Daily Archives: June 26, 2022

Shapiros Nature Ecology study also focused on what may have happened to other polar bear genomes during periods of low icein this case, around 120,000 or 125,000 years ago when, according to Shapiro, Arctic ice levels were similar to the present days. But here, she looked at the relationship between polar bears and brown bears.

Her team constructed a phylogenetic treesort of like an evolutionary map showing how the bears diverged from a common ancestor over timeusing Brunos genome and those of currently living polar bears, brown bears, and a black bear. (Shapiro was able to utilize one of Laidres Southeast Greenland polar bear genomes in her analyses, although the time gap between its life and Brunos is enormous. The sample pool, she says, is missing 100,000 years of evolution.)

From this and other analyses, the scientists gained some evidence that about 20,000 years before Bruno was born, brown bears and polar bears mixed to generate hybrid offspring. The scientists hypothesized that during this warm period, polar bears might have made their way on shore. The carcasses of the marine mammals they hunted could have attracted brown bearsleading to mating opportunities. As a potential result of this ancient interbreeding, Shapiro says, up to 10 percent of the genome of the modern brown bear comes from polar bear ancestry.

Figuring out how and when polar bears and brown bears commingled, further specialized, or diverged is a difficult task, given the limited fossil record and complexities of evolution. Evolution is a messy process, says Andrew Derocher, a polar bear researcher at the University of Alberta who was unaffiliated with the studies. He likens the process of evolutionary speciation to a massive bunch of vines that are creeping up the base of a tree, crisscrossing and entangling. Eventually, some of those vines might get their own trajectory, and thats what our species are, he says. But in this process, they can cross over, they can reconnect and fuse, and its certainly impossible to pull it apart, because theyre so interconnected.

Still, these two studies are linked, Laidre says, in the sense of: Where have polar bears persisted when sea ice was low, and how? The research may provide some insight into how bears in the pastand todays Southeast Greenland bearshave survived in warmer climates with less ice.

But how genetic changes manifest in physical form, and how those changes may have helped bears survive past warming events, are still open questions, the scientists say. And these study results shouldnt make us feel that the problem of Arctic warming is resolved, or that todays bears can easily adapt to rapidly shrinking levels of sea ice. It seems like global warming is happening too fast, Lindqvist says. She wonders if the polar bears can keep up.

After all, polar bears depend on seals as their food sourceand those seals depend on sea ice. Theres parts of the Arctic that used to be excellent seal habitats and excellent polar bear habitats, Derocher says. But theres no sea ice there anymore. And as a result, theres virtually no bears. Theres very few seals, and the ecosystem has basically unraveled.

What, then, might actually help? Global action on climate change, Laidre says. Thats it.

More here:
What Polar Bear Genomes May Reveal About Life in a Low-Ice Arctic - WIRED

The grant is being used to fund a first-of-its-kind clinical trial that will recruit healthy individuals through a genome-first approach and perform deep metabolic phenotyping to understand the underlying mechanisms responsible for the regulation of the human bodys metabolism through natriuretic peptide hormones.

The grant is being used to fund a first-of-its-kind clinical trial that will recruit healthy individuals through a genome-first approach and perform deep metabolic phenotyping to understand the underlying mechanisms responsible for the regulation of the human bodys metabolism through natriuretic peptide hormones.Researchers from the University of Alabama at Birmingham Division of Cardiovascular Disease have been awarded a $3.7 million grant from the National Heart Lung and Blood Institute to study how genetically determined differences in natriuretic peptide levels (heart hormones) regulate the handling of glucose metabolism and use of energy while resting and while exercising.

The grant is being used to fund a first-of-its-kind clinical trial that will recruit healthy individuals through a genome-first approach and perform deep metabolic phenotyping to understand the underlying mechanisms responsible for the regulation of the bodys metabolism through NPs.

NPs are hormones produced by the heart that regulate cardiometabolic health. These hormones are released in response to changes in pressure inside the heart. These hormones are also responsible for regulating how the body responds to glucose and how it utilizes energy at rest and while working out.

Pankaj Arora, M.D., associate professor of medicine and the director of the $11 million NIH-funded Cardiovascular Clinical and Translational Research Program and the UAB Cardiogenomics Clinic, received the grant.

An estimated 37 million adults in the United States have diabetes, and an additional 96 million adults have pre-diabetes, which predisposes them to a higher risk of potentially fatal cardiovascular events such as heart attack, stroke and heart failure.

Researchers believe that genetically determined low NP levels may contribute to some individuals having a poor glucose metabolism and a low amount of any exercise. Individuals with lower circulating NP levels are predisposed to a higher risk of cardiometabolic diseases such as diabetes, high blood pressure, heart attacks, stroke and heart failure.

Pankaj Arora, M.D., associate professor of medicine and the director of the $11 million NIH-funded Cardiovascular Clinical and Translational Research Program and the UAB Cardiogenomics Clinic, received the grant.The study is employing an innovative genome-first strategy to assess the role of NPs in regulating the cardiovascular and metabolic health of an individual, Arora said. We will be enrolling individuals with and without a common genetic variant that predisposes them to have low NP levels. The study participants will then undergo a comprehensive metabolic assessment to understand the influence of genetically determined low NP levels.

The study is the result of decades of interdisciplinary research conducted by UAB scientists in collaboration with investigators across the country. Through past research, Arora and colleagues have shown that certain RNA-based regulators control the production of NPs and serve as potential therapeutic targets. Arora and his colleagues are studying how these regulators can be targeted for a precision medicine approach to the treatment of common cardiometabolic diseases.

There are certain RNA-based regulators that control the production of these good heart hormones that were discovered by our group of researchers, Arora said. These regulators reduce the production of NPs in individuals with a low NP genotype and may serve as potential therapeutic targets for the treatment of high blood pressure, diabetes, pre-diabetes and heart failure.

In addition to an innovative genome-first approach, the study by Arora and colleagues may also unravel a potentially new line of personalized therapeutics that follow the same genome-first precision medicine approach.

Arora believes that innovative studies like these build upon the advances in genomic medicine and bring the knowledge of decades of research back to the benefit of the patients at their bedside. UAB has been supporting such bench-to-bedside initiatives that translate scientific evidence accumulated from large-scale population genomic studies and bench research to the patient bedside. UAB physician-scientists are leading several such initiatives to enhance clinical and translational research in the domains of cardiometabolic disease.

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Arora receives $3.7 million grant to assess a genome-first approach to improving cardiometabolic health through heart hormo - University of Alabama at...

Different structures were detected between the Brazilian and US genetic bases

Principal component analysis (PCA) revealed that most Brazilian cultivars (red circle) were grouped with a subgroup of US cultivars (green circle). Most of them belonged to MG VI, VII, VIII and IX (Fig.1A). Based on the Evanno criterion (Fig.1B), the structure results based on four groups (K=4) showed a high K value (312.35), but the upper-most level of the structure was in two groups (K=2; K=1885.43).

