“Polygenic Adaptation”
“CRISPR-Cas9 is a method for quickly and accurately editing the genome of virtually any living thing… “
Article by Adam Bluestein / Illustration by Jamie Cullen
This is free art suggesting “mutation”.
Tumor cell populations are full of mutations, mutations that provide genetic diversity that can allow some to survive chemotherapeutic agents. A series of single nucleotide changes may lead to tumor growth but most dramatic changes in genome composition, and increases in genetic variation, occur after tumor cells begin replicating without regard for their chromosome number and composition. This chromosomal instability creates variation in tumors, allowing for the most aggressive subpopulations to proliferate and generating a diverse pool of genotypes—all of which need to be wiped out if the cancer is to be eradicated. While it has been possible to identify the effects of chromosomal instability (wide-spread aneuploidy) for a long time, studying the mechanisms directly has been difficult given the limited amount of genome sampling and karyotyping (chromosome imaging) that was possible compared to the amount of change in tumors.
A recent paper by Bolhaqueiro et al. describes a technological advance for studying chromosome instability that involves genetically engineering cancer cells to express fluorescent proteins that label chromosomes and culturing those cells into organoids, 3D clusters that more accurately mimic how cell grow in vivo than cells in a flat culture. This approach allows rapid single cell karyotyping and imaging of chromosome behavior during cell division. Paired with single cell sequencing, the direct study of the chromosome instability in organoids may have broad applicability. Cancer cells all start with a fairly similar toolkit and undergo a finite number of replications, so with sufficient study more of their vulnerabilities become apparent and possible to target.
Below is the introduction and link to a longer summary of the Bolhaqueiro article; the original article is interesting but longer, geared towards an expert audience, and behind a paywall.
The genomes of cancer cells are littered with mutations (errors in individual nucleotides), some of which might contribute to growth of the cancer by activating tumour-promoting genes called oncogenes, or by switching off genes belonging to a class known as tumour suppressors, which fight cancer. Yet, arguably even more important are the genomic abnormalities that occur in tumour cells on a much larger scale. For example, such a cell might contain anomalous numbers of entire chromosomes (a situation termed aneuploidy). As the tumour evolves, chromosomal abnormalities can vary between neighbouring cancer cells. This suggests that chromosomal changes can occur by repeated chromosomal ‘shuffling’ during each cell division, resulting in a high rate of genomic change, termed chromosomal instability.
This an interesting article about the some of efforts in take advantage of Stem Cell therapies happening in China, and some of actions being taken to slow these efforts down to a responsible rate.
David Cyranoski, Nature
An international stem-cell body says the country’s proposed law could put patients at risk.
An international group of stem-cell researchers is urging China to cancel draft regulations that would permit some hospitals to sell therapies developed from patients’ own cells, without approval from the nation’s drug regulator.
The International Society for Stem Cell Research (ISSCR) sent a statement outlining its concerns to Jiao Hong, director of China’s National Medical Products Administration in Beijing, on 20 May. The society, which is based in Skokie, Illinois, represents more than 4,000 scientists, clinicians and ethicists around the world.
“We are deeply concerned that China’s newly proposed regulations will provide incentives for hospitals to market unsafe and ineffective interventions directly to consumers. This has the potential to harm the people of China, undermine public health and discredit the international standing of the Chinese regenerative medicine community,” warns the statement, which was signed by society president Doug Melton.
A paper in PLoS Genetics has identified a selection of genetic variants that clearly distinguish horses breeds that pace (running with the two legs on the same side move together) and those that trot (opposite front and back move together). Thought no physiological role has been demonstrated for the these mutations, yet, they appear to be good candidates for connecting single nucleotide changes to discrete and clearly recognizable inherited differences in behavior—and maybe a step towards understanding instincts.
