A mouse-specific retrotransposon drives a conserved Cdk2ap1 isoform essential for development.

Retrotransposons mediate gene regulation in important developmental and pathological processes. Here, we characterized the transient retrotransposon induction during preimplantation development of eight mammals. Induced retrotransposons exhibit similar preimplantation profiles across species, conferring gene regulatory activities, particularly through long terminal repeat (LTR) retrotransposon promoters. A mouse-specific MT2B2 retrotransposon promoter generates an N-terminally truncated Cdk2ap1ΔN that peaks in preimplantation embryos and promotes proliferation. In contrast, the canonical Cdk2ap1 peaks in mid-gestation and represses cell proliferation. This MT2B2 promoter, whose deletion abolishes Cdk2ap1ΔN production, reduces cell proliferation and impairs embryo implantation, is developmentally essential. Intriguingly, Cdk2ap1ΔN is evolutionarily conserved in sequence and function yet is driven by different promoters across mammals. The distinct preimplantation Cdk2ap1ΔN expression in each mammalian species correlates with the duration of its preimplantation development. Hence, species-specific transposon promoters can yield evolutionarily conserved, alternative protein isoforms, bestowing them with new functions and species-specific expression to govern essential biological divergence.

Rapid non-uniform adaptation to conformation-specific KRAS(G12C) inhibition.

KRAS GTPases are activated in one-third of cancers, and KRAS(G12C) is one of the most common activating alterations in lung adenocarcinoma1,2. KRAS(G12C) inhibitors3,4 are in phase-I clinical trials and early data show partial responses in nearly half of patients with lung cancer. How cancer cells bypass inhibition to prevent maximal response to therapy is not understood. Because KRAS(G12C) cycles between an active and inactive conformation4-6, and the inhibitors bind only to the latter, we tested whether isogenic cell populations respond in a non-uniform manner by studying the effect of treatment at a single-cell resolution. Here we report that, shortly after treatment, some cancer cells are sequestered in a quiescent state with low KRAS activity, whereas others bypass this effect to resume proliferation. This rapid divergent response occurs because some quiescent cells produce new KRAS(G12C) in response to suppressed mitogen-activated protein kinase output. New KRAS(G12C) is maintained in its active, drug-insensitive state by epidermal growth factor receptor and aurora kinase signalling. Cells without these adaptive changes-or cells in which these changes are pharmacologically inhibited-remain sensitive to drug treatment, because new KRAS(G12C) is either not available or exists in its inactive, drug-sensitive state. The direct targeting of KRAS oncoproteins has been a longstanding objective in precision oncology. Our study uncovers a flexible non-uniform fitness mechanism that enables groups of cells within a population to rapidly bypass the effect of treatment. This adaptive process must be overcome if we are to achieve complete and durable responses in the clinic.

Epigenetic evolution and lineage histories of chronic lymphocytic leukaemia.

Genetic and epigenetic intra-tumoral heterogeneity cooperate to shape the evolutionary course of cancer1. Chronic lymphocytic leukaemia (CLL) is a highly informative model for cancer evolution as it undergoes substantial genetic diversification and evolution after therapy2,3. The CLL epigenome is also an important disease-defining feature4,5, and growing populations of cells in CLL diversify by stochastic changes in DNA methylation known as epimutations6. However, previous studies using bulk sequencing methods to analyse the patterns of DNA methylation were unable to determine whether epimutations affect CLL populations homogeneously. Here, to measure the epimutation rate at single-cell resolution, we applied multiplexed single-cell reduced-representation bisulfite sequencing to B cells from healthy donors and patients with CLL. We observed that the common clonal origin of CLL results in a consistently increased epimutation rate, with low variability in the cell-to-cell epimutation rate. By contrast, variable epimutation rates across healthy B cells reflect diverse evolutionary ages across the trajectory of B cell differentiation, consistent with epimutations serving as a molecular clock. Heritable epimutation information allowed us to reconstruct lineages at high-resolution with single-cell data, and to apply this directly to patient samples. The CLL lineage tree shape revealed earlier branching and longer branch lengths than in normal B cells, reflecting rapid drift after the initial malignant transformation and a greater proliferative history. Integration of single-cell bisulfite sequencing analysis with single-cell transcriptomes and genotyping confirmed that genetic subclones mapped to distinct clades, as inferred solely on the basis of epimutation information. Finally, to examine potential lineage biases during therapy, we profiled serial samples during ibrutinib-associated lymphocytosis, and identified clades of cells that were preferentially expelled from the lymph node after treatment, marked by distinct transcriptional profiles. The single-cell integration of genetic, epigenetic and transcriptional information thus charts the lineage history of CLL and its evolution with therapy.

