PEPseq quantifies transcriptome-wide changes in protein occupancy and reveals selective translational repression after translational stress.

Post-transcriptional gene regulation is accomplished by the interplay of the transcriptome with RNA-binding proteins, which occurs in a dynamic manner in response to altered cellular conditions. Recording the combined occupancy of all proteins binding to the transcriptome offers the opportunity to interrogate if a particular treatment leads to any interaction changes, pointing to sites in RNA that undergo post-transcriptional regulation. Here, we establish a method to monitor protein occupancy in a transcriptome-wide fashion by RNA sequencing. To this end, peptide-enhanced pull-down for RNA sequencing (or PEPseq) uses metabolic RNA labelling with 4-thiouridine (4SU) for light-induced protein-RNA crosslinking, and N-hydroxysuccinimide (NHS) chemistry to isolate protein-crosslinked RNA fragments across all long RNA biotypes. We use PEPseq to investigate changes in protein occupancy during the onset of arsenite-induced translational stress in human cells and reveal an increase of protein interactions in the coding region of a distinct set of mRNAs, including mRNAs coding for the majority of cytosolic ribosomal proteins. We use quantitative proteomics to demonstrate that translation of these mRNAs remains repressed during the initial hours of recovery after arsenite stress. Thus, we present PEPseq as a discovery platform for the unbiased investigation of post-transcriptional regulation.

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

BACKGROUND: 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 address the need for an intuitive and interactive web application to explore the transcriptional signatures of heart failure with this work. METHODS: We reanalysed 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 (NFD) from organ donors and patient characteristics that allowed us to model confounding factors. We analyse differential gene expression, associated pathway signatures and reconstruct signaling networks based on inferred transcription factor activities through integer linear programming. We additionally focus, for the first time, on differential RNA transcript isoform usage (DTU) changes and predict RNA-binding protein (RBP) to target transcript interactions using a Global test approach. We report results for all pairwise comparisons (DCM, HCM, NFD). RESULTS: Focusing on the DCM versus HCM contrast (DCMvsHCM), we identified 201 differentially expressed genes, some of which can be clearly associated with changes in ERK1 and ERK2 signaling. Interestingly, the signs of the predicted activity for these two kinases have been inferred to be opposite to each other: In the DCMvsHCM contrast, we predict ERK1 to be consistently less activated in DCM while ERK2 was more activated in DCM. In the DCMvsHCM contrast, we identified 149 differently used transcripts. One of the top candidates is the O-linked N-acetylglucosamine (GlcNAc) transferase (OGT), which catalyzes a common post-translational modification known for its role in heart arrhythmias and heart hypertrophy. Moreover, we reconstruct RBP - target interaction networks and showcase the examples of CPEB1, which is differentially expressed in the DCMvsHCM contrast. CONCLUSION: Magnetique ( https://shiny.dieterichlab.org/app/magnetique ) is the first online application to provide an interactive view of the HF transcriptome at the RNA isoform level and to include transcription factor signaling and RBP:RNA interaction networks. 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.

Full-Length Spatial Transcriptomics Reveals the Unexplored Isoform Diversity of the Myocardium Post-MI.

We introduce Single-cell Nanopore Spatial Transcriptomics (scNaST), a software suite to facilitate the analysis of spatial gene expression from second- and third-generation sequencing, allowing to generate a full-length near-single-cell transcriptional landscape of the tissue microenvironment. Taking advantage of the Visium Spatial platform, we adapted a strategy recently developed to assign barcodes to long-read single-cell sequencing data for spatial capture technology. Here, we demonstrate our workflow using four short axis sections of the mouse heart following myocardial infarction. We constructed a de novo transcriptome using long-read data, and successfully assigned 19,794 transcript isoforms in total, including clinically-relevant, but yet uncharacterized modes of transcription, such as intron retention or antisense overlapping transcription. We showed a higher transcriptome complexity in the healthy regions, and identified intron retention as a mode of transcription associated with the infarct area. Our data revealed a clear regional isoform switching among differentially used transcripts for genes involved in cardiac muscle contraction and tissue morphogenesis. Molecular signatures involved in cardiac remodeling integrated with morphological context may support the development of new therapeutics towards the treatment of heart failure and the reduction of cardiac complications.

Analysis of myocardial cellular gene expression during pressure overload reveals matrix based functional intercellular communication.

