant role in SLE development. For example, in North America, the prevalence of SLE for Black and Hispanic populations is 3-4 times higher compared to Caucasian populations6. Clinical manifestations among non-white/non-European ancestral populations are also more severe, often showing increased organ damage6. For Dr. Hiraki, the issue is that the under-representation of non-European populations in SLE genetic studies exacerbates disparities in our understanding of SLE across populations, and thus impedes our ability to use genetic data broadly in clinical care.

While describing the lack of diversity in genetic data, Dr. Hiraki also discussed the societal inequities that confound this issue. A complex matter in its own right, Dr. Hiraki simplifies the discussion, stating “disparities in health care access and health outcomes fall along ethnic and socioeconomic lines”. Not only does the link between lower socioeconomic status and ethnicity affect health at a rudimentary level, such as poorer diet, but it affects how those people are treated within the healthcare system7. A historic lack of adequate, accessible, and ethical healthcare based on socioeconomic and ethnic status has resulted in deep mistrust of the healthcare system by some groups. In a follow up response, Dr. Hiraki explained how this affects the ability to conduct well-represented research for lupus:

“Engaging persons of all backgrounds in research continues to be a challenge. There is a complex relationship between the medical, scientific research community and different sectors of society… it’s understandable why certain groups may mistrust the medical community and be reluctant to participate in research.”

Beyond the difficulties of getting broad ethnic participation in research, lupus presentation and progression is linked to lower socioeconomic status and ethnicity7. Factors associated with inequities are so heavily associated with SLE that they are often considered predictive of SLE development and progression7. For Dr. Hiraki, this becomes a challenge of “trying to disentangle how much of th[e] disparities are a consequence of inequity when it comes to access to health care and how much of [it] is a reflection of genetics.”

As a clinician and researcher, Dr. Hiraki has made it her mission to proactively study underrepresented and admixed populations, stating “it [is] because of that imbalance in representation that we are inclusive in our recruitment.” True to her word, in 2009, Dr. Hiraki conducted one of the largest, single centers, transethnic cSLE studies out of SickKids in Toronto6. This study was done with the purpose of delineating ethnic involvement in cSLE as most multiracial studies prior had focused on adult SLE6. In contrast to predominant American studies that comprised of Caucasian, African American, and Hispanic sample groups, Dr. Hiraki’s team took advantage of Toronto’s multiculturalism and recruited Asian and South-Asian sample groups as well. This resulted in a majority non-Caucasian cohort. Overall, this diverse study agreed with the consensus in adult SLE research that non-Caucasian populations had higher disease prevalence and younger age of diagnosis6. However, severity and disease progression were independent of ethnicity6. More recently, Dr. Hiraki has focused more on neonatal SLE (cases where the patient has lupus at birth). Dr. Hiraki and her team found that ethnicity is not associated with lupus risk or a specific disease manifestation in neonatal SLE cases, suggesting gene loci that differ among different ethnicities are involved in a gene-environment dynamic that results in specific manifestations8. Such studies done by Dr. Hiraki and her team allow us to move forward to fully understanding the exact role of genetics in lupus versus that of the environment. Thus, there is a demonstrated need to account for ancestry in genetic association studies for lupus, while not discounting its potential involvement.

Furthermore, Dr. Hiraki’s research also demonstrates the value of including genetically diverse data in research. Another 2009 study described autoantibody differences among different ethnic populations9. This was the first study suggesting autoantibody clustering was linked to different clinical outcomes in cSLE. They found that Caucasian patients predominantly associated with autoantibodies that were linked to mild SLE and minimal organ damage, while non-Caucasians clustered with more severe manifestations and complications9. These types of classifications can provide important clinical context, suggesting that full autoantibody profiling could help predict disease progression and potential organ involvement. In line with what she hopes to accomplish, this and subsequent work provides valuable insights into lupus pathogenesis, making data “relevant not just for certain ancestral groups but making it relevant for everybody, irrespective of what their ancestral background is.”

