Vitamin D constrains inflammation by modulating the expression of key genes on Chr17q12-21.1

Vitamin D possesses immunomodulatory functions and vitamin D deficiency has been associated with the rise in chronic inflammatory diseases, including asthma (Litonjua and Weiss, 2007). Vitamin D supplementation studies do not provide insight into the molecular genetic mechanisms of vitamin D-mediated immunoregulation. Here, we provide evidence for vitamin D regulation of two human chromosomal loci, Chr17q12-21.1 and Chr17q21.2, reliably associated with autoimmune and chronic inflammatory diseases. We demonstrate increased vitamin D receptor (Vdr) expression in mouse lung CD4+ Th2 cells, differential expression of Chr17q12-21.1 and Chr17q21.2 genes in Th2 cells based on vitamin D status and identify the IL-2/Stat5 pathway as a target of vitamin D signaling. Vitamin D deficiency caused severe lung inflammation after allergen challenge in mice that was prevented by long-term prenatal vitamin D supplementation. Mechanistically, vitamin D induced the expression of the Ikzf3-encoded protein Aiolos to suppress IL-2 signaling and ameliorate cytokine production in Th2 cells. These translational findings demonstrate mechanisms for the immune protective effect of vitamin D in allergic lung inflammation with a strong molecular genetic link to the regulation of both Chr17q12-21.1 and Chr17q21.2 genes and suggest further functional studies and interventional strategies for long-term prevention of asthma and other autoimmune disorders.

Detecting and dissecting signaling crosstalk via the multilayer network integration of signaling and regulatory interactions

The versatility of cellular response arises from the communication, or crosstalk, of signaling pathways in a complex network of signaling and transcriptional regulatory interactions. Understanding the various mechanisms underlying crosstalk on a global scale requires untargeted computational approaches. We present a network-based statistical approach, MuXTalk, that uses high-dimensional edges called multilinks to model the unique ways in which signaling and regulatory interactions can interface. We demonstrate that the signaling-regulatory interface is located primarily in the intermediary region between signaling pathways where crosstalk occurs, and that multilinks can differentiate between distinct signaling-transcriptional mechanisms. Using statistically over-represented multilinks as proxies of crosstalk, we infer crosstalk among 60 signaling pathways, expanding currently available crosstalk databases by more than five-fold. MuXTalk surpasses existing methods in terms of model performance metrics, identifies additions to manual curation efforts, and pinpoints potential mediators of crosstalk. Moreover, it accommodates the inherent context-dependence of crosstalk, allowing future applications to cell type- and disease-specific crosstalk.

Multiomics of Tissue Extracellular Vesicles Identifies Unique Modulators of Atherosclerosis and Calcific Aortic Valve Stenosis

BACKGROUND

Fewer than 50% of patients who develop aortic valve calcification have concomitant atherosclerosis, implying differential pathogenesis. Although circulating extracellular vesicles (EVs) act as biomarkers of cardiovascular diseases, tissue-entrapped EVs are associated with early mineralization, but their cargoes, functions, and contributions to disease remain unknown.

METHODS

Disease stage-specific proteomics was performed on human carotid endarterectomy specimens (n=16) and stenotic aortic valves (n=18). Tissue EVs were isolated from human carotid arteries (normal, n=6; diseased, n=4) and aortic valves (normal, n=6; diseased, n=4) by enzymatic digestion, (ultra)centrifugation, and a 15-fraction density gradient validated by proteomics, CD63-immunogold electron microscopy, and nanoparticle tracking analysis. Vesiculomics, comprising vesicular proteomics and small RNA-sequencing, was conducted on tissue EVs. TargetScan identified microRNA targets. Pathway network analyses prioritized genes for validation in primary human carotid artery smooth muscle cells and aortic valvular interstitial cells.

RESULTS

Disease progression drove significant convergence (P<0.0001) of carotid artery plaque and calcified aortic valve proteomes (2318 proteins). Each tissue also retained a unique subset of differentially enriched proteins (381 in plaques; 226 in valves; q<0.05). Vesicular gene ontology terms increased 2.9-fold (P<0.0001) among proteins modulated by disease in both tissues. Proteomics identified 22 EV markers in tissue digest fractions. Networks of proteins and microRNA targets changed by disease progression in both artery and valve EVs revealed shared involvement in intracellular signaling and cell cycle regulation. Vesiculomics identified 773 proteins and 80 microRNAs differentially enriched by disease exclusively in artery or valve EVs (q<0.05); multiomics integration found tissue-specific EV cargoes associated with procalcific Notch and Wnt signaling in carotid arteries and aortic valves, respectively. Knockdown of tissue-specific EV-derived molecules FGFR2, PPP2CA, and ADAM17 in human carotid artery smooth muscle cells and WNT5A, APP, and APC in human aortic valvular interstitial cells significantly modulated calcification.

CONCLUSIONS

The first comparative proteomics study of human carotid artery plaques and calcified aortic valves identifies unique drivers of atherosclerosis versus aortic valve stenosis and implicates EVs in advanced cardiovascular calcification. We delineate a vesiculomics strategy to isolate, purify, and study protein and RNA cargoes from EVs entrapped in fibrocalcific tissues. Integration of vesicular proteomics and transcriptomics by network approaches revealed novel roles for tissue EVs in modulating cardiovascular disease.

Sortilin enhances fibrosis and calcification in aortic valve disease by inducing interstitial cell heterogeneity

AIMS

Calcific aortic valve disease (CAVD) is the most common valve disease, which consists of a chronic interplay of inflammation, fibrosis, and calcification. In this study, sortilin (SORT1) was identified as a novel key player in the pathophysiology of CAVD, and its role in the transformation of valvular interstitial cells (VICs) into pathological phenotypes is explored.

METHODS AND RESULTS

An aortic valve (AV) wire injury (AVWI) mouse model with sortilin deficiency was used to determine the effects of sortilin on AV stenosis, fibrosis, and calcification. In vitro experiments employed human primary VICs cultured in osteogenic conditions for 7, 14, and 21 days; and processed for imaging, proteomics, and transcriptomics including single-cell RNA-sequencing (scRNA-seq). The AVWI mouse model showed reduced AV fibrosis, calcification, and stenosis in sortilin-deficient mice vs. littermate controls. Protein studies identified the transition of human VICs into a myofibroblast-like phenotype mediated by sortilin. Sortilin loss-of-function decreased in vitro VIC calcification. ScRNA-seq identified 12 differentially expressed cell clusters in human VIC samples, where a novel combined inflammatory myofibroblastic-osteogenic VIC (IMO-VIC) phenotype was detected with increased expression of SORT1, COL1A1, WNT5A, IL-6, and serum amyloid A1. VICs sequenced with sortilin deficiency showed decreased IMO-VIC phenotype.

CONCLUSION

Sortilin promotes CAVD by mediating valvular fibrosis and calcification, and a newly identified phenotype (IMO-VIC). This is the first study to examine the role of sortilin in valvular calcification and it may render it a therapeutic target to inhibit IMO-VIC emergence by simultaneously reducing inflammation, fibrosis, and calcification, the three key pathological processes underlying CAVD.

Proprotein Convertase Subtilisin/Kexin 9 (PCSK9) Promotes Macrophage Activation via LDL Receptor-Independent Mechanisms

BACKGROUND

Activated macrophages contribute to the pathogenesis of vascular disease. Vein graft failure is a major clinical problem with limited therapeutic options. PCSK9 (proprotein convertase subtilisin/kexin 9) increases low-density lipoprotein (LDL)-cholesterol levels via LDL receptor (LDLR) degradation. The role of PCSK9 in macrophage activation and vein graft failure is largely unknown, especially through LDLR-independent mechanisms. This study aimed to explore a novel mechanism of macrophage activation and vein graft disease induced by circulating PCSK9 in an LDLR-independent fashion.