Improving replicability in single-cell RNA-Seq cell type discovery with Dune.

BACKGROUND: Single-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable. RESULTS: Here, we propose Dune, a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results-or partitions-on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. Dune is available as an R package on Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/Dune.html . CONCLUSIONS: Cluster refinement by Dune helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets.

benchdamic: benchmarking of differential abundance methods for microbiome data.

SUMMARY: Recently, an increasing number of methodological approaches have been proposed to tackle the complexity of metagenomics and microbiome data. In this scenario, reproducibility and replicability have become two critical issues, and the development of computational frameworks for the comparative evaluations of such methods is of utmost importance. Here, we present benchdamic, a Bioconductor package to benchmark methods for the identification of differentially abundant taxa. AVAILABILITY AND IMPLEMENTATION: benchdamic is available as an open-source R package through the Bioconductor project at https://bioconductor.org/packages/benchdamic/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Curated single cell multimodal landmark datasets for R/Bioconductor.

BACKGROUND: The majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurement of genomic, proteomic, epigenetic, and/or spatial information on the same cells. The development of new statistical and computational methods in Bioconductor for such data will be facilitated by easy availability of landmark datasets using standard data classes. RESULTS: We collected, processed, and packaged publicly available landmark datasets from important single-cell multimodal protocols, including CITE-Seq, ECCITE-Seq, SCoPE2, scNMT, 10X Multiome, seqFISH, and G&T. We integrate data modalities via the MultiAssayExperiment Bioconductor class, document and re-distribute datasets as the SingleCellMultiModal package in Bioconductor's Cloud-based ExperimentHub. The result is single-command actualization of landmark datasets from seven single-cell multimodal data generation technologies, without need for further data processing or wrangling in order to analyze and develop methods within Bioconductor's ecosystem of hundreds of packages for single-cell and multimodal data. CONCLUSIONS: We provide two examples of integrative analyses that are greatly simplified by SingleCellMultiModal. The package will facilitate development of bioinformatic and statistical methods in Bioconductor to meet the challenges of integrating molecular layers and analyzing phenotypic outputs including cell differentiation, activity, and disease.

Does sweetness exposure drive 'sweet tooth'?

It is widely believed that exposure to sweetened foods and beverages stimulates the liking and desire for sweetness. Here we provide an updated review of the empirical evidence from human research examining whether exposure to sweet foods or beverages influences subsequent general liking for sweetness ('sweet tooth'), based on the conclusions of existing systematic reviews and more recent research identified from a structured search of literature. Prior reviews have concluded that the evidence for a relationship between sweet taste exposure and measures of sweet taste liking is equivocal, and more recent primary research generally does not support the view that exposure drives increased liking for sweetness, in adults or children. In intervention trials using a range of designs, acute exposure to sweetness usually has the opposite effect (reducing subsequent liking and desire for sweet taste), while sustained exposures have no significant effects or inconsistent effects. Recent longitudinal observational studies in infants and children also report no significant associations between exposures to sweet foods and beverages with measures of sweet taste preferences. Overall, while it is widely assumed that exposure to sweetness stimulates a greater liking and desire for sweetness, this is not borne out by the balance of empirical evidence. While new research may provide a more robust evidence base, there are also a number of methodological, biological and behavioural considerations that may underpin the apparent absence of a positive relationship between sweetness exposure and liking.

