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The road ahead in genetics and genomics

Amy l mcguire, stacey gabriel, sarah a tishkoff, ambroise wonkam, aravinda chakravarti, eileen e m furlong, barbara treutlein, alexander meissner, howard y chang, núria lópez-bigas, jin-soo kim.

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Accepted 2020 Jul 21; Issue date 2020.

This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

In celebration of the 20th anniversary of Nature Reviews Genetics , we asked 12 leading researchers to reflect on the key challenges and opportunities faced by the field of genetics and genomics. Keeping their particular research area in mind, they take stock of the current state of play and emphasize the work that remains to be done over the next few years so that, ultimately, the benefits of genetic and genomic research can be felt by everyone.

Subject terms: Genetics, Genomics, Genetic techniques

To celebrate the first 20 years of Nature Reviews Genetics , we asked 12 leading scientists to reflect on the key challenges and opportunities faced by the field of genetics and genomics.

The contributors

Amy L. McGuire is the Leon Jaworski Professor of Biomedical Ethics and Director of the Center for Medical Ethics and Health Policy at Baylor College of Medicine. She has received numerous teaching awards at Baylor College of Medicine, was recognized by the Texas Executive Women as a Woman on the Move in 2016 and was invited to give a TedMed talk titled “There is No Genome for the Human Spirit” in 2014. In 2020, she was elected as a Hastings Center Fellow. Her research focuses on ethical and policy issues related to emerging technologies, with a particular focus on genomic research, personalized medicine and the clinical integration of novel neurotechnologies.

Stacey Gabriel is the Senior Director of the Genomics Platform at the Broad Institute since 2012 and has led platform development, execution and operation since its founding. She is Chair of Institute Scientists and serves on the institute’s executive leadership team. She is widely recognized as a leader in genomic technology and project execution. She has led the Broad’s contributions to numerous flagship projects in human genetics, including the International HapMap Project, the 1000 Genomes Project, The Cancer Genome Atlas, the National Heart, Lung, and Blood Institute’s Exome Sequencing Project and the TOPMed programme. She is Principal Investigator of the Broad’s All of Us (AoU) Genomics Center and serves on the AoU Program Steering Committee.

Sarah A. Tishkoff is the David and Lyn Silfen University Associate Professor in Genetics and Biology at the University of Pennsylvania, Philadelphia, USA, and holds appointments in the School of Medicine and the School of Arts and Sciences. She is a member of the US National Academy of Sciences and a recipient of an NIH Pioneer Award, a David and Lucile Packard Career Award, a Burroughs/Wellcome Fund Career Award and an American Society of Human Genetics Curt Stern Award. Her work focuses on genomic variation in Africa, human evolutionary history, the genetic basis of adaptation and phenotypic variation in Africa, and the genetic basis of susceptibility to infectious disease in Africa.

Ambroise Wonkam is Professor of Medical Genetics, Director of GeneMAP (Genetic Medicine of African Populations Research Centre) and Deputy Dean Research in the Faculty of Health Sciences, University of Cape Town, South Africa. He has successfully led numerous NIH- and Wellcome Trust-funded projects over the past decade to investigate clinical variability in sickle cell disease, hearing impairment genetics and the return of individual findings in genetic research in Africa. He won the competitive Clinical Genetics Society International Award for 2014 from the British Society of Genetic Medicine. He is president of the African Society of Human Genetics.

Aravinda Chakravarti is Director of the Center for Human Genetics and Genomics, the Muriel G. and George W. Singer Professor of Neuroscience and Physiology, and Professor of Medicine at New York University School of Medicine. He is an elected member of the US National Academy of Sciences, the US National Academy of Medicine and the Indian National Science Academy. He has been a key participant in the Human Genome Project, the International HapMap Project and the 1000 Genomes Project. His research attempts to understand the molecular basis of multifactorial disease. He was awarded the 2013 William Allan Award by the American Society of Human Genetics and the 2018 Chen Award by the Human Genome Organization.

Eileen E. M. Furlong is Head of the Genome Biology Department at the European Molecular Biology Laboratory (EMBL) and a member of the EMBL Directorate. She is an elected member of the European Molecular Biology Organization (EMBO) and the Academia Europaea, and a European Research Council (ERC) advanced investigator. Her group dissects fundamental principles of how the genome is regulated and how it drives cell fate decisions during embryonic development, including how developmental enhancers are organized and function within the 3D nucleus. Her work combines genetics, (single-cell) genomics, imaging and computational approaches to understand these processes. Her research has advanced the development of genomic methods for use in complex multicellular organisms.

Barbara Treutlein is Associate Professor of Quantitative Developmental Biology in the Department of Biosystems Science and Engineering of ETH Zurich in Basel, Switzerland. Her group uses and develops single-cell genomics approaches in combination with stem cell-based 2D and 3D culture systems to study how human organs develop and regenerate and how cell fate is regulated. For her work, Barbara has received multiple awards, including the Friedmund Neumann Prize of the Schering Foundation, the Dr. Susan Lim Award for Outstanding Young Investigator of the International Society of Stem Cell Research and the EMBO Young Investigator Award.

Alexander Meissner is a scientific member of the Max Planck Society and currently Managing Director of the Max Planck Institute (MPI) for Molecular Genetics in Berlin, Germany. He heads the Department of Genome Regulation and is a visiting scientist in the Department of Stem Cell and Regenerative Biology at Harvard University. Before his move to the MPI, he was a tenured professor at Harvard University and a senior associate member of the Broad Institute, where he co-directed the epigenomics programme. In 2018, he was elected as an EMBO member. His laboratory uses genomic tools to study developmental and disease biology with a particular focus on epigenetic regulation.

Howard Y. Chang is the Virginia and D. K. Ludwig Professor of Cancer Genomics at Stanford University and an investigator at the Howard Hughes Medical Institute. He is a physician–scientist who has focused on deciphering the hidden information in the non-coding genome. His laboratory is best known for studies of long non-coding RNAs in gene regulation and development of new epigenomic technologies. He is an elected member of the US National Academy of Sciences, the US National Academy of Medicine, and the American Academy of Arts and Sciences.

Núria López-Bigas is ICREA research Professor at the Institute for Research in Biomedicine and Associate Professor at the University Pompeu Fabra. She obtained an ERC Consolidator Grant in 2015 and was elected as an EMBO member in 2016. Her work has been recognized with the prestigious Banc de Sabadell Award for Research in Biomedicine, the Catalan National Award for Young Research Talent and the Career Development Award from the Human Frontier Science Program. Her research focuses on the identification of cancer driver mutations, genes and pathways across tumour types and in understanding the mutational processes that lead to the accumulation of mutations in cancer cells.

Eran Segal is Professor in the Department of Computer Science and Applied Mathematics at the Weizmann Institute of Science, heading a multidisciplinary laboratory with extensive experience in machine learning, computational biology and analysis of heterogeneous high-throughput genomic data. His research focuses on the microbiome, nutrition and genetics, and their effect on health and disease and aims to develop personalized medicine based on big data from human cohorts. He has published more than 150 publications and received several awards and honours for his work, including the Overton and the Michael Bruno awards. He was recently elected as an EMBO member and as a member of the Israel Young Academy.

Jin-Soo Kim is Director of the Center for Genome Engineering in the Institute for Basic Science in Daejon, South Korea. He has received numerous awards, including the 2017 Asan Award in Medicine, the 2017 Yumin Award in Science and the 2019 Research Excellence Award (Federation of Asian and Oceanian Biochemists and Molecular Biologists). He was featured as one of ten Science Stars of East Asia in Nature ( 558 , 502–510 (2018)) and has been recognized as a highly cited researcher by Clarivate Analytics since 2018. His work focuses on developing tools for genome editing in biomedical research.

Making genomics truly equitable

Amy McGuire. For the field of genetics and genomics, the first decade of the twenty-first century was a time of rapid discovery, transformative technological development and plummeting costs. We moved from mapping the human genome, an international endeavour that took more than a decade and cost billions of dollars, to sequencing individual genomes for a mere fraction of the cost in a relatively short time.

During the subsequent decade, the field turned towards making sense of the vast amount of genomic information being generated and situating it in the context of one’s environment, lifestyle and other non-genetic factors. Much of the hype that characterized the previous decade was tempered as we were reminded of the exquisite complexity of human biology. A vision of medicine driven by genetically determined risk predictions was replaced with a vision of precision in which genetics, environment and lifestyle all converge to deliver the right treatment to the right patient at the right time 1 .

As we embark on the third decade of this century, we are now faced with the prospect of being able not only to more accurately predict disease risk and tailor existing treatments on the basis of genetic and non-genetic factors but also to potentially cure or even eliminate some diseases entirely with gene-editing technologies.

These advancements raise many ethical and policy issues, including concerns about privacy and discrimination, the right of access to research findings and direct-to-consumer genetic testing, and informed consent. Significant investment has been made to better understand the risks and benefits of clinical genomic testing, and there has been vigorous debate about the ethics of human gene editing, with many prominent scientists and bioethicists calling for a moratorium on human germline editing until it is proven to be safe and effective and there is broad societal consensus on its appropriate application 2 .

These are all important issues that we need to continue to explore, but as the technologies that have been developed and tested at warp speed over the past two decades begin to be integrated into routine clinical care, it is imperative that we also confront one of the most difficult and fundamental challenges in genomics, in medicine and in society — rectifying structural inequities and addressing factors that privilege some while disadvantaging others. The genomics of the future must be a genomics for all, regardless of ethnicity, geography or ability to pay.

This audacious goal of making genomics truly equitable requires multifaceted solutions. The disproportionate burden of illness and death among racial and ethnic minorities associated with the global COVID-19 pandemic 3 and recent protests against police brutality towards African American citizens 4 have strengthened the antiracism movement and amplified demands for racial equity.

To be part of this movement and effect change will require humility. We must actively listen and learn from each other, especially when it is uncomfortable and our own complicity may be implicated. It will require solidarity and a recognition that we are all connected through our common humanity. And it will require courage. It may seem like a platitude, but it is true that nothing will change unless actual change is made. If we continue to do things as they have always been done, we will end up where we have always been. It is time to step into the discomfort and dare to do something different.

So what can we do differently to make genomics more equitable? I propose three areas where we should focus attention to address this important question. First, we must ensure equitable representation in genomic research. Examining 2,511 studies involving nearly 35 million samples from the GWAS Catalog in 2016, Popejoy and Fullerton found that the vast majority (81%) come from individuals of European descent, with only 5% coming from non-Asian minority populations 5 . This has created an ‘information disparity’ that has an impact on the reliability of clinical genomic interpretation for under-represented minorities 6 . The US National Institutes of Health (NIH) has invested in efforts to increase diversity in genomic research, but to be successful these efforts must be accompanied by serious attention to earning the trust of disadvantaged and historically mistreated populations. This will require, at a minimum, more meaningful engagement, improved transparency, robust systems of accountability, and a commitment to creating opportunities that promote and support a genomics workforce that includes scientists and clinicians from under-represented populations.

It is insufficient to achieve diverse representation in genomic research; however, there must also be equitable access to the fruits of that research. An analysis of the US Centers for Disease Control and Prevention’s 2018 Behavioural Risk Factor Surveillance System found that non-elderly adults from self-identified racial or ethnic minority groups are significantly less likely to see a doctor because of cost than non-elderly white adults 7 . This finding reflects how the structure and financing of health care in the United States perpetuates inequities and contributes to the larger web of social injustice that is at the heart of the problem. Even when socio-economic factors are controlled for, racial disparities in access to genetic services persist 8 . Large-scale, sustained research is needed to better understand and actively address the multitude of factors that contribute to this, including issues related to structural racism, mistrust, implicit and explicit bias, a lack of knowledge of genetic testing, and concerns about misuse of genetic information.

Finally, and perhaps most daunting, we must strive to achieve more equitable outcomes from genomic medicine. Many racial and ethnic minorities disproportionately experience chronic disease and premature death compared with white individuals. Disparities also exist by gender, sexual orientation, age, disability status, socio-economic status and geographical location. Health outcomes are heavily influenced by social, economic and environmental factors. Thus, although providing more equitable access to genomic services and ensuring more equitable representation in genomic research are necessary first steps, they are not enough 9 . Genomics can only be part of the solution if it is integrated with broader social, economic and political efforts aimed at addressing disparities in health outcomes. For genomics to be truly equitable, it must operate within a just health-care system and a just society.

we must strive to achieve more equitable outcomes from genomic medicine

Genome sequencing at population scale

Stacey Gabriel. Twenty years ago, I finished a PhD project that involved laboriously sequencing one gene — a rather complicated one, RET — in a couple of hundred people to catalogue pathogenic variants for Hirschsprung disease. This work required designing primers on the basis of genome sequence data as they were gradually released, amplifying the gene exon by exon (all 20!), running sequencing gels and manually scoring sequence changes. The notion of sequencing the whole genome to catalogue sequence changes was something to wish for in our wildest dreams.

Thanks to great strides in technology and the hard work of geneticists, engineers, epidemiologists and clinicians, much progress has been made; huge numbers of genomes (and exomes) have been sequenced across the world. Disease gene-finding projects such as my graduate work are now done routinely, rather than one gene at a time, using whole-exome or whole-genome sequencing (WGS) in families and affected individuals, enabling the identification of genes and causative mutations in thousands of Mendelian diseases and some complex diseases.

But the real promise of genome sequencing lies in true population-scale sequencing, ultimately at the scale of tens of millions of individuals, whereby genome sequencing of unselected people enables the unbiased, comprehensive study of our genome and the variation therein. It provides a ‘lookup table’ to catalogue disease-causing and benign variants (our ‘allelic series’). The genome sequence should become part of the electronic health record; it is a stable, persistent source of information about a person akin to physical measurements such as weight or blood pressure, exposures such as smoking or alcohol use, and (in many ways better than) self-reported family history.

the real promise of genome sequencing lies in true population-scale sequencing, ultimately at the scale of tens of millions of individuals

What can we learn? What needs to be solved? Even fairly small numbers of genomes aggregated in a consistent and searchable form have enabled a new way to use and interpret genomic data, just in the past couple of years providing a glimpse at the future. Efforts such as gnomAD 10 are a start — this database contains data from more than 15,000 genomes and 125,0000 exomes. With this resource, the frequency of genetic variants within populations is readily available. A clinician interpreting the genome of a patient can ask whether a variant has been observed before. The data provide a starting point for assessing the functional impact of classes of genetic variation and the ability to ask questions about ‘missing’ genetic variation where there is constraint.

Coupled with clinical data, building up population-scale databases of genomic plus clinical information will fuel the application of better risk interpretation using polygenic risk scores (PRSs) 11 . More routine WGS will shorten the ‘diagnostic odyssey’, in which patients suffer through rounds of testing and parents are left uncertain about future reproductive planning. More efficient clinical trials might be built using genomic information. With existing genomic information on all individuals in a health system, trials could be designed in a way that selects individuals most likely to have an event. This enrichment could provide more promising, shorter, smaller and cheaper trial design.

These databases must also rapidly be built in such a way that is representative of the population, representing the actual racial and ethnic diversity, not just what was available as banked sample collections. These are well known to be predominantly European-descent samples and thus preclude application of risk prediction tools in non-white individuals and have limited the ability to find population-specific genetic associations, such as those that have been demonstrated in type 2 diabetes mellitus (T2DM) 12 .

We have to solve important issues — data sharing, privacy and getting the data to scale. Sharing genomic and clinical data is of key importance to drive forward discovery and our understanding of how to use these data in the health-care setting. To do this well and responsibly, trust must be built and maintained through adherence to the rights of privacy, protection and non-discrimination. Progress is being made through the creation of data platforms and the development of frameworks for data protection and sharing; for example, by the work of the Global Alliance for Genomics and Health (GA4GH).

Several large biobanks are already being established to launch population-scale efforts. The UK Biobank is a vanguard programme that contains genotype data, questionnaire-based health and physical measurements on 500,000 individuals and some linkage to their medical records. Other efforts such as the All of Us research programme have been launched with goals directed at true population-based representation, and biobanks that link genomic data to comprehensive medical records in specific health-care systems (for example, Geisinger) or in specific countries or regions (for example, Estonia and Iceland) are also under way.

A big piece of this puzzle is generating comprehensive genome sequence data in these programmes and far beyond. For this aim, large-scale, affordable sequencing is key. No problem, right? Is sequencing not always getting cheaper? The problem is that this assumption is no longer true. We have got to where we are today because for a long time, from 2008 to 2013, sequencing costs dropped exponentially. However, in recent years, the sequencing cost curve has flattened, as is apparent in publicly reported cost estimates provided by the US National Human Genome Research Institute 13 . The cost per megabase of sequence data has remained largely unchanged since around 2016, hovering around a list price of US$0.01 per megabase, which translates to a US$1,000 genome. Gone are the days of our field touting the impressive decrease of cost in comparison with Moore’s law, and this development is worrying.

Some discounting does happen at considerable volume, and whole genomes can be priced in the range of US$500 to US$700. However, large projects (more than 500,000 samples) sequenced at these prices are few and far between, and are generally dependent on pharmaceutical or biotech funding, which can bring with it restrictions on data sharing. It is my belief that a fivefold to sevenfold reduction in total costs is needed to unlock more sequencing at the population scale and, ultimately, for genome sequencing to be more widely applied in the health-care setting. At US$100 per genome, the cost represents less than 1% of the annual average health-care expenditure per person in the United States, and a genome sequence is a one-time investment that can be referenced again and again over the entire lifespan of a person. Getting that cost curve down will be important to inspire health-care systems to adopt genome sequencing routinely.

I see three main drivers that will get us to US$100 per genome: innovation, scale and competition.

Innovation . Generating sequence data requires multiple components, and there are multiple areas ripe for innovation. Sample preparation can be improved through more efficient methods that decrease the labour required, or miniaturization can decrease the cost of the reagents used in library preparation. Developments to decrease data processing costs are also ripe for innovation. Recently, we showed that processing using optimized computing power lowered the time and cost of creating a sequence file by ~50% (S.G., unpublished observations). While decreases in the costs of sample preparation and data processing are important, they represent a small component of the total cost. Roughly 70% of the cost of sequencing a human genome is the sequencing reagent (flow cell) and the instrument. Appreciable cost decrease is made possible only by decreasing these marginal costs, as was demonstrated in the period from 2010 to 2014, when flow-cell densities doubled and sequencing cost dropped by an order of magnitude (US$100 per gigabase to US$10 per gigabase).

