Biology subjects

Vazquez, M.

Publications and source records attributed to Vazquez, M..

At least 19 recordsLinked to original sources

bioRxiv2026

A Principled Statistical Framework for Analyzing Spatial Patterns in Spatially Resolved Multi-Omics

Emerging spatial multi-omics technologies enable the profiling of molecular variation within its tissue context, yet existing methods for identifying spatially variable features lack principled approaches to experimental design and cross-sample inference. Here, we present STORM, a principled Statistical TOol for spatially Resolved Multi-omics, for rigorously analyzing spatial patterns in spatial multi-omics research. STORM incorporates a robust and efficient nonparametric test that quantifies local deviations in molecular feature measurements to detect spatial dependence across transcriptomic and proteomic data. It further estimates an interpretable spatial effect size, supports power calculations for both spatial locations and biological replicates, and enables formal group-level comparisons. In several simulated and experimental spatial multi-omics case studies, STORM demonstrates reliable performance in detecting spatial structures while offering quantitative support for study design decisions. Overall, STORM provides a principled statistical framework that unifies spatial hypothesis testing, effect size estimation, power analysis, and experimental design for spatial multi-omics data.

bioinformatics↗

bioRxiv2026

A biatrial digital twin integrating electrophysiology, mechanics, and circulation: from physiology to atrial fibrillation

Atrial electromechanics plays a key role in cardiac function by regulating ventricular filling and global hemodynamics, yet remains challenging to model consistently across scales. In this work, a multiscale atrial digital twin for simulations of normal and pathological atrial function is presented, formulated as an electromechanical framework for biatrial simulations that couples three-dimensional atrial electrophysiology and mechanics with a closed-loop zero-dimensional circulatory model. The framework is calibrated on a patient-specific biatrial anatomy to reproduce physiological regional activation times, atrial volumes, ejection fractions, and pressure-volume loop characteristics. The simulations capture all atrial functional phases throughout the cardiac cycle, including realistic figure-eight pressure-volume loops, an aspect hard to achieve in computational studies. A systematic sensitivity analysis quantifies the influence of active contraction, passive stiffness, boundary conditions, and circulatory parameters on atrial function. Finally, application to a pathological scenario through induced persistent atrial fibrillation demonstrates how electrophysiological remodelling propagates across scales, leading to loss of effective atrial contraction, altered atrioventricular flow patterns, and a clinically relevant reduction in cardiac output. Overall, this multiphysics and multiscale framework provides a robust platform to investigate how atrial electrical alterations drive mechanical and hemodynamic alterations in both healthy and pathological conditions.

bioengineering↗

bioRxiv2026

AI-enhanced cardiac digital twins extend drug proarrhythmic risk assessment through experimental data uncertainty propagation and overdose exploration: a loperamide case study

Drug-induced QT interval prolongation is a key biomarker of proarrhythmic risk and central to drug cardiac safety evaluation alongside in vitro assays and animal studies. Current preclinical frameworks, however, provide limited insight into how experimental uncertainty and extreme exposures translate into real-world arrhythmic risk, despite both factors critically modulating outcomes. To address this, we used sex-specific machine learning surrogate models trained on 3D cardiac digital twins--mechanistic electrophysiology models of anatomically detailed ventricles that integrate multichannel ion-channel block data. These emulators combine the realism of 3D simulations with high-throughput capability, enabling rapid, ethically unconstrained assessment of proarrhythmic risk. We illustrate the approach using loperamide, safe at therapeutic doses but linked to fatal arrhythmias at extreme exposures. Two analyses were performed: propagating experimental IC50 and Hill coefficient variability to quantify its effect on predicted QT prolongation and arrhythmic probability, and simulating extreme exposures to identify sex-specific arrhythmogenic thresholds. Experimental variability substantially broadened predicted QT prolongation and arrhythmic risk near decision thresholds. Extreme exposure simulations identified arrhythmogenic thresholds of approximately 107-109 x Cmax in female models and 213-286 x Cmax in male models. This framework offers a scalable, physics-based tool for early-stage drug cardiac safety evaluation.

bioengineering↗

bioRxiv2025

Can In Silico Models Predict Drug-Induced Cardiac Risk in Vulnerable Populations?