METHODS

We used Ldlr^-/- mice to examine the LDLR-independent roles of circulating PCSK9 in experimental vein grafts. Adeno-associated virus (AAV) vector encoding a gain-of-function mutant of PCSK9 (rAAV8/D377Y-mPCSK9) induced hepatic PCSK9 overproduction. To explore novel inflammatory targets of PCSK9, we used systems biology in Ldlr^-/- mouse macrophages.

RESULTS

In Ldlr^-/- mice, AAV-PCSK9 increased circulating PCSK9, but did not change serum cholesterol and triglyceride levels. AAV-PCSK9 promoted vein graft lesion development when compared with control AAV. In vivo molecular imaging revealed that AAV-PCSK9 increased macrophage accumulation and matrix metalloproteinase activity associated with decreased fibrillar collagen, a molecular determinant of atherosclerotic plaque stability. AAV-PCSK9 induced mRNA expression of the pro-inflammatory mediators IL-1β (interleukin-1 beta), TNFα (tumor necrosis factor alpha), and MCP-1 (monocyte chemoattractant protein-1) in peritoneal macrophages underpinned by an in vitro analysis of Ldlr^-/- mouse macrophages stimulated with endotoxin-free recombinant PCSK9. A combination of unbiased global transcriptomics and new network-based hyperedge entanglement prediction analysis identified the NF-κB (nuclear factor-kappa B) signaling molecules, lectin-like oxidized LOX-1 (LDL receptor-1), and SDC4 (syndecan-4) as potential PCSK9 targets mediating pro-inflammatory responses in macrophages.

CONCLUSIONS

Circulating PCSK9 induces macrophage activation and vein graft lesion development via LDLR-independent mechanisms. PCSK9 may be a potential target for pharmacologic treatment for this unmet medical need.

Identifying novel mechanisms of abdominal aortic aneurysm via unbiased proteomics and systems biology

Background

Abdominal aortic aneurysm (AAA), characterized by a continued expansion of the aorta, leads to rupture if not surgically repaired. Mice aid the study of disease progression and its underlying mechanisms since sequential studies of aneurysm development are not feasible in humans. The present study used unbiased proteomics and systems biology to understand the molecular relationship between the mouse models of AAA and the human disease.

Methods and results

Aortic tissues of developing and established aneurysms produced by either angiotensin II (AngII) infusion in Apoe -/- and Ldlr -/- mice or intraluminal elastase incubation in wildtype C57BL/6J mice were examined. Aortas were dissected free and separated into eight anatomical segments for proteomics in comparison to their appropriate controls. High-dimensional proteome cluster analyses identified site-specific protein signatures in the suprarenal segment for AngII-infused mice (159 for Apoe -/- and 158 for Ldlr -/-) and the infrarenal segment for elastase-incubated mice (173). Network analysis revealed a predominance of inflammatory and coagulation factors in developing aneurysms, and a predominance of fibrosis-related pathways in established aneurysms for both models. To further substantiate our discovery platform, proteomics was performed on human infrarenal aortic aneurysm tissues as well as aortic tissue collected from age-matched controls. Protein processing and inflammatory pathways, particularly neutrophil-associated inflammation, dominated the proteome of the human aneurysm abdominal tissue. Aneurysmal tissue from both mouse and human had inflammation, coagulation, and protein processing signatures, but differed in the prevalence of neutrophil-associated pathways, and erythrocyte and oxidative stress-dominated networks in the human aneurysms.

Conclusions

Identifying changes unique to each mouse model will help to contextualize model-specific findings. Focusing on shared proteins between mouse experimental models or between mouse and human tissues may help to better understand the mechanisms for AAA and establish molecular bases for novel therapies.

A disease-driver population within interstitial cells of human calcific aortic valves identified via single-cell and proteomic profiling

Cellular heterogeneity of aortic valves complicates the mechanistic evaluation of the calcification processes in calcific aortic valve disease (CAVD), and animal disease models are lacking. In this study, we identify a disease-driver population (DDP) within valvular interstitial cells (VICs). Through stepwise single-cell analysis, phenotype-guided omic profiling, and network-based analysis, we characterize the DDP fingerprint as CD44highCD29+CD59+CD73+CD45low and discover potential key regulators of human CAVD. These DDP-VICs demonstrate multi-lineage differentiation and osteogenic properties. Temporal proteomic profiling of DDP-VICs identifies potential targets for therapy, including MAOA and CTHRC1. In vitro loss-of-function experiments confirm our targets. Such a stepwise strategy may be advantageous for therapeutic target discovery in other disease contexts.

Dynamin-related protein 1 inhibition reduces hepatic PCSK9 secretion

AIMS

Proteostasis maintains protein homeostasis and participates in regulating critical cardiometabolic disease risk factors including proprotein convertase subtilisin/kexin type 9 (PCSK9). Endoplasmic reticulum (ER) remodeling through release and incorporation of trafficking vesicles mediates protein secretion and degradation. We hypothesized that ER remodeling that drives mitochondrial fission participates in cardiometabolic proteostasis.

METHODS AND RESULTS

We used in vitro and in vivo hepatocyte inhibition of a protein involved in mitochondrial fission, dynamin-related protein 1 (DRP1). Here, we show that DRP1 promotes remodeling of select ER microdomains by tethering vesicles at ER. A DRP1 inhibitor, mitochondrial division inhibitor 1 (mdivi-1) reduced ER localization of a DRP1 receptor, mitochondrial fission factor, suppressing ER remodeling-driven mitochondrial fission, autophagy, and increased mitochondrial calcium buffering and PCSK9 proteasomal degradation. DRP1 inhibition by CRISPR/Cas9 deletion or mdivi-1 alone or in combination with statin incubation in human hepatocytes and hepatocyte-specific Drp1-deficiency in mice reduced PCSK9 secretion (-78.5%). In HepG2 cells, mdivi-1 increased low-density lipoprotein receptor via c-Jun transcription and reduced PCSK9 mRNA levels via suppressed sterol regulatory binding protein-1c. Additionally, mdivi-1 reduced macrophage burden, oxidative stress, and advanced calcified atherosclerotic plaque in aortic roots of diabetic Apoe-deficient mice and inflammatory cytokine production in human macrophages.

CONCLUSIONS

We propose a novel tethering function of DRP1 beyond its established fission function, with DRP1-mediated ER remodeling likely contributing to ER constriction of mitochondria that drives mitochondrial fission. We report that DRP1-driven remodeling of select ER micro-domains may critically regulate hepatic proteostasis and identify mdivi-1 as a novel small molecule PCSK9 inhibitor.

Systems Approach to Discovery of Therapeutic Targets for Vein Graft Disease: PPARα Pivotally Regulates Metabolism, Activation, and Heterogeneity of Macrophages and Lesion Development

Supplemental Digital Content is available in the text.

Vein graft failure remains a common clinical challenge. We applied a systems approach in mouse experiments to discover therapeutic targets for vein graft failure.

CROT (Carnitine O-Octanoyltransferase) Is a Novel Contributing Factor in Vascular Calcification via Promoting Fatty Acid Metabolism and Mitochondrial Dysfunction

OBJECTIVE

Vascular calcification is a critical pathology associated with increased cardiovascular event risk, but there are no Food and Drug Administration-approved anticalcific therapies. We hypothesized and validated that an unbiased screening approach would identify novel mediators of human vascular calcification. Approach and Results: We performed an unbiased quantitative proteomics and pathway network analysis that identified increased CROT (carnitine O-octanoyltransferase) in calcifying primary human coronary artery smooth muscle cells (SMCs). Additionally, human carotid artery atherosclerotic plaques contained increased immunoreactive CROT near calcified regions. CROT siRNA reduced fibrocalcific response in calcifying SMCs. In agreement, histidine 327 to alanine point mutation inactivated human CROT fatty acid metabolism enzymatic activity and suppressed SMC calcification. CROT siRNA suppressed type 1 collagen secretion, and restored mitochondrial proteome alterations, and suppressed mitochondrial fragmentation in calcifying SMCs. Lipidomics analysis of SMCs incubated with CROT siRNA revealed increased eicosapentaenoic acid, a vascular calcification inhibitor. CRISPR/Cas9-mediated Crot deficiency in LDL (low-density lipoprotein) receptor-deficient mice reduced aortic and carotid artery calcification without altering bone density or liver and plasma cholesterol and triglyceride concentrations.