Scale . One component of cost is the fixed cost borne by the sequencing centre or the sequencing vendor. With high scale, centres can become more efficient and offset costs such as the costs of personnel, equipment and facilities. Scale can also result in volume discounting of the reagents, although this process is tightly controlled and approached cautiously depending on overall market dynamics.

Competition . Innovation and scale can only achieve so much. The cost of generating the data (the cost per gigabase) dominates and thus must come down considerably. The current market requires alternative options to drive this advance. Presently, the market for short-read sequencing is lacking viable, proven competition that would force flow-cell densities and machine yield to be increased and put pressures on volume discounting. While options for long-read sequencing exist and play a role in particular applications, such as de novo sequencing and structural variant resolution, they are at present far from cost competitive and, therefore, do not apply pressure to bring down the cost of routine WGS.

We need innovation, great economies of scale and/or real competition to come to play in the marketplace. When it comes to sequencing technology, particularly at a large scale, we cannot be complacent and work around the current barriers to realize small gains and one-off wins. This might involve specific types of investment beyond just financial ones; adopting and vetting new technology requires time, creativity, commitment and patience. It is a challenge for our community to take on now. In 5 years’ time, I hope we can look back at the era of the US$100 genome and progress towards real population-scale databases that fuel discovery, enriching our knowledge of the human allelic series and, importantly, the routine use of genomic data in the health-care setting.

A global view of human evolution

Sarah Tishkoff. The past 10 years saw an exponential increase in SNP array and high-coverage WGS data owing to innovations in genomic technologies. It is now possible to generate WGS data from tens of thousands of individuals (for example, GenomeAsia 100K 14 and NIH TOPMed 15 ). An increase in medical biobanks with access to electronic health records (for example, the UK Biobank 16 , the Million Veteran Project 17 and BioBank Japan 18 ) is enabling the mapping of hundreds of genetic associations with complex traits and diseases, as well as phenome-wide association studies 19 to map pleiotropic associations of phenotypes with genes. The genetic associations identified in these and other studies have been used to calculate PRSs for predicting complex phenotypes and risk of diseases.

Yet despite these advances, as of 2019, nearly 80% of individuals in genome-wide association studies (GWAS) were of European ancestries, ~10% were of East Asian ancestries, ~2% were of African ancestries, ~1.5% were of Hispanic ancestries and less than 1% were of other ancestries 20 . There is also a strong European bias in genomic reference databases, such as gnomAD and GTEx . These biases limit our knowledge of genetic risk factors for disease in ethnically diverse populations and could exacerbate health inequities 20 . Furthermore, PRSs that were estimated using European data do not accurately predict phenotypes and disease risk in non-European populations, performing worst in individuals with African ancestry 21 . The lack of transportability of PRSs across ethnic groups is likely due to differences in patterns of linkage disequilibrium and haplotype structure (resulting in different SNPs tagging causal variants), differences in allele frequencies, gene × gene effects and gene × environment effects. It is also possible that the genetic architecture of complex traits and diseases may differ across ethnic groups owing to different demographic histories and adaptation to diverse environments.

Although there have been initiatives to increase inclusion of ethnically diverse populations in human genomics research (for example, the NIH TOPMed 15 and H3Africa consortia), Indigenous populations remain under-represented. Great care must be taken to ensure that genomic research of minority and Indigenous populations is conducted in an ethical manner. This involves establishing partnerships with local research scientists, being sensitive to local customs and cultural concerns, obtaining both community and individual consent, and returning results to communities that participated when possible. In addition, there should be training and capacity building so that genomic research can be conducted locally, where feasible.

A particular area of focus in the future should be developing tools and resources that make genomic data and analyses accessible in low- and middle-income countries. We have to ensure that all people benefit from the genomics revolution and advances in precision medicine and gene editing. Thus, several of the biggest challenges in the next decade will be (1) to increase inclusion of ethnically diverse populations in human genomics research; (2) the generation of more diverse reference genomes using methods that generate long sequencing reads, and haplotype phasing, to account for the large amount of structural variation that likely exists within and between populations; (3) the training of a more diverse community of genomic research scientists; and (4) the development of better methods for accurately predicting phenotypes and genetic risk across ethnically diverse populations and for distinguishing gene × environment effects.

The inclusion of ethnically diverse populations, including Indigenous populations, is also critical for reconstructing human evolutionary history and understanding the genetic basis of adaptation to diverse environments and diets. While there have been a number of success stories for identifying genes of large effect that play a role in local adaptation (for example, lactose tolerance and sickle cell disease (SCD) associated with malaria resistance), identifying signatures of polygenic selection has been considerably more challenging 22 . Genomic signatures of polygenic adaptation are based on the ability to detect subtle shifts in allele frequencies at hundreds or thousands of loci with minor effect on the phenotype of a complex trait and to determine whether that shift is a result of demography or natural selection. A more daunting challenge arises from the same issues of portability of PRSs described earlier — variants associated with a complex trait may not tag well across ethnic groups and/or the genetic architecture of a trait may differ in different populations. Furthermore, it has recently been shown that uncorrected population stratification can result in a false signal of polygenic selection 23 . For example, several studies have identified signatures of polygenic adaptation for height across European populations (selection for increased height in northern Europeans and for decreased height in southern Europeans). However, it was recently shown that these results were influenced by population structure that could not be easily corrected using standard approaches, particularly for SNPs below genome-wide levels of significance 23 . When this analysis was repeated with variants identified in a more homogenous set of individuals of European ancestry from the UK Biobank, these signatures of polygenic adaptation were erased 23 . Thus, methods for detecting polygenic adaptation that are less biased by population structure and by population ascertainment bias will need to be developed in the future. These studies will also benefit from inclusion of more ethnically diverse populations in GWAS and identification of better tag SNPs as described earlier. A challenge of inclusion of minority populations in GWAS is that sample sizes are often small relative to majority populations. However, the high levels of genetic diversity and extremes of phenotypic diversity observed in some populations, particularly those from Africa, make them particularly informative for GWAS. For example, a GWAS of skin pigmentation in fewer than 1,600 Africans was informative for identifying novel genetic variants that affect skin colour, including a previously uncharacterized gene, MFSD12 (ref. 24 ). Thus, genomic studies in the future must make inclusion of minority populations a priority.

A challenge in both GWAS and selection scans has been the identification of causal genetic variants that directly have an impact on variable traits. Most of these variants are in non-coding regions of the genome. The development of high-throughput approaches, such as massively parallel luciferase expression assays to identify gene regulatory regions and high-throughput CRISPR screens in vitro and in vivo to identify functional variants influencing the trait of interest, will be useful 25 . There is also a need to better understand cell type-specific variation and gene regulation at the single-cell level, including response to stimuli such as immune, pharmacological and nutrient challenges, in ethnically diverse populations. However, these approaches are still limited by the need to have informative cell lines. This can be particularly challenging to obtain for Indigenous populations living in remote regions. Improvements in the differentiation of induced pluripotent stem cells (iPS cells) into assorted cell types and into organoids will be important for facilitating functional genomic studies. Establishment of iPS cells and organoids from diverse non-human primate species will also be informative for comparative genomic studies to identify the evolution of human-specific traits such as brain development and cognition. However, iPS cell-derived cells may not accurately reflect the impact of mutations acting on developmental phenotypes, which will require development of more efficient in vivo approaches in model organisms.

Perhaps the biggest revolution in the study of recent human evolutionary history has been the development of methods that make it feasible to sequence and/or obtain targeted genotypes from ancient DNA samples. The generation of high-coverage reference genomes for archaic hominid species such as Neanderthals and Denisovans, located in Eurasia, has made it feasible to identify archaic introgressed segments within the genomes of non-Africans. Some of these regions have been shown to play a role in adaptive traits such as adaptation to high altitude and immune response 26 . Furthermore, there has been an explosion of studies of ancient genetic variation in Europeans within the past 30,000 years that has demonstrated a much more complex model of the peopling of Europe, and the recent evolution of adaptive traits, than previously known from the archaeological record or from studies of modern populations 27 . The biggest challenge has been the inability to get high-quality ancient DNA from regions with a tropical climate, such as Africa and Asia. While there has been success in analysing DNA samples as old as 15,000 years in Africa, which has been informative for tracing recent migration and admixture events 28 , the lack of a more ancient African reference genome makes it very challenging to detect archaic introgression, which currently relies on statistical modelling approaches. Thus, the biggest challenge in the next 10 years will be the successful sequencing of ancient DNA more than 20,000 years old from all regions of the world, so that we may have a better understanding of the complex web of population histories from across the globe.

African genomics — the next frontier

Ambroise Wonkam. To fully meet the potential of global genetic medicine, research into African genomic variation is a scientific imperative, with equitable access being a major challenge to be addressed. Studying African genomic variation represents the next frontier of genetic medicine for three major reasons: ancestry, ecology and equity.

On the basis of a ‘pan-genome’ generated from 910 individuals of African descent, at least 300 million DNA variants (10%) are not found in the current human reference genome 29 , and 2–19% of the genome of ancestral Africans derives from poorly investigated archaic populations that diverged before the split of Neanderthals and modern humans 30 . Neanderthal genome contributions make up ~2% of the genome in present-day Europeans and are enriched for variations in genes involved in dermatological phenotypes, neuropsychiatric disorders and immunological functions 31 . Once technical challenges in sequencing poor-quality DNA have been overcome and approaches to investigate the genomic contribution of African archaic populations have been refined, it is likely that associations between variants in ancient African DNA and human traits or diseases will be found, providing insights that can benefit modern-day humans.

As a consequence of the 300,000–500,000 years of genomic history of modern humans in Africa, ancestral African populations are the most genetically diverse in the world. By contrast, there is an extreme genetic bottleneck, resulting in much less variation, in all non-African populations who evolved from the thousands of humans who migrated out of Africa approximately 70,000 years ago. Current PRSs, which aim to predict the risk for an individual of a specific disease on the basis of the genetic variants that individual harbours, exhibit a bias regarding usability and transferability across populations, as most PRSs do not account for multiple alleles that are either limited or of high frequency among Africans. A GWAS on the genetic susceptibility to T2DM identified a previously unreported African-specific significant locus, while showing transferability of 32 established T2DM loci 32 . In addition, nonsense mutations found commonly among Africans in PCSK9 , which are rare in Europeans 33 , are associated with a 40% reduction in plasma levels of low-density lipoprotein, supporting PCSK9 as a target for dyslipidaemia therapeutics. In the largest GWAS meta-analysis for 34 complex traits, conducted in 14,345 Africans, several loci had limited transferability among cohorts 34 , further illustrating that genomic variation is highest among Africans compared with other populations. As a consequence, linkage disequilibrium is lower in Africans, which improves fine mapping and identification of causative variants. Indeed, while only 2.4% of participants in large GWAS are African individuals, they account for 7% of all associations 35 . Moreover, whole-exome sequencing of nearly 1,000 African study participants of Xhosa ancestry with schizophrenia found very rare damaging mutations in multiple genes 36 , a finding that could be replicated in a Swedish cohort of 5,000 individuals. In comparison, results for the Xhosa cohort yielded larger effect sizes, which shows that for the same number of cases and controls, the greater genetic variation in African populations provides more power to detect genotype–phenotype relationships. Therefore, millions of African genomes must be sequenced, with genotyping and analysis tools optimized for their interrogation.

Greater availability of African genomes will improve our understanding of genomic variation and complex trait associations in all populations but will also support research into common monogenic diseases. The discovery of a single African origin of the SCD mutation, about 5,000–7,000 years ago, not only suggested recent migration and admixture events between Africans and Mediterranean and/or Middle Eastern populations but also enhanced our understanding of genetic variation in general as well as its potential impact on haemoglobinopathies 37 . For example, variants in the HBB -like gene cluster linked with high levels of fetal haemoglobin have been associated with less severe SCD; because the level of fetal haemoglobin is under genetic control, it is amenable to therapeutic manipulation by gene editing 38 . Moreover, knowledge of an individual’s genetic variants can have an impact on secondary prevention of and treatment strategies for SCD. For example, variants in APOL1 and HMOX1 and co-inheritance of α-thalassaemia are associated with kidney dysfunctions 39 ; stroke in SCD is associated with targeted genetic variants used in a Bayesian model; and overall SCD mortality has been associated with circulating transcriptomic profiles. It is estimated that 75% of the 305,800 babies with SCD born each year are born in Africa; SCD in Africa will serve as a model for understanding the impact of genetic variation on common monogenic traits and help to illustrate the multiple layers of genomic medicine implementation.

Greater availability of African genomes will improve our understanding of genomic variation and complex trait associations in all populations

Exploring African genomic diversity will also increase discovery of novel variants and genes for rare monogenic conditions. Indeed, allelic and locus heterogeneity display important differences in African individuals compared with other populations; for example, mutations in GJB2 account for nearly 50% of cases of congenital non-syndromic hearing impairment among Eurasians but are nearly non-existent in Africans, and there is evidence that novel variants in hearing impairment-associated genes are more likely to be found in Africans than in populations of European or Asian ancestries 40 . Higher fertility rate, consanguinity practices and regional genetic bottlenecks will improve novel gene discovery for monogenic diseases in Africa, as well as disease–gene pair curation, and will address existing challenges surrounding database biases and inference of variant deleteriousness, which have led to the misclassification of variants.

Differential population genomic variant frequencies are shaped by natural evolutionary selection as an adaptation to environmental pressures. The African continent follows a North–South axis, which is associated with variable climates and biodiversity, both motors of natural selection. This specific African ecology has shaped genetic variation accordingly, which can have a detrimental or positive impact on health. Obvious examples are variants that cause SCD but confer resistance to malaria 37 , APOL1 variants that are protective against trypanosomes (the parasites that cause sleeping sickness) 41 and variants of OSBPL10 and RXRA that protect against dengue fever 42 . Unfortunately, APOL1 variants also increase susceptibility to chronic kidney disease in populations of African ancestry 39 , 41 . A better understanding of the functional impact of genetic variants specific to African populations, particularly those that have been selected under environmental pressure, and the way they interact with each other is needed and will have a positive impact on genetic medicine practice. Moreover, immunogenetic studies among Africans will further our understanding of natural selection and responses to emerging infectious diseases, such as COVID-19.

The scientific imperative of genomic research of African populations is expected to enhance genetic medicine knowledge and practice in Africa but will face the challenges of overburdened and under-resourced public health-care systems, and often absent ethical, legal and social implication frameworks 43 , requiring international collaboration to be managed. Developing an African genomics workforce will be necessary to meet the major need for research across the lifespan for cohorts of millions of individuals with complex or monogenic diseases. Such endeavours can thrive on the foundation of recently established initiatives such as H3Africa. Indeed, equitable access for Africans is essential if African genomics is to reach its full potential as the next frontier of global genetic medicine.

Decoding multifactorial phenotypes

Aravinda Chakravarti. We live in a time of great technological progress in genomics and computing. And we live in a time when ‘genetics’ is a household word, with a public increasingly adept at understanding its relevance to their own lives. Not surprisingly, the study of genetics is being reinvented, rediscovered and reshaped, and we are beginning to understand the science of human heredity at a resolution that was impossible before.

The most significant genetics puzzle today, in my view, is the dissection of ‘family resemblance’ of complex phenotypes, both for intellectual (raison d'être of genetics) and practical (disease diagnosis and therapy) reasons. We have long known that family resemblance arises from shared alleles, declining as genetic relationship wanes, but the precise molecular components and composition of this resemblance are still poorly understood. At the turn of the twentieth century, the components were a matter of bitter and acrimonious debate 44 between the ‘Mendelians’ and the ‘Biometricians’, until the opposing views were reconciled by Ronald Fisher’s 1918 analysis 45 that complex inheritance could be explained through segregation of many genes, each individually Mendelian. In 1920, its publication delayed by World War I, this notion was elegantly demonstrated by the experimental studies of Altenburg and Muller using truncate wing , an “inconstant and modifiable character” 46 in Drosophila .

Fisher’s model assumed an infinite number of genes additively contributing to a trait, with common genetic variation at each component locus comprising two alleles that differ only slightly in their genetic effects 45 ; these genetic assumptions were quite contrary to what was then known 44 . Throughout the past century, this view matured, as segregation analyses of human phenotypes taught us that — beyond the effects of some major genes — most trait variation was polygenic, modulated by family-specific and random environmental factors 47 . Today, we have empirical evidence from GWAS, which use dense maps of genetic variants on hundreds of thousands of individuals measured for many traits and diseases, that the genetic architecture of most multifactorial traits is from common sequence variants with small allelic differences at thousands of sites across the genome 48 . This replacement of a pan-Mendelian view with a pan-polygenic view of traits is one of the most important contributions of genomics to genetics. Unfortunately, this mapping success has not clarified the number of genes involved, the identity of those genes or how those genes specify the phenotype. Indeed, some have concluded that many of the mapped GWAS loci are unrelated to the core biology of each phenotype 49 . Thus, for a deeper understanding, we need radically different approaches to understand complex trait biology in contrast to merely expanding GWAS in larger and larger samples.

for a deeper understanding, we need radically different approaches to understand complex trait biology

Yet, the most significant biology to emerge from GWAS is that most of the likely trait-causing variants fall outside coding sequences, in regulatory elements, most frequently enhancers 50 , 51 . This important finding has uncovered four new genetic puzzles. First, the non-coding regulatory machinery is vast; how much of this regulation is compromised, and how does it affect phenotypes? Second, regulatory changes affect RNA expression at many genes and protein expression at others; how does a cell ‘read’ these numerous changes as specific signals? Third, how is this coordinated expression response translated into cellular responses affecting phenotypes? Fourth, if specific environmental factors affect the same phenotype, which components do they dysregulate? In my opinion, we need to answer these questions for specific traits and diseases to truly understand their polygenic biology. Finally, these explanations must also answer the question of why some traits are decidedly Mendelian whereas others are not.

