GLI1+ Cells Contribute to Vascular Remodeling in Pulmonary Hypertension.

BACKGROUND: The precise origin of newly formed ACTA2+ (alpha smooth muscle actin-positive) cells appearing in nonmuscularized vessels in the context of pulmonary hypertension is still debatable although it is believed that they predominantly derive from preexisting vascular smooth muscle cells (VSMCs). METHODS: Gli1Cre-ERT2; tdTomatoflox mice were used to lineage trace GLI1+ (glioma-associated oncogene homolog 1-positive) cells in the context of pulmonary hypertension using 2 independent models of vascular remodeling and reverse remodeling: hypoxia and cigarette smoke exposure. Hemodynamic measurements, right ventricular hypertrophy assessment, flow cytometry, and histological analysis of thick lung sections followed by state-of-the-art 3-dimensional reconstruction and quantification using Imaris software were used to investigate the contribution of GLI1+ cells to neomuscularization of the pulmonary vasculature. RESULTS: The data show that GLI1+ cells are abundant around distal, nonmuscularized vessels during steady state, and this lineage contributes to around 50% of newly formed ACTA2+ cells around these normally nonmuscularized vessels. During reverse remodeling, cells derived from the GLI1+ lineage are largely cleared in parallel to the reversal of muscularization. Partial ablation of GLI1+ cells greatly prevented vascular remodeling in response to hypoxia and attenuated the increase in right ventricular systolic pressure and right heart hypertrophy. Single-cell RNA sequencing on sorted lineage-labeled GLI1+ cells revealed an Acta2high fraction of cells with pathways in cancer and MAPK (mitogen-activated protein kinase) signaling as potential players in reprogramming these cells during vascular remodeling. Analysis of human lung-derived material suggests that GLI1 signaling is overactivated in both group 1 and group 3 pulmonary hypertension and can promote proliferation and myogenic differentiation. CONCLUSIONS: Our data highlight GLI1+ cells as an alternative cellular source of VSMCs in pulmonary hypertension and suggest that these cells and the associated signaling pathways represent an important therapeutic target for further studies.

Multilineage murine stem cells generate complex organoids to model distal lung development and disease.

Organoids derived from mouse and human stem cells have recently emerged as a powerful tool to study organ development and disease. We here established a three-dimensional (3D) murine bronchioalveolar lung organoid (BALO) model that allows clonal expansion and self-organization of FACS-sorted bronchioalveolar stem cells (BASCs) upon co-culture with lung-resident mesenchymal cells. BALOs yield a highly branched 3D structure within 21 days of culture, mimicking the cellular composition of the bronchioalveolar compartment as defined by single-cell RNA sequencing and fluorescence as well as electron microscopic phenotyping. Additionally, BALOs support engraftment and maintenance of the cellular phenotype of injected tissue-resident macrophages. We also demonstrate that BALOs recapitulate lung developmental defects after knockdown of a critical regulatory gene, and permit modeling of viral infection. We conclude that the BALO model enables reconstruction of the epithelial-mesenchymal-myeloid unit of the distal lung, thereby opening numerous new avenues to study lung development, infection, and regenerative processes in vitro.

Hybrid modelling of biological systems: current progress and future prospects.

Integrated modelling of biological systems is becoming a necessity for constructing models containing the major biochemical processes of such systems in order to obtain a holistic understanding of their dynamics and to elucidate emergent behaviours. Hybrid modelling methods are crucial to achieve integrated modelling of biological systems. This paper reviews currently popular hybrid modelling methods, developed for systems biology, mainly revealing why they are proposed, how they are formed from single modelling formalisms and how to simulate them. By doing this, we identify future research requirements regarding hybrid approaches for further promoting integrated modelling of biological systems.

Hybrid modelling of biological systems using fuzzy continuous Petri nets.

Integrated modelling of biological systems is challenged by composing components with sufficient kinetic data and components with insufficient kinetic data or components built only using experts' experience and knowledge. Fuzzy continuous Petri nets (FCPNs) combine continuous Petri nets with fuzzy inference systems, and thus offer an hybrid uncertain/certain approach to integrated modelling of such biological systems with uncertainties. In this paper, we give a formal definition and a corresponding simulation algorithm of FCPNs, and briefly introduce the FCPN tool that we have developed for implementing FCPNs. We then present a methodology and workflow utilizing FCPNs to achieve hybrid (uncertain/certain) modelling of biological systems illustrated with a case study of the Mercaptopurine metabolic pathway. We hope this research will promote the wider application of FCPNs and address the uncertain/certain integrated modelling challenge in the systems biology area.

