Targeting histone acetylation dynamics and oncogenic transcription by catalytic P300/CBP inhibition.

To separate causal effects of histone acetylation on chromatin accessibility and transcriptional output, we used integrated epigenomic and transcriptomic analyses following acute inhibition of major cellular lysine acetyltransferases P300 and CBP in hematological malignancies. We found that catalytic P300/CBP inhibition dynamically perturbs steady-state acetylation kinetics and suppresses oncogenic transcriptional networks in the absence of changes to chromatin accessibility. CRISPR-Cas9 screening identified NCOR1 and HDAC3 transcriptional co-repressors as the principal antagonists of P300/CBP by counteracting acetylation turnover kinetics. Finally, deacetylation of H3K27 provides nucleation sites for reciprocal methylation switching, a feature that can be exploited therapeutically by concomitant KDM6A and P300/CBP inhibition. Overall, this study indicates that the steady-state histone acetylation-methylation equilibrium functions as a molecular rheostat governing cellular transcription that is amenable to therapeutic exploitation as an anti-cancer regimen.

Complementarity and redundancy of IL-22-producing innate lymphoid cells.

Intestinal T cells and group 3 innate lymphoid cells (ILC3 cells) control the composition of the microbiota and gut immune responses. Within the gut, ILC3 subsets coexist that either express or lack the natural cytoxicity receptor (NCR) NKp46. We identified here the transcriptional signature associated with the transcription factor T-bet-dependent differentiation of NCR(-) ILC3 cells into NCR(+) ILC3 cells. Contrary to the prevailing view, we found by conditional deletion of the key ILC3 genes Stat3, Il22, Tbx21 and Mcl1 that NCR(+) ILC3 cells were redundant for the control of mouse colonic infection with Citrobacter rodentium in the presence of T cells. However, NCR(+) ILC3 cells were essential for cecal homeostasis. Our data show that interplay between intestinal ILC3 cells and adaptive lymphocytes results in robust complementary failsafe mechanisms that ensure gut homeostasis.

The tidyomics ecosystem: enhancing omic data analyses.

The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools.

Genome sequencing and analysis of the Tasmanian devil and its transmissible cancer.

The Tasmanian devil (Sarcophilus harrisii), the largest marsupial carnivore, is endangered due to a transmissible facial cancer spread by direct transfer of living cancer cells through biting. Here we describe the sequencing, assembly, and annotation of the Tasmanian devil genome and whole-genome sequences for two geographically distant subclones of the cancer. Genomic analysis suggests that the cancer first arose from a female Tasmanian devil and that the clone has subsequently genetically diverged during its spread across Tasmania. The devil cancer genome contains more than 17,000 somatic base substitution mutations and bears the imprint of a distinct mutational process. Genotyping of somatic mutations in 104 geographically and temporally distributed Tasmanian devil tumors reveals the pattern of evolution and spread of this parasitic clonal lineage, with evidence of a selective sweep in one geographical area and persistence of parallel lineages in other populations.

Author Correction: Determination of RNA structural diversity and its role in HIV-1 RNA splicing.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Determination of RNA structural diversity and its role in HIV-1 RNA splicing.

Human immunodeficiency virus 1 (HIV-1) is a retrovirus with a ten-kilobase single-stranded RNA genome. HIV-1 must express all of its gene products from a single primary transcript, which undergoes alternative splicing to produce diverse protein products that include structural proteins and regulatory factors1,2. Despite the critical role of alternative splicing, the mechanisms that drive the choice of splice site are poorly understood. Synonymous RNA mutations that lead to severe defects in splicing and viral replication indicate the presence of unknown cis-regulatory elements3. Here we use dimethyl sulfate mutational profiling with sequencing (DMS-MaPseq) to investigate the structure of HIV-1 RNA in cells, and develop an algorithm that we name 'detection of RNA folding ensembles using expectation-maximization' (DREEM), which reveals the alternative conformations that are assumed by the same RNA sequence. Contrary to previous models that have analysed population averages4, our results reveal heterogeneous regions of RNA structure across the entire HIV-1 genome. In addition to confirming that in vitro characterized5 alternative structures for the HIV-1 Rev responsive element also exist in cells, we discover alternative conformations at critical splice sites that influence the ratio of transcript isoforms. Our simultaneous measurement of splicing and intracellular RNA structure provides evidence for the long-standing hypothesis6-8 that heterogeneity in RNA conformation regulates splice-site use and viral gene expression.

sccomp: Robust differential composition and variability analysis for single-cell data.

