Ubiquitin, keratin and skin fragility

Posted by Catherine Potenski | Categories:

Lin et al. Nature Genetics, 2016

Lin et al. Nature Genetics, 2016

Protein degradation is a highly coordinated process with multiple levels of regulation, including both targeted and autodegradation.  This sophisticated cascade of protein turnover must be precisely balanced to maintain proper physiological function. A recent article published in Nature Genetics reports the discovery of gene with protein-truncating mutations that lead to the skin condition epidermolysis bullosa, which is characterized by tendency to blister, itching and other abnormalities. The authors found 5 patients all with start codon mutations in the KLHL24 gene, which encodes Kelch-like protein 24, a substrate receptor of the cullin 3 (CUL3)–RBX1–KLHL24 ubiquitin ligase complex.

Lin et al., Nature Genetics, 2016

Lin et al., Nature Genetics, 2016

The mutant proteins from these patients were found to be stabilized, with increased levels in patient samples, leading the authors to hypothesize that KLHL24 may target a substrate that is important for the structural integrity of the skin.  Indeed, through mass spectrometry and biochemical analysis, they identify keratin 14 (KRT14) as a KLHL24 substrate, and find that KRT14 levels are decreased in patient samples. Keratin 14 is an intermediate filament component important for maintaining keratinocyte integrity and mutations in the gene are found in some epidermolysis bullosa patients. The authors further show that KLHL24 is autoubiquitinated and that the truncated mutant has reduced levels of autoubiquitination, stabilizing the protein. This increased KLHL24 stability leads to increased KRT14 degradation, resulting in the skin fragility phenotype observed in the patients.

Lin et al., Nature Genetics, 2016

Lin et al., Nature Genetics, 2016

 

 

 

 

 

 

 

 

 

 

 

Although dynamic regulation of keratins by the ubiquitin–proteasome system had been proposed, no targeting E3 ligases had been identified. This work established KLHL24 as a keratin-targeting E3 ligase.

 

We spoke with authors Dr. Xu Tan and Dr. Yong Yang to get some background on their research.

Can you briefly describe how you found the KLHL24 mutations in these different patients?

The first three epidermolysis bullosa (EB) patients were first screened for the 18 previously known causative genes but no mutations were found. Then we performed whole exome sequencing and pinned down only one common variant gene among all three patients, namely KLHL24. We then acquired samples from two additional patients without mutations in the 18 known causative genes and used Sanger sequencing to show that both of them also have the mutations in the same KLHL24 gene, confirming that this is a new causative gene of EB.

All the patients you studied had start codon mutations leading to truncations in the protein. This must have been intriguing. What where your initial thoughts about this finding?

We were shocked. The first thought was that these must be gain-of-function mutations, unlike all the other EB mutations, which are loss-of-function mutations that can occur all over the places.

 

You very nicely demonstrate a model whereby Keratin 14 is an ubiquitination substrate of KLHL24, and that the truncated mutant is stabilized, thus leading to greater Keratin 14 degradation and the skin fragility phenotype. Can you walk us through how you teased apart this model? What do you consider the key piece of evidence that supports this model?

We used an unbiased “pull down + mass spectrometry” method to look for the binding proteins to the substrate binding domain of KLHL24 and Keratin 14 was the only one we found that specifically binds KLHL24 but not a carefully designed mutant that is predicted structurally to lose the substrate binding capacity. We immediately verified the binding and also showed that knocking down/overexpressing KLHL24 can increase/decrease Keratin 14 levels. A key piece of evidence is that transfection of KLHL24 in cell lines can boost Keratin 14 ubiquitination. Afterwards, we obtained two important pieces of in vivo evidence to show the anti-correlation of KLHL24 level and Keratin 14 level (in human skin samples and a knock-in mouse model), nicely confirming that Keratin 14 is a ubiquitination substrate of KLHL24.

 

You make a knock-in mouse, which recapitulates the decreased Keratin 14 levels similar to what is seen in patients, but not the skin fragility phenotype. Can you comment on why this might be so?

Many differences exist between human and mouse skin, the most obvious is the presence of fur in the mouse skin, which might afford better mechanic support of the epidermis than that in the human skin. In addition, there is actually a small but significant difference between the degrees of Keratin 14 decrease in patients and the mouse model (~70% decrease in patients vs. ~50% decrease in mice). Previously mouse models having ~50% decrease of Keratin 14 (the Krt14+/- mouse model) also did not show skin fragility. We don’t yet know the reason for the differential decrease of levels in human and mouse skin but are working on finding out the answers.

