Disruption of DLL4/NOTCH1 Causes Dysregulated PPARγ/AKT Signaling in Pulmonary Arterial Hypertension.

Pulmonary arterial hypertension (PAH) is a progressive cardiopulmonary disease characterized by vascular remodeling of small pulmonary arteries. Endothelial dysfunction in advanced PAH is associated with proliferation, apoptosis resistance, and endothelial to mesenchymal transition (EndoMT) due to aberrant signaling. DLL4, a cell membrane associated NOTCH ligand, activates NOTCH1 signaling and plays a pivotal role maintaining vascular integrity. Inhibition of DLL4 has been associated with the development of pulmonary hypertension, but the mechanism is incompletely understood. Here we report that BMPR2 silencing in PAECs activated AKT and decreased DLL4 expression. DLL4 loss was also seen in lungs of patients with IPAH and HPAH. Over-expression of DLL4 in PAECs induced BMPR2 promoter activity and exogenous DLL4 increased BMPR2 mRNA through NOTCH1 activation. Furthermore, DLL4/NOTCH1 signaling blocked AKT activation, decreased proliferation and reversed EndoMT in BMPR2 - silenced PAECs and ECs from IPAH patients. PPARγ, suppressed by BMPR2 loss, was induced and activated by DLL4/NOTCH1 signaling in both BMPR2 -silenced and IPAH PAECs, reversing aberrant phenotypic changes, in part through AKT inhibition. Finally, leniolisib, a well-tolerated oral PI3K8/AKT inhibitor, decreased cell proliferation, induced apoptosis and reversed markers of EndoMT in BMPR2 -silenced PAECs. Restoring DLL4/NOTCH1/PPARγ signaling and/or suppressing AKT activation may be beneficial in preventing or reversing the pathologic vascular remodeling of PAH.

Pathway-based analyses of gene expression profiles at low doses of ionizing radiation.

Radiation exposure poses a significant threat to human health. Emerging research indicates that even low-dose radiation once believed to be safe, may have harmful effects. This perception has spurred a growing interest in investigating the potential risks associated with low-dose radiation exposure across various scenarios. To comprehensively explore the health consequences of low-dose radiation, our study employs a robust statistical framework that examines whether specific groups of genes, belonging to known pathways, exhibit coordinated expression patterns that align with the radiation levels. Notably, our findings reveal the existence of intricate yet consistent signatures that reflect the molecular response to radiation exposure, distinguishing between low-dose and high-dose radiation. Moreover, we leverage a pathway-constrained variational autoencoder to capture the nonlinear interactions within gene expression data. By comparing these two analytical approaches, our study aims to gain valuable insights into the impact of low-dose radiation on gene expression patterns, identify pathways that are differentially affected, and harness the potential of machine learning to uncover hidden activity within biological networks. This comparative analysis contributes to a deeper understanding of the molecular consequences of low-dose radiation exposure.

BMPR2 Loss Activates AKT by Disrupting DLL4/NOTCH1 and PPARγ Signaling in Pulmonary Arterial Hypertension.

Pulmonary arterial hypertension (PAH) is a progressive cardiopulmonary disease characterized by pathologic vascular remodeling of small pulmonary arteries. Endothelial dysfunction in advanced PAH is associated with proliferation, apoptosis resistance, and endothelial to mesenchymal transition (EndoMT) due to aberrant signaling. DLL4, a cell membrane associated NOTCH ligand, plays a pivotal role maintaining vascular integrity. Inhibition of DLL4 has been associated with the development of pulmonary hypertension, but the mechanism is incompletely understood. Here we report that BMPR2 silencing in pulmonary artery endothelial cells (PAECs) activated AKT and suppressed the expression of DLL4. Consistent with these in vitro findings, increased AKT activation and reduced DLL4 expression was found in the small pulmonary arteries of patients with PAH. Increased NOTCH1 activation through exogenous DLL4 blocked AKT activation, decreased proliferation and reversed EndoMT. Exogenous and overexpression of DLL4 induced BMPR2 and PPRE promoter activity, and BMPR2 and PPARG mRNA in idiopathic PAH (IPAH) ECs. PPARγ, a nuclear receptor associated with EC homeostasis, suppressed by BMPR2 loss was induced and activated by DLL4/NOTCH1 signaling in both BMPR2-silenced and IPAH ECs, reversing aberrant phenotypic changes, in part through AKT inhibition. Directly blocking AKT or restoring DLL4/NOTCH1/PPARγ signaling may be beneficial in preventing or reversing the pathologic vascular remodeling of PAH.

Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery.

Significant acceleration of the future discovery of novel functional materials requires a fundamental shift from the current materials discovery practice, which is heavily dependent on trial-and-error campaigns and high-throughput screening, to one that builds on knowledge-driven advanced informatics techniques enabled by the latest advances in signal processing and machine learning. In this review, we discuss the major research issues that need to be addressed to expedite this transformation along with the salient challenges involved. We especially focus on Bayesian signal processing and machine learning schemes that are uncertainty aware and physics informed for knowledge-driven learning, robust optimization, and efficient objective-driven experimental design.

Optimal decision-making in high-throughput virtual screening pipelines.

The need for efficient computational screening of molecular candidates that possess desired properties frequently arises in various scientific and engineering problems, including drug discovery and materials design. However, the enormous search space containing the candidates and the substantial computational cost of high-fidelity property prediction models make screening practically challenging. In this work, we propose a general framework for constructing and optimizing a high-throughput virtual screening (HTVS) pipeline that consists of multi-fidelity models. The central idea is to optimally allocate the computational resources to models with varying costs and accuracy to optimize the return on computational investment. Based on both simulated and real-world data, we demonstrate that the proposed optimal HTVS framework can significantly accelerate virtual screening without any degradation in terms of accuracy. Furthermore, it enables an adaptive operational strategy for HTVS, where one can trade accuracy for efficiency.

Nicotinamide Antagonizes Lipopolysaccharide-Induced Hypoxic Cell Signals in Human Macrophages.

Mechanisms to control the immune response are important to pathogen evasion and host defense. Gram-negative bacteria are common pathogens that can activate host immune responses through their outer membrane component, LPS. Macrophage activation by LPS induces cell signals that promote hypoxic metabolism, phagocytosis, Ag presentation, and inflammation. Nicotinamide (NAM) is a vitamin B3 derivative and precursor in the formation of NAD, which is a required cofactor in cellular function. In this study, treatment of human monocyte-derived macrophages with NAM promoted posttranslational modifications that antagonized LPS-induced cell signals. Specifically, NAM inhibited AKT and FOXO1 phosphorylation, decreased p65/RelA acetylation, and promoted p65/RelA and hypoxia-inducible transcription factor-1α (HIF-1α) ubiquitination. NAM also increased prolyl hydroxylase domain 2 (PHD2) production, inhibited HIF-1α transcription, and promoted the formation of the proteasome, resulting in reduced HIF-1α stabilization, decreased glycolysis and phagocytosis, and reductions in NOX2 activity and the production of lactate dehydrogenase A. These NAM responses were associated with increased intracellular NAD levels formed through the salvage pathway. NAM and its metabolites may therefore decrease the inflammatory response of macrophages and protect the host against excessive inflammation but potentially increase injury through reduced pathogen clearance. Continued study of NAM cell signals in vitro and in vivo may provide insight into infection-associated host pathologies and interventions.

Inflammation and DKK1-induced AKT activation contribute to endothelial dysfunction following NR2F2 loss.