Cellular omics such as single-cell genomics, proteomics, and microbiomics allow the characterization of tissue and microbial community composition, which can be compared between conditions to identify biological drivers. This strategy has been critical to revealing markers of disease progression, such as cancer and pathogen infection. A dedicated statistical method for differential variability analysis is lacking for cellular omics data, and existing methods for differential composition analysis do not model some compositional data properties, suggesting there is room to improve model performance. Here, we introduce sccomp, a method for differential composition and variability analyses that jointly models data count distribution, compositionality, group-specific variability, and proportion mean-variability association, being aware of outliers. sccomp provides a comprehensive analysis framework that offers realistic data simulation and cross-study knowledge transfer. Here, we demonstrate that mean-variability association is ubiquitous across technologies, highlighting the inadequacy of the very popular Dirichlet-multinomial distribution. We show that sccomp accurately fits experimental data, significantly improving performance over state-of-the-art algorithms. Using sccomp, we identified differential constraints and composition in the microenvironment of primary breast cancer.

A single-cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast.

To examine global changes in breast heterogeneity across different states, we determined the single-cell transcriptomes of > 340,000 cells encompassing normal breast, preneoplastic BRCA1+/- tissue, the major breast cancer subtypes, and pairs of tumors and involved lymph nodes. Elucidation of the normal breast microenvironment revealed striking changes in the stroma of post-menopausal women. Single-cell profiling of 34 treatment-naive primary tumors, including estrogen receptor (ER)+ , HER2+ , and triple-negative breast cancers, revealed comparable diversity among cancer cells and a discrete subset of cycling cells. The transcriptomes of preneoplastic BRCA1+/- tissue versus tumors highlighted global changes in the immune microenvironment. Within the tumor immune landscape, proliferative CD8+ T cells characterized triple-negative and HER2+ cancers but not ER+ tumors, while all subtypes comprised cycling tumor-associated macrophages, thus invoking potentially different immunotherapy targets. Copy number analysis of paired ER+ tumors and lymph nodes indicated seeding by genetically distinct clones or mass migration of primary tumor cells into axillary lymph nodes. This large-scale integration of patient samples provides a high-resolution map of cell diversity in normal and cancerous human breast.

A systematic review of computational methods for designing efficient guides for CRISPR DNA base editor systems.

In only a few years, as a breakthrough technology, clustered regularly interspaced short palindromic repeats/CRISPR-associated protein (CRISPR/Cas) gene-editing systems have ushered in the era of genome engineering with a plethora of applications. One of the most promising CRISPR tools, so-called base editors, opened an exciting avenue for exploring new therapeutic approaches through controlled mutagenesis. However, the efficiency of a base editor guide varies depending on several biological determinants, such as chromatin accessibility, DNA repair proteins, transcriptional activity, factors related to local sequence context and so on. Thus, the success of genetic perturbation directed by CRISPR/Cas base-editing systems relies on an optimal single guide RNA (sgRNA) design, taking those determinants into account. Although there is 11 commonly used software to design guides specifically for base editors, only three of them investigated and implemented those biological determinants into their models. This review presents the key features, capabilities and limitations of all currently available software with a particular focus on predictive model-based algorithms. Here, we summarize existing software for sgRNA design and provide a base for improving the efficiency of existing available software suites for precise target base editing.

A risk-reward examination of sample multiplexing reagents for single cell RNA-Seq.