This study evaluates virtual cardiac populations for preclinical assessment of drug-induced QT interval prolongation and arrhythmic risk. Traditional predictions often rely on small, healthy cohorts, excluding vulnerable populations. Using computational models of realistic heart anatomies and electrophysiology, we generated a virtual cohort of 512 subjects across healthy and diseased hearts (heart failure, dilated and hypertrophic cardiomyopathy, ischaemia, and myocardial infarction). We assessed QT prolongation and arrhythmic events following administration of moxifloxacin (benchmark antibiotic) and contraindicated drugs including quinidine, bepridil, and flecainide. Patients with heart failure, hypertrophic and dilated cardiomyopathy showed greater QT prolongation to moxifloxacin, unlike ischaemia and myocardial infarction, which resembled healthy subjects. Females exhibited consistently higher QT prolongation than males. Contraindicated drugs markedly increased arrhythmia risk in populations with heart failure, dilated and hypertrophic cardiomyopathy, and ischaemia, frequently leading to lethal arrhythmias such as Torsades des Pointes or ventricular fibrillation, particularly in females. These findings demonstrate that computational models capture variability in drug response across pathologies and sexes, offering a predictive framework for preclinical safety evaluations and supporting safer, more personalized drug development strategies.

bioengineering↗

bioRxiv2025

Intelligent Tool Orchestration for Rapid Mechanistic Model Prototyping: MCP Servers as AI-Biology Interfaces

The construction of multicellular mechanistic models in systems biology typically requires months of literature research, programming expertise, and deep knowledge of specialized computational tools. Here we present intelligent tool orchestration through Model Context Protocol (MCP) servers that enable Large Language Models (LLMs) agents to function as AI laboratory assistants for rapid model prototyping. We demonstrate this approach by constructing a multiscale model of cancer cell fate in response to TNF, entirely through natural language interactions, using an AI agent connected to MCP servers that interface with three complementary modeling software: NeKo for constructing gene regulatory networks from prior-knowledge databases, MaBoSS for simulating and analyzing Boolean models, and PhysiCell for setting up multicellular agent-based models. Our architecture encompasses more than 60 specialized tools, covering the entire workflow from data collection to multicellular simulation setup. From this use case we derived three key design principles for biological AI-tool integration: first, optimal tool granularity is best defined at biological decision points, where domain knowledge guides modeling choices; second, comprehensive session management is essential for tracking complex workflows and ensuring reproducibility across long interactions; and third, effective orchestration must be established from the start to let LLMs combine tools flexibly while maintaining biological coherence. In applying this framework across three different LLMs and scenarios, we observed consistent end-to-end orchestration as well as model-dependent differences in network size, dynamical behaviors, and integration details. This variability highlights both the portability of our approach and the importance of careful reporting and validation when deploying different LLMs in scientific contexts. While the resulting models require refinement, this work establishes the foundation for AI-assisted rapid prototyping in systems biology, enabling researchers to explore computational hypotheses at unprecedented speed while maintaining biological fidelity.

bioinformatics↗

bioRxiv2025

rDNAcaller: a fast and robust pipeline to call ribosomal DNA variants

Ribosomal DNA (rDNA) genes are essential components of the ribosome, organized as tandem repeats on the mammalian genomes. Extensive genetic and copy number variation in rDNA has been reported both within and between individuals, contributing to phenotypic diversity. However, previous rDNA variant calling strategies have relied on methods designed for diploid regions and have not been systematically benchmarked. With the recent availability of a telomere-to-telomere (T2T) genome assembly, rDNA regions have been fully assembled for the first time, enabling benchmarking and optimization of rDNA variant calling strategies. We developed a customized simulator that replicates real intra- and inter-individual rDNA variation based on the T2T assembly to benchmark the performance of commonly used variant callers, including GATK, Mutect2, and Lofreq. Additionally, we optimized the preprocessing and mapping steps to remove pseudogenes and significantly improve accuracy. Based on these optimizations, we introduce rDNAcaller, a novel pipeline for accurate rDNA variant detection using short-read whole-genome sequencing. rDNAcaller integrates optimized preprocessing with the top-performing variant caller and achieves 94% precision on experimental data from the T2T cell line. Applying our pipeline to data from the 1000 Genomes Project, we identify 5,607 novel rDNA variant positions across human populations, with African individuals showing the highest number of variants. Overall, rDNAcaller is a robust and versatile tool for analyzing rDNA variation, addressing the limitations of existing methods in handling high ploidies. By enabling accurate detection of rDNA variants, it facilitates deeper exploration of rDNAs role in phenotypic diversity, supporting future genomic studies and broadening our understanding of rDNA biology in health and disease. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=72 SRC="FIGDIR/small/653643v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@148419borg.highwire.dtl.DTLVardef@cb3b42org.highwire.dtl.DTLVardef@9fa802org.highwire.dtl.DTLVardef@1cf4ff0_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Calibration and validation strategy for electromechanical cardiac digital twins

