Loss of Neurological Disease HSAN-I-Associated Gene SPTLC2 Impairs CD8+ T Cell Responses to Infection by Inhibiting T Cell Metabolic Fitness.

Patients with the neurological disorder HSAN-I suffer frequent infections, attributed to a lack of pain sensation and failure to seek care for minor injuries. Whether protective CD8+ T cells are affected in HSAN-I patients remains unknown. Here, we report that HSAN-I-associated mutations in serine palmitoyltransferase subunit SPTLC2 dampened human T cell responses. Antigen stimulation and inflammation induced SPTLC2 expression, and murine T-cell-specific ablation of Sptlc2 impaired antiviral-T-cell expansion and effector function. Sptlc2 deficiency reduced sphingolipid biosynthetic flux and led to prolonged activation of the mechanistic target of rapamycin complex 1 (mTORC1), endoplasmic reticulum (ER) stress, and CD8+ T cell death. Protective CD8+ T cell responses in HSAN-I patient PBMCs and Sptlc2-deficient mice were restored by supplementing with sphingolipids and pharmacologically inhibiting ER stress-induced cell death. Therefore, SPTLC2 underpins protective immunity by translating extracellular stimuli into intracellular anabolic signals and antagonizes ER stress to promote T cell metabolic fitness.

Misexpression of inactive genes in whole blood is associated with nearby rare structural variants.

Gene misexpression is the aberrant transcription of a gene in a context where it is usually inactive. Despite its known pathological consequences in specific rare diseases, we have a limited understanding of its wider prevalence and mechanisms in humans. To address this, we analyzed gene misexpression in 4,568 whole-blood bulk RNA sequencing samples from INTERVAL study blood donors. We found that while individual misexpression events occur rarely, in aggregate they were found in almost all samples and a third of inactive protein-coding genes. Using 2,821 paired whole-genome and RNA sequencing samples, we identified that misexpression events are enriched in cis for rare structural variants. We established putative mechanisms through which a subset of SVs lead to gene misexpression, including transcriptional readthrough, transcript fusions, and gene inversion. Overall, we develop misexpression as a type of transcriptomic outlier analysis and extend our understanding of the variety of mechanisms by which genetic variants can influence gene expression.

Small-molecule-induced polymerization triggers degradation of BCL6.

Effective and sustained inhibition of non-enzymatic oncogenic driver proteins is a major pharmacological challenge. The clinical success of thalidomide analogues demonstrates the therapeutic efficacy of drug-induced degradation of transcription factors and other cancer targets1-3, but a substantial subset of proteins are resistant to targeted degradation using existing approaches4,5. Here we report an alternative mechanism of targeted protein degradation, in which a small molecule induces the highly specific, reversible polymerization of a target protein, followed by its sequestration into cellular foci and subsequent degradation. BI-3802 is a small molecule that binds to the Broad-complex, Tramtrack and Bric-à-brac (BTB) domain of the oncogenic transcription factor B cell lymphoma 6 (BCL6) and leads to the proteasomal degradation of BCL66. We use cryo-electron microscopy to reveal how the solvent-exposed moiety of a BCL6-binding molecule contributes to a composite ligand-protein surface that engages BCL6 homodimers to form a supramolecular structure. Drug-induced formation of BCL6 filaments facilitates ubiquitination by the SIAH1 E3 ubiquitin ligase. Our findings demonstrate that a small molecule such as BI-3802 can induce polymerization coupled to highly specific protein degradation, which in the case of BCL6 leads to increased pharmacological activity compared to the effects induced by other BCL6 inhibitors. These findings open new avenues for the development of therapeutic agents and synthetic biology.

The CDK inhibitor CR8 acts as a molecular glue degrader that depletes cyclin K.

Molecular glue compounds induce protein-protein interactions that, in the context of a ubiquitin ligase, lead to protein degradation1. Unlike traditional enzyme inhibitors, these molecular glue degraders act substoichiometrically to catalyse the rapid depletion of previously inaccessible targets2. They are clinically effective and highly sought-after, but have thus far only been discovered serendipitously. Here, through systematically mining databases for correlations between the cytotoxicity of 4,518 clinical and preclinical small molecules and the expression levels of E3 ligase components across hundreds of human cancer cell lines3-5, we identify CR8-a cyclin-dependent kinase (CDK) inhibitor6-as a compound that acts as a molecular glue degrader. The CDK-bound form of CR8 has a solvent-exposed pyridyl moiety that induces the formation of a complex between CDK12-cyclin K and the CUL4 adaptor protein DDB1, bypassing the requirement for a substrate receptor and presenting cyclin K for ubiquitination and degradation. Our studies demonstrate that chemical alteration of surface-exposed moieties can confer gain-of-function glue properties to an inhibitor, and we propose this as a broader strategy through which target-binding molecules could be converted into molecular glues.

