An atlas of combinatorial transcriptional regulation in mouse and man.

Combinatorial interactions among transcription factors are critical to directing tissue-specific gene expression. To build a global atlas of these combinations, we have screened for physical interactions among the majority of human and mouse DNA-binding transcription factors (TFs). The complete networks contain 762 human and 877 mouse interactions. Analysis of the networks reveals that highly connected TFs are broadly expressed across tissues, and that roughly half of the measured interactions are conserved between mouse and human. The data highlight the importance of TF combinations for determining cell fate, and they lead to the identification of a SMAD3/FLI1 complex expressed during development of immunity. The availability of large TF combinatorial networks in both human and mouse will provide many opportunities to study gene regulation, tissue differentiation, and mammalian evolution.

Functional annotation of human long noncoding RNAs via molecular phenotyping.

Long noncoding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes, and yet, their functions remain largely unknown. As part of the FANTOM6 project, we systematically knocked down the expression of 285 lncRNAs in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNAs exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest-to-date lncRNA knockdown data set with molecular phenotyping (over 1000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2.

Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory.

The bipartite network representation of the drug-target interactions (DTIs) in a biosystem enhances understanding of the drugs' multifaceted action modes, suggests therapeutic switching for approved drugs and unveils possible side effects. As experimental testing of DTIs is costly and time-consuming, computational predictors are of great aid. Here, for the first time, state-of-the-art DTI supervised predictors custom-made in network biology were compared-using standard and innovative validation frameworks-with unsupervised pure topological-based models designed for general-purpose link prediction in bipartite networks. Surprisingly, our results show that the bipartite topology alone, if adequately exploited by means of the recently proposed local-community-paradigm (LCP) theory-initially detected in brain-network topological self-organization and afterwards generalized to any complex network-is able to suggest highly reliable predictions, with comparable performance with the state-of-the-art-supervised methods that exploit additional (non-topological, for instance biochemical) DTI knowledge. Furthermore, a detailed analysis of the novel predictions revealed that each class of methods prioritizes distinct true interactions; hence, combining methodologies based on diverse principles represents a promising strategy to improve drug-target discovery. To conclude, this study promotes the power of bio-inspired computing, demonstrating that simple unsupervised rules inspired by principles of topological self-organization and adaptiveness arising during learning in living intelligent systems (like the brain) can efficiently equal perform complicated algorithms based on advanced, supervised and knowledge-based engineering.

Controlling distinct signaling states in cultured cancer cells provides a new platform for drug discovery.

Cancer cells can switch between signaling pathways to regulate growth under different conditions. In the tumor microenvironment, this likely helps them evade therapies that target specific pathways. We must identify all possible states and utilize them in drug screening programs. One such state is characterized by expression of the transcription factor Hairy and Enhancer of Split 3 (HES3) and sensitivity to HES3 knockdown, and it can be modeled in vitro. Here, we cultured 3 primary human brain cancer cell lines under 3 different culture conditions that maintain low, medium, and high HES3 expression and characterized gene regulation and mechanical phenotype in these states. We assessed gene expression regulation following HES3 knockdown in the HES3-high conditions. We then employed a commonly used human brain tumor cell line to screen Food and Drug Administration (FDA)-approved compounds that specifically target the HES3-high state. We report that cells from multiple patients behave similarly when placed under distinct culture conditions. We identified 37 FDA-approved compounds that specifically kill cancer cells in the high-HES3-expression conditions. Our work reveals a novel signaling state in cancer, biomarkers, a strategy to identify treatments against it, and a set of putative drugs for potential repurposing.-Poser, S. W., Otto, O., Arps-Forker, C., Ge, Y., Herbig, M., Andree, C., Gruetzmann, K., Adasme, M. F., Stodolak, S., Nikolakopoulou, P., Park, D. M., Mcintyre, A., Lesche, M., Dahl, A., Lennig, P., Bornstein, S. R., Schroeck, E., Klink, B., Leker, R. R., Bickle, M., Chrousos, G. P., Schroeder, M., Cannistraci, C. V., Guck, J., Androutsellis-Theotokis, A. Controlling distinct signaling states in cultured cancer cells provides a new platform for drug discovery.