To identify cellular mechanisms responsible for pressure overload triggered heart failure, we isolated cardiomyocytes, endothelial cells, and fibroblasts as most abundant cell types from mouse hearts in the subacute and chronic stages after transverse aortic constriction (TAC) and performed RNA-sequencing. We detected highly cell-type specific transcriptional responses with characteristic time courses and active intercellular communication. Cardiomyocytes after TAC exerted an early and sustained upregulation of inflammatory and matrix genes and a concomitant suppression of metabolic and ion channel genes. Fibroblasts, in contrast, showed transient early upregulation of inflammatory and matrix genes and downregulation of angiogenesis genes, but sustained induction of cell cycle and ion channel genes during TAC. Endothelial cells transiently induced cell cycle and extracellular matrix genes early after TAC, but exerted a long-lasting upregulation of inflammatory genes. As we found that matrix production by multiple cell types triggers pathological cellular responses, it might serve as a future therapeutic target.

Muscle-specific Cand2 is translationally upregulated by mTORC1 and promotes adverse cardiac remodeling.

The mechanistic target of rapamycin (mTOR) promotes pathological remodeling in the heart by activating ribosomal biogenesis and mRNA translation. Inhibition of mTOR in cardiomyocytes is protective; however, a detailed role of mTOR in translational regulation of specific mRNA networks in the diseased heart is unknown. We performed cardiomyocyte genome-wide sequencing to define mTOR-dependent gene expression control at the level of mRNA translation. We identify the muscle-specific protein Cullin-associated NEDD8-dissociated protein 2 (Cand2) as a translationally upregulated gene, dependent on the activity of mTOR. Deletion of Cand2 protects the myocardium against pathological remodeling. Mechanistically, we show that Cand2 links mTOR signaling to pathological cell growth by increasing Grk5 protein expression. Our data suggest that cell-type-specific targeting of mTOR might have therapeutic value against pathological cardiac remodeling.

Corrigendum: A Multi-Network Comparative Analysis of Transcriptome and Translatome Identifies Novel Hub Genes in Cardiac Remodeling.

[This corrects the article DOI: 10.3389/fgene.2020.583124.].

A comparison of metabolic labeling and statistical methods to infer genome-wide dynamics of RNA turnover.

Metabolic labeling of newly transcribed RNAs coupled with RNA-seq is being increasingly used for genome-wide analysis of RNA dynamics. Methods including standard biochemical enrichment and recent nucleotide conversion protocols each require special experimental and computational treatment. Despite their immediate relevance, these technologies have not yet been assessed and benchmarked, and no data are currently available to advance reproducible research and the development of better inference tools. Here, we present a systematic evaluation and comparison of four RNA labeling protocols: 4sU-tagging biochemical enrichment, including spike-in RNA controls, SLAM-seq, TimeLapse-seq and TUC-seq. All protocols are evaluated based on practical considerations, conversion efficiency and wet lab requirements to handle hazardous substances. We also compute decay rate estimates and confidence intervals for each protocol using two alternative statistical frameworks, pulseR and GRAND-SLAM, for over 11 600 human genes and evaluate the underlying computational workflows for their robustness and ease of use. Overall, we demonstrate a high inter-method reliability across eight use case scenarios. Our results and data will facilitate reproducible research and serve as a resource contributing to a fuller understanding of RNA biology.

Unsupervised Bayesian Prediction of RNA Translation from Ribosome Profiling Data.

Ribosome profiling has been instrumental in leading to important discoveries in several fields of life sciences. Here we describe a computational approach that enables identification of translation events on a genome-wide scale from ribosome profiling data. Periodic fragment sizes indicative of active translation are selected without supervision for each library. Our workflow allows to map the whole translational landscape of a given cell, tissue, or organism, under varying conditions, and can be used to expand the search for novel, uncharacterized open reading frames, such as regulatory upstream translation events. Through a detailed workflow example, we show how to perform qualitative and quantitative analysis of translatomes.

Updated and enhanced pig cardiac transcriptome based on long-read RNA sequencing and proteomics.