Certain horse breeds have been developed over generations specifically for the ability to perform alternative patterns of movement, or gaits. Current understanding of the genetic basis for these gaits is limited to one known mutation apparently necessary, but not sufficient, for explaining variability in “gaitedness.” The Standardbred breed includes two distinct groups, trotters, which exhibit a two-beat gait in which the opposite forelimb and hind limb move together, and pacers, which exhibit an alternative two-beat gait where the legs on the same side of the body move together. Our long-term objective is to identify variants underlying the ability of certain Standardbreds to pace. In this study, we were able to identify several regions of the genome highly associated with pacing and, within these regions, a number of specific highly associated variants. Although the biological function of these variants has yet to be determined, we developed a model based on seven variants that was > 99% accurate in predicting whether an individual was a pacer or a trotter in two independent populations. This predictive model can be used by horse owners to make breeding and training decisions related to this economically important trait, and by scientists interested in understanding the biology of coordinated gait development.
Want remarkably clear insights into genetics and public health with a bare minimum of reading? Well, some corners of Twitter have recently become an incredible resource if you’re interested in learning something about predictive statistics, epidemiology, genomics, and population genetics. There are no better examples of this than the tweetorials that Dr. Cecile Janssen posts. Dr. Janssen is a professor of translational epidemiology in the department of Epidemiology of the Rollins School of Public Health, Emory University, and her website, like her posts, contains insightful guides for thinking critically about DNA sequence data, heritability and health.
If you would like some key insights into predicting complex traits from DNA in a handful of tweets, follow this link: Why it is so hard to predict complex diseases and traits from DNA?
For a slightly longer read, here’s her article from WIRED on how DNA is best applied: DNA tells great stories -- about the past, not future
And a more advanced read, still aimed at a fairly general audience: Designing babies through gene editing: science or science fiction?
Abstract
While the genome sequencing revolution has led to the sequencing and assembly of many thousands of new genomes, genome annotation still uses very nearly the same technology that we have used for the past two decades. The sheer number of genomes necessitates the use of fully automated procedures for annotation, but errors in annotation are just as prevalent as they were in the past, if not more so. How are we to solve this growing problem?
A powerful new resource, one that is actually a new kind of resource, has come online and, hopefully, will help accelerate advances in genomics and the fight against many types of disease. The scale of genome data is so large that computational tools are required for every major step of acquiring, organizing, and analyzing genomes. Generating useful models from large genomic datasets, the kind you generate when studying human disease, is often difficult and time consuming and many aspects of this are now being automated using various types of machine learning approaches. Machine learning in this context can be roughly summarized as using computers to generate and evaluate huge numbers of statistical models in order to clarify relationships in datasets. To do this, the machine learning program needs to train on useful datasets. So for many cutting edge applications, the program doesn’t just need to be written but also trained—and this second step can require large amounts of time and computational resources, making the transmission and broader application of these programs less likely, until now. The Kipoi repository is the first open resource for machine learning methods in genomics, making cutting edge approaches available to clinicians and smaller labs. This resource is sure to speed the application and innovation in machine learning based genomics approaches, and hopefully we will all benefit from this new site for the free exchange of ideas.
For more information, here’s a nice summary from Technology Networks.
Here is the introduction from the original article, published in Nature Biotechnology.
Advances in machine learning, coupled with rapidly growing genome sequencing and molecular profiling datasets, are catalyzing progress in genomics1. In particular, predictive machine learning models, which are mathematical functions trained to map input data to output values, have found widespread usage. Prominent examples include calling variants from whole-genome sequencing data2,3, estimating CRISPR guide activity4,5 and predicting molecular phenotypes, including transcription factor binding, chromatin accessibility and splicing efficiency, from DNA sequence1,6,7,8,9,10,11. Once trained, these models can be probed in silico to infer quantitative relationships between diverse genomic data modalities, enabling several key applications such as the interpretation of functional genetic variants and rational design of synthetic genes.
However, despite the pivotal importance of predictive models in genomics, it is surprisingly difficult to share and exchange models effectively. In particular, there is no established standard for depositing and sharing trained models. This lack is in stark contrast to bioinformatics software and workflows, which are commonly shared through general-purpose software platforms such as the highly successful Bioconductor project12. Similarly, there exist platforms to share genomic raw data, including Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/), ArrayExpress (https://www.ebi.ac.uk/arrayexpress) and the European Nucleotide Archive (https://www.ebi.ac.uk/ena). In contrast, trained genomics models are made available via scattered channels, including code repositories, supplementary material of articles and author-maintained web pages. The lack of a standardized framework for sharing trained models in genomics hampers not only the effective use of these models—and in particular their application to new data—but also the use of existing models as building blocks to solve more complex tasks.
