A diseasome cluster-based drug repurposing of soluble guanylate cyclase activators from smooth muscle relaxation to direct neuroprotection

Soluble GC stimulators and activators: Past, present and future

The discovery of soluble GC (sGC) stimulators and sGC activators provided valuable tools to elucidate NO-sGC signalling and opened novel pharmacological opportunities for cardiovascular indications and beyond. The first-in-class sGC stimulator riociguat was approved for pulmonary hypertension in 2013 and vericiguat very recently for heart failure. sGC stimulators enhance sGC activity independent of NO and also act synergistically with endogenous NO. The sGC activators specifically bind to, and activate, the oxidised haem-free form of sGC. Substantial research efforts improved on the first-generation sGC activators such as cinaciguat, culminating in the discovery of runcaciguat, currently in clinical Phase II trials for chronic kidney disease and diabetic retinopathy. Here, we highlight the discovery and development of sGC stimulators and sGC activators, their unique modes of action, their preclinical characteristics and the clinical studies. In the future, we expect to see more sGC agonists in new indications, reflecting their unique therapeutic potential.

Current and future directions in network biology

Summary

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

Availability and implementation

Not applicable.

Inhaled nitric oxide suppresses neuroinflammation in experimental ischemic stroke

Ischemic stroke is a major global health issue and characterized by acute vascular dysfunction and subsequent neuroinflammation. However, the relationship between these processes remains elusive. In the current study, we investigated whether alleviating vascular dysfunction by restoring vascular nitric oxide (NO) reduces post-stroke inflammation. Mice were subjected to experimental stroke and received inhaled NO (iNO; 50 ppm) after reperfusion. iNO normalized vascular cyclic guanosine monophosphate (cGMP) levels, reduced the elevated expression of intercellular adhesion molecule-1 (ICAM-1), and returned leukocyte adhesion to baseline levels. Reduction of vascular pathology significantly reduced the inflammatory cytokines interleukin-1β (Il-1β), interleukin-6 (Il-6), and tumor necrosis factor-α (TNF-α), within the brain parenchyma. These findings suggest that vascular dysfunction is responsible for leukocyte adhesion and that these processes drive parenchymal inflammation. Reversing vascular dysfunction may therefore emerge as a novel approach to diminish neuroinflammation after ischemic stroke and possibly other ischemic disorders.

Promising Cerebral Blood Flow Enhancers in Acute Ischemic Stroke

Ischemic stroke presents a major global economic and public health burden. Although recent advances in available endovascular therapies show improved functional outcome, a good number of stroke patients are either ineligible or do not have access to these treatments. Also, robust collateral flow during acute ischemic stroke independently predicts the success of endovascular therapies and the outcome of stroke. Hence, adjunctive therapies for cerebral blood flow (CBF) enhancement are urgently needed. A very clear overview of the pial collaterals and the role of genetics are presented in this review. We review available evidence and advancement for potential therapies aimed at improving CBF during acute ischemic stroke. We identified heme-free soluble guanylate cyclase activators; Sanguinate, remote ischemic perconditioning; Fasudil, S1P agonists; and stimulation of the sphenopalatine ganglion as promising potential CBF-enhancing therapeutics requiring further investigation. Additionally, we outline and discuss the critical steps required to advance research strategies for clinically translatable CBF-enhancing agents in the context of acute ischemic stroke models.

Soluble guanylyl cyclase: A novel target for the treatment of vascular cognitive impairment?

