Category Archives: Genome

Valerii Plotnikov (left) from the Academy of Sciences of the Republic of Sakha, Yakutsk, Russia, and Daniel Fisher of the University of Michigan examine a woolly mammoth unearthed during a 2018 expedition. Love Daln hide caption

Scientists have recreated the three-dimensional structure of the woolly mammoths genetic blueprint.

The accomplishment, described Thursday in the journal Cell, marks what is believed to be the first time scientists have been able to produce a multidimensional version of the genome of a complex extinct species.

The advance should provide important new insights into the biology of a creature that has long sparked fascination. In addition, the work could aid efforts to breed a living version of the animal, the researchers and others said.

Its exciting, says Erez Lieberman Aiden, a professor of of molecular and human genetics and director of the Center for Genome Architecture at the Baylor College of Medicine in Houston. We think its going to be very valuable.

For years, scientists have been able to peer back in time by analyzing fragments of ancient DNA recovered from bones, fossilized teeth, mummies and even strands of hair.

In biology, one of the most powerful tools for understanding the history of life on this planet is ancient DNA, Aiden says. Its an incredibly powerful tool for understanding the history of life.

But theres only so much scientists could learn from snippets of DNA. So Aiden and his colleagues launched an international effort to try to recreate the three-dimensional structure of the DNA, including the chromosomes, of an extinct creature.

In so doing, you would be able to see exactly how that chromosome was shaped in a living cell, and youd be able to both get a deeper understanding of the genomes of ancient and extinct species and how those genomes worked which genes were on and off in particular tissues, Aiden says.

The scientists focused on the wooly mammoth, a big, shaggy species of elephant that roamed the tundra thousands of years ago.

Initially we had embarrassingly bad ideas. Im a little ashamed to admit it, Aiden told NPR. We said, Oh, you know, that looks like a good-looking piece of mammoth on eBay. Lets try that. Its kind of a little cringe, right, to tell you that. Ebay is a bad place to get your mammoth samples.

After searching for five years, the team finally found a well-preserved mammoth sample: skin from behind the ear of a 52,000-year-old female that was discovered freeze-dried in Siberia in 2018.

It was a piece of a mammoth skin that was, you know, wooly. True to the name it was indeed woolly mammoth skin, says Olga Dudchenko, an assistant professor at the Baylor Center for Genome Architecture who worked on the research. And thats actually not as trivial as it sounds because very often the hair would be lost. So this one was hairy. And that actually is an interesting indicator in and of itself that this is a sample of substantial quality. And that immediately piqued our attention.

In fact, the quality of the sample enabled the team to extract DNA and use a technique known as Hi-C to reconstruct the three-dimensional structure of all 28 of the mammoths chromosomes the extinct creatures entire genome, the researchers reported.

We were able to assemble the genome of a woolly mammoth just as 25 years ago humans were excited for the first time to assemble our own genomes, Aiden says. Now we can do that for animals that were long extinct. Thats obviously a milestone.

Not only that, the team has been able to peer into the genome to start learning what individual genes did.

And thats really exciting to be able to look at an extinct creature and be able to say, Oh, yes. I can see this gene was on. That gene was on. This gene was off. Oh, isnt that surprising? Aiden says. To be able to do all these specific things in a woolly mammoth is exciting.

In fact, by comparing the mammoth genome to DNA from modern elephants, the scientists have already discovered clues to what made the woolly mammoth woolly.

Weve been internally discussing whether we should start Hair Club for mammoths? Dudchekno jokes.

But seriously, that insight could help efforts that are already underway to try to bring a version of the mammoth back from extinction by endowing modern-day Asian elephants with mammoth traits, such as their hairiness, and perhaps even release them to graze the tundra again.

I do think that this can be helpful for de-extinction, Aiden says.

Other scientists praised the work.

I think its pretty cool, says Vincent Lynch, an associate professor of biological sciences at the University at Buffalo who was not involved in the research.

But Lynch isnt a fan of trying to bring back the mammoth. The unintended consequences of that could be disastrous, he says. And the money for such a project would be much better spent trying to save the elephants that still roam the planet today.

Theres an huge potential for unintended consequences, Lynch says. Just think about all the other invasive species that are in the world. You dont really know the effect that species is going to have in the environment until it gets there.

And Karl Flessa, a professor of geosciences at the University of Arizona agrees on the scientific accomplishment and the foolishness of trying to bring back the extinct pachyderm.

The preservation of genetic architectures from the woolly mammoth is really remarkable, Flessa says. But just because you can do it, doesnt mean that it should be done. A genetically modified Asian elephant is not a wooly mammoth. And releasing such an animal into the wild would be arrogant and irresponsible.

Others disagree.

"It's exciting to see that 3D architecture can be preserved in ancient samples. This will help move toward a complete de novo assembled mammoth genome, which could reveal features of the genome that might be relevant to mammoth de-extinction, Eriona Hysolli, who leads a project to create an Asian elephant with mammoth traits at Colossal Laboratories & Biosciences in Dallas, wrote NPR in an email.

Still, Robert Fleischer, a senior scientist for the Center for Conservation Genomics at the Smithsonians National Zoo & Conservation Institute in Washington, says that prospect is exciting.

If I was a 12-year-old in my science class in junior high school Id probably think this was pretty cool, Fleischer says. And I still think its pretty cool.

Excerpt from:
A 3D reconstruction of the woolly mammoth genome might help revive the extinct species : Shots - Health News - NPR

Neanderthals may not have truly gone extinct but instead may have been absorbed into the modern human population. That's one of the implications of a new study, which finds modern human DNA may have made up 2.5% to 3.7% of the Neanderthal genome.

"This research really highlights that what we think as a separate Neanderthal lineage really was more interconnected with our ancestors," Fernando Villanea, a population geneticist at the University of Colorado Boulder who was not involved in the study, told Live Science. Both modern human and Neanderthal populations "shared a long history of exchanging individuals."

Neanderthals were among the closest extinct relatives of modern humans, with our lineages diverging around 500,000 years ago. More than a decade ago, scientists revealed that Neanderthals interbred with the ancestors of modern humans who journeyed out of Africa. Today, the genomes of modern human groups outside Africa contain about 1% to 2% of Neanderthal DNA.

Related: 'More Neanderthal than human': How your health may depend on DNA from our long-lost ancestors

However, researchers know less about how modern human DNA may have entered the Neanderthal genome. That's largely because there are currently only three known high-quality examples of a complete Neanderthal genome that have survived from specimens unearthed in Vindija cave in Croatia, which date to 50,000 to 65,000 years ago, and Chagyrskaya and Denisova caves in Russia, which date to about 80,000 and 50,000 years ago, respectively.

In comparison, scientists have sequenced the genomes of hundreds of thousands of modern humans since the completion of the Human Genome Project in 2003.

"There has been a considerable amount of research focused on how matings between Neanderthals and modern humans affected our DNA and evolutionary history," study senior author Joshua Akey, a population geneticist at Princeton University in New Jersey, told Live Science. "However, we know much less about how these encounters impacted the genomes of Neanderthals."

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In the new study, scientists relied on the fact that both modern humans and Neanderthals generally possess two versions of every gene, one inherited from the father, the other from the mother. Because the two groups were more different from each other than they were from others of their own kind, interbreeding between Neanderthals and humans would lead to offspring that had a higher chance of possessing two different versions of each gene a situation known as heterozygosity than children that did not result from such interbreeding.

The researchers compared the genomes of the three Neanderthals with those of 2,000 modern humans. They discovered the Neanderthal genome may consist of 2.5% to 3.7% modern human DNA. That is akin to 1 in 30 modern human parents in the ancestral Neanderthal population.

The research team's analysis suggested that modern human DNA entered the Neanderthal genome during at least two distinct epochs of interbreeding one about 200,000 to 250,000 years ago, and the other about 100,000 to 120,000 years ago. Interbreeding may have taken place at other times, but such events may not have left any detectable traces in the Neanderthal genome, Akey said.