Orchestrating single-cell analysis with Bioconductor.

Recent technological advancements have enabled the profiling of a large number of genome-wide features in individual cells. However, single-cell data present unique challenges that require the development of specialized methods and software infrastructure to successfully derive biological insights. The Bioconductor project has rapidly grown to meet these demands, hosting community-developed open-source software distributed as R packages. Featuring state-of-the-art computational methods, standardized data infrastructure and interactive data visualization tools, we present an overview and online book (https://osca.bioconductor.org) of single-cell methods for prospective users.

Publisher Correction: Orchestrating single-cell analysis with Bioconductor.

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

NewWave: a scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA-seq data.

SUMMARY: We present NewWave, a scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA sequencing data. To achieve scalability, NewWave uses mini-batch optimization and can work with out-of-memory data, enabling users to analyze datasets with millions of cells. AVAILABILITY AND IMPLEMENTATION: NewWave is implemented as an open-source R package available through the Bioconductor project at https://bioconductor.org/packages/NewWave/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SpatialExperiment: infrastructure for spatially-resolved transcriptomics data in R using Bioconductor.

SUMMARY: SpatialExperiment is a new data infrastructure for storing and accessing spatially-resolved transcriptomics data, implemented within the R/Bioconductor framework, which provides advantages of modularity, interoperability, standardized operations and comprehensive documentation. Here, we demonstrate the structure and user interface with examples from the 10x Genomics Visium and seqFISH platforms, and provide access to example datasets and visualization tools in the STexampleData, TENxVisiumData and ggspavis packages. AVAILABILITY AND IMPLEMENTATION: The SpatialExperiment, STexampleData, TENxVisiumData and ggspavis packages are available from Bioconductor. The package versions described in this manuscript are available in Bioconductor version 3.15 onwards. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Low-no-calorie sweeteners exert marked compound-specific impact on the human gut microbiota ex vivo.

Low-no-calorie sweeteners (LNCS) are used as sugar substitutes as part of strategies to reduce the risk of chronic diseases related to high sugar intake (e.g. type 2 diabetes (T2D)). This study investigated how a range of sweeteners [tagatose (TA)/maltitol (MA)/sorbitol (SO)/stevia (ST)/sucralose (SU)/acesulfame K (ACK)] impact the gut microbiota of T2D subjects and healthy human adults using the ex vivo SIFR® technology (n = 12). The cohort covered clinically relevant interpersonal and T2D-related differences. ACK/SU remained intact while not impacting microbial composition and metabolite production. In contrast, TA/SO and ST/MA were respectively readily and gradually fermented. ST and particularly TA/SO/MA increased bacterial density and SCFA production product-specifically: SO increased acetate (∼Bifidobacterium adolescentis), whilst MA/ST increased propionate (∼Parabacteroides distasonis). TA exerted low specificity as it increased butyrate for healthy subjects, yet propionate for T2D subjects. Overall, LNCS exerted highly compound-specific effects stressing that results obtained for one LNCS cannot be generalised to other LNCS.

Age-Dependent Prebiotic Effects of Soluble Corn Fiber in M-SHIME® Gut Microbial Ecosystems.

Soluble corn fiber (SCF) has demonstrated prebiotic effects in clinical studies. Using an in vitro mucosal simulator of the human intestinal microbial ecosystem (M-SHIME®) model, the effects of SCF treatment on colonic microbiota composition and metabolic activity and on host-microbiome interactions were evaluated using fecal samples from healthy donors of different ages (baby [≤ 2 years], n = 4; adult [18-45 years], n = 2; elderly [70 years], n = 1). During the 3-week treatment period, M-SHIME® systems were supplemented with SCF daily (baby, 1.5, 3, or 4.5 g/d; adult, 3 or 8.5 g/d; and elderly, 8.5 g/d). M-SHIME® supernatants were evaluated for their effect on the intestinal epithelial cell barrier and inflammatory responses in lipopolysaccharide. (LPS)-stimulated cells. Additionally, short-chain fatty acid (SCFA) production and microbial community composition were assessed. In the baby and adult models, M-SHIME® supernatants from SCF treated vessels protected Caco-2 membrane integrity from LPS-induced damage. SCF treatment resulted in the expansion of Bacteroidetes, Firmicutes, and Bifidobacterial, as well as increased SCFA production in all age groups. SCF tended to have the greatest effect on propionate production. These findings demonstrate the prebiotic potential of SCF in babies, adults, and the elderly and provide insight into the mechanisms behind the observed prebiotic effects.