Vascular cognitive impairment (VCI) describes neurodegenerative disorders characterized by a vascular component. Pathologically, it involves decreased cerebral blood flow (CBF), white matter lesions, endothelial dysfunction, and blood-brain barrier (BBB) impairments. Molecularly, oxidative stress and inflammation are two of the major underlying mechanisms. Nitric oxide (NO) physiologically stimulates soluble guanylate cyclase (sGC) to induce cGMP production. However, under pathological conditions, NO seems to be at the basis of oxidative stress and inflammation, leading to a decrease in sGC activity and expression. The native form of sGC needs a ferrous heme group bound in order to be sensitive to NO (Fe(II)sGC). Oxidation of sGC leads to the conversion of ferrous to ferric heme (Fe(III)sGC) and even heme-loss (apo-sGC). Both Fe(III)sGC and apo-sGC are insensitive to NO, and the enzyme is therefore inactive. sGC activity can be enhanced either by targeting the NO-sensitive native sGC (Fe(II)sGC), or the inactive, oxidized sGC (Fe(III)sGC) and the heme-free apo-sGC. For this purpose, sGC stimulators acting on Fe(II)sGC and sGC activators acting on Fe(III)sGC/apo-sGC have been developed. These sGC agonists have shown their efficacy in cardiovascular diseases by restoring the physiological and protective functions of the NO-sGC-cGMP pathway, including the reduction of oxidative stress and inflammation, and improvement of vascular functioning. Yet, only very little research has been performed within the cerebrovascular system and VCI pathology when focusing on sGC modulation and its potential protective mechanisms on vascular and neural function. Therefore, within this review, the potential of sGC as a target for treating VCI is highlighted.

Targeting Soluble Guanylyl Cyclase during Ischemia and Reperfusion

Ischemia and reperfusion (IR) damage organs and contribute to many disease states. Few effective treatments exist that attenuate IR injury. The augmentation of nitric oxide (NO) signaling remains a promising therapeutic target for IR injury. NO binds to soluble guanylyl cyclase (sGC) to regulate vasodilation, maintain endothelial barrier integrity, and modulate inflammation through the production of cyclic-GMP in vascular smooth muscle. Pharmacologic sGC stimulators and activators have recently been developed. In preclinical studies, sGC stimulators, which augment the reduced form of sGC, and activators, which activate the oxidized non-NO binding form of sGC, increase vasodilation and decrease cardiac, cerebral, renal, pulmonary, and hepatic injury following IR. These effects may be a result of the improved regulation of perfusion and decreased oxidative injury during IR. sGC stimulators are now used clinically to treat some chronic conditions such as heart failure and pulmonary hypertension. Clinical trials of sGC activators have been terminated secondary to adverse side effects including hypotension. Additional clinical studies to investigate the effects of sGC stimulation and activation during acute conditions, such as IR, are warranted.

Molecular Biomarker Identification Using a Network-Based Bioinformatics Approach That Links COVID-19 With Smoking

The COVID-19 coronavirus, which primarily affects the lungs, is the source of the disease known as SARS-CoV-2. According to "Smoking and COVID-19: a scoping review," about 32% of smokers had a severe case of COVID-19 pneumonia at their admission time and 15% of non-smokers had this case of COVID-19 pneumonia. We were able to determine which genes were expressed differently in each group by comparing the expression of gene transcriptomic datasets of COVID-19 patients, smokers, and healthy controls. In all, 37 dysregulated genes are common in COVID-19 patients and smokers, according to our analysis. We have applied all important methods namely protein-protein interaction, hub-protein interaction, drug-protein interaction, tf-gene interaction, and gene-MiRNA interaction of bioinformatics to analyze to understand deeply the connection between both smoking and COVID-19 severity. We have also analyzed Pathways and Gene Ontology where 5 significant signaling pathways were validated with previous literature. Also, we verified 7 hub-proteins, and finally, we validated a total of 7 drugs with the previous study.

The soluble guanylyl cyclase pathway is inhibited to evade androgen deprivation-induced senescence and enable progression to castration resistance

