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SNP-ChIP: a versatile and tag-free method to quantify changes in protein binding across the genome

ABSTRACT: SNP-ChIP is a novel method leveraging small-scale intra-species genetic polymorphisms, mainly SNPs, to allow quantitative spike-in normalization of ChIP-seq results. SNP-ChIP uses a different strain of the same organism as the spike-in material and can be applied to any organism for which genome assemblies are available for two different strains or individuals with sufficient genetic diversity. This ensures antibody cross-reactivity and thus extends the applicability of the method beyond the small number of highly conserved proteins. It also ensures complete physiological coherence between the test and the spike-in cells. In this work we develop and validate the method using test cases from budding yeast meiosis. We use strains with ~0.7% genomic sequence divergence as test strain background and the spike-in strain, respectively. Sequencing reads are mapped to a hybrid genome, with naturally occurring sequence polymorphisms allowing assignment of most reads to one of the two genomes. By targeting the yeast chromosomal protein Red1, we show that SNP-ChIP reliably identifies previously reported changes in overall protein levels, irrespective of changes in binding distribution. We also show that SNP-ChIP is robust to wide changes in sequencing depth, as well as the amount of spike-in material. SNP-ChIP allowed discovery of novel regulators of global Red1 protein accumulation and is also shown to allow quantitative analysis of the DNA-damage associated histone modification gamma-H2AX. SNP-ChIP is a robust and versatile spike-in normalization method that can be used with any target against which a ChIP-grade antibody is available and for any organisms with sufficient intra-species diversity, including most model organisms as well as human cells. Grant ID: FY16-208 Grant title: Meiotic segregation of small chromosomes Funding source: March of Dimes Grantee name: Andreas Hochwagen, New York University, New York, NY, United States

ORGANISM(S): Saccharomyces cerevisiae

PROVIDER: GSE115092 | GEO | 2018/11/21

REPOSITORIES: GEO

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Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that was first reported in Wuhan, China in December of 2019 and has since caused a global pandemic resulting in millions of deaths and tens of millions of patients testing positive for infection. Analysis of different viral strains has identified a D614G change in the spike protein that is correlated with the virus becoming more transmissible. While studies have shown G614 viruses to be more transmissible, the effects of this mutation on the host response, especially on the cellular level, are yet to be fully elucidated. In this experiment we infected NHBE cells with the Washington (D614) strain or the New York (G614) strains of SARS-CoV-2. We generated RNA sequencing data at three different time points to improve our understanding of how the intracellular host response differs between infections with these two strains. We analyzed these data with a bioinformatics pipeline that identifies differentially expressed genes, enriched Gene Ontology terms and dysregulated signaling pathways. We detected over 2,000 differentially expressed genes, over 600 Gene Ontology terms, and 29 affected pathways between the two treatments. Many of these entities play a role in immune signaling and response. When comparing the different strains and different time points we found more overall similarities between matched time points than across different time points with the same strain. When specifically comparing the affected pathways, we saw that the 24hr time point of the New York strain was more similar to the 12hr time point of the Washington strain with a large number of pathways related to translation being inhibited in both strains at these time points. These results suggest that D614G substitution in the spike protein, combined with other nonsynonymous changes in the viral gene products cause distinct responses in infected host cells, especially relating to how quickly translation is dysregulated after infection. These observed differences in the intracellular host response to infection could play a role in driving the increase in pathogenicity and mortality seen in the New York outbreaks versus the Washington outbreaks at the beginning of the SARS-CoV-2 pandemic.

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SNP-ChIP: a versatile and tag-free method to quantify changes in protein binding across the genome

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