Denaturing purifications demonstrate that PRC2 and other widely reported chromatin proteins do not appear to bind directly to RNA in vivo.

Polycomb repressive complex 2 (PRC2) is reported to bind to many RNAs and has become a central player in reports of how long non-coding RNAs (lncRNAs) regulate gene expression. Yet, there is a growing discrepancy between the biochemical evidence supporting specific lncRNA-PRC2 interactions and functional evidence demonstrating that PRC2 is often dispensable for lncRNA function. Here, we revisit the evidence supporting RNA binding by PRC2 and show that many reported interactions may not occur in vivo. Using denaturing purification of in vivo crosslinked RNA-protein complexes in human and mouse cell lines, we observe a loss of detectable RNA binding to PRC2 and chromatin-associated proteins previously reported to bind RNA (CTCF, YY1, and others), despite accurately mapping bona fide RNA-binding sites across others (SPEN, TET2, and others). Taken together, these results argue for a critical re-evaluation of the broad role of RNA binding to orchestrate various chromatin regulatory mechanisms.

In vivo CRISPR editing with no detectable genome-wide off-target mutations.

CRISPR-Cas genome-editing nucleases hold substantial promise for developing human therapeutic applications1-6 but identifying unwanted off-target mutations is important for clinical translation7. A well-validated method that can reliably identify off-targets in vivo has not been described to date, which means it is currently unclear whether and how frequently these mutations occur. Here we describe 'verification of in vivo off-targets' (VIVO), a highly sensitive strategy that can robustly identify the genome-wide off-target effects of CRISPR-Cas nucleases in vivo. We use VIVO and a guide RNA deliberately designed to be promiscuous to show that CRISPR-Cas nucleases can induce substantial off-target mutations in mouse livers in vivo. More importantly, we also use VIVO to show that appropriately designed guide RNAs can direct efficient in vivo editing in mouse livers with no detectable off-target mutations. VIVO provides a general strategy for defining and quantifying the off-target effects of gene-editing nucleases in whole organisms, thereby providing a blueprint to foster the development of therapeutic strategies that use in vivo gene editing.

Analysis of off-target effects in CRISPR-based gene drives in the human malaria mosquito.

CRISPR-Cas9 nuclease-based gene drives have been developed toward the aim of control of the human malaria vector Anopheles gambiae Gene drives are based on an active source of Cas9 nuclease in the germline that promotes super-Mendelian inheritance of the transgene by homology-directed repair ("homing"). Understanding whether CRISPR-induced off-target mutations are generated in Anopheles mosquitoes is an important aspect of risk assessment before any potential field release of this technology. We compared the frequencies and the propensity of off-target events to occur in four different gene-drive strains, including a deliberately promiscuous set-up, using a nongermline restricted promoter for SpCas9 and a guide RNA with many closely related sites (two or more mismatches) across the mosquito genome. Under this scenario we observed off-target mutations at frequencies no greater than 1.42%. We witnessed no evidence that CRISPR-induced off-target mutations were able to accumulate (or drive) in a mosquito population, despite multiple generations' exposure to the CRISPR-Cas9 nuclease construct. Furthermore, judicious design of the guide RNA used for homing of the CRISPR construct, combined with tight temporal constriction of Cas9 expression to the germline, rendered off-target mutations undetectable. The findings of this study represent an important milestone for the understanding and managing of CRISPR-Cas9 specificity in mosquitoes, and demonstrates that CRISPR off-target editing in the context of a mosquito gene drive can be reduced to minimal levels.

Novel Biomarkers for Inflammatory Bowel Disease and Colorectal Cancer: An Interplay between Metabolic Dysregulation and Excessive Inflammation.