Population structure analysis between Brazilian and US germplasms. (A) Principal component analysis of Brazilian and US soybean cultivars based on SNPs markers; (B) Delta K as a function of the number of groups (K); (C) assignment coefficients of individual cultivars (bar plots) considering K=2; and (D) considering K=4.

Considering K=2 (Fig.1C), the Brazilian cultivars jointly presented an assignment to the Q1 group (green) equal to 86.7% which was much higher than that observed for the US cultivars (43.9%). Considering K=4 (Fig.1D), the Brazilian cultivars jointly presented an assignment to the Q2 group (red) of only 4.7% while the US cultivars jointly presented an assignment to the Q2 group of 27.4%. The Q1 group (green) has a lower assignment in Brazilian cultivars than US accessions (11.1%, and 30.1%, respectively). These results demonstrate that the set of Brazilian cultivars has a narrower genetic base compared to US cultivars.

When we compared the cultivars between maturity groups, we observed a clear differentiation between early and late groups. The highest genetic distances (0.4158) observed were between MG 000 and MG VIII-IX cultivars (Supplementary Table S1).

To examine the influence of maturity groups on population structure, we analyzed the average assignment coefficients (K=4) of Brazilian and US cultivars for each maturity group (Supplementary Figure S1). Brazilian cultivars from maturity group V presented Q1, Q2, Q3, and Q4 equal to 30.4%, 1.9%, 32.1, and 32.0%, respectively; US cultivars from this same maturity group (V) presented means of Q1, Q2, Q3, and Q4 equal to 9.2%, 8.2%, 65.1%, and 17.6%, respectively. This result indicates that, although belonging to the same maturity group, the Brazilian group V cultivars present considerably different allelic frequencies than the US cultivar group V cultivars, especially for Q3 and Q4. US cultivars belonging to earlier maturity groups (00, 0, I, and II) had significantly higher mean assignment coefficient to Q2 group (red) compared to other later maturity groups (V=8.2%, VI=8.1%, VIII=5.0%, and IX=13.6%). In the case of Brazilian cultivars, the average assignment coefficients for Q2 were much lower (V=1.9%, VI=4.2%, VII=5.6%, VIII=4.9% and IX=4.9%). These results demonstrate an important allelic pool that distinguishes early to late genetic materials present in Q2.

In general, the Brazilian germplasm showed few differences between maturity groups (Supplementary Table S1 and Fig.2A). This was also observed when we generated a population structure analysis exclusively with these cultivars (Fig.2C). In contrast, the US germplasm showed a high variation of genetic distance when we analyzed their maturity groups (Supplementary Table S1) with a clear clustering of cultivars (Fig.2B), which is more obvious when we observed their exclusive population structure analysis (Fig.2D). The results show that early cultivars tend to be genetically distant from late cultivars in the US. The maturity groups from the southern-breeding program of the US (V, VI, VII, VIII, and IX) tend to be less genetically divergent versus northern groups (00, 0, I, II, III, and IV). This agrees with previous studies indicating distinct Northern and Southern genetic pools in the US6. There is a low divergence among US soybean cultivars from maturity groups higher than V (Fig.2B). In contrast, cultivars from MG 00 and 0 were more genetically distant from cultivars of MG III and IV while maturity groups I-II were an intermediate group. The population structure analysis showed a high influence of Q2 in cultivars with MG 00-II. For cultivars in MG III and IV, we observed an increase of Q1. Finally, there is a high influence of Q3 in cultivars with maturity groups higher than V, which agrees with the genetic distance data.

Population structure analysis of Brazilian and US cultivars according to their maturity groups. Principal component analysis (PCA) within Brazilian (A) and US (B) germplasms for each maturity groups; population structure of the Brazilian (C) and the US (D) genetic basis arranged according to their maturity groups.

The results demonstrate that both genetic bases had few increases in genetic distance among modern genetic materials (releases after 2000) when compared to cultivars from the 1950s to 1970s (Supplementary Table S2). According to the IBS genetic distance mean, the Brazilian genetic base was more diverse over the decades compared to US germplasm especially when we compared cultivars released before the 1970s and released after the 2000s (Supplementary Table S2).

Average assignment coefficients (Q1, Q2, Q3, and Q4) from genetic structure results were calculated for both germplasm pools. All accessions were sorted according to their origin and decade of release (Fig.3). We observed high genomic modifications over the decades in the Brazilian germplasm. Modern genetic materials (20002010) had Q1, Q2, Q3, and Q4 values of 36.8%, 2.3%, 31.7%, and 26.0%, respectively, while old accessions (1950-1960s) had means of Q1, Q2, Q3, and Q4 equal to 1.6%, 6.6%, 7.0%, and 84.7%, respectively. A high decrease was observed for Q4 starting in the 1990s whereas Q1 and Q3 highly increased during the same period. For the US genetic base, we observed an increase of Q3 and a decrease of Q2 over time. Old cultivars (19501970) had Q1, Q2, Q3, and Q4 values of 36.0%, 33.7%, 12.3%, and 18.1%, respectively, while modern cultivars (20002010) had Q1, Q2, Q3, and Q4 of 24.3%, 17.5%, 40.3%, and 17.8%, respectively.

Mean assignment coefficients of the Brazilian and US cultivars belonging to the different decades of release (1950 to 2010) to STRUCTURE groups (Q1, Q2, Q3, and Q4) considering K=4.

Modification during the 1990s became more evident upon analysis of the PCA and genetic structure results of the Brazilian genetic base considering the decades of release (Fig.4A and C). We observed an increase in the influence of the Q2 in modern genetic materials (20002010) when we compared the results to old genetic materials (19501970). In contrast, the US genetic base showed few variations over time according to the average of genetic distance (Supplementary Table S2), PCA, and the exclusive population structure analysis (Fig.4B and D). These results suggest a large influence of new alleles in the Brazilian germplasm after the 1990s.

Population structure of Brazilian and US cultivars according to their decade of release. Principal component analysis (PCA) within Brazilian (A) and US (B) germplasm for each decade; population structure of the Brazilian (C) and the US (D) genetic bases arranged according to their decade of release.