Clinically translatable large animal models have become indispensable for cardiovascular research, clinically relevant proof of concept studies and for novel therapeutic interventions. In particular, the pig has emerged as an essential cardiovascular disease model, because its heart, circulatory system, and blood supply are anatomically and functionally similar to that of humans. Currently, molecular and omics-based studies in the pig are hampered by the incompleteness of the genome and the lack of diversity of the corresponding transcriptome annotation. Here, we employed Nanopore long-read sequencing and in-depth proteomics on top of Illumina RNA-seq to enhance the pig cardiac transcriptome annotation. We assembled 15,926 transcripts, stratified into coding and non-coding, and validated our results by complementary mass spectrometry. A manual review of several gene loci, which are associated with cardiac function, corroborated the utility of our enhanced annotation. All our data are available for download and are provided as tracks for integration in genome browsers. We deem this resource as highly valuable for molecular research in an increasingly relevant large animal model.

A Multi-Network Comparative Analysis of Transcriptome and Translatome Identifies Novel Hub Genes in Cardiac Remodeling.

Our understanding of the transition from physiological to pathological cardiac hypertrophy remains elusive and largely based on reductionist hypotheses. Here, we profiled the translatomes of 15 mouse hearts to provide a molecular blueprint of altered gene networks in early cardiac remodeling. Using co-expression analysis, we showed how sub-networks are orchestrated into functional modules associated with pathological phenotypes. We discovered unappreciated hub genes, many undocumented for their role in cardiac hypertrophy, and genes in the transcriptional network that were rewired in the translational network, and associated with semantically different subsets of enriched functional terms, such as Fam210a, a novel musculoskeletal modulator, or Psmd12, implicated in protein quality control. Using their correlation structure, we found that transcriptome networks are only partially reproducible at the translatome level, providing further evidence of post-transcriptional control at the level of translation. Our results provide novel insights into the complexity of the organization of in vivo cardiac regulatory networks.

Monitoring Cell-Type-Specific Gene Expression Using Ribosome Profiling In Vivo During Cardiac Hemodynamic Stress.

RATIONALE: Gene expression profiles have been mainly determined by analysis of transcript abundance. However, these analyses cannot capture posttranscriptional gene expression control at the level of translation, which is a key step in the regulation of gene expression, as evidenced by the fact that transcript levels often poorly correlate with protein levels. Furthermore, genome-wide transcript profiling of distinct cell types is challenging due to the fact that lysates from tissues always represent a mixture of cells. OBJECTIVES: This study aimed to develop a new experimental method that overcomes both limitations and to apply this method to perform a genome-wide analysis of gene expression on the translational level in response to pressure overload. METHODS AND RESULTS: By combining ribosome profiling (Ribo-seq) with a ribosome-tagging approach (Ribo-tag), it was possible to determine the translated transcriptome in specific cell types from the heart. After pressure overload, we monitored the cardiac myocyte translatome by purifying tagged cardiac myocyte ribosomes from cardiac lysates and subjecting the ribosome-protected mRNA fragments to deep sequencing. We identified subsets of mRNAs that are regulated at the translational level and found that translational control determines early changes in gene expression in response to cardiac stress in cardiac myocytes. Translationally controlled transcripts are associated with specific biological processes related to translation, protein quality control, and metabolism. Mechanistically, Ribo-seq allowed for the identification of upstream open reading frames in transcripts, which we predict to be important regulators of translation. CONCLUSIONS: This method has the potential to (1) provide a new tool for studying cell-specific gene expression at the level of translation in tissues, (2) reveal new therapeutic targets to prevent cellular remodeling, and (3) trigger follow-up studies that address both, the molecular mechanisms involved in the posttranscriptional control of gene expression in cardiac cells, and the protective functions of proteins expressed in response to cellular stress.

m6A-mRNA methylation regulates cardiac gene expression and cellular growth.

Conceptually similar to modifications of DNA, mRNAs undergo chemical modifications, which can affect their activity, localization, and stability. The most prevalent internal modification in mRNA is the methylation of adenosine at the N6-position (m6A). This returns mRNA to a role as a central hub of information within the cell, serving as an information carrier, modifier, and attenuator for many biological processes. Still, the precise role of internal mRNA modifications such as m6A in human and murine-dilated cardiac tissue remains unknown. Transcriptome-wide mapping of m6A in mRNA allowed us to catalog m6A targets in human and murine hearts. Increased m6A methylation was found in human cardiomyopathy. Knockdown and overexpression of the m6A writer enzyme Mettl3 affected cell size and cellular remodeling both in vitro and in vivo. Our data suggest that mRNA methylation is highly dynamic in cardiomyocytes undergoing stress and that changes in the mRNA methylome regulate translational efficiency by affecting transcript stability. Once elucidated, manipulations of methylation of specific m6A sites could be a powerful approach to prevent worsening of cardiac function.