Persistent inflammation can trigger altered epigenetic, inflammatory, and bioenergetic states. Inflammatory bowel disease (IBD) is an idiopathic disease characterized by chronic inflammation of the gastrointestinal tract, with evidence of subsequent metabolic syndrome disorder. Studies have demonstrated that as many as 42% of patients with ulcerative colitis (UC) who are found to have high-grade dysplasia, either already had colorectal cancer (CRC) or develop it within a short time. The presence of low-grade dysplasia is also predictive of CRC. Many signaling pathways are shared among IBD and CRC, including cell survival, cell proliferation, angiogenesis, and inflammatory signaling pathways. Current IBD therapeutics target a small subset of molecular drivers of IBD, with many focused on the inflammatory aspect of the pathways. Thus, there is a great need to identify biomarkers of both IBD and CRC, that can be predictive of therapeutic efficacy, disease severity, and predisposition to CRC. In this study, we explored the changes in biomarkers specific for inflammatory, metabolic, and proliferative pathways, to help determine the relevance to both IBD and CRC. Our analysis demonstrated, for the first time in IBD, the loss of the tumor suppressor protein Ras associated family protein 1A (RASSF1A), via epigenetic changes, the hyperactivation of the obligate kinase of the NOD2 pathogen recognition receptor (receptor interacting protein kinase 2 [RIPK2]), the loss of activation of the metabolic kinase, AMP activated protein kinase (AMPKα1), and, lastly, the activation of the transcription factor and kinase Yes associated protein (YAP) kinase, that is involved in proliferation of cells. The expression and activation status of these four elements are mirrored in IBD, CRC, and IBD-CRC patients and, importantly, in matched blood and biopsy samples. The latter would suggest that biomarker analysis can be performed non-invasively, to understand IBD and CRC, without the need for invasive and costly endoscopic analysis. This study, for the first time, illustrates the need to understand IBD or CRC beyond an inflammatory perspective and the value of therapeutics directed to reset altered proliferative and metabolic states within the colon. The use of such therapeutics may truly drive patients into remission.

Therapeutic avenues for cancer neuroscience: translational frontiers and clinical opportunities.

With increasing attention on the essential roles of the tumour microenvironment in recent years, the nervous system has emerged as a novel and crucial facilitator of cancer growth. In this Review, we describe the foundational, translational, and clinical advances illustrating how nerves contribute to tumour proliferation, stress adaptation, immunomodulation, metastasis, electrical hyperactivity and seizures, and neuropathic pain. Collectively, this expanding knowledge base reveals multiple therapeutic avenues for cancer neuroscience that warrant further exploration in clinical studies. We discuss the available clinical data, including ongoing trials investigating novel agents targeting the tumour-nerve axis, and the therapeutic potential for repurposing existing neuroactive drugs as an anti-cancer approach, particularly in combination with established treatment regimens. Lastly, we discuss the clinical challenges of these treatment strategies and highlight unanswered questions and future directions in the burgeoning field of cancer neuroscience.

HiChIRP reveals RNA-associated chromosome conformation.

Modular domains of long non-coding RNAs can serve as scaffolds to bring distant regions of the linear genome into spatial proximity. Here, we present HiChIRP, a method leveraging bio-orthogonal chemistry and optimized chromosome conformation capture conditions, which enables interrogation of chromatin architecture focused around a specific RNA of interest down to approximately ten copies per cell. HiChIRP of three nuclear RNAs reveals insights into promoter interactions (7SK), telomere biology (telomerase RNA component) and inflammatory gene regulation (lincRNA-EPS).

Activities and specificities of CRISPR/Cas9 and Cas12a nucleases for targeted mutagenesis in maize.

CRISPR/Cas9 and Cas12a (Cpf1) nucleases are two of the most powerful genome editing tools in plants. In this work, we compared their activities by targeting maize glossy2 gene coding region that has overlapping sequences recognized by both nucleases. We introduced constructs carrying SpCas9-guide RNA (gRNA) and LbCas12a-CRISPR RNA (crRNA) into maize inbred B104 embryos using Agrobacterium-mediated transformation. On-target mutation analysis showed that 90%-100% of the Cas9-edited T0 plants carried indel mutations and 63%-77% of them were homozygous or biallelic mutants. In contrast, 0%-60% of Cas12a-edited T0 plants had on-target mutations. We then conducted CIRCLE-seq analysis to identify genome-wide potential off-target sites for Cas9. A total of 18 and 67 potential off-targets were identified for the two gRNAs, respectively, with an average of five mismatches compared to the target sites. Sequencing analysis of a selected subset of the off-target sites revealed no detectable level of mutations in the T1 plants, which constitutively express Cas9 nuclease and gRNAs. In conclusion, our results suggest that the CRISPR/Cas9 system used in this study is highly efficient and specific for genome editing in maize, while CRISPR/Cas12a needs further optimization for improved editing efficiency.

Comparison of SHAPE reagents for mapping RNA structures inside living cells.