 

Do your findings have any potential implications for novel therapies for epidermolysis bullosa?

Absolutely, as I mentioned these are the first gain-of-function mutations found for EB, which should be easier to target therapeutically than loss-of-function mutations. Inhibiting KLHL24 in patients that we identified with these types of mutations should be able to effectively treat the conditions. We are now actively working on finding a specific KLHL24 inhibitor. In addition, because KLHL24 is a negative regulator of Keratin 14, other EB patients with partial loss-of-function mutations of Keratin 14 could also be helped by treatment with a KLHL24 inhibitor. In general, drug development targeting the ubiquitin-proteasome pathway has been given high hopes but it is not very obvious how to target the pathway specifically. Our studies provide a good example showing the importance of autoubiquitination of an E3 ligase, which might suggest previously over-looked strategies to target E3s.

 

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October issue cover: What’s going on here?

Posted by Brooke LaFlamme | Categories: , ,

Oct

Convergent cabbages by Keyong Chang

For all of October, we at Nature Genetics have been admiring the lovely cabbages on our cover. The image, created by photographer Keyong Chang, was contributed by the authors of the study on page 1218 of the issue.

But what is the story behind these pretty green cabbages?

Xiaowu Wang, corresponding author of the study, gave us a behind-the-scenes look at the process that led to the picture on our cover.

The image conveys the main idea of the study, namely that Brassica oleracea (cabbage, left) and Brassica rapa (Chinese cabbage, right) have taken similar evolutionary paths to arrive at their similar, but distinct, appearances. During domestication, farmers selected for cabbages of both species to have the large, leafy heads for which they are known. As shown in the study, the farmers were unknowingly selecting for orthologous genes in these two species. Read more

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Learning every way to break a gene

Posted by Brooke LaFlamme | Categories: ,

From Fig. 1 in Majithia et al. Nature Genetics 2016

From Fig. 1 in Majithia et al. Nature Genetics 2016

Finding the genetic cause of a disease—a mutation or genetic variant—is a lot like looking for a needle in a haystack. Except in the case of exome sequencing, it’s not always clear what a needle even looks like.

When a clinician finds a protein-altering variant in a gene known to cause disease, it could be the cause of the patient’s disease…or it could be nothing. This is the definition of a variant of uncertain significance (VUS). VUS’s often stay unknown unless someone puts in the time and resources to functionally characterize the variant. For obvious reasons, functional characterization of each individual VUS of every gene implicated in human disease is not practical.

One way to determine which variants cause disease and which don’t is to look at the DNA of many healthy individuals. If a healthy person carries a variant, it is unlikely to cause disease. The Exome Aggregation Consortium (ExAC) is the largest such effort—over 60,000 people have contributed their exome sequences to ExAC and thus provided a unique resource for clinicians to de-prioritize specific VUS’s as causes of disease.

But what if your variant is too rare or isn’t in ExAC?

Amit Majithia, David Altshuler and colleagues from the Broad Institute of Harvard and MIT developed a different strategy, published last week in Nature Genetics. The authors chose a gene, PPARG, that can cause Mendelian lipodystrophy when mutated. Some variants in this gene can also increase the risk of developing type 2 diabetes. PPARG also has many VUS’s in the population.

To find out which variants are likely to be pathogenic, the authors constructed a library of all 9,595 possible protein-altering variants of PPARG and tested them in pools using a functional assay in human macrophages. Cell pools that showed a positive result were sequenced and the numbers of each variant were counted, allowing the authors to assign a function score to each variant. These scores were then used, along with known benign and pathogenic variants from the prior literature, to train a machine-learning algorithm that could then classify each variant as pathogenic or not. The classifier found 6 new likely pathogenic variants, which were then validated through additional tests.

Summary of strategy used by Majithia et al.

Summary of strategy used by Majithia et al.

The strategy used by Majithia et al. could potentially be used by other researchers to study other proteins implicated in disease. Although it is somewhat of a brute-force approach to test every possible variant, the use of a pooled functional assay and computational classifier increases both the efficiency and accuracy of the result. We asked Dr. Majithia to tell us a little more about this study.

Author Q&A:

Why did you choose to focus your study on PPARG? 

