Daily Archives: January 9, 2021

When the British zoologist George Shaw first encountered a platypus specimen in 1799, he was so befuddled that he checked for stitches, thinking someone might be trying to trick him with a Frankencreature. Its hard to blame him: What other animal has a rubbery bill, ankle spikes full of venom, luxurious fur that glows under black light and a tendency to lay eggs?

Centuries later, were still trying to tease the platypus apart, now with subtler tools. What we find may lead us to ask: Is the platypus normal, and are we the thing that turned out strange?

On Wednesday in Nature, researchers presented the most complete platypus genome yet assembled, along with the genome of a close relation, the short-beaked echidna. By diving into their DNA, researchers can uncover the genes and proteins that underpin some of these creatures distinctive traits, and better understand how mammals like us evolved to be so unlike them.

The platypus and four echidna species, all native to Australia, are the worlds only living monotremes a group perhaps best known for their unique reproductive strategy, which involves laying eggs and then nursing their young once theyve hatched.

They are very bizarre in many ways, said Guojie Zhang, a genomicist at the University of Copenhagen and a leader of the sequencing effort.

But because the monotremes diverged from other mammals so early about 187 million years ago they are also very important for understanding mammalian evolution, he said. Indeed, some monotreme traits that seem so strange to us may have actually been present in the ancestor we all share.

The platypus genome was first sequenced in 2008. Since then, improvements in technology have made it much easier to map the placement of particular genes onto chromosomes. In the earlier attempt, only about 25 percent of the platypus genome was contextualized in such a way, Dr. Zhang said, while the new version is 96 percent mapped.

Its very complete, he said. We find a lot of genes that have been missed in previous assemblies.

The new genomes validate many previous findings about the platypus and, combined with the new echidna genome, add much more clarity to the evolutionary mechanisms involved, said Wesley Warren, a professor of genomics at the University of Missouri, who led the 2008 sequencing study but was not involved in this one.

In my opinion, among mammals, the platypus is the most fascinating species of all, he added. They represent the ancestral state of what terrestrial mammal genomes could have been before adapting to various environments.

Having such a comprehensive map enables comparisons among the genomes of different species, and helps fill gaps in the step-by-step story of how mammals appeared and then diverged. For instance, many birds and insects have multiple copies of a gene called vitellogenin, which is involved in the production of egg yolks.

Most mammals dont have the vitellogenin gene, said Dr. Zhang. But the new genomes reveal that platypuses and echidnas have one copy of it, helping to explain their anomalous egg-laying and suggesting that this gene (and perhaps the reproductive strategy itself) may have been something the rest of us lost, rather than an innovation of the monotremes. Meanwhile, they also have milk-producing genes similar to ours and those of other mammals, allowing them to nourish their young.

Other traits took other paths. The new genome reveals that monotremes, which are toothless, have lost multiple genes associated with dental development that are present in other mammals. Platypuses also have venom-producing genes that other mammals lack, but that are similar to those found in some reptiles, perhaps explaining their toxic foot spikes.

Less visible, but equally perplexing, is the fact that while other mammals generally have one pair of sex chromosomes, monotremes have five pairs. The structure of the newly revealed genomes suggests that these sex chromosomes were once in a ring formation, and then broke into pieces although more research is needed to figure out how that happened.

Dr. Zhang and his colleagues plan to continue investigating the many monotreme mysteries that remain. They are a very important lineage to understand, he said.

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A Question Hidden in the Platypus Genome: Are We the Weird Ones? - The New York Times

Abstract

The UKs COVID-19 epidemic during early 2020 was one of worlds largest and unusually well represented by virus genomic sampling. Here we reveal the fine-scale genetic lineage structure of this epidemic through analysis of 50,887 SARS-CoV-2 genomes, including 26,181 from the UK sampled throughout the countrys first wave of infection. Using large-scale phylogenetic analyses, combined with epidemiological and travel data, we quantify the size, spatio-temporal origins and persistence of genetically-distinct UK transmission lineages. Rapid fluctuations in virus importation rates resulted in >1000 lineages; those introduced prior to national lockdown tended to be larger and more dispersed. Lineage importation and regional lineage diversity declined after lockdown, while lineage elimination was size-dependent. We discuss the implications of our genetic perspective on transmission dynamics for COVID-19 epidemiology and control.