State-of-the-art cardiac electromechanical modelling and simulation form the basis for recent developments in cardiac Digital Twin technologies. However, a comprehensive evaluation of electromechanical models at cellular, tissue, and organ level has yet to be performed that addresses both ECG and pressure-volume biomarkers. Such an evaluation would build credibility for applications of cardiac Digital Twins in clinical research and therapy development. We aimed to follow ASME V&V40 standards for calibration, validation, and sensitivity analysis of ventricular electromechanical models under healthy conditions, to progress towards the Digital Twin vision. We performed a multi-scaled review of ventricular electromechanics and compiled a dataset for calibration and validation incorporating ECG, pressure-volume, displacement, and strain biomarkers. When applied to a biventricular multiscale model, we achieved healthy calibrated values for the QRS duration (89 ms), QT interval (360 ms), left ventricular ejection fraction (LVEF) (51 %), peak systolic pressure (14 kPa), end diastolic (105 mL) and end systolic volumes (51 mL), peak ejection flow rate (180 mL/ms). Model validation was performed by comparison to displacement and strain biomarkers including systolic atrioventricular plane displacement (1.5 cm), systolic fibre strain (-0.18) and longitudinal strain (-0.15). Sensitivity analysis of model parameters at cellular and ventricular scales was also performed to inform model calibration and as a first step towards uncertainty quantification. We quantified the effects of variability in ionic conductance, mechanical stiffness, cross-bridge cycling dynamics, and systemic circulation on action potential and active tension dynamics at the cellular scale, and on ECG, pressure-volume, displacement, and strain biomarkers at the ventricular scale. Simulations showed that the relationship between healthy LVEF and T wave biomarkers was primarily underpinned by variability in L-type calcium channel conductance and SERCA activity through multi-scale effects. In this study, we provide a systematic framework for credibility assessment of cardiac electromechanical models based on both ECG and mechanical biomarkers, as a foundational step towards the cardiac Digital Twin vision.

bioengineering↗

bioRxiv2024

The R-loop Grammar predicts R-loop formation under different topological constraints

R-loops are transient three-stranded nucleic acids that form during transcription when the nascent RNA hybridizes with the template DNA, freeing the DNA non-template strand. There is growing evidence that R-loops play important roles in physiological processes such as control of gene expression, and that they contribute to chromosomal instability and disease. It is known that R-loop formation is influenced by both the sequence and the topology of the DNA substrate, but many questions remain about how R-loops form and the 3-dimensional structures that they adopt. Here we represent an R-loop as a word in a formal grammar called the R-loop grammar and predict R-loop formation. We train the R-loop grammar on experimental data obtained by single-molecule R-loop footprinting and sequencing (SMRF-seq). Despite not containing explicit topological information, the R-loop grammar accurately predicts R-loop formation on plasmids with varying starting topologies and outperforms previous methods in R-loop prediction. Author summaryR-loops are prevalent triple helices that play regulatory roles in gene expression and are involved in various diseases. Our work improves the understanding of the relationship between the nucleotide sequence and DNA topology in R-loop formation. We use a mathematical approach from formal language theory to define an R-loop language and a set of rules to model R-loops as words in that language. We train the resulting R-loop grammar on experimental data of co-transcriptional R-loops formed on different DNA plasmids of varying topology. The model accurately predicts R-loop formation and outperforms prior methods. The R-loop grammar distills the effect of topology versus sequence, thus advancing our understanding of R-loop structure and formation.