NR2F2 is expressed in endothelial cells (ECs) and Nr2f2 knockout produces lethal cardiovascular defects. In humans, reduced NR2F2 expression is associated with cardiovascular diseases including congenital heart disease and atherosclerosis. Here, NR2F2 silencing in human primary ECs led to inflammation, endothelial-to-mesenchymal transition (EndMT), proliferation, hypermigration, apoptosis-resistance, and increased production of reactive oxygen species. These changes were associated with STAT and AKT activation along with increased production of DKK1. Co-silencing DKK1 and NR2F2 prevented NR2F2-loss-induced STAT and AKT activation and reversed EndMT. Serum DKK1 concentrations were elevated in patients with pulmonary arterial hypertension (PAH) and DKK1 was secreted by ECs in response to in vitro loss of either BMPR2 or CAV1, which are genetic defects associated with the development of PAH. In human primary ECs, NR2F2 suppressed DKK1, whereas its loss conversely induced DKK1 and disrupted endothelial homeostasis, promoting phenotypic abnormalities associated with pathologic vascular remodeling. Activating NR2F2 or blocking DKK1 may be useful therapeutic targets for treating chronic vascular diseases associated with EC dysfunction.NEW & NOTEWORTHY NR2F2 loss in the endothelial lining of blood vessels is associated with cardiovascular disease. Here, NR2F2-silenced human endothelial cells were inflammatory, proliferative, hypermigratory, and apoptosis-resistant with increased oxidant stress and endothelial-to-mesenchymal transition. DKK1 was induced in NR2F2-silenced endothelial cells, while co-silencing NR2F2 and DKK1 prevented NR2F2-loss-associated abnormalities in endothelial signaling and phenotype. Activating NR2F2 or blocking DKK1 may be useful therapeutic targets for treating vascular diseases associated with endothelial dysfunction.

Sensitivity Analysis of Genome-Scale Metabolic Flux Prediction.

TRIMER, Transcription Regulation Integrated with MEtabolic Regulation, is a genome-scale modeling pipeline targeting at metabolic engineering applications. Using TRIMER, regulated metabolic reactions can be effectively predicted by integrative modeling of metabolic reactions with a transcription factor-gene regulatory network (TRN), which is modeled through a Bayesian network (BN). In this article, we focus on sensitivity analysis of metabolic flux prediction for uncertainty quantification of BN structures for TRN modeling in TRIMER. We propose a computational strategy to construct the uncertainty class of TRN models based on the inferred regulatory order uncertainty given transcriptomic expression data. With that, we analyze the prediction sensitivity of the TRIMER pipeline for the metabolite yields of interest. The obtained sensitivity analyses can guide optimal experimental design (OED) to help acquire new data that can enhance TRN modeling and achieve specific metabolic engineering objectives, including metabolite yield alterations. We have performed small- and large-scale simulated experiments, demonstrating the effectiveness of our developed sensitivity analysis strategy for BN structure learning to quantify the edge importance in terms of metabolic flux prediction uncertainty reduction and its potential to effectively guide OED.

Probabilistic boolean networks predict transcription factor targets to induce transdifferentiation.

We developed a computational approach to find the best intervention to achieve transcription factor (TF) mediated transdifferentiation. We construct probabilistic Boolean networks (PBNs) from single-cell RNA sequencing data of two different cell states to model hematopoietic transcription factors cross-talk. This was achieved by a "sampled network" approach, which enabled us to construct large networks. The interventions to induce transdifferentiation consisted of permanently activating or deactivating each of the TFs and determining the probability mass transfer of steady-state probabilities from the departure to the destination cell type or state. Our findings support the common assumption that TFs that are differentially expressed between the two cell types are the best intervention points to achieve transdifferentiation. TFs whose interventions are found to transdifferentiate progenitor B cells into monocytes include EBF1 down-regulation, CEBPB up-regulation, TCF3 down-regulation, and STAT3 up-regulation.

Robust importance sampling for error estimation in the context of optimal Bayesian transfer learning.

Classification has been a major task for building intelligent systems because it enables decision-making under uncertainty. Classifier design aims at building models from training data for representing feature-label distributions-either explicitly or implicitly. In many scientific or clinical settings, training data are typically limited, which impedes the design and evaluation of accurate classifiers. Atlhough transfer learning can improve the learning in target domains by incorporating data from relevant source domains, it has received little attention for performance assessment, notably in error estimation. Here, we investigate knowledge transferability in the context of classification error estimation within a Bayesian paradigm. We introduce a class of Bayesian minimum mean-square error estimators for optimal Bayesian transfer learning, which enables rigorous evaluation of classification error under uncertainty in small-sample settings. Using Monte Carlo importance sampling, we illustrate the outstanding performance of the proposed estimator for a broad family of classifiers that span diverse learning capabilities.

Synthetic data for design and evaluation of binary classifiers in the context of Bayesian transfer learning.