Castration-resistant prostate cancer (CRPC) is fatal and therapeutically under-served. We describe a novel CRPC-restraining role for the vasodilatory soluble guanylyl cyclase (sGC) pathway. We discovered that sGC subunits are dysregulated during CRPC progression and its catalytic product, cyclic GMP (cGMP), is lowered in CRPC patients. Abrogating sGC heterodimer formation in castration-sensitive prostate cancer (CSPC) cells inhibited androgen deprivation (AD)-induced senescence, and promoted castration-resistant tumor growth. We found sGC is oxidatively inactivated in CRPC. Paradoxically, AD restored sGC activity in CRPC cells through redox-protective responses evoked to protect against AD-induced oxidative stress. sGC stimulation via its FDA-approved agonist, riociguat, inhibited castration-resistant growth, and the anti-tumor response correlated with elevated cGMP, indicating on-target sGC activity. Consistent with known sGC function, riociguat improved tumor oxygenation, decreasing the PC stem cell marker, CD44, and enhancing radiation-induced tumor suppression. Our studies thus provide the first evidence for therapeutically targeting sGC via riociguat to treat CRPC.

Statement of significance

Prostate cancer is the second highest cancer-related cause of death for American men. Once patients progress to castration-resistant prostate cancer, the incurable and fatal stage, there are few viable treatment options available. Here we identify and characterize a new and clinically actionable target, the soluble guanylyl cyclase complex, in castration-resistant prostate cancer. Notably we find that repurposing the FDA-approved and safely tolerated sGC agonist, riociguat, decreases castration-resistant tumor growth and re-sensitizes these tumors to radiation therapy. Thus our study provides both new biology regarding the origins of castration resistance as well as a new and viable treatment option.

Lacking mechanistic disease definitions and corresponding association data hamper progress in network medicine and beyond

A long-term objective of network medicine is to replace our current, mainly phenotype-based disease definitions by subtypes of health conditions corresponding to distinct pathomechanisms. For this, molecular and health data are modeled as networks and are mined for pathomechanisms. However, many such studies rely on large-scale disease association data where diseases are annotated using the very phenotype-based disease definitions the network medicine field aims to overcome. This raises the question to which extent the biases mechanistically inadequate disease annotations introduce in disease association data distort the results of studies which use such data for pathomechanism mining. We address this question using global- and local-scale analyses of networks constructed from disease association data of various types. Our results indicate that large-scale disease association data should be used with care for pathomechanism mining and that analyses of such data should be accompanied by close-up analyses of molecular data for well-characterized patient cohorts.

Re-Addressing Dementia by Network Medicine and Mechanism-Based Molecular Endotypes

Alzheimer's disease (AD) and other forms of dementia are together a leading cause of disability and death in the aging global population, imposing a high personal, societal, and economic burden. They are also among the most prominent examples of failed drug developments. Indeed, after more than 40 AD trials of anti-amyloid interventions, reduction of amyloid-β (Aβ) has never translated into clinically relevant benefits, and in several cases yielded harm. The fundamental problem is the century-old, brain-centric phenotype-based definitions of diseases that ignore causal mechanisms and comorbidities. In this hypothesis article, we discuss how such current outdated nosology of dementia is a key roadblock to precision medicine and articulate how Network Medicine enables the substitution of clinicopathologic phenotypes with molecular endotypes and propose a new framework to achieve precision and curative medicine for patients with neurodegenerative disorders.

Computational systems biology in disease modeling and control, review and perspectives

Omics-based approaches have become increasingly influential in identifying disease mechanisms and drug responses. Considering that diseases and drug responses are co-expressed and regulated in the relevant omics data interactions, the traditional way of grabbing omics data from single isolated layers cannot always obtain valuable inference. Also, drugs have adverse effects that may impair patients, and launching new medicines for diseases is costly. To resolve the above difficulties, systems biology is applied to predict potential molecular interactions by integrating omics data from genomic, proteomic, transcriptional, and metabolic layers. Combined with known drug reactions, the resulting models improve medicines' therapeutical performance by re-purposing the existing drugs and combining drug molecules without off-target effects. Based on the identified computational models, drug administration control laws are designed to balance toxicity and efficacy. This review introduces biomedical applications and analyses of interactions among gene, protein and drug molecules for modeling disease mechanisms and drug responses. The therapeutical performance can be improved by combining the predictive and computational models with drug administration designed by control laws. The challenges are also discussed for its clinical uses in this work.

