STING-seq provides a roadmap to identify variants and genes, allowing for a deeper understanding of the noncoding genome and targets of therapies — ScienceDaily

A new approach developed by researchers at New York University and the New York Genome Center combines genetic association studies, gene editing, and single cell sequencing to address these challenges and discover causal variants and genetic mechanisms for traits in cells. blood cells.

Their approach, called STING-seq and published in Scienceaddresses the challenge of directly connecting genetic variants to human traits and health, and can help scientists identify drug targets for genetically-based diseases.

In the last two decades, genome-wide association studies (GWAS) have become an important tool for studying the human genome. Using GWAS, scientists have identified thousands of mutations or genetic variants associated with many diseases, from schizophrenia to diabetes, as well as traits such as height. These studies are carried out by comparing the genomes of large populations to find variants that occur more frequently in those with a specific disease or trait.

GWAS can reveal which regions of the genome and possible variants are involved in diseases or traits. However, these associations are almost always found in the 98% of the genome that does not code for proteins, which is much less well known than the well-studied 2% of the genome that code for proteins. Another complication is that many variants lie in close proximity to each other within the genome and travel together through generations, a concept known as linkage. This can make it difficult to separate which variant plays a truly causal role from other variants that lie nearby. Even when scientists can identify which variant is causing a disease or trait, they don’t always know which gene is affected by the variant.

“An important goal for the study of human diseases is to identify causative genes and variants, which can clarify biological mechanisms and inform drug targets for these diseases,” said Neville Sanjana, an associate professor of biology at NYU. , associate professor of neuroscience and physiology at NYU Grossman School of Medicine, senior faculty member at the New York Genome Center, and co-senior author of the study.

“The enormous success of GWAS has highlighted the challenge of extracting information about disease biology from these massive data sets. Despite all our efforts over the past 10 years, the glass was still half full, at best.” We needed a new approach,” said Tuuli Lappalainen, senior associate faculty member at the New York Genome Center, professor of genomics at the KTH Royal Institute of Technology in Sweden and co-senior author of the study.

A cure for sickle cell anemia

A recent scientific breakthrough in the treatment of sickle cell disease, a genetic disorder marked by episodes of severe pain, illustrates how combining GWAS with cutting-edge molecular tools such as gene editing can identify causative variants and lead to innovative therapies. Using GWAS, the scientists identified areas of the genome important for producing fetal hemoglobin, a target based on its promise to reverse sickle cell disease, but they did not know which exact variant drives its production.

The researchers turned to CRISPR, a gene-editing tool that uses “molecular scissors to cut DNA,” according to Sanjana, to edit the regions identified by GWAS. When CRISPR edits were made to a specific location in the noncoding genome near a gene called BCL11Aresulted in elevated fetal hemoglobin levels.

CRISPR has now been used in clinical trials to edit this region in bone marrow cells from dozens of sickle cell patients. After the modified cells are reinfused into patients, they begin to produce fetal hemoglobin, which displaces the mutated adult form of hemoglobin, effectively curing them of sickle cell disease.

“This success story in the treatment of sickle cell disease is the result of combining insights from GWAS with gene editing,” said Sanjana. “But it took years of research in a single disease. How do we scale this up to better identify the causative variants and target genes of GWAS?”

GWAS is CRISPR compliant and single cell sequencing

The research team created a workflow called STING-seq: Systematic Targeting and Inhibition of Noncoding GWAS Loci with Single Cell Sequencing. STING-seq works by taking GWAS at the biobank scale and searching for potential causative variants using a combination of biochemical features and regulatory elements. The researchers then use CRISPR to target each of the regions of the genomes implicated by GWAS and perform single-cell sequencing to assess gene and protein expression.

In their study, the researchers illustrated the use of STING-seq to discover target genes for non-coding variants of blood traits. Blood traits, such as percentages of platelets, white blood cells, and red blood cells, are easy to measure in routine blood tests and have been well studied in GWAS. As a result, the researchers were able to use GWAS representing nearly 750,000 people of diverse origins to study blood traits.

Once the researchers identified 543 candidate regions of the genome that may play a role in blood traits, they used a version of CRISPR called CRISPR inhibition that can silence precise regions of the genome.

After CRISPR silencing of regions identified by GWAS, the researchers looked at the expression of nearby genes in individual cells to see if certain genes were turned on or off. If they saw a difference in gene expression between cells where the variants were and were not silenced, they could link specific non-coding regions to target genes. By doing this, the researchers were able to identify which non-coding regions are central to specific traits (and which are not), and often also the cellular pathways through which these non-coding regions function.

“The power of STING-seq is that we can apply this approach to any disease or trait,” said John Morris, a postdoctoral associate at the New York Genome Center and New York University and the study’s first author.

Using STING-seq to test groups of likely variants and see their impact on genes removes the guesswork scientists previously encountered when faced with linkage between variants or genes closest to variants, which often, but not always , are the target gene. In the case of a blood trait called monocyte count, the application of CRISPR triggered a gene, CD52to stand out clearly as significantly altered – and while CD52 it was close to the variant of interest, it was not the closest gene, so it may have been missed by previous methods.

In another analysis, the researchers identified a gene called PTPRC which is associated with 10 blood traits, including those related to red and white blood cells and platelets. However, there are several uncoded variants identified by GWAS in close proximity and it was challenging to understand which (if any) might modulate PTPRC expression. Applying STING-seq allowed them to isolate which variants were causal by seeing which ones changed. PTPRC expression.

STING-seq and beyond

While STING-seq can identify the target gene and the causative variant by silencing the variants, it does not explain the direction of the effect: whether a specific noncoding variant will increase or decrease the expression of a nearby gene. The researchers took their approach a step further to create a complementary approach they call beeSTING-seq (STING-seq base editing) that uses CRISPR to precisely insert a genetic variant rather than simply inhibit that region of the genome.

The researchers envision STING-seq and beeSTING-seq being used to identify causative variants of a wide range of diseases that can be treated with gene editing, as used in sickle cell disease, or with drugs that target genes or specific cells. roads

“Now that we can connect noncoding variants to target genes, this gives us evidence that small molecule or antibody therapies could be developed to change the expression of specific genes,” Lappalainen said.

Other study authors include Christina Caragine, Zharko Daniloski, Lu Lu, and Kyrie Davis, of NYU and the New York Genome Center; Júlia Domingo, Marcello Ziosi, Dafni Glinos, Stephanie Hao, Eleni P. Mimitou, and Peter Smibert of the New York Genome Center; Timothy Barry and Kathryn Roeder of Carnegie Mellon University; and Eugene Katsevich of the University of Pennsylvania.

The research was supported by the National Institutes of Health (DP2HG010099, R01CA279135, R01CA218668, R01AI176601, R01MH106842, UM1HG008901, R01GM122924, K99HG012792, R01MH123184), the National Science Foundation (DMS-2113 072), Canadian Institutes of Health Research, the European Molecular Biology Organization (ALTF 345-2021), American Heart Association (20POST35220040), Simons Foundation for Autism Research, MacMillan Center for the Study of the Non-Coding Cancer Genome, Wharton Data Science and Business Analytics Fund, New York University, and the New York Genome Center.