The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks.

Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods.

NLRP3 Inflammasome Activation in Peripheral Arterial Disease.

Background Peripheral arterial disease (PAD) is estimated to affect 7% of the adult population in the United States; however, there is currently little understanding of the key cellular and molecular pathways at play. With PAD characterized by vascular inflammation and associated calcification, the current study set out to elucidate the role of NLRP3 (nucleotide oligomerization domain-like receptor family, pyrin domain containing 3) inflammasome activation in the current cohort. Methods and Results Global proteomics of human vessels with and without PAD from a total of 14 donors revealed an increase of proinflammatory associated ontologies, specifically acute phase and innate immunity. Targeted mass spectrometry showed a significant increase in NLRP3, confirmed by NLRP3 ELISA. Histological analysis from the same patients demonstrated expression of NLRP3, colocalizing in immunoreactive CD68 (cluster of differentiation 68) and CD209 (cluster of differentiation 209) macrophages. Moreover, transmission electron microscopy showed the locality of macrophage-like cells in the presence of calcification, with confocal microscopy further validating the localization of CD68, NLRP3, and calcification via near-infrared calcium tracer. Systemic inflammation and the presence of the NLRP3 inflammasome was assessed via flow cytometry and ELISA, respectively. Compared with patients without PAD, NLRP3 expression was significantly increased in serum. In addition, proinflammatory cytokine presence was significantly increased in disease versus control, with IL (interleukin)-1ß, TNF-α (tumor necrosis factor α), and IL-33 demonstrating the greatest disparity, correlating with NLRP3 activation. Conclusions The current findings demonstrate a link between NLRP3, macrophage accumulation, and calcification in arteries of patients with PAD, suggesting an association or possible driver of PAD in these patients.

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.

Induced pluripotent stem cell-derived smooth muscle cells to study cardiovascular calcification.

Cardiovascular calcification is the lead predictor of cardiovascular events and the top cause of morbidity and mortality worldwide. To date, only invasive surgical options are available to treat cardiovascular calcification despite the growing understanding of underlying pathological mechanisms. Key players in vascular calcification are vascular smooth muscle cells (SMCs), which transform into calcifying SMCs and secrete mineralizing extracellular vesicles that form microcalcifications, subsequently increasing plaque instability and consequential plaque rupture. There is an increasing, practical need for a large scale and inexhaustible source of functional SMCs. Here we describe an induced pluripotent stem cell (iPSC)-derived model of SMCs by differentiating iPSCs toward SMCs to study the pathogenesis of vascular calcification. Specifically, we characterize the proteome during iPSC differentiation to better understand the cellular dynamics during this process. First, we differentiated human iPSCs toward an induced-SMC (iSMC) phenotype in a 10-day protocol. The success of iSMC differentiation was demonstrated through morphological analysis, immunofluorescent staining, flow cytometry, and proteomics characterization. Proteomics was performed throughout the entire differentiation time course to provide a robust, well-defined starting and ending cell population. Proteomics data verified iPSC differentiation to iSMCs, and functional enrichment of proteins on different days showed the key pathways changing during iSMC development. Proteomics comparison with primary human SMCs showed a high correlation with iSMCs. After iSMC differentiation, we initiated calcification in the iSMCs by culturing the cells in osteogenic media for 17 days. Calcification was verified using Alizarin Red S staining and proteomics data analysis. This study presents an inexhaustible source of functional vascular SMCs and calcifying vascular SMCs to create an in vitro model of vascular calcification in osteogenic conditions, with high potential for future applications in cardiovascular calcification research.

A Novel Spectral Annotation Strategy Streamlines Reporting of Mono-ADP-ribosylated Peptides Derived from Mouse Liver and Spleen in Response to IFN-γ.

