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.

"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.

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.

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-1ß (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.

Prevalence, Characteristics, and Outcomes of COVID-19-Associated Acute Myocarditis.

BACKGROUND: Acute myocarditis (AM) is thought to be a rare cardiovascular complication of COVID-19, although minimal data are available beyond case reports. We aim to report the prevalence, baseline characteristics, in-hospital management, and outcomes for patients with COVID-19-associated AM on the basis of a retrospective cohort from 23 hospitals in the United States and Europe. METHODS: A total of 112 patients with suspected AM from 56 963 hospitalized patients with COVID-19 were evaluated between February 1, 2020, and April 30, 2021. Inclusion criteria were hospitalization for COVID-19 and a diagnosis of AM on the basis of endomyocardial biopsy or increased troponin level plus typical signs of AM on cardiac magnetic resonance imaging. We identified 97 patients with possible AM, and among them, 54 patients with definite/probable AM supported by endomyocardial biopsy in 17 (31.5%) patients or magnetic resonance imaging in 50 (92.6%). We analyzed patient characteristics, treatments, and outcomes among all COVID-19-associated AM. RESULTS: AM prevalence among hospitalized patients with COVID-19 was 2.4 per 1000 hospitalizations considering definite/probable and 4.1 per 1000 considering also possible AM. The median age of definite/probable cases was 38 years, and 38.9% were female. On admission, chest pain and dyspnea were the most frequent symptoms (55.5% and 53.7%, respectively). Thirty-one cases (57.4%) occurred in the absence of COVID-19-associated pneumonia. Twenty-one (38.9%) had a fulminant presentation requiring inotropic support or temporary mechanical circulatory support. The composite of in-hospital mortality or temporary mechanical circulatory support occurred in 20.4%. At 120 days, estimated mortality was 6.6%, 15.1% in patients with associated pneumonia versus 0% in patients without pneumonia (P=0.044). During hospitalization, left ventricular ejection fraction, assessed by echocardiography, improved from a median of 40% on admission to 55% at discharge (n=47; P<0.0001) similarly in patients with or without pneumonia. Corticosteroids were frequently administered (55.5%). CONCLUSIONS: AM occurrence is estimated between 2.4 and 4.1 out of 1000 patients hospitalized for COVID-19. The majority of AM occurs in the absence of pneumonia and is often complicated by hemodynamic instability. AM is a rare complication in patients hospitalized for COVID-19, with an outcome that differs on the basis of the presence of concomitant pneumonia.

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.

Medial prefrontal and occipito-temporal activity at encoding determines enhanced recognition of threatening faces after 1.5 years.

Studies demonstrated that faces with threatening emotional expressions are better remembered than non-threatening faces. However, whether this memory advantage persists over years and which neural systems underlie such an effect remains unknown. Here, we employed an individual difference approach to examine whether the neural activity during incidental encoding was associated with differential recognition of faces with emotional expressions (angry, fearful, happy, sad and neutral) after a retention interval of > 1.5 years (N = 89). Behaviorally, we found a better recognition for threatening (angry, fearful) versus non-threatening (happy and neutral) faces after a delay of > 1.5 years, which was driven by forgetting of non-threatening faces compared with immediate recognition after encoding. Multivariate principal component analysis (PCA) on the behavioral responses further confirmed the discriminative recognition performance between threatening and non-threatening faces. A voxel-wise whole-brain analysis on the concomitantly acquired functional magnetic resonance imaging (fMRI) data during incidental encoding revealed that neural activity in bilateral inferior occipital gyrus (IOG) and ventromedial prefrontal/orbitofrontal cortex (vmPFC/OFC) was associated with the individual differences in the discriminative emotional face recognition performance measured by an innovative behavioral pattern similarity analysis (BPSA). The left fusiform face area (FFA) was additionally determined using a regionally focused analysis. Overall, the present study provides evidence that threatening facial expressions lead to persistent face recognition over periods of > 1.5 years, and that differential encoding-related activity in the medial prefrontal cortex and occipito-temporal cortex may underlie this effect.

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.

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.

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.

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.

Three-dimensional facial-image analysis to predict heterogeneity of the human ageing rate and the impact of lifestyle.

