Modeling transcriptional regulation of the cell cycle using a novel cybernetic-inspired approach.

Quantitative understanding of cellular processes, such as cell cycle and differentiation, is impeded by various forms of complexity ranging from myriad molecular players and their multilevel regulatory interactions, cellular evolution with multiple intermediate stages, lack of elucidation of cause-effect relationships among the many system players, and the computational complexity associated with the profusion of variables and parameters. In this paper, we present a modeling framework based on the cybernetic concept that biological regulation is inspired by objectives embedding rational strategies for dimension reduction, process stage specification through the system dynamics, and innovative causal association of regulatory events with the ability to predict the evolution of the dynamical system. The elementary step of the modeling strategy involves stage-specific objective functions that are computationally determined from experiments, augmented with dynamical network computations involving endpoint objective functions, mutual information, change-point detection, and maximal clique centrality. We demonstrate the power of the method through application to the mammalian cell cycle, which involves thousands of biomolecules engaged in signaling, transcription, and regulation. Starting with a fine-grained transcriptional description obtained from RNA sequencing measurements, we develop an initial model, which is then dynamically modeled using the cybernetic-inspired method, based on the strategies described above. The cybernetic-inspired method is able to distill the most significant interactions from a multitude of possibilities. In addition to capturing the complexity of regulatory processes in a mechanistically causal and stage-specific manner, we identify the functional network modules, including novel cell cycle stages. Our model is able to predict future cell cycles consistent with experimental measurements. We posit that this innovative framework has the promise to extend to the dynamics of other biological processes, with a potential to provide novel mechanistic insights.

Modeling transcriptional regulation of the cell cycle using a novel cybernetic-inspired approach.

Quantitative understanding of cellular processes, such as cell cycle and differentiation, is impeded by various forms of complexity ranging from myriad molecular players and their multilevel regulatory interactions, cellular evolution with multiple intermediate stages, lack of elucidation of cause-effect relationships among the many system players, and the computational complexity associated with the profusion of variables and parameters. In this paper, we present an elegant modeling framework based on the cybernetic concept that biological regulation is inspired by objectives embedding entirely novel strategies for dimension reduction, process stage specification through the system dynamics, and innovative causal association of regulatory events with the ability to predict the evolution of the dynamical system. The elementary step of the modeling strategy involves stage-specific objective functions that are computationally-determined from experiments, augmented with dynamical network computations involving end point objective functions, mutual information, change point detection, and maximal clique centrality. We demonstrate the power of the method through application to the mammalian cell cycle, which involves thousands of biomolecules engaged in signaling, transcription, and regulation. Starting with a fine-grained transcriptional description obtained from RNA sequencing measurements, we develop an initial model, which is then dynamically modeled using the cybernetic-inspired method (CIM), utilizing the strategies described above. The CIM is able to distill the most significant interactions from a multitude of possibilities. In addition to capturing the complexity of regulatory processes in a mechanistically causal and stage-specific manner, we identify the functional network modules, including novel cell cycle stages. Our model is able to predict future cell cycles consistent with experimental measurements. We posit that this state-of-the-art framework has the promise to extend to the dynamics of other biological processes, with a potential to provide novel mechanistic insights. STATEMENT OF SIGNIFICANCE: Cellular processes like cell cycle are overly complex, involving multiple players interacting at multiple levels, and explicit modeling of such systems is challenging. The availability of longitudinal RNA measurements provides an opportunity to "reverse-engineer" for novel regulatory models. We develop a novel framework, inspired using goal-oriented cybernetic model, to implicitly model transcriptional regulation by constraining the system using inferred temporal goals. A preliminary causal network based on information-theory is used as a starting point, and our framework is used to distill the network to temporally-based networks containing essential molecular players. The strength of this approach is its ability to dynamically model the RNA temporal measurements. The approach developed paves the way for inferring regulatory processes in many complex cellular processes.

Modular and mechanistic changes across stages of colorectal cancer.