In the diabetes community PPARG is a very important and storied gene. It has been linked to both common type 2 diabetes and rare familial forms. PPARG is also the target of multiple FDA approved drugs to treat diabetes. So on one hand, PPARG has been studied in humans and in the lab for two decades. On the other hand, we showed in 2014 (Majithia et al. PNAS) that even though PPARG had been sequenced in humans for so many years, we had only scratched the surface of the possible missense mutations that people in the general populations carry. Most of these mutations were benign, some strongly increased diabetes risk, and it took laboratory experiments with each and every mutation to sort them out. So PPARG was the perfect test case for our prospective experimental approach to test all possible missense mutations: it is relevant to common and rare genetic disease, has mutations of known function we could use for validation of our method, and has many unknown mutations, i.e. variants of uncertain significance that need to be functionally characterized.

Do you think that a similar strategy could be employed for other genes with many variants of unknown significance in the population? What would be the major challenges for applying this strategy to other genes?

Absolutely. A major purpose of this study was to demonstrate proof-of-concept that other investigators and clinicians could utilize for VUS in other genes/diseases. In principle there are three challenges in applying a prospective experimental approach to generate a “lookup table” for missense VUS: 1) making every possible missense mutation 2) building an assay with the scale and throughput to study every missense mutation and 3) connecting the lab experiments to what happens in people (i.e. phenotypes)

  1. The mutation synthesis technique we use, which was pioneered by Tarjei Mikkelsen, applies to any gene. Other groups like Jay Shendure’s in Seattle have independently developed methods to mutate genes at scale and now companies like TWIST Biosciences offer high quality mutation libraries for any gene. This is no longer a barrier to entry.
  2. Genes have myriad functions and so building an appropriately scaled, high throughput readout is a gene by gene process. No single assay can test the function of every gene, but there are partially generalizable strategies that can be used to study certain classes of genes. The strategy we used for PPARG, combining reporter gene expression and FACS, could be deployed for any gene that activates transcription. In fact we are taking this approach with another diabetes relevant transcription factor, HNF1A.
  3. Establishing clinical relevance of saturation mutagenesis data is a critical step. In our study we set a criterion for our assay, that in order for us to be able to discriminate variants of “unknown” significance, we should be able to accurately discriminate variants of “known” significance. For PPARG we benefitted from decades of research that had resulted in a series of missense variants with known function and disease effect. Many genes do not have such “allelic series” but with the increasingly widespread use exome/genome sequencing our knowledge of allelic series for genes is rapidly growing.

From your perspective, was the most surprising aspect of the study?

To independently prove the findings from our “lookup table” our collaborators in Cambridge took a series of VUS from patients referred to their clinic and tested them in single variant assays that are the standard for PPARG functional testing. In one case, our “lookup table” had a major discrepancy from the single variant assay. Careful follow-up work by our Cambridge colleagues resolved that the single variant assay that has been used for decades was actually incorrect and the “lookup table” made the correct call. We were surprised that, in this case, our new, high throughput assay had higher fidelity with human biology and it introduces a degree of nuance into what we consider “gold standard” when discrepancies inevitably arise.

How do you see your findings impacting on future research?

We hope this study will have its largest impact as a proof-of-concept for other genetic diseases and VUS interpretation. Our method is capable of improvement. For example, it does not assess missense effects on gene splicing, but others in the community are developing complementary methods that will overcome this (for example, see this paper from Gregory Findlay et al. published in Nature). We are particularly excited that our lookup table approach opens the door to systematically characterizing drug-by-genotype interactions that could be useful to guide treatment.

 

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Spreadsheets have misprints – it is known

Posted by Brooke LaFlamme | Categories: ,

by Myles Axton

Normally we do not re-examine supplementary information in this detail, but there is a common minor problem that systematically affects a small number of gene IDs within long lists of gene names copied into spreadsheets in the supplementary tables of many articles. We suggest checking for this problem before submitting tables to journals. It is easy to see the altered gene names by sorting the column in a separate version of the file and then searching for the misspelled name to correct it in the replacement version intended for publication.

Example of the Excel formatting issue

Example of the Excel formatting issue

The authors of this paper claim that gene names in a large proportion of papers reporting gene expression data have this problem. Here we list the supplementary tables they identified in the journal prior to 2015 and from the first nine months of 2016 that we found to contain one or more misprints. We think that these mistakes do not prevent reuse of the datasets provided and as stated in the accompanying editorial Legible ledgers we do not propose to publish formal Corrigenda for the supplementary tables of these articles.

 

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August issue cover: What’s going on here?

Posted by Brooke LaFlamme | Categories: ,

Rhinopithecus bieti

Rhinopithecus bieti

Yong-cheng Long

This month’s cover image is inspired by the paper on page 947 reporting the reference genome sequence of the black snub-nosed monkey, the second snub-nosed monkey genome paper published in Nature Genetics. The golden snub-nosed monkey genome was published in 2014.