Seventy-two SNPs with FST0.4 between Brazilian and US cultivars were identified (Supplementary Table S3). These SNPs are located on chromosomes 1, 4, 6, 7, 9, 10, 12, 16, 18, and 19 (Supplementary Figure S2). Twenty-six 100-Kbp genomic regions with a high degree of diversification between Brazilian and US genetic bases were also found (Table 1). The results for Tajimas D showed that these regions had balancing events that maintained the diversity of their bases. Two regions on chromosome 6 (47.3 47.4 Mbp and 47.347.4 Mbp) and another on chromosome 16 (31.1031.20 Mbp) had few variations in Brazilian accessions (Supplementary Table S4). In contrast, the allele distribution for most of the SNPs present in these genomic regions in US germplasm was higher compared to Brazilian germplasm. An opposite scenario was observed for the other three regions located on chromosomes 7 (6.30 6.40 Mbp), 16 (30.70 30.80), and 19 (3.00 3.10) (Supplementary Table S4). The allele variance was higher in the Brazilian genetic base than US germplasm for these three intervals.

Six SNPs located close to maturity loci E1 (Chr06: 20,207,077 to 20,207,940bp)14, E2 (Chr10: 45,294,735 to 45,316,121bp)15, and FT2a (Chr16: 31,109,999 to 31,114,963)16 had a large influence on the differentiation of the Brazilian and US genetic bases (Fig.5). For the SNPs ss715607350 (Chr10: 44,224,500), ss715607351 (Chr10: 44,231,253), and ss715624321 (Chr16: 30,708,368), we found that the alternative allele was barely present in US germplasm whereas the Brazilian genetic base had an equal distribution between reference and alternative alleles. When we examined the SNPs ss715624371 (Chr16: 31,134,540) and ss715624379 (Chr16: 31,181,902), the frequency of the alternative allele remains low in the US germplasm. However, the alternative alleles of these two SNPs were present in more than 78% of the Brazilian accessions in contrast to the previous three SNPs. Finally, the alternative allele for SNPs ss715593836 (Chr06: 20,019,602) and ss715593843 (Chr06: 20,353,073) were extremely rare in Brazilian germplasm with only 2% of the accessions carrying them. In contrast, the US germplasm had an equal distribution of reference and alternative alleles in their accessions. However, all accessions with the alternative alleles belonged to MGs lower than VI with less than five cultivars in MG V.

The allele frequency distribution for SNPs close to loci (A) E1 (chromosome 6), (B) E2 (chromosome 10), and (C) FT2a (chromosome 16) in Brazilian and US germplasms.

Ten SNPs were identified related to the genes modifier mutations present in Brazilian and US germplasm; these were distributed on chromosomes 4, 6, 10, 12, 16, and 19 (Supplementary Table S5). These SNPs had differing allele frequencies and could distinguish both genetic bases. Six modifications had a clear influence on the maturity of the accessions whereas two of these had a large influence in some decades of breeding (Supplementary Figure S3). The SNP ss715593833 had a similar haplotype as two SNPs described as close to the E1 loci (ss715593836 and ss715593843) due to the linkage disequilibrium (LD) among them. At the end of this chromosome, we also observed another three relevant SNPs in LD: ss715594746, ss715594787, and ss715594990. In the US germplasm, we observed a decrease in the alternative allele in accessions with MG values lower than IV. We detected other relevant modifications on chromosome 12 for SNPs ss715613204 and ss715613207. Both SNPs had a minor allele frequency higher than 0.35 in Brazilian germplasm with an increase in the alternative allele in cultivars with MGs higher than VII. In contrast, alternative alleles for both SNPs were extremely rare in the US germplasm except for accessions with MG higher than VII.

There were 312 genomic regions that differentiate northern (00 IV MG) and southern (V IX MG) cultivar groups (Supplementary Table S6), which included the Dt1 locus. We compared the SNPs observed in the genomic region close to the Dt1 gene (Chr19: 45.2045.30 Mbp) with the growth habit phenotype data available for 284 lines at the USDA website (www.ars-grin.gov). The phenotypic data suggests that these SNPs are associated with growth habit. Moreover, our diversity analysis demonstrated a putative selective sweep for the Dt1 gene in the northern germplasm, which has the dominant loci fixed for Dt1; the southern lines tend to be more diverse compared to the northern US cultivars (Supplementary Table S7). In contrast, other genomic regions have lower nucleotide diversity in southern accessions compared to the northern accessions. An important disease resistance gene cluster was observed on chromosome 13 bearing four loci: Rsv1, Rpv1, Rpg1, and Rps317,18,19,20. In this interval, we observed two genomic regions (29.70 29.80 Mbp and 31.90 32.00 Mbp) under putative selective sweeps in the southern germplasm (Supplementary Table S8).

Besides these regions, 1,401 SNPs with FST values higher than 0.40 between northern and southern US cultivars were also identified (Supplementary Table S9). In addition, there were 23 SNPs with FST values higher than 0.70 spread on chromosomes 1, 3, 6, and 19. Seven of them were located close to another important soybean locus: E1 (involved in soybean maturity control) (Supplementary Table S10). These SNPs clearly differentiate northern and southern US cultivars with the reference allele fixed in northern genetic materials, and the alternative alleles in southern accessions. Gene modification in US germplasm was also detected in our study. One hundred twenty-six SNPs were identified in FST analysis modifying 125 genes (Supplementary Table S11).

Finally, we detected 1,557 SNPs with FST values higher than 0.40 between super-early cultivars (00 0 MG) and early cultivars (III IV MG) (Supplementary Table S12). Seventeen SNPs had FST values higher than 0.70 spread on chromosomes 4, 7, 8, and 10. The SNPs identified on chromosome 10 were close to the E2 locus. We also detected 168 SNPs associated with modifications in 164 genes (Supplementary Table S13).

We observed two SNPs with large differences in allelic frequencies in the Brazilian germplasm (Supplementary Figure S4). On chromosome 4, SNP ss715588874 (50,545,890bp) had a decrease of the allele A in cultivars released after 2000 with only nine of the 45 Brazilian cultivars with this allele. A similar situation was observed on chromosome 19 for ss715633722 (3,180,152bp) with half of the modern accessions having the presence of allele C. Both SNPs had similar distribution according to their decades in the US genetic base with a large influence of reference alleles.

There were 126 genomic regions spread on almost all soybean chromosomes in Brazilian cultivars. The only exception was chromosome 20 (Supplementary Table S14). Our analysis between cultivars released before and after 1996 identified 30 putative regions under breeding sweep events. Thirteen regions had a decrease in diversity in modern genetic cultivars according to Tajimas D and results. Two genomic regions observed were close to important disease resistance loci: one on chromosome 13 (30.30 30.40 Mbp) close to the resistance gene cluster (with Rsv1, Rpv1, Rpg1, and Rps3)17,18,19,20 and another on chromosome 14 (1.70 1.80 Mbp) with a southern stem canker resistance loci21,22. In contrast, thirty-one genomic regions had an increase in diversity in modern cultivars, which suggested putative introgression events in these accessions. Two genomic regions were observed, on chromosome 2 (40.90 40.10 Mbp) and 9 (40.3040.40 Mbp). Thesewere previously reported to have an association with ureide content and iron nutrient content, respectively23,24.