The Future is Collaborative

When asked to identify the biggest hurdle(s) in lupus research, Dr. Hiraki reiterated the lack of inclusivity as a major obstacle, despite the push for representation over the last couple years. She describes “still having to generalize European [data] to non-European populations because there is no information” and how she “find[s] that very frustrating.”

This issue is demonstrated in one of Dr. Hiraki’s recent studies investigating the genetic link between schizophrenia and SLE in a multiethnic cohort. Here, the data on schizophrenia susceptibility genes was primarily from populations of European ancestry which affected the ability of the study to truly assess the link for non-Europeans10. This example demonstrates the sacrifices that are still being made in lupus research that potentially alter the conclusions made and subsequent clinical treatments.

Further discussing the issues in lupus research, Dr. Hiraki addressed the need for standardization and “speaking the same language” when it comes to the definition of lupus across research. Due to the variability in clinical presentation, lupus can be commonly misdiagnosed and misclassified (for example, lupus is commonly misdiagnosed as rheumatoid arthritis). Additionally, she explained “lupus is not only heterogeneous between people but it’s heterogeneous within a person over time.” That is, lupus involves periods of active inflammation and followed by periods of inactivity. Also, lupus manifestations can fluctuate over time, creating potential for new organ involvement years after the initial diagnosis.

To address these challenges, Dr. Hiraki urges collaboration, expressing:

“Science has evolved in such a way that we’re increasingly collaborative …By expanding [our] circle[s], not only [are we] connecting with people who have very different skill sets but again have different perspectives.”

The challenges of lupus, like the disease itself are complex and require unique troubleshooting that truly only arises from effective collaboration. On her end, Dr. Hiraki involves herself in international groups of lupus researchers aiming to ensure that data is being collected with consistent methods and definitions. By standardizing how research is done and data is collected, it will be easier to centralize and harmonize globally. Overall, this affords lupus researchers access to more diverse and usable data.

Much of her current work focuses on using these connections to coordinate large-scale, long-term, transethnic studies to delineate immunological profiles of individual patients. She is working to characterize single cell populations within lupus patients to distinguish inflammation over the course of a disease. In doing this, “the hope is as we have better characterization of each individual person’s disease and trajectory, [and] we’ll be able to treat them more effectively,” explains Dr. Hiraki. At the end of the day, Dr. Hiraki wants her research to provide broadly applicable data that translates to more personalized patient experiences.

Dr. Hiraki has established herself as a reputable powerhouse at the forefront of lupus research. Her research affords invaluable genetic context for a disease that is incredibly multifaceted and difficult to understand. Furthermore, her actions as an advocate for diversity in lupus research will have profound effects for patients worldwide. As the scientific community continues towards the trend of individualized medicine, Dr. Hiraki’s work will shine as a key driver of progression, exemplifying the need and value of inclusive and collaborative research.


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  10. Ulloa, A. C. et al. Schizophrenia genetics and neuropsychiatric features in childhood-onset systemic lupus erythematosus. The Journal of Rheumatology 49, 192–196 (2021).

Bringing the dark matter of proteins to light using deep learning

Ilham Abbasi

ProtENN, a deep learning approach for protein function prediction, increases coverage of the protein family database, Pfam, by 9.5%, comparable to the coverage achieved over a decade by alignment-based methods.

Protein domains with similar amino acid sequences tend to have similar functions– this is the very backbone of existing computational tools that leverage sequence homology to predict protein function. Despite the success of these algorithms in providing functional annotations for a large number of proteins, they struggle in predicting the function of proteins with low sequence homology to known proteins. However, recent work by Google Research presents a deep learning solution called ProtENN, which has effectively produced functional annotations for 6.8 million previously unannotated protein domains1.

State-of-the-art methods for protein function prediction, such as Protein Basic Local Alignment Tool (BLASTp), primarily rely on pairwise alignment-based techniques2. In these methods, a protein sequence is aligned to sequences with known function. If there is at least 30% homology between the sequences, they are inferred to share function. To further refine these techniques, probability-based methods have been introduced, in which the degree of conservation of a multiple sequence alignment is determined. For instance, profile hidden Markov models (HMM), such as HMMER, compare the protein sequence to a profile HMM that serves as a representation of a known protein domain or protein family3. If the sequence is matched to a profile HMM, its function can be inferred.