The questions of tomorrow will need to focus on four areas: the biology of enhancers and the transcription factors that bind them 51 ; the effect of genetic variation in enhancers 50 ; gene regulatory networks (GRNs) that regulate expression of multiple genes 52 ; and how GRN changes lead to specific cellular responses 53 . Despite many advances, the number of enhancers regulating expression of a specific gene remains unknown. How many enhancers are cell type specific versus ubiquitous? How many are constitutive rather than stage specific? And do they act additively or synergistically in gene expression? Additionally, which cognate transcription factors bind these enhancers, with what dynamics and how are they regulated 54 ? These details of a gene’s ‘enhancer code’ are critical for assessing its relative effect on a trait. Next, how does enhancer sequence variation affect a gene’s activity? Does such variation affect transcription factor binding only or its interaction with the promoter? Is the enhancer variant’s effect evident in all cellular states or only some? Is variation in only one enhancer sufficient to alter gene expression, or are multiple changes in multiple elements necessary?

Additional critical questions include which genes are involved in the core pathway underlying a trait, and how do we identify them 49 ? Elegant work has shown how genes are regulated within integrated modular GRNs, whereby one gene’s product is required in a subsequent step by another gene, with feedback interactions 52 . These GRNs comprise elements from the genome, transcriptome and proteome, with rate-limiting steps that require regulation. As our work on Hirschsprung disease has shown 50 , 53 , a GRN is composed of core genes, is the logic diagram of regulation of a major rate-limiting cellular step, is enriched in coding and enhancer disease variants with disease susceptibility scaling with increasing number of variants, and with disease resulting from effects on its rate-limiting gene product 53 . That is, the GRN integrates the expression of multiple genes. Finally, we need to understand how GRN changes alter cell properties and behaviour. I speculate that rate-limiting steps in GRNs are major regulators of broad cell properties, be they differentiation, migration, proliferation or apoptosis, the cellular integrator of GRN variation. Thus, genetic variation across the genome affects enhancers dysregulating many genes, but only when they dysregulate GRNs through rate-limiting steps do they affect cell and tissue biology 55 . This offers the promise of a mechanistic understanding of human polygenic disease.

The way forward for complex trait biology, including disease, is to shift our approach from reverse to forward genetics, using genome-wide approaches to cell type-specific gene perturbation. I believe we can construct cell-type GRNs en masse, inclusive of their enhancers, transcription factors and feedback or feedforward interactions, to then assay functionally defined variation in phenotypes. But, even this approach will be insufficient. We need to test our success by solving at least a few complex traits completely and demonstrating their veracity using a synthetic biology approach to recapitulate the phenotype in a model system; similarly to the field of chemistry, analysis has to be followed by de novo synthesis. Our genomic technologies are getting up to the task to enable this advance; as geneticists, are we?

Enhancers and embryonic development

Eileen Furlong. The work of my group sits at the interface of genome regulation and animal development, and there have been many exciting advances in both during the past decade. Developmental biology studies fundamental processes such as tissue and organ development and how complexity emerges through the combined action of cell communication, movement and mechanical forces. After the discovery that differentiated cells could be reprogrammed to a naive embryonic stem cell-like state, the past decade has witnessed an explosion in in vitro cellular reprogramming and differentiation studies. Organoids are a very exciting extension of this. The extent to which these fairly simple systems can self-organize and generate complexity 56 is one of the unexpected surprises of the past 5–10 years. The buzz around stem cells has also renewed interest in cellular plasticity in vivo and has uncovered an unexpected degree of transdifferentiation and dedifferentiation 57 . In the mouse heart, for example, cardiomyocytes dedifferentiate and proliferate to regenerate heart tissue when damaged within the first week after birth 58 .

Our understanding of the molecular changes that accompany differentiation has hugely advanced owing to the jump in scale, resolution and sensitivity of next-generation sequencing technologies over the past decade. This has led to a flood of studies in embryonic stem cells, iPS cells and embryos that revealed new concepts underlying genome regulation by measuring transcript diversity, transcription factor occupancy, chromatin accessibility and conformation, and chromatin, DNA and RNA modifications. The future challenge will be to connect this information to the physical characteristics of cells and how they form complex tissues. New technologies that solve many challenges of working with embryos will help, including CRISPR to engineer genomes, optogenetics to perturb proteins, lattice light-sheet and selective plane illumination microscopy to image processes in vivo, and low-input methods to overcome issues with scarce material. Particularly exciting to me are recent advances in single-cell genomics, which, although they are in their early days, will dramatically change the way we study embryogenesis. Many new insights have already emerged, including the discovery of unknown cell types and new developmental trajectories for well-established cell types. Even the concept of ‘cell identity’ has come into question.

Cell identities are largely driven by transcription factors, which act through cis -regulatory elements called ‘enhancers.’ One of the most exciting unsolved mysteries, in my opinion, is how enhancers relay information to their target genes. The textbook view of enhancers is of elements with exclusive function that regulate a specific target gene through direct promoter interactions, which occur sequentially if multiple enhancers are involved. However, emerging concepts in the past decade question many of these ‘dogmas’. Some enhancers have dual functions, whereas others may even regulate two genes. Enhancer–promoter communication is now viewed in the light of spatial genome organization, including topologically associating domains (TADs) and membraneless nuclear microcompartments (that is, hubs or condensates) 59 . Being present within the same TAD likely increases the frequency of enhancer–promoter interactions, but how a specific enhancer finds its correct promoter within a TAD, or when TADs are rearranged 60 , 61 , remains a mystery. Hubs or condensates are dynamic microcompartments 62 that contain high local concentrations of proteins, including transcription factors and the transcriptional machinery. One potential implication of condensates is that enhancers may not need to ‘directly’ touch a gene’s promoter to regulate transcription — rather, it may be sufficient to come in close proximity within the same condensate. Presumably, once proteins reach a critical concentration, transcription will be initiated. While this model fits a lot of emerging data, there are still many open questions. What is the required distance between an enhancer and a promoter to trigger transcription? Does this distance differ for different enhancers 63 depending on their transcription factor–DNA affinities? Do different chromatin environments 64 influence the process? At some loci, mutation of a single transcription factor-binding site in a single enhancer can have dramatic effects on gene expression and development. It is difficult to reconcile such cases with a shared condensate model, as other proteins bound to the enhancers and promoter should still phase separate. By contrast, there are many examples where mutation of a single transcription factor-binding site, or even an entire enhancer, has minimal impact on the expression of a gene. These observations suggest that there may be different types of loci, with requirements for different types of chromatin topologies and local nuclear environments, which will be important to tease apart in the coming years.

The genetic dissection of model loci in the 1990s and the first decade of the twenty-first century led to much of our understanding of how genes are regulated. The power of genomics in the past few decades has captured regulatory information for all genes genome-wide, providing more unbiased views of regulatory signatures, leading to new models of gene regulation. What is missing is empirical testing at a large scale. A major challenge is to move to more systematic in vivo functional dissection in organisms. CRISPR-based pooled screens have advanced the interrogation of genomic regions in cell culture systems. However, scaling functional assays in embryos remains a huge challenge. The task is enormous — even long-standing model organisms, such as Drosophila and mice, lack knockout strains for all protein-coding genes, and the number of regulatory elements is at least an order of magnitude higher. There has been little progress in developing scalable methods to quantify the contribution of a transcription factor’s input to an enhancer’s activity, and gene expression, in embryos. More systematic unbiased data will uncover more generalizable regulatory principles, increase our predictive abilities of gene regulation and developmental programmes, and enhance our understanding of the impact of genetic variation.

A major challenge is to move to more systematic in vivo functional dissection in organisms

Perhaps the most promising and exciting prospects in the coming years are to use single-cell genomics, imaging and the integration of the two to dissect the amazing complexity of embryonic development. Single-cell genomics can reveal information about developmental transitions in a way that was unfeasible before. When combined with temporal information, such data can reconstruct developmental trajectories 65 , 66 and identify the regulatory regions and transcription factors likely responsible for each transition 67 . The scale and unbiased nature of the data, profiling tens to hundreds of thousands of cells, provides much richer information than anyone envisaged just 5 years ago, bringing a new level of inference and causal modelling. The ability to measure single-cell parameters in situ (called ‘spatial omics’) will be transformative in the context of developing embryos to reveal the functional impact of spatial gradients, inductive signals and cell–cell interactions, and to move to digital 4D embryos. Combining these approaches with genetic perturbations holds promise to decode developmental programmes as they unfold. Will this bring us to a predictive understanding of the regulatory networks driving embryonic development during the next decade? ‘Simple’ model organisms are a fantastic test case to determine the types and scale of data required and to develop the computational framework to build predictive networks. The systematic functional dissection of gene regulation and true integration of single-cell genomics with single-cell imaging will bring many exciting advances in our understanding of the programmes driving embryonic development in the coming years.

Spatial multi-omics in single cells

Barbara Treutlein. Incredibly, the first single-cell transcriptome was sequenced just over a decade ago 68 ! Since this milestone, transcriptomes of millions of cells have been sequenced and analysed from diverse organisms, tissues and other cellular biosystems, and these maps of cell states are revolutionizing the life sciences. The technologies and associated computational methods have matured and been democratized to such an extent that nearly all laboratories can apply the approach to their particular system or question.

Of course, the transcriptome is not enough, and protocols have already been developed to measure chromatin accessibility, histone modifications, protein abundances, cell lineages and other features linked to genome activity in single cells 69 . Currently, many studies use dissociation-based single-cell genomics methods, where the spatial context is disrupted to facilitate the capture of single cells for downstream processing. Methods are improving to measure genomic features in situ 70 , as well as to computationally map features to spatial contexts 71 , 72 . The stage is set for the next phase of single-cell genomics, where spatial registration of multimodal genome activity across molecular, cellular and tissue or ecosystem scales will enable virtual reconstructions with extraordinary resolution and predictive capacity. These virtual maps will rely on multi-omic profiling of healthy and perturbed tissues and organisms, which presents major challenges and opportunities for innovation.

Cell throughput remains a challenge, and it is unclear what role dissociation-based single-cell sequencing protocols will play in the future. These protocols are fairly easy to implement, and laboratories around the world can execute projects with tens of thousands of cells analysed per experiment. However, there are scenarios in which measuring millions of cells per experiment would be desired, such as in perturbation screens. Combinatorial barcoding methods push cell-throughput boundaries 73 ; however, it is unclear how to scale full transcriptome sequencing economically to millions of cells using current sequencing technologies. ‘Compressed sensing’ modalities — whereby a limited, selected and/or random number of features are measured per cell, and high-dimensional feature levels are recovered through inference or similarity to a known reference — provide an interesting possibility to increasing cell throughput 74 .

Most single-cell transcriptome protocols are currently limited to priming the polyadenylation track present on all cellular mRNAs; however, this approach leads to biased sampling of highly expressed mRNAs. Clever innovations for random or targeted RNA enrichment could be a way to build up composite representations of cell states. Image-based in situ sequencing methods provide a means for increasing the number ofcells measured per experiment, as millions of cells can be imaged without a substantial increase in financial cost, although imaging time is a limiting factor. There remains a lot of room for experimental and computational optimizations to measure the transcriptome, random barcodes, DNA conformations and protein abundances from the micrometre scale to the centimetre scale spatially, and it will be interesting to see how methods for spatial registration advance over the next 5 years.

Currently, most high-throughput measurements are performed on cell suspensions or on intact tissues using one modality. That said, studies are emerging that measure several features from the same cell; for example, mRNA and chromatin accessibility 75 or mRNA and lineage 76 . To build virtual maps, independent measurements from different cells can be integrated with use of data integration tools 77 , although it can be difficult to align cell states across modalities in particular in developing systems. Therefore, the ultimate goal is to directly measure as many features as possible (for example, RNA, lineage, chromatin, proteins and DNA methylation) in the same cell 78 , ideally with spatial resolution. Furthermore, combining genetic and pharmacological perturbation screens with single-cell multi-omic measures will be informative to understand cell state landscapes and underlying regulatory networks for each cell type. The CRISPR–Cas field continues to develop creative tools for precise single-locus editing and other manipulations 79 , and incorporation of these toolkits with single-cell sequencing readouts will certainly bring new mechanistic insight.

Life forms are inherently dynamic, and each cell has a story to tell. Static measurements do not provide sufficient insight into the mechanisms that give rise to each cell state observed in a tissue. Computational approaches to stitch together independent measurements across time can be used to reconstruct potential histories; however, these are indirect inferences. Long-term live imaging in 2D cultures using confocal microscopy and in 3D tissues using light-sheet microscopy provides morphology, behaviour, location and, in some cases, molecular information on the history of a cell. Indeed, such long-term imaging experiments revealed that cell fates or states can be predicted from cell behaviour across many generations 80 . Cell tracking combined with end point single-cell genomics experiments can help to understand how cell states came to be; however, these experiments lack molecular resolution of the intermediates. There are strategies using CRISPR–Cas systems to capture highly prevalent RNAs inside cells at given times and insert these RNAs into DNA for storage and subsequent readout 81 . Together with live tracking and end-point single-cell genomics, such methods could provide unprecedented insight into cell histories.

My vision is that the emerging technologies described above can be applied to human 2D cell culture and 3D organoid biosystems to understand human development and disease mechanisms. My team and others are working to build virtual human organs that are based on high-throughput, multimodal single-cell genomics data. Organoid counterparts provide opportunities to perturb the system and understand lineage histories. Together, the next generation of single-cell genomics methods and human organoid technologies will provide unprecedented opportunities to develop new therapies for human disease.

the next generation of single-cell genomics methods and human organoid technologies will provide unprecedented opportunities

Unravelling the layers of the epigenome

Alexander Meissner. Around 1975, the idea that 5-methylcytosine could provide a mechanism to control gene expression gained traction, despite little knowledge of its genomic distribution or the associated enzymes 82 . With similarly limited genomic information or knowledge of the players involved, the histone code hypothesis was put forward in 2000 to explain how multiple different covalent modifications of chromatin may be coordinated to direct specific regulatory functions 83 . Tremendous progress has been made since, and the list of core epigenetic regulators that have been discovered and characterized seems largely complete 84 .

DNA sequencing has continued to dominate the past decade and contributed to an exponential growth of genome-wide maps of all layers of regulation. In the early days, individual CpG sites could be measured by restriction enzymes, whereas now we have generated probably well over a trillion cytosine methylation measurements. An equally astonishing number of genome-wide data sets have been collected for transcriptomes, histone modifications, transcription factor occupancy and DNA accessibility. Furthermore, the number of single-cell transcriptome and epigenome data sets continues to grow at an unprecedented pace.

On the basis of this overabundance of data across many normal and diseased cell states, for instance, we now clearly understand the non-random distribution of cytosine methylation across many different organisms. These maps have helped to refine our understanding of its relationship to gene expression, including the realization that only a few promoters are normally controlled via this modification, whereas gene bodies are actively targeted, and most dynamic changes occur at distal regulatory sites. Similar insights exist for many core histone modifications, and, in general, we have an improved appreciation of the epigenetic writers, readers and erasers involved. Over the past decade, we have seen substantially integrated and multilayered epigenomic analyses that provide a fairly comprehensive picture of epigenomic landscapes, including their dynamics across development and disease.

Additional innovation is now needed around data access and sharing. As noted, there is certainly no shortage of data, but to enable individual researchers to generate and verify hypotheses quickly improved tools are required to access and browse these data. Over the past decade, large coordinated projects such as ENCODE , the Roadmap Epigenomics Project and Blueprint Epigenome have initiated such efforts, but it remains a reality that data are not at everyone’s fingertips quite yet.

Moreover, despite decades of steady and recently accelerated progress, many important questions remain regarding the molecular coordination and developmental functions of these epigenetic modifications. For instance, cytosine methylation at gene bodies has been preserved for more than a billion years of evolution and yet its precise function is still under investigation. How and why did genomic methylation switch to a global mechanism in vertebrates compared with the selected methylation observed in invertebrates? What is the precise function of this modification in each of its regulatory contexts, and how are its ubiquitously acting enzymes recruited to specific sites in the genome? The latter is particularly timely given recent observations that enhancers, but also some repetitive elements, show ongoing recruitment of both de novo methylation and demethylation activity. Moreover, extraembryonic tissues show redirected activity that shares notable similarities with the long observed altered DNA methylation landscape found across most cancer types 85 . Lastly, it is abundantly clear that DNA methylation is essential for mammalian development; but despite us knowing this for nearly three decades, it is not clear how and why developing knockout embryos die. The specific developmental requirements are also largely true for many histone-modifying enzymes; however, it remains incompletely understood how exactly these modifications interact to support gene regulation.

A decade ago it seemed likely that we would answer questions such as these using newly gained sequencing power as a potent tool for generating hypotheses. However, for the most part, epigenomic analyses have expanded a highly valuable, but still largely descriptive, understanding of numerous epigenetic layers. So one may ask, what is different now and why should we expect to answer these questions in the coming years?

Technological innovation has always played a key role in biology, and some broadly applicable, recent breakthroughs will enable us to drive progress in the coming years. These include the transfer of the bacterial innate immunity CRISPR–Cas system as a universal genome-targeting tool 86 as well as for base editing, epigenome editing and various genome manipulations. Similarly, new fast-acting endogenous protein degradation systems have been developed that further enhance our ability to probe for precise function 87 . The past decade also saw major improvements in imaging technologies as well as cell and molecular biology, moving from the 2D space into the 3D space with both organoid cell culture models 88 and chromosome conformation capture approaches for exploring nuclear organization 89 .