Mass-spectrometry-enabled ADP-ribosylation workflows are developing rapidly, providing researchers a variety of ADP-ribosylome enrichment strategies and mass spectrometric acquisition options. Despite the growth spurt in upstream technologies, systematic ADP-ribosyl (ADPr) peptide mass spectral annotation methods are lacking. HCD-dependent ADP-ribosylome studies are common, but the resulting MS2 spectra are complex, owing to a mixture of b/y-ions and the m/p-ion peaks representing one or more dissociation events of the ADPr moiety (m-ion) and peptide (p-ion). In particular, p-ions that dissociate further into one or more fragment ions can dominate HCD spectra but are not recognized by standard spectral annotation workflows. As a result, annotation strategies that are solely reliant upon the b/y-ions result in lower spectral scores that in turn reduce the number of reportable ADPr peptides. To improve the confidence of spectral assignments, we implemented an ADPr peptide annotation and scoring strategy. All MS2 spectra are scored for the ADPr m-ions, but once spectra are assigned as an ADPr peptide, they are further annotated and scored for the p-ions. We implemented this novel workflow to ADPr peptides enriched from the liver and spleen isolated from mice post 4 h exposure to systemic IFN-γ. HCD collision energy experiments were first performed on the Orbitrap Fusion Lumos and the Q Exactive, with notable ADPr peptide dissociation properties verified with CID (Lumos). The m-ion and p-ion series score distributions revealed that ADPr peptide dissociation properties vary markedly between instruments and within instrument collision energy settings, with consequences on ADPr peptide reporting and amino acid localization. Consequentially, we increased the number of reportable ADPr peptides by 25% (liver) and 17% (spleen) by validation and the inclusion of lower confidence ADPr peptide spectra. This systematic annotation strategy will streamline future reporting of ADPr peptides that have been sequenced using any HCD/CID-based method.

Sex Differences in Gene Expression and Regulatory Networks across 29 Human Tissues.

Sex differences manifest in many diseases and may drive sex-specific therapeutic responses. To understand the molecular basis of sex differences, we evaluated sex-biased gene regulation by constructing sample-specific gene regulatory networks in 29 human healthy tissues using 8,279 whole-genome expression profiles from the Genotype-Tissue Expression (GTEx) project. We find sex-biased regulatory network structures in each tissue. Even though most transcription factors (TFs) are not differentially expressed between males and females, many have sex-biased regulatory targeting patterns. In each tissue, genes that are differentially targeted by TFs between the sexes are enriched for tissue-related functions and diseases. In brain tissue, for example, genes associated with Parkinson's disease and Alzheimer's disease are targeted by different sets of TFs in each sex. Our systems-based analysis identifies a repertoire of TFs that play important roles in sex-specific architecture of gene regulatory networks, and it underlines sex-specific regulatory processes in both health and disease.

Microbiome-Transcriptome Interactions Related to Severity of Respiratory Syncytial Virus Infection.

Respiratory syncytial virus (RSV) is a major cause of lower respiratory tract infections and hospital visits during infancy and childhood. Although risk factors for RSV infection have been identified, the role of microbial species in the respiratory tract is only partially known. We aimed to understand the impact of interactions between the nasal microbiome and host transcriptome on the severity and clinical outcomes of RSV infection. We used 16 S rRNA sequencing to characterize the nasal microbiome of infants with RSV infection. We used RNA sequencing to interrogate the transcriptome of CD4+ T cells obtained from the same set of infants. After dimension reduction through principal component (PC) analysis, we performed an integrative analysis to identify significant co-variation between microbial clade and gene expression PCs. We then employed LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples) to estimate the clade-gene association patterns for each infant. Our network-based integrative analysis identified several clade-gene associations significantly related to the severity of RSV infection. The microbial taxa with the highest loadings in the implicated clade PCs included Moraxella, Corynebacterium, Streptococcus, Haemophilus influenzae, and Staphylococcus. Interestingly, many of the genes with the highest loadings in the implicated gene PCs are encoded in mitochondrial DNA, while others are involved in the host immune response. This study on microbiome-transcriptome interactions provides insights into how the host immune system mounts a response against RSV and specific infectious agents in nasal microbiota.

Network Medicine in the Age of Biomedical Big Data.

Network medicine is an emerging area of research dealing with molecular and genetic interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale biomedical data generation offers a unique opportunity to assess the effect and impact of cellular heterogeneity and environmental perturbations on the observed phenotype. Marrying the two, network medicine with biomedical data provides a framework to build meaningful models and extract impactful results at a network level. In this review, we survey existing network types and biomedical data sources. More importantly, we delve into ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied. We provide three paradigms, mainly dealing with three major biological network archetypes: protein-protein interaction, expression-based, and gene regulatory networks. For each of these paradigms, we discuss a broad overview of philosophies under which various network methods work. We also provide a few examples in each paradigm as a test case of its successful application. Finally, we delineate several opportunities and challenges in the field of network medicine. We hope this review provides a lexicon for researchers from biological sciences and network theory to come on the same page to work on research areas that require interdisciplinary expertise. Taken together, the understanding gained from combining biomedical data with networks can be useful for characterizing disease etiologies and identifying therapeutic targets, which, in turn, will lead to better preventive medicine with translational impact on personalized healthcare.

Epigenome-wide association study reveals methylation pathways associated with childhood allergic sensitization.