genomics↗

bioRxiv2024

Integration of spatial multiplexed protein imaging and transcriptomics in the human kidney tracks the regenerative potential timeline of proximal tubules

The organizational principles of nephronal segments are based on longstanding anatomical and physiological attributes that are closely linked to the homeostatic functions of the kidney. Novel molecular approaches have recently uncovered layers of deeper signatures and states in tubular cells that arise at various timepoints on the spectrum between health and disease. For example, a dedifferentiated state of proximal tubular cells with mesenchymal stemness markers is frequently seen after injury. The persistence of such a state is associated with failed repair. Here, we introduce a novel analytical pipeline applied to highly multiplexed spatial protein imaging to characterize proximal tubular subpopulations and neighborhoods in reference and disease human kidney tissue. The results were validated and extended through integration with spatial and single cell transcriptomics. We demonstrate that, in reference tissue, a large proportion of S1 and S2 proximal tubular epithelial cells express THY1, a mesenchymal stromal and stem cell marker that regulates differentiation. Kidney disease is associated with loss of THY1 and transition towards expression of PROM1, another stem cell marker shown recently to be linked to failed repair. We demonstrate that the trajectory of proximal tubular cells to THY1 expression is clearly distinct from that of PROM1, and that a state with PROM1 expression is associated with niches of inflammation. Our data support a model in which the interplay between THY1 and PROM1 expression in proximal tubules associates with their regenerative potential and marks the timeline of disease progression.

bioinformatics↗

bioRxiv2024

Real-time prediction of drug-induced proarrhythmic risk with sex-specific cardiac emulators

In silico trials for drug safety assessment require a large number of high-fidelity 3D cardiac electrophysiological simulations to predict drug-induced QT interval prolongation, making the process computationally expensive and time-consuming. These simulations, while necessary to accurately model the complex physiological conditions of the human heart, are often cost-prohibitive when scaled to large populations or diverse conditions. To overcome this challenge, we develop sex-specific emulators for the real-time prediction of QT interval prolongation, with separate models for each sex. Building an extensive dataset from 900 simulations allows us to show the superior sensitivity of 3D models over 0D single-cell models in detecting abnormal electrical propagation in response to drug effects as the risk level increases. The resulting emulators trained on this dataset showed high accuracy level, with an average relative error of 4% compared to simulation results. This enables global sensitivity analysis and the replication of in silico cardiac safety clinical trials with accuracy comparable to that of simulations when validated against in vivo data. With our emulators, we carry out in silico clinical trials in seconds on a standard laptop, drastically reducing computational time compared to traditional high-performance computing methods. This efficiency enables the rapid testing of drugs across multiple concentration ranges without additional computational cost. This approach directly addresses several key challenges faced by the biopharmaceutical industry: optimizing trial designs, accounting for variability in biological assays, and enabling rapid, cost-effective drug safety evaluations. By integrating these emulators into the drug development process, we can enhance the reliability of preclinical assessments, streamline regulatory submissions, and advance the practical application of digital twins in biomedicine. This work represents a significant step toward more efficient and personalized drug development, ultimately benefiting patient safety and accelerating the path to market.

pharmacology and toxicology↗

bioRxiv2023

The Wingless Planar Cell Polarity pathway is essential for optimal activity-dependent synaptic plasticity.

From fly to man, the Wingless (Wg) / Wnt signaling molecule is essential for both the stability and plasticity of the nervous system. The Drosophila neuromuscular junction (NMJ) has proven to be a useful system for deciphering the role of Wg in directing activity-dependent synaptic plasticity, which, in the motoneuron, has been shown to be dependent on both the canonical and the noncanonical calcium Wg pathways. Here we show that the noncanonical planar cell polarity (PCP) pathway is an essential component of the Wg signaling system controlling plasticity at the motoneuron synapse. We present evidence that disturbing the PCP pathway leads to a perturbation in activity-dependent synaptic plasticity (ADSP). We first show that a PCP-specific allele of dishevelled (dsh) affects the de novo synaptic structures produced during ADSP. We then show that the Rho GTPases downstream of Dsh in the PCP pathway are also involved in regulating the morphological changes that take place after repeated stimulation. Finally, we show that Jun kinase is essential for this phenomenon, whereas we found no indication of the involvement of the transcription factor complex AP1 (Jun/Fos). This work shows the involvement of the neuronal PCP signaling pathway in supporting ADSP. Because we find that AP1 mutants can perform ADSP adequately, we hypothesize that, upon Wg activation, the Rho GTPases and Jun kinase are involved locally at the synapse, in instructing cytoskeletal dynamics responsible for the appearance of the morphological changes occurring during ADSP.