Effects of Proton Pump Inhibitors on the Gastric Mucosa-Associated Microbiota in Dyspeptic Patients.

Besides being part of anti-Helicobacter pylori treatment regimens, proton pump inhibitors (PPIs) are increasingly being used to treat dyspepsia. However, little is known about the effects of PPIs on the human gastric microbiota, especially those related to H. pylori infection. The goal of this study was to characterize the stomach microbial communities in patients with dyspepsia and to investigate their relationships with PPI use and H. pylori status. Using 16S rRNA gene pyrosequencing, we analyzed the mucosa-associated microbial populations of 24 patients, of whom 12 were treated with the PPI omeprazole and 9 (5 treated and 4 untreated) were positive for H. pylori infection. The Proteobacteria, Firmicutes, Bacteroidetes, Fusobacteria, and Actinobacteria phyla accounted for 98% of all of the sequences, with Helicobacter, Streptococcus, and Prevotella ranking among the 10 most abundant genera. H. pylori infection or PPI treatment did not significantly influence gastric microbial species composition in dyspeptic patients. Principal-coordinate analysis of weighted UniFrac distances in these communities revealed clear but significant separation according to H. pylori status only. However, in PPI-treated patients, Firmicutes, particularly Streptococcaceae, were significantly increased in relative abundance compared to those in untreated patients. Consistently, Streptococcus was also found to significantly increase in relation to PPI treatment, and this increase seemed to occur independently of H. pylori infection. Our results suggest that Streptococcus may be a key indicator of PPI-induced gastric microbial composition changes in dyspeptic patients. Whether the gastric microbiota alteration contributes to dyspepsia needs further investigation. IMPORTANCE: Although PPIs have become a popular treatment choice, a growing number of dyspeptic patients may be treated unnecessarily. We found that patients treated with omeprazole showed gastric microbial communities that were different from those of untreated patients. These differences regarded the abundances of specific taxa. By understanding the relationships between PPIs and members of the gastric microbiota, it will be possible to envisage new strategies for better managing patients with dyspepsia.

Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding.

MOTIVATION: Most functions within the cell emerge thanks to protein-protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable. METHODS: Network embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions. Furthermore, we introduce (i) a new valid variation of MCE, named non-centred MCE (ncMCE); (ii) two automatic strategies for selecting the appropriate embedding dimension; and (iii) two new randomized procedures for evaluating predictions. RESULTS: We compared our method against several unsupervised and supervisedly tuned embedding approaches and node neighbourhood techniques. Despite its computational simplicity, ncMCE-SP was the overall leader, outperforming the current methods in topological link prediction. CONCLUSION: Minimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs. The rationale for our approach is that biological and evolutionary information is imprinted in the non-linear patterns hidden behind the protein network topology, and can be exploited for predicting new protein links. The predicted PPIs represent good candidates for testing in high-throughput experiments or for exploitation in systems biology tools such as those used for network-based inference and prediction of disease-related functional modules. AVAILABILITY: https://sites.google.com/site/carlovittoriocannistraci/home. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Exploitation of genetic interaction network topology for the prediction of epistatic behavior.

Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks. We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks. Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab.

"Stealing fire or stacking knowledge" by machine intelligence to model link prediction in complex networks.

Current methodologies to model connectivity in complex networks either rely on network scientists' intelligence to discover reliable physical rules or use artificial intelligence (AI) that stacks hundreds of inaccurate human-made rules to make a new one that optimally summarizes them together. Here, we provide an accurate and reproducible scientific analysis showing that, contrary to the current belief, stacking more good link prediction rules does not necessarily improve the link prediction performance to nearly optimal as suggested by recent studies. Finally, under the light of our novel results, we discuss the pros and cons of each current state-of-the-art link prediction strategy, concluding that none of the current solutions are what the future might hold for us. Future solutions might require the design and development of next generation "creative" AI that are able to generate and understand complex physical rules for us.

Spatial Reconstruction of Oligo and Single Cells by De Novo Coalescent Embedding of Transcriptomic Networks.

