Biology subjects

Soneson, C.

Publications and source records attributed to Soneson, C..

At least 19 recordsLinked to original sources

bioRxiv2025

Cleavage of MEP-1 by DPF-3 Reveals Novel Substrate Specificity and Its Impact on Reproductive Fitness

Proteases are enzymes that catalyse the hydrolysis of peptide bonds in proteins for their functional, modification or degradation. Members of the Dipeptidyl Peptidase IV (DPPIV) family are exopeptidases that cleave dipeptides off the N-termini of their substrate peptides, typically after proline or alanine. Recently, we showed that human DPP4 and Caenorhabditis elegans DPF-3 have a larger target repertoire in vitro, permitting cleavage after additional amino acids. Here, we use TAILS (Terminal Amine Isotopic Labelling of Substrates) to identify DPF-3 targets in vivo and observe cleavage of MEP-1 after threonine, confirming a broader substrate specificity of DPF-3 also in vivo. Demonstrating physiological relevance, we show that rendering MEP-1 resistant to cleavage disrupts its stability, leading to developmental abnormalities such as defective gonadal migration and reproductive issues. Collectively, our findings highlight a previously unappreciated complexity in the substrate specificity of DPPIV family proteases and suggest that their physiological roles may extend beyond what is currently known. IMPORTANTO_LIManuscripts submitted to Review Commons are peer reviewed in a journal-agnostic way. C_LIO_LIUpon transfer of the peer reviewed preprint to a journal, the referee reports will be available in full to the handling editor. C_LIO_LIThe identity of the referees will NOT be communicated to the authors unless the reviewers choose to sign their report. C_LIO_LIThe identity of the referee will be confidentially disclosed to any affiliate journals to which the manuscript is transferred. C_LI GUIDELINESO_LIFor reviewers: https://www.reviewcommons.org/reviewers C_LIO_LIFor authors: https://www.reviewcommons.org/authors C_LI CONTACTThe Review Commons office can be contacted directly at: office@reviewcommons.org

molecular biology↗

bioRxiv2025

The genetic architecture of the human bZIP family

Generative biology holds the promise to transform our ability to design and understand living systems by creating novel proteins, pathways, and organisms with tailored functions that address challenges in medicine, sustainability, and technology. However, training generative models requires large quantities of data that captures the genetic architecture of protein function in all its complexity, but these are currently scarce. Here, we systematically mutagenized all 54 human basic-leucine zipper (bZIP) domains and quantified their interactions with each other using bindingPCA, a quantitative deep mutational scanning assay. This resulted in [~]2 million interaction measurements, capturing the effect of all single amino acid substitutions at each of the 35 interfacial positions. We found that mutation effects are largely additive in the vicinity of each wild-type bZIPs, but diverge across the family, indicating strong context dependency. A global additive thermodynamic model provided moderate prediction of mutation effects, while individual models per bZIP achieved higher performance, supporting a model of local simplicity and global complexity. Our results therefore suggest that the genetic architecture of protein function is more complex than previously anticipated, which could hinder predictability. However, a convolutional neural network trained on this dataset could accurately predict binding scores from sequence alone. Furthermore, the model enabled the design of synthetic bZIPs with high target specificity, demonstrating practical applicability for bioengineering purposes. Our study shows that capturing family-wide diversity is essential to reveal context dependencies and train accurate quantitative models of protein-protein interactions.

biophysics↗

bioRxiv2025

An organoid model of the menstrual cycle reveals the role of the luminal epithelium in regeneration of the human endometrium

Menstruation is an unusual physiological process whereby the human endometrium undergoes cyclical shedding yet scarless regeneration. Despite its pivotal role in reproductive health, the cellular states and interactions that coordinate this process are incompletely defined. Here, we establish an in vitro menstrual cycle (IVMC) protocol using human endometrial organoids that faithfully recapitulates the epithelium across the key phases of the menstrual cycle including differentiation, hormonal withdrawal, breakdown and regeneration. This IVMC protocol enables intricate study of the early phases of the cycle, which remain difficult to access in vivo. Using this system, we define transcriptional transitions in ciliated and non-ciliated epithelial cells during regeneration, revealing immediate stress-responsive states followed by wound-responsive ones that precede proliferation. We demonstrate that, in response to breakdown, organoids acquire a transcriptomic signature resembling the in vivo luminal epithelium in the menstrual and proliferative phases of the cycle. Luminal epithelial cells are crucial for this re-epithelialization and express factors such as WNT7A which we show to be required for long-term epithelial maintenance. Moreover, cell-cell communication analyses identify luminal epithelium as a signaling hub, interacting with endothelial and immune cells via pathways including CXCL8, consistent with promotion of angiogenesis and immune recruitment during the regenerative window. Taken together, our study establishes a physiologically relevant paradigm to dissect epithelial renewal and cell-cell interactions during menstruation with potential to extend towards modelling common and distressing conditions such as menstrual disorders and endometriosis.