neuroscience↗

bioRxiv2023

Analyzing the link between RNA secondary structures and R-loop formation with tree polynomials

R-loops are a class of non-canonical nucleic acid structures that typically form during transcription when the nascent RNA hybridizes the DNA template strand, leaving the DNA coding strand unpaired. Co-transcriptional R-loops are abundant in nature and biologically relevant. Recent research shows that DNA sequence and topology affect R-loops, yet it remains unclear how these and other factors drive R-loop formation. In this work, we investigate a link between the secondary structure of the nascent RNA and the probability of R-loop formation. We introduce tree-polynomial representations, a class of mathematical objects that enable accurate and efficient data analysis of RNA secondary structures. With tree-polynomials, we establish a strong correlation between the secondary structure of the RNA transcript and the probability of R-loop formation. We identify that branches with short stems separated by multiple bubbles in the RNA secondary structure are associated with the strong correlation and are predictive of R-loop formation.

molecular biology↗

bioRxiv2023

VLP-mediated delivery of structure-selected neoantigens demonstrates immunogenicity and antitumoral activity in mice

BackgroundNeoantigens are patient- and tumor-specific peptides that arise from somatic mutations. They stand as promising targets for personalized therapeutic cancer vaccines. The identification process for neoantigens has evolved with the use of next-generation sequencing technologies and bioinformatic tools in tumor genomics. However, in silico strategies for selecting immunogenic neoantigens still have very low accuracy rates, since they mainly focus on predicting peptide binding to Major Histocompatibility Complex (MHC) molecules, which is key but not the sole determinant for immunogenicity. MethodsWe developed a novel neoantigen selection pipeline based on existing software combined with a novel prediction method, the Neoantigen Optimization Algorithm (NOAH), which takes into account structural features of the peptide/MHC-I interaction in its prediction strategy. Moreover, to maximize neoantigens therapeutic potential, neoantigen-based vaccines should be manufactured in an optimal delivery platform that elicits robust de novo immune responses and bypasses central and peripheral tolerance. ResultsWe generated a highly immunogenic vaccine platform based on engineered HIV-1 Gag-based Virus-Like Particles (VLPs) expressing a high copy number of each in silico selected neoantigen. We tested different neoantigen-loaded VLPs (neoVLPs) in a B16-F10 melanoma mouse model to evaluate their capability to generate new immunogenic specificities. NeoVLPs were used in in vivo immunogenicity and tumor challenge experiments. ConclusionsNeoVLPs can promote the generation of de novo antitumor-specific immune responses, resulting in a delay in tumor growth. Vaccination with the neoVLP platform is a robust alternative to current therapeutic vaccine approaches and a promising candidate for future personalized immunotherapy. WHAT IS ALREADY KNOWN ON THIS TOPICIdentification of highly immunogenic neoantigens is still challenging, currently available pipelines base their prediction on MHC-I binding affinity. Moreover, neoantigen-based vaccine delivery needs to be improved to increase the potency of anti-tumor immune response. WHAT THIS STUDY ADDSNOAH is a novel pipeline for the identification and selection of neoantigens that combines binding affinity and structural features of the peptide/MHC-I interaction. Preclinical studies show highly immunogenic vaccine platform based on HIV-1 Gag based VLPs (neoVLPs) generates antitumor-specific immune responses, delaying tumor growth. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICYThe combination of NOAH and neoVLP platform represents an alternative to current therapeutic vaccine approaches and a promising candidate for future personalized immunotherapy.