In their paper, Li Yu and colleagues present the de novo genome sequence assembly of Rhinopithecus bieti as well as whole genome resequencing of all four other snub-nosed monkey species. All five species are among the world’s most endangered primate species. Three species, R. bieti, R. roxellana and R. strykeri, live at very high altitudes—above 3,000 meters. R. bieti lives exclusively on the Yunnan and Tibetan plateaus. The other two species, R. brelichi and R. avunculus, inhabit lowland regions. The authors compared the genome sequences between these species to identify genomic regions showing evidence of positive selection that could be related to living at high altitudes.

The photograph on the cover image was taken by one of the study’s co-authors, Dr. Yong-Cheng Long, who was profiled by the Nature Conservancy for his work on conservation of R. bieti (also called the Yunnan golden monkey by the locals). We asked Dr. Long to tell us a little about the monkey shown in the picture.

“The monkey is [a] male, whose name is ‘Big Guy’, and he is feeding on some leaves,” he said by email. “The Big Guy used to have 4 wives (about 6 years ago) and now has only 2, as he is getting old and is not strong enough to hold all of them because the females are more likely to find a strong shoulder to cry on.”

Dr. Long said there are 57 R. bieti individuals in the habituated “Yunnan snuby” group, which is open to the public. Because many of the individuals in the area are fully habituated to human presence, it is not difficult to get photographs of them. The group is only a small portion of the largest natural monkey troop (approximately 1,000 in total) in the world. Dr. Long emphasized the impact that illegal poaching has had on the monkeys. “This species has been endangered by human’s killing, and the monkeys can certainly survive once the killing is stopped.” In China, 2016 is the Year of the Monkey, and it has turned out to also be a lucky year for these particular monkeys. “We found the monkey group has boomed,” said Dr. Long. “12 of the 57 are the infants born this year.”

monkey

Nature Genetics office mascot

The lead author of the study, Dr. Yu, became interested in studying these species because of his focus on conservation genetics of endangered mammals distributed in Yunnan Province, China. This is one of the core regions of biodiversity in the world. “The most notable among the endangered mammals distributed in Yunnan Province is R. bieti, which is found exclusively on Yunnan and Tibetan Plateau”, said Dr. Yu by email. “It is unique in that it is the only primate having a red mouth like most humans, which [is why it’s called] one of the most beautiful animals.” Dr. Yu also noted that it is the highest altitude-dwelling nonhuman primate. It can survive in very cold and hypoxic environments that other primates cannot tolerate. “So, I was deeply attracted by this mysterious and interesting species, and was eager to come to understand it.”

 

IMG_1863We at Nature Genetics are also celebrating the Chinese Year of the Monkey. Our office mascot is this golden snub-nosed monkey (right), which was produced for marketing purposes in China (I snagged one during a recent visit to the Shanghai office). Scanning a barcode on the monkey’s rear end (left) will take you to the publication of the R. roxellana (golden snub-nosed monkey) genome paper.

 

 

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Joint calling of the ExAC publications

Posted by Orli Bahcall | Categories: , , ,

ExAC publications in Nature

We report this week in Nature and Nature Genetics the first publications from the Exome Aggregation Consortium (ExAC), a project that has generated the largest catalogue to date of variation in the protein-coding regions of the genome (known collectively as the exome), aggregating sequence data from over 60,000 individuals from across 21 research studies. Most importantly, they have provided a publicly accessible database (http://exac.broadinstitute.org), which has already become a critical resource for research and clinical studies. While an estimated over 1 million individuals have been exome or whole genome sequenced, only a small fraction of this data has been made publicly available, as there are many challenges to sharing and providing open access to these datasets. We applaud the authors for recognizing this need and meeting these challenges.

This work comes 15 years after we published the Human Genome Project, and follows in a series of community resources to catalogue variation in human genomes within and across populations. We continue to support these efforts, recognizing the necessity of developing these resources to further studies to understand the information encoded in our genome, genetic variation and genetic basis of disease.

Mapping ExAC publications

Very rare genetic variation: a first look

The scale of this sequencing dataset in ExAC has provided some of our first glimpses into very rare genetic variation across populations, with several important early insights. Firstly, the authors identify more than 7.4 million high-confidence genetic variants, on average one every 8 bases, the majority of them entirely novel (not present in any existing database) and extremely rare (more than half of the variants are seen only once across all 60,706 samples). Second, they are able to document recurrent rare mutations emerging independently, providing an estimate of the frequency of recurrence, never observed systematically before due to the need for such large sample sizes. Third, they are able to examine the level of selective constraint against protein-truncating variation, identifying 3,230 genes that appear highly loss-of-function-intolerant. Reassuringly, this includes most known human haploinsufficient disease genes, however 72% do not yet have an established human disease phenotype. While some of these genes may be associated with weaker phenotypes or embryonic lethality, this points to how much more we have yet to understand about the phenotypic consequences of loss of function in human genes.