cell biology↗

bioRxiv2024

A comprehensive Schizosaccharomyces pombe atlas of physical transcription factor interactions with proteins and chromatin

Transcription factors (TFs) are key regulators of gene expression, yet many of their targets and modes of action remain unknown. In Schizosaccharomyces pombe, one-third of TFs are solely homology-predicted, with few experimentally validated. We created a comprehensive library of 89 endogenously tagged S. pombe TFs, mapping their protein and chromatin interactions using immunoprecipitation-mass spectrometry and chromatin immunoprecipitation sequencing. Our study identified protein interactors for half the TFs, with over a quarter potentially forming stable complexes. We discovered DNA binding sites for most TFs across 2,027 unique genomic regions, revealing motifs for 38 TFs and uncovering a complex regulatory network of extensive TF cross- and autoregulation. Characterization of the largest TF family revealed conserved DNA sequence preferences but diverse binding patterns, and identified a repressive heterodimer, Ntu1/Ntu2, linked to perinuclear gene localization. Our TFexplorer webtool makes all data interactively accessible, offering new insights into TF interactions and regulatory mechanisms with broad biological relevance. HIGHLIGHTSO_LIComprehensive strain library of endogenously tagged S. pombe TFs C_LIO_LIExperimentally determined atlas of TF interactions with proteins and chromatin C_LIO_LITFexplorer web application for interactive exploration of TF interactomes C_LIO_LIIdentification of repressive Nattou complex linked to perinuclear gene localization C_LI

molecular biology↗

bioRxiv2024

Ubiquitination mediates protein localization in RNA virus-infected cells

Viruses trigger monocytes proinflammatory and antiviral responses. Ubiquitination, a post-translational modification primarily marking proteins for degradation, regulates cellular responses to virus infection. However, a comprehensive analysis of virus-induced ubiquitination in monocytes is lacking. Here we identified a widespread increase of ubiquitination under viral RNA challenge or influenza infection in monocytes. Systematic proteome studies revealed that influenza infection elicits dynamic ubiquitinome alterations, with a notable transition from early to late stage. Most of this increased ubiquitination is not proteolytic and targets proteins involved in subcellular localization, such as the mitochondrial protein COA7 which, when ubiquitinated during infection, translocates to the nucleus and inhibits stress granules formation and TNF- expression. Blocking ubiquitination halts viral ribonucleoproteins nuclear export, highlighting ubiquitinations importance for protein localization during virus infection.

molecular biology↗

bioRxiv2023

Massively parallel mapping of substrate cleavage sites defines dipeptidyl peptidase four subsite cooperativity

Substrate specificity determines protease functions in physiology and in clinical and biotechnological application, yet quantitative cleavage information is often unavailable, biased, or limited to a small number of events. Here, we develop qPISA (quantitative Protease specificity Inference from Substrate Analysis) to study Dipeptidyl Peptidase Four (DPP4), a key regulator of blood glucose levels. We use mass spectrometry to quantify its effects on >40,000 peptides from a complex, commercially available peptide mixture. By determining substrate turnover instead of focusing on product identification, we can reveal cooperative interactions within DPP4s active pocket and derive a sequence motif that predicts activity quantitatively. qPISA distinguishes DPP4 from the related C. elegans DPF-3 (a DPP8/9 orthologue), and we relate the differences to structural features of the two enzymes. We demonstrate that qPISA can direct protein engineering efforts like stabilization of GLP-1, a key DPP4 substrate used in treatment of diabetes and obesity. Thus, qPISA offers a versatile approach for profiling protease and especially exopeptidase specificity, facilitating insight into enzyme mechanisms and biotechnological and clinical applications.