immunology↗

bioRxiv2023

Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities

Gene regulation plays a critical role in the cellular processes that underlie human health and disease. The regulatory relationship between transcription factors (TFs), key regulators of gene expression, and their target genes, the so called TF regulons, can be coupled with computational algorithms to estimate the activity of TFs. However, to interpret these findings accurately, regulons of high reliability and coverage are needed. In this study, we present and evaluate a collection of regulons created using the CollecTRI meta-resource containing signed TF-gene interactions for 1,183 TFs. In this context, we introduce a workflow to integrate information from multiple resources and assign the sign of regulation to TF-gene interactions that could be applied to other comprehensive knowledge bases. We find that the signed CollecTRI-derived regulons outperform other public collections of regulatory interactions in accurately inferring changes in TF activities in perturbation experiments. Furthermore, we showcase the value of the regulons by investigating hallmarks of TF activity profiles inferred from the transcriptomes of three different cancer types. Overall, the CollecTRI-derived TF regulons enable the accurate and comprehensive estimation of TF activities and thereby help to interpret transcriptomics data. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/534849v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@ad6521org.highwire.dtl.DTLVardef@1ca8f37org.highwire.dtl.DTLVardef@1808ccdorg.highwire.dtl.DTLVardef@9c2613_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

Using machine learning to detect coronaviruses potentially infectious to humans

Establishing the host range for novel viruses remains a challenge. Here, we address the challenge of identifying non-human animal coronaviruses that may infect humans by creating an artificial neural network model that learns from the binding of the spike protein of alpha and beta coronaviruses to their host receptor. The proposed method produces a human-Binding Potential (h-BiP) score that distinguishes, with high accuracy, the binding potential among human coronaviruses. Two viruses, previously unknown to bind human receptors, were identified: Bat coronavirus BtCoV/133/2005 (a MERS related virus) and Rhinolophus affinis coronavirus isolate LYRa3 a SARS related virus. We further analyze the binding properties of these viruses using molecular dynamics. To test whether this model can be used for surveillance of novel coronaviruses, we re-trained the model on a set that excludes SARS-COV-2 viral sequences. The results predict the binding of SARS-CoV-2 with a human receptor, indicating that machine learning methods are an excellent tool for the prediction of host expansion events.

microbiology↗

bioRxiv2022

Different transcriptional responses by the CRISPRa system in distinct types of heterochromatin in Drosophila melanogaster

Transcription factors (TFs) activate gene expression by binding to elements close to promoters or enhancers. Some TFs can bind to heterochromatic regions to initiate gene activation, suggesting that if a TF is able to bind to any type of heterochromatin, it can activate transcription. To investigate this possibility, we used the CRISPRa system based on dCas9-VPR as an artificial TF in Drosophila. dCas9-VPR was targeted to the TAHRE subtelomeric element, an example of constitutive heterochromatin, and to promoters and enhancers of the HOX Ultrabithorax (Ubx) and Sex Combs Reduced (Scr) genes in the context of facultative heterochromatin. dCas9-VPR robustly activated TAHRE transcription, showing that although this element is heterochromatic, dCas9-VPR was sufficient to activate its expression. In the case of HOX gene promoters, although these genes are epigenetically silenced by Polycomb complexes, both were ectopically activated. When the artificial TF was directed to enhancers, we found that the expression pattern was different compared to the effect on the promoters. In the case of the Scr upstream enhancer, dCas9-VPR activated the gene ectopically but with less expressivity; however, ectopic activation also occurred in different cells. In the case of the bxI enhancer located in the third intron of Ubx, the presence of dCas9-VPR is capable of increasing transcription initiation while simultaneously blocking transcription elongation, generating a lack of functional phenotype. Our results show that transcription can be activated in any type of heterochromatin by CRISPRa; nevertheless, its effect on transcription is subject to the intrinsic characteristics of each gene or regulatory element. SignificanceWhether transcription only depends on activating factors binding to chromatin, even though it is found in a silent state as heterochromatin, remains an open question. In this work, we addressed this question using the CRISPRa system via dCas9-VPR as a synthetic transcriptional activator in Drosophila. This activator was directed to a constitutive heterochromatin element and to promoters and enhancers of two HOX genes, which in the tissues where they are not expressed, are present as facultative heterochromatin. In all cases, the CRISPRa system was able to activate transcription, showing that its sole presence is sufficient for this to occur. Although transcription in constitutive heterochromatin was very robust, in the case of promoters and enhancers of HOX genes, the degree of expressivity, penetrance and ectopic effect was different between promoters and enhancers. These results indicate that the presence of a synthetic activator can activate transcription by binding to transcriptional regulatory elements; however, its effect depends on the particular characteristics of each one. These results show how artificial transcription factors can be used to understand transcription regulation at the organismal level.