Although these methods have progressed protein function prediction, the well-known database for protein annotation, Pfam (now hosted by InterPro4) has seen a mere 5% coverage expansion over the past 5 years5. Dependence on sequence alignment limits the ability of such approaches to annotate proteins that diverge in sequence to known protein families and families that contain relatively few sequences. Additionally, proteins are not simply linearly arranged – the secondary and tertiary structure of proteins can influence function, which alignment-based methods fail to consider1.

To overcome the limitations of alignment-based approaches, Bileschi and colleagues1 propose a deep  learning model that predicts protein function without reliance on sequence alignment (fig. 1). They use a one-dimensional Convolutional Neural Network (CNN) that classifies proteins into one of 17,929 possible functional classes found in the Pfam database. Their model, ProtENN, considers both local and global protein sequence information to recognize sequence characteristics that are indicative of specific functions. Within ProtENN, a filter moves along the inputted amino acid sequence to identify features and patterns in the sequence. These patterns are then processed through multiple layers of the model, where higher layers identify increasingly complicated patterns. The function of novel protein domains can then be predicted, offering a quick, autonomous approach for annotation with minimal human intervention.

Figure 1: Comparison of ProtENN with alignment-based approaches. In alignment-based algorithms, an unknown protein sequence is aligned to known proteins and sequence similarity is used to infer protein domain function. In the ProtENN deep learning algorithm, an unknown protein sequence is processed through multiple model layers, which outputs a predicted protein domain classification without sequence alignment.

The greatest challenge in developing an accurate model for protein function prediction is not in building the model itself, but in designing train and test datasets that can apply to diverse sequences andprevent model bias1. To account for this, sequences in Pfam obtained from UniProtKB reference proteomes were split into train and test sets (1) randomly or (2) by grouping sequence families together and placing the entire group in either the training set or the testing set. The latter ensures that sequence homology between the datasets is low, allowing for accurate classification of proteins with low sequence similarity.

To benchmark model performance, the team at Google Research compared ProtENN against the well-established alignment-based methods, BLASTp and HMMER. Remarkably, ProtENN outperformed the two methods, achieving the lowest error rate and highest accuracy in both the random and grouped split datasets. This showcases ProtENN’s ability to make accurate predictions for diverse sequences.

Strikingly, the authors found that merging ProtENN with alignment-based methods improves prediction accuracy more than either method can individually. Not only did combining ProtENN with HMMER further reduce error rates by 38.6%, but the ensemble increased protein coverage in Pfam by 9.5%, or 6.8 million sequence regions. This added annotations for 1.8 million full-length proteins with no previous annotations, including 360 human proteins. These annotations have publicly been released as Pfam-N, available on the European Bioinformatics Institute website. Since this work, Pfam-N now has 5.2 million protein sequences, expanding UniProtKB reference proteome coverage by 8% (fig. 2)4.

Figure 2: Pfam coverage over the last decade. The orange line depicts the amount of annotations matched to UniProtKB reference proteomes with each Pfam update. The blue line depicts the amount of Pfam annotations added by Pfam-N. Figure taken from 1.

As an emerging space in proteomics, deep learning still faces many challenges. The information ProtENN uses to make predictions is largely unknown. Uncovering this information is crucial in understanding the relationship between protein sequence and function, however, this remains a difficult task6. Additionally, deep learning models heavily rely on a high volume of sequence data to learn meaningful patterns. To overcome this, a machine learning technique called transfer learning has recently been tested in conjunction with ProtENN to show further increases in protein prediction accuracy7. This suggests that despite its limitations, deep learning will likely become a core component of future tools for protein function prediction.

Alongside these advancements, integrative models will likely be developed that combine deep learning with approaches that consider protein information beyond sequence, such as structure and phylogenetic relationships. This will be useful for developments in biomedicine and therapeutics, such as de novo protein design, which requires precise protein sequence evaluation and functional prediction8. To facilitate the usefulness and buildability of ProtENN for various applications, the authors have made the information used to build ProtENN publicly available.