CONCLUSIONS

CROT is a novel contributing factor in vascular calcification via promoting fatty acid metabolism and mitochondrial dysfunction, as such CROT inhibition has strong potential as an antifibrocalcific therapy.

Gene Expression Profiling Reveals the Shared and Distinct Transcriptional Signatures in Human Lung Epithelial Cells Infected With SARS-CoV-2, MERS-CoV, or SARS-CoV: Potential Implications in Cardiovascular Complications of COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative virus for the current global pandemic known as coronavirus disease 2019 (COVID-19). SARS-CoV-2 belongs to the family of single-stranded RNA viruses known as coronaviruses, including the MERS-CoV and SARS-CoV that cause Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS), respectively. These coronaviruses are associated in the way that they cause mild to severe upper respiratory tract illness. This study has used an unbiased analysis of publicly available gene expression datasets from Gene Expression Omnibus to understand the shared and unique transcriptional signatures of human lung epithelial cells infected with SARS-CoV-2 relative to MERS-CoV or SARS-CoV. A major goal was to discover unique cellular responses to SARS-CoV-2 among these three coronaviruses. Analyzing differentially expressed genes (DEGs) shared by the three datasets led to a set of 17 genes, suggesting the lower expression of genes related to acute inflammatory response (TNF, IL32, IL1A, CXCL1, and CXCL3) in SARS-CoV-2. This subdued transcriptional response to SARS-CoV-2 may cause prolonged viral replication, leading to severe lung damage. Downstream analysis of unique DEGs of SARS-CoV-2 infection revealed changes in genes related to apoptosis (NRP1, FOXO1, TP53INP1, CSF2, and NLRP1), coagulation (F3, PROS1, ITGB3, and TFPI2), and vascular function (VAV3, TYMP, TCF4, and NR2F2), which may contribute to more systemic cardiovascular complications of COVID-19 than MERS and SARS. The study has uncovered a novel set of transcriptomic signatures unique to SARS-CoV-2 infection and shared by three coronaviruses, which may guide the initial efforts in the development of prognostic or therapeutic tools for COVID-19.

ApoC-III is a novel inducer of calcification in human aortic valves

Calcific aortic valve disease (CAVD) occurs when subpopulations of valve cells undergo specific differentiation pathways, promoting tissue fibrosis and calcification. Lipoprotein particles carry oxidized lipids that promote valvular disease, but low-density lipoprotein-lowering therapies have failed in clinical trials, and there are currently no pharmacological interventions available for this disease. Apolipoproteins are known promoters of atherosclerosis, but whether they possess pathogenic properties in CAVD is less clear. To search for a possible link, we assessed 12 apolipoproteins in nonfibrotic/noncalcific and fibrotic/calcific aortic valve tissues by proteomics and immunohistochemistry to understand if they were enriched in calcified areas. Eight apolipoproteins (apoA-I, apoA-II, apoA-IV, apoB, apoC-III, apoD, apoL-I, and apoM) were enriched in the calcific versus nonfibrotic/noncalcific tissues. Apo(a), apoB, apoC-III, apoE, and apoJ localized within the disease-prone fibrosa and colocalized with calcific regions as detected by immunohistochemistry. Circulating apoC-III on lipoprotein(a) is a potential biomarker of aortic stenosis incidence and progression, but whether apoC-III also induces aortic valve calcification is unknown. We found that apoC-III was increased in fibrotic and calcific tissues and observed within the calcification-prone fibrosa layer as well as around calcification. In addition, we showed that apoC-III induced calcification in primary human valvular cell cultures via a mitochondrial dysfunction/inflammation-mediated pathway. This study provides a first assessment of a broad array of apolipoproteins in CAVD tissues, demonstrates that specific apolipoproteins associate with valvular calcification, and implicates apoC-III as an active and modifiable driver of CAVD beyond its potential role as a biomarker.

Robustness and lethality in multilayer biological molecular networks

Robustness is a prominent feature of most biological systems. Most previous related studies have been focused on homogeneous molecular networks. Here we propose a comprehensive framework for understanding how the interactions between genes, proteins and metabolites contribute to the determinants of robustness in a heterogeneous biological network. We integrate heterogeneous sources of data to construct a multilayer interaction network composed of a gene regulatory layer, a protein-protein interaction layer, and a metabolic layer. We design a simulated perturbation process to characterize the contribution of each gene to the overall system's robustness, and find that influential genes are enriched in essential and cancer genes. We show that the proposed mechanism predicts a higher vulnerability of the metabolic layer to perturbations applied to genes associated with metabolic diseases. Furthermore, we find that the real network is comparably or more robust than expected in multiple random realizations. Finally, we analytically derive the expected robustness of multilayer biological networks starting from the degree distributions within and between layers. These results provide insights into the non-trivial dynamics occurring in the cell after a genetic perturbation is applied, confirming the importance of including the coupling between different layers of interaction in models of complex biological systems.

Annexin A1-dependent tethering promotes extracellular vesicle aggregation revealed with single-extracellular vesicle analysis

Extracellular vesicles (EVs) including plasma membrane-derived microvesicles and endosomal-derived exosomes aggregate by unknown mechanisms, forming microcalcifications that promote cardiovascular disease, the leading cause of death worldwide. Here, we show a framework for assessing cell-independent EV mechanisms in disease by suggesting that annexin A1 (ANXA1)-dependent tethering induces EV aggregation and microcalcification. We present single-EV microarray, a method to distinguish microvesicles from exosomes and assess heterogeneity at a single-EV level. Single-EV microarray and proteomics revealed increased ANXA1 primarily on aggregating and calcifying microvesicles. ANXA1 vesicle aggregation was suppressed by calcium chelation, altering pH, or ANXA1 neutralizing antibody. ANXA1 knockdown attenuated EV aggregation and microcalcification formation in human cardiovascular cells and acellular three-dimensional collagen hydrogels. Our findings explain why microcalcifications are more prone to form in vulnerable regions of plaque, regulating critical cardiovascular pathology, and likely extend to other EV-associated diseases, including autoimmune and neurodegenerative diseases and cancer.

Exploring the cross-phenotype network region of disease modules reveals concordant and discordant pathways between chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis

Chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) are two pathologically distinct chronic lung diseases that are associated with cigarette smoking. Genetic studies have identified shared loci for COPD and IPF, including several loci with opposite directions of effect. The existence of additional shared genetic loci, as well as potential shared pathobiological mechanisms between the two diseases at the molecular level, remains to be explored. Taking a network-based approach, we built disease modules for COPD and IPF using genome-wide association studies-implicated genes. The two disease modules displayed strong disease signals in an independent gene expression data set of COPD and IPF lung tissue and showed statistically significant overlap and network proximity, sharing 19 genes, including ARHGAP12 and BCHE. To uncover pathways at the intersection of COPD and IPF, we developed a metric, NetPathScore, which prioritizes the pathways of a disease by their network overlap with another disease. Applying NetPathScore to the COPD and IPF disease modules enabled the determination of concordant and discordant pathways between these diseases. Concordant pathways between COPD and IPF included extracellular matrix remodeling, Mitogen-activated protein kinase (MAPK) signaling and ALK pathways, whereas discordant pathways included advanced glycosylation end product receptor signaling and telomere maintenance and extension pathways. Overall, our findings reveal shared molecular interaction regions between COPD and IPF and shed light on the congruent and incongruent biological processes lying at the intersection of these two complex diseases.