Single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for understanding cellular heterogeneity and function. However the choice of sample multiplexing reagents can impact data quality and experimental outcomes. In this study, we compared various multiplexing reagents, including MULTI-Seq, Hashtag antibody, and CellPlex, across diverse sample types such as human peripheral blood mononuclear cells (PBMCs), mouse embryonic brain and patient-derived xenografts (PDXs). We found that all multiplexing reagents worked well in cell types robust to ex vivo manipulation but suffered from signal-to-noise issues in more delicate sample types. We compared multiple demultiplexing algorithms which differed in performance depending on data quality. We find that minor improvements to laboratory workflows such as titration and rapid processing are critical to optimal performance. We also compared the performance of fixed scRNA-Seq kits and highlight the advantages of the Parse Biosciences kit for fragile samples. Highly multiplexed scRNA-Seq experiments require more sequencing resources, therefore we evaluated CRISPR-based destruction of non-informative genes to enhance sequencing value. Our comprehensive analysis provides insights into the selection of appropriate sample multiplexing reagents and protocols for scRNA-Seq experiments, facilitating more accurate and cost-effective studies.

cellsig plug-in enhances CIBERSORTx signature selection for multidataset transcriptomes with sparse multilevel modelling.

MOTIVATION: The precise characterization of cell-type transcriptomes is pivotal to understanding cellular lineages, deconvolution of bulk transcriptomes, and clinical applications. Single-cell RNA sequencing resources like the Human Cell Atlas have revolutionised cell-type profiling. However, challenges persist due to data heterogeneity and discrepancies across different studies. One limitation of prevailing tools such as CIBERSORTx is their inability to address hierarchical data structures and handle nonoverlapping gene sets across samples, relying on filtering or imputation. RESULTS: Here, we present cellsig, a Bayesian sparse multilevel model designed to improve signature estimation by adjusting data for multilevel effects and modelling for gene-set sparsity. Our model is tailored to large-scale, heterogeneous pseudobulk and bulk RNA sequencing data collections with nonoverlapping gene sets. We tested the performances of cellsig on a novel curated Human Bulk Cell-type Catalogue, which harmonizes 1435 samples across 58 datasets. We show that cellsig significantly enhances cell-type marker gene ranking performance. This approach is valuable for cell-type signature selection, with implications for marker gene validation, single-cell annotation, and deconvolution benchmarks. AVAILABILITY AND IMPLEMENTATION: Codes and the interactive app are available at https://github.com/stemangiola/cellsig; and the database is available at https://doi.org/10.5281/zenodo.7582421.

Uncovering molecular mechanisms for amelanotic/hypopigmented primary cutaneous melanoma.

BACKGROUND: A portion of approximately 2-20% of cutaneous melanoma (CM) are diagnosed as amelanotic/hypopigmented melanoma (AHM) and represent a challenge for early diagnosis. OBJECTIVES: Since the degree to which somatic mutations and copy number aberrations (CNA) in genes associated with skin-lightening or albinism may contribute to the loss of tumour pigmentation in AHM samples has not yet been addressed, we have investigated loss of function mutations of key pigmentation genes in matched germline and AHM as well as pigmented melanoma (PM) tumour DNA samples. METHODS: An analysis of clinical and histopathological characteristics together with whole exome sequencing data of 34 fresh frozen primary CM, graded according to the amount of pigmentation present was performed. Together with germline and somatic variant analysis, 30 samples were previously analysed for CNA changes. This study focussed on germline and somatic variants in the coding region of 16 genes known to be associated with albinism/hypopigmentation or variation in human pigmentation in all samples. Chromosomal regions encompassing these 16 genes were examined for DNA copy loss or gain. RESULTS: The finding that red hair related MC1R and TYR R402Q loss of activity gene variant alleles and genotypes are associated with AHM was validated in this study. Germline AHM-related gene variants were enriched in 70% (n=7 of 10) of AHM patients vs 8.3% (n=2 of 24) of PM patients. This surprisingly high frequency of rare germline variants in AHM patients constitutes the "first hit" and confirms that AHM patients are more likely to be albinism allele carriers than patients with PM. Next, in CNA analysis of each tumour sample, 50% (n=4 of 8) AHM samples with a pigmentation gene variant had LOH in the region containing the corresponding gene, and 25% (=2 of 8) had loss-of-heterozygosity (LOH) in chromosomal regions of two AHM-related genes. CONCLUSIONS: This study proposes that the likely molecular mechanism for development of amelanogenesis in AHM is carriage of an albinism/hypopigmentation allele followed by LOH of the corresponding gene in the tumour.