Transfer learning (TL) techniques can enable effective learning in data scarce domains by allowing one to re-purpose data or scientific knowledge available in relevant source domains for predictive tasks in a target domain of interest. In this Data in Brief article, we present a synthetic dataset for binary classification in the context of Bayesian transfer learning, which can be used for the design and evaluation of TL-based classifiers. For this purpose, we consider numerous combinations of classification settings, based on which we simulate a diverse set of feature-label distributions with varying learning complexity. For each set of model parameters, we provide a pair of target and source datasets that have been jointly sampled from the underlying feature-label distributions in the target and source domains, respectively. For both target and source domains, the data in a given class and domain are normally distributed, where the distributions across domains are related to each other through a joint prior. To ensure the consistency of the classification complexity across the provided datasets, we have controlled the Bayes error such that it is maintained within a range of predefined values that mimic realistic classification scenarios across different relatedness levels. The provided datasets may serve as useful resources for designing and benchmarking transfer learning schemes for binary classification as well as the estimation of classification error.

Protocol for condition-dependent metabolite yield prediction using the TRIMER pipeline.

This protocol explains the pipeline for condition-dependent metabolite yield prediction using Transcription Regulation Integrated with MEtabolic Regulation (TRIMER). TRIMER targets metabolic engineering applications via a hybrid model integrating transcription factor (TF)-gene regulatory network (TRN) with a Bayesian network (BN) inferred from transcriptomic expression data to effectively regulate metabolic reactions. For E. coli and yeast, TRIMER achieves reliable knockout phenotype and flux predictions from the deletion of one or more TFs at the genome scale. For complete details on the use and execution of this protocol, please refer to Niu et al. (2021).

Mineralocorticoid receptor antagonist treatment of established pulmonary arterial hypertension improves interventricular dependence in the SU5416-hypoxia rat model.

Treatment with mineralocorticoid receptor (MR) antagonists beginning at the outset of disease, or early thereafter, prevents pulmonary vascular remodeling in preclinical models of pulmonary arterial hypertension (PAH). However, the efficacy of MR blockade in established disease, a more clinically relevant condition, remains unknown. Therefore, we investigated the effectiveness of two MR antagonists, eplerenone (EPL) and spironolactone (SPL), after the development of severe right ventricular (RV) dysfunction in the rat SU5416-hypoxia (SuHx) PAH model. Cardiac magnetic resonance imaging (MRI) in SuHx rats at the end of week 5, before study treatment, confirmed features of established disease including reduced RV ejection fraction and RV hypertrophy, pronounced septal flattening with impaired left ventricular filling and reduced cardiac index. Five weeks of treatment with either EPL or SPL improved left ventricular filling and prevented the further decline in cardiac index compared with placebo. Interventricular septal displacement was reduced by EPL whereas SPL effects were similar, but not significant. Although MR antagonists did not significantly reduce pulmonary artery pressure or vessel remodeling in SuHx rats with established disease, animals with higher drug levels had lower pulmonary pressures. Consistent with effects on cardiac function, EPL treatment tended to suppress MR and proinflammatory gene induction in the RV. In conclusion, MR antagonist treatment led to modest, but consistent beneficial effects on interventricular dependence after the onset of significant RV dysfunction in the SuHx PAH model. These results suggest that measures of RV structure and/or function may be useful endpoints in clinical trials of MR antagonists in patients with PAH.

Network Classification Based on Reducibility With Respect to the Stability of Canalizing Power of Genes in a Gene Regulatory Network - A Boolean Network Modeling Perspective.

A key objective of studying biological systems is to design therapeutic intervention strategies for beneficially altering cell dynamics. Derivation of control policies is hindered by the high-dimensional state spaces associated with gene regulatory networks. Hence, it is critical to reduce the network complexity and the paper aims to address this issue by focusing on the distribution of the canalizing power (CP) of the genes in the model. Canalizing genes enforce broad corrective actions on cellular processes and play a crucial role in producing optimal reactions to external stimuli. Therefore, it is critical to reduce the network while preserving the canalizing power of genes. We reduce Boolean networks with perturbation by removing genes with the smallest canalizing power consecutively, and evaluate the stability of canalizing power. A systematic empirical study demonstrates that there are two classes of networks, reducible and irreducible with respect to the preservation of canalizing power of the genes. Based on these observations, we introduce the definition of reducible networks and proceed with the problem of selecting the relevant network features that allow for discriminating networks from the two different classes. We demonstrate the efficacy of the selected features on synthetic and real gene regulatory networks.