Another major shift included the reappreciation that membraneless organelles are a widespread mechanism of cellular organization 90 . In particular, there have been many advances in our understanding of how condensates form and function, including for transcriptional regulation. Together with known properties of modified histones on DNA and the fact that many epigenetic regulators also contain intrinsically disordered regions, it is reasonable to assume that these physical properties will have a major impact on our understanding of chromatin. Importantly, changes in topology have been linked to disease 91 , and similar connections have been reported recently for condensates 92 . This will likely be an exciting area to follow in the coming years.

there have been many advances in our understanding of how condensates form and function, including for transcriptional regulation

Lastly, our research continues to be more and more reliant on multidisciplinary skills, with mathematics, physics, chemistry and computer science playing an ever-more central role in biology, which will require some rethinking in training and institutional organization to accomplish our goals. Going forward, we will need more functional integration, which in part due to the aforementioned selected discoveries is now very tractable. In particular, more refined perturbation of gene activity, which for many chromatin regulators should be separated into catalytic and regulatory functions, together with readouts at multiple levels of resolution will bring us closer to the insights needed. We recently exemplified this with a pipeline that explores epigenetic regulator mutant phenotypes at single-cell resolution 93 . From these studies, we may be able to understand how epigenetic regulators interact with the environment to influence or protect the organismal phenotype, connecting detailed molecular genetics to classical theories of epigenetic phenomena.

As we approach the 100-year anniversary of the detection of 5-methylcytosine in DNA 94 , it seems we can hope to declare at least for some layers of the epigenome that we fully understand the rules under which they operate. This may enable the exploration of more precise therapeutic interventions, for instance by redirecting chromatin modifiers rather than blocking their universal catalytic activities, which are shared between normal and diseased states. Of course, looking back at predictions made just 10 years ago 95 , one should expect many additional unforeseen advances that are just as difficult to predict now as they were back then.

Long non-coding RNAs: a time to build

Howard Chang. Long non-coding RNAs (lncRNAs) are the dominant transcriptional output of many eukaryotic genomes. Although studies over the past decade have revealed diverse mechanisms and disease implications for many lncRNAs, the vast majority of lncRNAs remain mysterious. The fundamental challenge is that we lack the knowledge to systematically transform lncRNA sequence into function. Progress in the next decade may come from a paradigm shift from ‘reading’ to ‘writing’ lncRNAs.

Gene regulation was once thought to be the exclusive province of proteins. Intense efforts for disease diagnosis and treatment focused almost entirely on protein-coding genes and their products, ignoring the vast majority of the genome. Even at the time of the completion of the Human Genome Project, only a handful of functional lncRNAs were known that silenced the expression of neighbouring genes. Thus, it was widely believed that the genome contained mostly ‘junk’, which sometimes made RNA as transcriptional noise.

The human genome is currently estimated to encode nearly 60,000 lncRNAs, ranging from several hundred to tens of thousands of bases, that apparently do not function by encoding proteins 96 . Studies over the past decade discovered that many lncRNAs act at the interface between chromatin modification machinery and the genome. Specific lncRNAs can act as guides, scaffolds or decoys to control the recruitment of specific chromatin modification enzymes or transcription factors to DNA or their dismissal from DNA 97 . lncRNAs can activate as well as silence genes, and these RNAs can target neighbouring genes as a function of local chromosomal folding (in cis ) or at a distance throughout the genome (in trans ). Detailed dissections of individual lncRNAs have revealed that lncRNAs are composed of modular RNA motifs that enable one lncRNA to connect proteins that read, write or erase specific chromatin marks. These findings have galvanized substantial excitement about lncRNAs; laboratories around the world are now investigating the roles of lncRNAs in diverse systems, ranging from control of flowering time in plants to mutations in human genetic disorders.

Nonetheless, the notable progress to date can be viewed as anecdotal — each lncRNA is its own story. When a new lncRNA sequence is recognized in a genome database or RNA profiling experiment, we are still in the dark about what may happen to the cell or organism (if anything) when the lncRNA is removed. Indeed, efforts to ‘read’ lncRNAs have been the dominant experimental strategy over the past two decades. Systematic efforts in the ENCODE, FANTOM and emerging cell atlas consortia have mapped the transcriptional landscape, transcript isoforms and, more recently, single-cell expression profiles of lncRNAs. These powerful data are now combined with genome-scale CRISPR-based methods to inactivate tens of thousands of lncRNAs, one at a time, to observe possible cell defects 98 , 99 . However, many challenges remain. Positive hits require further exploratory studies to define possible mechanisms of action, and we lack a principled strategy to combine lncRNA knockouts to address genetic redundancy and compensation.

A potentially fruitful and complementary direction is the pivot from ‘reading’ to ‘writing’ long RNA scripts. On the basis of the systematic dissection of RNA sequences and secondary structures in lncRNAs, we and others believe that the information in lncRNAs resembles that on a billboard (in which keywords and catchphrases are repeated) rather than a finely honed legal document (where every comma counts). Small units of RNA shapes are repeated within lncRNAs to build up the meaning in the lncRNA billboard, but these RNA shapes can be rearranged in different orders or locations without affecting meaning. These insights have allowed scientists to recognize lncRNA genes from different species that perform the same function even though the primary sequences bear little similarity 100 . Moreover, investigators were able to strip down lncRNAs to their essential ‘words’, composed of these key repeating shapes and one-tenth the size of the original lncRNA, which still functioned in vivo to control chromatin state over a whole chromosome 100 , 101 . Finally, it is now possible to successfully create synthetic lncRNAs. By adding RNA shapes to carefully chosen RNA templates, investigators are starting to create designer lncRNAs that can regulate chromatin in vivo 100 , suffice to partly rescue the physiological lncRNA gene knockout 102 , or target RNAs to specific cytotopic locations within the cell 103 , 104 .

The shift from reading to writing lncRNAs will challenge us on the technical front, leading to potential transformative technologies. Current technologies for massively parallel reporter gene assays are built on short sequence inserts. A plan to build tens of thousands of synthetic lncRNAs will require accurate long DNA or RNA synthesis. These designer sequences will need to be placed into the appropriate locations in the genome and controlled to have proper developmental expression, splicing pattern and RNA chemical modifications. Landmark studies using the XIST lncRNA, which normally silences the second X chromosome in female cells, to silence the ectopic chromosome 21 in Down syndrome cells highlight the biomedical promise of such an approach 105 .

As the field develops technologies for large-scale creation and testing of synthetic lncRNAs, we can rigorously test our understanding of the information content in the language of RNA sequences and shapes. The next decade promises to be an exciting time for building non-coding RNAs and to create entirely new tools to manipulate gene function for biology and medicine.

FAIR genomics to track tumorigenesis

Núria López-Bigas. Cancer research is one of the fields that has probably benefited the most from the technological and methodological advances of genomics. In the span of less than two decades, the field has witnessed an incredible boost in the generation of cancer genomic, epigenomic and transcriptomic data of patients’ tumours, both in bulk and more recently at the single-cell level. My dream as a cancer researcher is to have a full understanding of the path that cells follow towards tumorigenesis. Which events in the life of an individual, a tissue and a particular cell lead to the malignant transformation of some cells? Of course I do not expect to have a deterministic answer, as this is not a deterministic process. Instead we should aim for a quantitative or probabilistic understanding of the key events that drive tumorigenesis. We have solid epidemiological evidence showing that smoking increases the probability of lung cancer, exposure to the Sun raises the probability of developing melanoma and some anticancer treatments increase the probability of secondary neoplasms. But which specific mechanisms at the molecular and cellular levels influence these increases?

One first clear goal of cancer genomics is to catalogue all genes involved in tumorigenesis across different tissues. Although this is a daunting task, it is actually feasible 106 . By analysing the mutational patterns of genes across tumours, one can identify those with significant deviations from what is expected under neutrality, which indicates that these mutations provide a selective advantage in tumorigenesis and are thus driver mutations. We can imagine a future in which through the systematic analysis of millions of sequenced tumour genomes this catalogue or compendium moves closer and closer to completion. For this to happen, not only do we need genome sequencing to expand — this process is already in motion in research, clinical settings and the pharmaceutical industry — but more importantly the resulting data must be made FAIR (findable, accessible, interoperable and reusable) 107 . To this end, consortia and initiatives that promote, catalyse and facilitate the sharing of genomic data, such as the Beyond 1 Million Genomes consortium, the GA4GH or the cBioPortal for Cancer Genomics , are necessary.

Of note, cataloguing genes and mutations involved in cancer development, albeit a very important first step, is still far from the final goal of understanding how and under which conditions they drive tumorigenesis. Framing cancer development as a Darwinian evolutionary process helps me to navigate the path towards this final objective. As is true of any Darwinian process, its two key features are variation and selection. Thanks to the past 15 years of cancer genomics, we now have a much better grasp of the origin of somatic genetic variation between cells across different tissues. The study of the variability in the number, type and genomic distribution of mutations across tumours provides a window into the life history of cells across the somatic tissues of an individual 108 , 109 . In addition, recent studies sequencing the genome of healthy cells in different tissues 110 – 112 have shown that mutations accumulate in hundreds and thousands in our cells in normal conditions over time. These studies have also detected positive selection in some genes across healthy tissues. Hence, positive selection is a pervasive process that operates not only in tumorigenesis but also in healthy tissues, where it is a hallmark of somatic development of skin, oesophagus, blood and other tissues. Take, for example, clonal haematopoiesis: it results from a continuous Darwinian evolutionary process in which over time (with age) some haematopoietic cells harbouring mutations in certain blood development genes, such as DNMT3A and TET2 , outcompete other cells in the compartment 113 , 114 . This process is part of normal haematopoietic development. Problems arise only when this process gets out of control, leading to leukaemia in the case of blood, or a malignant tumour in solid tissues. Why is it only in rare cases that this ubiquitous interplay between variation and selection becomes uncontrollable and results in full-blown tumorigenesis? Which events, beside known tumorigenic mutations, drive this process?

we now have a much better grasp of the origin of somatic genetic variation between cells across different tissues

If we have learnt something in recent years, it is that virtually all tumours harbour driver mutations 115 – 117 , implying that driver genomic events are necessary. However, they are clearly not sufficient for tumorigenesis to occur. So, what are these other triggers of the tumorigenic process? What happens in the lung cells of a smoker or in the haematopoietic cells of a patient treated with chemotherapy that increases their chances to become malignant? Epigenetic modifications and changes in selective constraints, such as evolutionary bottlenecks, for example, at the time of chemotherapy, may be part of the answer.

For the near future, my dream is to see a further increase in FAIR cancer genomics data to help us disentangle the step-by-step game of variation and selection in our tissues that leads to tumorigenesis and likely other ageing-related diseases.

Integrating genomics into medicine

Eran Segal. The past 20 years in genomics have been extraordinary. We developed high-throughput sequencing and learned how to use it to efficiently sequence full genomes and measure gene expression and epigenetic marks at the genome-wide scale and even at the single-cell level 118 . Using these capabilities, we created unprecedented catalogues of novel genomes, functional DNA elements and non-coding RNAs from all kingdoms of life 119 . But — perhaps with the exception of cancer 120 and gene therapy for some monogenic diseases 121 — genomics has yet to deliver on its promise to have an impact on our everyday life. For example, drugs and diagnostics are still being developed in the traditional way, with screening assays to find lead compounds for targets typically arising from animal studies, without involving genomics in any of the steps. Moreover, when the global COVID-19 pandemic hit, the genome of the spreading severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was rapidly sequenced, but why some infected individuals exhibit severe disease and others do not remains unknown.

Indeed, our next challenge is to translate the incredible resources and technologies developed in genomics into an improved understanding of health and disease. This improved understanding should transform the field of medicine to use genomics in its transition to personalized medicine, which promises individualized treatment by targeting the right medication to the right person at the right time on the basis of that person’s unique profile. By continuing to focus on more and more measurements and the creation of more atlases and catalogues, we run the danger of drowning in ever-growing amounts of data and correlative findings. Walking down this path can lead to an endless endeavour, as bulk measurements can always be replaced with single-cell ones, or measures at higher temporal and spatial resolution, across more conditions and wider biological contexts.

Instead, we should use genomics to tackle big unanswered questions such as what causes the variation that we see across people in phenotypes, disease susceptibility and drug responses? What is the relative contribution of genetic, epigenetic, microbiome and environmental factors? How are their effects mediated, and what would be the effect of different interventions? Ultimately, we should strive to use genomics to generate actionable and personalized insights that lead to better health. We are now at an inflexion point in genomics that allows us for the first time to apply it to study human biology and realize these ambitious aims 122 .

At the cellular level, we can use iPS cells from patients to derive cellular models of multiple diseases and prioritize treatments based on measuring both their cellular and molecular response (for example, gene expression and epigenetics) to existing drugs and drug combinations. We can even use massively parallel assays to separately measure the effect of each of tens of thousands of rationally designed mutations, including patient-specific mutations, as we have done, for example, in testing the effect of all clinically identified mutations in TP53 on cellular function 123 . Measuring the molecular effects of directed mutations in genes encoding transcription factors and signalling molecules and in other genes can reveal the underlying pathways and regulatory networks of the disease studied and identify putative therapeutic targets. The application of such approaches to fields that are still poorly understood, such as neurodegenerative diseases, can be particularly impactful.

But we can be much more ambitious and directly profile large cohorts of human individuals using diverse ‘omics’ assays. As molecular changes typically precede clinical disease manifestations, longitudinal measurements coupled with clinical phenotyping have the potential of identifying novel disease diagnostics and therapeutic targets. Indeed, biobanks that track large samples of hundreds of thousands of individuals have recently emerged and are proving highly informative 124 . However, at the molecular level their focus has thus far been on genetics. Technological advances and cost reductions now allow us to obtain much deeper person-specific multi-omic profiles that include transcriptome, proteome, methylome, microbiome, immune system and metabolome measurements. Having these data on the same individual and at multiple time points can reveal which omic layer is more perturbed and informative for each disease and identify associations between molecular markers and disease.

The challenge in using such observational data from human cohorts is to identify which of the associations are causal. One way to address this is to wisely select the nature and type of the associations studied. For example, in working with microbiome data, we can move from analyses at the level of species composition to analyses at the level of SNPs in bacterial genes. Such associations are more specific and more likely to be causal, as in the case of a SNP in the dadH bacterial gene, which correlated with metabolism of the primary medication to treat Parkinson disease and the gut microbiota from patients 125 . Another approach is to use longitudinal measurements and separation of time to emulate target trials from observational data 126 . For example, we can select distinct subsets from the cohort that match on several known risk factors (for example, age or body mass index) but differ on a marker of interest (for example, expression of a gene or presence of an epigenetic mark), and compare future disease onset or progression in these two populations. Similarly, retrospective analysis of baseline multi-omic measurements from participants in randomized clinical trials may identify markers that distinguish responders from non-responders and be used for patient stratification or for identifying additional putative targets.

Ultimately, biomarkers identified from observational cohorts need to be tested in randomized clinical trials to establish causality and assess efficacy. In the case of microbial strains extracted from humans, we may be able to skip animal testing and go directly to human trials. In other cases, such as when human genes are being manipulated, we will need to start with cell culture assays and animal testing before performing clinical trials in humans. However, in all cases, tested omic targets should have already shown associations in human individuals, thus making them more likely to be relevant and succeed in trials, as is the case with drug targets for which genetic evidence links them to the disease 127 .

Beyond these scientific challenges, there is the challenge of engaging the public and diverse ethnic and socio-economic groups to participate in such large-scale multi-omic profiling endeavours even before we can present them with immediate benefits. We can start with incentives in the form of informational summary reports of the data measured and gradually move towards carefully and responsibly conveyed actionable insights as we learn more.

Overcoming the aforementioned challenges is not an easy task, but with the breathtaking advances that genomics has undergone in the past two decades, the time may be right to tackle them. Success can transform genomics from being applied mostly in research settings to having it become an integral and inseparable part of medicine.

CRISPR genome editing enters the clinic

Jin-Soo Kim. In the past several years, genome editing has come of age 128 , in particular because of the repurposing of CRISPR systems. Genomic DNA can be modified in a targeted manner in vivo or in vitro with high efficiency and precision, potentially enabling therapeutic genome editing for the treatment of both genetic and non-genetic diseases. All three types of programmable nucleases developed for genome editing, namely zinc-finger nucleases, transcription activator-like effector nucleases and CRISPR nucleases, are now under clinical investigation. In the next several years, we will be able to learn whether these genome-editing tools will be effective and safe enough to treat patients with an array of diseases, including HIV infection, leukaemia, blood disorders and hereditary blindness, heralding a new era in medicine.

If the history of the development of novel drugs or treatments such as gene therapy and monoclonal antibodies is any guide, the road to therapeutic genome editing is likely to be bumpy but ultimately worth travelling. Key questions related to medical applications of programmable nucleases concern their mode of delivery, specificity, on-target activity and immunogenicity. First, in vivo delivery (or direct delivery into patients) of genes or mRNAs encoding programmable nucleases or preassembled Cas9 ribonucleoproteins can be a challenge, given the large size of these nucleases. Ex vivo (or indirect) delivery is, in general, more efficient than in vivo delivery but is limited to cells from blood or bone marrow, which can be collected with ease, edited in vitro and transfused back into patients. Ongoing developments of nanoparticles and viral vectors are expected to enhance and expand in vivo genome editing in tissues or organs not readily accessible with current delivery systems, such as the brain.

Second, programmable nucleases, including CRISPR nucleases, can cause unwanted on-target and off-target mutations, which may contribute to oncogenesis. Several cell-based and cell-free methods have been developed to identify genome-wide CRISPR off-target sites in an unbiased manner 129 – 131 . But it remains a challenge to validate off-target activity at sites with low mutation frequencies (less than 0.1%) in a population of cells, owing to the intrinsic error rates of current sequencing technologies. Even at on-target sites, CRISPR–Cas9 can induce unexpected outcomes such as large deletions of chromosomal segments 132 . It will be important to understand the mechanisms behind the unusual on-target activity and to measure and reduce the frequencies of such events.

Last but not least, Cas9 and other programmable nucleases can be immunogenic, potentially causing undesired innate and adaptive immune responses. In this regard, it makes sense that initial clinical trials have focused on ex vivo delivery of Cas9 ribonucleoproteins into T cells or in vivo gene editing in the eye, an immunologically privileged organ. Cas9 epitope engineering or novel Cas9 orthologues derived from non-pathogenic bacteria may avoid some of the immune responses, offering therapeutic modalities for in vivo genome editing in tissues or organs with little or no immune privilege.