Single cell RNA-seq (scRNA-seq) profiles conceal temporal and spatial tissue developmental information. De novo reconstruction of single cell temporal trajectory has been fairly addressed, but reverse engineering single cell 3D spatial tissue organization is hitherto landmark based, and de novo spatial reconstruction is a compelling computational open problem. Here it is shown that a proposed algorithm for de novo coalescent embedding (D-CE) of oligo/single cell transcriptomic networks can help to address this problem. Relying on the spatial information encoded in the expression patterns of genes, it is found that D-CE of cell-cell association transcriptomic networks, by preserving mesoscale network organization, captures spatial domains, identifies spatially expressed genes, reconstructs cell samples' 3D spatial distribution, and uncovers spatial domains and markers necessary for understanding the design principles on spatial organization and pattern formation. Comparison to the novoSpaRC and CSOmap (the only available de novo 3D spatial reconstruction methods) on 14 datasets and 497 reconstructions, reveals a significantly superior performance of D-CE.

Geometrical congruence, greedy navigability and myopic transfer in complex networks and brain connectomes.

We introduce in network geometry a measure of geometrical congruence (GC) to evaluate the extent a network topology follows an underlying geometry. This requires finding all topological shortest-paths for each nonadjacent node pair in the network: a nontrivial computational task. Hence, we propose an optimized algorithm that reduces 26 years of worst scenario computation to one week parallel computing. Analysing artificial networks with patent geometry we discover that, different from current belief, hyperbolic networks do not show in general high GC and efficient greedy navigability (GN) with respect to the geodesics. The myopic transfer which rules GN works best only when degree-distribution power-law exponent is strictly close to two. Analysing real networks-whose geometry is often latent-GC overcomes GN as marker to differentiate phenotypical states in macroscale structural-MRI brain connectomes, suggesting connectomes might have a latent neurobiological geometry accounting for more information than the visible tridimensional Euclidean.

Salt-Specific Gene Expression Reveals Elevated Auxin Levels in Arabidopsis thaliana Plants Grown Under Saline Conditions.

Soil salinization is increasing globally, driving a reduction in crop yields that threatens food security. Salinity stress reduces plant growth by exerting two stresses on plants: rapid shoot ion-independent effects which are largely osmotic and delayed ionic effects that are specific to salinity stress. In this study we set out to delineate the osmotic from the ionic effects of salinity stress. Arabidopsis thaliana plants were germinated and grown for two weeks in media supplemented with 50, 75, 100, or 125 mM NaCl (that imposes both an ionic and osmotic stress) or iso-osmolar concentrations (100, 150, 200, or 250 mM) of sorbitol, that imposes only an osmotic stress. A subsequent transcriptional analysis was performed to identify sets of genes that are differentially expressed in plants grown in (1) NaCl or (2) sorbitol compared to controls. A comparison of the gene sets identified genes that are differentially expressed under both challenge conditions (osmotic genes) and genes that are only differentially expressed in plants grown on NaCl (ionic genes, hereafter referred to as salt-specific genes). A pathway analysis of the osmotic and salt-specific gene lists revealed that distinct biological processes are modulated during growth under the two conditions. The list of salt-specific genes was enriched in the gene ontology (GO) term "response to auxin." Quantification of the predominant auxin, indole-3-acetic acid (IAA) and IAA biosynthetic intermediates revealed that IAA levels are elevated in a salt-specific manner through increased IAA biosynthesis. Furthermore, the expression of NITRILASE 2 (NIT2), which hydrolyses indole-3-acetonitile (IAN) into IAA, increased in a salt-specific manner. Overexpression of NIT2 resulted in increased IAA levels, improved Na:K ratios and enhanced survival and growth of Arabidopsis under saline conditions. Overall, our data suggest that auxin is involved in maintaining growth during the ionic stress imposed by saline conditions.

Energy-sparing by 2-methyl-2-thiazoline protects heart from ischaemia/reperfusion injury.