BACKGROUND: While mechanisms contributing to the progression and metastasis of colorectal cancer (CRC) are well studied, cancer stage-specific mechanisms have been less comprehensively explored. This is the focus of this manuscript. METHODS: Using previously published data for CRC (Gene Expression Omnibus ID GSE21510), we identified differentially expressed genes (DEGs) across four stages of the disease. We then generated unweighted and weighted correlation networks for each of the stages. Communities within these networks were detected using the Louvain algorithm and topologically and functionally compared across stages using the normalized mutual information (NMI) metric and pathway enrichment analysis, respectively. We also used Short Time-series Expression Miner (STEM) algorithm to detect potential biomarkers having a role in CRC. RESULTS: Sixteen Thousand Sixty Two DEGs were identified between various stages (p-value ≤ 0.05). Comparing communities of different stages revealed that neighboring stages were more similar to each other than non-neighboring stages, at both topological and functional levels. A functional analysis of 24 cancer-related pathways indicated that several signaling pathways were enriched across all stages. However, the stage-unique networks were distinctly enriched only for a subset of these 24 pathways (e.g., MAPK signaling pathway in stages I-III and Notch signaling pathway in stages III and IV). We identified potential biomarkers, including HOXB8 and WNT2 with increasing, and MTUS1 and SFRP2 with decreasing trends from stages I to IV. Extracting subnetworks of 10 cancer-relevant genes and their interacting first neighbors (162 genes in total) revealed that the connectivity patterns for these genes were different across stages. For example, BRAF and CDK4, members of the Ser/Thr kinase, up-regulated in cancer, displayed changing connectivity patterns from stages I to IV. CONCLUSIONS: Here, we report molecular and modular networks for various stages of CRC, providing a pseudo-temporal view of the mechanistic changes associated with the disease. Our analysis highlighted similarities at both functional and topological levels, across stages. We further identified stage-specific mechanisms and biomarkers potentially contributing to the progression of CRC.

MetGENE: gene-centric metabolomics information retrieval tool.

BACKGROUND: Biomedical research often involves contextual integration of multimodal and multiomic data in search of mechanisms for improved diagnosis, treatment, and monitoring. Researchers need to access information from diverse sources, comprising data in various and sometimes incongruent formats. The downstream processing of the data to decipher mechanisms by reconstructing networks and developing quantitative models warrants considerable effort. RESULTS: MetGENE is a knowledge-based, gene-centric data aggregator that hierarchically retrieves information about the gene(s), their related pathway(s), reaction(s), metabolite(s), and metabolomic studies from standard data repositories under one dashboard to enable ease of access through centralization of relevant information. We note that MetGENE focuses only on those genes that encode for proteins directly associated with metabolites. All other gene-metabolite associations are beyond the current scope of MetGENE. Further, the information can be contextualized by filtering by species, anatomy (tissue), and condition (disease or phenotype). CONCLUSIONS: MetGENE is an open-source tool that aggregates metabolite information for a given gene(s) and presents them in different computable formats (e.g., JSON) for further integration with other omics studies. MetGENE is available at https://bdcw.org/MetGENE/index.php.

The maternal blood lipidome is indicative of the pathogenesis of severe preeclampsia.

Preeclampsia is a pregnancy-specific syndrome characterized by hypertension and proteinuria after 20 weeks of gestation. However, it is not well understood what lipids are involved in the development of this condition, and even less is known how these lipids mediate its formation. To reveal the relationship between lipids and preeclampsia, we conducted lipidomic profiling of maternal sera of 44 severe preeclamptic and 20 healthy pregnant women from a multiethnic cohort in Hawaii. Correlation network analysis showed that oxidized phospholipids have increased intercorrelations and connections in preeclampsia, whereas other lipids, including triacylglycerols, have reduced network correlations and connections. A total of 10 lipid species demonstrate significant changes uniquely associated with preeclampsia but not any other clinical confounders. These species are from the lipid classes of lysophosphatidylcholines, phosphatidylcholines (PCs), cholesteryl esters, phosphatidylethanolamines, lysophosphatidylethanolamines, and ceramides. A random forest classifier built on these lipids shows highly accurate and specific prediction (F1 statistic = 0.94; balanced accuracy = 0.88) of severe preeclampsia, demonstrating their potential as biomarkers for this condition. These lipid species are enriched in dysregulated biological pathways, including insulin signaling, immune response, and phospholipid metabolism. Moreover, causality inference shows that various PCs and lysophosphatidylcholines mediate severe preeclampsia through PC 35:1e. Our results suggest that the lipidome may play a role in the pathogenesis and serve as biomarkers of severe preeclampsia.