Cyclic GMP modulating drugs in cardiovascular diseases: mechanism-based network pharmacology

Mechanism-based therapy centred on the molecular understanding of disease-causing pathways in a given patient is still the exception rather than the rule in medicine, even in cardiology. However, recent successful drug developments centred around the second messenger cyclic guanosine-3'-5'-monophosphate (cGMP), which is regulating a number of cardiovascular disease modulating pathways, are about to provide novel targets for such a personalized cardiovascular therapy. Whether cGMP breakdown is inhibited or cGMP synthesis is stimulated via guanylyl cyclases or their upstream regulators in different cardiovascular disease phenotypes, the outcomes seem to be so far uniformly protective. Thus, a network of cGMP-modulating drugs has evolved that act in a mechanism-based, possibly causal manner in a number of cardiac conditions. What remains a challenge is the detection of cGMPopathy endotypes amongst cardiovascular disease phenotypes. Here, we review the growing clinical relevance of cGMP and provide a glimpse into the future on how drugs interfering with this pathway may change how we treat and diagnose cardiovascular diseases altogether.

Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders

Despite advances in pharmacology and neuroscience, the path to new medications for psychiatric disorders largely remains stagnated. Drug repurposing offers a more efficient pathway compared with de novo drug discovery with lower cost and less risk. Various computational approaches have been applied to mine the vast amount of biomedical data generated over recent decades. Among these methods, network-based drug repurposing stands out as a potent tool for the comprehension of multiple domains of knowledge considering the interactions or associations of various factors. Aligned well with the poly-pharmacology paradigm shift in drug discovery, network-based approaches offer great opportunities to discover repurposing candidates for complex psychiatric disorders. In this review, we present the potential of network-based drug repurposing in psychiatry focusing on the incentives for using network-centric repurposing, major network-based repurposing strategies and data resources, applications in psychiatry and challenges of network-based drug repurposing. This review aims to provide readers with an update on network-based drug repurposing in psychiatry. We expect the repurposing approach to become a pivotal tool in the coming years to battle debilitating psychiatric disorders.

Online in silico validation of disease and gene sets, clusterings or subnetworks with DIGEST

As the development of new drugs reaches its physical and financial limits, drug repurposing has become more important than ever. For mechanistically grounded drug repurposing, it is crucial to uncover the disease mechanisms and to detect clusters of mechanistically related diseases. Various methods for computing candidate disease mechanisms and disease clusters exist. However, in the absence of ground truth, in silico validation is challenging. This constitutes a major hurdle toward the adoption of in silico prediction tools by experimentalists who are often hesitant to carry out wet-lab validations for predicted candidate mechanisms without clearly quantified initial plausibility. To address this problem, we present DIGEST (in silico validation of disease and gene sets, clusterings or subnetworks), a Python-based validation tool available as a web interface (https://digest-validation.net), as a stand-alone package or over a REST API. DIGEST greatly facilitates in silico validation of gene and disease sets, clusterings or subnetworks via fully automated pipelines comprising disease and gene ID mapping, enrichment analysis, comparisons of shared genes and variants and background distribution estimation. Moreover, functionality is provided to automatically update the external databases used by the pipelines. DIGEST hence allows the user to assess the statistical significance of candidate mechanisms with regard to functional and genetic coherence and enables the computation of empirical $P$-values with just a few mouse clicks.

The sGC stimulator BAY-747 and activator runcaciguat can enhance memory in vivo via differential hippocampal plasticity mechanisms