Besides these regions, there were also 409 SNPs with FST values higher than 0.40, distributed across all soybean chromosomes. There were 73 SNPs with FST values higher than 0.70 (Supplementary Table S15). Some of these SNPs were also reported to be associated with important soybean traits such as plant height, seed mass, water use efficiency, nutrient content, and ureide content23,24,25,26,27.

We also identified gene modifications with a high impact on the Brazilian genetic base when we compared cultivars according to their decade of release. Of the 409 SNPs identified in FST analysis, we observed 40 SNPs causing modifications in 39 soybean genes (Supplementary Table S16). Three SNPs with FST values higher than 0.70 were associated with non-synonymous modifications: ss715588896 (Glyma.04G239600 a snoaL-like polyketide cyclase), ss715607653 (Glyma.10g051900 a gene with a methyltransferase domain), and ss715632020 (Glyma.18G256700 a PQQ enzyme repeat).

Continued here:
Genetic relationships and genome selection signatures between soybean cultivars from Brazil and United States after decades of breeding | Scientific...

An illustration of Demodex folliculorum. Photo: Shutterstock (Shutterstock)

Scientists have finally unraveled the genetic secrets of humanitys coziest roommates: Demodex folliculorum, also known as the skin mite. Among other things, the findings confirm that these mites actually do have anuses, contrary to previous speculation. They also indicate that the microscopic animals may not be as potentially harmful as commonly thought and that theyre evolving into co-dependent, symbiotic creatures that might provide us some benefits to boot.

D. folliculorum is actually one of two mite species that call us home, along with Demodex brevis. Both species are arachnidsmore closely related to ticks than spidersbut D. folliculorum mites are the ones that usually reside (and mate) on our faces. These stubby worm-shaped critters live for two to three weeks, all the while embedded in our pores, clinging to our hair follicles, and primarily feeding off our sebum, the oily substance provided by our body to protect and moisturize the skin.

Despite virtually every person in the world having their own mite collection, theres still much we dont understand about them. But in a new study published Tuesday in the journal Molecular Biology and Evolution, researchers in Europe say theyve now fully sequenced the genome of D. folliculoruman accomplishment that might answer some lingering questions about their inner workings.

Some researchers have argued, for instance, that these mites lack an anus. Without an anus, the theory goes, their fecal waste simply accumulates inside them over their brief lifespan and is only released all at once when they die. Some have also speculated that an overabundance of mites can cause a skin condition known as rosacea, perhaps due to bacteria thats released from this explosion of poop upon a mites death. Other research has cast doubt on that claim, though, and the researchers behind the new study say theyve confirmed that mites do indeed have an anus.

Study author Alejandra Perotti, a researcher at the University of Reading in the UK, notes that the larger presence of mites in people who develop rosacea and other skin conditions may very well be a consequence of the condition and not its actual cause. And if mites arent leaving behind huge amounts of poop behind when they die, then theres a less clear rationale as to how they would make us sick in the first place. Other studies, for what its worth, have continued to find a link between the mites and rosacea, though they may only be one of many triggersinvolved.

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It is easier and faster to just blame the mites, she said in an email to Gizmodo.

The teams other findings show that these mites have evolved to become incredibly lazy, genetically speaking, as a result of hitching their wagon to humans. They have a very simple genome compared to other related species, and they seem to be surviving with the bare minimum of cells and proteins needed to function (Their leg pairs are even powered by a single muscle cell each). Theyve lost the ability to survive exposure to ultraviolet light, which explains why they hunker deep down into our pores and only move and mate at night, and they dont appear to even produce their own melatonin anymore, like many animals doinstead, they seem to mooch it from us. Theyre also passed down from mother to child, often through breastfeeding, meaning that populations have relatively low genetic diversity. And their lack of natural predators, host competition, and generally sheltered existence suggests that the mites are only likely to lose more genes over time.

The researchers theorize that these trends could one day lead to the end of D. folliculorum mites as a distinct entitya process thats been observed with bacteria but never an animal, they say. Eventually, the mites might no longer live externally on our skin as parasites but instead become wholly internal symbiotes. If so, then we might be seeing that transition taking place now, though this transformation likely wouldnt be finished for a long time.

Regardless of the future fate of these mites, the scientists say theyre perhaps doing some good for us now. They might help clear the skin of excess dead cells and other materials, for instance, at least when their populations are kept in check. Perotti also hopes that their research will provide people proper knowledge of these permanent companions, which have been blamed too long for our skin problems.

More:
The Mites That Live and Breed on Your Face Have Anuses, Genome Study Finds - Gizmodo

NEW YORK An analysis of monkeypox virus (MPXV) genomes from the ongoing global outbreak has found that the samples cluster together, indicating a single origin for them.

Between the beginning of the year and the middle of June, there have been more than 2,100 laboratory-confirmed cases of monkeypox, most of which have been reported since the start of May, according to the World Health Organization. More than 80 percent of these cases have been reported in Europe and 12 percent in the Americas, where the virus is not endemic and the cases have no known links to endemic regions.

Researchers in Portugal where there have been about 300 cases, according to the European Centre for Disease Prevention and Control have now conducted a phylogenetic analysis of 2022 MPXV and found that the outbreak likely has a single source related to a 2017/2018 outbreak in Nigeria. They additionally reported in Nature Medicine on Friday that the virus samples appeared to be undergoing accelerated evolution, likely influenced by host APOBEC3, a class of mRNA-editing enzymes that help defend against viruses.

"The accelerated evolution is an observation, but we do not know yet how that happened. It was quite unexpected to find so many mutations in the 2022 MPXV," senior author Joo Paulo Gomes from the National Institute of Health Doutor Ricardo Jorge in Lisbon said in an email.

He and his colleagues analyzed the first 2022 MPXV genome from the outbreak, which they released publicly on May 19, in conjunction with 14 other MPXVgenome sequences, most of which were also from Portugal.

A phylogenetic analysis placed the 2022 outbreak samples among clade 3, within what was formerly known as the "West African" clade. All the outbreak samples clustered tightly together, indicating a single origin for the ongoing outbreak.

At the same time, the outbreak samples formed a branch that diverges from viruses linked to cases in the UK, Israel, and Singapore in 2018 and 2019, which themselves stemmed from an outbreak in Nigeria from 2017/2018. This suggested to the researchers that the 2022 outbreak could be due to the continuous circulation and evolution of the virus from the Nigeria outbreak.

However, 2022 MPXV differs from the 2018/2019 virus by an average 50 SNPs, which Gomes noted is many more than expected. For this type of virus, he said, one or two mutations would be expected to arise each year. As 2022 MPXV is likely a descendant of the 2017/2018 Nigeria outbreak which led to the UK, Israel, and Singapore cases in 2018/2019 about 5 to 10 additional mutations would be expected, not 50.