Infectious disease epidemics are composed of chains of transmission, yet surprisingly little is known about how co-circulating transmission lineages vary in size, spatial distribution, and persistence, and how key properties such as epidemic size and duration arise from their combined action. While individual-level contact tracing investigations can reconstruct the structure of small-scale transmission clusters [e.g., (13)] they cannot be extended practically to large national epidemics. However, recent studies of Ebola, Zika, influenza and other viruses have demonstrated that virus emergence and spread can be instead tracked using large-scale pathogen genome sequencing [e.g., (47)]. Such studies show that regional epidemics can be highly dynamic at the genetic level, with recurrent importation and extinction of transmission chains within a given location. In addition to measuring genetic diversity, understanding pathogen lineage dynamics can help target interventions effectively [e.g., (8, 9)], track variants with potentially different phenotypes [e.g., (10, 11)], and improve the interpretation of incidence data [e.g., (12, 13)].

The rate and scale of virus genome sequencing worldwide during the COVID-19 pandemic has been unprecedented, with >100,000 SARS-CoV-2 genomes shared online by 1 October 2020 (14). Notably, approximately half of these represent UK infections and were generated by the national COVID-19 Genomics UK (COG-UK) consortium (15). The UK experienced one of the largest epidemics worldwide during the first half of 2020. Numbers of positive SARS-CoV-2 tests rose in March and peaked in April; by 26 June there had been 40,453 nationally-notified COVID-19 deaths in the UK (deaths occurring 28 days of first positive test; (16). Here, we combine this large genomic data set with epidemiological and travel data to provide a full characterisation of the genetic structure and lineage dynamics of the UK epidemic.

Our study encompasses the initial epidemic wave of COVID-19 in the UK and comprises all SARS-CoV-2 genomes available before 26 June 2020 (50,887 genomes, of which 26,181 were from the UK; Fig. 1A) (17). The data represents genomes from 9.29% of confirmed UK COVID-19 cases by 26 June (16). Further, using an estimate of the actual size of the UK epidemic (18) we infer virus genomes were generated for 0.66% (95% CI = 0.46-0.95%) of all UK infections by 5th May (Fig. 1B).

(A) Collection dates of the 50,887 genomes analyzed here (left-hand axis). Genomes are colored by sampling location (England = red, Scotland = dark blue, Wales = yellow, Northern Ireland = light blue, elsewhere = grey). The solid line shows the cumulative number of UK virus genomes (right-hand axis). The dashed and dotted lines show, respectively, the cumulative number of laboratory-confirmed UK cases (by specimen date) and the estimated number of UK infections (18); grey shading = 95% CI; right-hand axis). Due to retrospective screening, the cumulative number of genomes early in the epidemic exceeds that of confirmed cases. (B) Proportion of weekly estimated UK infections (18) included in our genome sequence dataset.

We first sought to identify and enumerate all independently introduced, genetically-distinct chains of infection within the UK. We developed a large-scale molecular clock phylogenetic pipeline to identify UK transmission lineages that (i) contain two or more UK genomes and (ii) descend from an ancestral lineage inferred to exist outside of the UK (Fig. 2, A and B). Sources of statistical uncertainty in lineage assignation were taken into account (17). We identified a total of 1179 (95% HPD 1143-1286) UK transmission lineages. Although each is intended to capture a chain of local transmission arising from a single importation event, some UK transmission lineages will be unobserved or aggregated due to limited SARS-CoV-2 genetic diversity (19) or incomplete or uneven genome sampling (20, 21). Therefore we expect this number to be an underestimate (17). In our phylogenetic analysis 1650 (95% HPD 1611-1783) UK genomes could not be allocated to a UK transmission lineage (singletons). Had more genomes been sequenced, it is likely that many of these singletons would have been assigned to a UK transmission lineage. Further, many singleton importations are likely to be unobserved.