genetics↗

bioRxiv2022

Ionic remodelling following myocardial infarction explains phenotypic variability in ECG and arrhythmic substrate but not in ejection fraction

AimsSudden death after myocardial infarction (MI) is associated with electrophysiological heterogeneities and ionic remodelling, which are reflected as variable phenotypes. Low ejection fraction (EF) is used in risk stratification, but its mechanistic links with the post-MI pro-arrhythmic heterogeneities are unknown. We aim to provide a mechanistic explanation of clinical phenotypes in acute and chronic MI, from ionic remodeling to ECG and EF, using human electromechanical modelling and simulation to augment experimental and clinical investigations. Methods and ResultsA human ventricular electromechanical modelling and simulation framework is constructed and validated with rich experimental and clinical datasets. Abnormalities caused by scar and border zone ionic remodeling are introduced in varying degrees as reported in experimental data obtained in acute and chronic infarction. Simulations enabled reproducing and explaining clinical phenotypes post-MI, from ionic remodelling to ECGs and pressure-volume loops. In acute MI, T-wave inversion and Brugada phenocopy were explained by up to 57 ms of local APD prolongation and activation failure due to the inhibition of potassium, sodium and calcium channels in the border zone. In chronic MI, upright tall T-waves highlight large repolarisation dispersion caused by uneven potassium channel expression in border and remote zones, which promoted ectopic propagation at fast pacing. Post-MI ionic remodelling reduced EF by up to 10% through inhibition of calcium transient amplitude due to weaker calcium currents or SERCA activity, but the EF at resting heart rate was not sensitive to the extent of repolarisation heterogeneity and the risk of repolarisation abnormalities at fast pacing. ConclusionsMulti-scale modelling and simulation coherently integrates experimental and clinical data at subcellular, tissue, and organ scales to unravel electromechanical disease mechanisms in MI. In acute post-MI, ionic remodelling and its effect on refractoriness and propagation failure in the BZ have a strong impact on phenotypic ECG variability, whereas in chronic post-MI, the repolarisation dispersion across the BZ is crucial. T-wave and QT abnormalities are better indicators of repolarisation heterogeneities than EF in post-MI.

bioengineering↗

bioRxiv2022

Fluid-structure interaction analysis of eccentricity and leaflet rigidity on thrombosis biomarkers in bioprosthetic aortic valve replacements

This work introduces the first 2-way fluid-structure interaction (FSI) computational model to study the effect of aortic annulus eccentricity on the performance and thrombogenic risk of cardiac bioprostheses. The model predicts that increasing eccentricities yield lower geometric orifice areas (GOAs) and higher normalized transvalvular pressure gradients (TPGs) for healthy cardiac outputs during systole, agreeing with in vitro experiments. Regions with peak values of residence time and shear rate are observed to grow with eccentricity in the sinus of Valsalva, indicating an elevated risk of thrombus formation for eccentric configurations. In addition, the computational model is used to analyze the effect of varying leaflet rigidity on both performance, thrombogenic and calcification risks with applications to tissue-engineered prostheses, observing an increase in systolic and diastolic TPGs, and decrease in systolic GOA, which translates to decreased valve performance for more rigid leaflets. An increased thrombogenic risk is detected for the most rigid valves. Peak solid stresses are also analyzed, and observed to increase with rigidity, elevating risk of valve calcification and structural failure. The immersed FSI method was implemented in a high-performance computing multi-physics simulation software, and validated against a well known FSI benchmark. The aortic valve bioprosthesis model is qualitatively contrasted against experimental data, showing good agreement in closed and open states. To the authors knowledge this is the first computational FSI model to study the effect of eccentricity or leaflet rigidity on thrombogenic biomarkers, providing a novel tool to aid device manufacturers and clinical practitioners.

bioengineering↗