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1. A dual, catalytic role for the fission yeast Ccr4-Not complex in gene silencing and heterochromatin spreading

   https://doi.org/10.1093/genetics/iyad108  

   Challal, Drice ; Menant, Alexandra ; Goksal, Can ; Leroy, Estelle ; Al-Sady, Bassem ; Rougemaille, Mathieu ; Rusche, ed., L. ( June 2023 , GENETICS)

   Heterochromatic gene silencing relies on combinatorial control by specific histone modifications, the occurrence of transcription, and/or RNA degradation. Once nucleated, heterochromatin propagates within defined chromosomal regions and is maintained throughout cell divisions to warrant proper genome expression and integrity. In the fission yeast Schizosaccharomyces pombe, the Ccr4-Not complex partakes in gene silencing, but its relative contribution to distinct heterochromatin domains and its role in nucleation versus spreading have remained elusive. Here, we unveil major functions for Ccr4-Not in silencing and heterochromatin spreading at the mating type locus and subtelomeres. Mutations of the catalytic subunits Caf1 or Mot2, involved in RNA deadenylation and protein ubiquitinylation, respectively, result in impaired propagation of H3K9me3 and massive accumulation of nucleation-distal heterochromatic transcripts. Both silencing and spreading defects are suppressed upon disruption of the heterochromatin antagonizing factor Epe1. Overall, our results position the Ccr4-Not complex as a critical, dual regulator of heterochromatic gene silencing and spreading.
2. Relating enhancer genetic variation across mammals to complex phenotypes using machine learning

   https://doi.org/10.1126/science.abm7993  

   Kaplow, Irene M. ; Lawler, Alyssa J. ; Schäffer, Daniel E. ; Srinivasan, Chaitanya ; Sestili, Heather H. ; Wirthlin, Morgan E. ; Phan, BaDoi N. ; Prasad, Kavya ; Brown, Ashley R. ; Zhang, Xiaomeng ; et al ( April 2023 , Science)

   INTRODUCTION Diverse phenotypes, including large brains relative to body size, group living, and vocal learning ability, have evolved multiple times throughout mammalian history. These shared phenotypes may have arisen repeatedly by means of common mechanisms discernible through genome comparisons. RATIONALE Protein-coding sequence differences have failed to fully explain the evolution of multiple mammalian phenotypes. This suggests that these phenotypes have evolved at least in part through changes in gene expression, meaning that their differences across species may be caused by differences in genome sequence at enhancer regions that control gene expression in specific tissues and cell types. Yet the enhancers involved in phenotype evolution are largely unknown. Sequence conservation–based approaches for identifying such enhancers are limited because enhancer activity can be conserved even when the individual nucleotides within the sequence are poorly conserved. This is due to an overwhelming number of cases where nucleotides turn over at a high rate, but a similar combination of transcription factor binding sites and other sequence features can be maintained across millions of years of evolution, allowing the function of the enhancer to be conserved in a particular cell type or tissue. Experimentally measuring the function of orthologous enhancers across dozens of species ismore »currently infeasible, but new machine learning methods make it possible to make reliable sequence-based predictions of enhancer function across species in specific tissues and cell types. RESULTS To overcome the limits of studying individual nucleotides, we developed the Tissue-Aware Conservation Inference Toolkit (TACIT). Rather than measuring the extent to which individual nucleotides are conserved across a region, TACIT uses machine learning to test whether the function of a given part of the genome is likely to be conserved. More specifically, convolutional neural networks learn the tissue- or cell type–specific regulatory code connecting genome sequence to enhancer activity using candidate enhancers identified from only a few species. This approach allows us to accurately associate differences between species in tissue or cell type–specific enhancer activity with genome sequence differences at enhancer orthologs. We then connect these predictions of enhancer function to phenotypes across hundreds of mammals in a way that accounts for species’ phylogenetic relatedness. We applied TACIT to identify candidate enhancers from motor cortex and parvalbumin neuron open chromatin data that are associated with brain size relative to body size, solitary living, and vocal learning across 222 mammals. Our results include the identification of multiple candidate enhancers associated with brain size relative to body size, several of which are located in linear or three-dimensional proximity to genes whose protein-coding mutations have been implicated in microcephaly or macrocephaly in humans. We also identified candidate enhancers associated with the evolution of solitary living near a gene implicated in separation anxiety and other enhancers associated with the evolution of vocal learning ability. We obtained distinct results for bulk motor cortex and parvalbumin neurons, demonstrating the value in applying TACIT to both bulk tissue and specific minority cell type populations. To facilitate future analyses of our results and applications of TACIT, we released predicted enhancer activity of >400,000 candidate enhancers in each of 222 mammals and their associations with the phenotypes we investigated. CONCLUSION TACIT leverages predicted enhancer activity conservation rather than nucleotide-level conservation to connect genetic sequence differences between species to phenotypes across large numbers of mammals. TACIT can be applied to any phenotype with enhancer activity data available from at least a few species in a relevant tissue or cell type and a whole-genome alignment available across dozens of species with substantial phenotypic variation. Although we developed TACIT for transcriptional enhancers, it could also be applied to genomic regions involved in other components of gene regulation, such as promoters and splicing enhancers and silencers. As the number of sequenced genomes grows, machine learning approaches such as TACIT have the potential to help make sense of how conservation of, or changes in, subtle genome patterns can help explain phenotype evolution. Tissue-Aware Conservation Inference Toolkit (TACIT) associates genetic differences between species with phenotypes. TACIT works by generating open chromatin data from a few species in a tissue related to a phenotype, using the sequences underlying open and closed chromatin regions to train a machine learning model for predicting tissue-specific open chromatin and associating open chromatin predictions across dozens of mammals with the phenotype. [Species silhouettes are from PhyloPic]« less
3. Dri1 mediates heterochromatin assembly via RNAi and histone deacetylation