(A) Figurative illustration of the international context of UK transmission lineages. Note only half of the cases in the top UK transmission lineage are observed and the bottom UK transmission lineage is unobserved. To be detected, a UK transmission lineage must contain two or more sampled genomes; singletons are not classified here as UK transmission lineages. (B) Detailed view of one of the UK transmission lineages from (A), used to illustrate the terms TMRCA, detection lag, and importation lag. The lineage TMRCA is sample-dependent; for example, TMRCA A is observed if genomes 16 are sampled and TMRCA B is observed if only genomes 35 are sampled. (C) Distribution of UK transmission lineage sizes. Blue bars show the number of transmission lineages of each size (red bars = 95% HPD of these sizes across the posterior tree distribution). Inset: the corresponding cumulative frequency distribution of lineage size (blue line), on double logarithmic axes (red shading = 95% HPD of this distribution across the posterior tree distribution). Values either side of vertical dashed line show coefficients of power-law distributions (P[X x] ~ x) fitted to lineages containing 50 (1) and >50 (2) virus genomes, respectively. (D) Partition of 26,181 UK genomes into UK transmission lineages and singletons, colored by (i) lineage, for the 8 largest lineages, or (ii) duration of lineage detection (time between the lineages oldest and most recent genomes) for the remainder. The sizes of the 8 largest lineages are also shown in the figure.

Most transmission lineages are small and 72.4% (95% HPD 69.3-72.9%) contain <10 genomes (Fig. 2C). However the lineage size distribution is strongly skewed and follows a power-law distribution (Fig. 2C, inset), such that the 8 largest UK transmission lineages contain >25% of all sampled UK genomes (Fig. 2D; figs. S2 to S5 show further visualizations). Although the two largest transmission lineages are estimated to comprise >1500 UK genomes each, there is phylogenetic uncertainty in their sizes (95% HPDs = 1280-2133 and 1342-2011 genomes). Since our dataset comprises only a small fraction of all UK infections, these observed lineage sizes will underestimate true lineage size. However, the true distribution of relative lineage sizes will closely match our observation, and its power-law shape indicates that almost all unobserved lineages will be small. All 8 largest lineages were first detected before the UK national lockdown was announced on 23 March and, as expected, larger lineages were observed for longer (Pearsons r = 0.82; 95% CI = 0.8-0.83; fig. S7). The sampling frequency of lineages of varying sizes differed over time (Fig. 3A and figs. S8 and S9); while UK transmission lineages containing >100 genomes consistently accounted for >40% of weekly sampled genomes, the proportion of small transmission lineages (10 genomes) and singletons decreased over the course of the epidemic (Fig. 3A).

(A) Lineage size breakdown of UK genomes collected each week. Colors of the 8 largest lineages are as depicted in Fig. 2D. (B) Trends through time in the detection of UK transmission lineages. For each day, all lineages detected up to that day are colored by the time since the transmission lineage was last sampled. Isoclines correspond to weeks. Shaded area = transmission lineages that were first sampled <1 week ago. The red arrow indicates the start of the UK lockdown. (C) Red line = daily rate of detecting new transmission lineages. Blue line = rate at which lineages have not been observed for >4 weeks, shading = 95% HPD across the posterior distribution of trees.

The detection of UK transmission lineages in our data changed markedly through time. In early March the epidemic was characterised by lineages first observed within the previous week (Fig. 3B). The per-genome rate of appearance of new lineages was initially high, then declined throughout March and April (Fig. 3C), such that by 1st May 96.2% of sampled genomes belonged to transmission lineages that were first observed >7 days previously. By 1st June, a growing number of lineages (>73%) had not been detected by genomic sampling for >4 weeks, suggesting that they were rare or had gone extinct, a result that is robust to the sampling rate (Fig. 1, A and B, and Fig. 3C). Together, these results indicate that the UKs first epidemic wave resulted from the concurrent growth of many hundreds of independently-introduced transmission lineages, and that the introduction of non-pharmaceutical interventions (NPIs) was followed by the apparent extinction of lineages in a size-dependent manner.

We also characterised the spatial distribution of UK transmission lineages using available data on 107 virus genome sampling locations, which correspond broadly to UK counties or metropolitan regions (data S1). Although genomes were not collected randomly (some lineages and regions will be over-represented due to targeted investigation of local outbreaks; e.g., (22) the number of UK lineages detected in each region correlates with the number of genomes sequenced (Fig. 4A, Pearsons r = 0.96, 95% CI = 0.95-0.98) and the number of reported cases (fig. S10, Pearsons r = 0.53, 95% CI = 0.35-0.67, data S2) in each region. Further, larger lineages were observed in more locations; every 100 additional genomes in a lineage increases its observed range by 6-7 regions (Fig. 4B; Pearsons r = 0.8, 95% CI = 0.78-0.82). Thus, bigger regional epidemics comprised a greater diversity of transmission lineages, and larger lineages were more geographically widespread. These observations indicate substantial dissemination of a subset of lineages across the UK and suggest many regions experienced a series of introductions of new lineages from elsewhere, potentially hindering the impact of local interventions.