"So, unquestionably, we are facing a scenario of accelerated evolution," Gomes said.

The changes also tended to follow a certain pattern of incorporating more adenines and thymines into an already A/T-rich viral genome, which suggested that the human APOBEC3 system could be involved in this accelerated evolution.

APOBEC3 is a host antiviral mechanism that induces mutations into viruses, but that could lead to hypermutation if the enzymes do not fully restrict the viruses. Gomes noted that this mechanism has already been described in HIV and HPV.

"We do not know about the consequences but we know, for instance, that [a number] of these mutations are affecting viral proteins that are associated with the interaction with the human immune system, so, hypothetically, a mechanism of immune evasion cannot be completely discarded," he added.

In all, the researchers said that viral genome sequencing of outbreak samples may enable scientists to better understand how 2022 MPXV is spreading and provide insight into ways to control that spread. "We will focus on identifying and monitoring the mutations that will arise in real time during the ongoing transmission in order to better understand the host adaptation," Gomes said.

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Monkeypox Genome Analysis Points to Single Origin of Recent Outbreak - GenomeWeb

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Genome B.C.'s boss talks about how his agency's work helps people he and elsewhere

Author of the article:

Do you know your As, Cs, Gs and Ts?

They are the four types of bases in a DNA molecule which consists of two strands wound around each other to form an organisms complete set of DNA, called its genome.

DNA carries the instructions for making specific proteins or sets of proteins. There are about 20,000 genes in the human genome located on 23 pairs of chromosomes which are packed into the nucleus of a human cell.

Its remarkably complex and remarkably important, says Pascal Spothelfer, CEO of Genome B.C., as we move toward a future that will allow us to make informed and specific decisions about our health, the health of all other living beings, plants and the environment.

Here in Vancouver, Genome B.C. has been leading the way in the expansion of knowledge and specific beneficial advances in science and technology. COVID- 19 research was one such benefactor.

Pascal Spothelfer joined a Conversation That Matters about the role Genome B.C. is playing in our lives and in the expansion of scientific research and biomedical technology.

You can join a Conversations Live event. Sign up for advance notice about upcoming events at ohboy.ca/conversations.

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Conversations That Matter: Knowing your genome - Vancouver Sun

Index case and named contact partner epidemiology

In December 2020, researchers and public health officials in the United Kingdom identified a rapidly spreading SARS-CoV-2 variant within England, then designated as PANGO lineage B.1.1.721, now designated as the Alpha variant of concern in the WHO nomenclature. In NYC, a SARS-CoV-2 genome sequence classified as belonging to the Alpha lineage was obtained from a sample on 4 January 2021 (the index case): NYCPHL-002130 (GISAID accession number EPI_ISL_857200). Due to the potential public health importance of Alpha variant cases in NYC in early 2021, NYC DOHMH conducted a public health investigation related to the individual from which this sample had been obtained. This investigation determined that the individual had recently traveled to Ghana (late December/early January) and developed symptoms consistent with COVID-19 while in Ghana. Contact tracing in New York City identified another case of an Alpha variant infection, sampled on 14 January 2021, in a named contact with a similar travel history (the named contact partner): NYCPHL-002461 (GISAID accession EPI_ISL_883324). The named contact partner had also developed symptoms consistent with COVID-19 while in Ghana, prior to returning the United States.

Typical of the Alpha variant21, NYCPHL-002130 from the index case exhibited S gene target failure (SGTF) phenotype with the TaqPath COVID-19 RT-PCR assay (Table1). NYC PHL uses the ARTIC amplicon-based protocol V3 to sequence full viral genomes and capture intra-host diversity. All 24 mutations diagnostic of the Alpha variant were found in >90% of reads (Table2). The viral genome from this index case showed limited intra-host viral diversity (Fig.1). A single variable site was found at position 23099, with C in 20.4% of reads and A in 79.6% of reads.

Frequencies of individual alleles shown as ticks, a smoothed kernel density plot is used to highlight clustering patterns, and colors represent allele types.

During the initial PCR screening of the sample collected from the named contact partner (NYCPHL-002461-A), the SGTF characteristic of the Alpha variant was not observed (Table1). Furthermore, genome sequencing revealed substantial intra-host viral diversity within the viral genome, a possible signature of superinfection (Fig.1). To confirm that this intra-host diversity was not attributable to experimental or sequencing artifacts, the original sample was re-extracted and re-sequenced (NYCPHL-002461-B) and similar SGTF was observed. Additional extractions were then performed in duplicate from the original stock (NYCPHL-002461-C and -D) and sequenced. The same signature of intra-host diversity was confirmed in all four sequenced extractions. Four nucleotide (nt) substitutions differentiating this sequence from the reference genome were identified at >90% frequency: C241T, C3037T, C14408T, and A23403G (Fig.1; Table2). These four substitutions were all present in the lineage B.1 virus that is ancestral to the named SARS-CoV-2 variants. Numerous additional substitutions, including A23063T (S N501Y), were present, but at slightly lower frequencies. Nonetheless, this genome was classified as an Alpha variant. Notably, the 69/70 and 144 deletions were found at >97% in the sequencing reads, despite the lack of SGTF.

NYCPHL-002461-A, -B, and -D extracts exhibited low Ct values for the ORF1ab and N gene targets, ranging between 15 and 16 (Table1). The S gene target Ct values were around 2 to 3 cycles higher. The difference suggests a reduction of viral template in the S gene target region, but not SGTF. We note NYCPHL-002461-C yielded an invalid result, as the TaqPath assay showed no amplification on all targets, including the MS2 phage extraction-control target.

The presence of multiple intermediate frequency alleles and the lack of SGTF in the TaqPath assay prompted us to investigate the intra-host diversity in the named contact partner, NYCPHL-002461. Using the previously described and validated Galaxy SARS-CoV-2 allelic variation pipeline22, we identified four categories of allelic frequencies: shared, major strain, minor strain, and other (see Fig.1, interactive notebook at https://observablehq.com/@spond/nyc-superinfection). The four replicate sequencing runs for NYCPHL-002461 yielded remarkably similar patterns of these allelic frequencies.

Alleles that fell into the shared category were present at 90% allele frequency in three or more samples. Shared alleles included all four substitutions characteristic of B.1 (Table2) and two deletions in the S gene (69-70 and 144) diagnostic of the Alpha variant.

Major strain occurred at frequencies between 60 and 80% (in at least 3 samples). Major alleles included all 21 substitutions defining the Alpha variant, which we observed at a median allele frequency of 74.1%, and ORF1A deletion (Table2). The remaining major alleles are shared with genome from the index case.

