Biology subjects

Marini, F.

Publications and source records attributed to Marini, F..

34 records · Page 2Linked to original sources

bioRxiv2022

Impact of long-term storage on mid-infrared spectral patterns of serum and synovial fluid samples of dogs with osteoarthritis

ObjectiveTo evaluate impact of long-term storage on mid-infrared (MIR) spectral patterns of serum and synovial fluid (SF) of dogs with knee OA and controls. DesignSerum (52 OA and 49 control) and SF (51 OA and 51 control) samples from dogs that had been in short-term (<3 years) frozen state (-80{degrees}C) had their MIR spectra obtained. The remaining aliquots were maintained in long-term (>5 years) frozen state before having MIR spectra acquired under the same testing conditions. Multi-level simultaneous component analysis was used to evaluate the effect of time. Partial least squares discriminant analysis was used to compare performance of predictive models built for discriminating OA from control spectra from each time point. ResultsMedian interval of storage between sample measurements was 5.7 years. Spectra obtained at two time points were significantly different (P <0.0001), however, contribution of sample aging accounted for only 1.61% and 2.98% of serum and SF profiles variability, respectively. Predictive models for discriminating serum of OA from controls for short-term storage showed 87.3{+/-}3.7% sensitivity, 88.9{+/-}2.4% specificity and 88.1{+/-}2.3% accuracy, while, for long-term storage, values of the same figures of merit were 92.5{+/-}2.6%, 97.1{+/-}1.7% and 94.8{+/-}1.4%, respectively. Predictive models based on short-term stored SF spectra had 97.3{+/-}1.6% sensitivity, 89.4{+/-}2.6% specificity and 93.4{+/-}1.6% accuracy, while the values for long-term storage 95.7{+/-}2.1%, 95.7{+/-}0.8% and 95.8{+/-}1.1%, respectively. ConclusionsLong-term storage of serum and SF results in significant differences in spectral variables, however, these changes do not significantly alter the performance of predictive algorithms for discriminating OA samples from controls.

biochemistry↗

bioRxiv2022

Development of a Novel Air-Liquid Interface Airway Tissue Equivalent Model for In Vitro Respiratory Modeling Studies

The human airways are complex structures with important interactions between cells, extracellular matrix (ECM) proteins and the biomechanical microenvironment. A robust, well-differentiated in vitro culture system that accurately models these interactions would provide a useful tool for studying normal and pathological airway biology. Here, we report the feasibility and analysis of a physiologically relevant air-liquid interface (ALI) 3D airway organ tissue equivalent (OTE) model with three novel features: native pulmonary fibroblasts, solubilized lung ECM, and hydrogel substrate with tunable stiffness and porosity. We demonstrate the versatility of the OTE model by evaluating the impact of these features on human bronchial epithelial (HBE) cell phenotype. Variations of this model were analyzed during 28 days of ALI culture by evaluating epithelial confluence, trans-epithelial resistance, and epithelial phenotype via multispectral immuno-histochemistry and next-generation sequencing. Cultures that included both solubilized lung ECM and native pulmonary fibroblasts within the hydrogel substrate formed well-differentiated ALI cultures that maintained a barrier function and expressed mature epithelial markers relating to goblet, club and ciliated cells. Modulation of hydrogel stiffness did not negatively impact HBE differentiation and could be a valuable variable to alter epithelial phenotype. This study highlights the feasibility and versatility of a 3D airway OTE model to model the multiple components of the human airway 3D microenvironment.

cell biology↗

bioRxiv2022

Enhanced methionine cycle suppresses naïve CD8+ T-cell maturation

The metabolic pathways controlling naive CD8+ T (Tn) cell maturation following thymic egress remain mostly undefined. This is important because immature Tn are a major component of peripheral immune tolerance in newborns and under lymphopenia. In this study we demonstrate that TMRM, a mitochondrial membrane potential marker, could be applied to rapidly identify an immature Tn cell population in the periphery. Applying this marker to perform metabolic and proteomic analysis, we show that immature Tn cells maintain accelerated methionine cycle in respect to mature Tn. This unique metabolic state was associated with restricted Tbx21 locus and diminished immune response in vitro and in vivo. Following our findings, we demonstrate that inhibition of methionine cycle leads to rapid functional maturation of Tn and recovery of immune response to stimuli. Our work provides insight into the way the rate of methionine cycling regulates T cell maturation, opening a path for metabolic manipulation of immune tolerance.