biochemistry↗

bioRxiv2023

The tidyomics ecosystem: Enhancing omic data analyses

The growth of omic data presents evolving challenges in data manipulation, analysis, and integration. Addressing these challenges, Bioconductor1 provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming2 offers a revolutionary standard for data organisation and manipulation. 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 analysing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas3, spanning six data frameworks and ten analysis tools.

bioinformatics↗

bioRxiv2023

DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes

MotivationAlthough transcriptomics data is typically used to analyse mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g., healthy vs. diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, i.e., reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions. ResultsHere, we present DifferentialRegulation, a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, versus state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data. Availability and implementationDifferentialRegulation is distributed as a Bioconductor R package.

bioinformatics↗

bioRxiv2023

einprot: flexible, easy-to-use, reproducible workflows for statistical analysis of quantitative proteomics data

We describe einprot, an R package providing easy-to-use reproducible workflows for quality control, statistical analysis and visualization of quantitative proteomics data. einprot is applicable to tabular output from MaxQuant, Proteome Discoverer and FragPipe, and a single function call generates an html report that describes the full analysis pipeline applied to the data and contains static and interactive figures and tables for further exploration. This has the potential to facilitate routine analyses as well as to provide a standardized, yet comprehensive way to communicate results to collaborators and the broader community. The source file underlying the report is also returned, giving the user full flexibility to further modify the workflow according to their needs.

bioinformatics↗

bioRxiv2023

Convergence of multiple RNA-silencing pathways on GW182/TNRC6

The RNA-binding protein TRIM71/LIN-41 is a phylogenetically conserved developmental regulator that functions in mammalian stem cell reprogramming, brain development and cancer. TRIM71 recognizes target mRNAs through hairpin motifs and silences them through molecular mechanisms that await identification. Here, we uncover that TRIM71 silences its targets by recruiting TNRC6/GW182, a core component of the miRNA-induced silencing complex (miRISC). Co-immunoprecipitation reveals interaction of TNRC6A with additional RNA-binding proteins and we demonstrate that AGO2, TRIM71, and UPF1 each recruit TNRC6 to specific, largely distinct sets of transcripts to silence them. As cellular TNRC6 levels are limiting, competition occurs among the silencing pathways, such that loss of AGO2 protein, or of AGO2 binding to TNRC6, enhances the activities of the other pathways. We conclude that a miRNA-like silencing activity is shared among different mRNA silencing pathways and that use of TNRC6 as a central hub provides a means to integrate their activities.

molecular biology↗

bioRxiv2023

TAR Syndrome-associated Rbm8a deficiency causes hematopoietic defects and attenuates Wnt/PCP signaling

Defects in blood development frequently occur among syndromic congenital anomalies. Thrombocytopenia-Absent Radius (TAR) syndrome is a rare congenital condition with reduced platelets (hypomegakaryocytic thrombocytopenia) and forelimb anomalies, concurrent with more variable heart and kidney defects. TAR syndrome associates with hypomorphic gene function for RBM8A/Y14 that encodes a component of the exon junction complex involved in mRNA splicing, transport, and nonsense-mediated decay. How perturbing a general mRNA-processing factor causes the selective TAR Syndrome phenotypes remains unknown. Here, we connect zebrafish rbm8a perturbation to early hematopoietic defects via attenuated non-canonical Wnt/Planar Cell Polarity (PCP) signaling that controls developmental cell re-arrangements. In hypomorphic rbm8a zebrafish, we observe a significant reduction of cd41-positive thrombocytes. rbm8a-mutant zebrafish embryos accumulate mRNAs with individual retained introns, a hallmark of defective nonsense-mediated decay; affected mRNAs include transcripts for non-canonical Wnt/PCP pathway components. We establish that rbm8a-mutant embryos show convergent extension defects and that reduced rbm8a function interacts with perturbations in non-canonical Wnt/PCP pathway genes wnt5b, wnt11f2, fzd7a, and vangl2. Using live-imaging, we found reduced rbm8a function impairs the architecture of the lateral plate mesoderm (LPM) that forms hematopoietic, cardiovascular, kidney, and forelimb skeleton progenitors as affected in TAR Syndrome. Both mutants for rbm8a and for the PCP gene vangl2 feature impaired expression of early hematopoietic/endothelial genes including runx1 and the megakaryocyte regulator gfi1aa. Together, our data propose aberrant LPM patterning and hematopoietic defects as consequence of attenuated non-canonical Wnt/PCP signaling upon reduced rbm8a function. These results also link TAR Syndrome to a potential LPM origin and a developmental mechanism. HIGHLIGHTSO_LIZebrafish mutants for the TAR Syndrome gene rbm8a show thrombocyte reduction C_LIO_LIAttenuated Rbm8a function results in retained introns in mRNAs encoding PCP components C_LIO_LIEarly PCP defects result in lateral plate mesoderm anomalies and hematopoietic defects C_LIO_LIPCP anomalies selectively impact cell fate patterning C_LI