Multiorgan Systems Study Reveals Igfbp7 as a Suppressor of Gluconeogenesis after Gastric Bypass Surgery

Roux-en-Y gastric bypass (RYGB) surgery reduces weight in obese patients. A marked decrease in blood glucose levels occurs before weight loss; however, key molecules that improve the glycemic profile remain largely unknown. Using a murine RYGB surgery model, we performed multiorgan proteomics and bioinformatics to monitor the proteins and molecular pathways that change in this early glycemic response. Multiplexed proteomic kinetics data analysis revealed that the Roux limb, biliopancreatic limb, liver, and pancreas each exhibited unique temporal and molecular responses to the RYGB surgery. In addition, protein-protein network analysis indicated that the changes to the microbial environment in the intestine may play a crucial role in the beneficial effects of RYGB surgery. Furthermore, insulin-like growth factor binding protein 7 (Igfbp7) was identified as an early induced protein in the Roux limb. Known secretory properties of Igfbp7 prompted us to further investigate its role as a remote organ regulator of glucose metabolism. Igfbp7 overexpression decreased blood glucose levels in diet-induced obese mice and attenuated gluconeogenic gene expression in the liver. Secreted Igfbp7 appeared to mediate these beneficial effects. These results demonstrate that organs responded differentially to RYGB surgery and indicate that Igfbp7 may play an important role in improving blood glucose levels.

Spatiotemporal Multi-Omics Mapping Generates a Molecular Atlas of the Aortic Valve and Reveals Networks Driving Disease

BACKGROUND

No pharmacological therapy exists for calcific aortic valve disease (CAVD), which confers a dismal prognosis without invasive valve replacement. The search for therapeutics and early diagnostics is challenging because CAVD presents in multiple pathological stages. Moreover, it occurs in the context of a complex, multi-layered tissue architecture; a rich and abundant extracellular matrix phenotype; and a unique, highly plastic, and multipotent resident cell population.

METHODS

A total of 25 human stenotic aortic valves obtained from valve replacement surgeries were analyzed by multiple modalities, including transcriptomics and global unlabeled and label-based tandem-mass-tagged proteomics. Segmentation of valves into disease stage-specific samples was guided by near-infrared molecular imaging, and anatomic layer-specificity was facilitated by laser capture microdissection. Side-specific cell cultures were subjected to multiple calcifying stimuli, and their calcification potential and basal/stimulated proteomes were evaluated. Molecular (protein-protein) interaction networks were built, and their central proteins and disease associations were identified.

RESULTS

Global transcriptional and protein expression signatures differed between the nondiseased, fibrotic, and calcific stages of CAVD. Anatomic aortic valve microlayers exhibited unique proteome profiles that were maintained throughout disease progression and identified glial fibrillary acidic protein as a specific marker of valvular interstitial cells from the spongiosa layer. CAVD disease progression was marked by an emergence of smooth muscle cell activation, inflammation, and calcification-related pathways. Proteins overrepresented in the disease-prone fibrosa are functionally annotated to fibrosis and calcification pathways, and we found that in vitro, fibrosa-derived valvular interstitial cells demonstrated greater calcification potential than those from the ventricularis. These studies confirmed that the microlayer-specific proteome was preserved in cultured valvular interstitial cells, and that valvular interstitial cells exposed to alkaline phosphatase-dependent and alkaline phosphatase-independent calcifying stimuli had distinct proteome profiles, both of which overlapped with that of the whole tissue. Analysis of protein-protein interaction networks found a significant closeness to multiple inflammatory and fibrotic diseases.

CONCLUSIONS

A spatially and temporally resolved multi-omics, and network and systems biology strategy identifies the first molecular regulatory networks in CAVD, a cardiac condition without a pharmacological cure, and describes a novel means of systematic disease ontology that is broadly applicable to comprehensive omics studies of cardiovascular diseases.

The multiplex network of human diseases

Untangling the complex interplay between phenotype and genotype is crucial to the effective characterization and subtyping of diseases. Here we build and analyze the multiplex network of 779 human diseases, which consists of a genotype-based layer and a phenotype-based layer. We show that diseases with common genetic constituents tend to share symptoms, and uncover how phenotype information helps boost genotype information. Moreover, we offer a flexible classification of diseases that considers their molecular underpinnings alongside their clinical manifestations. We detect cohesive groups of diseases that have high intra-group similarity at both the molecular and the phenotypic level. Inspecting these disease communities, we demonstrate the underlying pathways that connect diseases mechanistically. We observe monogenic disorders grouped together with complex diseases for which they increase the risk factor. We propose potentially new disease associations that arise as a unique feature of the information flow within and across the two layers.

XINA: A Workflow for the Integration of Multiplexed Proteomics Kinetics Data with Network Analysis

Quantitative proteomics experiments, using for instance isobaric tandem mass tagging approaches, are conducive to measuring changes in protein abundance over multiple time points in response to one or more conditions or stimulations. The aim is often to determine which proteins exhibit similar patterns within and across experimental conditions, since proteins with coabundance patterns may have common molecular functions related to a given stimulation. In order to facilitate the identification and analyses of coabundance patterns within and across conditions, we previously developed a software inspired by the isobaric mass tagging method itself. Specifically, multiple data sets are tagged in silico and combined for subsequent subgrouping into multiple clusters within a single output depicting the variation across all conditions, converting a typical inter-data-set comparison into an intra-data-set comparison. An updated version of our software, XINA, not only extracts coabundance profiles within and across experiments but also incorporates protein-protein interaction databases and integrative resources such as KEGG to infer interactors and molecular functions, respectively, and produces intuitive graphical outputs. In this report, we compare the kinetics profiles of >5600 unique proteins derived from three macrophage cell culture experiments and demonstrate through intuitive visualizations that XINA identifies key regulators of macrophage activation via their coabundance patterns.

Uremic Toxin Indoxyl Sulfate Promotes Proinflammatory Macrophage Activation Via the Interplay of OATP2B1 and Dll4-Notch Signaling

BACKGROUND

Chronic kidney disease (CKD) increases cardiovascular risk. Underlying mechanisms, however, remain obscure. The uremic toxin indoxyl sulfate is an independent cardiovascular risk factor in CKD. We explored the potential impact of indoxyl sulfate on proinflammatory activation of macrophages and its underlying mechanisms.

METHODS

We examined in vitro the effects of clinically relevant concentrations of indoxyl sulfate on proinflammatory responses of macrophages and the roles of organic anion transporters and organic anion transporting polypeptides (OATPs). A systems approach, involving unbiased global proteomics, bioinformatics, and network analysis, then explored potential key pathways. To address the role of Delta-like 4 (Dll4) in indoxyl sulfate-induced macrophage activation and atherogenesis in CKD in vivo, we used 5/6 nephrectomy and Dll4 antibody in low-density lipoprotein receptor-deficient (Ldlr-/-) mice. To further determine the relative contribution of OATP2B1 or Dll4 to proinflammatory activation of macrophages and atherogenesis in vivo, we used siRNA delivered by macrophage-targeted lipid nanoparticles in mice.

RESULTS

We found that indoxyl sulfate-induced proinflammatory macrophage activation is mediated by its uptake through transporters, including OATP2B1, encoded by the SLCO2B1 gene. The global proteomics identified potential mechanisms, including Notch signaling and the ubiquitin-proteasome pathway, that mediate indoxyl sulfate-triggered proinflammatory macrophage activation. We chose the Notch pathway as an example of key candidates for validation of our target discovery platform and for further mechanistic studies. As predicted computationally, indoxyl sulfate triggered Notch signaling, which was preceded by the rapid induction of Dll4 protein. Dll4 induction may result from inhibition of the ubiquitin-proteasome pathway, via the deubiquitinating enzyme USP5. In mice, macrophage-targeted OATP2B1/Slco2b1 silencing and Dll4 antibody inhibited proinflammatory activation of peritoneal macrophages induced by indoxyl sulfate. In low-density lipoprotein receptor-deficient mice, Dll4 antibody abolished atherosclerotic lesion development accelerated in Ldlr-/- mice. Moreover, coadministration of indoxyl sulfate and OATP2B1/Slco2b1 or Dll4 siRNA encapsulated in macrophage-targeted lipid nanoparticles in Ldlr-/- mice suppressed lesion development.