As public protein databases continue to grow, the need for accurate protein function predictions becomes increasingly important. To meet this challenge, ProtENN has paved the way for the use of deep learning in protein classification. Although in its infancy, ProtENN’s full capabilities are only beginning to be explored.


1.        Bileschi, M. L. et al. Using deep learning to annotate the protein universe. Nat Biotechnol 40, 932–937 (2022).

2.        Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J Mol Biol 215, 403–410 (1990).

3.        Finn, R. D., Clements, J. & Eddy, S. R. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res 39, W29 (2011).

4.        Paysan-Lafosse, T. et al. InterPro in 2022. Nucleic Acids Res 51, (2023).

5.        Mistry, J. et al. Pfam: The protein families database in 2021. Nucleic Acids Res 49, D412–D419 (2021).

6.        de Crécy-Lagard, V. et al. A roadmap for the functional annotation of protein families: a community perspective. Database (Oxford) 2022, (2022).

7.        Bugnon, L. A. et al. Transfer learning: The key to functionally annotate the protein universe. Patterns 4, 100691 (2023).

8.        Unsal, S. et al. Learning functional properties of proteins with language models. Nat Mach Intell 4, 227–245 (2022).

Epigenetics behind age reversal in mice- what does that mean for humans?

Syed A K Shifat Ahmed

New research findings in mice suggest old cells retain a copy of their young state that can be reactivated to regain phenotypes lost from aging

Let’s admit it- in conscious or subconscious minds we all have been lured to that anti-aging skincare commercial on the roadside billboard or that anti-aging hack post on our social feed.  The desire to look young and stay away from age-related health complications is real. Science for decades have been trying to understand and control aging1. It is known that specific signatures like methylation markers tags DNA stretches (collectively known as epigenome) that is critical in deciding the functional DNA sites in a biological phenomenon called epigenetics2.  A recent article published in Cell by Yang et al. 2023, provides evidence that disrupting this epigenome accelerated molecular, cognitive, and physiological aging in mice3. Alternatively restoring the disrupted areas was able to reset the epigenome to its younger biological state3. This suggest epigenetic information is not completely lost, rather the cell saves the information in a form that can be retrieved upon appropriate activation (Figure 1a). Understanding this age reversal epigenetic mechanism can open novel therapies for a host of age-related disorders.

Aging has largely been associated with mutational or unwanted changes in DNA as the cell responds to DNA damages such as double stranded breaks (DSB)1,4.  However recent research suggests the aging associated changes are also triggered from loss of epigenetic information4,5.  A previous study from this research group showed that that when subjected to DNA damage, the cells recruit special chromatin repair proteins to the sites of damage where they are tasked with repairing the errors5. These proteins would normally leave after fixing the damage allowing the DNA to return to its usual compactness. But with repeated DNA damage-repairs, these proteins may get displaced inappropriately. Since DNA compactness influences which genes get exposed and expressed, this displacement can result in altered expressions of genes that are critical to aging.

In this study, the researchers investigated if regulating the epigenetic landscape could alter cell aging3. This was experimented through genetically modified mice containing a gene for a scissor-like-enzyme called Ppo1 endonuclease. The enzyme upon induction with drug tamoxifen, which is known to increase oxidative cellular stress, produced cuts in non-protein coding DNA regions mimicking the DSBs as seen in physiological state. The increased DNA damages induced in transgenic mice resulted in rapid modifications of the epigenetic landscape and the system being termed as ICE (Inducible Changes to Epigenome).  Both the transgenic ICE mice (with Ppo1 endonuclease) and non-transgenic control mice (without Ppo1 endonuclease) were treated with tamoxifen for 3 weeks and phenotypes observed for a period. There were not any noticeable differences in the first 4-6 months post-treatment. However, after 10 months the ICE mice started exhibiting features typical of aging like grey hair, reduced body weight, lower activity, and cognitive decline – all these features were absent in the control mice group (Figure 1b). This was reasoned to occur from higher rate of DNA damage and epigenetic reshuffling due to the endonuclease mediated DNA cutting in the ICE mice. ChIP sequencing which is performed to study interactions between epigenetic regulators and DNA confirmed there was increased epigenetic disruption in the ICE mice that resulted in advancement of the epigenetic clock by 50% causing the ICE mice to biologically age faster3.