FGF10 Triggers De Novo Alveologenesis in a Bronchopulmonary Dysplasia Model: Impact on Resident Mesenchymal Niche Cells.

Bronchopulmonary dysplasia (BPD) is a neonatal lung disease developing in premature babies characterized by arrested alveologenesis and associated with decreased Fibroblast growth factor 10 (FGF10) expression. One-week hyperoxia (HYX) exposure of newborn mice leads to a permanent arrest in alveologenesis. To test the role of Fgf10 signaling to promote de novo alveologenesis following hyperoxia, we used transgenic mice allowing inducible expression of Fgf10 and recombinant FGF10 (rFGF10) protein delivered intraperitoneally. We carried out morphometry analysis, and IF on day 45. Alveolospheres assays were performed co-culturing AT2s from normoxia (NOX) with FACS-isolated Sca1Pos resident mesenchymal cells (rMC) from animals exposed to NOX, HYX-PBS, or HYX-FGF10. scRNAseq between rMC-Sca1Pos isolated from NOX and HYX-PBS was also carried out. Transgenic overexpression of Fgf10 and rFGF10 administration rescued the alveologenesis defects following HYX. Alveolosphere assays indicate that the activity of rMC-Sca1Pos is negatively impacted by HYX and partially rescued by rFGF10 treatment. Analysis by IF demonstrates a significant impact of rFGF10 on the activity of resident mesenchymal cells. scRNAseq results identified clusters expressing Fgf10, Fgf7, Pdgfra, and Axin2, which could represent the rMC niche cells for the AT2 stem cells. In conclusion, we demonstrate that rFGF10 administration is able to induce de novo alveologenesis in a BPD mouse model and identified subpopulations of rMC-Sca1Pos niche cells potentially representing its cellular target.

Fuzzy Petri nets for modelling of uncertain biological systems.

The modelling of biological systems is accompanied with epistemic uncertainties that range from structural uncertainty to parametric uncertainty due to such limitations as insufficient understanding of the underlying mechanism and incomplete measurement data of a system. Fuzzy logic approaches such as fuzzy Petri nets (FPNs) are effective in addressing these issues. In this paper, we review FPNs that have been used for modelling uncertain biological systems, which we classify in three categories: basic fuzzy Petri nets, fuzzy quantitative Petri nets and Petri nets with fuzzy kinetic parameters. For each category of these FPNs, we summarize its modelling capabilities and current applications, discuss its merits and drawbacks and give suggestions for further research. This understanding on how to use FPNs for modelling uncertain biological systems will assist readers in selecting appropriate FPN classes for specific modelling circumstances. This review may also promote the extensive research and application of FPNs in the systems biology area.

Characterization in mice of the resident mesenchymal niche maintaining AT2 stem cell proliferation in homeostasis and disease.

Resident mesenchymal cells (rMCs defined as Cd31Neg Cd45Neg EpcamNeg ) control the proliferation and differentiation of alveolar epithelial type 2 (AT2) stem cells in vitro. The identity of these rMCs is still elusive. Among them, Axin2Pos mesenchymal alveolar niche cells (MANCs), which are expressing Fgf7, have been previously described. We propose that an additional population of rMCs, expressing Fgf10 (called rMC-Sca1Pos Fgf10Pos ) are equally important to maintain AT2 stem cell proliferation. The alveolosphere model, based on the AT2-rMC co-culture in growth factor-reduced Matrigel, was used to test the efficiency of different rMC subpopulations isolated by FACS from adult murine lung to sustain the proliferation and differentiation of AT2 stem cells. We demonstrate that rMC-Sca1Pos Fgf10Pos cells are efficient to promote the proliferation and differentiation of AT2 stem cells. Co-staining of adult lung for Fgf10 mRNA and Sftpc protein respectively, indicate that 28% of Fgf10Pos cells are located close to AT2 cells. Co-ISH for Fgf7 and Fgf10 indicate that these two populations do not significantly overlap. Gene arrays comparing rMC-Sca1Pos Axin2Pos and rMC-Sca1Pos Fgf10Pos support that these two cell subsets express differential markers. In addition, rMC function is decreased in obese ob/ob mutant compared to WT mice with a much stronger loss of function in males compared to females. In conclusion, rMC-Sca1Pos Fgf10Pos cells play important role in supporting AT2 stem cells proliferation and differentiation. This result sheds a new light on the subpopulations of rMCs contributing to the AT2 stem cell niche in homeostasis and in the context of pre-existing metabolic diseases.

Identification of a novel subset of alveolar type 2 cells enriched in PD-L1 and expanded following pneumonectomy.