Copy number variation in ExAC

In a companion paper in Nature Genetics, Douglas Rudefer, Shaun Purcell and colleagues examine rare copy number variation (CNV) with the ExAC dataset, specifically the rates and properties of genic CNVs with <0.5% frequency. They use their previous method XHMM to characterize CNV calls from this exome sequencing dataset. They find that ~70% of individuals carry at least one rare genic CNV, with an average of 0.81 deleted and 1.75 duplicated genes. The authors also estimate relative intolerance to CNVs for each gene. This CNV dataset is incorporated into ExAC and will be useful for continuing population and disease association studies, together with other measures of genic intolerance, and the authors provide an example of this in analysis of a schizophrenia case-control study.

Clinical genetics: classifying pathogenic variation

The current work also brings an important message for clinical genetics in the need for reexamining the literature on classifying pathogenic variation for rare disorders. The average ExAC participant harbors ~54 variants that have previously been classified as causal for a disease, and considering the ascertainment of the study it is likely that most of this may be due to misclassified variants.

Using ExAC as a reference panel for classifying disease relevant variation, Lek et al. review the evidence for pathogenicity of 192 previously reported pathogenic variants for rare Mendelian disorders. Only 9 of these variants had sufficient support for disease association, with a high proportion of these variants present at an implausibly high frequency in the ExAC dataset. This suggests that many of these were false positive associations and incorrectly classified as pathogenic, the implications of which are not merely academic, as these findings are often used in clinical diagnoses and treatment.

In two additional companion publications, the authors take this a step further and demonstrate what is needed to move towards resolution of the nature of these prior associations, by bringing together large case series combined with ExAC. Walsh et al. (Genetics in Medicine, 10.1038/GIM.2016.90 published online August 17, 2016) systematically reexamine evidence for genes implicated in cardiomyopathy, one of the most common and severe rare disorders, and find many well known purported cardiomyopathy genes do not show support for pathogenicity, including some that are included in routine clinical genetic testing. Similarly, Minikel et al. collect 16,025 prion disease cases, the largest case series ever available for prion disease, for which ~10-15% of cases are estimated to be caused by mutations in the PRNP gene. They find a number of variants in PRNP thought to be pathogenic and with high penetrance appear to be likely benign (Minikel et al. Science Translational Medicine 10.1126/scitranslmed.aad5169). This led to a corrected patient diagnosis soon after this report, as Robert Green explained in his Perspective accompanying this publication (Lebo et al. Science Translational Medicine 10.1126/scitranslmed.aad9460).

These findings highlight the necessity to carefully evaluate the literature for rare genetic disorders. This also reinforces the value of large reference panels such as ExAC for filtering variants seen in patient exomes, a practice most of the genomics community has adopted in establishing standards for assessing sequence variants in human disease (MacArthur et al. Nature 508, 469–476 (2014), 10.1038/nature13127). The ExAC project continues to expand in size, hoping to increase to more than 120,000 exome sequences over this next year, as well as 20,000 whole genome sequences, bringing additional sample size, diversity and exploration of non-coding regions that will aid these efforts.

ClinVar and contributing to variant interpretation databases

This project, which relied on the willingness of many large research consortia to provide their raw data, demonstrates the extreme value of promoting the sharing, aggregation and harmonization of genomic data. This is true also for patient genetic variants, as there is a need for databases that provide greater confidence in variant interpretation. NCBI’s ClinVar database, which accepts contributions of clinically annotated genetic variation from clinical labs, clinicians and researchers, has become a key resource for clinical variant interpretation.

Improvements to the landscape of clinical genetics will require continued investment in such variant databases, continued expansion of human genetic reference panels, as well as efforts to link these to phenotype data. Recontacting to obtain phenotype data will be trialed on a subset of the ExAC dataset where consents allow, while new initiatives such as the UK 100,000 Genomes Project and the US Precision Medicine Initiative will also provide linked genome and phenotype information. Finally, enabling the ethical sharing of linked genetic and clinical data without violating participant privacy will require fundamental innovation in regulation and ethics policy, work that has been started by bodies such as the NIH and the Global Alliance for Genomics and Health, but around which considerable uncertainty remains.