Longitudinal shear stress response in human endothelial cells to atheroprone and atheroprotective conditions.

The two main blood flow patterns, namely, pulsatile shear (PS) prevalent in straight segments of arteries and oscillatory shear (OS) observed at branch points, are associated with atheroprotective (healthy) and atheroprone (unhealthy) vascular phenotypes, respectively. The effects of blood flow-induced shear stress on endothelial cells (ECs) and vascular health have generally been studied using human umbilical vein endothelial cells (HUVECs). While there are a few studies comparing the differential roles of PS and OS across different types of ECs at a single time point, there is a paucity of studies comparing the temporal responses between different EC types. In the current study, we measured OS and PS transcriptomic responses in human aortic endothelial cells (HAECs) over 24 h and compared these temporal responses of HAECs with our previous findings on HUVECs. The measurements were made at 1, 4, and 24 h in order to capture the responses at early, mid, and late time points after shearing. The results indicate that the responses of HAECs and HUVECs are qualitatively similar for endothelial function-relevant genes and several important pathways with a few exceptions, thus demonstrating that HUVECs can be used as a model to investigate the effects of shear on arterial ECs, with consideration of the differences. Our findings show that HAECs exhibit an earlier response or faster kinetics as compared to HUVECs. The comparative analysis of HAECs and HUVECs presented here offers insights into the mechanisms of common and disparate shear stress responses across these two major endothelial cell types.

Time varying causal network reconstruction of a mouse cell cycle.

BACKGROUND: Biochemical networks are often described through static or time-averaged measurements of the component macromolecules. Temporal variation in these components plays an important role in both describing the dynamical nature of the network as well as providing insights into causal mechanisms. Few methods exist, specifically for systems with many variables, for analyzing time series data to identify distinct temporal regimes and the corresponding time-varying causal networks and mechanisms. RESULTS: In this study, we use well-constructed temporal transcriptional measurements in a mammalian cell during a cell cycle, to identify dynamical networks and mechanisms describing the cell cycle. The methods we have used and developed in part deal with Granger causality, Vector Autoregression, Estimation Stability with Cross Validation and a nonparametric change point detection algorithm that enable estimating temporally evolving directed networks that provide a comprehensive picture of the crosstalk among different molecular components. We applied our approach to RNA-seq time-course data spanning nearly two cell cycles from Mouse Embryonic Fibroblast (MEF) primary cells. The change-point detection algorithm is able to extract precise information on the duration and timing of cell cycle phases. Using Least Absolute Shrinkage and Selection Operator (LASSO) and Estimation Stability with Cross Validation (ES-CV), we were able to, without any prior biological knowledge, extract information on the phase-specific causal interaction of cell cycle genes, as well as temporal interdependencies of biological mechanisms through a complete cell cycle. CONCLUSIONS: The temporal dependence of cellular components we provide in our model goes beyond what is known in the literature. Furthermore, our inference of dynamic interplay of multiple intracellular mechanisms and their temporal dependence on one another can be used to predict time-varying cellular responses, and provide insight on the design of precise experiments for modulating the regulation of the cell cycle.

Topological and functional comparison of community detection algorithms in biological networks.