Minor strain alleles occurred at frequencies between 10 and 25% (in at least 3 samples). All but one of the 12 diagnostic Epsilon mutations was found in this set: A28272T is absent in NYCPHL-002461. All remaining minor alleles have been observed in other Epsilon genomes.

The other category encompasses all other variable sites, i.e. those occurring at frequency between 25 and 60% or those found in only one or two samples. The two alleles were found in all four replicate sequences at intermediate frequencies: G7723A (30.3%) and C23099A (46.7%). These frequencies are suggestive of intra-host variation in the major strain.

In contrast to the allelic mixture detected in the named partner (NYCPHL-002461), we observed allele frequencies >90% for all Alpha defining mutations in the sequencing data for the index case, NYCPHL-002130 (Table2). The C23099A mutation, which was at intermediate frequency in NYCPHL-002461 from the named contact partner, was present at 88.1% in NYCPHL-002130 from the index case, consistent with the transmission of a mixed viral population between these individuals.

We identified sub-clades within Alpha and Epsilon that shared substitutions with the major and minor strains (Fig.2). We inferred a maximum likelihood (ML) phylogenetic tree in IQTree2 for the major strain and 3655 related Alpha (B.1.1.7) genomes containing the C2110T, C14120T, C19390T, and T7984C substitutions found in the major strain (Fig.2A). We also inferred an ML tree for the minor strain and 2275 related Epsilon (B.1.429) genomes containing the C8947T, C12100T, and C10641T substitutions found in the minor strain (Fig.2C).

A Phylogeny of Alpha variant immediate relatives. B Root-to-tip regression for Alpha variant. C Phylogeny of Epsilon variant immediate relatives. D Root-to-tip regression for Epsilon variant. NY-NYCPHL-002461 is the genome deposited in GISAID from the case of putative superinfection.NY-NYCPHL-002130 is the genome from the index case.

Root-to-tip regression analyses show that the NYCPHL-002461 sampling date is consistent with the molecular clock for both the major and minor strain sequences (Fig.2B/D), indicating that one would expect viruses of this degree of genetic divergence to have been circulating in mid-January 2021. In fact, genomes identical to the major variant were sampled in both NYC (the NYCPHL-002130 index case) and in Ghana on 8 January 2021 (EPI_ISL_944711), consistent with a scenario in which this particular Alpha virus was acquired in Ghana. These three viruses share a common ancestor around 4 January 2021 and are separated from additional viruses sampled in Ghana by two mutations: C912T and C23099A. Notably, the latter mutation appears at intermediate frequency in both NYCPHL-002130 and NYCPHL-002461.

The minor variant is genetically distinct from all other sampled genomes, including any genome sequenced by NYC DOHMH (Fig.2C). The closest relatives were sampled in California (EPI_ISL_3316023, EPI_ILS_1254173, EPI_ISL_2825578), the United Kingdom (EPI_ILS_873881), and Cameroon (EPI_ISL_1790107, EPI_ISL_1790108, EPI_ISL_1790109). The most similar of these relatives is EPI_ISL_3316023, which was sampled on 11 January 2021 in California and represents the direct ancestor of the minor variant on the phylogeny. The only mutation separating this California genome from the minor variant is T28272A, which is a reversion away from an Epsilon-defining mutation (Table2).

It is unlikely that this minor variant is a laboratory contaminant, as there are no closely related Epsilon genomes sequenced from NYC. That said, NYC represents the probable source of this Epsilon virus. Of the 145 SARS-CoV-2 genomes sequenced by NYC public health surveillance between 10 January 2020 through 16 January 2020, 4 (2.8%) were Epsilon. A similar proportion of Epsilon genomes deposited in GISAID were sampled by other labs during this same period: 11 out of 431 genomes (2.6%)23. No Epsilon genome has been reported to date from Ghana.

A preliminary inquiry of the genome sequencing data from the S gene (12 contiguous read fragments) and N gene (nucleoprotein; 3 contiguous read fragments) regions was suggestive of recombinant genome fragments within the named contact partner. To determine whether pairs of polymorphic sites within individual read fragments displayed evidence of recombination we employed three different four-gamete based recombination detection tests: PHI24, MCL, and R2 vs Dist25 (Table3). The power of each of these tests to detect recombination was seriously constrained by the short lengths of the read fragments and the low numbers of both variant-defining sites and other polymorphic sites with minor allele frequencies >1% within each of the fragments. Only three of the 15 read fragments (read fragments 6 and 8 in the S gene and read fragment 3 in the N-gene) encompassed two or more of the variant-defining sites that were expected to provide the best opportunities to detect recombination. Nevertheless, pairs of sites within four read fragments in the S gene (positions 2312324467 covering fragments 7, 8, 9 and 10) and one read fragment in the nucleoprotein gene (positions 2898629378 covering fragment 3) exhibited signals of significant phylogenetic incompatibility with at least two of the three tests (p<0.05): signals which are consistent with recombination. The only read fragment for which evidence of recombination was supported by all three tests was fragment 3 in the N gene: a fragment that was one among only three that contained multiple variant-defining substitutions. Eight of the fifteen analyzed read-fragment alignments exhibited no signals of recombination using any of the tests, which is unsurprising given the lack within these fragments of both variant-defining substitutions and polymorphic sites with minor allele frequencies greater than 1%.

The four gamete tests on genomic sequencing data is limited by the short length of amplified fragments. To obtain data from longer sequence fragments, we PCR-amplified three regions of the genome from the original nucleic acid extracts, cloned them, and then sequenced individual clones. These longer genomic fragments provide greater resolution for detecting recombination, compared with the short fragments from deep sequencing analysis, because they include more differentiating sites spread out farther across the genome.

The longest cloned region spanned 947 nt within the S gene (positions 2290423850) and contained 5 nt substitutions differentiating the major and minor strains plus a variable site in the major variant. Of the 104 clones sequenced within this region, 60 (57.7%) were major strain haplotypes, 13 (12.5%) were minor strain haplotypes, whereas the remaining 31 clones (29.8%) contained both major and minor strain mutations, consistent with recombination (Fig.3). We observed 11 distinct combinations of major and minor strain mutations across these clones, with two distinct haplotypes present in 6 clones apiece. Most recombinant haplotypes (n=24) are consistent with only a single recombination breakpoint. However, 7 clones are consistent with 2 breakpoints (representing 3 different haplotypes), and 1 clone is consistent with 3 distinct breakpoints.

Each row represents a sequenced clone (n=104). Colored markings denote mutations from the reference genome. Major strain mutations are those found in the Alpha variant. Minor strain mutations are those found in Epsilon variant. Other mutations are found at intermediate or low frequencies. Shared mutations are those shared by B.1 viruses.