CONCLUSIONS

These results suggest that novel crosstalk between OATP2B1 and Dll4-Notch signaling in macrophages mediates indoxyl sulfate-induced vascular inflammation in CKD.

Context-enriched interactome powered by proteomics helps the identification of novel regulators of macrophage activation

The role of pro-inflammatory macrophage activation in cardiovascular disease (CVD) is a complex one amenable to network approaches. While an indispensible tool for elucidating the molecular underpinnings of complex diseases including CVD, the interactome is limited in its utility as it is not specific to any cell type, experimental condition or disease state. We introduced context-specificity to the interactome by combining it with co-abundance networks derived from unbiased proteomics measurements from activated macrophage-like cells. Each macrophage phenotype contributed to certain regions of the interactome. Using a network proximity-based prioritization method on the combined network, we predicted potential regulators of macrophage activation. Prediction performance significantly increased with the addition of co-abundance edges, and the prioritized candidates captured inflammation, immunity and CVD signatures. Integrating the novel network topology with transcriptomics and proteomics revealed top candidate drivers of inflammation. In vitro loss-of-function experiments demonstrated the regulatory role of these proteins in pro-inflammatory signaling.

When human cells or tissues are injured, the body triggers a response known as inflammation to repair the damage and protect itself from further harm. However, if the same issue keeps recurring, the tissues become inflamed for longer periods of time, which may ultimately lead to health problems. This is what could be happening in cardiovascular diseases, where long-term inflammation could damage the heart and blood vessels.

Many different proteins interact with each other to control inflammation; gaining an insight into the nature of these interactions could help to pinpoint the role of each molecular actor. Researchers have used a combination of unbiased, large-scale experimental and computational approaches to develop the interactome, a map of the known interactions between all proteins in humans. However, interactions between proteins can change between cell types, or during disease. Here, Halu et al. aimed to refine the human interactome and identify new proteins involved in inflammation, especially in the context of cardiovascular disease.

Cells called macrophages produce signals that trigger inflammation whey they detect damage in other cells or tissues. The experiments used a technique called proteomics to measure the amounts of all the proteins in human macrophages. Combining these data with the human interactome made it possible to predict new links between proteins known to have a role in inflammation and other proteins in the interactome. Further analysis using other sets of data from macrophages helped identify two new candidate proteins – GBP1 and WARS – that may promote inflammation. Halu et al. then used a genetic approach to deactivate the genes and decrease the levels of these two proteins in macrophages, which caused the signals that encourage inflammation to drop.

These findings suggest that GBP1 and WARS regulate the activity of macrophages to promote inflammation. The two proteins could therefore be used as drug targets to treat cardiovascular diseases and other disorders linked to inflammation, but further studies will be needed to precisely dissect how GBP1 and WARS work in humans.

The Transcriptional Signature of Growth in Human Fetal Aortic Valve Development

BACKGROUND

In the second trimester of human fetal development, a tenfold increase in fetal size occurs while cardiac valves grow and retain their function. Patterns of transcription in normally growing human aortic valves are unknown.

METHODS

Discarded human aortic valve samples were collected from the second trimester, 6 from early (14, 15, 17 weeks) and 6 from late (20, 21, 22 weeks) gestation. Network analysis of RNA sequencing data identified subnetworks of significantly increasing and decreasing transcripts. Subsequent cluster analysis identified patterns of transcription through the time course. Pathway enrichment analysis determined the predominant biological processes at each interval.

RESULTS

We observed phasic transcription over the time course, including an early decrease in cell proliferation and developmental genes (14 to 15 weeks). Pattern specification, shear stress, and adaptive immune genes were induced early. Cell adhesion genes were increased from 14 to 20 weeks. A phase involving cell differentiation and apoptosis (17 to 20 weeks) was followed by downregulation of endothelial-to-mesenchymal transformation genes and then by increased extracellular matrix organization and stabilization (20 to 22 weeks).

CONCLUSIONS

We present a unique data set, comprehensively characterizing human valve development after valve primordia are formed, focusing on key processes displayed by normal aortic valves undergoing significant growth. We build a time course of genes and processes in second trimester fetal valve growth and observe the sequential regulation of gene clusters over time. Critical valve growth genes are potential targets for therapeutic intervention in congenital heart disease and have implications for regenerative medicine and tissue engineering.

Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes

Probing the dynamic control features of biological networks represents a new frontier in capturing the dysregulated pathways in complex diseases. Here, using patient samples obtained from a pancreatic islet transplantation program, we constructed a tissue-specific gene regulatory network and used the control centrality (Cc) concept to identify the high control centrality (HiCc) pathways, which might serve as key pathobiological pathways for Type 2 Diabetes (T2D). We found that HiCc pathway genes were significantly enriched with modest GWAS p-values in the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) study. We identified variants regulating gene expression (expression quantitative loci, eQTL) of HiCc pathway genes in islet samples. These eQTL genes showed higher levels of differential expression compared to non-eQTL genes in low, medium, and high glucose concentrations in rat islets. Among genes with highly significant eQTL evidence, NFATC4 belonged to four HiCc pathways. We asked if the expressions of T2D-associated candidate genes from GWAS and literature are regulated by Nfatc4 in rat islets. Extensive in vitro silencing of Nfatc4 in rat islet cells displayed reduced expression of 16, and increased expression of four putative downstream T2D genes. Overall, our approach uncovers the mechanistic connection of NFATC4 with downstream targets including a previously unknown one, TCF7L2, and establishes the HiCc pathways' relationship to T2D.

Abstract 708: Using Global Proteomics and Network Science to Explore Therapeutic Targets for Abdominal Aortic Aneurysm (AAA)

Objective: To understand mechanisms critical to aneurysm development and identify potential therapeutic targets, we performed global proteomics and network analysis in mouse models of AAA.

Methods and Results: AAAs were produced by luminal perfusion of the infrarenal aorta of C57BL/6 (wild-type) mice with elastase or subcutaneous infusion of angiotensin II (AngII) in Apoe-/- and Ldlr-/- mice (on congenic C57BL/6 background). Aortas were harvested at two intervals corresponding to developing or end-point aneurysm phenotypes that are specific to each model (n=3 for each procedure, interval, and genotype). Aortas were dissected into 8 segments spanning the arch to infrarenal portion, resulting in combined 288 aortic segments for label-free proteomics. Proteins were classified as significantly correlated to aneurysm according to their fold-change over control. Additional correlations were derived from a high-dimensional data analysis tool (“Xina”), developed in our laboratory, which enables clustering of proteins according to model, genotype, aortic region, and interval. Using our criteria, we identified lists of 159 proteins in the Apoe-/- and 158 proteins in the Ldlr-/- mice (AngII infused), and 173 proteins in wild-type mice (elastase perfused). Network analysis of these protein lists reveal commonalities between the models to include protein pathways related to protein translation, immune function, platelet activation, and extracellular matrix organization. Pathways enriched following AngII infusion included phagosome function and platelet aggregation, while pathways enriched in the elastase model included apoptosis, smooth muscle cell contraction, and Fc gamma R-mediated phagocytosis.

Conclusion: Identification of pathways and proteins shared among these models may present master regulators of aneurysmal events and identify therapeutic targets. Conversely, identifying aneurysmal events unique to each model will provide valuable information regarding the model(s) chosen for use in a study and help contextualize model-specific findings.

Abstract 175: Dynamin-Related Protein 1 Regulates Proteostasis and Proprotein Convertase Subtilisin/Kexin Type 9 Secretion

Objective: Dysfunctional protein homeostasis (proteostasis) contributes to cardiovascular and metabolic disorders. We and others associated the mitochondrial fission protein, Dynamin-related protein 1 (DRP1) with cardiometabolic disease. Liver DRP1-deficiency reduces serum lipids and very-low density lipoprotein secretion in high-fat fed mice; whether DRP1 mediates these effects via proteostasis regulation is unknown.