A recent, not-yet peer-reviewed study suggests that most Neanderthal DNA seen in the modern human genome resulted from a single major period of interbreeding about 47,000 years ago that lasted about 6,800 years. Interbreeding that occurred at other times, such as the earlier events that impacted the Neanderthal genome, likely did not leave a detectable trace in our genome.

Skulls found in the Skhul and Qafzeh caves in Israel date to around 100,000 years ago around the same time as one of the major interbreeding events identified in the study. Those fossils appear to be modern human remains, but they still have relatively primitive features such as larger brows, which might "be signs of gene flow from Neanderthals," Chris Stringer, a paleoanthropologist at the Natural History Museum in London who was not involved in the new study, told Live Science.

By analyzing the level of genetic variation seen between the three Neanderthal genomes, the new study also suggested the long-term average Neanderthal population was about 20% smaller than previously estimated. "This doesn't sound like a large difference, but given that Neanderthals were already estimated to have a fairly small population size, the fact that it was even smaller is an important insight," Akey said.

These new smaller estimates of Neanderthal population size suggest that Neanderthals may have disappeared because "they were simply absorbed into the modern human population," Akey said. "Recurrent waves of modern human migrations out of Africa eventually overwhelmed the ability of Neanderthals to remain a distinct population, and they were ultimately just assimilated into the modern human gene pool."

Future research could study the biological effects, good or bad, that modern human DNA may have had in Neanderthals, Akey said.

The scientists detailed their findings online Thursday (July 11) in the journal Science.

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Neanderthals didn't truly go extinct, but were rather absorbed into the modern human population, DNA study suggests - Livescience.com

Scientists from Baylor College of Medicine and elsewhere say they have discovered subfossils of ancient chromosomes in the remains of a female woolly mammoth (Mammuthus primigenius) that died 52,000 years ago in what is now Siberia. The fossils preserve the structure of the ancient chromosomes down to the nanometer scale billionths of a meter. The researchers hypothesize that the mammoth skin spontaneously freeze-dried in the Siberian cold, leading to a glass transition that preserved the fossils.

Sandoval-Velasco et al. assembled the genome and 3D chromosomal structures of a 52,000-year-old woolly mammoth. Image credit: Sandoval-Velasco et al., doi: 10.1016/j.cell.2024.06.002.

This is a new type of fossil, and its scale dwarfs that of individual ancient DNA fragments a million times more sequence, said Dr. Erez Lieberman Aiden, director of the Center for Genome Architecture at Baylor College of Medicine.

It is also the first time a karyotype of any sort has been determined for an ancient sample.

Knowing the three-dimensional architecture of a genome provides a lot of additional information beyond its sequence, but most ancient DNA specimens consist of very small, scrambled DNA fragments.

Building off work mapping the 3D structure of the human genome, Dr. Aiden and colleagues thought that if the right ancient DNA sample could be found it would be possible to use the same strategies to assemble ancient genomes.

They tested dozens of samples over the course of five years before landing on an unusually well-preserved woolly mammoth that was excavated near Belaya Gora, Sakha Republic, northeastern Siberia in September 2018.

We think it spontaneously freeze-dried shortly after its death. The nuclear architecture in a dehydrated sample can survive for an incredibly long period of time, said Dr. Olga Dudchenko, also from the Center for Genome Architecture at Baylor College of Medicine.

To reconstruct the mammoths genomic architecture, the authors extracted DNA from a skin sample taken behind the mammoths ear.

They used a method called Hi-C that allows them to detect which sections of DNA are likely to be in close spatial proximity and interact with each other in their natural state in the nucleus.

Imagine you have a puzzle that has three billion pieces, but you dont have the picture of the final puzzle to work from, said Professor Marc Marti-Renom, a structural genomicist at the Centre Nacional dAnlisi Genmica and the Centre for Genomic Regulation.

Hi-C allows you to have an approximation of that picture before you start putting the puzzle pieces together.

Then, they combined the physical information from the Hi-C analysis with DNA sequencing to identify the interacting DNA sections and create an ordered map of the mammoths genome, using the genomes of present-day elephants as a template.

The analysis revealed that woolly mammoths had 28 chromosomes the same number as present-day Asian and African elephants.

Remarkably, the fossilized mammoth chromosomes also retained a huge amount of physical integrity and detail, including the nanoscale loops that bring transcription factors in contact with the genes they control.

By examining the compartmentalization of genes within the nucleus, the scientists were able to identify genes that were active and inactive within the mammoths skin cells a proxy for epigenetics or transcriptomics.

The mammoth skin cells had distinct gene activation patterns compared to the skin cells of its closest relative, the Asian elephant, including for genes potentially related to its woolly-ness and cold tolerance.

For the first time, we have a woolly mammoth tissue for which we know roughly which genes were switched on and which genes were off, Professor Marti-Renom said.

This is an extraordinary new type of data, and its the first measure of cell-specific gene activity of the genes in any ancient DNA sample.

The teams results appear today in the journal Cell.

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Marcela Sandoval-Velasco et al. 2024. Three-dimensional genome architecture persists in a 52,000-year-old woolly mammoth skin sample. Cell 187 (14): 3541-3562; doi: 10.1016/j.cell.2024.06.002

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52,000-Year-Old Woolly Mammoth Skin Retained Its Ancient Genome Architecture: Study - Sci.News

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A team led by Mass Eye and Ear researchers has demonstrated for the first time a successful restoration of hearing through a novel, in vivo genome editing approach in an adult mouse model with a form of inherited deafness caused by mutations in microRNA. The researchers note that mouse and human microRNAs have identical sequences, and accordingly, hope their new study lays the foundation for translational research into applications in humans with deafness caused by these types of mutations.

The study, led byZheng-Yi Chen, DPhil, an associate scientist in the Eaton-Peabody Laboratories at Mass Eye and Ear (a member of the Mass General Brigham healthcare system), was published July 10 inScience Translational Medicine.

"Our findings provide a promising pathway for developing treatments by editing for many forms of genetic hearing loss, said Chen, who is the Ines and Fredrick Yeatts Chair in Otolaryngology at Mass Eye and Ear and an associate professor in OtolaryngologyHead and Neck Surgery at Harvard Medical School. With further study, our intervention using genome editing could potentially halt or reverse hearing loss progression in affected individuals, including adults."

About one in 500 newborns suffer from genetic hearing loss and currently there are not any approved therapeutics to treat deafness.

In the new study, researchers targeted a specific mutation in the microRNA-96 (MiR-96) gene that causes progressive hearing loss in mice and plays a crucial role in regulating gene expression in hair cells (sensory cells responsible for hearing) of mammals. In humans, this mutation has been identified as a cause of a form of dominant inherited progressive hearing loss called DFNA50. The researchers created a mouse model carrying the mutation that mirrored the progressive hearing loss in humans with DFNA50; by four weeks of age, these models exhibited complete hearing loss at high frequencies.

The team employed a CRISPR/Cas9 genome editing approach to target and disrupt this mutation, that was delivered to the inner ear through an injection of an adeno-associated virus (AAV) carrying the editing machinery. They compared injections at two time points, during early development and adult stages, and demonstrated robust preservation of auditory function in both cases long term, with earlier intervention proving most optimal.

The study also looked at safety of the AAV-mediated genome editing approach and found it had a good safety profile that includes little off-target effect and no detectable long-term integration of the AAV vector in the genome. Our research suggested minimal potential risk and supports the feasibility of future clinical applications in humans, said Wenliang Zhu, PhD, and physician-scientist Wan Du, MD, PhD, members of Chens lab at Mass Eye and Ear and first authors on the paper.

Chen and his team have designed a construct to contain all known microRNA mutations to be used in humans, and in conjunction withMass General Brighams Gene and Cell Therapy Institute, plan to conduct IND-enabling studies in additional preclinical models in the hopes of moving this treatment approach into a first-in-human clinical trial. Studies like this one show the promise of gene therapy for treating conditions such as hearing loss.Mass General BrighamsGene and Cell Therapy Instituteis helping to translate scientific discoveries made by researchers into first-in-human clinical trials and, ultimately, life-changing treatments for patients.