The second cloned S region spanned 657 nt in the S gene (positions 2144222098) including the 6970 and 144 deletions characteristic of the major strain and two 2 substitutions in the minor strain. Of the 93 clones sequenced, 69 (74.1%) were major strain haplotypes, 17 (18.3%) were minor strain haplotypes, and 7 (7.5%) were mixed haplotypes (Fig.4). Five of these mixed haplotypes contained only one of the two deletions. One mixed haplotype was consistent with multiple recombination breakpoints. Unlike in the primary sequencing analyses where the 6970 and 144 deletions were present in >98% of sequences, 69-70 was observed in only 72 (77.4%) clones and 144 was observed in only 71 (76.3%). These frequencies are consistent with the frequency of the other major strain substitutions in the primary sequencing analysis.

Each row represents a sequenced clone (n=93). Colored markings denote mutations from the reference genome. Major strain mutations are those found in the Alpha variant. Minor strain mutations are those found in Epsilon variant. Other mutations are found at intermediate or low frequencies.

The third, and shortest, cloned region spanned 476 nt of ORF8 (positions 2779828273), surrounding 4 substitutions defining the major strain and 1 minor strain substitution. Of the 36 cloned sequences, 30 (83.3%) had the major strain haplotype, 2 (5.6%) had the minor variant haplotype, and 4 (11.1%) had mixed haplotypes consistent with a single recombination breakpoint (Fig.5). Note the discriminating substitutions only span 223 nt of this region.

Each row represents a sequenced clone (n=36). Colored markings denote mutations from the reference genome. Major strain mutations are those found in the Alpha variant. Minor strain mutations are those found in Epsilon variant. Other mutations are found at intermediate or low frequencies.

Three cloned sequences from the 947 nt S gene fragment contained single nucleotide deletions resulting in non-sense mutations. In the 657 nt S gene fragment, we observed 8 clones with similar deletions, detected in both the forward and reverse direction during sequencing. These deletions were seen in the non-recombinant Alpha and Epsilon haplotypes and likely reflect non-functional viral particles, expected to constitute a substantial fraction of genomes within an infected individual26,27.

In vitro recombination can be introduced by reverse-transcription and PCR amplification, which are part of both genome sequencing and cloning protocols28. These in vitro effects have a strong stochastic component and would result in substantially different recombinant haplotype frequencies across different extracts and PCR experiments. To determine the extent to which these protocols could have led to biased inference of recombination, we compared the haplotype frequencies across the four extracts from NYCPHL-002461, which had each independently been subjected to reverse transcription and PCR amplification, and the frequency of these haplotypes in the cloning experiment, which included PCR amplification.

Within the 947 nt cloned S gene fragment, the major haplotype was present between 76.4% and 78.6%, and the minor haplotype was between 13.7% and 15.4% (Supplementary Table1). The recombinant haplotype positions 23604A and 23709C was present at 3.9% allele frequency (standard deviation of 0.34% across extracts), whereas recombinant haplotype 23604C and 23709T was present at 4.3% (standard deviation of 0.37% across extracts). Although the haplotype frequencies among extracts were significantly different (p=0.029; chi-square test), the magnitude of these differences were unremarkable. Furthermore, there was no significant difference between the frequency of these haplotypes in cloning experiment and extracts (p=0.190 versus -A; p=0.189 versus -B; p=0.357 versus -C; p=0.206 versus -D; Fishers Exact Test).

A similar pattern was observed within the 476 nt cloned fragment in the ORF8 region, which included four discrimination sites: 27972, 28048, 28095, and 28111 (Supplementary Table2). The predominant recombinant haplotypes were consistent across the four extracts, and the frequencies differed only slightly (p=0.077; chi-square test). As in S, the frequency of these recombinant haplotypes in the cloning experiment was not significantly different from any of the extracts (p=0.405 versus -A; p=0.413 versus -B; p=0.199 versus -C; p=0.408 versus -D; Fishers exact test).

Hence, in vitro recombination induced by either reverse-transcription or PCR amplification, does not appear to have been the dominant contributor to the recombinant haplotype distribution reported here.

To determine whether there was onward transmission of a recombinant descendent of these major and minor strains, we queried the 27,806 genomes sequenced by NYC public health surveillance and deposited to GISAID through 5 September 2021. We tested these genomes for mosaicism (3SEQ29; with Dunn-Sidak correction for multiple comparisons) of the major and minor strains; however, we were unable to reject the null hypothesis of non-reticulate evolution for any of these genomes. We also did not find any genomes in the PHL dataset with a superset of the identifying substitutions present in the major and minor variants (e.g., C912T and C27406G) among the genomes in the PHL dataset. There is no evidence of an Alpha/Epsilon recombinant that circulated in New York City.

Since the Dunn-Sidak correction done in the 3SEQ analysis applies a conservative type-1 error threshold of 0.05, we reran the analysis using a more permissive threshold of 0.25 (see methods) and were able to reject the null hypothesis for a single genome (EPI_ISL_2965250; p=2.24106 and Dunn-Sidak corrected p=0.117). Although this genome (Fig.6) contains many of the mutations characteristic of the Alpha variant throughout the genome, it does not possess mutations unique to the major strain nor any Epsilon-specific mutations. Rather, within the putative recombinant regions, the EPI_ISL_2965250 genome has C8809T, C27925T, C28311T, and T28879G. All of these mutations are characteristic of the B.1.526 Iota-variant, prevalent in NYC in early 2021. Therefore, this genome is likely not a descendant of the major and minor strains. Instead it appears to be a recombinant descendant of Alpha and Iota viruses.

The distribution of the nucleotide variation found in the major, minor, Iota (B.1.526; EPI_ISL_1635735), and single putative recombinant (EPI_ISL_2965250) strains relative to the reference genome (Wuhan Hu-1; bottom gray sequence).

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Detection of SARS-CoV-2 intra-host recombination during superinfection with Alpha and Epsilon variants in New York City - Nature.com

The oversubscribed series B financing led by Healthcare Venture Partners, added SoftBank Corp. and Natera to existing investors Sequoia Capital, Foresite Capital, Founders Fund, among others

MENLO PARK, Calif., June 21, 2022 /PRNewswire/ --MyOme, a clinical whole genome platform analysis company, today announced that the company has raised $23 million in an oversubscribed series B financing round led by Healthcare Venture Partners, bringing the total raised to over $36M. MyOme plans to use the funds to begin commercialization of their clinical whole genome analysis platform technology and clinical reports to health systems. The MyOme platform will help families understand and manage their risk for inherited disease.

"Through whole genome analysis, MyOme will address the large market of predominantly healthy individuals who have known disease susceptibility in their family that can be preventatively managed or better cared for today with genetic insights, but who don't have a known rare genetic mutation," said Premal Shah, PhD, CEO of MyOme. "Moving forward, the whole genome backbone enables the delivery of future genomic reports on-demand for a lifetime of insights."