Recent advances in SHAPE technology have converted the classic primer extension method to next-generation sequencing platforms, allowing transcriptome-level analysis of RNA secondary structure. In particular, icSHAPE and SHAPE-MaP, using NAI-N3 and 1M7 reagents, respectively, are methods that claim to measure in vivo structure with high-throughput sequencing. However, these compounds have not been compared on an unbiased, raw-signal level. Here, we directly compare several in vivo SHAPE acylation reagents using the simple primer extension assay. We conclude that while multiple SHAPE technologies are effective at measuring purified RNAs in vitro, acylimidazole reagents NAI and NAI-N3 give markedly greater signals with lower background than 1M7 for in vivo measurement of the RNA structurome.

Strategic sequences in fat graft survival.

Although lipotransfer, or fat grafting, is a commonly used procedure in aesthetic and reconstructive surgery, there is still variability in graft survival and neoadipogenesis from one procedure to the next. A better understanding of the sequential molecular events occurring with grafting would allow us to strategize methods to improve the regenerative potency of the grafted tissue. These steps begin with an autophagic process, followed by the inclusion of stromal vascular fraction and matrix components. By tailoring and modifying each of these steps for a particular type of aesthetic or reconstructive procedure, strategic sequencing represents a dynamic approach to lipotransfer with the aim of maximizing adipocyte viability and growth. In the implementation of the strategic sequence, it remains important to consider the clinical viability of each step and its compliance with the US Food and Drug Administration regulations. This review highlights the basic science behind clinically translatable approaches to supplementing various fat grafting procedures.

ChIP-DIP: A multiplexed method for mapping hundreds of proteins to DNA uncovers diverse regulatory elements controlling gene expression.

Gene expression is controlled by the dynamic localization of thousands of distinct regulatory proteins to precise regions of DNA. Understanding this cell-type specific process has been a goal of molecular biology for decades yet remains challenging because most current DNA-protein mapping methods study one protein at a time. To overcome this, we developed ChIP-DIP (ChIP Done In Parallel), a split-pool based method that enables simultaneous, genome-wide mapping of hundreds of diverse regulatory proteins in a single experiment. We demonstrate that ChIP-DIP generates highly accurate maps for all classes of DNA-associated proteins, including histone modifications, chromatin regulators, transcription factors, and RNA Polymerases. Using these data, we explore quantitative combinations of protein localization on genomic DNA to define distinct classes of regulatory elements and their functional activity. Our data demonstrate that ChIP-DIP enables the generation of 'consortium level', context-specific protein localization maps within any molecular biology lab.

SPIDR: a highly multiplexed method for mapping RNA-protein interactions uncovers a potential mechanism for selective translational suppression upon cellular stress.

RNA binding proteins (RBPs) play crucial roles in regulating every stage of the mRNA life cycle and mediating non-coding RNA functions. Despite their importance, the specific roles of most RBPs remain unexplored because we do not know what specific RNAs most RBPs bind. Current methods, such as crosslinking and immunoprecipitation followed by sequencing (CLIP-seq), have expanded our knowledge of RBP-RNA interactions but are generally limited by their ability to map only one RBP at a time. To address this limitation, we developed SPIDR (Split and Pool Identification of RBP targets), a massively multiplexed method to simultaneously profile global RNA binding sites of dozens to hundreds of RBPs in a single experiment. SPIDR employs split-pool barcoding coupled with antibody-bead barcoding to increase the throughput of current CLIP methods by two orders of magnitude. SPIDR reliably identifies precise, single-nucleotide RNA binding sites for diverse classes of RBPs simultaneously. Using SPIDR, we explored changes in RBP binding upon mTOR inhibition and identified that 4EBP1 acts as a dynamic RBP that selectively binds to 5'-untranslated regions of specific translationally repressed mRNAs only upon mTOR inhibition. This observation provides a potential mechanism to explain the specificity of translational regulation controlled by mTOR signaling. SPIDR has the potential to revolutionize our understanding of RNA biology and both transcriptional and post-transcriptional gene regulation by enabling rapid, de novo discovery of RNA-protein interactions at an unprecedented scale.

Precise DNA cleavage using CRISPR-SpRYgests.