RNA Modification Level Estimation with pulseR.

RNA modifications regulate the complex life of transcripts. An experimental approach called LAIC-seq was developed to characterize modification levels on a transcriptome-wide scale. In this method, the modified and unmodified molecules are separated using antibodies specific for a given RNA modification (e.g., m6A). In essence, the procedure of biochemical separation yields three fractions: Input, eluate, and supernatent, which are subjected to RNA-seq. In this work, we present a bioinformatics workflow, which starts from RNA-seq data to infer gene-specific modification levels by a statistical model on a transcriptome-wide scale. Our workflow centers around the pulseR package, which was originally developed for the analysis of metabolic labeling experiments. We demonstrate how to analyze data without external normalization (i.e., in the absence of spike-ins), given high efficiency of separation, and how, alternatively, scaling factors can be derived from unmodified spike-ins. Importantly, our workflow provides an estimate of uncertainty of modification levels in terms of confidence intervals for model parameters, such as gene expression and RNA modification levels. We also compare alternative model parametrizations, log-odds, or the proportion of the modified molecules and discuss the pros and cons of each representation. In summary, our workflow is a versatile approach to RNA modification level estimation, which is open to any read-count-based experimental approach.

Estimating the accuracy of a reduced-order model for the calculation of fractional flow reserve (FFR).

Image-based noninvasive fractional flow reserve (FFR) is an emergent approach to determine the functional relevance of coronary stenoses. The present work aimed to determine the feasibility of using a method based on coronary computed tomography angiography (CCTA) and reduced-order models (0D-1D) for the evaluation of coronary stenoses. The reduced-order methodology (cFFRRO ) was kept as simple as possible and did not include pressure drop or stenosis models. The geometry definition was incorporated into the physical model used to solve coronary flow and pressure. cFFRRO was assessed on a virtual cohort of 30 coronary artery stenoses in 25 vessels and compared with a standard approach based on 3D computational fluid dynamics (cFFR3D ). In this proof-of-concept study, we sought to investigate the influence of geometry and boundary conditions on the agreement between both methods. Performance on a per-vessel level showed a good correlation between both methods (Pearson's product-moment R=0.885, P<0.01), when using cFFR3D  as the reference standard. The 95% limits of agreement were -0.116 and 0.08, and the mean bias was -0.018 (SD =0.05). Our results suggest no appreciable difference between cFFRRO  and cFFR3D with respect to lesion length and/or aspect ratio. At a fixed aspect ratio, however, stenosis severity and shape appeared to be the most critical factors accounting for differences in both methods. Despite the assumptions inherent to the 1D formulation, asymmetry did not seem to affect the agreement. The choice of boundary conditions is critical in obtaining a functionally significant drop in pressure. Our initial data suggest that this approach may be part of a broader risk assessment strategy aimed at increasing the diagnostic yield of cardiac catheterisation for in-hospital evaluation of haemodynamically significant stenoses.

Modelling accidental hypothermia effects on a human body under different pathophysiological conditions.

Accidental exposure to cold water environment is one of the most challenging situations in which hypothermia occurs. In the present work, we aim to characterise the energy balance of a human body subjected to such extreme environmental conditions. This study is carried out using a recently developed computational model and by setting boundary conditions needed to simulate the effect of cold surrounding environment. A major finding is the capacity of the body core regions to maintain their temperature high for a substantial amount of time, even under the most extreme environmental conditions. We also considered two disease states that highlight the spectrum of possible pathologies implicated in thermal regulation of the human body. These states are (i) cardiomyopathy, which affects the operating capacity of the heart, and (ii) malnutrition, which directly impairs the body's ability to regulate heat exchange with the environment. We have found that cardiomyopathy has little influence on the thermal balance of the human body, whereas malnutrition has a profound negative effect on the thermal balance and leads to dramatic reduction in core temperature.

A novel method for non-invasively detecting the severity and location of aortic aneurysms.

The influence of an aortic aneurysm on blood flow waveforms is well established, but how to exploit this link for diagnostic purposes still remains challenging. This work uses a combination of experimental and computational modelling to study how aneurysms of various size affect the waveforms. Experimental studies are carried out on fusiform-type aneurysm models, and a comparison of results with those from a one-dimensional fluid-structure interaction model shows close agreement. Further mathematical analysis of these results allows the definition of several indicators that characterize the impact of an aneurysm on waveforms. These indicators are then further studied in a computational model of a systemic blood flow network. This demonstrates the methods' ability to detect the location and severity of an aortic aneurysm through the analysis of flow waveforms in clinically accessible locations. Therefore, the proposed methodology shows a high potential for non-invasive aneurysm detectors/monitors.