AIMS: Cardiac ischaemia/reperfusion (I/R) injury remains a critical issue in the therapeutic management of ischaemic heart failure. Although mild hypothermia has a protective effect on cardiac I/R injury, more rapid and safe methods that can obtain similar results to hypothermia therapy are required. 2-Methyl-2-thiazoline (2MT), an innate fear inducer, causes mild hypothermia resulting in resistance to critical hypoxia in cutaneous or cerebral I/R injury. The aim of this study is to demonstrate the protective effect of systemically administered 2MT on cardiac I/R injury and to elucidate the mechanism underlying this effect. METHODS AND RESULTS: A single subcutaneous injection of 2MT (50 mg/kg) was given prior to reperfusion of the I/R injured 10 week-old male mouse heart and its efficacy was evaluated 24 h after the ligation of the left anterior descending coronary artery. 2MT preserved left ventricular systolic function following I/R injury (ejection fraction, %: control 37.9 ± 6.7, 2MT 54.1 ± 6.4, P < 0.01). 2MT also decreased infarct size (infarct size/ischaemic area at risk, %: control 48.3 ± 12.1, 2MT 25.6 ± 4.2, P < 0.05) and serum cardiac troponin levels (ng/mL: control 8.9 ± 1.1, 2MT 1.9 ± 0.1, P < 0.01) after I/R. Moreover, 2MT reduced the oxidative stress-exposed area within the heart (%: control 25.3 ± 4.7, 2MT 10.8 ± 1.4, P < 0.01). These results were supported by microarray analysis of the mouse hearts. 2MT induced a transient, mild decrease in core body temperature (°C: -2.4 ± 1.4), which gradually recovered over several hours. Metabolome analysis of the mouse hearts suggested that 2MT minimized energy metabolism towards suppressing oxidative stress. Furthermore, 18F-fluorodeoxyglucose-positron emission tomography/computed tomography imaging revealed that 2MT reduced the activity of brown adipose tissue (standardized uptake value: control 24.3 ± 6.4, 2MT 18.4 ± 5.8, P < 0.05). 2MT also inhibited mitochondrial respiration and glycolysis in rat cardiomyoblasts. CONCLUSIONS: We identified the cardioprotective effect of systemically administered 2MT on cardiac I/R injury by sparing energy metabolism with reversible hypothermia. Our results highlight the potential of drug-induced hypothermia therapy as an adjunct to coronary intervention in severe ischaemic heart disease.

Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes.

MOTIVATION: Nonlinear small datasets, which are characterized by low numbers of samples and very high numbers of measures, occur frequently in computational biology, and pose problems in their investigation. Unsupervised hybrid-two-phase (H2P) procedures-specifically dimension reduction (DR), coupled with clustering-provide valuable assistance, not only for unsupervised data classification, but also for visualization of the patterns hidden in high-dimensional feature space. METHODS: 'Minimum Curvilinearity' (MC) is a principle that-for small datasets-suggests the approximation of curvilinear sample distances in the feature space by pair-wise distances over their minimum spanning tree (MST), and thus avoids the introduction of any tuning parameter. MC is used to design two novel forms of nonlinear machine learning (NML): Minimum Curvilinear embedding (MCE) for DR, and Minimum Curvilinear affinity propagation (MCAP) for clustering. RESULTS: Compared with several other unsupervised and supervised algorithms, MCE and MCAP, whether individually or combined in H2P, overcome the limits of classical approaches. High performance was attained in the visualization and classification of: (i) pain patients (proteomic measurements) in peripheral neuropathy; (ii) human organ tissues (genomic transcription factor measurements) on the basis of their embryological origin. CONCLUSION: MC provides a valuable framework to estimate nonlinear distances in small datasets. Its extension to large datasets is prefigured for novel NMLs. Classification of neuropathic pain by proteomic profiles offers new insights for future molecular and systems biology characterization of pain. Improvements in tissue embryological classification refine results obtained in an earlier study, and suggest a possible reinterpretation of skin attribution as mesodermal. AVAILABILITY: https://sites.google.com/site/carlovittoriocannistraci/home.

Age-sex population adjusted analysis of disease severity in epidemics as a tool to devise public health policies for COVID-19.