BACKGROUND: Community detection algorithms are fundamental tools to uncover important features in networks. There are several studies focused on social networks but only a few deal with biological networks. Directly or indirectly, most of the methods maximize modularity, a measure of the density of links within communities as compared to links between communities. RESULTS: Here we analyze six different community detection algorithms, namely, Combo, Conclude, Fast Greedy, Leading Eigen, Louvain and Spinglass, on two important biological networks to find their communities and evaluate the results in terms of topological and functional features through Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology term enrichment analysis. At a high level, the main assessment criteria are 1) appropriate community size (neither too small nor too large), 2) representation within the community of only one or two broad biological functions, 3) most genes from the network belonging to a pathway should also belong to only one or two communities, and 4) performance speed. The first network in this study is a network of Protein-Protein Interactions (PPI) in Saccharomyces cerevisiae (Yeast) with 6532 nodes and 229,696 edges and the second is a network of PPI in Homo sapiens (Human) with 20,644 nodes and 241,008 edges. All six methods perform well, i.e., find reasonably sized and biologically interpretable communities, for the Yeast PPI network but the Conclude method does not find reasonably sized communities for the Human PPI network. Louvain method maximizes modularity by using an agglomerative approach, and is the fastest method for community detection. For the Yeast PPI network, the results of Spinglass method are most similar to the results of Louvain method with regard to the size of communities and core pathways they identify, whereas for the Human PPI network, Combo and Spinglass methods yield the most similar results, with Louvain being the next closest. CONCLUSIONS: For Yeast and Human PPI networks, Louvain method is likely the best method to find communities in terms of detecting known core pathways in a reasonable time.

miRNAs that Induce Human Cardiomyocyte Proliferation Converge on the Hippo Pathway.

Understanding the mechanisms that control human cardiomyocyte proliferation might be applicable to regenerative medicine. We screened a whole genome collection of human miRNAs, identifying 96 to be capable of increasing proliferation (DNA synthesis and cytokinesis) of human iPSC-derived cardiomyocytes. Chemical screening and computational approaches indicated that most of these miRNAs (67) target different components of the Hippo pathway and that their activity depends on the nuclear translocation of the Hippo transcriptional effector YAP. 53 of the 67 miRNAs are present in human iPSC cardiomyocytes, yet anti-miRNA screening revealed that none are individually essential for basal proliferation of hiPSC cardiomyocytes despite the importance of YAP for proliferation. We propose a model in which multiple endogenous miRNAs redundantly suppress Hippo signaling to sustain the cell cycle of immature cardiomyocytes.

Enhancer-associated long non-coding RNA LEENE regulates endothelial nitric oxide synthase and endothelial function.

The optimal expression of endothelial nitric oxide synthase (eNOS), the hallmark of endothelial homeostasis, is vital to vascular function. Dynamically regulated by various stimuli, eNOS expression is modulated at transcriptional, post-transcriptional, and post-translational levels. However, epigenetic modulations of eNOS, particularly through long non-coding RNAs (lncRNAs) and chromatin remodeling, remain to be explored. Here we identify an enhancer-associated lncRNA that enhances eNOS expression (LEENE). Combining RNA-sequencing and chromatin conformation capture methods, we demonstrate that LEENE is co-regulated with eNOS and that its enhancer resides in proximity to eNOS promoter in endothelial cells (ECs). Gain- and Loss-of-function of LEENE differentially regulate eNOS expression and EC function. Mechanistically, LEENE facilitates the recruitment of RNA Pol II to the eNOS promoter to enhance eNOS nascent RNA transcription. Our findings unravel a new layer in eNOS regulation and provide novel insights into cardiovascular regulation involving endothelial function.

Systems biology analysis of longitudinal functional response of endothelial cells to shear stress.

Blood flow and vascular shear stress patterns play a significant role in inducing and modulating physiological responses of endothelial cells (ECs). Pulsatile shear (PS) is associated with an atheroprotective endothelial phenotype, while oscillatory shear (OS) is associated with an atheroprone endothelial phenotype. Although mechanisms of endothelial shear response have been extensively studied, most studies focus on characterization of single molecular pathways, mainly at fixed time points after stress application. Here, we carried out a longitudinal time-series study to measure the transcriptome after the application of PS and OS. We performed systems analyses of transcriptional data of cultured human vascular ECs to elucidate the dynamics of endothelial responses in several functional pathways such as cell cycle, oxidative stress, and inflammation. By combining the temporal data on differentially expressed transcription factors and their targets with existing knowledge on relevant functional pathways, we infer the causal relationships between disparate endothelial functions through common transcriptional regulation mechanisms. Our study presents a comprehensive temporally longitudinal experimental study and mechanistic model of shear stress response. By comparing the relative endothelial expressions of genes between OS and PS, we provide insights and an integrated perspective into EC function in response to differential shear. This study has significant implications for the pathogenesis of vascular diseases.