Having provided evidence of epigenome erosion in accelerated aging, the team decided to test if the epigenome could be reversed to the original landscape in post treated ICE mice. The researchers used a subset of Yamanaka factors called OSK3. The Yamanaka factors are proteins known for their role in reversing adult stem cells to embryolike ones and can alleviate old-age phenotypes and increase lifespan of progeroid mice6. In this study, cyclic expression of Yamanaka factors reversed age-associated gene expressions in ICE mice, with the genes associated with chromatin modification showing expression profiles similar to younger mice3 (Fig 1c). The results consolidated earlier findings from this research group where Yamanaka factors was used to cure blindness in mice by restoring the youthful epigenome7.

The concept of age reversing has attracted a lot of interest among researchers and investors. While our lifespan has improved considerably, has our health span improved equally? Exploring mechanisms involved in aging and cell rejuvenation could pave the way for novel treatment interventions for conditions like cancer, diabetes, and blindness. This work has shown our perceived idea of cell aging being driven by accumulation of DNA mutations only – is a bit misleading. When DNA from ICE mice and non-ICE mice were sequenced, they did not reveal significant differences despite the former demonstrating higher aging phenotypes3.  This led to the authors conclude that epigenetics holds cues to cell aging and reprograming the epigenome would be a more feasible option than correcting mutations in the DNA as scientists continue to tackle cell aging.

When Steve Horvath first developed the concept of epigenetic clock to measure biological age, based on the epigenetic markers the DNA has accumulated, scientists thought of dialing this clock -up and – down to regulate aging8. The current work not only give hope of gaining control to such a dialing meter but also shows promise in reversing age by retrieving the encrypted copy of the “young epigenome” information. At present how and where this information is stored and what signals could authorise the cell to download this epigenetic software permanently are questions for further investigation.

Figure 1: Regulating the epigenetic landscape to accelerate and reverse aging in mice. a) The image shows chromatin reorganization and restoration following double stranded breaks (DSB) repair. When the chromatin modifier gets displaced, it induces changes in epigenetic landscape to promote normal aging. In the study the epigenetic clock was accelerated by increasing DNA damage in ICE (induced change to epigenome) mice that caused changes in gene expression and promoted faster aging in mice. b) The image shows the epigenome was restored by dialing the epigenetic clock backwards using OSK proteins. The OSK proteins aided in reprogramming of the epigenome that returned ICE mice to its “youthful” state. c) ICE and control (CRE) mice from same litter post treatment (1-month and 10-month) showing accelerated aging in ICE mice. Adapted from Yang et al 20233


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Mapping the genetic risk of common diseases with blood metabolites

Nowrin Aman

What if the key predictors of common diseases lie in the components of blood? Genome-wide association studies have predicted the role of blood metabolites as a risk factor and potential treatment targets for 12 common traits and diseases.

Human blood is a mystery, composed of small biochemical constituents called metabolites which are small molecules essential for maintenance of normal homeostasis and physiological processes(Figure 1)1,2. Metabolites are produced because of various catalytic metabolic reactions and go on to form a network of chemical reaction pathways known as the metabolome2. The metabolome now presents as a vivid picture of how the cell operates, and trace back to how the genome acts to give a functional product for everyday life1. In a recently published Nature Article, Chen et al3. elucidated the pivotal role of blood metabolites as functional genomic candidates in determining treatment strategies for common traits and diseases. Defining the specific function of the molecules, with their involved cellular pathways, deviation from this normal physiology, and heritability can provide indications of disease condition3–5.

Figure 1. Illustration showing a conversation between a patient and physician unveiling the mystery of blood metabolites and related genes for treatment.