Epigenetic mechanisms integrate both genetic variability and environmental exposures. However, comprehensive epigenome-wide analysis has not been performed across major childhood allergic phenotypes. We examined the association of epigenome-wide DNA methylation in mid-childhood peripheral blood (Illumina HumanMethyl450K) with mid-childhood atopic sensitization, environmental/inhalant and food allergen sensitization in 739 children in two birth cohorts (Project Viva-Boston, and the Generation R Study-Rotterdam). We performed covariate-adjusted epigenome-wide association meta-analysis and employed pathway and regional analyses of results. Seven-hundred and five methylation sites (505 genes) were significantly cross-sectionally associated with mid-childhood atopic sensitization, 1411 (905 genes) for environmental and 45 (36 genes) for food allergen sensitization (FDR<0.05). We observed differential methylation across multiple genes for all three phenotypes, including genes implicated previously in innate immunity (DICER1), eosinophilic esophagitis and sinusitis (SIGLEC8), the atopic march (AP5B1) and asthma (EPX, IL4, IL5RA, PRG2, SIGLEC8, CLU). In addition, most of the associated methylation marks for all three phenotypes occur in putative transcription factor binding motifs. Pathway analysis identified multiple methylation sites associated with atopic sensitization and environmental allergen sensitization located in/near genes involved in asthma, mTOR signaling, and inositol phosphate metabolism. We identified multiple differentially methylated regions associated with atopic sensitization (8 regions) and environmental allergen sensitization (26 regions). A number of nominally significant methylation sites in the cord blood analysis were epigenome-wide significant in the mid-childhood analysis, and we observed significant methylation - time interactions among a subset of sites examined. Our findings provide insights into epigenetic regulatory pathways as markers of childhood allergic sensitization.

A Network Analysis of Biomarkers for Type 2 Diabetes.

Numerous studies have investigated individual biomarkers in relation to risk of type 2 diabetes. However, few have considered the interconnectivity of these biomarkers in the etiology of diabetes as well as the potential changes in the biomarker correlation network during diabetes development. We conducted a secondary analysis of 27 plasma biomarkers representing glucose metabolism, inflammation, adipokines, endothelial dysfunction, IGF axis, and iron store plus age and BMI at blood collection from an existing case-control study nested in the Nurses' Health Study (NHS), including 1,303 incident diabetes case subjects and 1,627 healthy women. A correlation network was constructed based on pairwise Spearman correlations of the above factors that were statistically different between case and noncase subjects using permutation tests (P < 0.0005). We further evaluated the network structure separately among diabetes case subjects diagnosed <5, 5-10, and >10 years after blood collection versus noncase subjects. Although pairwise biomarker correlations tended to have similar directions comparing diabetes case subjects to noncase subjects, most correlations were stronger in noncase than in case subjects, with the largest differences observed for the insulin/HbA1c and leptin/adiponectin correlations. Leptin and soluble leptin receptor were two hubs of the network, with large numbers of different correlations with other biomarkers in case versus noncase subjects. When examining the correlation network by timing of diabetes onset, there were more perturbations in the network for case subjects diagnosed >10 years versus <5 years after blood collection, with consistent differential correlations of insulin and HbA1c C-peptide was the most highly connected node in the early-stage network, whereas leptin was the hub for mid- or late-stage networks. Our results suggest that perturbations of the diabetes-related biomarker network may occur decades prior to clinical recognition. In addition to the persistent dysregulation between insulin and HbA1c, our results highlight the central role of the leptin system in diabetes development.

Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data.

BACKGROUND: Although ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data - critical first steps for any subsequent analysis. RESULTS: We find that analysis of large RNA-Seq data sets requires both careful quality control and the need to account for sparsity due to the heterogeneity intrinsic in multi-group studies. We developed Yet Another RNA Normalization software pipeline (YARN), that includes quality control and preprocessing, gene filtering, and normalization steps designed to facilitate downstream analysis of large, heterogeneous RNA-Seq data sets and we demonstrate its use with data from the Genotype-Tissue Expression (GTEx) project. CONCLUSIONS: An R package instantiating YARN is available at http://bioconductor.org/packages/yarn .

Regulatory network changes between cell lines and their tissues of origin.