developmental biology↗

bioRxiv2023

Understanding and evaluating ambiguity in single-cell and single-nucleus RNA-sequencing

Recently, a new modification has been proposed by Hjorleifsson and Sullivan et al. to the model used to classify the splicing status of reads (as spliced (mature), unspliced (nascent), or ambiguous) in single-cell and single-nucleus RNA-seq data. Here, we evaluate both the theoretical basis and practical implementation of the proposed method. The proposed method is highly-conservative, and therefore, unlikely to mischaracterize reads as spliced (mature) or unspliced (nascent) when they are not. However, we find that it leaves a large fraction of reads classified as ambiguous, and, in practice, allocates these ambiguous reads in an all-or-nothing manner, and differently between single-cell and single-nucleus RNA-seq data. Further, as implemented in practice, the ambiguous classification is implicit and based on the index against which the reads are mapped, which leads to several drawbacks compared to methods that consider both spliced (mature) and unspliced (nascent) mapping targets simultaneously -- for example, the ability to use confidently assigned reads to rescue ambiguous reads based on shared UMIs and gene targets. Nonetheless, we show that these conservative assignment rules can be obtained directly in existing approaches simply by altering the set of targets that are indexed. To this end, we introduce the spliceu reference and show that its use with alevin-fry recapitulates the more conservative proposed classification. We also observe that, on experimental data, and under the proposed allocation rules for ambiguous UMIs, the difference between the proposed classification scheme and existing conventions appears much smaller than previously reported. We demonstrate the use of the new piscem index for mapping simultaneously against spliced (mature) and unspliced (nascent) targets, allowing classification against the full nascent and mature transcriptome in human or mouse in <3GB of memory. Finally, we discuss the potential of incorporating probabilistic evidence into the inference of splicing status, and suggest that it may provide benefits beyond what can be obtained from discrete classification of UMIs as splicing-ambiguous.

bioinformatics↗

bioRxiv2022

mutscan - a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data

Multiplexed assays of variant effect (MAVE) experimentally measure the fitness of large numbers of sequence variants by selective enrichment of sequences with desirable properties followed by quantification by sequencing. mutscan is an R package for flexible analysis of such experiments, covering the entire workflow from raw reads up to statistical analysis and visualization. Core components are implemented in C++ for efficiency. Various experimental designs are supported, including single or paired reads with optional unique molecular identifiers. To find variants with changed relative abundance, mutscan employs established statistical models provided in the edgeR and limma packages. mutscan is available from https://github.com/fmicompbio/mutscan.

bioinformatics↗

bioRxiv2022

Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability

Computational methods represent the lifeblood of modern molecular biology. Benchmarking is important for all methods, but with a focus here on computational methods, benchmarking is critical to dissect important steps of analysis pipelines, formally assess performance across common situations as well as edge cases, and ultimately guide users on what tools to use. Benchmarking can also be important for community building and advancing methods in a principled way. We conducted a meta-analysis of recent single-cell benchmarks to summarize the scope, extensibility, neutrality, as well as technical features and whether best practices in open data and reproducible research were followed. The results highlight that while benchmarks often make code available and are in principle reproducible, they remain difficult to extend, for example, as new methods and new ways to assess methods emerge. In addition, embracing containerization and workflow systems would enhance reusability of intermediate benchmarking results, thus also driving wider adoption.