Metabolite-ratios are an estimate of substrate to product for enzymatic reaction3. Chen et al. explored this relationship by using genome-wide association studies (GWAS) to identify metabolites and metabolite-ratios with related genes and chromosome positions, in addition to their causal effect in disease mechanisms. In addition, twelve common traits and diseases were selected as study outcomes in these three categories: 1)estimated bone mineral density (eBMD) from ultrasound measurements, Alzheimer’s Disease, Parkinson’s Disease and osteoarthritis influenced by aging; 2)body mass index (BMI), coronary artery disease (CAD), ischemic stroke, and type 2 diabetes(T2D) influenced by metabolism; 3)type 1 diabetes (T1D), inflammatory bowel disease (IBD), multiple sclerosis (MS) and asthma influenced by immunity (Figure 2)3.

Gene-metabolite associations have been studied in previous GWAS since 2012, often termed as “metabolomics profiling”. 2–4,6,7 However, sample size limitations made it difficult to infer the causal role of the functional variants, which has been addressed by Chen and colleagues. They used a series of large GWAS studies encompassing 1091 metabolites and 309 metabolites8. Participants had genome-wide genotyped and circulating plasma metabolite levels measured. The relationship between blood metabolites have not only highlighted the previously known eight superpathways for each molecule but have also discovered their own other pathways for metabolites, related to multiple gene associations, with majority identified for lipids and amino acids3,6.For example, genetic markers for the ratio of phosphate and 21 metabolites were identified from 4 different superpathways. The authors reported around 20% heritability for metabolites and up to 84% for metabolite-ratios.

Figure 2. An overview of the study showing3: 1) study cohort used8 2) disease mechanisms selected on possible genes relating to common traits and diseases as outcomes measured; comprehensive list of main causal metabolites and metabolite-ratios identified in the study; Orotate is identified as a major risk for incidental hip fractures by correlating with eBMD. Highlighting major findings: 3) superpathways of metabolite-gene variant associations identified 4) 14 genes, mostly for encoding enzymes and transporters finalized as potential therapeutic targets using knockout mice models, Mendelian disease traits and drug target information. 5) Future prospects of metabolome studies highlighted by increasing ethnicity, functional studies, and controlling the roles of diet, environment and gene interaction. Illustration created by

Analyzing the genetic markers, and metabolomes using different databases, the authors identified 94 genes for 109 metabolites and 48 metabolite-ratios3. The results resonated the observations from other studies, where a single gene expressed its effect on multiple conditions, or the opposite where a single trait is influenced by multiple genes3,5,6. Examples include the fatty acid desaturase (FADS) gene family on FADS locus, chromosome 11, which is responsible for its effect on 79 metabolites including 75 lipids and 1 amino acid, also showed the highest number of associated metabolite-ratios in a locus3. About 26% of the genetic markers identified for metabolite-ratios encode enzymes that are used to make the metabolite pairs. Examples include the gene effecting bilirubin-glucuronide conjugates, encodes for a catalytic enzyme of glucuronidation reaction. Delving into disease-gene association in drug database9, the authors discovered 42 genes related to ~580 pre-clinical and clinical drugs, which act as antagonists, agonists, substrates, inhibitors, or inducers of the encoded proteins. Integrating this pharmacological information, Mendelian disease traits and murine knockout models, 14 genes were deduced to have therapeutic potential as drug targets for regulating metabolite levels.3

The authors further validated their analysis to target 22 metabolites and 20 metabolite-ratios that conferred a causal relationship with one or more traits and disease outcomes3. The most implicated causal finding was the triangular correlation of genetically predicted plasma orotate levels regulating eBMD, which is a known strong risk factor for hip fracture and delved deeper to validate the debilitating relation of higher orotate levels with increased fracture risk in a separate nested study3,10. This could be a novel progression in the diagnosis of osteoporosis, which is one of the topmost causes of hip fracture and disability in aging population, especially women globally10. This is perhaps the shining light which shows the application of GWAS in designing the metabolomes as diagnostic or prognostic biomarkers for common diseases.