Evolutionary analyses of the major variant surface antigen-encoding genes reveal population structure of Plasmodium falciparum within and between continents.

Malaria remains a major public health problem in many countries. Unlike influenza and HIV, where diversity in immunodominant surface antigens is understood geographically to inform disease surveillance, relatively little is known about the global population structure of PfEMP1, the major variant surface antigen of the malaria parasite Plasmodium falciparum. The complexity of the var multigene family that encodes PfEMP1 and that diversifies by recombination, has so far precluded its use in malaria surveillance. Recent studies have demonstrated that cost-effective deep sequencing of the region of var genes encoding the PfEMP1 DBLα domain and subsequent classification of within host sequences at 96% identity to define unique DBLα types, can reveal structure and strain dynamics within countries. However, to date there has not been a comprehensive comparison of these DBLα types between countries. By leveraging a bioinformatic approach (jumping hidden Markov model) designed specifically for the analysis of recombination within var genes and applying it to a dataset of DBLα types from 10 countries, we are able to describe population structure of DBLα types at the global scale. The sensitivity of the approach allows for the comparison of the global dataset to ape samples of Plasmodium Laverania species. Our analyses show that the evolution of the parasite population emerging out of Africa underlies current patterns of DBLα type diversity. Most importantly, we can distinguish geographic population structure within Africa between Gabon and Ghana in West Africa and Uganda in East Africa. Our evolutionary findings have translational implications in the context of globalization. Firstly, DBLα type diversity can provide a simple diagnostic framework for geographic surveillance of the rapidly evolving transmission dynamics of P. falciparum. It can also inform efforts to understand the presence or absence of global, regional and local population immunity to major surface antigen variants. Additionally, we identify a number of highly conserved DBLα types that are present globally that may be of biological significance and warrant further characterization.

StructuralVariantAnnotation: a R/Bioconductor foundation for a caller-agnostic structural variant software ecosystem.

SUMMARY: StructuralVariantAnnotation is an R/Bioconductor package that provides a framework for decoupling downstream analysis of structural variant breakpoints from upstream variant calling methods. It standardizes the representational format from BEDPE, or any of the three different notations supported by VCF into a breakpoint GRanges data structure suitable for use by the wider Bioconductor ecosystem. It handles both transitive breakpoints and duplication/insertion notational differences of identical variants-both common scenarios when comparing short/long read-based call sets that confound downstream analysis. StructuralVariantAnnotation provides the caller-agnostic foundation needed for a R/Bioconductor ecosystem of structural variant annotation, classification and interpretation tools able to handle both simple and complex genomic rearrangements. AVAILABILITY AND IMPLEMENTATION: StructuralVariantAnnotation is implemented in R and available for download as the Bioconductor StructuralVariantAnnotation package. Details can be found at https://www.bioconductor.org/packages/release/bioc/html/StructuralVariantAnnotation.html. It has been released under a GPL license.

The transcription factor Nerfin-1 prevents reversion of neurons into neural stem cells.