Base editing 133 , 134 and prime editing 135 are promising new approaches that may overcome some of the limitations of nuclease-mediated genome editing. Base editors and prime editors are composed of a Cas9 nickase, rather than the wild-type Cas9 nuclease, and a nucleobase deaminase and a reverse transcriptase, respectively. Because a nickase, unlike a nuclease, produces DNA single-strand breaks or nicks, but not double-strand breaks (DSBs), base editors and prime editors are unlikely to induce large deletions at on-target sites and chromosomal rearrangements resulting from non-homologous end joining (NHEJ) repair of concurrent on-target and off-target DSBs. Furthermore, when it comes to gene correction rather than gene disruption, these new types of gene editors are much more efficient and ‘cleaner’ than DSB-producing nucleases because they neither require donor template DNA nor rely on error-prone NHEJ; in human cells, DSBs are preferentially repaired by NHEJ, leading to small insertions or deletions (indels), rather than by homologous recombination involving donor DNA.

Base editors and prime editors are also well suited for germline editing and in utero editing (that is, gene editing in the fetus), which should be done with caution, in full consideration of ethical, legal and societal issues. In principle, CRISPR–Cas9 can be used for the correction of pathogenic mutations in human embryos; however, donor DNA is seldom used as a repair template in human embryos 136 . Recurrent or non-recurrent de novo mutations are responsible for the vast majority of genetic diseases. Cell-free fetal DNA in the maternal blood can be used to detect these de novo mutations in fetuses, which are absent in the parents. Some de novo mutations are manifested even before birth, leading to miscarriage, disability or early death after birth; it is often too late and inefficient to attempt gene editing in newborns. These mutations could be corrected in utero using base editors or prime editors without inducing unwanted indels and without relying on inefficient homologous recombination. Compared with germline editing or preimplantation genetic diagnosis, in utero editing, if proven safe and effective in the future, should be ethically more acceptable because it does not involve the creation or destruction of human embryos.

As promising and powerful as they are, current versions of base editors and prime editors can be further optimized and improved. For instance, Cas9 evolved in microorganisms as a nuclease rather than a nickase. Current Cas9 nickases used for base editing (D10A SpCas9 variant) and prime editing (H840A variant) can be engineered to increase their activities and specificities. In parallel, deaminase and reverse transcriptase moieties in base editors and prime editors, respectively, can be engineered or replaced with appropriate orthologues to increase the efficiency and scope of genome editing. It has been shown that base editors can cause both guide RNA-dependent and guide RNA-independent DNA or RNA off-target mutations, raising concerns for their applications in medicine. Prime editors may also cause unwanted on-target and off-target mutations, which must be carefully studied before moving on to therapeutic applications.

Biomedical researchers are now equipped with powerful tools for genome editing. I expect that these tools will be developed further and applied more broadly in both research and medicine in the coming years.

Acknowledgements

A.C. acknowledges that the ideas in his contribution were developed through studies on Hirschsprung disease and thanks the many trainees who have contributed to this work over the past 5 years. A.L.M. acknowledges A. Gutierrez, K. Kostick, G. Lazaro, M. Majumder, K. Munoz, S. Pereira, H. Smith and P. Zuk for feedback. A.M. thanks D. Hnisz, Z. D. Smith, J. Charlton and H. Kretzmer for feedback and the Max Planck Society for funding. A.W. is supported by NIH awards U54HG009790, U01HG009716, U01HG007459 and U24HL135600, and Wellcome Trust award H3A/18/001, and states that the funders had no role in study design, and analysis, decision to publish or preparation of the manuscript. B.T. acknowledges J. G. Camp for helpful discussions. E.E.M.F. is very grateful to A. Ephrussi, M. Mir, M. Perino, Y. Kherdjemil, T. Pollex and S. Secchia for useful comments. E. E. M. F is supported by European Research Council (Advanced Grant) agreement no. 787611 (DeCRyPT). E.S. is supported by grants from the European Research Council and the Israel Science Foundation. H.Y.C. is supported by NIH RM1-HG007735 and R35-CA209919. H.Y.C. is an investigator of the Howard Hughes Medical Institute. J.-S.K. is supported by the Institute for Basic Science (IBS-R021-D1). N.L-B. acknowledges funding from the European Research Council (Consolidator Grant 682398), the Spanish Ministry of Economy and Competitiveness (SAF2015-66084-R, European Regional Development Fund) and the Asociación Española Contra el Cáncer (GC16173697BIGA). S.A.T. is funded by NIH grants R35 GM134957-01 and NIAMS R01AR076241-01A1 and American Diabetes Association Pathway to Stop Diabetes grant #1-19-VSN-02.

Competing interests

H.Y.C. is a co-founder of Accent Therapeutics and Boundless Bio and an advisor of 10x Genomics, Arsenal Biosciences and Spring Discovery. J.-S.K. is a co-founder of and holds stock in ToolGen Inc. A.C., A.L.M., A.M., A.W., B.T., E.E.M.F., E.S., N.L.-B., S.G. and S.A.T. declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Beyond 1 Million Genomes : https://b1mg-project.eu/

Blueprint Epigenome : https://www.blueprint-epigenome.eu/

cBioPortal for Cancer Genomics : https://www.cbioportal.org/

ENCODE : https://www.encodeproject.org/

Global Alliance for Genomics and Health : https://www.ga4gh.org/

gnomAD : https://gnomad.broadinstitute.org/

GTEx : https://www.gtexportal.org/home/

GWAS Catalog : https://www.ebi.ac.uk/gwas

H3Africa : https://h3africa.org

Roadmap Epigenomics Project : http://www.roadmapepigenomics.org/

Contributor Information

Amy L. McGuire, Email: [email protected]

Stacey Gabriel, Email: [email protected].

Sarah A. Tishkoff, Email: [email protected]

Ambroise Wonkam, Email: [email protected].

Aravinda Chakravarti, Email: [email protected].

Eileen E. M. Furlong, Email: [email protected]

Barbara Treutlein, Email: [email protected].

Alexander Meissner, Email: [email protected].

Howard Y. Chang, Email: [email protected]

Núria López-Bigas, Email: [email protected].

Eran Segal, Email: [email protected].

Jin-Soo Kim, Email: [email protected].

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Mapping Alzheimer’s: UCSB’s Cristina Venegas follows a genetic puzzle tracing Alzheimer’s roots from Colombia to California

There’s a set of childhood memories that UC Santa Barbara film and media studies professor Cristina Venegas returns to once in a while, recollections that stand out because of their peculiar nature.

  “I have memories of being a child in Montería, which is an interesting city, and it’s on the banks of the Sinú river,” she said of a municipality in her native northern Colombia. “I remember the town square facing the church where sometimes you could escape the  heat and humidity. And there I can almost still see people who would sit in the park who appeared to be mentally … gone… and people referred to them as ‘crazy.’ There was also an old man who taught me how to play the guitar. The wife walked around with a cardboard on her forehead all day repeatedly asking what time it was.”

This bit of surreality is not hers alone; it’s embedded in the cultural memory of South America. Colombian master storyteller Gabriel García Márquez alluded to it in his 1967 tour de force “One Hundred Years of Solitude,” calling it “a plague of forgetfulness” that sweeps a fictional remote Colombian town, wiping memory, identity and meaning from its denizens. In the 1980s, Colombian neurologist Dr. Francisco Lopera would begin to assemble a real-life medical puzzle, following

reports of young people complaining of memory loss, and finding entire families with the same symptoms going back for generations. He concluded it was an early-onset form of Alzheimer’s disease, encoded in the families’ genes.

So when Venegas learned about a collaboration in the foothills of the Colombian Andes between Lopera and UCSB neuroscientist Kenneth S. Kosik , something blossomed in her own brain – a story that she and her sister, Emmy award-winning science journalist Marisa Venegas, were uniquely equipped to tell.

“There are so many dimensions to this story that tap into our history and our background,” she said, “about how this disease has ravaged the region, and not only the region, but worldwide.” With the working title  “ Mapping Alzheimer’s, ” the Venegas sisters’ film aims to tell a story that spans centuries and reaches around the world, to document a fight that has been joined by numerous scientists and advocates, and to highlight the bonds that provide hope for a future without the disease.

“I’m humbled by what they all go through,” Cristina Venegas said, “and that’s the story we need to tell.”

“Serendipity” is probably the word that most aptly describes Kenneth Kosik’s career: If he hadn’t traveled to the University of Antioquia School of Medicine in Medellín to lecture on the biology of Alzheimer’s disease in 1992, he wouldn’t have been introduced to Francisco Lopera. If he hadn’t met Lopera, he wouldn’t have been let into the insular world of the paisa — the name the locals of Antioquia have taken for themselves. And if he hadn’t been let into their lives, he perhaps wouldn’t have embarked on what he calls “an odyssey both geographical and personal” — tracing the path of mutations to the presenilin1 gene around the world and backward in time.

“I’m enchanted,” he said. “That’s why I’ve gone back for 30 years. It exerts some sort of draw over me.”

Indeed, the enchantment has drawn him deep into the lush and rugged landscapes of the Colombian Cordillera Central , even at the height of violence in the recent history of the country. The clinical and scientific collaboration has navigated its way around tense situations in conflict zones and conducted exploits worthy of any popular thriller. They've visited rough, poor neighborhoods and taken family histories marked by the collateral damage caused by nearly half a century of internecine conflict.

Physician-scientists Kosik and Lopera and their research teams live with one foot in each of two worlds — the highly controlled domain of the lab and the tumultuous field environment. Their goals? To not only investigate the genetic origins of Alzheimer’s, but also to pave the way for a cure.

At the heart of the researchers’ work is an extended family of about 6,000, each of whom bear a heavy burden: They’re either destined to develop Alzheimer’s by the time they’re about 45 years old, or they will bear witness to the inevitable decline of parents, siblings and cousins… often both. This genetic version of Alzheimer’s — as opposed to the “sporadic” type people get much later in life — is autosomal dominant, which means the individual need inherit only one copy of the mutant gene from either parent to develop the dementia themselves. In the presence of the gene the disease is nearly inevitable, coming on like clockwork.

Because of this kindred’s size and homogeneity, they provide an ideal population through which to hone in on the cellular and molecular mechanisms that lead to the development of the sticky plaques and neurofibrillary tangles that are the hallmarks of the disease. Thanks to Lopera’s dedication and relationship-building over the years, the family has time and again volunteered to cooperate with the scientists, welcoming them into their homes, participating in clinical trials,

offering their blood, and at the ends of their lives, their brains.

These conditions set the stage for some difficult dilemmas for the researchers, who have the power to predict who will develop Alzheimer’s, but not to cure or even mitigate the condition. They build family trees and run gene tests — to date, there are a rather astonishing 12 extended families found in Colombia, each with their own distinct mutations to the same gene. In the early days of the collaboration the researchers had to grapple with whether to deliver the harsh news or remain silent. Kosik writes about this experience in a 1999 account for the journal “The Sciences:”

  “I told the grown children that we could now determine which of them would get the disease, and I asked whether they would want to take the test. ‘Before answering,’ I told them, ‘remember that there is no treatment.’ All the children said they would want to take the test. ‘What would they do differently once they knew the result?’ I asked. At that point no one had an answer, except 23-year old González, who later told our nurse that if his test were positive, he would shoot himself.”

Two decades later, and thanks to the modest promise some drugs have shown in slowing down the disease, some of Lopera’s asymptomatic families have been invited to participate in clinical trials with the Dominantly Inherited Alzheimer Network Trials Unit (DIAN-TU), a project of the Washington University School of Medicine. For the trials they will all receive the FDA-approved anti-amyloid drug lecanemab as well as either an experimental anti-tau agent or a placebo, requiring the would-be participants to know their status in order to enroll. Members of Lopera’s Grupo Neurociencias de Antioquia have prepared for this effort by receiving specialized training in genetic counseling.

Into this maze of high stakes and errant genes, clinical trials and incremental progress plunge the filmmakers, who since 2019 have followed the scientists onto the field and into the lab as they trace the origins of and piece together the puzzle that is Alzheimer’s disease. Along the way the documentary team has uncovered clues about Latin America’s colonial past and the role it played in the emergence of such a high concentration of genetic anomalies in a relatively small part of the world. Importantly, they highlight the relationships that don’t show up in clinical trials and in scientific papers, but nevertheless are the reason for and the foundation of the scientists’ progress.

“We wanted to focus on the scientists and this transcultural and collaborative experience of doing research over such a long period of time,” Cristina Venegas explained. “And out of that comes this friendship and incredible bond, and the kinds of discoveries that they’ve been able to make because they’ve been working together, bringing different kinds of methodologies and cultural situations that have led them to ask new questions.”

Teaming up with award-winning documentary filmmaker Marc Shaffer, the storytellers have traveled far and wide within Colombia — as well as Mexico, the United States and Europe — to follow the scientists’ efforts to advance the field of Alzheimer’s research.

“But the whole point is to concentrate on Latin America, and on the idea that these communities are participants in science and are giving their lives and histories to participate in this long-running research process,” Venegas added. Hopes were high that a recently concluded 10-year clinical trial for the drug crenezumab would result in a therapy that would abate or delay the onset of the disease. But the results were disappointing and the researchers, she said, were crushed.

Still, nature always has the final say, and in an act of serendipity has yielded a rare gift: an individual with the PSEN1 mutation who managed to live well into her 70s without developing the dementia that begins to take down relatives half her age. Thanks to her family’s donation of her brain , Kosik and colleagues from Harvard and the University of Hamburg are among the scientists across the globe unraveling this mystery, to which the filmmakers are just as irresistibly drawn.

“Although part of my motivation for embarking on this project stemmed from seeing several members of my significant other’s family succumb to the disease, I have been fascinated with Alzheimer’s disease since I began my career as a science journalist,” Marisa Venegas said. While she and Cristina don’t share any concerns about having the PSEN1 mutation themselves, she added, “seeing so many newly identified families with the mutation in the area so close to where I was born certainly makes me realize how fortunate we are to have been spared its grip.”

Anyone who becomes involved in Alzheimer’s research quickly learns that it is an exacting, painstaking process to unravel this sticky mystery. But in this story of faith, fortitude, family and fellowship, there’s still so much joy to glean from the relatively short but fierce lives of the paisas and the other families with the genetic form of the disease, lessons we all can draw from.

“Their attitude toward all of this is incredible,” Kosik said. “They just have a wonderful way of mixing tragedy with love for life.” This potent mix no doubt buoys patient, researcher and filmmaker alike in the face of overwhelming odds.

“Obviously, we’re not going to get to a point in the story where they solve the mystery,” Cristina Venegas said. Indeed, piecing together the Alzheimer’s puzzle is akin to building a cathedral, brick by molecular brick, in the hope that future generations of the Colombian families — and by extension all those around the world afflicted by the disease — will benefit.

In the meantime, it’s important to squeeze as much life out of every moment as possible, whether it’s the researchers traveling to remote towns on the trail of stories of forgetfulness, or the rural country folk on the front lines of the disease, or filmmakers hoping to complete their film — finances allowing — in the next couple years. By day, Venegas said, the patients are grappling with the worst news of their lives with the researchers. By night, the boundaries soften between physician and patient, researcher and

subject, as they drop their heavy burdens for a few precious hours.

“It’s just incredible to see Ken (Kosik) talking in Spanish and dancing,” said Venegas, dubbing him an “ honorary Colombian ” for the deep connections he has developed with the families in Antioquia. “He’s in his element.”

To learn more about the film-in-progress, visit Mapping Alzheimer’s . ​​​​​​

Dr. Francisco Lopera died on September 10, 2024 at his home in Medellín. He was 73 years old. His death has deeply affected his family, friends, colleagues and the community for which he worked tirelessly for decades to treat and find an Alzheimer's cure.

“Lopera’s approach to clinical medicine was built on his philosophy: ‘They don’t come to us; we go to them,’” Kosik said in an obituary he wrote in the journal Nature . “It produced good science and goodwill, opening the way for new clinical trials and the discovery of rare protective variants.”

Lopera’s dream of finding a cure now falls to his proteges and colleagues around the world who have pledged to carry on the work begun by their legendary mentor.

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Nutrigenomics: The Genome–Food Interface

M nathaniel mead.

• Copyright and License information

Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.

Efforts to unveil the etiology of human disease often recapitulate the nature versus nurture debate. But today’s biologists concede that neither nature nor nurture alone can explain the molecular processes that ultimately govern human health. The presence of a particular gene or mutation in most cases merely connotes a predisposition to a particular disease process. Whether that genetic potential will eventually manifest as a disease depends on a complex interplay between the human genome and environmental and behavioral factors. This understanding has helped spawn numerous multidisciplinary gene-based approaches to the study of health and disease.

One such endeavor is nutrigenomics, the integration of genomic science with nutrition and, when possible, other lifestyle variables such as cigarette smoking and alcohol consumption. Although genes are critical for determining function, nutrition modifies the extent to which different genes are expressed and thereby modulates whether individuals attain the potential established by their genetic background.

Nutrigenomics therefore initially referred to the study of the effects of nutrients on the expression of an individual’s genetic makeup. More recently, this definition has been broadened to encompass nutritional factors that protect the genome from damage. Ultimately, nutrigenomics is concerned with the impact of dietary components on the genome, the proteome (the sum total of all proteins), and the metabolome (the sum of all metabolites). As in pharmacogenomics, where a drug will have diverse impacts on different segments of the population, researchers recognize that only a portion of the population will respond positively to specific nutritional interventions, while others will be unresponsive, and still other could even be adversely affected.

A Focus on Polymorphisms

Numerous studies in humans, animals, and cell cultures have demonstrated that macronutrients (e.g., fatty acids and proteins), micronutrients (e.g., vitamins), and naturally occurring bioreactive chemicals (e.g., phytochemicals such as flavonoids, carotenoids, coumarins, and phytosterols; and zoochemicals such as eicosapentaenoic acid and docosahexaenoic acid) regulate gene expression in diverse ways. Many of the micronutrients and bioreactive chemicals in foods are directly involved in metabolic reactions that determine everything from hormonal balances and immune competence to detoxification processes and the utilization of macronutrients for fuel and growth. Some of the biochemicals in foods (e.g., genistein and resveratrol) are ligands for transcription factors and thus directly alter gene expression. Others (e.g., choline) alter signal transduction pathways and chromatin structure, thus indirectly affecting gene expression.