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This latest research from Chen and colleagues marks a significant step forward in the field of gene therapy for hearing disorders, offering hope for future clinical trials aimed at restoring auditory function in people with genetic forms of hearing impairment. Chen and his collaborators have also conducted clinical trials looking at a different gene therapy approach for another form of deafness, DFNB9 caused by mutations in theOTOFgene. That clinical trial in China has demonstrated positive results in childrentreated in oneandboth ears. Chen hopes the technology developed in the OTOF trial, such as minimally invasive AAV delivery into the human inner ears, will accelerate the development of editing therapy into the clinic.

With more than 150 forms of genetic deafness, our research offers further hope for patients that previously lacked any options beyond a cochlear implant, said Chen. These findings suggest a need for more rigorous studies building on proof-of-concept papers like these, to achieve our goal of developing different treatment approaches to target every one of these mutations.

Reference:Zhu W, Du W, Rameshbabu AP, et al. Targeted genome editing restores auditory function in adult mice with progressive hearing loss caused by a human microRNA mutation. Sci. Transl. Medicine. 2024;16(755):eadn0689. doi:10.1126/scitranslmed.adn0689

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Novel Genome Editing Approach Restores Hearing in Mouse Model of Inherited Deafness - Technology Networks

Optimized SGE approach improves experiment quality

We developed a HAP1 DNA ligase 4 (LIG4)-knockout (KO) line with genomic integration of a clonally derived Cas9 (HAP1-A5) and confirmed BAP1 essentiality in this line (Figs. 1 and 2a), high Cas9 activity (Fig. 2b and Extended Data Fig. 1a) and robust maintenance of haploidy (Extended Data Fig. 1b). We also optimized plasmids and transfection protocols, increasing transfection efficiency in HAP1 cells from <5% to >60% compared to other12 SGE experiments (Extended Data Fig. 1c and Methods). To screen all coding exons of BAP1, we used five time points: day (D)4, D7, D10, D14 and D21. Our optimized SGE protocol led to increased editing by homology-directed repair (HDR), with ~1% unedited reads (Fig. 2c and Supplementary Table 1).

a, Target regions of 245 bp were designed for all coding exons of the canonical BAP1 transcript: ENST00000460680.6 (ref. 52). Target regions were processed in separate experiments to sequentially cover all regions. For each region, LIG4-KO, Cas9-expressing HAP1-A5 cells were transfected in triplicate with an sgRNA-expressing plasmid and a corresponding variant library; homologous recombination with this template library at the Cas9 lesion/cut site results in the introduction of variants into the genome, generating populations of edited cells. This allows for assessment of variant function, because only benign variants will rescue cell fitness following CRISPRCas9-mediated disruption of BAP1, an essential gene. Each region was edited separately using two independent template librarysgRNA pairs; each variant library (library A or library B) contained saturating mutations (colored squares) and library-specific synonymous PPEs (dark red line) to prevent sgRNACas9-mediated recutting of incorporated genomic tracts. dsDNA, double-stranded DNA. b, Cells were cultured over time with pellets collected at D4, D7, D10, D14 and D21. gDNA, genomic DNA. c, Sequencing was used to assess the population dynamics of genomic DNA libraries, generating counts for each variant using the QUANTS pipeline. DESeq2 was used to convert counts into an LFC of variant abundance over time. LFCs were then median scaled and a single functional score was computed through the aggregation of library A and library B data. Functional scores were categorized on the basis of a significance threshold and assessed for accuracy against variants with known pathogenic or benign classifications.

a, A targeted CRISPRCas9 screen in HAP1-A5 cells confirmed BAP1 essentiality and permitted selection of sgRNAs with favorable depletion kinetics for use in SGE (Methods). b, FACS analysis counts (green fluorescent protein (GFP)-positive cells) demonstrated that the HAP1-A5 clone has very high Cas9 activity (arrow), measured at 48 and 72 h after transduction with a GFP/blue fluorescent protein (BFP) activity construct (Methods: Ploidy and FACS analysis), compared to the parental Polyclonal (Cas9+ LIG4) line. A total of 10,00020,000 cells were analyzed for each line. Cell count percentages derived from negative-control lines with no Cas9 showed expected low levels of Cas9 activity (see Extended Data Fig. 1a and Supplementary Fig. 1a for representative FACS data). c, Editing using pilot SGE conditions: a template library (496 variants) coupled with sgRNA-A targeting exon 5 of BAP1 was transfected into the polyclonal (Cas9+ LIG4) line and cells were sampled at D5 and D11 (time points previously10 used in SGE). More than 10% of the counts were unedited (wild type), which decreased to <1% when the clonal (Cas9+ LIG4) cell line (HAP1-A5) was edited using the same sgRNA and HDR homology arms with optimized SGE conditions, including a high-complexity template library (1,040 variants) sampled over five time points. d, Count abundance for variants that resulted in synonymous changes or edited intronic regions did not change significantly over a 21-day SGE screen (two-sided MannWhitneyWilcoxon test; D4 versus D21 counts, P=0.3; NS, not significant), whereas variants resulting in stop-gained and frameshift consequences were significantly depleted (****P<2.21016; n=8,707 synonymous and intronic variants; n=5,628 frameshift and stop-gained variants; mean z-score counts of three biological replicates at each time point). Boxes show the interquartile range, the horizontal lines show the median z-score count and whiskers show the maximum and minimum values that are not outliers. e, Density plot showing functional scores colored by Ensembl Variant Effect Predictor (VEP)53 mutational consequence. Black tick marks represent single variant values. f, Jitter plot showing VEP mutational consequence categories versus functional score. Data points that have FDR0.01 are semitransparent and the median synonymous functional score differs significantly from that for all other categories except UTR (KruskalWallis test: P<2.21016, H=6,692.2; two-sided Dunns BH FDR ****q<2.21016).

Source data

Of note, the canonical BAP1 transcript (ENST00000460680.6) has 17 exons (Fig. 1a), and because oligonucleotide synthesis lengths are limited, 22 SGE target regions of 245 bp were designed to saturate all of the coding sequence, with 20- to 90-bp exon-flanking sequences also saturated (intron, 5 UTR, 3 UTR). For larger exons, partially overlapping regions were designed. All HDR template libraries were designed using VaLiAnT13. These libraries contained two different synonymous protospacer adjacent motif (PAM)/protospacer protection edits (PPEs) that were refractory to single guide RNA (sgRNA)Cas9 cutting, preventing cleavage of incorporated tracts. Each SGE region was targeted in two separate experiments; HDR template library A contained a PPE for one sgRNA (A) and library B contained a different PPE for a different sgRNA (B) within the same target region. Transfections were performed in triplicate for both library A and library B for all 22 regions, with samples collected at the five time points mentioned above (Fig. 1b).

In total, data from 598 genomic DNA time point-replicate libraries progressed to data analysis (Fig. 1c), with an average variant coverage of 535 generated on the Illumina platform (Supplementary Table 2).

We used cell fitness as a biological readout of BAP1 function, first rigorously reconfirming BAP1 essentiality (Extended Data Fig. 2ac) and SGE efficacy (Extended Data Fig. 2d) in HAP1 cells. To aid the selection of appropriate sgRNAs for experimentation, we performed a targeted CRISPRCas9 screen with 193 sgRNAs tiled across all 17 BAP1 exons (Fig. 2a). sgRNAs for SGE were selected based principally on design parameters (as previously described13), with depletion kinetics also considered (Methods). We deployed these sgRNAs and variant libraries across all 22 BAP1 target regions and confirmed editing (Extended Data Fig. 3). As expected for an essential gene amenable to SGE, scaled counts between D4 and D21 for stop-gained and frameshift variants were reduced, suggesting the depletion of cells with these variants, whereas synonymous and intron variant counts remained unchanged (Fig. 2d). By combining library A and library B, we calculated a single functional score for each variant (Methods). This is the apparent growth rate across D4, D7, D10, D14 and D21 computed by log-linear regression in DESeq2 (ref. 14) and represents log2-transformed fold change (LFC) per unit time (Methods). Stop-gained, frameshift and splice donor/acceptor variants exhibited predominantly negative functional scores, whereas synonymous, intron and UTR variants had a unimodal distribution centered at 0 (Fig. 2e). Missense variants exhibited a continuum of functional scores with a negatively skewed unimodal distribution (Fig. 2e).