Predicting base editing outcomes using position-specific sequence determinants.

CRISPR/Cas base editors promise nucleotide-level control over DNA sequences, but the determinants of their activity remain incompletely understood. We measured base editing frequencies in two human cell lines for two cytosine and two adenine base editors at ∼14 000 target sequences and find that base editing activity is sequence-biased, with largest effects from nucleotides flanking the target base. Whether a base is edited depends strongly on the combination of its position in the target and the preceding base, acting to widen or narrow the effective editing window. The impact of features on editing rate depends on the position, with sequence bias efficacy mainly influencing bases away from the center of the window. We use these observations to train a machine learning model to predict editing activity per position, with accuracy ranging from 0.49 to 0.72 between editors, and with better generalization across datasets than existing tools. We demonstrate the usefulness of our model by predicting the efficacy of disease mutation correcting guides, and find that most of them suffer from more unwanted editing than pure outcomes. This work unravels the position-specificity of base editing biases and allows more efficient planning of editing campaigns in experimental and therapeutic contexts.

Multiplex generation and single cell analysis of structural variants in a mammalian genome.

The functional consequences of structural variants (SVs) in mammalian genomes are challenging to study. This is due to several factors, including: 1) their numerical paucity relative to other forms of standing genetic variation such as single nucleotide variants (SNVs) and short insertions or deletions (indels); 2) the fact that a single SV can involve and potentially impact the function of more than one gene and/or cis regulatory element; and 3) the relative immaturity of methods to generate and map SVs, either randomly or in targeted fashion, in in vitro or in vivo model systems. Towards addressing these challenges, we developed Genome-Shuffle-seq, a straightforward method that enables the multiplex generation and mapping of several major forms of SVs (deletions, inversions, translocations) throughout a mammalian genome. Genome-Shuffle-seq is based on the integration of "shuffle cassettes" to the genome, wherein each shuffle cassette contains components that facilitate its site-specific recombination (SSR) with other integrated shuffle cassettes (via Cre-loxP), its mapping to a specific genomic location (via T7-mediated in vitro transcription or IVT), and its identification in single-cell RNA-seq (scRNA-seq) data (via T7-mediated in situ transcription or IST). In this proof-of-concept, we apply Genome-Shuffle-seq to induce and map thousands of genomic SVs in mouse embryonic stem cells (mESCs) in a single experiment. Induced SVs are rapidly depleted from the cellular population over time, possibly due to Cre-mediated toxicity and/or negative selection on the rearrangements themselves. Leveraging T7 IST of barcodes whose positions are already mapped, we further demonstrate that we can efficiently genotype which SVs are present in association with each of many single cell transcriptomes in scRNA-seq data. Finally, preliminary evidence suggests our method may be a powerful means of generating extrachromosomal circular DNAs (ecDNAs). Looking forward, we anticipate that Genome-Shuffle-seq may be broadly useful for the systematic exploration of the functional consequences of SVs on gene expression, the chromatin landscape, and 3D nuclear architecture. We further anticipate potential uses for in vitro modeling of ecDNAs, as well as in paving the path to a minimal mammalian genome.

Base editing screens define the genetic landscape of cancer drug resistance mechanisms.

Drug resistance is a principal limitation to the long-term efficacy of cancer therapies. Cancer genome sequencing can retrospectively delineate the genetic basis of drug resistance, but this requires large numbers of post-treatment samples to nominate causal variants. Here we prospectively identify genetic mechanisms of resistance to ten oncology drugs from CRISPR base editing mutagenesis screens in four cancer cell lines using a guide RNA library predicted to install 32,476 variants in 11 cancer genes. We identify four functional classes of protein variants modulating drug sensitivity and use single-cell transcriptomics to reveal how these variants operate through distinct mechanisms, including eliciting a drug-addicted cell state. We identify variants that can be targeted with alternative inhibitors to overcome resistance and functionally validate an epidermal growth factor receptor (EGFR) variant that sensitizes lung cancer cells to EGFR inhibitors. Our variant-to-function map has implications for patient stratification, therapy combinations and drug scheduling in cancer treatment.

Predicting Mutations Generated by Cas9, Base Editing, and Prime Editing in Mammalian Cells.

The first fruits of the CRISPR-Cas revolution are starting to enter the clinic, with gene editing therapies offering solutions to previously incurable genetic diseases. The success of such applications hinges on control over the mutations that are generated, which are known to vary depending on the targeted locus. In this review, we present the current state of understanding and predicting CRISPR-Cas cutting, base editing, and prime editing outcomes in mammalian cells. We first provide an introduction to the basics of DNA repair and machine learning that the models rely on. We then overview the datasets and methods created for characterizing edits at scale, as well as the insights that have been derived from them. The predictions generated from these models serve as a foundation for designing efficient experiments across the broad contexts where these tools are applied.