immunology↗

bioRxiv2022

Magnetique: An interactive web application to explore transcriptome signatures of heart failure

Despite a recent increase in the number of RNA-seq datasets investigating heart failure (HF), accessibility and usability remain critical issues for medical researchers. We present Magnetique (https://shiny.dieterichlab.org/app/magnetique), an interactive web application to explore the transcriptional signatures of heart failure. We reanalyzed the Myocardial Applied Genomics Network RNA-seq dataset, one of the largest publicly available datasets of left ventricular RNA-seq samples from patients with dilated (DCM) or hypertrophic (HCM) cardiomyopathy, as well as unmatched non-failing hearts from organ donors and patient characteristics that allowed us to model confounding factors. Focusing on the DCM versus HCM contrast, we identified 201 differentially expressed genes and associated pathway signatures. Moreover, we predict underlying signaling networks based on inferred transcription factor activities. To the best of our knowledge, Magnetique is the first online application to provide an interactive view of the HF transcriptome by analyzing differential transcript isoform usage. Finally, another graphical view on statistically predicted RNA-binding protein to target transcript interactions complements the Magnetique web application. The source code for both the analyses (https://github.com/dieterich-lab/magnetiqueCode2022) and the web application (https://github.com/AnnekathrinSilvia/magnetique) is available to the public. We hope that our application will help users to uncover the molecular basis of heart failure.

bioinformatics↗

bioRxiv2022

An Atlas of Cells in the Human Tonsil

Palatine tonsils are secondary lymphoid organs representing the first line of immunological defense against inhaled or ingested pathogens. Here, we present a comprehensive census of cell types forming the human tonsil by applying single-cell transcriptome, epigenome, proteome and adaptive immune repertoire sequencing as well as spatial transcriptomics, resulting in an atlas of >357,000 cells. We provide a glossary of 121 annotated cell types and states, and disentangle gene regulatory mechanisms that drive cells through specialized lineage trajectories. Exemplarily, we stratify multiple tonsil-resident myeloid slancyte subtypes, establish a distant BCL6 superenhancer as locally active in both follicle-associated T and B cells, and describe SIX5 as a potentially novel transcriptional regulator of plasma cell maturation. Further, our atlas is a reference map to understand alterations observed in disease. Here, we discover immune-phenotype plasticity in tumoral cells and microenvironment shifts of mantle cell lymphomas (MCL). To facilitate such reference-based analysis, we develop HCATonsilData and SLOcatoR, a computational framework that provides programmatic and modular access to our dataset; and allows the straightforward annotation of future single-cell profiles from secondary lymphoid organs.

immunology↗

bioRxiv2022

SimBu: Bias-aware simulation of bulk RNA-seq data with variable cell type composition

MotivationAs complex tissues are typically composed of various cell types, deconvolution tools have been developed to computationally infer their cellular composition from bulk RNA sequencing (RNA-seq) data. To comprehensively assess deconvolution performance, gold-standard datasets are indispensable. Gold-standard, experimental techniques like flow cytometry or immunohistochemistry are resource-intensive and cannot be systematically applied to the numerous cell types and tissues profiled with high-throughput transcriptomics. The simulation of pseudo-bulk data, generated by aggregating single-cell RNA-seq (scRNA-seq) expression profiles in pre-defined proportions, offers a scalable and cost-effective alternative. This makes it feasible to create in silico gold standards that allow fine-grained control of cell-type fractions not conceivable in an experimental setup. However, at present, no simulation software for generating pseudo-bulk RNA-seq data exists. ResultsWe developed SimBu, an R package capable of simulating pseudo-bulk samples based on various simulation scenarios, designed to test specific features of deconvolution methods. A unique feature of SimBu is the modelling of cell-type-specific mRNA bias using experimentally-derived or data-driven scaling factors. Here, we show that SimBu can generate realistic pseudo-bulk data, recapitulating the biological and statistical features of real RNA-seq data. Finally, we illustrate the impact of mRNA bias on the evaluation of deconvolution tools and provide recommendations for the selection of suitable methods for estimating mRNA content. ConclusionSimBu is a user-friendly and flexible tool for simulating realistic pseudo-bulk RNA-seq datasets serving as in silico gold-standard for assessing cell-type deconvolution methods. AvailabilitySimBu is freely available at https://github.com/omnideconv/SimBu as an R package under the GPL-3 license. Contactalex.dietrich@tum.de and markus.list@tum.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2022