The authors have used only European ancestry to reduce population stratification bias and a separate small cohort of other ethnicities, and so the limitations remain the same as other studies3. Moreover, they acknowledged that the study has been based on possible gene-metabolite causes, and so the playground remains to be explored for other metabolites regulating daily life. Ethnicity and diet could be major variables in shaping the diversity of genetics and associated metabolomics, hence more studies are needed to find disease-specific biomarkers for other populations3,5,6. The authors managed to scratch the surface for the heritability of the metabolomes, and this calls for more functional studies to understand their role in disease pattern7.

Overall, Chen and colleagues emphasized the genes and their role in metabolites, through pathways, specific enzymes, transporters or proteins, that could act as a modification target for therapeutic interventions in common chronic conditions3.This paper leaves an open floor for the clinicians, diagnostic labs, pharmaceutical companies and basic scientists to collaborate their thoughts on developing clinical screening panels, prognostic tests to include metabolomes, and eventually implementing them in clinical settings to guide treatment goals6. The authors advocated to refocus our aim of scientific advances? should it not get easier for patient? – Like just a simple, quick blood test.


1.        Zhang, A., Sun, H., Xu, H., Qiu, S. & Wang, X. Cell Metabolomics. OMICS 17, 495 (2013).

2.        Roberts, L. D., Souza, A. L., Gerszten, R. E. & Clish, C. B. Targeted Metabolomics. Curr Protoc Mol Biol 98, (2012).

3.        Chen, Y. et al. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases. Nature Genetics 2023 55:1 55, 44–53 (2023).

4.        Shin, S. Y. et al. An atlas of genetic influences on human blood metabolites. Nat. Genet. 46, 543–550 (2014).

5.        Gallois, A. et al. A comprehensive study of metabolite genetics reveals strong pleiotropy and heterogeneity across time and context. Nat Commun 10, (2019).

6.        Bar, N. et al. A reference map of potential determinants for the human serum metabolome. Nature 2020 588:7836 588, 135–140 (2020).

7.        Yousri, N. A. et al. Long term conservation of human metabolic phenotypes and link to heritability. Metabolomics 10, 1005–1017 (2014).

8.        Raina, P. et al. Cohort Profile: The Canadian Longitudinal Study on Aging (CLSA). Int J Epidemiol 48, 1752–1753j (2019).

9.        Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46, D1074–D1082 (2018).

10.      Lu, T., Forgetta, V., Greenwood, C. M. T. & Richards, J. B. Identifying causes of fracture beyond bone mineral density: evidence from human genetics. J. Bone Min. Res. 37, 1592–1602 (2022).

Past history of obesity remodels the epigenetic landscape of the immune system in mice

Nina Anggala

New insights into the molecular relationship between obesity and Age-related Macular Degeneration (AMD) are the first evidence of the systemic impact of obesity, even after weight loss.

Until recently, obesity was considered a disease of overindulgence rather than biological fact.The advent of Genome Wide Association Studies (GWAS) challenged these preconceptions. With the ensuing flood of candidate genes, specifically those regulating satiety pathways, came the realization that obesity is a complex disorder with a significant contribution from our DNA1.

Hata et al.2 report for the first time that obesity not only has a genetic basis, but that living with obesity impacts the genome – even after weight loss. Specifically, they focus on the previously reported association between obesity and Age-Related Macular Degeneration (AMD), a leading cause of vision loss for adults over the age of 503.

Currently, no causal genes have been identified for AMD4. The implication: AMD likely owes its etiology to environmental challenges, specifically blue light damage to the eye, that accumulate over a lifetime. Over time, the immune system’s response to repeated assaults manifests in the form of retinal thinning, invasive neovascularization, and Drusen deposits: insoluble extracellular material containing lipids, proteins, and inflammatory factors secreted by immune cells5 (Fig 1).