Cellular dedifferentiation is the regression of a cell from a specialized state to a more multipotent state and is implicated in cancer. However, the transcriptional network that prevents differentiated cells from reacquiring stem cell fate is so far unclear. Neuroblasts (NBs), the Drosophila neural stem cells, are a model for the regulation of stem cell self-renewal and differentiation. Here we show that the Drosophila zinc finger transcription factor Nervous fingers 1 (Nerfin-1) locks neurons into differentiation, preventing their reversion into NBs. Following Prospero-dependent neuronal specification in the ganglion mother cell (GMC), a Nerfin-1-specific transcriptional program maintains differentiation in the post-mitotic neurons. The loss of Nerfin-1 causes reversion to multipotency and results in tumors in several neural lineages. Both the onset and rate of neuronal dedifferentiation in nerfin-1 mutant lineages are dependent on Myc- and target of rapamycin (Tor)-mediated cellular growth. In addition, Nerfin-1 is required for NB differentiation at the end of neurogenesis. RNA sequencing (RNA-seq) and chromatin immunoprecipitation (ChIP) analysis show that Nerfin-1 administers its function by repression of self-renewing-specific and activation of differentiation-specific genes. Our findings support the model of bidirectional interconvertibility between neural stem cells and their post-mitotic progeny and highlight the importance of the Nerfin-1-regulated transcriptional program in neuronal maintenance.

Identifying Targets of Protective Antibodies against Severe Malaria in Papua, Indonesia, Using Locally Expressed Domains of Plasmodium falciparum Erythrocyte Membrane Protein 1.

Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1), a diverse family of multidomain proteins expressed on the surface of malaria-infected erythrocytes, is an important target of protective immunity against malaria. Our group recently studied transcription of the var genes encoding PfEMP1 in individuals from Papua, Indonesia, with severe or uncomplicated malaria. We cloned and expressed domains from 32 PfEMP1s, including 22 that were upregulated in severe malaria and 10 that were upregulated in uncomplicated malaria, using a wheat germ cell-free expression system. We used Luminex technology to measure IgG antibodies to these 32 domains and control proteins in 63 individuals (11 children). At presentation to hospital, levels of antibodies to PfEMP1 domains were either higher in uncomplicated malaria or were not significantly different between groups. Using principal component analysis, antibodies to 3 of 32 domains were highly discriminatory between groups. These included two domains upregulated in severe malaria, a DBLß13 domain and a CIDRα1.6 domain (which has been previously implicated in severe malaria pathogenesis), and a DBLδ domain that was upregulated in uncomplicated malaria. Antibody to control non-PfEMP1 antigens did not differ with disease severity. Antibodies to PfEMP1 domains differ with malaria severity. Lack of antibodies to locally expressed PfEMP1 types, including both domains previously associated with severe malaria and newly identified targets, may in part explain malaria severity in Papuan adults.

Interfacing Seurat with the R tidy universe.

MOTIVATION: Seurat is one of the most popular software suites for the analysis of single-cell RNA sequencing data. Considering the popularity of the tidyverse ecosystem, which offers a large set of data display, query, manipulation, integration and visualization utilities, a great opportunity exists to interface the Seurat object with the tidyverse. This interface gives the large data science community of tidyverse users the possibility to operate with familiar grammar. RESULTS: To provide Seurat with a tidyverse-oriented interface without compromising efficiency, we developed tidyseurat, a lightweight adapter to the tidyverse. Tidyseurat displays cell information as a tibble abstraction, allowing intuitively interfacing Seurat with dplyr, tidyr, ggplot2 and plotly packages powering efficient data manipulation, integration and visualization. Iterative analyses on data subsets are enabled by interfacing with the popular nest-map framework. AVAILABILITY AND IMPLEMENTATION: The software is freely available at cran.r-project.org/web/packages/tidyseurat and github.com/stemangiola/tidyseurat. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

VIRUSBreakend: Viral Integration Recognition Using Single Breakends.

