Intracellular proteomics and extracellular vesiculomics as a metric of disease recapitulation in 3D-bioprinted aortic valve arrays.

In calcific aortic valve disease (CAVD), mechanosensitive valvular cells respond to fibrosis- and calcification-induced tissue stiffening, further driving pathophysiology. No pharmacotherapeutics are available to treat CAVD because of the paucity of (i) appropriate experimental models that recapitulate this complex environment and (ii) benchmarking novel engineered aortic valve (AV)-model performance. We established a biomaterial-based CAVD model mimicking the biomechanics of the human AV disease-prone fibrosa layer, three-dimensional (3D)-bioprinted into 96-well arrays. Liquid chromatography-tandem mass spectrometry analyses probed the cellular proteome and vesiculome to compare the 3D-bioprinted model versus traditional 2D monoculture, against human CAVD tissue. The 3D-bioprinted model highly recapitulated the CAVD cellular proteome (94% versus 70% of 2D proteins). Integration of cellular and vesicular datasets identified known and unknown proteins ubiquitous to AV calcification. This study explores how 2D versus 3D-bioengineered systems recapitulate unique aspects of human disease, positions multiomics as a technique for the evaluation of high throughput-based bioengineered model systems, and potentiates future drug discovery.

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.

Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases.

Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.

ETV2 regulates PARP-1 binding protein to induce ER stress-mediated death in tuberin-deficient cells.

Lymphangioleiomyomatosis (LAM) is a rare progressive disease, characterized by mutations in the tuberous sclerosis complex genes (TSC1 or TSC2) and hyperactivation of mechanistic target of rapamycin complex 1 (mTORC1). Here, we report that E26 transformation-specific (ETS) variant transcription factor 2 (ETV2) is a critical regulator of Tsc2-deficient cell survival. ETV2 nuclear localization in Tsc2-deficient cells is mTORC1-independent and is enhanced by spleen tyrosine kinase (Syk) inhibition. In the nucleus, ETV2 transcriptionally regulates poly(ADP-ribose) polymerase 1 binding protein (PARPBP) mRNA and protein expression, partially reversing the observed down-regulation of PARPBP expression induced by mTORC1 blockade during treatment with both Syk and mTORC1 inhibitors. In addition, silencing Etv2 or Parpbp in Tsc2-deficient cells induced ER stress and increased cell death in vitro and in vivo. We also found ETV2 expression in human cells with loss of heterozygosity for TSC2, lending support to the translational relevance of our findings. In conclusion, we report a novel ETV2 signaling axis unique to Syk inhibition that promotes a cytocidal response in Tsc2-deficient cells and therefore maybe a potential alternative therapeutic target in LAM.

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.

GRAND: a database of gene regulatory network models across human conditions.

Gene regulation plays a fundamental role in shaping tissue identity, function, and response to perturbation. Regulatory processes are controlled by complex networks of interacting elements, including transcription factors, miRNAs and their target genes. The structure of these networks helps to determine phenotypes and can ultimately influence the development of disease or response to therapy. We developed GRAND (https://grand.networkmedicine.org) as a database for computationally-inferred, context-specific gene regulatory network models that can be compared between biological states, or used to predict which drugs produce changes in regulatory network structure. The database includes 12 468 genome-scale networks covering 36 human tissues, 28 cancers, 1378 unperturbed cell lines, as well as 173 013 TF and gene targeting scores for 2858 small molecule-induced cell line perturbation paired with phenotypic information. GRAND allows the networks to be queried using phenotypic information and visualized using a variety of interactive tools. In addition, it includes a web application that matches disease states to potentially therapeutic small molecule drugs using regulatory network properties.

Constructing gene regulatory networks using epigenetic data.