Krüppel-Like Factor 4 Regulation of Cholesterol-25-Hydroxylase and Liver X Receptor Mitigates Atherosclerosis Susceptibility.

BACKGROUND: Atherosclerosis is a multifaceted inflammatory disease involving cells in the vascular wall (eg, endothelial cells [ECs]), as well as circulating and resident immunogenic cells (eg, monocytes/macrophages). Acting as a ligand for liver X receptor (LXR), but an inhibitor of SREBP2 (sterol regulatory element-binding protein 2), 25-hydroxycholesterol, and its catalyzing enzyme cholesterol-25-hydroxylase (Ch25h) are important in regulating cellular inflammatory status and cholesterol biosynthesis in both ECs and monocytes/macrophages. METHODS: Bioinformatic analyses were used to investigate RNA-sequencing data to identify cholesterol oxidation and efflux genes regulated by Krüppel-like factor 4 (KLF4). In vitro experiments involving cultured ECs and macrophages and in vivo methods involving mice with Ch25h ablation were then used to explore the atheroprotective role of KLF4-Ch25h/LXR. RESULTS: Vasoprotective stimuli increased the expression of Ch25h and LXR via KLF4. The KLF4-Ch25h/LXR homeostatic axis functions through suppressing inflammation, evidenced by the reduction of inflammasome activity in ECs and the promotion of M1 to M2 phenotypic transition in macrophages. The increased atherosclerosis in apolipoprotein E-/-/Ch25h-/- mice further demonstrates the beneficial role of the KLF4-Ch25h/LXR axis in vascular function and disease. CONCLUSIONS: KLF4 transactivates Ch25h and LXR, thereby promoting the synergistic effects between ECs and macrophages to protect against atherosclerosis susceptibility.

Systems Analysis of the Complement-Induced Priming Phase of Liver Regeneration.

Liver regeneration is a well-orchestrated process in the liver that allows mature hepatocytes to reenter the cell cycle to proliferate and replace lost or damaged cells. This process is often impaired in fatty or diseased livers, leading to cirrhosis and other deleterious phenotypes. Prior research has established the role of the complement system and its effector proteins in the progression of liver regeneration; however, a detailed mechanistic understanding of the involvement of complement in regeneration is yet to be established. In this study, we have examined the role of the complement system during the priming phase of liver regeneration through a systems level analysis using a combination of transcriptomic and metabolomic measurements. More specifically, we have performed partial hepatectomy on mice with genetic deficiency in C3, the major component of the complement cascade, and collected their livers at various time points. Based on our analysis, we show that the C3 cascade activates c-fos and promotes the TNF-α signaling pathway, which then activates acute-phase genes such as serum amyloid proteins and orosomucoids. The complement activation also regulates the efflux and the metabolism of cholesterol, an important metabolite for cell cycle and proliferation. Based on our systems level analysis, we provide an integrated model for the complement-induced priming phase of liver regeneration.

Computational Modeling of Competitive Metabolism between ω3- and ω6-Polyunsaturated Fatty Acids in Inflammatory Macrophages.