A Bartlett-type correction for likelihood ratio tests with application to testing equality of Gaussian graphical models.

This work defines a new correction for the likelihood ratio test for a two-sample problem within the multivariate normal context. This correction applies to decomposable graphical models, where testing equality of distributions can be decomposed into lower dimensional problems.

The Human-Specific BOLA2 Duplication Modifies Iron Homeostasis and Anemia Predisposition in Chromosome 16p11.2 Autism Individuals.

Human-specific duplications at chromosome 16p11.2 mediate recurrent pathogenic 600 kbp BP4-BP5 copy-number variations, which are among the most common genetic causes of autism. These copy-number polymorphic duplications are under positive selection and include three to eight copies of BOLA2, a gene involved in the maturation of cytosolic iron-sulfur proteins. To investigate the potential advantage provided by the rapid expansion of BOLA2, we assessed hematological traits and anemia prevalence in 379,385 controls and individuals who have lost or gained copies of BOLA2: 89 chromosome 16p11.2 BP4-BP5 deletion carriers and 56 reciprocal duplication carriers in the UK Biobank. We found that the 16p11.2 deletion is associated with anemia (18/89 carriers, 20%, p = 4e-7, OR = 5), particularly iron-deficiency anemia. We observed similar enrichments in two clinical 16p11.2 deletion cohorts, which included 6/63 (10%) and 7/20 (35%) unrelated individuals with anemia, microcytosis, low serum iron, or low blood hemoglobin. Upon stratification by BOLA2 copy number, our data showed an association between low BOLA2 dosage and the above phenotypes (8/15 individuals with three copies, 53%, p = 1e-4). In parallel, we analyzed hematological traits in mice carrying the 16p11.2 orthologous deletion or duplication, as well as Bola2+/- and Bola2-/- animals. The Bola2-deficient mice and the mice carrying the deletion showed early evidence of iron deficiency, including a mild decrease in hemoglobin, lower plasma iron, microcytosis, and an increased red blood cell zinc-protoporphyrin-to-heme ratio. Our results indicate that BOLA2 participates in iron homeostasis in vivo, and its expansion has a potential adaptive role in protecting against iron deficiency.

Per-sample standardization and asymmetric winsorization lead to accurate clustering of RNA-seq expression profiles.

MOTIVATION: Data transformations are an important step in the analysis of RNA-seq data. Nonetheless, the impact of transformation on the outcome of unsupervised clustering procedures is still unclear. RESULTS: Here, we present an Asymmetric Winsorization per-Sample Transformation (AWST), which is robust to data perturbations and removes the need for selecting the most informative genes prior to sample clustering. Our procedure leads to robust and biologically meaningful clusters both in bulk and in single-cell applications. AVAILABILITY AND IMPLEMENTATION: The AWST method is available at https://github.com/drisso/awst. The code to reproduce the analyses is available at https://github.com/drisso/awst_analysis. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PsiNorm: a scalable normalization for single-cell RNA-seq data.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) enables transcriptome-wide gene expression measurements at single-cell resolution providing a comprehensive view of the compositions and dynamics of tissue and organism development. The evolution of scRNA-seq protocols has led to a dramatic increase of cells throughput, exacerbating many of the computational and statistical issues that previously arose for bulk sequencing. In particular, with scRNA-seq data all the analyses steps, including normalization, have become computationally intensive, both in terms of memory usage and computational time. In this perspective, new accurate methods able to scale efficiently are desirable. RESULTS: Here, we propose PsiNorm, a between-sample normalization method based on the power-law Pareto distribution parameter estimate. Here, we show that the Pareto distribution well resembles scRNA-seq data, especially those coming from platforms that use unique molecular identifiers. Motivated by this result, we implement PsiNorm, a simple and highly scalable normalization method. We benchmark PsiNorm against seven other methods in terms of cluster identification, concordance and computational resources required. We demonstrate that PsiNorm is among the top performing methods showing a good trade-off between accuracy and scalability. Moreover, PsiNorm does not need a reference, a characteristic that makes it useful in supervised classification settings, in which new out-of-sample data need to be normalized. AVAILABILITY AND IMPLEMENTATION: PsiNorm is implemented in the scone Bioconductor package and available at https://bioconductor.org/packages/scone/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