Governments continue to update social intervention strategies to contain COVID-19 infections. However, investigation of COVID-19 severity indicators across the population might help to design more precise strategies, balancing the need to keep people safe and to reduce the socio-economic burden of generalized restriction precedures. Here, we propose a method for age-sex population-adjusted analysis of disease severity in epidemics that has the advantage to use simple and repeatable variables, which are daily or weekly available. This allows to monitor the effect of public health policies in short term, and to repeat these calculations over time to surveille epidemic dynamics and impact. Our method can help to define a risk-categorization of likeliness to develop a severe COVID-19 disease which requires intensive care or is indicative of a higher risk of dying. Indeed, analysis of suitable open-access COVID-19 data in three European countries indicates that individuals in the 0-40 age interval and females under 60 are significantly less likely to develop a severe condition and die, whereas males equal or above 60 are more likely at risk of severe disease and death. Hence, a combination of age-adaptive and sex-balanced guidelines for social interventions could represent key public health management tools for policymakers.

Cell Mechanics Based Computational Classification of Red Blood Cells Via Machine Intelligence Applied to Morpho-Rheological Markers.

Despite fluorescent cell-labelling being widely employed in biomedical studies, some of its drawbacks are inevitable, with unsuitable fluorescent probes or probes inducing a functional change being the main limitations. Consequently, the demand for and development of label-free methodologies to classify cells is strong and its impact on precision medicine is relevant. Towards this end, high-throughput techniques for cell mechanical phenotyping have been proposed to get a multidimensional biophysical characterization of single cells. With this motivation, our goal here is to investigate the extent to which an unsupervised machine learning methodology, which is applied exclusively on morpho-rheological markers obtained by real-time deformability and fluorescence cytometry (RT-FDC), can address the difficult task of providing label-free discrimination of reticulocytes from mature red blood cells. We focused on this problem, since the characterization of reticulocytes (their percentage and cellular features) in the blood is vital in multiple human disease conditions, especially bone-marrow disorders such as anemia and leukemia. Our approach reports promising label-free results in the classification of reticulocytes from mature red blood cells, and it represents a step forward in the development of high-throughput morpho-rheological-based methodologies for the computational categorization of single cells. Besides, our methodology can be an alternative but also a complementary method to integrate with existing cell-labelling techniques.

Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome.

The stomach is inhabited by diverse microbial communities, co-existing in a dynamic balance. Long-term use of drugs such as proton pump inhibitors (PPIs), or bacterial infection such as Helicobacter pylori, cause significant microbial alterations. Yet, studies revealing how the commensal bacteria re-organize, due to these perturbations of the gastric environment, are in early phase and rely principally on linear techniques for multivariate analysis. Here we disclose the importance of complementing linear dimensionality reduction techniques with nonlinear ones to unveil hidden patterns that remain unseen by linear embedding. Then, we prove the advantages to complete multivariate pattern analysis with differential network analysis, to reveal mechanisms of bacterial network re-organizations which emerge from perturbations induced by a medical treatment (PPIs) or an infectious state (H. pylori). Finally, we show how to build bacteria-metabolite multilayer networks that can deepen our understanding of the metabolite pathways significantly associated to the perturbed microbial communities.

Modular gateway-ness connectivity and structural core organization in maritime network science.

Around 80% of global trade by volume is transported by sea, and thus the maritime transportation system is fundamental to the world economy. To better exploit new international shipping routes, we need to understand the current ones and their complex systems association with international trade. We investigate the structure of the global liner shipping network (GLSN), finding it is an economic small-world network with a trade-off between high transportation efficiency and low wiring cost. To enhance understanding of this trade-off, we examine the modular segregation of the GLSN; we study provincial-, connector-hub ports and propose the definition of gateway-hub ports, using three respective structural measures. The gateway-hub structural-core organization seems a salient property of the GLSN, which proves importantly associated to network integration and function in realizing the cargo transportation of international trade. This finding offers new insights into the GLSN's structural organization complexity and its relevance to international trade.

Use of Steroid Profiling Combined With Machine Learning for Identification and Subtype Classification in Primary Aldosteronism.