   https://doi.org/10.1093/genetics/iyab032  

   Ban, Hyoju ; Sun, Wenqi ; Chen, Yu-hang ; Chen, Yong ; Li, Fei ( May 2021 , Genetics)

   Freitag, M (Ed.)

   Abstract Heterochromatin, a transcriptionally silenced chromatin domain, is important for genome stability and gene expression. Histone 3 lysine 9 methylation (H3K9me) and histone hypoacetylation are conserved epigenetic hallmarks of heterochromatin. In fission yeast, RNA interference (RNAi) plays a key role in H3K9 methylation and heterochromatin silencing. However, how RNAi machinery and histone deacetylases (HDACs) are coordinated to ensure proper heterochromatin assembly is still unclear. Previously, we showed that Dpb4, a conserved DNA polymerase epsilon subunit, plays a key role in the recruitment of HDACs to heterochromatin during S phase. Here, we identified a novel RNA-binding protein Dri1 that interacts with Dpb4. GFP-tagged Dri1 forms distinct foci mostly in the nucleus, showing a high degree of colocalization with Swi6/Heterochromatin Protein 1. Deletion of dri1+ leads to defects in silencing, H3K9me, and heterochromatic siRNA generation. We also showed that Dri1 physically associates with heterochromatic transcripts, and is required for the recruitment of the RNA-induced transcriptional silencing (RITS) complex via interacting with the complex. Furthermore, loss of Dri1 decreases the association of the Sir2 HDAC with heterochromatin. We further demonstrated that the C-terminus of Dri1 that includes an intrinsically disordered (IDR) region and three zinc fingers is crucial for its role in silencing.more »Together, our evidences suggest that Dri1 facilitates heterochromatin assembly via the RNAi pathway and HDAC.« less
4. Discovery of target genes and pathways at GWAS loci by pooled single-cell CRISPR screens

   https://doi.org/10.1126/science.adh7699  

   Morris, John A. ; Caragine, Christina ; Daniloski, Zharko ; Domingo, Júlia ; Barry, Timothy ; Lu, Lu ; Davis, Kyrie ; Ziosi, Marcello ; Glinos, Dafni A. ; Hao, Stephanie ; et al ( May 2023 , Science)