We next used functional scores and standard errors computed using DESeq2 to accurately define variant effects. For each variant tested, a z-score distribution of functional score divided by standard error was used to calculate P values using a two-tailed z-test (Methods). All unique variants were collated and the false discovery rate (FDR) was derived from the P value using the BenjaminiHochberg (BH) procedure15 to correct for multiple testing. The behavior of individual variants within this spectrum was intriguing, with, for example, specific synonymous alterations appearing disruptive and specific stop-gained and frameshift alleles, particularly those in the terminal exon, appearing nondisruptive. Codon deletions (in-frame, sequentially deleted codons) also exhibited a spectrum of scores with a bimodal distribution, which allowed us to refine key residues/domains within the BAP1 protein. Individual variant functional scores relative to the FDR threshold are shown in Fig. 2f. All mutational consequence categories, except UTR variants, had a significantly different median functional score from synonymous variants as measured by Dunns nonparametric pairwise multiple-comparisons procedure (q<0.0001; Supplementary Table 3).

Functional scores and FDR values were used to categorize variants into functional classes, following the integration and validation of data as described below. Variants with an FDR0.01 were categorized as unchanged, those with an FDR<0.01 and a negative functional score were categorized as depleted and those with an FDR<0.01 and a positive functional score were categorized as enriched. Data for 18,108 unique variants were collected after filtering steps with variants categorized as follows: 11,912 unchanged, 5,665 depleted and 531 enriched (Supplementary Table 2). Unchanged variants centered tightly around a zero functional score (median=0.00; range=0.09) and enriched variants had modestly increased scores (median=0.01; range=0.03), while depleted variants exhibited a wider score distribution (median=0.13; range=0.27; Fig. 3a). As above, stop-gained variants were depleted consistently across all exons, except exon 17, suggesting an escape of nonsense-mediated decay. No enriched variants were observed for stop-gained alleles (Fig. 3b). Functional scores for missense variants were significantly different between exons as measured by KruskalWallis rank sum test (P<0.0001, H=1,093.3). Interestingly, while missense variants were depleted in all exons, we noted that proportionally more of these variants were depleted in exons 19 and 1517, and that fewer missense variants were depleted in exons 1014. Exons 19 and 1517 encode conserved UCH and protein interaction motifs, respectively. Indeed, we found a significant positive correlation between the depleted missense functional classification and conservation as measured by ortholog identity at each amino acid position in the protein (Spearmans rank: rs=0.45, P<0.0001). This relationship was also observed for codon deletions (Spearmans rank: rs=0.44, P<0.0001).

a, Histogram showing all 18,108 unique variants assayed, grouped into 75 intervals and colored according to functional classification. Inset shows a magnified section of functional score intervals with 500 variants. b, Composition of functional classes by exon and mutational consequence (color key as in a). c, EVE scores for functional classes (n variants in class shown). Both depleted and enriched classes have significantly different median values from the unchanged class (KurskalWallis, P<2.21016; two-sided Dunns BH FDR, ****q<0.0001; depleted q<2.21016 and enriched q=3.4105), demonstrating that depleted and enriched variants are less represented over evolution compared to unchanged variants and are therefore more likely to be disruptive. Boxes show the interquartile range, horizontal lines show the median EVE score, whiskers show maximum and minimum values that are not outliers, and outliers are shown as points. d, The bar chart shows the number of variants in each class that are in gnomAD and not ClinVar (n shown) divided by the number of variants in each class assayed. Fewer depleted and enriched variants than unchanged variants were observed in gnomAD (two-sided chi-squared test: 2=49.1, P<2.141011). e, Heat map showing amino acid-level substitutions (A:stop) created by nucleotide-level saturation across 730 codons (single nucleotide variants (SNVs) only), colored by functional classification (SNV missense changes with discordant functional classifications between alternative codons were excluded; n=158). Of note, codon deletion, alanine scan and stop scan changes were designed to be incorporated at each of the 720 nonsplit codons (of 730 total codons). Bar chart shows the percentage identity calculated from Geneious alignment of the eight species shown in Fig. 6d. Key protein regions are shown (UCH, ubiquitin C-terminal hydrolase; HBM, HCF1 binding motif; BRCA1, BRCA1 binding domain; ASXL, additional sex combs like 1/2/3 interaction; YY1, Ying Yang 1 binding domain; NLS, nuclear localization signal). f,g, AlphaFold54 BAP1 model with SGE-depleted codon deletions colored dark blue (f). Depleted codon deletions accurately delineate the UCH domain (purple) and protein interaction region (cyan), as highlighted in g. Depletion also occurs in uncharacterized regions, including the -helix C terminal to the UCH domain, proximal to the protein interaction region (arrow, f).

Source data

Because Evolutionary model of Variant Effect (EVE)16 scores can be used as a measure of conservation for missense variants, we compared this metric to our SGE results and found that depleted and enriched variants were under more evolutionary constraint (8,525 of 8,822 total unique missense variant assessed; Fig. 3c). Variants under more evolutionary constraint are expected to be observed less frequently in population-ascertained cohorts of healthy controls from the gnomAD database, which was the case for both depleted and enriched variants compared to unchanged variants (chi-squared test; 2=49.1, P<0.0001; Fig. 3d). We also observed that the conserved N-terminal UCH domain of BAP1 showed greater intolerance to missense changes and codon deletions compared to the more central regions of the protein (Fig. 3e), in keeping with its amino acid conservation. The conserved C-terminal protein interaction motifs also demonstrated intolerance to change. Of note, codon deletions precisely delineated critical domains with high accuracy and highlight uncharacterized regions (Fig. 3f,g).

Before making comparisons to clinical data, we examined the reproducibility of the functional scores and functional classifications for each variant by comparing LFCs from separate genome editing experiments. Overall, 81% of variants (14,624/18,108) were separately examined using library A and library B HDR templates, with close to linear LFC value correlations (Pearsons R=0.95, P<0.0001; Fig. 4a). When functional classifications were computed using library A or library B LFCs and FDRs, a 90% concordance of variant classification was observed (13,106/14,624; Fig. 4a). As LFCs and functional classifications were found to be highly correlated, to increase robustness, library A and library B LFCs for each variant were combined into a single combined LFC and termed the abovementioned functional score (Methods). As expected, variant LFCs within PPE codons differed between libraries (Extended Data Fig. 4a,b). Therefore, variants in PPE codons examined by only library A or library B were excluded from downstream analyses (n=140). As above, our functional score was calculated as the apparent growth rate over five time points, an analysis previously used in SGE17. This approach is appropriate for our data, as LFCs between later time points were linearly related (Extended Data Fig. 4cf). The functional scores, functional classifications (depleted, unchanged and enriched) and downstream comparisons used throughout this study were derived from these combined LFC values.