Importance: Most patients with primary aldosteronism, a major cause of secondary hypertension, are not identified or appropriately treated because of difficulties in diagnosis and subtype classification. Applications of artificial intelligence combined with mass spectrometry-based steroid profiling could address this problem. Objective: To assess whether plasma steroid profiling combined with machine learning might facilitate diagnosis and treatment stratification of primary aldosteronism, particularly for patients with unilateral adenomas due to pathogenic KCNJ5 sequence variants. Design, Setting, and Participants: This diagnostic study was conducted at multiple tertiary care referral centers. Steroid profiles were measured from June 2013 to March 2017 in 462 patients tested for primary aldosteronism and 201 patients with hypertension. Data analyses were performed from September 2018 to August 2019. Main Outcomes and Measures: The aldosterone to renin ratio and saline infusion tests were used to diagnose primary aldosteronism. Subtyping was done by adrenal venous sampling and follow-up of patients who underwent adrenalectomy. Statistical tests and machine-learning algorithms were applied to plasma steroid profiles. Areas under receiver operating characteristic curves, sensitivity, specificity, and other diagnostic performance measures were calculated. Results: Primary aldosteronism was confirmed in 273 patients (165 men [60%]; mean [SD] age, 51 [10] years), including 134 with bilateral disease and 139 with unilateral adenomas (58 with and 81 without somatic KCNJ5 sequence variants). Plasma steroid profiles varied according to disease subtype and were particularly distinctive in patients with adenomas due to KCNJ5 variants, who showed better rates of biochemical cure after adrenalectomy than other patients. Among patients tested for primary aldosteronism, a selection of 8 steroids in combination with the aldosterone to renin ratio showed improved effectiveness for diagnosis over either strategy alone. In contrast, the steroid profile alone showed superior performance over the aldosterone to renin ratio for identifying unilateral disease, particularly adenomas due to KCNJ5 variants. Among 632 patients included in the analysis, machine learning-designed combinatorial marker profiles of 7 steroids alone both predicted primary aldosteronism in 1 step and subtyped patients with unilateral adenomas due to KCNJ5 variants at diagnostic sensitivities of 69% (95% CI, 68%-71%) and 85% (95% CI, 81%-88%), respectively, and at specificities of 94% (95% CI, 93%-94%) and 97% (95% CI, 97%-98%), respectively. The validation series yielded comparable diagnostic performance. Conclusions and Relevance: Machine learning-designed combinatorial plasma steroid profiles may facilitate both screening for primary aldosteronism and identification of patients with unilateral adenomas due to pathogenic KCNJ5 variants, who are most likely to show benefit from surgical intervention.

Systems Network Genomic Analysis Reveals Cardioprotective Effect of MURC/Cavin-4 Deletion Against Ischemia/Reperfusion Injury.

Background Ischemia/reperfusion (I/R) injury is a critical issue in the development of treatment strategies for ischemic heart disease. MURC (muscle-restricted coiled-coil protein)/Cavin-4 (caveolae-associated protein 4), which is a component of caveolae, is involved in the pathophysiology of dilated cardiomyopathy and cardiac hypertrophy. However, the role of MURC in cardiac I/R injury remains unknown. Methods and Results The systems network genomic analysis based on PC-corr network inference on microarray data between wild-type and MURC knockout mouse hearts predicted a network of discriminating genes associated with reactive oxygen species. To demonstrate the prediction, we analyzed I/R-injured mouse hearts. MURC deletion decreased infarct size and preserved heart contraction with reactive oxygen species-related molecule EGR1 (early growth response protein 1) and DDIT4 (DNA-damage-inducible transcript 4) suppression in I/R-injured hearts. Because PC-corr network inference integrated with a protein-protein interaction network prediction also showed that MURC is involved in the apoptotic pathway, we confirmed the upregulation of STAT3 (signal transducer and activator of transcription 3) and BCL2 (B-cell lymphoma 2) and the inactivation of caspase 3 in I/R-injured hearts of MURC knockout mice compared with those of wild-type mice. STAT3 inhibitor canceled the cardioprotective effect of MURC deletion in I/R-injured hearts. In cardiomyocytes exposed to hydrogen peroxide, MURC overexpression promoted apoptosis and MURC knockdown inhibited apoptosis. STAT3 inhibitor canceled the antiapoptotic effect of MURC knockdown in cardiomyocytes. Conclusions Our findings, obtained by prediction from systems network genomic analysis followed by experimental validation, suggested that MURC modulates cardiac I/R injury through the regulation of reactive oxygen species-induced cell death and STAT3-meditated antiapoptosis. Functional inhibition of MURC may be effective in reducing cardiac I/R injury.