A subsidiary of the Chinese sequencing giant BGI is filing a patent infringement suit agains the US sequencing giant. The BGI subsidiary, Complete Genomics, filed its complaint in the the District Court of Deleware, claiming infringement on a patent for “methods and compositions for efficient base calling in sequencing reactions,” which is all pretty central to high throughput sequencing. This appears to be a response to Illumina filing a complaint earlier this month against BGI Europe and Latvia MGI Tech, another BGI subsidiaries, earlier this year. These are probably just the early stages of what is likely to be a long series of antagonistic maneuvers between giants. It is unlikely that this Coke-versus-Pepsi style competition will do much to reduce the dominance of these groups, but one can hope that as this battle plays out some of the smaller sequencing players will grow and insert a little more competition into the market.
All the links in this post are from Genome-Web, another excellent genome news source.
Two exciting and complementary machine learning methods for assigning cell identity based on single-cell sequencing data were published in a paper from Johns Hopkins. The first program, scCoGAPS, defines latent spaces from a single-cell RNA-sequencing dataset to categorize cells and the second program, projectR, evaluates latent spaces in independent target datasets using transfer learning. These two methods are interesting advances towards a goal that is likely still far off—understanding exactly what makes each cell what it is. For an excellent summary read the press release, Finding A Cell’s True Identity.
The original article is a more complicated reading but interesting through out.
Analysis of gene expression in single cells allows for decomposition of cellular states as low-dimensional latent spaces. However, the interpretation and validation of these spaces remains a challenge. Here, we present scCoGAPS, which defines latent spaces from a source single-cell RNA-sequencing (scRNA-seq) dataset, and projectR, which evaluates these latent spaces in independent target datasets via transfer learning. Application of developing mouse retina to scRNA-Seq reveals intrinsic relationships across biological contexts and assays while avoiding batch effects and other technical features. We compare the dimensions learned in this source dataset to adult mouse retina, a time-course of human retinal development, select scRNA-seq datasets from developing brain, chromatin accessibility data, and a murine-cell type atlas to identify shared biological features. These tools lay the groundwork for exploratory analysis of scRNA-seq data via latent space representations, enabling a shift in how we compare and identify cells beyond reliance on marker genes or ensemble molecular identity.
There is no one human genome. Each person starts life with two non-identical copies of a genome, and variations both small and large begin to accumulate each time those copies are copied. And then there are the differences between individuals. If we think of the genome as a single list of bases at specific positions then point mutations—substitutions, small inserts and deletions—are easy enough to map to those position, however major structural variants—inversions, translocations and repetitive sequences—complicate how we map these mutations. Reference genomes, a consensus representation of deeply sequenced human genomes have traditionally been the basis of how we map nucleotides and variants to positions on chromosomes but long read technologies are making it increasingly apparent that structural variants are quite common and new methods for representing the human genome.
The first of the following articles lays out why a more advanced model for capturing the variation in the human genome is needed. The article after that describes how multiple genomes and their structural variation can be summarized using graphs, a computational improvement on the current linear reference genomes. The last article discusses the some of the single molecule sequencing technology bringing this issue to the fore. There are many other articles that deal with this topic, but these are a good start.
A recent study on human structural variation indicates insufficiencies and errors in the human reference genome, GRCh38, and argues for the construction of a human pan-genome.
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Here’s an article describing how structural variants can be captured in a graph.