a, Independent SGE libraries (A and B) were used to edit most target regions with 13,106 of 14,624 variants showing a concordant functional classification (dark blue) and 1,518 variants discordant between libraries (light blue). Of note, the degrees of LFC for each independent variant measurement were highly concordant based on Pearsons correlation coefficient (R) and two-tailed t-test P<2.21016. b, ROC curve for SGE functional score, with AUC value shown. Also shown is the ideal threshold for maximum diagnostic sensitivity and specificity (plotted as 1 specificity). Calculated using pROC (version 1.18.4)55 in R. c, Top, a histogram showing the 18,108 unique variants grouped within 75 intervals of functional score, colored by ClinVar clinical significance. Bottom, a magnified region highlights that pathogenic/likely pathogenic (dark blue) variants are depleted. The arrow shows the x-axis position of the ideal threshold. d, Top, functional classification by ClinVar clinical significance (1*, 4 September 2023). Bottom, functional classification by observation in ClinVar and gnomAD (n variants shown). e, Depleted variants (n=5,665) categorized into strongly depleted (lower 50%, dark blue) and weakly depleted (upper 50%, light blue) variants, either side of the median functional score (0.1260642). f, More frameshift and stop-gained variants and fewer missense variants were strongly depleted compared to weakly depleted variants (two-sided chi-squared test, 2 = 10,759, P<2.21016). g, Strongly and weakly depleted missense variants have significantly different EVE scores (two-sided MannWhitneyWilcoxon test, ****P<2.21016). Boxes show the interquartile range, horizontal lines show the median EVE score, whiskers show maximum and minimum values that are not outliers, and outliers are shown as points. h, Concordance of SGE functional classification and orthogonal functional assays for VUS in patients with cancer and developmental disorders9,25. Color indicates SGE classification and shape corresponds to orthogonal assay classification. Control variants (from a casecontrol study25) are shown in green text. SGE variants that were strongly depleted (dark blue) and not tolerated in orthogonal assays (triangles) are completely concordant. P12A, which was partially tolerated in an orthogonal assay, was weakly depleted in SGE. All tolerated variants (white squares) in assays were unchanged in SGE (gray), except for E406V, which was enriched (red).

Source data

Full nucleotide and protein-level variant effect maps are provided in Extended Data Figs. 5 and 6, respectively. The full dataset with annotations and scores is also available for download at https://github.com/team113sanger/Waters_BAP1_SGE and as Supplementary Data 1 and 2. Variant scores and classifications can also be searched on the BAP1 Viewer: https://bap1-viewer.shinyapps.io/bap1viewer/.

To further examine functional scores, we first identified variants with strong clinical/functional data in support of their classification, curating 851 benign (true negative) and 199 pathogenic (true positive) variants that had at least one star (1*) in ClinVar (downloaded 4 September 2023). We used the functional scores for these variants to generate a receiver operating characteristic (ROC) curve, with the area under the curve (AUC) computed (Fig. 4b). We found that our functional score was highly accurate at classifying these variants with a sensitivity of >99%, a specificity of >98%, a classification error rate close to 0 (<0.002%) and a precision-recall AUC of >0.999 (Supplementary Table 4). We also used our data to explore the relationship between functional score/classification and reported clinical classifications and found high concordance (Fig. 4c,d).

Of note, many clinically used in silico classifiers, including EVE16, SIFT18 and PolyPhen-219, use protein-level information to predict function, whereas SGE assesses function at the nucleotide level, capturing variant effects on splicing, RNA folding, codon usage and other non-protein-level processes. We observed that few synonymous variants were depleted in our screen (Figs. 2e,f and 3b). Importantly, synonymous variants that were classified as depleted had significantly higher SpliceAI scores than unchanged synonymous variants (P<0.0001, two-sided MannWhitneyWilcoxon test; Extended Data Fig. 7a), suggesting functional relevance. In the absence of functional or in silico data, synonymous variants are routinely classified as VUS20, suggesting that these variants could be misclassified without SGE. Importantly, we found that variants (missense, stop-gained and synonymous) created by SGE through different nucleotide-level changes had highly correlated LFCs, as expected (Pearsons R=0.91, P<0.0001; Extended Data Fig. 7b). Missense changes alone also showed a high correlation in LFCs between alternative codons (Pearsons R=0.89, P<0.0001). Of note, 8,822 unique nucleotide-level changes in our screen resulted in 4,619 unique missense changes at the protein level, of which 3,993 could be examined using alternative codon generation, with 16.7% (667/3,993) showing different functional classifications. Thus, not all missense changes have equal effects when encoded by alternative codons, further highlighting the importance and richness of SGE functional assessment at the nucleotide level.

Because very few BAP1 missense variants have been ascribed to be pathogenic or benign, a direct comparison of sensitivity and specificity using an AUC summary metric between in silico tools and SGE functional scores for missense variants alone is not possible. However, when we compared experimental data with in silico tools, we found that EVE, PolyPhen-2 and CADD21 predicted SGE classifications of non-splice region missense variants with 7779% accuracy (Extended Data Fig. 7c). With per-variant examination, it is notable that EVE, PolyPhen-2 and CADD classify proportionally more missense variants as pathogenic, probably damaging and likely pathogenic, respectively, suggesting that SGE may have a relatively higher specificity (Extended Data Fig. 7dg).

Next, we sought to quantify the evidence strength at which predictions from our assay could be applied using the American College of Medical Genetics and Genomics (ACMG) framework for variant interpretation20. To this end, we generated further truth sets of high-confidence pathogenic and benign variants (Methods and Supplementary Table 4) against which to evaluate assay performance using the established framework from Brnich et al.22. We aimed to evaluate assay performance in predicting the impact of missense variants, which are challenging to classify.

We observed that 99.8% (2,419/2,423) of variants in our pathogenicity truth set exhibited the expected depletion in the assay output, whereas 97.1% (134/138) of variants in our benignity truth set were unchanged or enriched in the assay (Table 1). These observations equate to likelihood ratios toward pathogenicity of 27.6 and benignity of 470.6, which correspond to strong and very strong evidence strengths, respectively22,23. Notably, when using truth sets constructed using ClinVar-classified missense variants only (1* review status), there was full concordance with assay results; however, due to small sample numbers, these truth sets yielded likelihood ratios toward pathogenicity and benignity of 6.0 and 7.0, respectively, both equating to a moderate strength of evidence. Further limiting truth sets by restriction to ClinVar variants of 2* did not allow the generation of evidence strengths due to the absence of pathogenic variants.

We were intrigued by the observation that some patients with germline BAP1 variants have been reported as being predisposed to tumors, whereas others have a neurodevelopmental disorder. SGE allows us to test whether these variants have different functional outcomes.

To this end, we ranked the 5,665 depleted variants (we excluded enriched variants) by categorizing them on either side of the median, defining them as strongly depleted (n=2,833) or weakly depleted (n=2,832) (Fig. 4e). We observed that the proportions of mutational consequences seen in strongly and weakly depleted categories were significantly different from one another (chi-squared test; 2=10,759, P<0.0001), with more missense and fewer stop-gained and frameshift mutations weakly depleted (Fig. 4f). We also observed that weakly depleted missense variants were less conserved (P<0.0001, two-sided MannWhitneyWilcoxon test; Fig. 4g). Strongly depleted variants were also depleted at an earlier time point (D10) in the screen compared to most weakly depleted variants (Extended Data Fig. 7h). Taking these findings together, it appears that a subset of missense variants (n=426; strongly depleted) behave similarly to stop-gained/frameshift variants and a larger number of missense variants (n=1,548; weakly depleted) have a less extreme LFC and slower change in variant abundance.

Sixteen BAP1 germline variants have been associated with developmental disorders9,24. In our screen, we assayed 15 of these 16 variants and found that 13 of 15 were classified as depleted (Supplementary Note 1 and Extended Data Fig. 7i). Functional studies have previously been performed on variants associated with development9 and cancer25, with perfect concordance observed between these orthogonal assays and SGE to the degree that a putative hypomorphic allele can be distinguished (Supplementary Note 1 and Fig. 4h).

Next, we analyzed data from a comprehensive clinical analysis of families with BAP1-tumor predisposition syndrome (TPDS)26 (Supplementary Note 1 and Supplementary Table 5). Interestingly, we found that carriers of depleted variants had a significantly earlier age of onset than carriers of unchanged variants (P<0.01, two-sided MannWhitneyWilcoxon test; Extended Data Fig. 7j). However, we saw no differences between strongly and weakly depleted classifications for age of onset or cancer type (Supplementary Note 1 and Extended Data Fig. 7j,k). Moreover, while there was a difference in molecular consequences (Fig. 4f), conservation (Fig. 4g) and effect sizes, germline cancer-associated variants did not have different functional score effect sizes compared to development-associated variants (Extended Data Fig. 7i,k).