An advanced computational bioheat transfer model for a human body with an embedded systemic circulation.

In the present work, an elaborate one-dimensional thermofluid model for a human body is presented. By contrast to the existing pure conduction-/perfusion-based models, the proposed methodology couples the arterial fluid dynamics of a human body with a multi-segmental bioheat model of surrounding solid tissues. In the present configuration, arterial flow is included through a network of elastic vessels. More than a dozen solid segments are employed to represent the heat conduction in the surrounding tissues, and each segment is constituted by a multilayered circular cylinder. Such multi-layers allow flexible delineation of the geometry and incorporation of properties of different tissue types. The coupling of solid tissue and fluid models requires subdivision of the arterial circulation into large and small arteries. The heat exchange between tissues and arterial wall occurs by convection in large vessels and by perfusion in small arteries. The core region, including the heart, provides the inlet conditions for the fluid equations. In the proposed model, shivering, sweating, and perfusion changes constitute the basis of the thermoregulatory system. The equations governing flow and heat transfer in the circulatory system are solved using a locally conservative Galerkin approach, and the heat conduction in the surrounding tissues is solved using a standard implicit backward Euler method. To investigate the effectiveness of the proposed model, temperature field evolutions are monitored at different points of the arterial tree and in the surrounding tissue layers. To study the differences due to flow-induced convection effects on thermal balance, the results of the current model are compared against those of the widely used modelling methodologies. The results show that the convection significantly influences the temperature distribution of the solid tissues in the vicinity of the arteries. Thus, the inner convection has a more predominant role in the human body heat balance than previously thought. To demonstrate its capabilities, the proposed new model is used to study different scenarios, including thermoregulation inactivity and variation in surrounding atmospheric conditions.

A benchmark study of numerical schemes for one-dimensional arterial blood flow modelling.

Haemodynamical simulations using one-dimensional (1D) computational models exhibit many of the features of the systemic circulation under normal and diseased conditions. Recent interest in verifying 1D numerical schemes has led to the development of alternative experimental setups and the use of three-dimensional numerical models to acquire data not easily measured in vivo. In most studies to date, only one particular 1D scheme is tested. In this paper, we present a systematic comparison of six commonly used numerical schemes for 1D blood flow modelling: discontinuous Galerkin, locally conservative Galerkin, Galerkin least-squares finite element method, finite volume method, finite difference MacCormack method and a simplified trapezium rule method. Comparisons are made in a series of six benchmark test cases with an increasing degree of complexity. The accuracy of the numerical schemes is assessed by comparison with theoretical results, three-dimensional numerical data in compatible domains with distensible walls or experimental data in a network of silicone tubes. Results show a good agreement among all numerical schemes and their ability to capture the main features of pressure, flow and area waveforms in large arteries. All the information used in this study, including the input data for all benchmark cases, experimental data where available and numerical solutions for each scheme, is made publicly available online, providing a comprehensive reference data set to support the development of 1D models and numerical schemes.

Synergy Between Intercellular Communication and Intracellular Ca(2+) Handling in Arrhythmogenesis.

Calcium is the primary signalling component of excitation-contraction coupling, the process linking electrical excitability of cardiac muscle cells to coordinated contraction of the heart. Understanding [Formula: see text] handling processes at the cellular level and the role of intercellular communication in the emergence of multicellular synchronization are key aspects in the study of arrhythmias. To probe these mechanisms, we have simulated cellular interactions on large scale arrays that mimic cardiac tissue, and where individual cells are represented by a mathematical model of intracellular [Formula: see text] dynamics. Theoretical predictions successfully reproduced experimental findings and provide novel insights on the action of two pharmacological agents (ionomycin and verapamil) that modulate [Formula: see text] signalling pathways via distinct mechanisms. Computational results have demonstrated how transitions between local synchronisation events and large scale wave formation are affected by these agents. Entrainment phenomena are shown to be linked to both intracellular [Formula: see text] and coupling-specific dynamics in a synergistic manner. The intrinsic variability of the cellular matrix is also shown to affect emergent patterns of rhythmicity, providing insights into the origins of arrhythmogenic [Formula: see text] perturbations in cardiac tissue in situ.