The human reference genome serves as the foundation for genomics by providing a scaffold for alignment of sequencing reads, but currently only reflects a single consensus haplotype, thus impairing analysis accuracy. Here we present a graph reference genome implementation that enables read alignment across 2,800 diploid genomes encompassing 12.6 million SNPs and 4.0 million insertions and deletions (indels). The pipeline processes one whole-genome sequencing sample in 6.5 h using a system with 36 CPU cores. We show that using a graph genome reference improves read mapping sensitivity and produces a 0.5% increase in variant calling recall, with unaffected specificity. Structural variations incorporated into a graph genome can be genotyped accurately under a unified framework. Finally, we show that iterative augmentation of graph genomes yields incremental gains in variant calling accuracy. Our implementation is an important advance toward fulfilling the promise of graph genomes to radically enhance the scalability and accuracy of genomic analyses.
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Here’s an article describing how next-next generation sequencing is illuminating the diversity of structural variants across human populations.
Advances in genome assembly and phasing provide an opportunity to investigate the diploid architecture of the human genome and reveal the full range of structural variation across population groups. Here we report the de novo assembly and haplotype phasing of the Korean individual AK1 (ref. 1) using single-molecule real-time sequencing2, next-generation mapping3, microfluidics-based linked reads4, and bacterial artificial chromosome (BAC) sequencing approaches. Single-molecule sequencing coupled with next-generation mapping generated a highly contiguous assembly, with a contig N50 size of 17.9 Mb and a scaffold N50 size of 44.8 Mb, resolving 8 chromosomal arms into single scaffolds. The de novoassembly, along with local assemblies and spanning long reads, closes 105 and extends into 72 out of 190 euchromatic gaps in the reference genome, adding 1.03 Mb of previously intractable sequence. High concordance between the assembly and paired-end sequences from 62,758 BAC clones provides strong support for the robustness of the assembly. We identify 18,210 structural variants by direct comparison of the assembly with the human reference, identifying thousands of breakpoints that, to our knowledge, have not been reported before. Many of the insertions are reflected in the transcriptome and are shared across the Asian population. We performed haplotype phasing of the assembly with short reads, long reads and linked reads from whole-genome sequencing and with short reads from 31,719 BAC clones, thereby achieving phased blocks with an N50 size of 11.6 Mb. Haplotigs assembled from single-molecule real-time reads assigned to haplotypes on phased blocks covered 89% of genes. The haplotigs accurately characterized the hypervariable major histocompatability complex region as well as demonstrating allele configuration in clinically relevant genes such as CYP2D6. This work presents the most contiguous diploid human genome assembly so far, with extensive investigation of unreported and Asian-specific structural variants, and high-quality haplotyping of clinically relevant alleles for precision medicine.
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Researchers at Mount Sinai have developed a novel approach to cancer immunotherapy, injecting immune stimulants directly into a tumor to teach the immune system to destroy it and other tumor cells throughout the body.
The “in situ vaccination” worked so well in patients with advanced-stage lymphoma that it is also undergoing trials in breast and head and neck cancer patients, according to a study published in Nature Medicine in April.
The treatment consists of administering a series of immune stimulants directly into one tumor site. The first stimulant recruits important immune cells called dendritic cells that act like generals of the immune army. The second stimulant activates the dendritic cells, which then instruct T cells, the immune system’s soldiers, to kill cancer cells and spare non-cancer cells. This immune army learns to recognize features of the tumor cells so it can seek them out and destroy them throughout the body, essentially turning the tumor into a cancer vaccine factory.
“The in situ vaccine approach has broad implications for multiple types of cancer,” said lead author Joshua Brody, MD, Director of the Lymphoma Immunotherapy Program at The Tisch Cancer Institute at the Icahn School of Medicine at Mount Sinai. “This method could also increase the success of other immunotherapies such as checkpoint blockade.”
After testing the lymphoma vaccine in the lab, it was tested in 11 patients in a clinical trial. Some patients had full remission from months to years. In lab tests in mice, the vaccine drastically increased the success of checkpoint blockade immunotherapy, the type of immunotherapy responsible for the complete remission of former President Jimmy Carter’s cancer and the focus of the 2018 Nobel Prize in Medicine.
Thank you for reading!