Farm to Genomes: African Rice

Posted by Catherine Potenski | Categories: ,

Meyer at al., Nature Genetics, 2016

Meyer at al., Nature Genetics, 2016

Rice is one of the most important crops on the planet, responsible for feeding billions of people. Given this global significance, studying rice in different geographies can be useful and aid in harnessing genetic diversity underlying particular traits and adaptations favorable to different environments. African rice (Oryza glaberrima Steud.) is mainly grown in sub-Saharan Africa and known for its stress tolerance. In a new article this week in Nature Genetics, Michael Purugganan and colleagues report the whole genome re-sequencing of 93 African rice landraces from various regions of Western coastal and sub-Saharan Africa. They create a genome-wide SNP map and through comparative genomic analysis study the domestication and population history of African rice. They use their map to perform GWAS for salt tolerance and find 11 significantly associated regions, highlighting the value of this unique genetic resource.

Meyer et al., Nature Genetics, 2016

Meyer et al., Nature Genetics, 2016

By studying various regions with distinct environments, the authors were able to get clues about adaptation and geographic spread of the populations. They focused on coastal Senegal and inland Togo, which have higher and lower levels of soil salinity, respectively, and interviewed farmers in the region to understand the agricultural practices they employ in each region. The knowledge of the farmers helped to inform the genetic analysis and contributed to the model of African rice domestication and dispersal.

You can watch some of the interviews with the farmers here:

African rice farmers- interviews

Additionally, we spoke with authors Michael Purugganan and Rachel Meyer to get some background on this research.

Why do you think that rice is understudied in Africa compared to other places?

MP: I think it’s because it is not widely grown, unlike its Asian counterpart which has pretty much taken over the world.  But there definitely is more interest in African rice as breeders are trying to figure out how to increase food production in Africa, as well as to try to see what genes in African rice can be used to improve Asian rice.

RM: There is a lot of great research on improving Asian rice for African farmers that is being done by brilliant AfricaRice scientists, and they are working hard on the social science side too. But there are so many challenges that Africa disproportionately faces – particularly climate variation – that demands ramping up rice research. There is insufficient support for programs that integrate crop experiments and trials into the different farmlands. A better connection between scientists and small-scale farmers would really help farmers adopt new varieties too- because there is sometimes resistance to trying new ones.

How did you choose which samples to include in your analysis?

RM: Recognizing that a lot of NGO work encouraging farmers to grow Asian rice ramped up in the 80’s and 90’s, we took advantage of the germplasm largely donated in the 70’s to the West Africa Rice Development Association, which were duplicated and available through IRRI (International Rice Research Institute). We chose accessions with the most metadata available, preferring ones with georeferenced location and a cultivar name. It wasn’t until later that we realized water tables far inland were high in salinity, so we just tried to make sure we had a fair number of samples within 250km of the coast, or along rivers connecting to the ocean.

Were you surprised by any of your findings?

MP: There definitely were a few surprises in the data, but the big revelation for me was the long time for the population bottleneck that led to domestication.  We found from the genomic data that it may have taken more than 10,000 years of steady population decline before full-blown domesticated African rice shows up in the archaeological record.  This suggests the possibility that humans were already cultivating or managing its ancestor for thousands of years, and I think if this pattern holds for other domesticated crop species it will change our thinking on how domestication has taken place.

RM: I was surprised we got nice GWAS results with so few samples, and even more surprised that we saw several of those exhibiting signatures of geographic selection. We were lucky to find a broad distribution of traits in the landraces we chose to sequence, for we had made the DNA libraries ahead of the phenotyping experiments.

What was it like to meet and talk with the farmers?

RM: It was one of the highlights of my life to meet the farmers! I’m grateful to have gotten a glimpse of their heritage, their pride, and their struggles. We were all so impressed with the generosity of women, in particular, to help each other. We were also shocked by how many farms are run by the elderly; their children don’t see farming as profitable and many have left. For the three of us in the field, it made us think hard about how we can give back to the communities that gave us their time. I hope that crop science, publicity (like this blog) and policy changes can raise the profile of the small-scale farmer.

In each interview, the farmers also had a chance to interview us, and that part was especially interesting. Several asked really good questions about African and Asian rice domestication. You could see the cultural value of the basic science.

You chose to focus on salinity tolerance as a trait particularly relevant to farming in Africa.  In what ways do you see your results being used for crop improvement?