Methods for in vitro DNA cleavage and molecular cloning remain unable to precisely cleave DNA directly adjacent to bases of interest. Restriction enzymes (REs) must bind specific motifs, whereas wild-type CRISPR-Cas9 or CRISPR-Cas12 nucleases require protospacer adjacent motifs (PAMs). Here we explore the utility of our previously reported near-PAMless SpCas9 variant, named SpRY, to serve as a universal DNA cleavage tool for various cloning applications. By performing SpRY DNA digests (SpRYgests) using more than 130 guide RNAs (gRNAs) sampling a wide diversity of PAMs, we discovered that SpRY is PAMless in vitro and can cleave DNA at practically any sequence, including sites refractory to cleavage with wild-type SpCas9. We illustrate the versatility and effectiveness of SpRYgests to improve the precision of several cloning workflows, including those not possible with REs or canonical CRISPR nucleases. We also optimize a rapid and simple one-pot gRNA synthesis protocol to streamline SpRYgest implementation. Together, SpRYgests can improve various DNA engineering applications that benefit from precise DNA breaks.

Therapy-associated remodeling of pancreatic cancer revealed by single-cell spatial transcriptomics and optimal transport analysis.

In combination with cell intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged high-plex single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cell-cell interactions in human pancreatic cancer associated with specific malignant subtypes and neoadjuvant chemotherapy/radiotherapy. We developed Spatially Constrained Optimal Transport Interaction Analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligand-receptor gene expression. Our results uncovered a marked change in ligand-receptor interactions between cancer-associated fibroblasts and malignant cells in response to treatment, which was supported by orthogonal datasets, including an ex vivo tumoroid co-culture system. Overall, this study demonstrates that characterization of the tumor microenvironment using high-plex single-cell spatial transcriptomics allows for identification of molecular interactions that may play a role in the emergence of chemoresistance and establishes a translational spatial biology paradigm that can be broadly applied to other malignancies, diseases, and treatments.

DNA repair and immune checkpoint blockade response.

Immune checkpoint blockade (ICB) has shown immense promise for treating patients with various cancer types, but its effectiveness relies on our ability to identify likely responders. Here, we examined the association between mutations in 25 core DNA repair genes and ICB outcomes in 6619 patients across 9 cancer types with advanced disease and MSK-IMPACT tumor sequencing. Notably, we observed that mutations in 7 of the DNA repair genes (ATM, ATR, POLE, ERCC4, NBN, RAD50, PARP1) were significantly associated with improved overall survival in ICB-treated patients (p < 0.05 for all) and had significant interaction with treatment (pinteraction <0.05 for all). Similarly, DNA repair mutations were enriched in other cancer types not previously assessed and primary tumors of unknown origins, suggesting that mutations could serve as a biomarker independent of cancer type. Although our cohort was enriched in certain cancer types, such as melanoma and non-small cell lung cancer, and clinically matched samples were not assessed, our study provides a robust approach in characterizing clinically-adoptable biomarkers that can select for potential ICB responders.

Treatment-associated remodeling of the pancreatic cancer endothelium at single-cell resolution.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most treatment refractory and lethal malignancies. The diversity of endothelial cell (EC) lineages in the tumor microenvironment (TME) impacts the efficacy of antineoplastic therapies, which in turn remodel EC states and distributions. Here, we present a single-cell resolution framework of diverse EC lineages in the PDAC TME in the context of neoadjuvant chemotherapy, radiotherapy, and losartan. We analyzed a custom single-nucleus RNA-seq dataset derived from 37 primary PDAC specimens (18 untreated, 14 neoadjuvant FOLFIRINOX + chemoradiotherapy, 5 neoadjuvant FOLFIRINOX + chemoradiotherapy + losartan). A single-nucleus transcriptome analysis of 15,185 EC profiles revealed two state programs (ribosomal, cycling), four lineage programs (capillary, arterial, venous, lymphatic), and one program that did not overlap significantly with prior signatures but was enriched in pathways involved in vasculogenesis, stem-like state, response to wounding and hypoxia, and endothelial-to-mesenchymal transition (reactive EndMT). A bulk transcriptome analysis of two independent cohorts (n = 269 patients) revealed that the lymphatic and reactive EndMT lineage programs were significantly associated with poor clinical outcomes. While losartan and proton therapy were associated with reduced lymphatic ECs, these therapies also correlated with an increase in reactive EndMT. Thus, the development and inclusion of EndMT-inhibiting drugs (e.g., nintedanib) to a neoadjuvant chemoradiotherapy regimen featuring losartan and/or proton therapy may be most effective in depleting both lymphatic and reactive EndMT populations and potentially improving patient outcomes.