Alveolar type 2 (AT2) cells are heterogeneous cells, with specialised AT2 subpopulations within this lineage exhibiting stem cell properties. However, the existence of quiescent, immature cells within the AT2 lineage that are activated during lung regeneration is unknown.SftpcCreERT2/+;tdTomatoflox/flox mice were used for the labelling of AT2 cells and labelled subpopulations were analysed by flow cytometry, quantitative PCR, assay for transposase-accessible chromatin using sequencing (ATAC-seq), gene arrays, pneumonectomy and culture of precision-cut lung slices. Single-cell RNA-sequencing (scRNA-seq) data from human lungs were analysed.In mice, we detected two distinct AT2 subpopulations, with low tdTomato level (TomLow) and high tdTomato level (TomHigh). TomLow cells express lower levels of the AT2 differentiation markers Fgfr2b and Etv5, while TomHigh, as bona fide mature AT2 cells, show higher levels of Sftpc, Sftpb, Sftpa1, Fgfr2b and Etv5 expression. ATAC-seq analysis indicates that TomLow and TomHigh cells constitute two distinct cell populations, with specific silencing of Sftpc, Rosa26 and cell cycle gene loci in the TomLow population. Upon pneumonectomy, the number of TomLow but not TomHigh cells increases and TomLow cells show upregulated expression of Fgfr2b, Etv5, Sftpc, Ccnd1 and Ccnd2 compared to Sham. TomLow cells overexpress programmed cell death 1 ligand 1 (PD-L1), an immune inhibitory membrane receptor ligand, which is used by flow cytometry to differentially isolate these two subpopulations. In the human lung, data mining of a recent scRNA-seq AT2 data set demonstrates the existence of a PD-L1 Pos population. Therefore, we have identified a novel population of AT2 quiescent, immature progenitor cells in mouse that expand upon pneumonectomy and we have provided evidence for the existence of such cells in human.

Towards dynamic genome-scale models.

The analysis of the dynamic behaviour of genome-scale models of metabolism (GEMs) currently presents considerable challenges because of the difficulties of simulating such large and complex networks. Bacterial GEMs can comprise about 5000 reactions and metabolites, and encode a huge variety of growth conditions; such models cannot be used without sophisticated tool support. This article is intended to aid modellers, both specialist and non-specialist in computerized methods, to identify and apply a suitable combination of tools for the dynamic behaviour analysis of large-scale metabolic designs. We describe a methodology and related workflow based on publicly available tools to profile and analyse whole-genome-scale biochemical models. We use an efficient approximative stochastic simulation method to overcome problems associated with the dynamic simulation of GEMs. In addition, we apply simulative model checking using temporal logic property libraries, clustering and data analysis, over time series of reaction rates and metabolite concentrations. We extend this to consider the evolution of reaction-oriented properties of subnets over time, including dead subnets and functional subsystems. This enables the generation of abstract views of the behaviour of these models, which can be large-up to whole genome in size-and therefore impractical to analyse informally by eye. We demonstrate our methodology by applying it to a reduced model of the whole-genome metabolism of Escherichia coli K-12 under different growth conditions. The overall context of our work is in the area of model-based design methods for metabolic engineering and synthetic biology.

Coloured Petri nets for multilevel, multiscale and multidimensional modelling of biological systems.

Owing to the availability of data of one biological phenomenon at different levels/scales, modelling of biological systems is moving from single level/scale to multiple levels/scales, which introduces a number of challenges. Coloured Petri nets (ColPNs) have been successfully applied to multilevel, multiscale and multidimensional modelling of some biological systems, addressing many of these challenges. In this article, we first review the basics of ColPNs and some popular extensions, and then their applications for multilevel, multiscale and multidimensional modelling of biological systems. This understanding of how to use ColPNs for modelling biological systems will assist readers in selecting appropriate ColPN classes for specific modelling circumstances.

Mediator complex regulates alternative mRNA processing via the MED23 subunit.

Mediator complex is an integrative hub for transcriptional regulation. Here we show that Mediator regulates alternative mRNA processing via its MED23 subunit. Combining tandem affinity purification and mass spectrometry, we identified a number of mRNA processing factors that bind to a soluble recombinant Mediator subunit, MED23, but not to several other Mediator components. One of these factors, hnRNP L, specifically interacts with MED23 in vitro and in vivo. Consistently, Mediator partially colocalizes with hnRNP L and the splicing machinery in the cell. Functionally, MED23 regulates a subset of hnRNP L-targeted alternative splicing (AS) and alternative cleavage and polyadenylation (APA) events, as shown by minigene reporters and exon array analysis. ChIP-seq analysis revealed that MED23 can regulate hnRNP L occupancy at their coregulated genes. Taken together, these results demonstrate a crosstalk between Mediator and the splicing machinery, providing a molecular basis for coupling mRNA processing to transcription.