Approach and Results: Using mass spectrometry integrated with network analysis to map the human liver secretome, we found DRP1 associated with cardiovascular disease modules and lipid pathways. Electron microscopy revealed human liver DRP1 at mitochondria, cytosol, vesicles, endoplasmic reticulum (ER), and clustered at membrane tethered to ER exit sites. DRP1 small molecule inhibition (Mdivi-1) or CRISPR/Cas9-mediated DRP1 deletion in human liver cells, and Drp1 -liver deficiency in mice reduced autophagic flux without impairing the amino acid metabolome, or activating the autophagy inhibitor, mammalian target of rapamycin complex 1. DRP1 partially co-localized and co-immunoprecipitated with the ER trafficking and autophagy regulator, Syntaxin 17, in human liver tissue and cells. DRP1 inhibition reduced Proprotein convertase subtilisin/kexin type 9 (PCSK9) secretion in human liver cells and mice (-78.5%), and altered trafficking of the PCSK9-binding and ER maintenance chaperone, Glucose-regulated protein 94. Co-treating human liver cells with Mdivi-1 and proteasome inhibitor (MG132), non-transcriptionally increased intracellular PCSK9, while maintaining Mdivi-1-mediated reduced PCSK9 secretion.

Conclusions: We propose a novel function of DRP1 in the regulation of proteostasis, wherein DRP1 may cluster, then tether and/or constrict nascent autophagy-associated membrane at the ER via its interaction with Syntaxin 17. DRP1 inhibition likely reduces lipoprotein and PCSK9 secretion in part by impairing autophagic flux leading to compensatory chaperone-mediated proteasomal degradation for ER maintenance. Proteostasis regulation and the cellular function of DRP1 is more complex than previously thought, potentially providing new avenues to therapeutically target cardiometabolic disease.

Abstract 608: Integrated Omics and Network Analysis Identify Drivers of Calcific Bicuspid Aortic Valve Disease

Objectives: Calcific aortic valve disease (CAVD) shares risk factors with atherosclerosis but has no pharmacotherapy. Bicuspid aortic valve (BAV) is the most common congenital cardiac defect (2% of humans), and individuals with BAVs acquire CAVD at a rate >15-25x of those with tricuspid aortic valves (TAVs). The causes and mechanisms of accelerated BAV-CAVD remain unclear. This study demonstrates the integrated transcriptome and proteome of BAV-CAVD.

Methods: Quantification of gated chest CTs assessed aortic valve calcification. We performed transcriptomics using Illumina sequencing and global unlabeled proteomics by LC-MS/MS in a total of 55 human aortic valves (24 stenotic TAVs and 25 stenotic BAVs from valve replacement surgeries segmented into fibrotic and calcific portions, and 6 non-diseased aortic valves from heart transplants).

Results: Calcific burden did not differ between BAVs and TAVs (Agatston score: p = 0.88), but BAV mineralization was significantly accelerated as BAVs underwent surgery 11.5±1.74 years earlier than TAVs (p < 0.01). Transcriptomics sequenced 11,223 genes and proteomics identified 1,798 proteins. Of these, 174 genes (fibrotic = 121, calcified = 53) and 98 proteins (fibrotic = 24, calcified = 74) were differentially expressed between BAV- and TAV-CAVD. Pathway analysis of proteins enriched in BAVs vs. TAVs found significant enhancement of iron metabolism and proteoglycan function in BAV-CAVD, while complement activation and collagen biosynthesis distinguished TAVs. When overrepresented proteins were mapped to the protein-protein interactome, network analysis identified disease stage- and valve type-specific subnetworks. While numerous protein-protein interactions existed between the fibrosis and calcification subnetworks of TAVs, a disconnection between these two pathological processes was identified in BAV subnetworks.

Conclusions: Integrated multi-omics of CAVD stratified by disease stage and valve type identify unique contributors to BAV pathogenesis and shed light on molecular drivers of disease. Network analysis finds that while fibrocalcific responses are linked in TAVs, fibrosis and calcification may develop independently in BAVs, with vital implications for targeting of therapeutics.

Abstract 228: Multi-omics Mapping Generates a Molecular Atlas of the Aortic Valve and Reveals Networks Driving Disease

Background: No pharmacological therapy exists for calcific aortic valve disease (CAVD), which confers a dismal prognosis without valve replacement. The search for therapeutics and early diagnostics is challenging since CAVD presents in multiple pathological stages.

Methods: A total of 25 human stenotic aortic valves obtained from valve replacement surgery were analyzed by multiple modalities, including transcriptomics and global unlabeled and tandem-mass-tagged proteomics by liquid chromatography-mass spectrometry.

Results: Global transcriptional and protein expression signatures differed between the non-diseased, fibrotic, and calcific stages of CAVD, with consistent trends in gene and protein expression across disease stages. Anatomical aortic valve microlayers exhibited unique proteome profiles that were maintained throughout disease progression, and revealed GFAP as a specific marker of valvular interstitial cells (VICs) from the spongiosa layer. CAVD disease progression was marked by an emergence of smooth muscle cell activation, inflammation, and calcification-related pathways. Proteins overrepresented in the disease-prone fibrosa are functionally annotated to fibrosis and calcification pathways, and we found that, in vitro , fibrosa-derived VICs demonstrated greater calcification potential than those from the ventricularis. These studies confirmed that the microlayer-specific proteome was preserved in cultured VICs, and that VICs exposed to TNAP-dependent and TNAP-independent calcifying stimuli had distinct proteome profiles, both of which overlapped with that of the whole tissue. Network analysis of protein-protein interaction networks found a significant closeness to multiple inflammatory and fibrotic diseases.

Conclusions: A spatially- and temporally-resolved multi-omics and systems biology strategy identifies the first molecular regulatory networks in CAVD, a cardiac condition without a pharmacological cure, and describes a strategy for endophenotype characterization that is broadly applicable to comprehensive omics studies of cardiovascular diseases.

Abstract 663: Nuclear RSK1 Induces Pro-inflammatory Activation of Macrophages through STAT1 Phosphorylation at Ser727

Objective: Maladaptive inflammatory responses involve macrophage activation by pro-inflammatory cytokines such as IFN-γ. Proteins that undergo nuclear translocation may regulate these processes.

Approach and Results: To explore novel key regulators of macrophage activation, we performed quantitative proteomics to monitor protein translocation to the nuclei of human primary macrophages elicited with IFN-γ for 0, 10, 20, 30, or 60 min. Bioinformatics identified RSK1, a ribosomal protein kinase, as one of the candidates. We found that IFN-γ stimulation promotes RSK1 phosphorylation at Ser380. STAT1 is a key mediator of IFN-γ-triggered cellular responses. Mass spectrometry identified RSK1-mediated phosphorylation sites within STAT1, including Ser727. siRNA silencing of RSK1 attenuated STAT1 phosphorylation in IFN-γ-stimulated macrophages. In concert with these results, RSK1 silencing hindered IFN-γ-induced secretion of pro-inflammatory chemokines in human primary macrophages, such as CCL2/MCP-1, CCL7/MCP-3, and CCL8/MCP-2.

Conclusion: We discovered that RSK1 nuclear translocation triggers STAT1 phosphorylation, resulting in pro-inflammatory activation of macrophages (Figure), a novel role for a ribosomal protein-associated kinase in nuclear signaling and inflammation.

Regulation of Nuclear Receptor Interacting Protein 1 (NRIP1) Gene Expression in Response to Weight Loss and Exercise in Humans

OBJECTIVE

Nuclear receptor interacting protein 1 (NRIP1) is an important energy regulator, but few studies have addressed its role in humans. This study investigated adipose tissue and skeletal muscle NRIP1 gene expression and serum levels in response to weight loss and exercise in humans.

METHODS

NRIP1 expression was measured by microarray and serum NRIP1 by ELISA and Western blotting. Skeletal muscle transcriptomes were analyzed from Gene Expression Omnibus databases. Network-based proximity analysis was performed on the proximity of NRIP1 interacting genes in the human interactome.