Genomic alterations predictive of poor clinical outcomes in pan-cancer.

BACKGROUND: Genomic alterations are highly frequent across cancers, but their prognostic impact is not well characterized in pan-cancer cohorts. Here, we use pan-cancer cohorts from TCGA and MSK-IMPACT to evaluate the associations of common genomic alterations with poor clinical outcome. MATERIALS AND METHODS: Genomic alterations in commonly altered genes were extracted from Pan-Cancer TCGA and MSK-IMPACT cohorts. Multivariable Cox regression analyses stratified by cancer type defined adjusted hazard ratios (AHRs) for disease-specific survival (DSS), progression-free survival (PFS) and overall survival (OS). RESULTS: Using TCGA we identified 32 mutated genes, and 15 copy number (CN) genes with frequency >= 4% in 9,104 patients across 28 cancers. On UVA, having a TP53-mutations or any mutation in the 31 genes (mut31) were associated with worse PFS (HR: 1.22, p < 0.0001 and HR: 1.1, p = 0.04, respectively) and DSS (HR: 1.38, p < 0.0001, and HR: 1.16, p = 0.03, respectively). CDKN2A, PTEN deletions, and MYC-amplifications were associated with PFS and DSS (p < 0.05 for all). On MVA, including TP53-mutations, mut31, CDKN2A-deletion, PTEN-deletion, and MYC-amplification, all five alterations were independently prognostic of poor PFS and DSS. Similar results were observed in an independent cohort from MSK-IMPACT (n = 7,051) where TP53 was associated with poor OS independent of mut31 and CN alterations in CDKN2A, PTEN, and MYC in primary tumors (p < 0.0001). CONCLUSIONS: TP53-mutations, CDKN2A-deletion, PTEN-deletion, and MYC-amplification are independent pan-cancer prognostic genomic alterations.

Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal and treatment-refractory cancer. Molecular stratification in pancreatic cancer remains rudimentary and does not yet inform clinical management or therapeutic development. Here, we construct a high-resolution molecular landscape of the cellular subtypes and spatial communities that compose PDAC using single-nucleus RNA sequencing and whole-transcriptome digital spatial profiling (DSP) of 43 primary PDAC tumor specimens that either received neoadjuvant therapy or were treatment naive. We uncovered recurrent expression programs across malignant cells and fibroblasts, including a newly identified neural-like progenitor malignant cell program that was enriched after chemotherapy and radiotherapy and associated with poor prognosis in independent cohorts. Integrating spatial and cellular profiles revealed three multicellular communities with distinct contributions from malignant, fibroblast and immune subtypes: classical, squamoid-basaloid and treatment enriched. Our refined molecular and cellular taxonomy can provide a framework for stratification in clinical trials and serve as a roadmap for therapeutic targeting of specific cellular phenotypes and multicellular interactions.

Pan-Cancer Survival Classification With Clinicopathological and Targeted Gene Expression Features.

Prognostication for patients with cancer is important for clinical planning and management, but remains challenging given the large number of factors that can influence outcomes. As such, there is a need to identify features that can robustly predict patient outcomes. We evaluated 8608 patient tumor samples across 16 cancer types from The Cancer Genome Atlas and generated distinct survival classifiers for each using clinical and histopathological data accessible to standard oncology workflows. For cancers that had poor model performance, we deployed a random-forest-embedded sequential forward selection approach that began with an initial subset of the 15 most predictive clinicopathological features before sequentially appending the next most informative gene as an additional feature. With classifiers derived from clinical and histopathological features alone, we observed cancer-type-dependent model performance and an area under the receiver operating curve (AUROC) range of 0.65 to 0.91 across all 16 cancer types for 1- and 3-year survival prediction, with some classifiers consistently outperforming those for others. As such, for cancers that had poor model performance, we posited that the addition of more complex biomolecular features could enhance our ability to prognose patients where clinicopathological features were insufficient. With the inclusion of gene expression data, model performance for 3 select cancers (glioblastoma, stomach/gastric adenocarcinoma, ovarian serous carcinoma) markedly increased from initial AUROC scores of 0.66, 0.69, and 0.67 to 0.76, 0.77, and 0.77, respectively. As a whole, this study provides a thorough examination of the relative contributions of clinical, pathological, and gene expression data in predicting overall survival and reveals cancer types for which clinical features are already strong predictors and those where additional biomolecular information is needed.