Spatial quorum sensing modelling using coloured hybrid Petri nets and simulative model checking.

BACKGROUND: Quorum sensing drives biofilm formation in bacteria in order to ensure that biofilm formation only occurs when colonies are of a sufficient size and density. This spatial behaviour is achieved by the broadcast communication of an autoinducer in a diffusion scenario. This is of interest, for example, when considering the role of gut microbiota in gut health. This behaviour occurs within the context of the four phases of bacterial growth, specifically in the exponential stage (phase 2) for autoinducer production and the stationary stage (phase 3) for biofilm formation. RESULTS: We have used coloured hybrid Petri nets to step-wise develop a flexible computational model for E.coli biofilm formation driven by Autoinducer 2 (AI-2) which is easy to configure for different notions of space. The model describes the essential components of gene transcription, signal transduction, extra and intra cellular transport, as well as the two-phase nature of the system. We build on a previously published non-spatial stochastic Petri net model of AI-2 production, keeping the assumptions of a limited nutritional environment, and our spatial hybrid Petri net model of biofilm formation, first presented at the NETTAB 2017 workshop. First we consider the two models separately without space, and then combined, and finally we add space. We describe in detail our step-wise model development and validation. Our simulation results support the expected behaviour that biofilm formation is increased in areas of higher bacterial colony size and density. Our analysis techniques include behaviour checking based on linear time temporal logic. CONCLUSIONS: The advantages of our modelling and analysis approach are the description of quorum sensing and associated biofilm formation over two phases of bacterial growth, taking into account bacterial spatial distribution using a flexible and easy to maintain computational model. All computational results are reproducible.

Emerging ensembles of kinetic parameters to characterize observed metabolic phenotypes.

BACKGROUND: Determining the value of kinetic constants for a metabolic system in the exact physiological conditions is an extremely hard task. However, this kind of information is of pivotal relevance to effectively simulate a biological phenomenon as complex as metabolism. RESULTS: To overcome this issue, we propose to investigate emerging properties of ensembles of sets of kinetic constants leading to the biological readout observed in different experimental conditions. To this aim, we exploit information retrievable from constraint-based analyses (i.e. metabolic flux distributions at steady state) with the goal to generate feasible values for kinetic constants exploiting the mass action law. The sets retrieved from the previous step will be used to parametrize a mechanistic model whose simulation will be performed to reconstruct the dynamics of the system (until reaching the metabolic steady state) for each experimental condition. Every parametrization that is in accordance with the expected metabolic phenotype is collected in an ensemble whose features are analyzed to determine the emergence of properties of a phenotype. In this work we apply the proposed approach to identify ensembles of kinetic parameters for five metabolic phenotypes of E. Coli, by analyzing five different experimental conditions associated with the ECC2comp model recently published by Hädicke and collaborators. CONCLUSIONS: Our results suggest that the parameter values of just few reactions are responsible for the emergence of a metabolic phenotype. Notably, in contrast with constraint-based approaches such as Flux Balance Analysis, the methodology used in this paper does not require to assume that metabolism is optimizing towards a specific goal.

Petri-net-based 2D design of DNA walker circuits.

We consider localised DNA computation, where a DNA strand walks along a binary decision graph to compute a binary function. One of the challenges for the design of reliable walker circuits consists in leakage transitions, which occur when a walker jumps into another branch of the decision graph. We automatically identify leakage transitions, which allows for a detailed qualitative and quantitative assessment of circuit designs, design comparison, and design optimisation. The ability to identify leakage transitions is an important step in the process of optimising DNA circuit layouts where the aim is to minimise the computational error inherent in a circuit while minimising the area of the circuit. Our 2D modelling approach of DNA walker circuits relies on coloured stochastic Petri nets which enable functionality, topology and dimensionality all to be integrated in one two-dimensional model. Our modelling and analysis approach can be easily extended to 3-dimensional walker systems.

The RNA-binding protein QKI suppresses cancer-associated aberrant splicing.

Lung cancer is the leading cause of cancer-related death worldwide. Aberrant splicing has been implicated in lung tumorigenesis. However, the functional links between splicing regulation and lung cancer are not well understood. Here we identify the RNA-binding protein QKI as a key regulator of alternative splicing in lung cancer. We show that QKI is frequently down-regulated in lung cancer, and its down-regulation is significantly associated with a poorer prognosis. QKI-5 inhibits the proliferation and transformation of lung cancer cells both in vitro and in vivo. Our results demonstrate that QKI-5 regulates the alternative splicing of NUMB via binding to two RNA elements in its pre-mRNA, which in turn suppresses cell proliferation and prevents the activation of the Notch signaling pathway. We further show that QKI-5 inhibits splicing by selectively competing with a core splicing factor SF1 for binding to the branchpoint sequence. Taken together, our data reveal QKI as a critical regulator of splicing in lung cancer and suggest a novel tumor suppression mechanism involving QKI-mediated regulation of the Notch signaling pathway.