RESULTS

In patients with obesity, adipose tissue NRIP1 mRNA expression increased during weight loss and weight maintenance and showed strong associations with metabolic markers and anthropometric parameters. Serum NRIP1 protein levels also increased after weight loss. In skeletal muscle, imposed rest increased NRIP1 expression by 80%, and strength training increased expression by ∼25% compared to baseline. Following rest, NRIP1 expression became sensitive to insulin stimulation. After re-training, NRIP1 expression decreased. Interactome analysis showed significant proximity of NRIP1 interacting partners to the obesity network/module.

CONCLUSIONS

NRIP1 gene expression and serum levels are strongly associated with metabolic states such as obesity, weight loss, different types of exercise, and peripheral tissue insulin resistance, potentially as a mediator of sedentary effects.

PARP9 and PARP14 cross-regulate macrophage activation via STAT1 ADP-ribosylation

Despite the global impact of macrophage activation in vascular disease, the underlying mechanisms remain obscure. Here we show, with global proteomic analysis of macrophage cell lines treated with either IFNγ or IL-4, that PARP9 and PARP14 regulate macrophage activation. In primary macrophages, PARP9 and PARP14 have opposing roles in macrophage activation. PARP14 silencing induces pro-inflammatory genes and STAT1 phosphorylation in M(IFNγ) cells, whereas it suppresses anti-inflammatory gene expression and STAT6 phosphorylation in M(IL-4) cells. PARP9 silencing suppresses pro-inflammatory genes and STAT1 phosphorylation in M(IFNγ) cells. PARP14 induces ADP-ribosylation of STAT1, which is suppressed by PARP9. Mutations at these ADP-ribosylation sites lead to increased phosphorylation. Network analysis links PARP9-PARP14 with human coronary artery disease. PARP14 deficiency in haematopoietic cells accelerates the development and inflammatory burden of acute and chronic arterial lesions in mice. These findings suggest that PARP9 and PARP14 cross-regulate macrophage activation.

Data-driven modeling of solar-powered urban microgrids

Distributed generation takes center stage in today's rapidly changing energy landscape. Particularly, locally matching demand and generation in the form of microgrids is becoming a promising alternative to the central distribution paradigm. Infrastructure networks have long been a major focus of complex networks research with their spatial considerations. We present a systemic study of solar-powered microgrids in the urban context, obeying real hourly consumption patterns and spatial constraints of the city. We propose a microgrid model and study its citywide implementation, identifying the self-sufficiency and temporal properties of microgrids. Using a simple optimization scheme, we find microgrid configurations that result in increased resilience under cost constraints. We characterize load-related failures solving power flows in the networks, and we show the robustness behavior of urban microgrids with respect to optimization using percolation methods. Our findings hint at the existence of an optimal balance between cost and robustness in urban microgrids.

Emergence of overlap in ensembles of spatial multiplexes and statistical mechanics of spatial interacting network ensembles

Spatial networks range from the brain networks, to transportation networks and infrastructures. Recently interacting and multiplex networks are attracting great attention because their dynamics and robustness cannot be understood without treating at the same time several networks. Here we present maximal entropy ensembles of spatial multiplex and spatial interacting networks that can be used in order to model spatial multilayer network structures and to build null models of real data sets. We show that spatial multiplexes naturally develop a significant overlap of the links, a noticeable property of many multiplexes that can affect significantly the dynamics taking place on them. Additionally, we characterize ensembles of spatial interacting networks and we analyze the structure of interacting airport and railway networks in India, showing the effect of space in determining the link probability.

Multiplex PageRank

Many complex systems can be described as multiplex networks in which the same nodes can interact with one another in different layers, thus forming a set of interacting and co-evolving networks. Examples of such multiplex systems are social networks where people are involved in different types of relationships and interact through various forms of communication media. The ranking of nodes in multiplex networks is one of the most pressing and challenging tasks that research on complex networks is currently facing. When pairs of nodes can be connected through multiple links and in multiple layers, the ranking of nodes should necessarily reflect the importance of nodes in one layer as well as their importance in other interdependent layers. In this paper, we draw on the idea of biased random walks to define the Multiplex PageRank centrality measure in which the effects of the interplay between networks on the centrality of nodes are directly taken into account. In particular, depending on the intensity of the interaction between layers, we define the Additive, Multiplicative, Combined, and Neutral versions of Multiplex PageRank, and show how each version reflects the extent to which the importance of a node in one layer affects the importance the node can gain in another layer. We discuss these measures and apply them to an online multiplex social network. Findings indicate that taking the multiplex nature of the network into account helps uncover the emergence of rankings of nodes that differ from the rankings obtained from one single layer. Results provide support in favor of the salience of multiplex centrality measures, like Multiplex PageRank, for assessing the prominence of nodes embedded in multiple interacting networks, and for shedding a new light on structural properties that would otherwise remain undetected if each of the interacting networks were analyzed in isolation.

Phase transition of light on complex quantum networks

Recent advances in quantum optics and atomic physics allow for an unprecedented level of control over light-matter interactions, which can be exploited to investigate new physical phenomena. In this work we are interested in the role played by the topology of quantum networks describing coupled optical cavities and local atomic degrees of freedom. In particular, using a mean-field approximation, we study the phase diagram of the Jaynes-Cummings-Hubbard model on complex networks topologies, and we characterize the transition between a Mott-like phase of localized polaritons and a superfluid phase. We found that, for complex topologies, the phase diagram is nontrivial and well defined in the thermodynamic limit only if the hopping coefficient scales like the inverse of the maximal eigenvalue of the adjacency matrix of the network. Furthermore we provide numerical evidences that, for some complex network topologies, this scaling implies an asymptotically vanishing hopping coefficient in the limit of large network sizes. The latter result suggests the interesting possibility of observing quantum phase transitions of light on complex quantum networks even with very small couplings between the optical cavities.

Monochromaticity in neutral evolutionary network models

Recent studies on epistatic networks of model organisms have unveiled a certain type of modular property called monochromaticity in which the networks are clustered into functional modules that interact with each other through the same type of epistasis. Here, we propose and study three epistatic network models that are inspired by the duplication-divergence mechanism to gain insight into the evolutionary basis of monochromaticity and to test if it can be explained as the outcome of a neutral evolutionary hypothesis. We show that the epistatic networks formed by these stochastic evolutionary models have monochromaticity conflict distributions that are centered close to zero and are statistically significantly different from their randomized counterparts. In particular, the last model we propose yields a strictly monochromatic solution. Our results agree with the monochromaticity findings in real organisms and point toward the possible role of a neutral mechanism in the evolution of this phenomenon.

Entropy rate of nonequilibrium growing networks

New entropy measures have been recently introduced for the quantification of the complexity of networks. Most of these entropy measures apply to static networks or to dynamical processes defined on static complex networks. In this paper we define the entropy rate of growing network models. This entropy rate quantifies how many labeled networks are typically generated by the growing network models. We analytically evaluate the difference between the entropy rate of growing tree network models and the entropy of tree networks that have the same asymptotic degree distribution. We find that the growing networks with linear preferential attachment generated by dynamical models are exponentially less than the static networks with the same degree distribution for a large variety of relevant growing network models. We study the entropy rate for growing network models showing structural phase transitions including models with nonlinear preferential attachment. Finally, we bring numerical evidence that the entropy rate above and below the structural phase transitions follows a different scaling with the network size.

Study of Z Boson Production in PbPb Collisions at √S(NN)=2.76 TeV

A search for Z bosons in the μ(+)μ(-) decay channel has been performed in PbPb collisions at √S(NN)=2.76  TeV with the CMS detector at the LHC, in a 7.2 μb(-1) data sample. The number of opposite-sign muon pairs observed in the 60-120 GeV/c(2) invariant mass range is 39, corresponding to a yield per unit of rapidity (y) and per minimum bias event of [33.8±5.5(stat)±4.4(syst)]×10(-8), in the |y|<2.0 range. Rapidity, transverse momentum, and centrality dependencies are also measured. The results agree with next-to-leading order QCD calculations, scaled by the number of incoherent nucleon-nucleon collisions.