Next, we used whole-exome sequencing data from 454,787 individuals in UK Biobank to explore the phenotypic consequences of BAP1-disruptive alleles27. We identified 57 SGE-depleted, 80 SGE-enriched and 754 SGE-unchanged variants in the exomes of 297, 1,960 and 61,333 carriers, respectively (Supplementary Table 6). We performed a phenome-wide association study (PheWAS) analysis (Supplementary Method 14), focusing on depleted variants only. To evaluate the association of these variants with overall cancer risk, we generated cancer-type phenotypic variables and rare variant burden test masks (variant sets) (Fig. 5a, Extended Data Fig. 8a and Supplementary Table 7). We found that SGE-depleted nonsynonymous variants were significantly associated with all-site cancer predisposition (P=7.85 1003; n=82) with this variant set/mask composed of missense and high-confidence protein-truncating variants (PTVs), which were classified as depleted by SGE.

a, PheWAS forest plot for all-site cancers using SGE-depleted variants and controls; regression model effect is shown by data points and effect standard error is shown by bars (Supplementary Table 7). Rare variant burden test masks (and CADD, EVE and REVEL56 predictors) are shown by color for BAP1 variants in UK Biobank (n carriers shown in key). Significance, according to the corrected P value determined by generalized linear modeling (Supplementary Method 14), is indicated by a triangle (significant) or a circle (not significant). SGE-depleted nonsynonymous variants (yellow) showed a significant effect and are therefore associated with increased cancer risk. SGE-depleted high-confidence (HC) protein-truncating variants (PTVs; orange) demonstrated a significant effect, as did HC PTVs (red). b, UK Biobank SGE-depleted nonsynonymous variant carriers (n=69) had a significantly higher median blood concentration of IGF-1 compared to noncarriers (n=398,505); P<0.005 (P=0.0033, two-sided MannWhitneyWilcoxon test). Violin plots are colored by BAP1 variant status, boxes show the interquartile range, horizontal lines show the median IGF-1 blood concentration (nmol l1), whiskers show maximum and minimum values that are not outliers, and outliers are shown as points. c, IGF1 mRNA expression levels in transcripts per million (TPM) obtained from TCGA for 80 uveal melanoma tumors28. BAP1-mutant tumors (n=35) have higher IGF1 expression than those with wild-type BAP1 (n=45). Colors, outliers and box description are as in b, except the horizontal line is the median IGF1 expression in tumors. P<0.001 (P=0.00029, two-sided MannWhitneyWilcoxon test). d, The 80 samples from patients with uveal melanoma were ranked by TCGA IGF1 expression level, with tumors with the top 50% highest expression levels classified as having high expression and the bottom 50% classified as having low expression. Top, KaplanMeier estimates, with deceased status (overall survival) shown by vertical tick marks and the model for survival probability based on the overall survival time (in days) shown by lines colored to indicate IGF1 expression level. The P value was calculated using the log-rank test and indicates a significant difference between the overall survival probability for tumors with high and low IGF1 expression from patients in the cohort. Bottom, number at-risk table shows a higher number of patients alive at each time increment for patients whose tumor expressed low versus high levels of IGF-1.

Source data

Beyond cancer, we also examined the association between UK Biobank BAP1 variants and quantitative traits (Supplementary Table 8). As a result, we identified that circulating IGF-1 levels were significantly increased in carriers of SGE-depleted nonsynonymous BAP1 variants compared to noncarriers (Fig. 5b; P<0.005, two-sided MannWhitneyWilcoxon test). Importantly, IGF-1 levels in carriers with and without a cancer diagnosis did not differ, indicating that significantly increased IGF-1 levels are specific to individuals with SGE-depleted nonsynonymous BAP1 variants rather than a cancer diagnosis, and suggests a possible mechanism of BAP1-mediated pathogenicity (Supplementary Note 2).

To further investigate the association of SGE-depleted alleles with increased IGF-1 levels, we obtained The Cancer Genome Atlas (TCGA) RNA-seq data28 for uveal melanomas and found a strong association of loss-of-function BAP1 alleles with IGF1 mRNA expression (P<0.001, two-sided MannWhitneyWilcoxon test) and poor prognosis by KaplanMeier estimate (P=0.004) (Fig. 5c,d).

As a further exemplar of the value of our BAP1 SGE data, we identified a family whose proband presented at the age of 26 years with uveal melanoma. A review of the family history revealed other BAP1-associated tumors segregating over three generations (Fig. 6a,b), with sequencing revealing a germline c.535C>T (R179W) variant in the BAP1 gene. c.535C>T was depleted in our SGE experiment, with a functional score of 0.122 and an FDR of<0.01 (Fig. 6c). This variant had been classified in the clinic as a VUS, but together with our SGE data it has been reclassified as likely pathogenic (ACMG, class IV), a result that will contribute to the clinical management of this kindred. Of note, R179W falls in a highly conserved region of BAP1, which includes the proton donor residue at H169. At codon R179, the only SGE-tolerated substitution is R179Q, with glutamine being the conserved residue in the Drosophila melanogaster BAP1 ortholog Calypso (Fig. 6d,e).

a, Pedigree with a proband carrying a c.535C>T variant (HGVSc, ENST00000460680.6:c.535C>T; HGVSp, ENSP00000417132.1:p.Arg179Trp; R179W) in exon 7 of BAP1. The proband was a 33-year-old male presenting with uveal melanoma (UM) at 26 years (arrow) whose father, uncle and grandmother presented with melanoma (ME), basal cell carcinoma (BCC) and renal cell carcinoma (RCC), respectively. The probands mother was not known to be a carrier and died of metastatic (M) cancer, possibly cholangiocarcinoma (CCA). The pedigree follows established nomenclature: black, clinically confirmed disease (malignant tumor); square, male; circle, female; diagonal line, deceased; d., age at death; number, age at disease presentation. An asterisk indicates the patient for whom samples are shown in b. b, Pathology of the primary cutaneous melanoma in the patient from a. Top, micrograph showing hematoxylin and eosin (H&E) staining. Bottom, micrograph showing BAP1 immunohistochemical staining; staining is absent in tumor tissue (black arrow) but is present (purple cells) in immune infiltrate (red arrow). Scale bars, 100m. Micrographs are representative of three histological sections. c, Functional scores across exon 7. Exonic/intronic ranges within the target region are shown, with points colored by VEP consequence. Transparency based on FDR. Shape denotes functional classification. The variant in a is labeled. d, Multiple-sequence alignment of exon 7 created by global alignment of BAP1 orthologs from eight species (gap open/extension penalty=12/3); numbers are protein positions of human BAP1 (ENSP00000417132.1) and residues are colored by identity (black, 100%; dark gray, 80100%; light gray, 6080%; white, <60%). R179 (and the highly conserved H169 proton donor) is highlighted by a red arrow. Note that the glutamine residue in Drosophila aligns at human position R179, the only missense variant at this position tolerated in SGE. e, Heat map (see Extended Data Fig. 6 for the full heat map) of amino acid substitutions for two key positions, H169 and R179, colored by functional classification. White space results from SNV saturation not producing all amino acid substitutions. c.535C>T produces R179W, which is depleted. R179R, a synonymous change, is unchanged, other missense changes (R179P/L/G/A/*) and R179 codon deletion are depleted and only R179Q is tolerated. H169 in the catalytic core is intolerant to all observed changes, except for a synonymous change. Black circle, key synonymous changes; white triangle, key missense changes.