MOTIVATION: Integration of viruses into infected host cell DNA can cause DNA damage and disrupt genes. Recent cost reductions and growth of whole genome sequencing has produced a wealth of data in which viral presence and integration detection is possible. While key research and clinically relevant insights can be uncovered, existing software has not achieved widespread adoption, limited in part due to high computational costs, the inability to detect a wide range of viruses, as well as precision and sensitivity. RESULTS: Here, we describe VIRUSBreakend, a high-speed tool that identifies viral DNA presence and genomic integration. It utilizes single breakends, breakpoints in which only one side can be unambiguously placed, in a novel virus-centric variant calling and assembly approach to identify viral integrations with high sensitivity and a near-zero false discovery rate. VIRUSBreakend detects viral integrations anywhere in the host genome including regions such as centromeres and telomeres unable to be called by existing tools. Applying VIRUSBreakend to a large metastatic cancer cohort, we demonstrate that it can reliably detect clinically relevant viral presence and integration including HPV, HBV, MCPyV, EBV and HHV-8. AVAILABILITY AND IMPLEMENTATION: VIRUSBreakend is part of the Genomic Rearrangement IDentification Software Suite (GRIDSS). It is available under a GPLv3 license from https://github.com/PapenfussLab/VIRUSBreakend. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Regulation of PRMT5-MDM4 axis is critical in the response to CDK4/6 inhibitors in melanoma.

Cyclin-dependent kinase 4/6 (CDK4/6) inhibitors are an established treatment in estrogen receptor-positive breast cancer and are currently in clinical development in melanoma, a tumor that exhibits high rates of CDK4 activation. We analyzed melanoma cells with acquired resistance to the CDK4/6 inhibitor palbociclib and demonstrate that the activity of PRMT5, a protein arginine methyltransferase and indirect target of CDK4, is essential for CDK4/6 inhibitor sensitivity. By indirectly suppressing PRMT5 activity, palbociclib alters the pre-mRNA splicing of MDM4, a negative regulator of p53, leading to decreased MDM4 protein expression and subsequent p53 activation. In turn, p53 induces p21, leading to inhibition of CDK2, the main kinase substituting for CDK4/6 and a key driver of resistance to palbociclib. Loss of the ability of palbociclib to regulate the PRMT5-MDM4 axis leads to resistance. Importantly, combining palbociclib with the PRMT5 inhibitor GSK3326595 enhances the efficacy of palbociclib in treating naive and resistant models and also delays the emergence of resistance. Our studies have uncovered a mechanism of action of CDK4/6 inhibitors in regulating the MDM4 oncogene and the tumor suppressor, p53. Furthermore, we have established that palbociclib inhibition of the PRMT5-MDM4 axis is essential for robust melanoma cell sensitivity and provide preclinical evidence that coinhibition of CDK4/6 and PRMT5 is an effective and well-tolerated therapeutic strategy. Overall, our data provide a strong rationale for further investigation of novel combinations of CDK4/6 and PRMT5 inhibitors, not only in melanoma but other tumor types, including breast, pancreatic, and esophageal carcinoma.

Optical mapping reveals a higher level of genomic architecture of chained fusions in cancer.

Genomic rearrangements are common in cancer, with demonstrated links to disease progression and treatment response. These rearrangements can be complex, resulting in fusions of multiple chromosomal fragments and generation of derivative chromosomes. Although methods exist for detecting individual fusions, they are generally unable to reconstruct complex chained events. To overcome these limitations, we adopted a new optical mapping approach, allowing megabase-length genome maps to be reconstructed and rearranged genomes to be visualized without loss of integrity. Whole-genome mapping (Bionano Genomics) of a well-studied highly rearranged liposarcoma cell line resulted in 3338 assembled consensus genome maps, including 72 fusion maps. These fusion maps represent 112.3 Mb of highly rearranged genomic regions, illuminating the complex architecture of chained fusions, including content, order, orientation, and size. Spanning the junction of 147 chromosomal translocations, we found a total of 28 Mb of interspersed sequences that could not be aligned to the reference genome. Traversing these interspersed sequences using short-read sequencing breakpoint calls, we were able to identify and place 399 sequencing fragments within the optical mapping gaps, thus illustrating the complementary nature of optical mapping and short-read sequencing. We demonstrate that optical mapping provides a powerful new approach for capturing a higher level of complex genomic architecture, creating a scaffold for renewed interpretation of sequencing data of particular relevance to human cancer.