   INTRODUCTION Genome-wide association studies (GWASs) have identified thousands of human genetic variants associated with diverse diseases and traits, and most of these variants map to noncoding loci with unknown target genes and function. Current approaches to understand which GWAS loci harbor causal variants and to map these noncoding regulators to target genes suffer from low throughput. With newer multiancestry GWASs from individuals of diverse ancestries, there is a pressing and growing need to scale experimental assays to connect GWAS variants with molecular mechanisms. Here, we combined biobank-scale GWASs, massively parallel CRISPR screens, and single-cell sequencing to discover target genes of noncoding variants for blood trait loci with systematic targeting and inhibition of noncoding GWAS loci with single-cell sequencing (STING-seq). RATIONALE Blood traits are highly polygenic, and GWASs have identified thousands of noncoding loci that map to candidate cis -regulatory elements (CREs). By combining CRE-silencing CRISPR perturbations and single-cell readouts, we targeted hundreds of GWAS loci in a single assay, revealing target genes in cis and in trans . For select CREs that regulate target genes, we performed direct variant insertion. Although silencing the CRE can identify the target gene, direct variant insertion can identify magnitude and direction of effect onmore »gene expression for the GWAS variant. In select cases in which the target gene was a transcription factor or microRNA, we also investigated the gene-regulatory networks altered upon CRE perturbation and how these networks differ across blood cell types. RESULTS We inhibited candidate CREs from fine-mapped blood trait GWAS variants (from ~750,000 individual of diverse ancestries) in human erythroid progenitors. In total, we targeted 543 variants (254 loci) mapping to candidate CREs, generating multimodal single-cell data including transcriptome, direct CRISPR gRNA capture, and cell surface proteins. We identified target genes in cis (within 500 kb) for 134 CREs. In most cases, we found that the target gene was the closest gene and that specific enhancer-associated biochemical hallmarks (H3K27ac and accessible chromatin) are essential for CRE function. Using multiple perturbations at the same locus, we were able to distinguished between causal variants from noncausal variants in linkage disequilibrium. For a subset of validated CREs, we also inserted specific GWAS variants using base-editing STING-seq (beeSTING-seq) and quantified the effect size and direction of GWAS variants on gene expression. Given our transcriptome-wide data, we examined dosage effects in cis and trans in cases in which the cis target is a transcription factor or microRNA. We found that trans target genes are also enriched for GWAS loci, and identified gene clusters within trans gene networks with distinct biological functions and expression patterns in primary human blood cells. CONCLUSION In this work, we investigated noncoding GWAS variants at scale, identifying target genes in single cells. These methods can help to address the variant-to-function challenges that are a barrier for translation of GWAS findings (e.g., drug targets for diseases with a genetic basis) and greatly expand our ability to understand mechanisms underlying GWAS loci. Identifying causal variants and their target genes with STING-seq. Uncovering causal variants and their target genes or function are a major challenge for GWASs. STING-seq combines perturbation of noncoding loci with multimodal single-cell sequencing to profile hundreds of GWAS loci in parallel. This approach can identify target genes in cis and trans , measure dosage effects, and decipher gene-regulatory networks.« less
5. The Drosophila HP1 family is associated with active gene expression across chromatin contexts

   https://doi.org/10.1093/genetics/iyab108  

   Schoelz, John M ; Feng, Justina X ; Riddle, Nicole C ( July 2021 , Genetics)

   Kaplan, C D (Ed.)

   Abstract Drosophila Heterochromatin Protein 1a (HP1a) is essential for heterochromatin formation and is involved in transcriptional silencing. However, certain loci require HP1a to be transcribed. One model posits that HP1a acts as a transcriptional silencer within euchromatin while acting as an activator within heterochromatin. However, HP1a has been observed as an activator of a set of euchromatic genes. Therefore, it is not clear whether, or how, chromatin context informs the function of HP1 proteins. To understand the role of HP1 proteins in transcription, we examined the genome-wide binding profile of HP1a as well as two other Drosophila HP1 family members, HP1B and HP1C, to determine whether coordinated binding of these proteins is associated with specific transcriptional outcomes. We found that HP1 proteins share many of their endogenous binding targets. These genes are marked by active histone modifications and are expressed at higher levels than nontarget genes in both heterochromatin and euchromatin. In addition, HP1 binding targets displayed increased RNA polymerase pausing compared with nontarget genes. Specifically, colocalization of HP1B and HP1C was associated with the highest levels of polymerase pausing and gene expression. Analysis of HP1 null mutants suggests these proteins coordinate activity at transcription start sites to regulate transcription.more »Depletion of HP1B or HP1C alters expression of protein-coding genes bound by HP1 family members. Our data broaden understanding of the mechanism of transcriptional activation by HP1a and highlight the need to consider particular protein–protein interactions, rather than broader chromatin context, to predict impacts of HP1 at transcription start sites.« less