Not all individuals age at the same rate. Methods such as the 'methylation clock' are invasive, rely on expensive assays of tissue samples and infer the ageing rate by training on chronological age, which is used as a reference for prediction errors. Here, we develop models based on convoluted neural networks through training on non-invasive three-dimensional (3D) facial images of approximately 5,000 Han Chinese individuals that achieve an average difference between chronological or perceived age and predicted age of ±2.8 and 2.9 yr, respectively. We further profile blood transcriptomes from 280 individuals and infer the molecular regulators mediating the impact of lifestyle on the facial-ageing rate through a causal-inference model. These relationships have been deposited and visualized in the Human Blood Gene Expression-3D Facial Image (HuB-Fi) database. Overall, we find that humans age at different rates both in the blood and in the face, but do so coherently and with heterogeneity peaking at middle age. Our study provides an example of how artificial intelligence can be leveraged to determine the perceived age of humans as a marker of biological age, while no longer relying on prediction errors of chronological age, and to estimate the heterogeneity of ageing rates within a population.

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.

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.

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.

Machine learning of human plasma lipidomes for obesity estimation in a large population cohort.

Obesity is associated with changes in the plasma lipids. Although simple lipid quantification is routinely used, plasma lipids are rarely investigated at the level of individual molecules. We aimed at predicting different measures of obesity based on the plasma lipidome in a large population cohort using advanced machine learning modeling. A total of 1,061 participants of the FINRISK 2012 population cohort were randomly chosen, and the levels of 183 plasma lipid species were measured in a novel mass spectrometric shotgun approach. Multiple machine intelligence models were trained to predict obesity estimates, i.e., body mass index (BMI), waist circumference (WC), waist-hip ratio (WHR), and body fat percentage (BFP), and validated in 250 randomly chosen participants of the Malmö Diet and Cancer Cardiovascular Cohort (MDC-CC). Comparison of the different models revealed that the lipidome predicted BFP the best (R2 = 0.73), based on a Lasso model. In this model, the strongest positive and the strongest negative predictor were sphingomyelin molecules, which differ by only 1 double bond, implying the involvement of an unknown desaturase in obesity-related aberrations of lipid metabolism. Moreover, we used this regression to probe the clinically relevant information contained in the plasma lipidome and found that the plasma lipidome also contains information about body fat distribution, because WHR (R2 = 0.65) was predicted more accurately than BMI (R2 = 0.47). These modeling results required full resolution of the lipidome to lipid species level, and the predicting set of biomarkers had to be sufficiently large. The power of the lipidomics association was demonstrated by the finding that the addition of routine clinical laboratory variables, e.g., high-density lipoprotein (HDL)- or low-density lipoprotein (LDL)- cholesterol did not improve the model further. Correlation analyses of the individual lipid species, controlled for age and separated by sex, underscores the multiparametric and lipid species-specific nature of the correlation with the BFP. Lipidomic measurements in combination with machine intelligence modeling contain rich information about body fat amount and distribution beyond traditional clinical assays.

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.

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.

Functional Brain Network Topology Discriminates between Patients with Minimally Conscious State and Unresponsive Wakefulness Syndrome.

Consciousness arises from the functional interaction of multiple brain structures and their ability to integrate different complex patterns of internal communication. Although several studies demonstrated that the fronto-parietal and functional default mode networks play a key role in conscious processes, it is still not clear which topological network measures (that quantifies different features of whole-brain functional network organization) are altered in patients with disorders of consciousness. Herein, we investigate the functional connectivity of unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) patients from a topological network perspective, by using resting-state EEG recording. Network-based statistical analysis reveals a subnetwork of decreased functional connectivity in UWS compared to in the MCS patients, mainly involving the interhemispheric fronto-parietal connectivity patterns. Network topological analysis reveals increased values of local-community-paradigm correlation, as well as higher clustering coefficient and local efficiency in UWS patients compared to in MCS patients. At the nodal level, the UWS patients showed altered functional topology in several limbic and temporo-parieto-occipital regions. Taken together, our results highlight (i) the involvement of the interhemispheric fronto-parietal functional connectivity in the pathophysiology of consciousness disorders and (ii) an aberrant connectome organization both at the network topology level and at the nodal level in UWS patients compared to in the MCS patients.