Arachidonic acid (AA), a representative ω6-polyunsaturated fatty acid (PUFA), is a precursor of 2-series prostaglandins (PGs) that play important roles in inflammation, pain, fever, and related disorders including cardiovascular diseases. Eating fish or supplementation with the ω3-PUFAs such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) is widely assumed to be beneficial in preventing cardiovascular diseases. A proposed mechanism for a cardio-protective role of ω3-PUFAs assumes competition between AA and ω3-PUFAs for cyclooxygenases (COX), leading to reduced production of 2-series PGs. In this study, we have used a systems biology approach to integrate existing knowledge and novel high-throughput data that facilitates a quantitative understanding of the molecular mechanism of ω3- and ω6-PUFA metabolism in mammalian cells. We have developed a quantitative computational model of the competitive metabolism of AA and EPA via the COX pathway through a two-step matrix-based approach to estimate the rate constants. This model was developed by using lipidomic data sets that were experimentally obtained from EPA-supplemented ATP-stimulated RAW264.7 macrophages. The resulting model fits the experimental data well for all metabolites and demonstrates that the integrated metabolic and signaling networks and the experimental data are consistent with one another. The robustness of the model was validated through parametric sensitivity and uncertainty analysis. We also validated the model by predicting the results from other independent experiments involving AA- and DHA-supplemented ATP-stimulated RAW264.7 cells using the parameters estimated with EPA. Furthermore, we showed that the higher affinity of EPA binding to COX compared with AA was able to inhibit AA metabolism effectively. Thus, our model captures the essential features of competitive metabolism of ω3- and ω6-PUFAs.

Serum microRNAs explain discordance of non-alcoholic fatty liver disease in monozygotic and dizygotic twins: a prospective study.

OBJECTIVE: In the setting where two individuals are genetically similar, epigenetic mechanisms could account for discordance in the presence or absence of non-alcoholic fatty liver disease (NAFLD). This study investigated if serum microRNAs (miRs) could explain discordance in NAFLD. DESIGN: This is a cross-sectional analysis of a prospective cohort study of 40 (n=80) twin-pairs residing in Southern California. All participants underwent a standardised research visit, liver MRI using proton-density fat fraction to quantify fat content and miR profiling of their serum. RESULTS: Among the 40 twin-pairs, there were 6 concordant for NAFLD, 28 were concordant for non-NAFLD and 6 were discordant for NAFLD. The prevalence of NAFLD was 22.5% (18/80). Within the six discordant twins, a panel of 10 miRs differentiated the twin with NAFLD from the one without. Two of these miRs, miR-331-3p and miR-30c, were also among the 21 miRs that were different between NAFLD and non-NAFLD groups (for miR-331-3p: 7.644±0.091 vs 8.057±0.071, respectively, p=0.004; for miR-30c: 10.013±0.126 vs 10.418±0.086, respectively, p=0.008). Both miRs were highly heritable (35.9% and 10.7%, respectively) and highly correlated with each other (R=0.90, p=2.2×10(-16)) suggesting involvement in a common mechanistic pathway. An interactome analysis of these two miRs showed seven common target genes. CONCLUSIONS: Using a novel human twin-study design, we demonstrate that discordancy in liver fat content between the twins can be explained by miRs, and that they are heritable.

Biomarkers of NAFLD progression: a lipidomics approach to an epidemic.

The spectrum of nonalcoholic fatty liver disease (NAFLD) includes steatosis, nonalcoholic steatohepatitis (NASH), and cirrhosis. Recognition and timely diagnosis of these different stages, particularly NASH, is important for both potential reversibility and limitation of complications. Liver biopsy remains the clinical standard for definitive diagnosis. Diagnostic tools minimizing the need for invasive procedures or that add information to histologic data are important in novel management strategies for the growing epidemic of NAFLD. We describe an "omics" approach to detecting a reproducible signature of lipid metabolites, aqueous intracellular metabolites, SNPs, and mRNA transcripts in a double-blinded study of patients with different stages of NAFLD that involves profiling liver biopsies, plasma, and urine samples. Using linear discriminant analysis, a panel of 20 plasma metabolites that includes glycerophospholipids, sphingolipids, sterols, and various aqueous small molecular weight components involved in cellular metabolic pathways, can be used to differentiate between NASH and steatosis. This identification of differential biomolecular signatures has the potential to improve clinical diagnosis and facilitate therapeutic intervention of NAFLD.

Integrating omics into the cardiac differentiation of human pluripotent stem cells.