BACKGROUND: Cell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. Although there are recognized important cellular and transcriptomic differences between cell lines and tissues, a systematic overview of the differences between the regulatory processes of a cell line and those of its tissue of origin has not been conducted. The RNA-Seq data generated by the GTEx project is the first available data resource in which it is possible to perform a large-scale transcriptional and regulatory network analysis comparing cell lines with their tissues of origin. RESULTS: We compared 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines (LCLs) and whole blood samples, and 244 paired primary fibroblast cell lines and skin samples. While gene expression analysis confirms that these cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, network analysis indicates that expression changes are the cumulative result of many previously unreported alterations in transcription factor (TF) regulation. More specifically, cell cycle genes are over-expressed in cell lines compared to primary tissues, and this alteration in expression is a result of less repressive TF targeting. We confirmed these regulatory changes for four TFs, including SMAD5, using independent ChIP-seq data from ENCODE. CONCLUSIONS: Our results provide novel insights into the regulatory mechanisms controlling the expression differences between cell lines and tissues. The strong changes in TF regulation that we observe suggest that network changes, in addition to transcriptional levels, should be considered when using cell lines as models for tissues.

Exploring regulation in tissues with eQTL networks.

Characterizing the collective regulatory impact of genetic variants on complex phenotypes is a major challenge in developing a genotype to phenotype map. Using expression quantitative trait locus (eQTL) analyses, we constructed bipartite networks in which edges represent significant associations between genetic variants and gene expression levels and found that the network structure informs regulatory function. We show, in 13 tissues, that these eQTL networks are organized into dense, highly modular communities grouping genes often involved in coherent biological processes. We find communities representing shared processes across tissues, as well as communities associated with tissue-specific processes that coalesce around variants in tissue-specific active chromatin regions. Node centrality is also highly informative, with the global and community hubs differing in regulatory potential and likelihood of being disease associated.

Manipulation of extreme events on scale-free networks.

Extreme events taking place on networks are not uncommon. We show that it is possible to manipulate the extreme event occurrence probabilities and distribution over the nodes of a scale-free network by tuning the nodal capacity. This can be used to reduce the number of extreme event occurrences. However, monotonic nodal capacity enhancements, beyond a point, do not lead to any substantial reduction in the number of extreme events. We point out the practical implication of this result for network design in the context of reducing extreme event occurrences.

Dynamic phase transition from localized to spatiotemporal chaos in coupled circle map with feedback.

We investigate coupled circle maps in the presence of feedback and explore various dynamical phases observed in this system of coupled high dimensional maps. We observe an interesting transition from localized chaos to spatiotemporal chaos. We study this transition as a dynamic phase transition. We observe that persistence acts as an excellent quantifier to describe this transition. Taking the location of the fixed point of circle map (which does not change with feedback) as a reference point, we compute a number of sites which have been greater than (less than) the fixed point until time t. Though local dynamics is high dimensional in this case, this definition of persistence which tracks a single variable is an excellent quantifier for this transition. In most cases, we also obtain a well defined persistence exponent at the critical point and observe conventional scaling as seen in second order phase transitions. This indicates that persistence could work as a good order parameter for transitions from fully or partially arrested phase. We also give an explanation of gaps in eigenvalue spectrum of the Jacobian of localized state.

Directed percolation criticality due to stochastic switching between attractive and repulsive coupling in coupled circle maps.

We study a lattice model where the coupling stochastically switches between repulsive (subtractive) and attractive (additive) at each site with probability p at every time instant. We observe that such a kind of coupling stabilizes the local fixed point of a circle map, with the resultant globally stable attractor providing a unique absorbing state. Interestingly, a continuous phase transition is observed from the absorbing state to spatiotemporal chaos via spatiotemporal intermittency for a range of values of p . It is interesting to note that the transition falls in class of directed percolation. Static and spreading exponents along with relevant scaling laws are found to be obeyed confirming the directed percolation universality class in spatiotemporal intermittency regime.

Synchronization in a network of model neurons.

We study the spatiotemporal dynamics of a network of coupled chaotic maps modelling neuronal activity, under variation of coupling strength epsilon and degree of randomness in coupling p. We find that at high coupling strengths (epsilon>epsilonfixed) the unstable saddle point solution of the local chaotic maps gets stabilized. The range of coupling where this spatiotemporal fixed point gains stability is unchanged in the presence of randomness in the connections, namely epsilonfixed is invariant under changes in p. As coupling gets weaker (epsilon0) one obtains spatial synchronization in the network. We find that this range of synchronized chaos increases exponentially with the fraction of random links in the network. Further, in the space of fixed coupling strengths, the synchronization transition occurs at a finite value of p, a scenario quite distinct from the many examples of synchronization transitions at p-->0. Further we show that the synchronization here is robust in the presence of parametric noise, namely in a network of nonidentical neuronal maps. Finally we check the generality of our observations in networks of neurons displaying both spiking and bursting dynamics.