There is increasing evidence that genome instability, in the absence of overt exposure to genotoxicants,is itself a sensitive marker of nutritional deficiency. –Michael Fenech, CSIRO Genome Health and Nutrigenomics Laboratory

Much of the nutrigenomic focus has been on single-nucleotide polymorphisms (SNPs), DNA sequence variations that account for 90% of all human genetic variation. SNPs that alter the function of “housekeeping genes” involved in the basic maintenance of the cell are assumed to alter the risk of developing a disease. Dietary factors may differentially alter the effect of one or more SNPs to increase or decrease disease risk.

An elegant example of a diet–SNP interaction involves the common C677T polymorphism of the methylenetetrahydrofolate reductase ( MTHFR ) gene. This variant causes MTHFR enzyme activity to slow down. This results in reduced capacity to use folate (or folic acid) to convert homocysteine to methionine and thence to the S -adenosyl-methionine required for the maintenance methylation of cytosine in DNA and control of gene expression, among many other reactions. But the same variant also may increase the form of folate that can be used to make thymidine, one of the bases in DNA, and to prevent mutagenic uracil from being incorporated instead. This shift in methylation status may explain why in a low-folate environment (for example, where there is low intake of folate-rich vegetables such as spinach and asparagus or a lack of supplemental folate) homozygous carriers of the C677T polymorphism may be more prone to developmental defects but at the same time could be protected against certain cancers.

The key point here is that the activity of the reaction catalyzed by the MTHFR gene can be modified depending on the amount of two essential nutrients: folate, which is the substrate for MTHFR , and riboflavin, a cofactor of MTHFR . “Therefore, the risks associated with MTHFR activity can be markedly modified, for better or for worse, depending on fortification and supplementation strategies,” says Michael Fenech, a research scientist at the CSIRO Genome Health and Nutrigenomics Laboratory in Adelaide, Australia. “For example, in those countries where mothers are required to supplement with high-dose folic acid to prevent neural tube defects in the infant, this practice may actually allow more babies to be born with the MTHFR C677T [polymorphism].” These children would be less able to convert folate to a usable form. On the other hand, if the dietary environment in which these individuals have to grow is low in folate and riboflavin, then they may struggle to survive in good health.

The field of nutrigenomics could not have been launched without the recent development of high-throughput -omic (genomic, transcriptomic, proteomic, and metabolomic) technologies. “These technologies enable us to identify and measure many molecules of each type at one time,” says Jim Kaput, director of the newly established Division of Personalized Nutrition and Medicine at the FDA National Center for Toxicological Research. “In the realm of genomics, for example, we can now measure many variations in DNA, including tens of thousands of single-nucleotide polymorphisms and copy number variants, as well as many RNA molecules. This is crucial, since most cases of chronic diseases are not caused by mutations in single genes but rather by complex interactions among variants of several . . . genes.”

These technologies currently enable identification of up to 500,000 SNPs per individual. Whereas nucleic acids can be analyzed with either sequencing or hybridization technologies, protein and metabolites may require slightly different techniques and equipment depending upon the type of protein and chemical nature of the metabolite. Nevertheless, Kaput says, the end result using various -omic technologies is an incredibly detailed window into the molecular makeup of each individual.

Meanwhile, nutritional biochemists have been busily cataloguing factors in food, including dozens of essential nutrients and tens of thousands of bioactive substances, that can be correlated with molecular patterns identified through the various -omic technologies. The intersection of the genomic and nutritional domains will require sophisticated analytic techniques and, in Kaput’s opinion, the open sharing of scientific research findings worldwide because of the value derived from studying genomic and nutritional patterns in different populations and ethnic groups.

The Sweet Spot for Genomic Health

Not only the expression of genes but also the physical integrity and stability of the genome—what has been referred to as “genome health”—is to a large degree determined by a steady supply of specific nutrients. “There is increasing evidence that genome instability, in the absence of overt exposure to genotoxicants, is itself a sensitive marker of nutritional deficiency,” says Fenech.

Fenech originated the concept of “genome health nutrigenomics,” the science of how nutritional deficiency or excess can cause genome mutations at the base sequence or chromosomal level. “The main goal of this particular research discipline is to define the optimal dietary intake and tissue culture medium concentration to maintain damage to the genome at its lowest possible level in vivo and in vitro , respectively,” says Fenech. “This is critically important because increased damage to the genome is among the fundamental causes of infertility, developmental defects, cancer, and neurodegenerative diseases.” By the same token, the selective use of genome-protective nutrients in individuals with specific gene variants could potentially result in improved resistance toward these major diseases. Fenech believes we need to start viewing foods and diets in terms of their content of genome-protective nutrients.

Folate is among the nutrients most often cited as critical to genomic stability. Controlled intervention study data published in the July 1998 issue of Carcinogenesis and the April 2001 issue of Mutation Research indicate that a folate intake greater than 200 μg/day is required for chromosomal stability. Fenech’s team has shown that reducing plasma folate concentration from 120 to 12 nmol/L in vitro , which is considered to be within the equivalent adequate range in vivo , causes as much genome damage as that induced by an acute exposure to 0.2 Gy of ionizing radiation. “We concluded that even moderate folate deficiency within the physiological range causes as much DNA damage in cultured lymphocytes as ten times the annual allowed limit of exposure to X rays and other forms of low linear energy transfer ionizing radiation for the general population,” says Fenech. He points out that the typical plasma folate concentration for most populations is only 10–30 nmol/L, a level adequate to prevent anemia “but apparently insufficient to minimize chromosomal damage.”

In the May 2005 issue of Carcinogenesis Fenech and his colleagues identified nine key nutrients that may affect genomic integrity in various ways. When consumed in increasing amounts in food, six of these nutrients (folate, vitamin B 12 , niacin, vitamin E, retinol, and calcium) are associated with a reduction in DNA damage, whereas three others (riboflavin, pantothenic acid, and biotin) are associated with an increase in DNA damage to the same extent observed with occupational exposure to genotoxic and carcinogenic chemicals. “These observations indicate that nutritional deficiency or excess can cause DNA damage on its own and that the effects are of the same magnitude as that of many common environmental toxicants,” Fenech says.

Paul Soloway, a nutrition professor at Cornell University, points out that characterizing diets or specific nutrients as being genome-damaging or genome-protecting on the basis of in vitro studies overlooks the variations in benefits that exist over a lifetime, particular relative to the timing of disease onset. Moreover, nutritionists have long understood that the optimal requirements for many nutrients fall within a range between deficiency and toxicity. In an environment of vitamin fortification and supplementation, Fenech’s findings may compel health officials to be more vigilant about not exceeding levels that could be harmful to the genome or that might even promote the growth of latent cancers. As an example of how controversial these concerns may be, some studies have reported protective benefits from folate for initiation of colorectal cancer, whereas others have found that this nutrient may promote the growth of this cancer once it is established.

Defining the optimal concentration of micronutrients required to maintain cells in a genomically stable state remains one of the main challenges for nutrigenomics researchers. This challenge becomes magnified in the context of requirements for diverse genetic backgrounds. Fenech cites the example of individuals who show inherited defects in DNA repair: these individuals may be more vulnerable to the DNA-damaging effects of moderate folate deficiency than those who do not have such defects.

There are thousands of DNA alterations in each human cell daily; if not efficiently repaired, our genome would rapidly be destroyed. Diet and lifestyle are major mediating factors in this equation. For example, DNA damage is accelerated by oxidative stressors such as tobacco smoke, strenuous exercise, and a high-fat diet, according to a study in the September 2002 issue of Carcinogenesis . On the flip side, diets low in fat and/or high in cruciferous vegetables have been shown to lower the oxidative DNA damage rate in humans, as indicated by reduced urinary excretion of 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG). In other reports, the dietary intake of vitamin C determined the concentration of 8-oxodG in human sperm DNA, while dietary fish oil and calcium reduced oxidative DNA damage rate in colonic epithelial cells.

When it comes to maintaining genomic integrity, epigenetic changes such as those involving DNA and histone modifications are as profound as the genetic ones. “The loss of normal epigenetic states can lead to genomic rearrangements and increased failure of mismatch repair,” says Soloway. The example of folate and MTHFR helps highlight the dynamic interplay between the genome and epigenome, he says: “Because there are considerable epigenetic influences of nutrients such as folate, one of the ways by which alleles of MTHFR might control nutrient-related phenotypes is through epigenetic mechanisms.” Changes in the epigenome in response to dietary factors may often precede changes in the genome, and yet those genomic changes help solidify the emergence of new epigenetic patterns within the organism.

In addition to folate, various antioxidant nutrients and phytochemicals are known to enhance DNA repair and reduce oxidative DNA damage, and such dietary contributions could theoretically compensate for inherited defects in repair mechanisms. Also, individuals with inherited polymorphisms that lower the activity of antioxidant enzyme systems such as manganese superoxide dismutase and glutathione peroxidase may have a higher requirement for dietary antioxidants to prevent DNA damage or cancer risk.

Although it is tempting to focus on single-nutrient effects such as the folate example mentioned above, nutrigenomics researchers contend that the real focus should be on the impact of multiple nutritional imbalances (both excess and deficiency) on the genome. In their May 2005 Carcinogenesis article, which described a study of 190 healthy men and women with an average age of 48 years, Fenech and his colleagues showed that high intakes of various B vitamins—riboflavin, pantothenic acid, and biotin—actually increased micronucleus frequency in lymphocytes, a standard measure of genome damage.

Going further, they studied the combined effects of calcium or riboflavin with different levels of folate intake, since earlier studies had indicated that these dietary factors tend to interact in modifying the risk of cancer, osteoporosis, and hip fractures. Increasing one’s calcium intake further enhanced the genome-protective effect of a high-folate diet whereas a high riboflavin intake further exacerbated genome damage associated with a low-folate diet. This is consistent with epidemiologic studies showing that cancer rates tend to be higher among populations that consume more red meat (which is very high in riboflavin), more alcohol (which depletes folate), and fewer vegetables (a rich source of folate).

The promise of nutrition-modulated DNA repair strategies has attracted the attention of cancer researchers in particular. “Dietary factors can act to stabilize the genome once genetic abnormalities have occurred,” says gastroenterologist Graeme Young, who directs the Flinders Centre for Innovation in Cancer in Adelaide, Australia. “The traditional diet–genome approach has related protection to dietary lifestyle and germline genotype,” he says. “Here we are discussing dietary interaction with the abnormal genome in potentially precancerous cells.” Young and his colleagues are now planning to explore the capacity of dietary factors to regulate DNA repair mechanisms.

Nutrigenomic Links to Chronic Disease

Ben van Ommen, director of the European Nutrigenomics Organization, and colleagues hypothesize that all diseases can be reduced to imbalances in four overarching processes: inflammatory, metabolic, oxidative, and psychological stress. Diseases arise because of genetic predispositions to one or more of these stressors. Nutrigenomics represents a major effort to improve our understanding of the role of nutrition and genomic interactions in at least the first three of these areas, says Kaput. In time, he adds, we will see important contributions from nutrigenomics for the prevention of many common modern maladies, including obesity, diabetes, cardiovascular disease, cancer, inflammatory disorders, age-related cognitive disorders, visual function, and of course many vitamin deficiency problems.

Diabetes, obesity, and cardiovascular diseases have been referred to by medical anthropologists and others as “diseases of civilization.” The reason is simple: when aboriginal populations begin to adopt a high-sugar, high-fat “Western diet” for the first time, obesity and diabetes suddenly begin to appear in those populations and typically increase at rates commensurate with the adoption of the new diet. Such observations have been dramatically borne out in studies of the Pima Indians of Arizona and the indigenous people of Hawaii. In both instances, the abandonment of the traditional plant-rich, high-fiber diet was followed by skyrocketing rates of diabetes, obesity, and later cancer.

Dietary factors can act to stabilize the genome once genetic abnormalities have occurred. The traditional diet–genome approach has related protection to dietary lifestyle and germline genotype. Here we are discussing dietary interaction with the abnormal genome in potentially precancerous cells. –Graeme Young, Flinders Centre for Innovation in Cancer

Over the course of human evolution, diet has profoundly molded human metabolic capacities and thus paved the way for the emergence of modern diseases. From an evolutionary perspective, diet is a limiting factor that imposes selective pressures on a population, much like other environmental factors. Some genotypes within a population are associated with higher nutrient needs, and when those needs are not met, there will be selection against those particular genotypes. However, when those needs are met—for example, the need for extra calories from carbohydrates and dietary fat—the gene that confers the high nutrient requirement will then persist in the population. This could well be the case for genes linked with obesity and diabetes.

Soloway notes that in cases where certain gene alleles confer some selective advantage, high levels of the required nutrient can actually lead to an expanded frequency of those alleles in a population. “In such cases, nutrient availability can provide a selective pressure that drives genotypic shifts in a population,” he says.

From the nutrigenomic perspective, diabetes and obesity are both the result of an imbalanced diet interacting with genes that were once functional and adaptive in an earlier phase of human evolution, when food was less abundant. In the modern context, these same genes are considered to code for hormonal or metabolic tendencies that have become maladaptive and pathological in the modern environment. Risk of developing these diseases is thought to be modulated by genetic susceptibility differences among ancestral groups to the effect of the Western diet in precipitating insulin resistance.

In addition, says Lynn Ferguson, a nutrition professor at the University of Auckland in New Zealand and program leader of the New Zealand National Centre for Research Excellence in Nutrigenomics, “the control of food intake is profoundly influenced by gene variants encoding taste receptors or those encoding a number of peripheral signaling peptides such as insulin, leptin, ghrelin, cholecystokinin, and corresponding receptors. Total dietary intake, and the satiety value of various foods, will profoundly modify the impact of these genes.” In volume 10, number 2 (2006) of Molecular Diagnosis & Therapy , Ferguson cites studies that have linked five common SNPs with increased obesity risk and resistance to weight reduction. “These SNPs represent promising targets for future nutrigenomic studies of people at risk for obesity,” she says. Taken together, these findings provide a strong scientific rationale for avoiding a generic, one-size-fits-all approach to the problem of obesity.

Given that obesity is itself a risk factor for diabetes, cardiovascular disease, and various cancers, it is worthwhile to focus on the nutrigenomic aspects of this disease. A study conducted at the University of Navarra in Pamplona, Spain, and published in the August 2003 issue of the Journal of Nutrition showed that women with a Glu27 variant and a carbohydrate intake constituting more than 49% of total caloric consumption had a nearly three-fold increase in their risk of developing obesity. Importantly, an alternative variant of that same gene was not linked with a greater obesity risk in relation to the same carbohydrate–calorie intake levels. This could help explain why some women on high-carbohydrate diets gain weight while others do not.

Abdominal obesity, independent of generalized adiposity, predicts insulin resistance, type 2 diabetes, dyslipidemia, and cardiovascular disease. Endocrinologist Jerry Greenfield and colleagues at St. Vincent’s Hospital in Sydney, Australia, recently reported that high polyunsaturated fat intake was associated with lower levels of abdominal fat in women at low genetic risk for abdominal obesity but not in women at high genetic risk. Also, a moderately high alcohol intake (1–1.5 drinks per day) was associated with approximately 20% less abdominal fat than lower intakes, but only in women genetically predisposed to abdominal obesity. This study, published in the November 2003 Journal of Clinical Endocrinology and Metabolism , indicates that various gene–diet interactions could be a key part of the abdominal obesity equation.

Diet–gene interactions are highly complex and hard to predict, thus demonstrating the need for highly controlled genotypes and environmental conditions that allow for identifying different regulatory patterns based on diet and genotype. The challenges we now face may ultimately require a nutrigenomics project on the scale of the Human Genome Project in order to identify genes that cause or promote chronic disease and the nutrients that regulate or influence the activity of these genes. –Jim Kaput, FDA National Center for Toxicological Research

The APOE gene offers another example of how certain polymorphisms may predispose their bearers to chronic diseases. Each of three phenotypes carries a different probability of cardiovascular disease risk and responds differently to lifestyle and environmental factors, including dietary variables such as the amount and type of dietary fat. Most people in the United States have the APOE3 phenotype and respond favorably to a lower intake of dietary fat and regular exercise: their cholesterol levels drop and overall cardiovascular health improves. However, about 20% of the U.S. population carries at least one variant denoted as APOE- ε4, a polymorphism associated with elevated total cholesterol level, as well as an increased risk of both type 2 diabetes and Alzheimer disease. The SNP also abrogates the protective effects seen with moderate alcohol consumption and greatly increases the cardiovascular risks associated with smoking, dramatically boosting the risk of heart attack in such individuals.

“The implication here is that anyone with this genotype should be rigorously attentive to their diet and lifestyle,” says Ferguson. “These individuals should avoid smoking and alcohol while undertaking exercise and eating a diet low in saturated fat. Nonetheless, at present, very few people are aware of their APOE genotype.” Lack of the awareness of such SNP–diet–lifestyle interactions is not only a drawback for public health education, but also may result in null findings in epidemiologic studies when in fact certain segments of the study population are highly vulnerable to diseases that are linked with a given SNP.

Future Research Directives and Challenges

Identifying the SNP–diet and SNP–nutrient interactions that cause chronic disease is challenging because of the complexities inherent in studying genotypes and in assessing dietary and nutrient intakes. At this time, few if any of the SNP–diet associations that have been reported in epidemiologic studies have been replicated, and many have been plagued by a lack of appropriate statistical power and other methodologic problems. Ultimately, because many cases of chronic diseases are influenced by different diets, nutrition–genome interactions will not be found unless diet and genotype are controlled and changed in the experimental design (same diet with different genotypes, and different genotypes with the same diet).

“Diet–gene interactions are highly complex and hard to predict, thus demonstrating the need for highly controlled genotypes and environmental conditions that allow for identifying different regulatory patterns based on diet and genotype,” Kaput says. “The challenges we now face may ultimately require a nutrigenomics project on the scale of the Human Genome Project in order to identify genes that cause or promote chronic disease and the nutrients that regulate or influence the activity of these genes.”

Because human intervention studies are costly and difficult to conduct, observational studies (which detect associations, not causal relationships) will likely continue to dominate the epidemiologic approach to nutrigenomics. For interventional and mechanistic data, in vivo animal studies will be heavily favored because lab animals can be selected for minimal genetic variation and shorter life spans. Moreover, it is much easier to control and monitor the dietary intakes of animals than those of humans.