Measurement of dijet angular distributions and search for quark compositeness in pp collisions at √s = 7 TeV

Dijet angular distributions are measured over a wide range of dijet invariant masses in pp collisions at √s = 7 TeV, at the CERN LHC. The event sample, recorded with the CMS detector, corresponds to an integrated luminosity of 36 pb⁻¹. The data are found to be in good agreement with the predictions of perturbative QCD, and yield no evidence of quark compositeness. With a modified frequentist approach, a lower limit on the contact interaction scale for left-handed quarks of Λ⁺ = 5.6 TeV (Λ⁻ = 6.7 TeV) for destructive (constructive) interference is obtained at the 95% confidence level.

Search for pair production of second-generation scalar leptoquarks in pp collisions at √s = 7 TeV

A search for pair production of second-generation scalar leptoquarks in the final state with two muons and two jets is performed using proton-proton collision data at √s = 7 TeV collected by the CMS detector at the LHC. The data sample used corresponds to an integrated luminosity of 34 pb⁻¹. The number of observed events is in good agreement with the predictions from the standard model processes. An upper limit is set on the second-generation leptoquark cross section times β² as a function of the leptoquark mass, and leptoquarks with masses below 394 GeV are excluded at a 95% confidence level for β = 1, where β is the leptoquark branching fraction into a muon and a quark. These limits are the most stringent to date.

Search for pair production of first-generation scalar leptoquarks in pp collisions at √s = 7 TeV

A search for pair production of first-generation scalar leptoquarks is performed in the final state containing two electrons and two jets using proton-proton collision data at √s = 7 TeV. The data sample used corresponds to an integrated luminosity of 33 pb⁻¹ collected with the CMS detector at the CERN LHC. The number of observed events is in good agreement with the predictions for the standard model background processes, and an upper limit is set on the leptoquark pair production cross section times β² as a function of the leptoquark mass, where β is the branching fraction of the leptoquark decay to an electron and a quark. A 95% confidence level lower limit is set on the mass of a first-generation scalar leptoquark at 384 GeV for β = 1, which is the most stringent direct limit to date.

Dijet azimuthal decorrelations in pp collisions at √s=7 TeV

Measurements of dijet azimuthal decorrelations in pp collisions at √s=7 TeV using the CMS detector at the CERN LHC are presented. The analysis is based on an inclusive dijet event sample corresponding to an integrated luminosity of 2.9 pb⁻¹. The results are compared to predictions from perturbative QCD calculations and various Monte Carlo event generators. The dijet azimuthal distributions are found to be sensitive to initial-state gluon radiation.

Measurement of the B(+) production cross section in pp collisions at sqrt[s]=7 TeV

Measurements of the total and differential cross sections dσ/dp(T)(B) and dσ/dy(B) for B(+) mesons produced in pp collisions at sqrt[s]=7  TeV are presented. The data correspond to an integrated luminosity of 5.8  pb(-1) collected by the CMS experiment operating at the LHC. The exclusive decay B(+)→J/ψK(+), with J/ψ→μ(+)μ(-), is used to detect B(+) mesons and to measure the production cross section as a function of p(T)(B) and y(B). The total cross section for p(T)(B)>5  GeV and |y(B)|<2.4 is measured to be 28.1±2.4±2.0±3.1  μb, where the first uncertainty is statistical, the second is systematic, and the last is from the luminosity measurement.

Measurement of the isolated prompt photon production cross section in pp collisions at √s=7 TeV

The differential cross section for the inclusive production of isolated prompt photons has been measured as a function of the photon transverse energy E(T)(γ) in pp collisions at √s=7  TeV using data recorded by the CMS detector at the LHC. The data sample corresponds to an integrated luminosity of 2.9  pb(-1). Photons are required to have a pseudorapidity |η(γ)|<1.45 and E(T)(γ)>21  GeV, covering the kinematic region 0.006<x(T)<0.086. The measured cross section is found to be in agreement with next-to-leading-order perturbative QCD calculations.

Search for stopped Gluinos in pp collisions at square root s=7 TeV

The results of the first search for long-lived gluinos produced in 7 TeV pp collisions at the CERN Large Hadron Collider are presented. The search looks for evidence of long-lived particles that stop in the CMS detector and decay in the quiescent periods between beam crossings. In a dataset with a peak instantaneous luminosity of 1×10(32) cm-2 s-1, an integrated luminosity of 10 pb-1, and a search interval corresponding to 62 hours of LHC operation, no significant excess above background was observed. Limits at the 95% confidence level on gluino pair production over 13 orders of magnitude of gluino lifetime are set. For a mass difference mg - mχ1(0) >100 GeV/c2, and assuming BR(g→gχ1(0))=100%, mg < 370 GeV/c2 are excluded for lifetimes from 10 μs to 1000 s.

Search for quark compositeness with the dijet centrality ratio in pp collisions at √s=7 TeV

A search for quark compositeness in the form of quark contact interactions, based on hadronic jet pairs (dijets) produced in proton-proton collisions at √s=7 TeV, is described. The data sample of the study corresponds to an integrated luminosity of 2.9 pb(-1) collected with the CMS detector at the LHC. The dijet centrality ratio, which quantifies the angular distribution of the dijets, is measured as a function of the invariant mass of the dijet system and is found to agree with the predictions of the standard model. A statistical analysis of the data provides a lower limit on the energy scale of quark contact interactions. The sensitivity of the analysis is such that the expected limit is 2.9 TeV; because the observed value of the centrality ratio at high invariant mass is below the expectation, the observed limit is 4.0 TeV at the 95% confidence level.

Search for dijet resonances in 7 TeV pp collisions at CMS

A search for narrow resonances in the dijet mass spectrum is performed using data corresponding to an integrated luminosity of 2.9 pb⁻¹ collected by the CMS experiment at the Large Hadron Collider. Upper limits at the 95% confidence level are presented on the product of the resonance cross section, branching fraction into dijets, and acceptance, separately for decays into quark-quark, quark-gluon, or gluon-gluon pairs. The data exclude new particles predicted in the following models at the 95% confidence level: string resonances, with mass less than 2.50 TeV, excited quarks, with mass less than 1.58 TeV, and axigluons, colorons, and E6 diquarks, in specific mass intervals. This extends previously published limits on these models.

First measurement of Bose-Einstein correlations in proton-proton collisions at √s=0.9 and 2.36 TeV at the LHC

Bose-Einstein correlations have been measured using samples of proton-proton collisions at 0.9 and 2.36 TeV center-of-mass energies, recorded by the CMS experiment at the CERN Large Hadron Collider. The signal is observed in the form of an enhancement of pairs of same-sign charged particles with small relative four-momentum. The size of the correlated particle emission region is seen to increase significantly with the particle multiplicity of the event.

Transverse-momentum and pseudorapidity distributions of charged hadrons in pp collisions at square root of s = 7 TeV

Charged-hadron transverse-momentum and pseudorapidity distributions in proton-proton collisions at square root of s = 7  TeV are measured with the inner tracking system of the CMS detector at the LHC. The charged-hadron yield is obtained by counting the number of reconstructed hits, hit pairs, and fully reconstructed charged-particle tracks. The combination of the three methods gives a charged-particle multiplicity per unit of pseudorapidity dN(ch)/dη|(|η|<0.5) = 5.78 ± 0.01(stat) ± 0.23(syst) for non-single-diffractive events, higher than predicted by commonly used models. The relative increase in charged-particle multiplicity from square root of s = 0.9 to 7 TeV is [66.1 ± 1.0(stat) ± 4.2(syst)]%. The mean transverse momentum is measured to be 0.545 ± 0.005(stat) ± 0.015(syst)  GeV/c. The results are compared with similar measurements at lower energies.