MyOme recently presented new data at the 2022 American Society of Clinical Oncology Annual Meeting on the performance of an enhanced cross-ancestry polygenic risk score (PRS) to improve breast cancer risk assessment for women across multiple ethnicities. These advanced technologies can be broadly applied across many diseases. MyOme also published data in Nature Medicine on their clinical study using whole genome reconstruction for application of PRS to in-vitro fertilization across cancers and cardiac, metabolic, and autoimmune diseases.

"The power of the whole genome has not yet been fully realized," said Matthew Rabinowitz, PhD, co-founder and chairman of MyOme. "Most common disease is not caused by single genes, but by an interaction of many genes and the environment.Whole genome analysis and PRStechnologies are accelerating and will play an increasingly crucial role in healthcare. MyOme is at the forefront of developing clinical applications to support families of all ethnicities."

"MyOme is building the future where best-in-class genomic platforms and tools can lead to a better understanding of disease with a meaningful impact on human health," said Michael Mashaal, MD, senior managing director ofHealthcare Venture Partners. "We are committed to supporting them in this endeavor and believe in their team of leading visionaries and bioinformaticians with extensive experience in developing innovative genomic products."

MyOme was founded by leaders in the field of genomics, including Matthew Rabinowitz, co-founder of Natera.

About MyOme

MyOme is a clinical whole genome analysis platform company helping families understand their risk for inherited diseases. MyOme leverages the power of the whole genome for a lifetime of actionable insights. Certified under the Clinical Laboratory Improvement Amendments (CLIA), MyOme is based in Menlo Park, California. For more information, visit myome.com.

SOURCE MyOme, Inc

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MyOme Secures $23M in Series B Funding to Deliver Clinical Whole Genome Insights to Help Families Manage Risk for Inherited Diseases - PR Newswire

10 million sequences of COVID-19s genomic code have now been organized into a phylogenetic tree in the UC Santa Cruz SARS-CoV-2 Browser, which is the largest tree of genomic sequences of a single species ever assembled. This accomplishment is impressive for both the computer engineering feat of processing such a massive amount of data and the incredible dedication and coordination of the researchers involved.

It is an astounding thing that has happened there, said Clay Fischer, Project Manager for the UCSC Genome Browser.

All of these sequences are assembled by the researchers into a phylogenetic tree that shows the evolutionary history of the virus, with different branches representing the lineages that have mutated throughout the pandemic. This tree is powered by a software tool called UShER that was developed at the UC Santa Cruz Genomics Institute and is hosted on the UCSC Genome Browser website.

Many hands from around the world have brought the Genomics Institute these 10 million sequences that live on the UShER tree. Clinicians worldwide have administered tests to be sent off to local labs, which then sent the samples on for sequencing. Once they are sequenced, they become digital files that are uploaded to databases for genomic information such as GISAID, GenBank, or the COG-UK database.

Angie Hinrichs, a senior software architect at the UCSC Genome Browser and self-described data wrangler, built a pipeline to pull these sequences into the UShER tree automatically. But this process was complicated as some databases, like GISAID, had restrictions that necessitated the manual download of sequences.

For the first half of 2021, I would download them every night before bed, Hinrichs said.

Hinrichs has worked at the UCSC Genome Browser for twenty years. She keeps a low profile, usually preferring to work behind the scenes than in the spotlight. But according to her colleagues, her work curating the tree of COVID-19 genomes and coordinating with the CDC and other health organizations has been of great importance to the pandemic relief effort. She is a part of the Pango team of volunteers who have been monitoring virus sequences to identify new variants. She takes on the ongoing, daily maintenance of updating and annotating the UShER tree, which recently became the default software used by the Pangolin tool, a system used by health officials worldwide to track the spread of variants in their community.

UShER was created early in the pandemic, when researchers at the UC Santa Cruz Genomics Institute recognized that tracing the evolution of a quickly evolving global pathogen like COVID-19 would require a phylogenetic tree that was able to handle an unprecedented amount of data. So, the Genomics Institutes scientific director David Haussler gathered together a team to focus on pathogen genomics, led by Assistant Professor of Biomolecular Engineering Russell Corbett-Detig and including then-postdoc Yatish Turakhia. Turakhia originally wrote the UShER software, which has the ability to rapidly add a new genome sequence to a very large tree of genome sequences.

Making a tree that can handle so much data is an incredible feat of computer engineering that has required herculean efforts from a number of researchers. Before the current pandemic, phylogenetic trees for comparing viral samples were relatively common, but they were built from comparatively small numbers of sequences.

As unprecedented numbers of SARS-CoV-2 sequences became available, the standard tree-building tools simply could not keep up, and researchers often struggled to make sure their analysis kept pace with the amount of samples they would receive. UShERs software and the sustained effort of the team made it possible to grow the tree apace with the pandemics flood of sequences.

Hinrichs says that her two decades of experience working with the massive amounts of data stored on the UCSC Genome Browser helped prepare her to work with the COVID-19 lineages on UShER.

This data coordination is what makes our resources really powerful, Hinrichs said. We have really great resources here, and really great people.

One of those great resources is UCSCs amazing computing hardware maintained by Jorge Garcia, Haifang Telc, and Erich Weiler. Hinrichs explained that having that computing power has been essential for this project.

Big data is our thing, so we were ready to jump on this, she said.

At the beginning of the pandemic, the UCSC pathogen genomics team made guesses as to how many COVID-19 sequences the tree would need to be able to handle. Only Corbett-Detig thought it would reach a million no one anticipated reaching 10 million.

I still get surprised at how far weve come, Turakhia said. The unimaginable amount of data we were able to handle and the fact that we are able to make sense of it quickly is mind-boggling as a computational genomicist.

As the tree has grown, it has required constant attention and updates. Cheng Ye, an undergrad in Turakhias new lab at UC San Diego, was also able to figure out a way to add new sequences faster when the tree had grown to contain millions of sequences already, and helped develop a tool called MatOptimize that moves sequences around on the tree when more data makes it apparent that the original placement was less optimal.

Accumulating reliable data has been instrumental to better understanding what we are up against in the fight against COVID-19 and all its variants. While little was known about this virus at the start of the pandemic, the tree-building tools developed at UC Santa Cruz have helped to put the history of the virus in some perspective and to predict its future, and researchers across campus have leveraged their expertise to aid in the relief efforts. The progress has been astounding; but for the researchers on the browser team, the urgency of their mission and the sheer amount of data that needs to be curated has also been overwhelming at times. Fischer acknowledges that this level of dedication comes at a cost.

It has been two years of blood, sweat, and tears, he said.

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The team behind a tree of 10 million Covid sequences - University of California, Santa Cruz