Kaput notes that assessments of dietary intake, albeit mundane to the outside world, may represent one of the biggest impediments to the success of large-scale human nutrigenomic studies. “Quantifying food intake is challenging because free-living humans simply do not regard daily life as a science experiment where the amount and type of food is accurately recorded,” he says. To avoid measurement problems such as misclassification, more reliable measurement tools for assessing nutrient intake will be needed in the years ahead.

Proponents of nutrigenomics research have cited the population-wide prevention and treatment of vitamin deficiency as a top public health priority. Since vitamin deficiencies are highly prevalent in socioeconomically challenged populations around the world, and because large sample sizes are needed to test nutrigenomic relationships, Kaput and his colleagues are pushing for an international effort to study micronutrient needs based on differing genetic makeups among different ancestral groups.

Bruce Ames, a molecular biologist at Children’s Hospital Oakland Research Institute in California, has documented a number of polymorphisms in genes that affect the binding of coenzymes, some of which are essential vitamins. “With these types of evidenced-based findings within the nutrigenomic framework, I believe we’ll have more ammunition to convince government and public health officials to tackle the issue of vitamin deficiency around the world,” Kaput says. “With this more targeted approach, we’re more likely to see political and economic forces fall in place to solve the problem. . . . Although the complexities are substantial, I believe nutrigenomic approaches offer the best hope for understanding the molecular processes that maintain health and prevent disease.”

For Fenech, one of the key objectives of nutrigenomics for society is to diagnose and nutritionally prevent DNA damage on an individual-by-individual basis. He has devised the concept of the Genome Health Clinic, a new mode of health care based on the diagnosis and nutritional prevention of DNA damage and the diseases that result therefrom. In recent years, a number of nutritional/metabolic/diagnostic testing companies such as Genova and MetaMetrix have started to sell genomic profiling tests to help guide decision making around dietary supplements. With the increasingly lower pricings for analyzing SNPs in individuals, the population-level potential for dietary optimization based on nutrigenomic approaches seems truly awesome. Even in the absence of information on an individual’s genotype, it is practical to use nutrition-sensitive genome damage biomarkers, such as the micronucleus assay, to determine whether dietary and/or supplement choices are causing benefit or harm to a person’s genome.

Says Fenech, “In the near future, instead of diagnosing and treating diseases caused by genome or epigenome damage, health care practitioners may be trained to diagnose and nutritionally prevent or even reverse genomic damage and aberrant gene expression. Nutrigenomics will help usher in the development of new functional foods and supplements for genome health that can be mixed and matched so that overall nutritional intake is appropriately tailored to an individual’s genotype and genome status.”

Research presented at a November 2007 meeting suggests that inositol (a member of the B vitamin family found in grains, seeds, nuts, brewer’s yeast, and many other foods) and its derivative inositol hexaphosphate (IP6) help protect against genetic damage from UVB and other radiation. In one experiment, human skin cells treated with IP6 were less likely than untreated cells to undergo apoptosis, indicating that they had less irreparable DNA damage. In another experiment, mice genetically engineered for a propensity to skin cancer drank water containing 2% IP6. Tumors developed in 23% of these mice compared with 51% of mice that did not receive IP6. Use of a cream containing inositol and IP6 also protected against tumor development in mice exposed to UVB radiation. The researchers suggest that people who are regularly exposed to ionizing radiation, such as airline pilots, frequent fliers, or people who handle radioactive materials, might take IP6 prophylactically to prevent possible long-term effects of exposure.

Source: Shamsuddin AM. Paper presented at: American Association for Cancer Research Centennial Conference on Translational Cancer Medicine: From Technology to Treatment; Singapore; 4–8 November 2007.

An article published in the October 2007 issue of the British Journal of Nutrition warns that fortifying flour with folic acid—a move intended to prevent neural tube defects among mothers who eat the flour—may lead to numerous unforeseen health problems. Unlike the natural folates found in leafy green vegetables, which are digested in the gut, synthetic supplements are now believed to be metabolized in the liver. The study authors hypothesize that the liver becomes saturated, and unmetabolized folic acid enters the blood stream, where it can contribute to leukemia, arthritis, colorectal cancer, and ectopic and multiple pregnancies. Other recent findings on a potential link between supplementation and colorectal cancer are examined in two commentaries in the November 2007 issue of Nutrition Reviews . The new data follow on the heels of the U.K. Food Standard Agency’s May 2007 approval of the addition of folic acid to flour. The United States, Canada, and Chile also currently fortify flour with folic acid, and the policy is being considered for implementation in Australia, New Zealand, and Ireland.

Sources: Wright AJA, et al. 2007. Folic acid metabolism in human subjects revisited: potential implications for proposed mandatory folic acid fortification in the UK. Br J Nutr 98(4):667–675; Kim Y-I. 2007. Folic acid fortification and supplementation—good for some but not so good for others. Nutr Rev 65:504–511; Solomons NW. 2007. Food fortification with folic acid: has the other shoe dropped? Nutr Rev 65:512–515.

Antioxidants are known for their ability to slow the oxidation that damages cells. But the human body doesn’t derive the same level of benefit from all antioxidants. Recently nutritionists with the USDA Agricultural Research Service measured the plasma antioxidant capacity (AOC) of study subjects following a single meal of blueberries, cherries, dried plums, dried plum juice, grapes, kiwis, or strawberries. They reported in the April 2007 Journal of the American College of Nutrition that blueberries, grapes, and kiwifruit yielded the greatest increases in plasma AOC. Plums—despite their high antioxidant content—did not raise plasma AOC levels, probably because chlorogenic acid, the antioxidant in which they are richest, is not readily absorbed by humans.

Norwegian researchers showed in the August 2007 issue of the Journal of Nutrition that anthocyanins from bilberries and black currants reduced levels of transcription factor NF-κB in cultured cells. NF-κB orchestrates a wide range of inflammatory responses. In humans, anthocyanin supplementation decreased interleukin-8, IFN, and normal T cell expression by 25%, 25%, and 15%, respectively, over placebo. The authors suggest that anthocyanins and/or their metabolites may serve as redox buffers capable of suppressing oxidative stress and thereby dampen the inflammatory response by direct reactive oxygen species scavenging.

Sources: Prior RL, et al. 2007. Plasma antioxidant capacity changes following a meal as a measure of the ability of a food to alter in vivo antioxidant status. J Am Coll Nutr 26(2):170–181; Karlsen A, et al. 2007. Anthocyanins inhibit nuclear factor-B activation in monocytes and reduce plasma concentrations of pro-inflammatory mediators in healthy adults. J Nutr 137:1951–1954.

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Scope and Standards: Defining the Advanced Practice Role in Genetics

Because knowledge concerning genetics and genomics and its application to oncology care is continuing to grow, oncology nurses must be aware of appropriate advanced scope of practice roles based on education and training. All nurses must develop and maintain knowledge of the field, but advanced practice nurses working in genetics have additional competencies and management expectations. Collaboration among practice levels and disciplines is essential. This article focuses on the advanced practice role as further defined in published resources outlining scope of practice in genetics.

AT A GLANCE

• Genetics should be considered an integral part of oncology advanced nursing practice.
• Nurses should be able to apply genetic nursing standards for the advanced practitioner to specific patient situations.
• Additional competencies are expected of the advanced practitioner specifically related to test selection, as well as interpretation and coordination of care for genetic evaluation and testing.
• Genetics & Genomics

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New $5 million DoE award supports KU startup’s green hydrogen energy research

Tue, 10/29/2024

Brendan M. Lynch

LAWRENCE — With $5 million in support from the U.S. Department of Energy, the University of Kansas and Avium — a startup firm founded by researchers from KU’s School of Engineering — aim to make clean hydrogen more affordable.

According to the DoE , the work at KU is part of $750 million in funding for 52 projects across 24 states “to dramatically reduce the cost of clean hydrogen and reinforce American leadership in the growing hydrogen industry.”

Green hydrogen is a key tool in the worldwide push to slash carbon emissions, especially in the industrial, transport and agricultural sectors. However, conventional hydrogen production emits greenhouse gases. By contrast, green hydrogen is produced with renewable energy, making it crucial to achieving net-zero goals.

“The whole world is interested in green hydrogen,” said Kevin Leonard, professor of chemical & petroleum engineering at KU, as well as a member of KU’s Center for Environmental Beneficial Catalysis and chief science officer of Avium. “Hydrogen is a commodity chemical — nearly 100 million tons are produced annually worldwide. It’s used in fertilizers, cement production, metal processing and refining. Traditionally, it’s made from natural gas, but that process emits CO 2 . This results in hundreds of millions of tons of greenhouse gas emissions.”

Back in 2017, Leonard and KU graduate student Joseph Barforoush developed new catalysts that make green hydrogen production more efficient, which led to the founding of Avium, based in Lawrence.

“We've gone through the Small Business Innovation Research grants, receiving funding from both the National Science Foundation and the DoE,” Leonard said. “As part of the Bipartisan Infrastructure Bill, $750 million was allocated to bolster green hydrogen efforts in the U.S., including the award to Avium and KU.”

The work at KU and Avium will develop new catalysts and technologies to improve the efficiency and reliability of green hydrogen production. According to Leonard, the benefits might well extend beyond sectors where hydrogen is already used.

“People are interested in green hydrogen for traditional applications like those I mentioned, but also for emerging ones,” he said. “One example is sustainable aviation fuels. Green hydrogen will be critical in creating sustainable, petroleum-free fuels, specifically for aviation.”

The KU researcher said clean hydrogen is also gaining interest for renewable energy storage.

“Take Arizona, for example,” he said. “During winter days, the solar panels on the grid can produce much more energy than is needed. However, in the summer, when it’s 110 degrees and air conditioners are running, solar energy alone cannot produce enough electricity, specifically in the evening. Storing excess energy from January and February to use in July and August is a challenge. However,  using green hydrogen to store that energy, then converting it back to electricity later, may prove effective for grid energy balancing.”

KU students and postdoctoral researchers will receive training as part of the work. But further, the award will support technical training and career-building opportunities for students at the Dwayne Peaslee Technical Training Center in Lawrence and Urban Tec in Kansas City, Missouri.

“We’ll collaborate with Peaslee to provide technical training for students entering fields like electrical work and HVAC, ensuring they are familiar with the specialized skills required for green hydrogen processes, such as handling high-voltage lines or understanding the systems involved in hydrogen energy,” Leonard said. “We are also partnering with Urban Tec to launch the Avium Summer Experience, where students from Kansas City will visit KU to explore university life. They'll also tour Avium and Peaslee Tech to learn about the different paths available to them — whether through apprenticeships or startup environments.”

Leonard said the transition to a clean-energy future, especially the DoE’s stated Hydrogen Shot goal, would depend in part on the development of technology like Avium’s catalysts.

“The U.S. is really pushing towards sustainability,” he said. “There's a federal target to produce green hydrogen for just a dollar per kilogram by 2031. The point is that green hydrogen will become a key part of the transition to clean energy. Green hydrogen can help make the chemical industry more sustainable by enabling the more sustainable production of fuels and fertilizers.”

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• Published: 30 October 2024

Myocardial infarction augments sleep to limit cardiac inflammation and damage

• Pacific Huynh 1 , 2 , 3 , 4 , 5   na1 ,
• Jan D. Hoffmann 1 , 2 , 3 , 4 , 5 , 6   na1 ,
• Teresa Gerhardt 1 , 2 , 3 , 4 , 5 , 7 ,
• Máté G. Kiss 1 , 2 , 3 , 4 , 5 ,
• Faris M. Zuraikat 8 , 9 ,
• Oren Cohen   ORCID: orcid.org/0009-0004-2827-9063 1 , 2 , 10 ,
• Christopher Wolfram   ORCID: orcid.org/0009-0002-8113-9102 7 ,
• Abi G. Yates 1 , 2 , 3 , 4 , 5 ,
• Alexander Leunig   ORCID: orcid.org/0000-0002-9179-9203 1 , 2 , 3 , 4 , 5 ,
• Merlin Heiser   ORCID: orcid.org/0000-0001-5232-0688 1 , 2 , 3 , 4 , 5 ,
• Lena Gaebel 1 , 2 , 3 , 4 , 5 ,
• Matteo Gianeselli   ORCID: orcid.org/0000-0003-3726-3595 1 , 2 , 3 , 4 , 5 ,
• Sukanya Goswami 1 , 2 , 3 , 4 , 5 ,
• Annie Khamhoung 1 , 2 , 3 , 4 , 5 ,
• Jeffrey Downey 1 , 2 , 3 , 4 , 5 ,
• Seonghun Yoon 1 , 2 ,
• Zhihong Chen   ORCID: orcid.org/0000-0001-7403-7015 11 ,
• Vladimir Roudko 11 ,
• Travis Dawson 11 ,
• Joana Ferreira da Silva 12 , 13 ,
• Natalie J. Ameral 13 ,
• Jarod Morgenroth-Rebin 12 ,
• Darwin D’Souza 11 ,
• Laura L. Koekkoek 1 , 2 , 3 , 4 , 5 ,
• Walter Jacob   ORCID: orcid.org/0000-0001-9649-4989 1 , 2 , 3 , 4 , 5 ,
• Jazz Munitz   ORCID: orcid.org/0000-0002-9173-2666 1 , 1 , 14 , 15 ,
• Donghoon Lee   ORCID: orcid.org/0000-0003-0453-6059 3 , 4 , 16 , 17 , 18 , 19 ,
• John F. Fullard   ORCID: orcid.org/0000-0001-9874-2907 3 , 4 , 16 , 17 , 18 , 19 ,
• Mandy M. T. van Leent   ORCID: orcid.org/0000-0002-9747-1022 1 , 2 , 14 , 15 ,
• Panos Roussos   ORCID: orcid.org/0000-0002-4640-6239 3 , 4 , 16 , 17 , 18 , 19 ,
• Seunghee Kim-Schulze 11 ,
• Neomi Shah 10 ,
• Benjamin P. Kleinstiver   ORCID: orcid.org/0000-0002-5469-0655 12 , 13 ,
• Filip K. Swirski   ORCID: orcid.org/0000-0002-3163-9152 1 , 2 , 5 , 14 ,
• David Leistner 7 , 20 ,
• Marie-Pierre St-Onge   ORCID: orcid.org/0000-0003-1354-1749 8 , 9 &
• Cameron S. McAlpine   ORCID: orcid.org/0000-0002-9832-6345 1 , 2 , 3 , 4 , 5  

Nature ( 2024 ) Cite this article

Metrics details

• Acute inflammation
• Myocardial infarction
• Neuroimmunology

Sleep is integral to cardiovascular health 1 , 2 . Yet, the circuits that connect cardiovascular pathology and sleep are incompletely understood. It remains unclear whether cardiac injury influences sleep and whether sleep-mediated neural outputs contribute to heart healing and inflammation. Here we report that in humans and mice, monocytes are actively recruited to the brain after myocardial infarction (MI) to augment sleep, which suppresses sympathetic outflow to the heart, limiting inflammation and promoting healing. After MI, microglia rapidly recruit circulating monocytes to the brain’s thalamic lateral posterior nucleus (LPN) via the choroid plexus, where they are reprogrammed to generate tumour necrosis factor (TNF). In the thalamic LPN, monocytic TNF engages Tnfrsf1a -expressing glutamatergic neurons to increase slow wave sleep pressure and abundance. Disrupting sleep after MI worsens cardiac function, decreases heart rate variability and causes spontaneous ventricular tachycardia. After MI, disrupting or curtailing sleep by manipulating glutamatergic TNF signalling in the thalamic LPN increases cardiac sympathetic input which signals through the β2-adrenergic receptor of macrophages to promote a chemotactic signature that increases monocyte influx. Poor sleep in the weeks following acute coronary syndrome increases susceptibility to secondary cardiovascular events and reduces the heart’s functional recovery. In parallel, insufficient sleep in humans reprogrammes β2-adrenergic receptor-expressing monocytes towards a chemotactic phenotype, enhancing their migratory capacity. Collectively, our data uncover cardiogenic regulation of sleep after heart injury, which restricts cardiac sympathetic input, limiting inflammation and damage.

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Data availability

scRNA-seq data have been deposited to NCBI Gene Expression Omnibus under the following accession numbers: GSE275071 for the human PBMC sequencing from the sleep restriction study and GSE275089 for all mouse sequencing. snRNA-seq data for human brain tissue have been deposited to NCBI Gene Expression Omnibus under the accession number GSE227781 . All other necessary data are contained within the manuscript. Requests for materials can be made to the corresponding author.  Source data are provided with this paper.

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Acknowledgements

The authors thank the Human Immune Monitoring Core at the Icahn School of Medicine at Mount Sinai for help with sequencing; the BioMedical Engineering and Imaging Institute and the small animal imaging facility at the Icahn School of Medicine at Mount Sinai for help with MRI and echo imaging and analysis; the Mount Sinai Neuropathology Brain bank for human brain tissue; the Mount Sinai Microscopy and Advanced Bioimaging Core for help with lightsheet, confocal and immunofluorescence imaging; and K. Joyes for copy editing the manuscript text. This work was funded by the National Institutes of health (NIH) R01HL158534, R00HL151750, the Cure Alzheimer’s Fund, and an ISMMS Karen Strauss Cook Research Scholar Award (to C.S.M.); NIH R01AG082185 (to C.S.M., P.R. and D. Lee); an American Heart Association postdoctoral fellowship 24POST1196847 (to P.H.); NIH 5T32HL007824-25 (supported J.D.H.); an EMBO Long Term Fellowship (ALTF 750-2022; to J.F.d.S.); a Kayden-Lambert MGH Research Scholar Award and NIH P01-HL142494 and DP2-CA281401 (to B.P.K.); NINIH R01HL128226 and R35HL155670 (to M.-P.S.-O.); NIH T32HL007343 (supported F.M.Z.); and NIH UL1TR001873 (to Columbia University).