IRF4 deficiency vulnerates B cell progeny for leukemogenesis via somatically acquired Jak3 mutations conferring IL-7 hypersensitivity

The processes leading from disturbed B cell development to adult B cell progenitor acute lymphoblastic leukemia (BCP-ALL) are poorly understood. Here, we describe Irf4-/- mice as prone to developing BCP-ALL with age. Irf4-/- preB-I cells exhibited impaired differentiation but enhanced proliferation in response to IL-7, along with reduced retention in the IL-7 providing bone marrow niche due to decreased CXCL12 responsiveness. Thus selected, preB-I cells acquired Jak3 mutations, probably following irregular AID activity, resulting in malignant transformation. We demonstrate heightened IL-7 sensitivity due to Jak3 mutants, devise a model to explain it and describe structural and functional similarities to Jak2 mutations often occurring in human Ph-like ALL. Finally, targeting JAK signaling with Ruxolitinib in vivo prolonged survival of mice bearing established Irf4-/- leukemia. Intriguingly, organ infiltration including leukemic meningeosis was selectively reduced without affecting blood blast counts. In this work, we present spontaneous leukemogenesis following IRF4 deficiency with potential implications for high-risk BCP-ALL in adult humans.

cancer biology↗

bioRxiv2021

easier: interpretable predictions of antitumor immune response from bulk RNA-seq data

Immunotherapy with immune checkpoint blockers (ICB) is associated with striking clinical success, but only in a small fraction of patients. Thus, we need computational biomarker-based methods that can anticipate which patients will respond to treatment. Current established biomarkers are imperfect due to their incomplete view of the tumor and its microenvironment. We have recently presented a novel approach that integrates transcriptomics data with biological knowledge to study tumors at a more holistic level. Validated in four different solid cancers, our approach outperformed the state-of-the-art methods to predict response to ICB. Here, we introduce estimate systems immune response (easier), an R/Bioconductor package that applies our approach to quantify biomarkers and assess patients likelihood to respond to immunotherapy, providing just the patients baseline bulk-tumor RNA-sequencing (RNA-seq) data as input.

systems biology↗

bioRxiv2021

GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data

BackgroundThe interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task, where the essential information is distributed among different tabular and list formats - normalized expression values, results from differential expression analysis, and results from functional enrichment analyses. A number of tools and databases are widely used for the purpose of identification of relevant functional patterns, yet often their contextualization within the data and results at hand is not straightforward, especially if these analytic components are not combined together efficiently. ResultsWe developed the GeneTonic software package, which serves as a comprehensive toolkit for streamlining the interpretation of functional enrichment analyses, by fully leveraging the information of expression values in a differential expression context. GeneTonic is implemented in R and Shiny, leveraging packages that enable HTML-based interactive visualizations for executing drilldown tasks seamlessly, viewing the data at a level of increased detail. GeneTonic is integrated with the core classes of existing Bioconductor workflows, and can accept the output of many widely used tools for pathway analysis, making this approach applicable to a wide range of use cases. Users can effectively navigate interlinked components (otherwise available as flat text or spreadsheet tables), bookmark features of interest during the exploration sessions, and obtain at the end a tailored HTML report, thus combining the benefits of both interactivity and reproducibility. ConclusionGeneTonic is distributed as an R package in the Bioconductor project (https://bioconductor.org/packages/GeneTonic/) under the MIT license. Offering both birds-eye views of the components of transcriptome data analysis and the detailed inspection of single genes, individual signatures, and their relationships, GeneTonic aims at simplifying the process of interpretation of complex and compelling RNA-seq datasets for many researchers with different expertise profiles.