RM: One of the authors, from AfricaRice, Dr. Kofi Bimpong, had actually been working on salt tolerance separately as well, and has two graduate student collecting African rice landraces in Casamance. If from this paper we can consider that domestication possibly occurred in the Inner Niger Delta region and also in the West, then these collecting efforts are all the more important because they are from a center of origin, promising more genetic variation than people would have ever estimated. If you look through the available germplasm there is so little that has been collected or studied from Casamance. It’s tricky collecting there, for there is social unrest, and landmines. Hats off to the young graduate students, Mamadou Sock and Bathe Diop, doing that fieldwork; I’m sure there is a lot of discovery to be made with those collections, and more promising salt tolerant landraces to integrate into breeding programs.

In addition, our results suggesting many of the salt tolerance genes are shared in both rice species make them more valuable to explore in other crops.  Shared adaptive mechanisms are especially fascinating to evolutionary biologists and are powerful assets of the breeder’s toolbox.

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July issue cover: What’s going on here?

Posted by Brooke LaFlamme | Categories:

JulyThis month’s cover features the inspiring block-like karst mountains of the Li River between Guilin and Yangshuo in Guangxi province. The image was inspired by a study in this month’s issue reporting deep sequencing of the MHC region in individuals of Han Chinese ancestry. The study represents an important resource for the study of immune-related disorders in Asian populations. It also identifies loci associated with risk of psoriasis, thus demonstrating the power of this resource.

In addition to simply being a beautiful image evocative of the mountains in Guangxi province, the image also brings to mind the peaks that might be observed in many types of genomic data, such as Sanger sequencing reads, ChIP-seq peaks, etc.

Our own chief editor, Myles Axton, did first-hand research leading to the selection of this cover image. As he found, the Yulong River in Yangshuo is less muddy than the Li River and better for swimming and sightseeing from bamboo rafts (arrow indicates NG editor in the field).

Yulong River

Yulong River

Myles Axton

 

Myles holding a 20 yuan note with drawing of karst mountains.

Myles holding a 20 yuan note with drawing of karst mountains.

Myles Axton

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June issue cover: What’s going on here?

Posted by Brooke LaFlamme | Categories:

Carrot canang sari by Rachel Meyer

Carrot canang sari by Rachel Meyer

As June comes to a close, it’s time to look back at our June issue and ask “what’s going on here?” with the cover image. As you may have guessed, the image is related to the publication of the carrot genome sequence in this month’s issue.

The cover image was provided by Rachel Meyer, a scientist who was not a co-author of the genome paper. Dr. Meyer was previously a postdoctoral researcher with Michael Purugganan at NYU and is an AAAS Science and Technology Policy fellow. She is also a co-founder of Shoots & Roots in New York.

Dr. Meyer gave us the following information about the carrot canang sari on the June cover:

Celebrating the recent availability of rainbow carrots year-round in Washington DC, I cut them in various ways and laid them out in a public dirt plot between the sidewalk and the street that was still bare because Spring had barely started and planting was far from beginning. The cold kept the carrots nicely preserved for three days. The installation took about 6 hours, and the design itself was lifted from a Persian carpet, sharing an origin with some of the earliest domesticated carrots. I had no intention to leave the installation there but people in the busy U-street/Shaw district, coming home late at night from the bars, would stop and photograph it, and even some of the suits interrupted their morning power walks to work to investigate it. After a few days, to my surprise it was not rats, but a middle-aged man who had decimated the carrots for a meal.

Shelby Ellison, an author of the carrot genome article this cover references, did this research as part of her NSF Plant Genome Postdoctoral Fellowship. We were in the same class of Fellows together and became friends because we would look for cool restaurants around DC together during our brief visits to NSF for annual Plant Genome meetings. I’m grateful to be able to explore the subject of her science through installation.

For more about the carrot genome paper, see our previous blog post, featuring Q&A with the corresponding author.

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Cancer clones- mixing and spreading

Posted by Catherine Potenski | Categories: ,

Shah 1

McPherson et al., Nature Genetics 2016

The trajectory of tumor cells during metastasis can be influenced by many factors, including the physical environment and the genetic makeup of metastatic clones. In high-grade serous ovarian cancer, there are limited barriers in the intraperitoneal space, allowing for extensive spreading and mixing of tumor cells. A recent article published in Nature Genetics explores these different patterns of clonal evolution in metastatic ovarian cancer using a combination of bulk and single cell sequencing.

The authors characterized the mutation landscapes of different metastatic tumors and find both monophyletic and polyphyletic clones. While in most patients there was unidirectional seeding from the original ovarian tumor, two patients exhibited polyclonal spread and reseeding. Therefore, high-grade serous ovarian cancer cells can migrate through and establish metastasis within the intraperitoneal space via different evolutionary routes.