That most cancers use the same set of molecular tools is a very powerful idea, but it has been hard to figure out what these tools are and how to target them. Follow the link below for a quick, and enthusiastic, summary of genome-scale CRISPR–Cas9 screens of 324 human cancer cell lines from 30 cancer types with the goal of developing a new, diverse and more effective portfolio of cancer drug targets.
Scientists have taken cancer apart piece-by-piece to reveal its weaknesses, and come up with new ideas for treatment. A team at the Wellcome Sanger Institute disabled every genetic instruction, one at a time, inside 30 types of cancer. It has thrown up 600 new cancer vulnerabilities and each could be the target of a drug.Cancer Research UK praised the sheer scale of the study.
The study heralds the future of personalised cancer medicine. At the moment drugs like chemotherapy cause damage throughout the body. One of the researchers is Dr Fiona Behan, whose mother died after getting cancer for the second time.
A recent post discussed an important paper that demonstrated an association between transposable elements, aka ‘jumping genes’, and cancer. Transposable elements are an important but often forgotten class of mutagen that can contribute to genome instability and may disrupt genes and their expression. The original article has an outstanding abundance of data and is not easy reading. The article below provides an accessible summary that’s worth reading if you aren’t an expert in genomics.
Catharine Paddock, Medical News Today
In cancer research, scientists usually look for cancer genes by scouring the genome for altered sequences — or mutations — in DNA. But a new study has now revealed that jumping genes, which customary sequencing overlooks, are also important drivers of tumor growth.
Scientists at the Washington University School of Medicine in St. Louis, MO, found that jumping genes are widespread in cancer and promote tumor growth by forcing cancer genes to remain switched on.
They analyzed 7,769 tumor samples from 15 different types of cancer and found 129 jumping genes that can drive tumor growth through their influence on 106 different cancer genes.
The jumping genes were functioning as "stealthy on-switches" in 3,864 of the tumors that the team analyzed. These tumors came from breast, colon, lung, skin, prostate, brain, and other types of cancer.
A recent Nature Genetics paper gives a full account of the study.
The following article provides insights into the promise of applying quantitative approaches in the context of tumor tissues and clinical environments. Genetics approaches have dominated cancer research because they generate such an abundance of data ( and because the methodology is so widely and readily generalizable). While genetic variations clearly play an important role in cancer, deviant signaling drives cancer progression and signaling molecules are the targets of most chemotherapeutics. This highlights the importance of understanding cancer signaling pathways with data-rich and quantitatively rigorous methods, similar to those used in genetics. The following article in Science Signaling discusses this topic and is both thorough and accessible. —RPR
Michael B. Yaffe, Science Signaling
Abstract—Genetic approaches to cancer research have dramatically advanced our understanding of the pathophysiology of this disease, leading to similar genetics-based approaches for precision therapy, which have been less successful. Reconfiguring and adapting the types of technologies that underlie genetic research to dissect tumor cell signaling in clinical samples may offer an alternative road forward.
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“Researchers from University of Southern Denmark, Wellcome Sanger Institute and BGI, today published a study in the journal Genome Biology comparing the library preparation and sequencing platforms for single-cell RNA-sequencing (scRNA-seq).
Single cell transcriptomics (i.e. scRNA-seq) is a next-generation sequencing approach that simultaneously measures the messenger RNA concentrations (encoded by DNA/genome/genetic blueprint) of thousands of genes, in individual cells. This enables researchers to gain a high-resolution view of cells to unravel heterogenous cell populations and better understand individual cell functions in the body. Although several single-cell protocols exist, the sequencing has traditionally been performed using Illumina technology and sequencing platforms.”
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“An international consortium has sequenced the entire genome of durum wheat--the source of semolina for pasta, a food staple for the world's population, according to an article published today in Nature Genetics.
The team has also discovered how to significantly reduce cadmium levels in durum grain, ensuring the safety and nutritional value of the grain through selective breeding.
"This ground-breaking work will lead to new standards for durum breeding and safety of durum-derived products, paving the way for production of durum wheat varieties better adapted to climate challenges, with higher yields, enhanced nutritional quality, and improved sustainability," said Luigi Cattivelli of Italy's Council for Agricultural Research and Economics (CREA).”