(A) Correlation between the number of transmission lineages detected in each region (points = median values, bars = 95% HPD intervals) and the number of UK virus genomes from each region (Pearsons r = 0.96, 95% CI = 0.95-0.98). (B) Correlation between the spatial range of each transmission lineage and the number of virus genomes it contains (Pearsons r = 0.8, 95% CI = 0.78-0.82,) (C) Map showing Shannons index (SI) for each region, calculated across the study period (2nd Feb-26th Jun). Yellow colors indicate higher SI values and darker colors lower values. (D) SI through time for the UK national capital cities. The dotted lines indicate the start of the UK national lockdown. (E) Illustration of the diverse spatial range distributions of UK transmission lineages. Colors represent the week of the first detected genome in the transmission lineage in each location. Circles show the number of sampled genomes per location. Insets show the distribution of geographic distances for all sequence pairs within the lineage (see data S4 and fig. S12 for further details). Colored boxes next to lineage names are as depicted in Fig. 2D.

We quantified the substantial variation among regions in the diversity of transmission lineages present using Shannons index (SI; this value increases as both the number of lineages and the evenness of their frequencies increase; Fig. 4C and data S3). We observed the highest SIs in Hertfordshire (4.77), Greater London (4.62) and Essex (4.49); these locations are characterised by frequent commuter travel to/within London and proximity to major international airports (23). Locations with the three lowest nonzero SIs were in Scotland (Stirling = 0.96, Aberdeenshire = 1.04, Inverclyde = 1.32; Fig. 4C). We speculate that regional differences in transmission lineage diversity may be related to the level of connectedness to other regions.

To illustrate temporal trends in transmission lineage diversity, we plot SI through time for each of the UKs national capital cities (Fig. 4D). Lineage diversities in each peaked in late March and declined after the UK national lockdown, congruent with Fig. 3, C and D. Greater Londons epidemic was the most diverse and characterised by an early, rapid rise in SI (Fig. 4D), consistent with epidemiological trends there (16, 24). Belfasts lineage diversity was notably lower (data S4 shows other locations).

We observe variation in the spatial range of individual UK transmission lineages. Although some lineages are widespread, most are more localized and the range size distribution is right-skewed (fig. S11), congruent with an observed abundance of small lineages (Figs. 2C and 4B) and biogeographic theory [e.g., (25)]. For example, lineage DTA_13 is geographically dispersed (>50% of sequence pairs sampled >234km apart) whereas DTA_290 is strongly local (95% of sequence pairs sampled <100km apart) and DTA_62 has multiple foci of sampled genomes (Fig. 4E and fig. S12). The national distribution of cases therefore arose from the aggregation of multiple heterogeneous lineage-specific patterns.

The process by which transmission lineages are introduced to an area is an important aspect of early epidemic growth [e.g., (26)]. To investigate this at a national scale we estimated the rate and source of SARS-CoV-2 importations into the UK. Since standard phylogeographic approaches were precluded by strong biases in genome sampling among countries (20), we developed a new approach that combines virus phylogenetics with epidemiological and travel data. First, we estimated the TMRCA (time of the most recent common ancestor) of each UK transmission lineage (17). The TMRCAs of most UK lineages are dated to March and early April (median = 21st March; IQR = 14th-29th March). UK lineages with earlier TMRCAs tend to be larger and longer-lived than those whose TMRCAs postdate the national lockdown (Fig. 5A and fig. S15).

(A) Histogram of lineage TMRCAs, colored by lineage size. Inset: expanded view of the days prior to UK lockdown. Left-hand arrow = collection date of the UKs first laboratory-confirmed case; right-hand arrow = collection date of the earliest UK virus genome in our dataset. (B) Estimated number of inbound travellers to the UK per day (black) and estimated number of infectious cases worldwide (dashed red). Arrows here show, from left to right, dates of the first self-isolation advice for returning travellers from China, Italy, and of the start of the UK national lockdown. (C) Estimated importation intensity (EII) curve (black) and the histogram of lineage TMRCAs (grey). (D) Estimated histogram of virus lineage importation events per day, obtained from our lag model. Colors show the proportion attributable each day to inbound travel from various countries (see table S4 and figs. S19 and S20). This assignment is statistical, i.e., we cannot ascribe a specific source location to any given lineage.