Prediction of prime editing insertion efficiencies using sequence features and DNA repair determinants.

Most short sequences can be precisely written into a selected genomic target using prime editing; however, it remains unclear what factors govern insertion. We design a library of 3,604 sequences of various lengths and measure the frequency of their insertion into four genomic sites in three human cell lines, using different prime editor systems in varying DNA repair contexts. We find that length, nucleotide composition and secondary structure of the insertion sequence all affect insertion rates. We also discover that the 3' flap nucleases TREX1 and TREX2 suppress the insertion of longer sequences. Combining the sequence and repair features into a machine learning model, we can predict relative frequency of insertions into a site with R = 0.70. Finally, we demonstrate how our accurate prediction and user-friendly software help choose codon variants of common fusion tags that insert at high efficiency, and provide a catalog of empirically determined insertion rates for over a hundred useful sequences.

BTBBCL6 dimers as building blocks for reversible drug-induced protein oligomerization.

Here, we characterize the BTB domain of the transcription factor BCL6 (BTBBCL6) as a small-molecule-controlled, reversible oligomerization switch, which oligomerizes upon BI-3802 treatment and de-oligomerizes upon addition of BI-3812. We show that the magnitude of oligomerization can be controlled in vitro by BI-3802 concentration and exposure time. In cellular models, exposure to BI-3802/BI-3812 can drive multiple cycles of foci formation consisting of BTBBCL6 fused to EGFP, which are not degraded due to the lack of a degron. We generated an epidermal growth factor receptor (EGFR)-BTBBCL6 fusion. Treatment with BI-3802, as an ON switch, induced EGFR-BTBBCL6 phosphorylation and activation of downstream effectors, which could in part be reversed by the addition of BI-3812, as an OFF switch. Finally, BI-3802-induced oligomerization of the EGFR-BTBBCL6 fusion enhanced proliferation of an EGF-dependent cell line in absence of EGF. These results demonstrate the successful application of small-molecule-induced, reversible oligomerization as a switch for synthetic biology.

Degradation of GSPT1 causes TP53-independent cell death in leukemia while sparing normal hematopoietic stem cells.

Targeted protein degradation is a rapidly advancing and expanding therapeutic approach. Drugs that degrade GSPT1 via the CRL4CRBN ubiquitin ligase are a new class of cancer therapy in active clinical development with evidence of activity against acute myeloid leukemia in early-phase trials. However, other than activation of the integrated stress response, the downstream effects of GSPT1 degradation leading to cell death are largely undefined, and no murine models are available to study these agents. We identified the domains of GSPT1 essential for cell survival and show that GSPT1 degradation leads to impaired translation termination, activation of the integrated stress response pathway, and TP53-independent cell death. CRISPR/Cas9 screens implicated decreased translation initiation as protective following GSPT1 degradation, suggesting that cells with higher levels of translation are more susceptible to the effects of GSPT1 degradation. We defined 2 Crbn amino acids that prevent Gspt1 degradation in mice, generated a knockin mouse with alteration of these residues, and demonstrated the efficacy of GSPT1-degrading drugs in vivo with relative sparing of numbers and function of long-term hematopoietic stem cells. Our results provide a mechanistic basis for the use of GSPT1 degraders for the treatment of cancer, including TP53-mutant acute myeloid leukemia.

Selection of Oncogenic Mutant Clones in Normal Human Skin Varies with Body Site.

Skin cancer risk varies substantially across the body, yet how this relates to the mutations found in normal skin is unknown. Here we mapped mutant clones in skin from high- and low-risk sites. The density of mutations varied by location. The prevalence of NOTCH1 and FAT1 mutations in forearm, trunk, and leg skin was similar to that in keratinocyte cancers. Most mutations were caused by ultraviolet light, but mutational signature analysis suggested differences in DNA-repair processes between sites. Eleven mutant genes were under positive selection, with TP53 preferentially selected in the head and FAT1 in the leg. Fine-scale mapping revealed 10% of clones had copy-number alterations. Analysis of hair follicles showed mutations in the upper follicle resembled adjacent skin, but the lower follicle was sparsely mutated. Normal skin is a dense patchwork of mutant clones arising from competitive selection that varies by location. SIGNIFICANCE: Mapping mutant clones across the body reveals normal skin is a dense patchwork of mutant cells. The variation in cancer risk between sites substantially exceeds that in mutant clone density. More generally, mutant genes cannot be assigned as cancer drivers until their prevalence in normal tissue is known.See related commentary by De Dominici and DeGregori, p. 227.This article is highlighted in the In This Issue feature, p. 211.