TRIMER: Transcription Regulation Integrated with Metabolic Regulation.

There has been extensive research in predictive modeling of genome-scale metabolic reaction networks. Living systems involve complex stochastic processes arising from interactions among different biomolecules. For more accurate and robust prediction of target metabolic behavior under different conditions, not only metabolic reactions but also the genetic regulatory relationships involving transcription factors (TFs) affecting these metabolic reactions should be modeled. We have developed a modeling and simulation pipeline enabling the analysis of Transcription Regulation Integrated with Metabolic Regulation: TRIMER. TRIMER utilizes a Bayesian network (BN) inferred from transcriptomes to model the transcription factor regulatory network. TRIMER then infers the probabilities of the gene states relevant to the metabolism of interest, and predicts the metabolic fluxes and their changes that result from the deletion of one or more transcription factors at the genome scale. We demonstrate TRIMER's applicability to both simulated and experimental data and provide performance comparison with other existing approaches.

Type I interferon activation and endothelial dysfunction in caveolin-1 insufficiency-associated pulmonary arterial hypertension.

Interferonopathies, interferon (IFN)-α/ß therapy, and caveolin-1 (CAV1) loss-of-function have all been associated with pulmonary arterial hypertension (PAH). Here, CAV1-silenced primary human pulmonary artery endothelial cells (PAECs) were proliferative and hypermigratory, with reduced cytoskeletal stress fibers. Signal transducers and activators of transcription (STAT) and phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) were both constitutively activated in these cells, resulting in a type I IFN-biased inflammatory signature. Cav1-/- mice that spontaneously develop pulmonary hypertension were found to have STAT1 and AKT activation in lung homogenates and increased circulating levels of CXCL10, a hallmark of IFN-mediated inflammation. PAH patients with CAV1 mutations also had elevated serum CXCL10 levels and their fibroblasts mirrored phenotypic and molecular features of CAV1-deficient PAECs. Moreover, immunofluorescence staining revealed endothelial CAV1 loss and STAT1 activation in the pulmonary arterioles of patients with idiopathic PAH, suggesting that this paradigm might not be limited to rare CAV1 frameshift mutations. While blocking JAK/STAT or AKT rescued aspects of CAV1 loss, only AKT inhibitors suppressed activation of both signaling pathways simultaneously. Silencing endothelial nitric oxide synthase (NOS3) prevented STAT1 and AKT activation induced by CAV1 loss, implicating CAV1/NOS3 uncoupling and NOS3 dysregulation in the inflammatory phenotype. Exogenous IFN reduced CAV1 expression, activated STAT1 and AKT, and altered the cytoskeleton of PAECs, implicating these mechanisms in PAH associated with autoimmune and autoinflammatory diseases, as well as IFN therapy. CAV1 insufficiency elicits an IFN inflammatory response that results in a dysfunctional endothelial cell phenotype and targeting this pathway may reduce pathologic vascular remodeling in PAH.

Optimal Bayesian supervised domain adaptation for RNA sequencing data.

MOTIVATION: When learning to subtype complex disease based on next-generation sequencing data, the amount of available data is often limited. Recent works have tried to leverage data from other domains to design better predictors in the target domain of interest with varying degrees of success. But they are either limited to the cases requiring the outcome label correspondence across domains or cannot leverage the label information at all. Moreover, the existing methods cannot usually benefit from other information available a priori such as gene interaction networks. RESULTS: In this article, we develop a generative optimal Bayesian supervised domain adaptation (OBSDA) model that can integrate RNA sequencing (RNA-Seq) data from different domains along with their labels for improving prediction accuracy in the target domain. Our model can be applied in cases where different domains share the same labels or have different ones. OBSDA is based on a hierarchical Bayesian negative binomial model with parameter factorization, for which the optimal predictor can be derived by marginalization of likelihood over the posterior of the parameters. We first provide an efficient Gibbs sampler for parameter inference in OBSDA. Then, we leverage the gene-gene network prior information and construct an informed and flexible variational family to infer the posterior distributions of model parameters. Comprehensive experiments on real-world RNA-Seq data demonstrate the superior performance of OBSDA, in terms of accuracy in identifying cancer subtypes by utilizing data from different domains. Moreover, we show that by taking advantage of the prior network information we can further improve the performance. AVAILABILITY AND IMPLEMENTATION: The source code for implementations of OBSDA and SI-OBSDA are available at the following link. https://github.com/SHBLK/BSDA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Optimal Bayesian Transfer Learning for Count Data.