Source data

Finally, to further explore the use of SGE data and identify novel BAP1 variants, we queried tumor sequence data for a cohort of 394,756 patient samples in the Foundation Medicine database29 and found 12,172 (3.1%) unique BAP1-altered specimens harboring 13,283 BAP1 alterations, including all possible changes at codon R146 (Extended Data Fig. 9ad). Because these variants were derived from tumor-only sequencing, germline DNA was obtained30 and sequenced from a patient with breast/cholangiocarcinoma whose sister was diagnosed with renal carcinoma (Extended Data Fig. 9ad). Both patients were confirmed to carry a germline R146K (c.437G>A) variant identified as disruptive by SGE (Extended Data Fig. 9ae), providing another example of how SGE data can help improve diagnostic precision.

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Telomere-to-telomere Citrullus super-pangenome provides direction for watermelon breeding - Nature.com

Lima, Peru, July 8, 2024 (PAHO) A critical training program launched in Lima, Peru, aimed at equipping researchers across Latin America with cutting-edge bioinformatics skills to tackle bacterial threats, particularly those transmitted by food and water. The "NGS Bioinformatics Essentials for Bacterial Genome Analysis" course, organized by the University of Cambridge and the International AIDS Vaccine Initiative (IAVI) in collaboration with the National Institute of Health (INS-Peru) and the Infectious Hazards Management Unit from the Health Emergencies Department at the Pan American Health Organization (PAHO), represented a major step forward in regional genomic surveillance capabilities.

This theoretical-practical course laid the groundwork for the PAHOGen genomic surveillance strategy, particularly within the PulseNet Latin America and Caribbean Network for food born bacterial diseases. The intensive week-long program focused on Next-Generation Sequencing (NGS) analysis of bacterial genomic datasets and was designed to deliver comprehensive bioinformatics training. Through a combination of lectures and hands-on exercises using real-world datasets, the objective was to equip participants with essential bioinformatics skills, allowing them to adeptly navigate the complex landscape of NGS data analysis.

"This course is a cornerstone of PAHO's PAHOGen strategy," said Dr. Isabel Chinen from PAHO. "By strengthening bioinformatics skills in the region, we can significantly improve our ability to track and respond to emerging bacterial threats."

Upon completion, participants had acquired proficiency in using the Linux command line for data analysis, understanding various data/file formats utilized in high-throughput sequencing data analysis, performing quality control (QC) of high-throughput sequencing data, practical knowledge of essential tools and pipelines for bacterial genome data analysis, and the ability to install and manage crucial bioinformatics software and pipelines for bacterial genome data analysis.

The course hosted 15 national participants from Peru, representing various health entities and academic institutions, along with five regional participants from Paraguay (Lab.Central Salud Pblica), Chile (Inst.Salud Pblica), Ecuador (Inst. Nac. Salud Pblica e Investigacin), Panam (Inst. Conmemorativo Gorgas Estudio de la Salud), and Colombia (Inst. Nac. Salud). The course was taught by a distinguished panel of experts, including Prof. Stephen Baker from the University of Cambridge and IAVI, London, UK; Bioch. Claudia Carolina Carbonari from the National Institute of Infectious Diseases ANLIS, Argentina; Dr. Megan Carey from IAVI, London, UK; Dr. Jacqui Keane from the University of Cambridge, UK; Dr. Elli Mylona from the University of Cambridge, UK; and Dr. Sushmita Sridhar from Universidad Peruana Cayetano Heredia, Peru. The course was organized by Ronnie Gaviln from INS-Peru, Isabel Chinen from IHM/PHE/PAHO, WDC, USA, and Prof. Stephen Baker from the University of Cambridge and IAVI, London, UK.

This collaborative effort showcased a significant investment in the future of public health in Latin America. By strengthening bioinformatics capabilities, the region gained a powerful tool for combating bacterial threats and ultimately improving health outcomes for all.

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Public Health Received a Boost: New Course Strengthened Bacterial Genome Analysis in Latin America - Pan American Health Organization

New research shows that recurrent episodes of gene flow, beginning 250,000 to 200,000 years ago, affected the genomes and biology of both modern humans and Neanderthals, and estimates that Neanderthals have 2.5 to 3.7% human ancestry.

Li et al. provide insights into the history of admixture between modern humans and Neanderthals, show that gene flow had substantial impacts on patterns of modern human and Neanderthal genomic variation, and show that accounting for human-introgressed sequences in Neanderthals enables more-accurate inferences of admixture and its consequences in both Neanderthals and modern humans. Image credit: Neanderthal Museum.

This is the first time that geneticists have identified multiple waves of modern human-Neanderthal admixture, said Southeast Universitys Professor Liming Li.

We now know that for the vast majority of human history, weve had a history of contact between modern humans and Neanderthals, added Princeton Universitys Professor Joshua Akey.

The hominins who are our most direct ancestors split from the Neanderthal family tree about 600,000 years ago, then evolved our modern physical characteristics about 250,000 years ago.

From then until the Neanderthals disappeared that is, for about 200,000 years modern humans have been interacting with Neanderthal populations.

Using genomes from 2,000 living humans as well as three Neanderthals and one Denisovan, the researchers mapped the gene flow between the hominin groups over the past quarter-million years.

They used a genetic tool they designed a few years ago called IBDmix, which uses machine learning techniques to decode the genome.

Scientists previously depended on comparing human genomes against a reference population of modern humans believed to have little or no Neanderthal or Denisovan DNA.

The study authors have established that even those referenced groups, who live thousands of miles south of the Neanderthal caves, have trace amounts of Neanderthal DNA, probably carried south by voyagers (or their descendants).

With IBDmix, they identified a first wave of contact about 200,000-250,000 years ago, another wave 100,000-120,000 years ago, and the largest one about 50,000-60,000 years ago. That contrasts sharply with previous genetic data.

To date, most genetic data suggests that modern humans evolved in Africa 250,000 years ago, stayed put for the next 200,000 years, and then decided to disperse out of Africa 50,000 years ago and go on to people the rest of the world, Professor Akey said.

Our models show that there wasnt a long period of stasis, but that shortly after modern humans arose, weve been migrating out of Africa and coming back to Africa, too.

To me, this story is about dispersal, that modern humans have been moving around and encountering Neanderthals and Denisovans much more than we previously recognized.

That vision of humanity on the move coincides with the archaeological and paleoanthropological research suggesting cultural and tool exchange between the hominin groups.

The key insight was to look for modern-human DNA in the genomes of the Neanderthals, instead of the other way around.

The vast majority of genetic work over the last decade has really focused on how mating with Neanderthals impacted modern human phenotypes and our evolutionary history but these questions are relevant and interesting in the reverse case, too, Professor Akey said.

They realized that the offspring of those first waves of Neanderthal-modern matings must have stayed with the Neanderthals, therefore leaving no record in living humans.

Because we can now incorporate the Neanderthal component into our genetic studies, we are seeing these earlier dispersals in ways that we werent able to before, Professor Akey said.

The final piece of the puzzle was discovering that the Neanderthal population was even smaller than previously believed.

Genetic modeling has traditionally used variation diversity as a proxy for population size. The more diverse the genes, the larger the population.

But using IBDmix, the team showed that a significant amount of that apparent diversity came from DNA sequences that had been lifted from modern humans, with their much larger population.

As a result, the effective population of Neanderthals was revised down from about 3,400 breeding individuals down to about 2,400.

Put together, the new findings paint a picture of how the Neanderthals vanished from the record, some 30,000 years ago.

I dont like to say extinction, because I think Neanderthals were largely absorbed, Professor Akey said.

The idea is that Neanderthal populations slowly shrank until the last survivors were folded into modern human communities.

This assimilation model was first articulated by Fred Smith, an anthropology professor at Illinois State University, in 1989. Our results provide strong genetic data consistent with Freds hypothesis, and I think thats really interesting, Professor Akey said.

Neanderthals were teetering on the edge of extinction, probably for a very long time.

If you reduce their numbers by 10 or 20%, which our estimates do, thats a substantial reduction to an already at-risk population.

Modern humans were essentially like waves crashing on a beach, slowly but steadily eroding the beach away.

Eventually we just demographically overwhelmed Neanderthals and incorporated them into modern human populations.