Due to incomplete sampling, TMRCAs best represent the date of the first inferred transmission event in a lineage, not its importation date (Fig. 2B). To infer the latter, and quantify the delay between importation and onward within-UK transmission, we generated daily estimates of the number of travellers arriving in the UK and of global SARS-CoV-2 infections (17) worldwide. Before March, the UK received ~1.75m inbound travellers per week (school holidays explain the end-February ~10% increase; Fig. 5B). International arrivals fell by ~95% during March and this reduction was maintained through April. Elsewhere, estimated numbers of infectious cases peaked in late March (Fig. 5B). We combined these two trends to generate an estimated importation intensity (EII) - a daily empirical measure of the intensity of SARS-CoV-2 importation into the UK (17). Since both travel volumes and epidemic incidence fluctuate rapidly over orders of magnitude, the EII is robust to other sources of variation in the relative importation risk among countries (17). The EII peaks in mid-March, when high UK inbound travel volumes coincided with growing numbers of infectious cases elsewhere (Fig. 5, B and C).

Crucially, the EIIs temporal profile closely matches, but precedes, that of the TMRCAs of UK transmission lineages (Fig. 5, A and C). The difference between the two represents the importation lag, the time elapsed between lineage importation and the first detected local transmission event (Fig. 2B). Using a statistical model (17), we estimate importation lag to be on average 8.22 5.21 days (IQR = 3.35-15.18) across all transmission lineages. Further, importation lag is strongly size-dependent; average lag is ~10 days for lineages comprising 10 genomes and <1 day for lineages of >100 genomes (table S2). This size-dependency likely arises because the earliest transmission event in a lineage is more likely to be captured if it contains many genomes (Fig. 2B) (17). We use this model to impute an importation date for each UK transmission lineage (Fig. 5D). Importation was unexpectedly dynamic, rising and falling substantially over only 4 weeks, hence 80% of importations (that gave rise to detectable UK transmission lineages) occurred between 27 February and 30 March. The delay between the inferred date of importation and the first genomic detection of each lineage was 14.13 5.61 days on average (IQR = 10-18) and declined through time (tables S2 and S3).

To investigate country-specific contributions to virus importation we generated separate importation intensity (EII) curves for each country (fig. S17). Using these values, we estimated the numbers of inferred importations each day attributable to inbound travel from each source location. This assignment is statistical and does not take the effects of superspreading events into account. As with the rate of importation (Fig. 5A), the relative contributions of arrivals from different countries were dynamic (Fig. 5D). Dominant source locations shifted rapidly in February and March and the diversity of source locations increased in mid-March (fig. S17). Earliest importations were most likely from China or elsewhere in Asia but were rare compared to those from Europe. Over our study period we infer ~33% of UK transmission lineages stemmed from arrivals from Spain, 29% from France, 12% from Italy and 26% from elsewhere (fig. S20 and table S4). These large-scale trends were not apparent from individual-level travel histories; routine collection of such data ceased on 12 March (27).

The exceptional size of our genomic survey provides insight into the micro-epidemiological patterns that underlie the features of a large, national COVID-19 epidemic, allowing us to quantify the abundance, size distribution, and spatial range of transmission lineages. Pre-lockdown, high travel volumes and few restrictions on international arrivals (Fig. 5B and table S5) led to the establishment and co-circulation of >1000 identifiable UK transmission lineages (Fig. 5A), jointly contributing to accelerated epidemic growth that quickly exceeded national contact tracing capacity (27). The relative contributions of importation and local transmission to initial epidemic dynamics under such circumstances warrants further investigation. We expect similar trends occurred in other countries with comparably large epidemics and high international travel volumes; virus genomic studies from regions with smaller or controlled COVID-19 epidemics have reported high importation rates followed by more transient lineage persistence [e.g., (2830)].