There is often a limited amount of omics data to design predictive models in biomedicine. Knowing that these omics data come from underlying processes that may share common pathways and disease mechanisms, it may be beneficial for designing a more accurate and reliable predictor in a target domain of interest, where there is a lack of labeled data to leverage available data in relevant source domains. Here, we focus on developing Bayesian transfer learning methods for analyzing next-generation sequencing (NGS) data to help improve predictions in the target domain. We formulate transfer learning in a fully Bayesian framework and define the relatedness by a joint prior distribution of the model parameters of the source and target domains. Defining joint priors acts as a bridge across domains, through which the related knowledge of source data is transferred to the target domain. We focus on RNA-seq discrete count data, which are often overdispersed. To appropriately model them, we consider the Negative Binomial model and propose an Optimal Bayesian Transfer Learning (OBTL) classifier that minimizes the expected classification error in the target domain. We evaluate the performance of the OBTL classifier via both synthetic and cancer data from The Cancer Genome Atlas (TCGA).

Modeling the combined impacts of host plant resistance and biological control on the population dynamics of a major pest of wheat.

BACKGROUND: Single-tool approaches often fail to provide effective long-term suppression of pest populations, such that combining several tools into an integrated management strategy is critical. Yet studies that harness the power of population models to explore the relative efficacy of various management tools and their combinations remain rare. We constructed a Leslie matrix population model to evaluate the potential of crop resistance, acting alone or in combination with biological control, to reduce populations of the wheat stem sawfly, Cephus cinctus Norton, a major pest of wheat in North America. RESULTS: Our model projections indicated that crop resistance reduced, but did not stop, C. cinctus population growth, suggesting that implementing multiple management tools will be necessary for longer term control of this pest. The levels of parasitism needed to curtail population growth were much lower in model projections for resistant solid-stemmed compared with susceptible hollow-stemmed cultivars (22% versus 86%). Furthermore, even when accounting for the reduced levels of parasitism observed in resistant cultivars, projected population growth rates for C. cinctus were always lower in resistant compared with susceptible wheat cultivars. CONCLUSION: Despite some empirical evidence for antagonistic interactions between resistance and biological control, our models suggest that combining these two approaches will always reduce population growth rates to lower levels than implementing either strategy alone. More work focused on integrating biological control into crop resistance breeding programs, and determining how these approaches affect performance of limiting life stages, will be important to optimize sustainable approaches to integrated pest management in this system and more broadly. Published 2020. This article is a U.S. Government work and is in the public domain in the USA.

Optimal clustering with missing values.

BACKGROUND: Missing values frequently arise in modern biomedical studies due to various reasons, including missing tests or complex profiling technologies for different omics measurements. Missing values can complicate the application of clustering algorithms, whose goals are to group points based on some similarity criterion. A common practice for dealing with missing values in the context of clustering is to first impute the missing values, and then apply the clustering algorithm on the completed data. RESULTS: We consider missing values in the context of optimal clustering, which finds an optimal clustering operator with reference to an underlying random labeled point process (RLPP). We show how the missing-value problem fits neatly into the overall framework of optimal clustering by incorporating the missing value mechanism into the random labeled point process and then marginalizing out the missing-value process. In particular, we demonstrate the proposed framework for the Gaussian model with arbitrary covariance structures. Comprehensive experimental studies on both synthetic and real-world RNA-seq data show the superior performance of the proposed optimal clustering with missing values when compared to various clustering approaches. CONCLUSION: Optimal clustering with missing values obviates the need for imputation-based pre-processing of the data, while at the same time possessing smaller clustering errors.