The findings were published in the journal Science.

_____

Liming Li et al. 2024. Recurrent gene flow between Neanderthals and modern humans over the past 200,000 years. Science 385 (6705); doi: 10.1126/science.adi1768

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Homo sapiens and Neanderthals Interacted Over 200,000-Year Period, Study Reveals - Sci.News

SequAna Core Facility, Department of Biology, University of Konstanz, Konstanz, Germany

Abdoallah Sharaf

Genetics Department, Faculty of Agriculture, Ain Shams University, Cairo, Egypt

Abdoallah Sharaf&Asmaa Mohammed Abushady

College of Agriculture and Environmental Sciences, University of South Africa, Florida, South Africa

Lucky Tendani Nesengani,Sinebongo Mdyogolo,Rae Marvin Smith,Appolinaire Djikeng&Ntanganedzeni Mapholi

Laboratory of Biodiversity, Ecology, and Genome, Department of Biology, Faculty of Sciences, Mohammed V University in Rabat, Rabat, Morocco

Ichrak Hayah&Bouabid Badaoui

Washington State University, Global Health, Nairobi, Kenya

Josiah Ochieng Kuja

Department of Biological Sciences, Elizade University, Ilara-Mokin, Nigeria

Taiwo Crossby Omotoriogun

A. P. Leventis Ornithological Research Institute, University of Jos, Jos, Nigeria

Taiwo Crossby Omotoriogun

Regional Centre for Biotechnology and Bioresources Research, University of Port Harcourt, Port Harcourt, Nigeria

Blessing Adanta Odogwu,Victor Ezebuiro&Julian O. Osuji

SouthSouth Zonal Centre of Excellence, National Biotechnology Development Agency, Port Harcourt, Nigeria

Blessing Adanta Odogwu,Victor Ezebuiro&Julian O. Osuji

Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne, UK

Girish Beedessee

Research Department, Institut Pasteur du Maroc, Casablanca, Morocco

Abdelhamid Barakat,Adil El Hamouchi,Fouzia Radouani,Hicham Charoute,Ichrak Benamri&Meriem Khyatti

Inqaba Biotec, Pretoria, South Africa

Acclaim M. Moila&Hamilton Ganesan

Laboratory of Bioinformatics, Biomathematics and Biostatistics-LR16IPT09, Institut Pasteur de Tunis, Universit de Tunis El Manar, Tunis, Tunisia

Alia Benkahla,Mariem Hanachi,Melek Chaouch&Oussema Souiai

Field Crops Laboratory, National Institute of Agricultural Research of Tunisia (INRAT), University of Carthage, Tunis, Tunisia

Amal Boukteb

Biotechnology Research Unit, Regional Center of Agricultural Research of Rabat, National Institute of Agricultural Research, Rabat, Morocco

Amine Elmouhtadi,Driss Iraqi,Rachid Mentag&Slimane Khayi

Laboratory of Molecular Biology, Department of Basic Sciences, University of Kinshasa, Kinshasa, Democratic Republic of Congo

Antoine Lusala Mafwila&Georges Lelo Mvumbi

Biotechnology School, Nile University, Giza, Egypt

Asmaa Mohammed Abushady,Assem Kadry Elsherif&Shaimaa Roshdy Abdullah Reda

African Genome Center, University Mohammed VI Polytechnic (UM6P), Ben Guerir, Morocco

Bulbul Ahmed,Khaoula Errafii&Mohamed Hijri

Separations (Pty) Ltd, Johannesburg, South Africa

Charles Wairuri,Maritte Kilian&Marija Kvas

University of Lagos, Lagos, Nigeria

Charlotte C. Ndiribe

Finima Nature Park, Port Harcourt, Nigeria

Chukwuike Ebuzome

South African Medical Research Council Genomics Platform, Cape Town, South Africa

Craig J. Kinnear

Science for Africa Foundation, Nairobi, Kenya

Deborah-Fay Ndlovu,Fatu Badiane Markey,Judy Omumbo&Thomas Kariuki

National Center for Scientific and Technical Research, Rabat, Morocco

Elmostafa El Fahime&Marouane Melloul

Bio and Emerging Technology Institute, Addis Ababa, Ethiopia

Ermias Assefa&Yonas Geberemichael

Faculty of Sciences, Mohammed V University, Rabat, Morocco

Faissal Ouardi

Applied Genetics in Agriculture, Ecology and Public Health Laboratory, University of Abou Bekr Belkaid Tlemcen, Tlemcen, Algeria

Fatima Zohra Belharfi,Ikram Mkedder,Imane Haddadi,Mohammed Rida Mediouni,Sarra Selka,Semir Bechir Suheil Gaouar&Soumia Ayed

Megaflex, Casablanca, Morocco

Fatim Zohra Tmimi&Mossaab Maaloum

Rutgers University-Newark, Newark, NJ, USA

Fatu Badiane Markey

Biotechnology Centre, University of Yaound 1, Yaound, Cameroon

Francis Zeukeng,Jude Bigoga Daiga,Libert Brice Tonfack,Pierre Franois Djocgoue&Rosette Megnekou

Department of Microbial Cellular and Molecular Biology, Addis Ababa University, Addis Ababa, Ethiopia

Helen Nigussie

Plant and Microbial Biotechnology Center, Moroccan Foundation for Advanced Science, Innovation and Research, University Mohammed VI Polytechnic, Ben Guerir, Morocco

Issam Meftah-Kadmiri

Department of Breeding and Reproduction, National Animal Genetic Resources Centre and Data Bank, Entebbe, Uganda

Jackson Franco Mubiru,Joan Bayowa Rokani&Joel Ogwang

International Livestock Research Institute, Nairobi, Kenya

Jean-Baka Kodjo Domelevo Entfellner,Cathrine Ziyomo&Appolinaire Djikeng

AbbVie Inc., North Chicago, IL, USA

Justin Eze Ideozu

Foundational Biodiversity Science, South African National Biodiversity Institute, Pretoria, South Africa

Kim Labuschagne,Mamohale Chaisi,Monica Mwale&Mudzuli Mavhunga

Laboratoire des Sciences Biomdicales, Alimentaires et de Sant Environnementale (LaSBASE), Dpartement des Analyses Biomdicales (AMB), Ecole Suprieure des Techniques Biologiques et Alimentaires (ESTBA), Universit de Lom, Lom, Togo

Komi Koukoura Komi

Illumina, Inc., Evry, France

Lydia Hadjeras,Michael Abdo,Sean Edwards,Tulsi Sahil,Xavier David&Zhiliang Chen

Agricultural Research Council, Biotechnology Platform, Pretoria, South Africa

Madeleine Ramantswana&Thabang Madisha

Division of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa

Marietjie W. Botes

MGI-Tech, Pretoria, South Africa

Mmatshepho Phasha-Muchemenye

Lakes and Fish Resources Protection and Development Agency (LFRPDA), Cairo, Egypt

Mohammed Ahmed Hassan

Veterinary Genetic Analysis Laboratory, Hassan II Agronomy and Veterinary Institute (IAV), Rabat, Morocco

Mohammed Piro,Oumaima Aminou&Siham Fellahi

Department of Plant and Soil Sciences, University of Pretoria, Pretoria, South Africa

Nicholas Abraham Olivier

Forestry and Agricultural Biotechnology Institute, University of Pretoria, Pretoria, South Africa

Nicholas Abraham Olivier&Renate Dorothea Zipfel

Department of Veterinary Pathology and Public Health, Hassan II Agronomy and Veterinary Institute (IAV), Rabat, Morocco

Oumayma Arbani

Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria, South Africa

Renate Dorothea Zipfel

Inqaba Biotec Central Africa, Yaound, Cameroon

Rolland Bantar Tata

University of Warwick, Coventry, UK

Sadik Muzemil

Department of Neurogenetics of Language, Rockefeller University, New York, NY, USA

Sadye Paez

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Establishing African genomics and bioinformatics programs through annual regional workshops - Nature.com