The biological processes that drive cellular function can be represented by a complex network of interactions between regulators (transcription factors) and their targets (genes). A cell's epigenetic state plays an important role in mediating these interactions, primarily by influencing chromatin accessibility. However, how to effectively use epigenetic data when constructing a gene regulatory network remains an open question. Almost all existing network reconstruction approaches focus on estimating transcription factor to gene connections using transcriptomic data. In contrast, computational approaches for analyzing epigenetic data generally focus on improving transcription factor binding site predictions rather than deducing regulatory network relationships. We bridged this gap by developing SPIDER, a network reconstruction approach that incorporates epigenetic data into a message-passing framework to estimate gene regulatory networks. We validated SPIDER's predictions using ChIP-seq data from ENCODE and found that SPIDER networks are both highly accurate and include cell-line-specific regulatory interactions. Notably, SPIDER can recover ChIP-seq verified transcription factor binding events in the regulatory regions of genes that do not have a corresponding sequence motif. The networks estimated by SPIDER have the potential to identify novel hypotheses that will allow us to better characterize cell-type and phenotype specific regulatory mechanisms.

Low dose environmental radon exposure and breast tumor gene expression.

BACKGROUND: The International Agency for Research on Cancer classified radon and its decay-products as Group-1-human-carcinogens, and with the current knowledge they are linked specifically to lung cancer. Biokinetic models predict that radon could deliver a carcinogenic dose to breast tissue. Our previous work suggested that low-dose radon was associated with estrogen-receptor (ER)-negative breast cancer risk. However, there is limited research to examine the role of radon in breast cancer biology at the tissue level. We aim to understand molecular pathways linking radon exposure with breast cancer biology using transcriptome-wide-gene-expression from breast tumor and normal-adjacent tissues. METHODS: Our study included 943 women diagnosed with breast cancer from the Nurses' Health Study (NHS) and NHSII. We estimated cumulative radon concentration for each participant up-to the year of breast cancer diagnosis by linking residential addresses with a radon exposure model. Transcriptome-wide-gene-expression was measured with the Affymetrix-Glue-Human-Transcriptome-Array-3.0 and Human-Transcriptome-Array-2.0. We performed covariate-adjusted linear-regression for individual genes and further employed pathway-analysis. All analyses were conducted separately for tumor and normal-adjacent samples and by ER-status. RESULTS: No individual gene was associated with cumulative radon exposure in ER-positive tumor, ER-negative tumor, or ER-negative normal-adjacent tissues at FDR < 5%. In ER-positive normal-adjacent samples, PLCH2-reached transcriptome-wide-significance (FDR < 5%). Gene-set-enrichment-analyses identified 2-upregulated pathways (MAPK signaling and phosphocholine biosynthesis) enriched at FDR < 25% in ER-negative tumors and normal-adjacent tissues, and both pathways have been previously reported to play key roles in ionizing radiation induced tumorigenesis in experimental settings. CONCLUSION: Our findings provide insights into the molecular pathways of radon exposure that may influence breast cancer etiology.

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.

Using network science tools to identify novel diet patterns in prodromal dementia.

OBJECTIVE: To use network science to model complex diet relationships a decade before onset of dementia in a large French cohort, the 3-City Bordeaux study. METHODS: We identified cases of dementia incident to the baseline food frequency questionnaire over 12 years of follow-up. For each case, we randomly selected 2 controls among individuals at risk at the age at case diagnosis and matched for age at diet assessment, sex, education, and season of the survey. We inferred food networks in both cases and controls using mutual information, a measure to detect nonlinear associations, and compared food consumption patterns between groups. RESULTS: In the nested case-control study, the mean (SD) duration of follow-up and number of visits were 5.0 (2.5) vs 4.9 (2.6) years and 4.1 (1.0) vs 4.4 (0.9) for cases (n = 209) vs controls (n = 418), respectively. While there were few differences in simple, average food intakes, food networks differed substantially between cases and controls. The network in cases was focused and characterized by charcuterie as the main hub, with connections to foods typical of French southwestern diet and snack foods. In contrast, the network of controls included several disconnected subnetworks reflecting diverse and healthier food choices. CONCLUSION: How foods are consumed (and not only the quantity consumed) may be important for dementia prevention. Differences in predementia diet networks, suggesting worse eating habits toward charcuterie and snacking, were evident years before diagnosis in this cohort. Network methods, which are designed to model complex systems, may advance our understanding of risk factors for dementia.

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 .

Understanding Tissue-Specific Gene Regulation.

Although all human tissues carry out common processes, tissues are distinguished by gene expression patterns, implying that distinct regulatory programs control tissue specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that the regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.