Time-dependent extracellular manipulations of human pluripotent stem cells can yield as much as 90% pure populations of cardiomyocytes. While the extracellular control of differentiation generally entails dynamic regulation of well-known pathways such as Wnt, BMP, and Nodal signaling, the underlying genetic networks are far more complex and are poorly understood. Notably, the identification of these networks holds promise for understanding heart disease and regeneration. The availability of genome-wide experimentation, such as RNA and DNA sequencing, as well as high throughput surveying with small molecule and small interfering RNA libraries, now enables us to map the genetic interactions underlying cardiac differentiation on a global scale. Initial studies demonstrate the complexity of the genetic regulation of cardiac differentiation, exposing unanticipated novel mechanisms. However, the large datasets generated tend to be overwhelming and systematic approaches are needed to process the vast amount of data to improve our mechanistic understanding of the complex biology. Systems biology methods spur high hopes for parsing vast amounts of data into genetic interaction models that can be verified experimentally and ultimately yield functional networks that expose the genetic connections underlying biological processes.

Modeling of eicosanoid fluxes reveals functional coupling between cyclooxygenases and terminal synthases.

Eicosanoids, including prostaglandins (PG) and leukotrienes, are lipid mediators derived from arachidonic acid. A quantitative and biochemical level understanding of eicosanoid metabolism would aid in understanding the mechanisms that govern inflammatory processes. Here, we present a combined experimental and computational approach to understanding the biochemical basis of eicosanoid metabolism in macrophages. Lipidomic and transcriptomic measurements and analyses reveal temporal and dynamic changes of the eicosanoid metabolic network in mouse bone marrow-derived macrophages (BMDM) upon stimulation of the Toll-like receptor 4 with Kdo2-Lipid A (KLA) and stimulation of the P2X7 purinergic receptor with adenosine 5'-triphosphate. Kinetic models were developed for the cyclooxygenase (COX) and lipoxygenase branches of arachidonic acid metabolism, and then the rate constants were estimated with a data set from ATP-stimulated BMDM, using a two-step matrix-based approach employing a constrained least-squares method followed by nonlinear optimization. The robustness of the model was validated through parametric sensitivity, uncertainty analysis, and predicting an independent dataset from KLA-primed ATP-stimulated BMDM by allowing the parameters to vary within the uncertainty range of the calculated parameters. We analyzed the functional coupling between COX isozymes and terminal enzymes by developing a PGH2-divided model. This provided evidence for the functional coupling between COX-2 and PGE2 synthase, between COX-1/COX-2 and PGD2 synthase, and also between COX-1 and thromboxane A2 synthase. Further, these functional couplings were experimentally validated using COX-1 and COX-2 selective inhibitors. The resulting fluxomics analysis demonstrates that the "multi-omics" systems biology approach can define the complex machinery of eicosanoid networks.

25-Hydroxycholesterol activates the integrated stress response to reprogram transcription and translation in macrophages.

25-Hydroxycholesterol (25OHC) is an enzymatically derived oxidation product of cholesterol that modulates lipid metabolism and immunity. 25OHC is synthesized in response to interferons and exerts broad antiviral activity by as yet poorly characterized mechanisms. To gain further insights into the basis for antiviral activity, we evaluated time-dependent responses of the macrophage lipidome and transcriptome to 25OHC treatment. In addition to altering specific aspects of cholesterol and sphingolipid metabolism, we found that 25OHC activates integrated stress response (ISR) genes and reprograms protein translation. Effects of 25OHC on ISR gene expression were independent of liver X receptors and sterol-response element-binding proteins and instead primarily resulted from activation of the GCN2/eIF2α/ATF4 branch of the ISR pathway. These studies reveal that 25OHC activates the integrated stress response, which may contribute to its antiviral activity.

Developing microRNA screening as a functional genomics tool for disease research.

Originally discovered as regulators of developmental timing in C. elegans, microRNAs (miRNAs) have emerged as modulators of nearly every cellular process, from normal development to pathogenesis. With the advent of whole genome libraries of miRNA mimics suitable for high throughput screening, it is possible to comprehensively evaluate the function of each member of the miRNAome in cell-based assays. Since the relatively few microRNAs in the genome are thought to directly regulate a large portion of the proteome, miRNAome screening, coupled with the identification of the regulated proteins, might be a powerful new approach to gaining insight into complex biological processes.