The physiology of AMD, by virtue of the inflammatory content within Drusen deposits, has been linked to immune processes3. Canonically, as macrophages infiltrate the site of injury, they initiate a cascade of events, including angiogenesis and cell death. Within the microenvironment of the retina specifically, debris from cells targeted for apoptosis form Drusen deposits. These deposits are themselves harmless – vision loss is a function of retinal thinning, not the accumulation of deposits – but the neovascularization that precedes their formation may be the catalyst for AMD. Specifically, it’s thought that invasive neovascularization into the retina, which facilitates infiltration of immune cells and is the culprit for the signs of inflammation with which we are all familiar (the red nose and fever that accompanies a cold, for example), directly causes retinal thinning. Similarly, researchers have time and again linked obesity to systemic inflammation6. Their investigation thus began with the question of if and how obesity lays the foundation for AMD, with the assumption that the two disorders interact with, and perhaps through, the immune system.

Figure 1: Early stages of AMD are characterized by Drusen deposits beneath the Retinal Pigment Epithelium (green arrows). As disease progresses, the immune response triggers neovascularization from the choroid layer into the retina. The retina itself thins, eventually leading to vision loss. Figure taken from 7.

To test their theory, Hata and colleagues placed experimental mice on a 20-week weight gain/weight loss (WG/WL) regimen to simulate a past history of obesity. After weight loss, experimental and control mice underwent laser-induced injury to the retina, mimicking the blue light damage that leads to AMD. Compared to mice kept on a regular diet throughout, experimental mice who had a history of obesity displayed increased neovascularization (Fig 2). Transplants of adipose tissue and bone marrow from experimental and control mice into naive recipients recapitulated the above phenotypes: macrophages in mice that received tissue from previously obese mice were hyperactive and, in their hyperactivity, destructive.

Hata and colleagues turned to the epigenome, as the mediator between gene regulation and environment, to determine how this change of behaviour, which must have occurred during the period of obesity, could be maintained after weight loss. Global patterns of chromatin accessibility, which are potentiated by epigenetic markers on the histone and nucleotide level, determine which gene programs are poised for expression within a cell and are typically stable within differentiated cells, unless acted on by external pressures (Fig 2)8. When they analyzed these patterns in the macrophages of mice that had undergone the WG/WL regimen, without receiving laser damage, the change in landscape was striking — accessible regions in the chromatin of previously obese mice were significantly shifted towards pro-inflammatory, pro-angiogenic pathways, compared to controls. Essentially, obesity primed the immune response of these mice, potentially accelerating disease progression in the future. To establish causality, they eradicated macrophage precursors in a separate cohort and repeated the blue light experiment in both WG/WL and control groups. In the absence of a fully functional innate immune system, previously obese mice did not develop AMD, substantiating their conclusion that immune system hyperactivity drives disease progression.

Figure 2: Experimental pipeline for WG/WL and control mice. (1-2) WG/WL mice were placed on a High-Fat Diet (HFD) for 11 weeks followed by a Regular Diet (RD) for 9 weeks to simulate a past history of obesity. (3-4) After laser-induced injury to the retina, WG/WL mice displayed increased neovascularization: damage to the eye initiated by immune activity. (5) Epigenetic profiling of macrophages within the eye of WG/WL mice showed increased chromatin accessibility at pro-inflammatory gene pathways compared to control mice. Epigenetic states can be stably transmitted through cell divisions, thus connecting a history of obesity to future retinal damage7. Me, red: methyl group associated with closed chromatin; Ac, blue: acetyl group associated with open chromatin.

            If these outcomes can be validated in human studies, primed immune cells could present a novel therapeutic target for AMD and other age-related diseases – with the caveat that immunosuppressive medications also pave the way for opportunistic pathogens9. Alternatively, one could curtail AMD well before onset by targeting inflammation at the source: obesity. The outcomes of this study are particularly serendipitous as they join a wave of obesity research fueled by groundbreaking weight loss drugs10. Their discoveries may very well motivate drug developers further, though caution must be paid to persistent weight stigma in both popular culture and health care. Wherever their next endeavors may lead, Hata et al.’s efforts have yielded insights into the lifelong impact of obesity and have uncovered, for the first time, a genetic basis for AMD.


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