A model-driven methodology for exploring complex disease comorbidities applied to autism spectrum disorder and inflammatory bowel disease.

We propose a model-driven methodology aimed to shed light on complex disorders. Our approach enables exploring shared etiologies of comorbid diseases at the molecular pathway level. The method, Comparative Comorbidities Simulation (CCS), uses stochastic Petri net simulation for examining the phenotypic effects of perturbation of a network known to be involved in comorbidities to predict new roles for mutations in comorbid conditions. To demonstrate the utility of our novel methodology, we investigated the molecular convergence of autism spectrum disorder (ASD) and inflammatory bowel disease (IBD) on the autophagy pathway. In addition to validation by domain experts, we used formal analyses to demonstrate the model's self-consistency. We then used CCS to compare the effects of loss of function (LoF) mutations previously implicated in either ASD or IBD on the autophagy pathway. CCS identified similar dynamic consequences of these mutations in the autophagy pathway. Our method suggests that two LoF mutations previously implicated in IBD may contribute to ASD, and one ASD-implicated LoF mutation may play a role in IBD. Future targeted genomic or functional studies could be designed to directly test these predictions.

Modeling biological gradient formation: combining partial differential equations and Petri nets.

Both Petri nets and differential equations are important modeling tools for biological processes. In this paper we demonstrate how these two modeling techniques can be combined to describe biological gradient formation. Parameters derived from partial differential equation describing the process of gradient formation are incorporated in an abstract Petri net model. The quantitative aspects of the resulting model are validated through a case study of gradient formation in the fruit fly.

YB-1 binds to CAUC motifs and stimulates exon inclusion by enhancing the recruitment of U2AF to weak polypyrimidine tracts.

The human Y box-binding protein-1 (YB-1) is a deoxyribonucleic acid (DNA)/ribonucleic acid (RNA)-binding protein with pleiotropic functions. Besides its roles in the regulation of transcription and translation, several recent studies indicate that YB-1 is a spliceosome-associated protein and is involved in alternative splicing, but the underlying mechanism has remained elusive. Here, we define both CAUC and CACC as high-affinity binding motifs for YB-1 by systematic evolution of ligands by exponential enrichment (SELEX) and demonstrate that these newly defined motifs function as splicing enhancers. Interestingly, on the endogenous CD44 gene, YB-1 appears to mediate a network interaction to activate exon v5 inclusion via multiple CAUC motifs in both the alternative exon and its upstream polypyrimidine tract. We provide evidence that YB-1 activates splicing by facilitating the recruitment of U2AF65 to weak polypyrimidine tracts through direct protein-protein interactions. Together, these findings suggest a vital role of YB-1 in activating a subset of weak 3' splice sites in mammalian cells.

Alveolar macrophage-expressed Plet1 is a driver of lung epithelial repair after viral pneumonia.

Influenza A virus (IAV) infection mobilizes bone marrow-derived macrophages (BMDM) that gradually undergo transition to tissue-resident alveolar macrophages (TR-AM) in the inflamed lung. Combining high-dimensional single-cell transcriptomics with complex lung organoid modeling, in vivo adoptive cell transfer, and BMDM-specific gene targeting, we found that transitioning ("regenerative") BMDM and TR-AM highly express Placenta-expressed transcript 1 (Plet1). We reveal that Plet1 is released from alveolar macrophages, and acts as important mediator of macrophage-epithelial cross-talk during lung repair by inducing proliferation of alveolar epithelial cells and re-sealing of the epithelial barrier. Intratracheal administration of recombinant Plet1 early in the disease course attenuated viral lung injury and rescued mice from otherwise fatal disease, highlighting its therapeutic potential.

Protocol for biomodel engineering of unilevel to multilevel biological models using colored Petri nets.

Biological systems inherently span multiple levels, which can pose challenges in spatial representation for modelers. We present a protocol that utilizes colored Petri nets to construct and analyze biological models of systems, encompassing both unilevel and multilevel scenarios. We detail a modeling workflow exploiting the PetriNuts platform comprising a set of tools linked together via common file formats. We describe steps for modeling preparation, component-level modeling and analysis, followed by system-level modeling and analysis, and model use.