Author information

These authors contributed equally: Pacific Huynh, Jan D. Hoffmann

Authors and Affiliations

Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Pacific Huynh, Jan D. Hoffmann, Teresa Gerhardt, Máté G. Kiss, Oren Cohen, Abi G. Yates, Alexander Leunig, Merlin Heiser, Lena Gaebel, Matteo Gianeselli, Sukanya Goswami, Annie Khamhoung, Jeffrey Downey, Seonghun Yoon, Laura L. Koekkoek, Walter Jacob, Jazz Munitz, Jazz Munitz, Mandy M. T. van Leent, Filip K. Swirski & Cameron S. McAlpine

Department of Medicine, Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Pacific Huynh, Jan D. Hoffmann, Teresa Gerhardt, Máté G. Kiss, Oren Cohen, Abi G. Yates, Alexander Leunig, Merlin Heiser, Lena Gaebel, Matteo Gianeselli, Sukanya Goswami, Annie Khamhoung, Jeffrey Downey, Seonghun Yoon, Laura L. Koekkoek, Walter Jacob, Mandy M. T. van Leent, Filip K. Swirski & Cameron S. McAlpine

Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Pacific Huynh, Jan D. Hoffmann, Teresa Gerhardt, Máté G. Kiss, Abi G. Yates, Alexander Leunig, Merlin Heiser, Lena Gaebel, Matteo Gianeselli, Sukanya Goswami, Annie Khamhoung, Jeffrey Downey, Laura L. Koekkoek, Walter Jacob, Donghoon Lee, John F. Fullard, Panos Roussos & Cameron S. McAlpine

Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Pacific Huynh, Jan D. Hoffmann, Teresa Gerhardt, Máté G. Kiss, Abi G. Yates, Alexander Leunig, Merlin Heiser, Lena Gaebel, Matteo Gianeselli, Sukanya Goswami, Annie Khamhoung, Jeffrey Downey, Laura L. Koekkoek, Walter Jacob, Filip K. Swirski & Cameron S. McAlpine

Department of Medicine, NYC Health and Hospitals/Elmhurst, Elmhurst, Queens, NY, USA

Jan D. Hoffmann

Department of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité, Germany and Berlin Institute of Health, Berlin, Germany

Teresa Gerhardt, Christopher Wolfram & David Leistner

Center of Excellence for Sleep and Circadian Research, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA

Faris M. Zuraikat & Marie-Pierre St-Onge

Division of General Medicine, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA

Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Oren Cohen & Neomi Shah

Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Zhihong Chen, Vladimir Roudko, Travis Dawson, Darwin D’Souza & Seunghee Kim-Schulze

Center for Genomic Medicine, Department of Pathology, Massachusetts General Hospital, Boston, MA, USA

Joana Ferreira da Silva, Jarod Morgenroth-Rebin & Benjamin P. Kleinstiver

Department of Pathology, Harvard Medical School, Boston, MA, USA

Joana Ferreira da Silva, Natalie J. Ameral & Benjamin P. Kleinstiver

BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Jazz Munitz, Mandy M. T. van Leent & Filip K. Swirski

Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Jazz Munitz & Mandy M. T. van Leent

Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Donghoon Lee, John F. Fullard & Panos Roussos

Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Department of Medicine, Cardiology/Angiology, Goethe University Hospital, Frankfurt, Germany

David Leistner

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Contributions

P.H. and J.D.H. conceived the project, designed and performed experiments, analysed and interpreted data, offered intellectual input and edited the manuscript. T.G. designed and performed experiments, analysed and interpreted data, conducted the sleep and ACS trial, offered intellectual input and edited the manuscript. M.G.K. aided experiments, interpreted data and offered intellectual input. F.M.Z. and M.-P.S.-O. conducted the human sleep restriction trial and offered intellectual input. O.C. aided in implementation of the sleep questionnaire for the ACS trial, interpreted data and offered intellectual input. C.W. and D. Leistner conducted the sleep and ACS trial. S.G., A.K., A.G.Y., J.D., L.L.K., W.J., L.G., M.H., A.L. and M.G. conducted and aided experiments. Z.C., V.R., T.D., J.M.-R., D.D. and S.K.-S. conducted sequencing experiments and aided in analysis. J.F.d.S., N.J.A. and B.P.K. designed and cloned the AAV vector for Tnfrsf1a knockout and offered technical advice. J.M. and M.M.T.v.L. conducted cMRI imaging and analysis. D. Lee, J.F.F. and P.R. conducted human brain snRNA-seq and analysis. S.Y. performed MI and sham surgeries. N.S. and F.K.S. aided in supervision and offered intellectual input. C.S.M. conceived the project, supervised, directed and managed the study, performed experiments, interpreted data, designed the figures and wrote the manuscript.

Corresponding author

Correspondence to Cameron S. McAlpine .

Ethics declarations

Competing interests.

C.S.M. is a consultant for Granite Bio. J.F.d.S. and B.P.K. are inventors on patents or patent applications filed by Mass General Brigham (MGB) that describe genome engineering technologies. B.P.K. is a consultant for EcoR1 capital and Novartis Venture Fund, and is on the scientific advisory board of Acrigen Biosciences, Life Edit Therapeutics and Prime Medicine. B.P.K. has a financial interest in Prime Medicine, a company that is developing therapeutic CRISPR–Cas technologies for gene editing. The interests of B.P.K. were reviewed and are managed by Massachusetts General Hospital (MGH) and MGB in accordance with their conflict-of-interest policies. The other authors declare no competing interests.

Peer review

Peer review information.

Nature thanks Hafid Ait-Oufella, Rachel Rowe and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.

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Extended data figures and tables

Extended data fig. 1 sleep parameters in mice with cardiovascular diseases..

a , Quantification of sleep in WT mice that consumed a chow diet and atherosclerotic Apoe −/− mice what consumed a HFD for 16 weeks. n = 8 WT mice; n = 5 Apoe −/− HFD mice. b , Quantification of SWS in sham controls and TAC mice 7 days after surgery. n = 4 sham; n = 3 TAC. c , Quantification of REM sleep in MI and sham mice up to 21 days after infarct. n = 5 mice per group. d , Hypnogram of sleep and wake states in sham and MI mice. e , Quantification of locomotor activity up to 21 days after infarct (p < 0.0001, F = 64.71). n = 4 mice per group. f , Quantification of body temperature up to 21 days after infarct (p < 0.0001, F = 19.68). n = 4 mice per group. g , SWS analysis in naïve and sham mice. n = 5 sham mice; n = 4 naive mice. Data are mean ± s.e.m. Statistical analysis was done using two-way analysis of variance. Experiments were conducted in female mice. *p < 0.05, **p < 0.01, ***p < 0.001.

Extended Data Fig. 2 Analysis of microglia after myocardial infarction.

a , UMAP of cells identified in scRNAseq of mouse brain. n = 5 pooled mice per group. b , UMAP of microglia subclusters and their frequency in day 3 sham and MI mice. n = 5 pooled mice per group. c , Expression of chemokines and activation markers in non-microglial macrophages. n = 5 pooled mice per group. d , Analysis of microglia activation markers in naive mice injected with sham or day 1 MI plasma and sacrificed 4 h later. n = 4 sham plasma; n = 5 MI plasma. e , Measurement of IL-3, IL-6, and IL-1β in plasma one day after sham or MI. n = 3-8  f , Analysis of microglia and quantification of brain monocytes 1 day after stereotactic injection of IL-3, IL-6, IL-1β or PBS into the thalamus of naive mice. n = 5.  g , Microglial responses to IL-3, IL-6, and IL-1β in an ex-vivo culture assay. n = 3. Data are mean ± s.e.m. Statistical analysis was done using one-way analysis of variance and two-tailed unpaired t -tests. Experiments were conducted in female mice. *p < 0.05, **p < 0.01, ***p < 0.001.

Extended Data Fig. 3 Brain immune parameters after myocardial infarction.

a , Flow cytometry quantification of brain macrophages, microglia, T cells and neutrophils in sham and MI mice 1, 3, and 7 days after infarct. Each data point represents an individual mouse. b , Histograms of tdTomato (TdT) positivity in blood monocytes and brain microglia in Ms4a3 Cre Rosa Tomato mice. c , Analysis of GFP+ cells in the heart, brain, lung, bone marrow, and liver 1 day after sham or infarct and adoptive transfer. n = 5 per group. d , Quantification of CCL2 protein in plasma and brain, and Ccl2 mRNA transcript in blood monocytes and brain tissue. n = 12 sham and n = 14 MI plasma; n = 4 per group for monocyte transcript; n = 5 sham and n = 4 MI brain CCL2 protein; n = 15 per group for brain transcript. e , Representative immunofluorescent images and quantification of CCR2+ monocytes in the cortex and hypothalamus. n = 7 per group. f , Representative image of secondary antibody only control on human liver sections. g , Analysis of FITC-dextran signal in the brain after peripheral delivery one day after MI or sham operation. scRNAseq analysis of brain endothelial and epithelial cells 3 days after sham or MI. n = 5 sham and n = 5 MI for FITC dextran; n = 5 pooled mice per group for scRNAseq. h , Microglial analysis in CCL2 RFP mice one day after sham or MI. n = 4 mice per group. i , Representative images and quantification of thalamic microglia morphology by skeletal analysis. n = 3, each dot represents one cell. j , Analysis of blood monocyte and brain microglia CD123 (IL-3Ra), brain monocytes, and microglia CCL2 in Il3ra fl/fl and Cx3cr1Cre ERT2 Il3ra fl/fl mice injected with tamoxifen over 5 consecutive days and subjected to MI 3 weeks later. n = 4 per group except brain monocytes n = 7 Il3ra fl/fl and n = 8 Cx3cr1Cre ERT2 Il3ra fl/fl mice. Data are mean ± s.e.m. Statistical analysis was done using two-tailed unpaired t -tests. Experiments were conducted in female mice. *p < 0.05, **p < 0.01, ***p < 0.001.

Extended Data Fig. 4 Analysis of brain monocytes, sleep regulation, and Tnfrsf1a targeting vector design.

a , Enumeration of blood Ly6C hi monocytes in MI and MI + CCR2 antagonist mice one day after injury. CCR2 antagonist was delivered to the brain via the cisterna magna. n = 5 mice per group. b , Quantification of brain Ly6C hi monocytes 1 day after MI and SWS analysis in WT sham, WT MI, and Ccr2 −/− MI mice. n = 4 for immune cell quantification; for sleep analysis n = 4 WT MI; n = 5 WT sham, n = 4 Ccr2 −/− MI. c , WT MI mice were injected with an anti-TCRβ antibody in the cisterna magna immediately after MI. Monocytes and SWS were quantified one day later. n = 4 control and n = 5 anti-TCRβ. d , Naive mice were injected with PBS or 30,000 monocytes sorted from the blood of a GFP mouse. Flow cytometry analysis of the transferred monocytes in the brain 2 h later. n = 5 mice per group. e , UMAP of brain monocytes and analysis of their frequencies in sham and MI mice. n = 5 pooled mice per group. f , Flow cytometry analysis of brain monocyte TNF. g , qPCR analysis of blood monocyte Tnf mRNA 1 day after sham or MI, n = 4 mice per group. scRNAseq analysis of blood monocytes 3 days after MI or sham. n = 5 pooled mice per group. h , Quantification of SWS in WT sham, WT MI, and Tnf −/− MI mice. n = 3 WT sham; n = 5 WT MI; n = 4 Tnf −/− MI. i , Schematic of the AAV genome encoding Cre expressed from the CaMKII promoter, along with an SpCas9 gRNA targeted to Tnfrsf1a and expressed from the human U6 promoter. j , Schematic for in vivo tissue specific knockdown or Tnfrsf1a , where AAV viral vectors encoding pCaMKII-Cre and the Tnfrsf1a -targeted SpCas9 gRNA are delivered via bilateral stereotactic injection into the thalamic LPN. Neuron-specific Cre recombination activates SpCas9 nuclease expression, leading to complexation of the nuclease with the Tnfrsf1a gRNA to target and knockout the Tnfrsf1a gene. k , Schematic of brain regions analysed by qPCR. Whole brain tissue was used. Expression of Tnfrsf1a in whole thalamic and cortex tissue. n = 5 WT + AAV2 MI mice; n = 4 Stop fl/fl -Cas9 GFP  + AAV2 MI mice. l , Enumeration of Ly6C hi monocytes in the brain of WT + AAV2 MI mice and Stop fl/fl -Cas9 GFP  + AAV2 MI mice 3 days after infarct. n = 3 WT + AAV2 MI mice; n = 4 Stop fl/fl -Cas9 GFP  + AAV2 MI mice. Data are mean ± s.e.m. Statistical analysis was done using two-way analysis of variance and two-tailed unpaired t -tests. Experiments were conducted in female mice. *p < 0.05, **p < 0.01, ***p < 0.001.

Extended Data Fig. 5 Extended analysis of sleep and cardiac function in MI and MI + SF mice.

a , Analysis of wake bouts (transitions from a sleep state to a wake state) in MI and MI + SF mice. n = 4 MI mice; n = 3 MI + SF mice. b , Evaluation of stroke volume (SV) and cardiac output (CO) by echocardiography in MI and MI + SF mice on days 3, 7, and 21 after infarct. n = 8 mice on day 3; n = 9 MI mice on day 7; n = 10 MI + SF mice on day 7; n = 8 MI mice on day 21; n = 10 MI + SF mice on day 21. c , Analysis of EF in sham mice, and mice that received a ‘medium’ or ‘large’ MI and exposed to SF or habitual sleep. Analysis was completed 3 days after sham or MI. n = 5 sham; n = 8 ‘medium’ MI ± SF; n = 5 ‘large’ MI ± SF. d , mRNA expression of Col3a1 and Col4a1 in infarcts 7 days after MI. n = 13 MI and n = 14 MI + SF. Data are mean ± s.e.m. Statistical analysis was done using one-way analysis of variance and two-tailed unpaired t -tests. Experiments were conducted in female mice. *p < 0.05, **p < 0.01, ***p < 0.001.

Extended Data Fig. 6 Inflammation and haematopoiesis in MI and MI + SF mice.

a , Flow cytometry enumeration of cardiac cells in MI and MI + SF mice. n = 7 MI; n = 8 MI + SF. b , Enumeration of monocytes and neutrophils in the infarcts of mice 3 days after receiving a ‘large’ MI and sleeping habitually or exposed to SF. n = 5 mice per group. c , Flow cytometry enumeration of blood leukocytes in MI and MI + SF mice. n = 11 MI on day 3; n = 12 MI + SF on day 3; n = 7 MI on day 7; n = 8 MI + SF on day 7; n = 7 MI day 21 monocytes; n = 9 MI + SF day 21 monocytes; n = 11 MI day 21 neutrophils; n = 12 MI + SF day 21 neutrophils. d , Flow cytometry gating and enumeration of progenitor cells in the BM of MI and MI + SF mice. Each data point represents an individual mouse. e , Analysis of BrdU incorporation into BM progenitor cells of MI and MI + SF mice 3 days after infarct. n = 5 per group. Data are mean ± s.e.m. Statistical analysis was done using two-tailed unpaired t -tests. Experiments were conducted in female mice. *p < 0.05, **p < 0.01, ***p < 0.001.

Extended Data Fig. 7 Assessment of cardiac and blood leukocytes and plasma corticosterone.

a , UMAP of heart leukocytes. n = 5 pooled mice per group. b , Subclustering of cardiac macrophages and cluster frequencies in MI and MI + SF mice. n = 5 pooled mice per group. c , UMAP depicting resident and recruited heart macrophages and defining genes. n = 5 pooled mice per group. d , DEGs in recruited macrophages. n = 5 pooled mice per group. e , UMAP of blood leukocytes and monocytes along with monocyte DEGs and top pathways of genes enriched in MI + SF versus MI mice. n = 5 pooled mice per group. f , Plasma corticosterone levels. Each data point represents an individual mouse. g , Leukocyte enumeration in the infarct. n = 9 MI + SF; n = 8 MI + SF + ADRβ2 blocker. h , Analysis of ejection fraction (EF) and infarct leukocyte abundance in WT mice transplanted with WT BM or Adrb2 −/− BM and exposed to MI and SF. Analysis was performed 3 days after infarct. n = 5 WT bmWT ; n = 4 WT bmAdrb2−/− . Data are mean ± s.e.m. Statistical analysis was done using two-tailed unpaired t -tests. Experiments were conducted in female mice. *p < 0.05, **p < 0.01, ***p < 0.001.

Extended Data Fig. 8 Extended analysis of human sleep restriction study and schematic of hypothesis.

a , Actigraphy measured nightly total sleep time of study participants during the habitual sleep (HS) and sleep restriction (SR) phases of the randomized crossover trial. n = 4 participants per condition. b , UMAP of scRNAseq data of PBMCs. n = 4 participants per condition. c , Pathway analysis of cluster defining genes among monocyte clusters and the top 10 cluster defining genes in cluster 2. n = 4 participants per condition. d , UMAP of chemotactic genes enriched in monocyte cluster 3. n = 4 participants per condition. e , Schematic of hypothesis. (1) Myocardial infarction activates microglia via IL-1β and (2) enhances their production of myeloid chemoattractants including CCL2 and CCL5. (3) Circulating monocytes are actively recruited to the MI brain where they release TNF which signals to glutamatergic neurons in the thalamic LPN to (4) augment sleep. (5) Enhanced sleep after MI suppresses sympathetic input to the heart which limits signalling through ADRB2 and the generation of the myeloid chemoattractants CCL3 and CCL4 to (6) suppress monocyte recruitment to the infarcted heart thus limiting inflammation and promoting healing. Data are mean ± s.e.m. Statistical analysis was done using one-way analysis of variance and two-tailed paired t -tests. *p < 0.05, **p < 0.01, ***p < 0.001.

Supplementary information

Reporting summary, supplementary video 1.

Representative iDISCO imaging of CCR2 + monocytes in the brain one day after Sham or MI operation. n  = 4, representative of one experiment.

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Huynh, P., Hoffmann, J.D., Gerhardt, T. et al. Myocardial infarction augments sleep to limit cardiac inflammation and damage. Nature (2024). https://doi.org/10.1038/s41586-024-08100-w

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Received : 15 November 2023

Accepted : 23 September 2024

Published : 30 October 2024

DOI : https://doi.org/10.1038/s41586-024-08100-w

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