Earlier lineages were larger, more dispersed, and harder to eliminate, highlighting the importance of rapid or pre-emptive interventions in reducing transmission [e.g., (3133)]. The high heterogeneity in SARS-CoV-2 transmission at the individual level (3436) appears to extend to whole transmission lineages, such that >75% of sampled viruses belong to the top 20% of lineages ranked by size. While the national lockdown coincided with limited importation and reduced regional lineage diversity, its impact on lineage extinction was size-dependent (Fig. 3, B and C). The over-dispersed nature of SARS-CoV-2 transmission likely exacerbated this effect (37), thereby favoring, as Rt declined, greater survival of larger widespread lineages and faster local elimination of lineages in low prevalence regions. The degree to which the surviving lineages contributed to the UKs ongoing second epidemic, including the effect of specific mutations on lineage growth rates [e.g., (11)], is currently under investigation. The transmission structure and dynamics measured here provide a new context in which future public health actions at regional, national, and international scales should be planned and evaluated.

S. A. Nadeau, T. G. Vaughan, J. Scir, J. S. Huisman, T. Stadler, The origin and early spread of SARS-CoV-2 in Europe. medRxiv [preprint]. 12 June 2020.pmid:20127738

C. Angus, CoVid Plots and Analysis. University of Sheffield (2020); .doi:10.15131/shef.data.12328226

J. L. Geoghegan, X. Ren, M. Storey, J. Hadfield, L. Jelley, S. Jefferies, J. Sherwood, S. Paine, S. Huang, J. Douglas, F. K. Mendes, A. Sporle, M. G. Baker, D. R. Murdoch, N. French, C. R. Simpson, D. Welch, A. J. Drummond, E. C. Holmes, S. Duchene, J. de Ligt, Genomic epidemiology reveals transmission patterns and dynamics of SARS-CoV-2 in Aotearoa New Zealand. medRxiv [preprint]. 20 August 2020.pmid:20168930

S. M. Nicholls et al., MAJORA: Continuous integration supporting decentralised sequencing for SARS-CoV-2 genomic surveillance. bioRxiv [preprint]. 7 October 2020.pmid:328328

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Establishment and lineage dynamics of the SARS-CoV-2 epidemic in the UK - Science

With no robust system to identify genetic variations of the coronavirus, experts warn that the U.S. is woefully ill-equipped to track a dangerous new mutant, leaving health officials blind as they try to combat the grave threat.

The variant, which is now surging in Britain, has the potential to explode in the U.S. the next few weeks, putting new pressures on hospitals already near the breaking point.

The U.S. has no large-scale, nationwide system for checking coronavirus genomes for new mutations, including the ones carried by the new variant. About 1.4 million people test positive for the virus each week, but researchers are only doing genome sequencing a method that can definitively spot the variant on fewer than 3,000 of those weekly samples. And that work is done by a patchwork of academic, state and commercial laboratories.

Scientists say that a national surveillance program would be able to determine just how widespread the new variant is and help contain emerging hot spots, extending the crucial window of time in which vulnerable people across the country could get vaccinated. That would cost several hundred million dollars or more. But that is a tiny fraction of the $16 trillion in economic losses that the U.S. is estimated to have sustained because of COVID-19.

"We need some sort of leadership," said Dr. Charles Chiu, a researcher at the University of California, San Francisco, whose team spotted some of the first California cases of the new variant. "This has to be a system that is implemented on a national level."

With such a system in place, health officials could warn the public in affected areas and institute new measures to contend with the variant such as using better masks, contact tracing, closing schools or temporary lockdowns and do so early, rather than waiting until a new surge flooded hospitals with the sick. The incoming Biden administration may be open to the idea.

Experts point to Britain as a model. British researchers sequence the genome the complete genetic material in a coronavirus from up to 10% of new positive samples. Even if the U.S. sequenced just 1% of genomes from across the country, or about 2,000 a day, that would shine a bright light on the new variant as well as other variants that may emerge.

But over the past month, U.S. researchers have only sequenced a few hundred genomes a day, said GISAID, an international database. And just a few states have been responsible for most of the effort. California is in the lead, with 8,896 genomes. In North Dakota, which has had more than 93,500 cases so far, researchers haven't sequenced a single genome.

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Experts warn U.S. is blind to new virus variant - Minneapolis Star Tribune

Researchers have successfully used a DNA-editing technique to extend the lifespan of mice with the genetic variation associated with progeria, a rare genetic disease that causes extreme premature aging in children and can significantly shorten their life expectancy. The study was published in the journal Nature, and was a collaboration between the National Human Genome Research Institute (NHGRI), part of the National Institutes of Health; Broad Institute of Harvard and MIT, Boston; and the Vanderbilt University Medical Center, Nashville, Tennessee.