McPherson et al., Nature Genetics 2016

McPherson et al., Nature Genetics 2016

We spoke to lead author, Sohrab Shah, to get some background on this research.

What features of this particular cancer made you want to study its metastasis? Were you surprised by your findings?

High grade serous ovarian cancers are often widespread through the peritoneal cavity at diagnosis.  We wanted to ask what are the characteristics of cells that spread and what is the distribution of these cells throughout the abdominal lesions.  The focus was to study the disease state prior to any treatment to characterize the diversity and take in inventory of the ‘substrate’ of clones upon which treatment selective pressures may be acting.  Many patients experience relapse after initial response to treatment.  Mapping which clones lead to relapse remains a key question in the field.  This was borne out in one patient in our study where specific clones that led to relapses were already present at diagnosis but only represented a minority of branches in the clonal phylogeny.

It is important to note that the mode of spread in this disease differs from most solid cancers, where spread is achieved through the bloodstream or lymphatics.  Ovarian cancer represents a unique opportunity to study disease spread through a relatively physically unencumbered anatomic space.  One might expect that in such an environment the potential for clonal intermixing is high.   This might lead to many clones co-existing at many sites.  But the majority of intraperitoneal samples were clonally pure, suggesting unidirectional spreading from ovary sites with diverse clonal repertoires, and a lack of clonal intermixing.

You provide evidence that the microenvironment influences the metastatic success of tumors. What does this say about in vitro cancer models that don’t account for tissue context?

One of the intriguing findings suggested that specific clones were present in specific sites.  This may indicate that particular microenvironments are differently suited to particular clones. Another surprising finding was that every patient harbored at least one lesion that was very diverse in its clonal make-up (typically within primary ovary sites).  This leads to the natural question of whether properties of specific microenvironments in some way promote or ‘tolerate’ clonal diversity.  If this were the case, then both in vitro and in vivo model systems such as cell lines, organoids and mouse xenografts may not adequately represent the natural disease state we find in patients prior to treatment.

How did you choose your sampling strategy?

The study results are naturally biased by the sampling strategy.  The study design was subject to what material could be obtained during the provision of care.  In our setup, we consented patients for collection and study of all material removed at primary debulking surgery.  Wherever possible tissue was cryopreserved, but inevitably many deposits were preserved in formalin.   Our strategy led to acquisition of a median n=10 samples per patient.  The nature of the samples and their locations are presented in Figure 4 and are also available in interactive web-form at:

http://compbio.bccrc.ca/research/tumour-evolution/

Users can click on the links for each patient and explore the clonal maps.

You utilize both bulk and single cell sequencing as complementary approaches to elucidating tumor evolution. Can you comment on the trade offs between cost and throughput and how you chose your sample sizes?

The field is entering an interesting time.   There are several limitations to both bulk and single cell sequencing strategies to define the clonal constituents of a tumor sample.  Most single cell techniques suffer from vast under-sampling of the clonal repertoire since they are limited in throughput and may only practically yield data from 100s of cells.  Furthermore, single cell techniques are prone to two key experimental sources of noise: missing data and allele-dropout.  We used targeted, multiplexed single cell sequencing as a form of validation from inferences made from the bulk sampling including validating co-occurrence of point mutations and structural variations in the same cells.  Hypotheses were generated from multi-site bulk analysis and were then tested using orthogonal single cell approaches.   Accordingly, the sample sizes in single cell were chosen to identify clones that were detected in bulk samples – in the range of 5% prevalence.  Notably, the noise properties of targeted multiplexed single cell data required some careful statistical treatment, the results of which were published as a standalone contribution in Nature Methods simultaneously with this publication.  As the field moves forward, it may become practical to sequence the whole genomes of 1000s of cells per sample. I look forward to the day when a single experimental design would be sufficient to dissect the important clones present in a cancer.  This would enable studying evolutionary properties at scale, leveraging richly defined principles and statistical models from the field of population genetics.

You find that there are differences in the potential for migration and metastasis across the tumors from your patients. What clinical implications might this have?

Our study is underpowered to provide a clear answer on this.  Our results hint anecdotally that cases with strong patterns of unidirectional spread fared poorly in their treatment trajectories.  Whether cancers harboring clones with strong potential to invade new micro-environments and dominate their local landscapes indicates potential to evade chemotherapy remains an important question to consider.  As we take this study forward in model systems derived from spatially distinct sites, reproducible treatment selection experiments can be carried out to robustly address this question.

 

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