bioinformatics↗

bioRxiv2020

TREND-DB - A Transcriptome-wide Atlas of the Dynamic Landscape of Alternative Polyadenylation

Alternative polyadenylation (APA) profoundly expands the transcriptome complexity. Perturbations of APA can disrupt biological processes, ultimately resulting in devastating disorders. A major challenge in identifying mechanisms and consequences of APA (and its perturbations) lies in the complexity of RNA 3end processing, involving poorly conserved RNA motifs and multi-component complexes consisting of far more than 50 proteins. This is further complicated in that RNA 3end maturation is closely linked to transcription, RNA processing, and even epigenetic (histone/DNA/RNA) modifications. Here we present TREND-DB (http://shiny.imbei.uni-mainz.de:3838/trend-db), a resource cataloging the dynamic landscape of APA after depletion of >170 proteins involved in various facets of transcriptional, co- and posttranscriptional gene regulation, epigenetic modifications, and further processes. TREND-DB visualizes the dynamics of transcriptome 3end diversification (TREND) in a highly interactive manner; it provides a global APA network map and allows interrogating genes affected by specific APA-regulators, and vice versa. It also permits condition-specific functional enrichment analyses of APA-affected genes, which suggest wide biological and clinical relevance across all RNAi conditions. The implementation of the UCSC Genome Browser provides additional customizable layers of gene regulation accounting for individual transcript isoforms (e.g. epigenetics, miRNA binding sites, RNA-binding proteins). TREND-DB thereby fosters disentangling the role of APA for various biological programs, including potential disease mechanisms, and helps to identify their diagnostic and therapeutic potential.

bioinformatics↗

bioRxiv2020

ideal: an R/Bioconductor package for Interactive Differential Expression Analysis

BackgroundRNA sequencing (RNA-seq) is an ever increasingly popular tool for transcriptome profiling. A key point to make the best use of the available data is to provide software tools that are easy to use but still provide flexibility and transparency in the adopted methods. Despite the availability of many packages focused on detecting differential expression, a method to streamline this type of bioinformatics analysis in a comprehensive, accessible, and reproducible way is lacking. ResultsWe developed the ideal software package, which serves as a web application for interactive and reproducible RNA-seq analysis, while producing a wealth of visualizations to facilitate data interpretation. ideal is implemented in R using the Shiny framework, and is fully integrated with the existing core structures of the Bioconductor project. Users can perform the essential steps of the differential expression analysis work-flow in an assisted way, and generate a broad spectrum of publication-ready outputs, including diagnostic and summary visualizations in each module, all the way down to functional analysis. ideal also offers the possibility to seamlessly generate a full HTML report for storing and sharing results together with code for reproducibility. Conclusionideal is distributed as an R package in the Bioconductor project (http://bioconductor.org/packages/ideal/), and provides a solution for performing interactive and reproducible analyses of summarized RNA-seq expression data, empowering researchers with many different profiles (life scientists, clinicians, but also experienced bioinformaticians) to make the ideal use of the data at hand.

bioinformatics↗

bioRxiv2019

Orchestrating Single-Cell Analysis with Bioconductor

Recent developments in experimental technologies such as single-cell RNA sequencing have enabled the profiling a high-dimensional number of genome-wide features in individual cells, inspiring the formation of large-scale data generation projects quantifying unprecedented levels of biological variation at the single-cell level. The data generated in such projects exhibits unique characteristics, including increased sparsity and scale, in terms of both the number of features and the number of samples. Due to these unique characteristics, specialized statistical methods are required along with fast and efficient software implementations in order to successfully derive biological insights. Bioconductor - an open-source, open-development software project based on the R programming language - has pioneered the analysis of such high-throughput, high-dimensional biological data, leveraging a rich history of software and methods development that has spanned the era of sequencing. Featuring state-of-the-art computational methods, standardized data infrastructure, and interactive data visualization tools that are all easily accessible as software packages, Bioconductor has made it possible for a diverse audience to analyze data derived from cutting-edge single-cell assays. Here, we present an overview of single-cell RNA sequencing analysis for prospective users and contributors, highlighting the contributions towards this effort made by Bioconductor.