bioinformatics↗

bioRxiv2022

Benchmark of Differential Gene Expression Analysis Methods for Inter-species RNA-Seq Data using a Phylogenetic Simulation Framework

Inter-species RNA-Seq datasets are increasingly common, and have the potential to answer new questions on gene expression patterns across the evolution. Single species differential expression analysis is a now well studied problem, that benefits from sound statistical methods. Extensive reviews on biological or synthetic datasets have provided the community with a clear picture on the relative performances of the available tools in various settings. Such benchmarks are still missing in the inter-species gene expression context. In this work, we take a first step in this direction by developing and implementing a new simulation framework. This tool builds on both the RNA-Seq and the Phylogenetic Comparative Methods literatures to generate realistic count datasets, while taking into account the phylogenetic relationships between the samples. We illustrate the features of this new framework through a targeted simulation study, that reveals some of the strengths and weaknesses of both the classical and phylogenetic approaches for inter-species differential expression analysis. The tool has been integrated in the R package compcodeR freely available on Bioconductor.

bioinformatics↗

bioRxiv2021

monaLisa: an R/Bioconductor package for identifying regulatory motifs

Proteins binding to specific nucleotide sequences, such as transcription factors, play key roles in the regulation of gene expression. Their binding can be indirectly observed via associated changes in transcription, chromatin accessibility, DNA methylation and histone modifications. Identifying candidate factors that are responsible for these observed experimental changes is critical to understand the underlying biological processes. Here we present monaLisa, an R/Bioconductor package that implements approaches to identify relevant transcription factors from experimental data. The package can be easily integrated with other Bioconductor packages and enables seamless motif analyses without any software dependencies outside of R. AvailabilitymonaLisa is implemented in R and available on Bioconductor at https://bioconductor.org/packages/monaLisa with the development version hosted on GitHub at https://github.com/fmicompbio/monaLisa. Contactmichael.stadler@fmi.ch

bioinformatics↗

bioRxiv2021

Built on sand: the shaky foundations of simulating single-cell RNA sequencing data

With the emergence of hundreds of single-cell RNA-sequencing (scRNA-seq) datasets, the number of computational tools to analyse aspects of the generated data has grown rapidly. As a result, there is a recurring need to demonstrate whether newly developed methods are truly performant - on their own as well as in comparison to existing tools. Benchmark studies aim to consolidate the space of available methods for a given task, and often use simulated data that provide a ground truth for evaluations. Thus, demanding a high quality standard for synthetically generated data is critical to make simulation study results credible and transferable to real data. Here, we evaluated methods for synthetic scRNA-seq data generation in their ability to mimic experimental data. Besides comparing gene- and cell-level quality control summaries in both one- and two-dimensional settings, we further quantified these at the batch- and cluster-level. Secondly, we investigate the effect of simulators on clustering and batch correction method comparisons, and, thirdly, which and to what extent quality control summaries can capture reference-simulation similarity. Our results suggest that most simulators are unable to accommodate complex designs without introducing artificial effects; they yield over-optimistic performance of integration, and potentially unreliable ranking of clustering methods; and, it is generally unknown which summaries are important to ensure effective simulation-based method comparisons.

bioinformatics↗

bioRxiv2021

Alevin-fry unlocks rapid, accurate, and memory-frugal quantification of single-cell RNA-seq data

The rapid growth of high-throughput single-cell and single-nucleus RNA sequencing technologies has produced a wealth of data over the past few years. The available technologies continue to evolve and experiments continue to increase in both number and scale. The size, volume, and distinctive characteristics of these data necessitate the development of new software and associated computational methods to accurately and efficiently quantify single-cell and single-nucleus RNA-seq data into count matrices that constitute the input to downstream analyses. We introduce the alevin-fry framework for quantifying single-cell and single-nucleus RNA-seq data. Despite being faster and more memory frugal than other accurate and scalable quantification approaches, alevin-fry does not suffer from the false positive expression or memory scalability issues that are exhibited by other lightweight tools. We demonstrate how alevin-fry can be effectively used to quantify single-cell and single-nucleus RNA-seq data, and also how the spliced and unspliced molecule quantification required as input for RNA velocity analyses can be seamlessly extracted from the same pre-processed data used to generate regular gene expression count matrices.

bioinformatics↗