mbkmeans: Fast clustering for single cell data using mini-batch k-means.

Single-cell RNA-Sequencing (scRNA-seq) is the most widely used high-throughput technology to measure genome-wide gene expression at the single-cell level. One of the most common analyses of scRNA-seq data detects distinct subpopulations of cells through the use of unsupervised clustering algorithms. However, recent advances in scRNA-seq technologies result in current datasets ranging from thousands to millions of cells. Popular clustering algorithms, such as k-means, typically require the data to be loaded entirely into memory and therefore can be slow or impossible to run with large datasets. To address this problem, we developed the mbkmeans R/Bioconductor package, an open-source implementation of the mini-batch k-means algorithm. Our package allows for on-disk data representations, such as the common HDF5 file format widely used for single-cell data, that do not require all the data to be loaded into memory at one time. We demonstrate the performance of the mbkmeans package using large datasets, including one with 1.3 million cells. We also highlight and compare the computing performance of mbkmeans against the standard implementation of k-means and other popular single-cell clustering methods. Our software package is available in Bioconductor at https://bioconductor.org/packages/mbkmeans.

An African-specific haplotype in MRGPRX4 is associated with menthol cigarette smoking.

In the U.S., more than 80% of African-American smokers use mentholated cigarettes, compared to less than 30% of Caucasian smokers. The reasons for these differences are not well understood. To determine if genetic variation contributes to mentholated cigarette smoking, we performed an exome-wide association analysis in a multiethnic population-based sample from Dallas, TX (N = 561). Findings were replicated in an independent cohort of African Americans from Washington, DC (N = 741). We identified a haplotype of MRGPRX4 (composed of rs7102322[G], encoding N245S, and rs61733596[G], T43T), that was associated with a 5-to-8 fold increase in the odds of menthol cigarette smoking. The variants are present solely in persons of African ancestry. Functional studies indicated that the variant G protein-coupled receptor encoded by MRGPRX4 displays reduced agonism in both arrestin-based and G protein-based assays, and alteration of agonism by menthol. These data indicate that genetic variation in MRGPRX4 contributes to inter-individual and inter-ethnic differences in the preference for mentholated cigarettes, and that the existence of genetic factors predisposing vulnerable populations to mentholated cigarette smoking can inform tobacco control and public health policies.

Moderate intakes of soluble corn fibre or inulin do not cause gastrointestinal discomfort and are well tolerated in healthy children.

We investigated the gastrointestinal (GI) tolerance of soluble corn fibre (SCF) compared with inulin in children 3-9 years old. SCF (3-8 g/d for 10d) was tolerated as well as inulin: no differences were identified in stool frequency and consistency, proportion of subjects with at least one loose stool or reporting symptoms during bowel movement. Compared to inulin, 6 g/d of SCF lowered gas severity in children aged 3-5 years old. No differences were noted for alpha and beta diversity, relative abundance of Bacteroidota, Firmicutes, Ruminococcaceae, or the Firmicutes to Bacteroidota ratio. Relative abundance of some specific strains (i.e. Anaerostipes, Bifidobacterium, Fusicatenibacter, Parabacteroides) varied depending on the fibre type and dose level. Fortification at a level of 6-8 g/d of SCF and/or inulin could help addressing the fibre gap without any GI discomfort.