An international team has unveiled a new CRISPR-based tool that acts more like a shredder than the usual scissor-like action of CRISPR-Cas9. The new approach, based on Type I CRISPR-Cas3, is able to wipe out long stretches of DNA in human cells with programmable targeting, and has been shown to work in human cells for the first time.
Thank you for reading!
In 2019, 'King' the Wire Fox Terrier won the Westminster Dog Show in Madison Square Garden, having competed against 2,800 dogs from 203 breeds. The sheer number of dog breeds points toward the major role played by genetics in shaping such variation in dogs.
In a new study, researchers at the National Human Genome Research Institute (NHGRI) have generated the largest catalog of genetic variants associated with physical traits for domesticated dog breeds. The findings, published in Nature Communication, will help researchers assess if variants associated with dog body structure, behavior and life span could also be implicated in related human diseases.
"This study included data from more than 722 dogs and 144 modern breeds," says Dr. Ostrander, NIH Distinguished Investigator and senior author of the paper. "Through the results, we've learned some of the fascinating genetics behind the variability observed in the world's 450 dog breeds."
After humans initially selected for specific traits during dog breeding centuries ago, dogs have since formed traits and characteristics spontaneously over time. Jocelyn Plassais, a postdoctoral researcher in Dr. Ostrander's laboratory and lead author of the study explained that dogs naturally develop disorders that are common to humans, such as various forms cancers, infections and even diabetes. In addition, a vast number of regions within the dog genome remain similar to the human genome. Thus, dog genomes can provide insight into the biological mechanisms of human health and disease.
The researchers used whole genome sequencing and genome-wide association studies to identify genomic variants associated with sixteen observable characteristics. Most of the blood samples from dogs were taken via The Dog Genome Project, a citizen science initiative that relies on donations from motivated dog owners.
A randomized phase 2 trial examining the assignment of treatment based on gene-expression profiling compared with standard chemotherapy for patients with cancer of unknown primary site showed no improvement in the 1-year survival rate with the more tailored approach. However, several caveats may limit the relevance of the findings. A report of this study was published in the Journal of Clinical Oncology.1
Cancer of unknown primary site (CUP) refers to malignancies in which the originating tumor type cannot be identified. As a result, determining the best treatment for this cancer, diagnosed in approximately 31,000 people in the US each year, is extremely difficult.2 In recent years, oncologists have looked to genetic testing to identify the cancer type as a way to improve care.
In the current study, a molecular analysis of biopsied tissue predicted the originating cancer site for all of the 101 patients treated. The analysis identified a total of 16 sites; cancers of the pancreas (21% of participants), gastric system (21% of participants), and malignant lymphomas (20% of patients) were the 3 most common sites to be predicted as the primary site of malignancy. The Japan-based researchers then randomized the patients to receive therapy appropriate to the predicted site of origin (50 patients) or the standard, empiric treatment of paclitaxel plus carboplatin (51 patients).
By Bio-IT World Staff
April 5, 2019 | Scientists at the Anthony Nolan Research Institute in the UK have demonstrated that ultra-high-resolution HLA typing performed with PacBio sequencing identified stronger matches associated with improved survival rates among patients who received hematopoietic stem cell transplants. The retrospective study was published this week in the Journal of Biology of Blood and Marrow Transplantation.
HLA typing involves analysis of the genes found in the human leukocyte antigen region of the human genome. For stem cell transplants, HLA typing is used to find the best donor/recipient match for the strongest chance of a positive outcome for transplant patients. The HLA genes are highly polymorphic and complex, making them very difficult to resolve fully with conventional technologies. They are also known to be important in immune-related diseases and drug hypersensitivity.
The Anthony Nolan Research Institute, which is funded by Anthony Nolan, a registered UK charity that maintains the world's oldest stem cell registry, has implemented Single Molecule, Real-Time (SMRT) Sequencing from PacBio to fully phase and characterize HLA genes with high accuracy. In this retrospective study, the scientists aimed to determine whether high-resolution HLA typing enabled by SMRT Sequencing would have made a difference for previously matched donors and recipients.