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Base editing for progeria treatmentProgeria is caused by a mutation in the nuclear lamin Agene in which one DNA base C is changed to a T. Researchers used the base editing method, which substitutes a single DNA letter for another without damaging the DNA, to reverse that change. Credit: Ernesto Del Aguila, NHGRI.

DNA is made up of four chemical bases A, C, G and T. Progeria, which is also known as Hutchinson-Gilford progeria syndrome, is caused by a mutation in the nuclear lamin A(LMNA) gene in which one DNA base C is changed to a T. This change increases the production of the toxic protein progerin, which causes the rapid aging process.

Approximately 1 in 4 million children are diagnosed with progeria within the first two years of birth, and virtually all of these children develop health issues in childhood and adolescence that are normally associated with old age, including cardiovascular disease (heart attacks and strokes), hair loss, skeletal problems, subcutaneous fat loss and hardened skin.

For this study, researchers used a breakthrough DNA-editing technique called base editing, which substitutes a single DNA letter for another without damaging the DNA, to study how changing this mutation might affect progeria-like symptoms in mice.

"The toll of this devastating illness on affected children and their families cannot be overstated," said Francis S. Collins, M.D., Ph.D., a senior investigator in NHGRI's Medical Genomics and Metabolic Genetics Branch, NIH director and a corresponding author on the paper. "The fact that a single specific mutation causes the disease in nearly all affected children made us realize that we might have tools to fix the root cause. These tools could only be developed thanks to long-term investments in basic genomics research.

The toll of this devastating illness on affected children and their families cannot be overstated.The fact that a single specific mutation causes the disease in nearly all affected children made us realize that we might have tools to fix the root cause. These tools could only be developed thanks to long-term investments in basic genomics research.

The study follows another recent milestone for progeria research, as the U.S. Food and Drug Administration approved the first treatment for progeria in November 2020, a drug called lonafarnib. The drug therapy provides some life extension, but it is not a cure. The DNA-editing method may provide an additional and even more dramatic treatment option in the future.

David Liu, Ph.D., and his lab at the Broad Institute developed the base-editing method in 2016, funded in part by NHGRI.

"CRISPR editing, while revolutionary, cannot yet make precise DNA changes in many kinds of cells," said Dr. Liu, a senior author on the paper. "The base-editing technique we've developed is like a find-and-replace function in a word processor. It is extremely efficient in converting one base pair to another, which we believed would be powerful in treating a disease like progeria.

To test the effectiveness of their base-editing method, the team initially collaborated with the Progeria Research Foundation to obtain connective tissue cells from progeria patients. The team used the base editor on theLMNAgene within the patients cells in a laboratory setting. The treatment fixed the mutation in 90% of the cells.

The Progeria Research Foundation was thrilled to collaborate on this seminal study with Dr. Collinss group at the NIH and Dr. Lius group at Broad Institute, said Leslie Gordon, M.D., Ph.D., a co-author and medical director of The Progeria Research Foundation, which partially funded the study. These study results present an exciting new pathway for investigation into new treatments and the cure for children with progeria.

Following this success, the researchers tested the gene-editing technique by delivering a single intravenous injection of the DNA-editing mix into nearly a dozen mice with the progeria-causing mutation soon after birth. The gene editor successfully restored the normal DNA sequence of theLMNAgene in a significant percentage of cells in various organs, including the heart and aorta.

Many of the mice cell types still maintained the corrected DNA sequence six months after the treatment. In the aorta, the results were even better than expected, as the edited cells seemed to have replaced those that carried the progeria mutation and dropped out from early deterioration. Most dramatically, the treated mice's lifespan increased from seven months to almost 1.5 years. The average normal lifespan of the mice used in the study is two years.

As a physician-scientist, its incredibly exciting to think that an idea youve been working on in the laboratory might actually have therapeutic benefit, said Jonathan D. Brown, M.D., assistant professor of medicine in the Division of Cardiovascular Medicine at Vanderbilt University Medical Center. Ultimately our goal will be to try to develop this for humans, but there are additional key questions that we need to first address in these model systems.

Funding for the study was supported in part by NHGRI, the NIH Common Fund, the National Institute of Allergy and Infectious Diseases, the National Institute of Biomedical Imaging and Engineering, the National Institute of General Medical Sciences, the National Heart, Lung and Blood Institute and the National Center for Advancing Translational Sciences.

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DNA-editing method shows promise to treat mouse model of progeria - National Human Genome Research Institute