genomics↗

bioRxiv2018

pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components

BackgroundPrincipal component analysis (PCA) is frequently used in genomics applications for quality assessment and exploratory analysis in high-dimensional data, such as RNA sequencing (RNA-seq) gene expression assays. Despite the availability of many software packages developed for this purpose, an interactive and comprehensive interface for performing these operations is lacking.\n\nResultsWe developed the pcaExplorer software package to enhance commonly performed analysis steps with an interactive and user-friendly application, which provides state saving as well as the automated creation of reproducible reports. pcaExplorer is implemented in R using the Shiny framework and exploits data structures from the open-source Bioconductor project. Users can easily generate a wide variety of publication-ready graphs, while assessing the expression data in the different modules available, including a general overview, dimension reduction on samples and genes, as well as functional interpretation of the principal components.\n\nConclusionpcaExplorer is distributed as an R package in the Bioconductor project (http://bioconductor.org/packages/pcaExplorer/). Being designed to assist a broad range of researchers in the critical step of interactive data exploration, pcaExplorer is a practical companion tool for any transcriptomic analysis, with an efficient design that facilitates hypothesis generation, while at the same time providing the foundation for reproducible analyses.

bioinformatics↗

bioRxiv2018

PCF11 connects alternative polyadenylation to formation and spontaneous regression of neuroblastoma

Diversification at the transcriptome 3end is an important and evolutionarily conserved layer of gene regulation associated with differentiation and dedifferentiation processes. However the underlying mechanisms and functional consequences are poorly defined. Here, we identify extensive transcriptome-3end-alterations in neuroblastoma, a tumour entity with a paucity of recurrent somatic mutations and an unusually high frequency of spontaneous regression. Utilising extensive RNAi-screening we reveal the landscape and drivers of transcriptome-3end-diversification, discovering PCF11 as critical regulator, directing alternative polyadenylation (APA) of hundreds of transcripts including a differentiation RNA-operon. PCF11 shapes inputs converging on WNT-signalling, and governs cell cycle, proliferation, apoptosis and neurodifferentiation. Postnatal PCF11 down-regulation induces a neurodifferentiation program, and low-level PCF11 in neuroblastoma associates with favourable outcome and spontaneous tumour regression. Our findings document a critical role for APA in tumourigenesis and describe a novel mechanism for cell fate reprogramming in neuroblastoma with important clinical implications. An interactive data repository of transcriptome-wide APA covering >170 RNAis, and an APA-network map with regulatory hubs is provided.

developmental biology↗

bioRxiv2018

TAF-ChIP: An ultra-low input approach for genome wide chromatin immunoprecipitation assay

Chromatin immunoprecipitation (ChIP) followed by next generation sequencing (ChIP-Seq) is powerful technique to study transcriptional regulation. However, the requirement of millions of cells to generate results with high signal-to-noise ratio precludes it in the study of small cell populations. Here, we present a Tagmentation-Assisted Fragmentation ChIP (TAF-ChIP) and sequencing method to generate high-quality results from low cell numbers. The data obtained from the TAF-ChIP approach is amenable to standard tools for ChIP-Seq analysis, owing to its high signal-to-noise ratio. The epigenetic profiles from TAF-ChIP approach showed high agreement with conventional ChIP-Seq datasets, thereby underlining the utility of this approach.

genomics↗

bioRxiv2018

The exon junction complex regulates the release and phosphorylation of paused RNA polymerase II

Promoter proximal pausing of RNA polymerase II (Pol II) is a widespread transcriptional regulatory step across metazoans. Here we find that the nuclear exon junction complex (pre-EJC) plays a critical and conserved role in this process. Depletion of pre-EJC subunits leads to a global decrease in Pol II pausing and to premature entry into elongation. This effect occurs, at least in part, via non-canonical recruitment of pre-EJC components at promoters. Failure to recruit the pre-EJC at promoters results in increased binding of the positive transcription elongation complex (P-TEFb) and in enhanced Pol II release. Notably, restoring pausing is sufficient to rescue exon skipping and the photoreceptor differentiation defect associated with depletion of pre-EJC components in vivo. We propose that the pre-EJC serves as an early transcriptional checkpoint to prevent premature entry into elongation, ensuring proper recruitment of RNA processing components that are necessary for exon definition.

molecular biology↗