Soluble guanylate cyclase (sGC) requires a heme-group bound in order to produce cGMP, a second messenger involved in memory formation, while heme-free sGC is inactive. Two compound classes can increase sGC activity: sGC stimulators acting on heme-bound sGC, and sGC activators acting on heme-free sGC. In this rodent study, we investigated the potential of the novel brain-penetrant sGC stimulator BAY-747 and sGC activator runcaciguat to enhance long-term memory and attenuate short-term memory deficits induced by the NOS-inhibitor L-NAME. Furthermore, hippocampal plasticity mechanisms were investigated. In vivo, oral administration of BAY-747 and runcaciguat to male Wistar rats enhanced memory acquisition in the object location task (OLT), while only BAY-747 reversed L-NAME induced memory impairments in the OLT. Ex vivo, both BAY-747 and runcaciguat enhanced hippocampal GluA1-containing AMPA receptor (AMPAR) trafficking in a chemical LTP model for memory acquisition using acute mouse hippocampal slices. In vivo only runcaciguat acted on the glutamatergic AMPAR system in hippocampal memory acquisition processes, while for BAY-747 the effects on the neurotrophic system were more pronounced as measured in male mice using western blot. Altogether this study shows that sGC stimulators and activators have potential as cognition enhancers, while the underlying plasticity mechanisms may determine disease-specific effectiveness.

Ceruletide and Alpha-1 Antitrypsin as a Novel Combination Therapy for Ischemic Stroke

Ischemic stroke is a primary cause of morbidity and mortality worldwide. Beyond the approved thrombolytic therapies, there is no effective treatment to mitigate its progression. Drug repositioning combinational therapies are becoming promising approaches to identify new uses of existing drugs to synergically target multiple disease-response mechanisms underlying complex pathologies. Here, we used a systems biology-based approach based on artificial intelligence and pattern recognition tools to generate in silico mathematical models mimicking the ischemic stroke pathology. Combinational treatments were acquired by screening these models with more than 5 million two-by-two combinations of drugs. A drug combination (CA) formed by ceruletide and alpha-1 antitrypsin showing a predicted value of neuroprotection of 92% was evaluated for their synergic neuroprotective effects in a mouse pre-clinical stroke model. The administration of both drugs in combination was safe and effective in reducing by 39.42% the infarct volume 24 h after cerebral ischemia. This neuroprotection was not observed when drugs were given individually. Importantly, potential incompatibilities of the drug combination with tPA thrombolysis were discarded in vitro and in vivo by using a mouse thromboembolic stroke model with t-PA-induced reperfusion, revealing an improvement in the forepaw strength 72 h after stroke in CA-treated mice. Finally, we identified the predicted mechanisms of action of ceruletide and alpha-1 antitrypsin and we demonstrated that CA modulates EGFR and ANGPT-1 levels in circulation within the acute phase after stroke. In conclusion, we have identified a promising combinational treatment with neuroprotective effects for the treatment of ischemic stroke.

Efficient link prediction in the protein–protein interaction network using topological information in a generative adversarial network machine learning model

Background

The investigation of possible interactions between two proteins in intracellular signaling is an expensive and laborious procedure in the wet-lab, therefore, several in silico approaches have been implemented to narrow down the candidates for future experimental validations. Reformulating the problem in the field of network theory, the set of proteins can be represented as the nodes of a network, while the interactions between them as the edges. The resulting protein–protein interaction (PPI) network enables the use of link prediction techniques in order to discover new probable connections. Therefore, here we aimed to offer a novel approach to the link prediction task in PPI networks, utilizing a generative machine learning model.

Results

We created a tool that consists of two modules, the data processing framework and the machine learning model. As data processing, we used a modified breadth-first search algorithm to traverse the network and extract induced subgraphs, which served as image-like input data for our model. As machine learning, an image-to-image translation inspired conditional generative adversarial network (cGAN) model utilizing Wasserstein distance-based loss improved with gradient penalty was used, taking the combined representation from the data processing as input, and training the generator to predict the probable unknown edges in the provided induced subgraphs. Our link prediction tool was evaluated on the protein–protein interaction networks of five different species from the STRING database by calculating the area under the receiver operating characteristic, the precision-recall curves and the normalized discounted cumulative gain (AUROC, AUPRC, NDCG, respectively). Test runs yielded the averaged results of AUROC = 0.915, AUPRC = 0.176 and NDCG = 0.763 on all investigated species.

Conclusion

We developed a software for the purpose of link prediction in PPI networks utilizing machine learning. The evaluation of our software serves as the first demonstration that a cGAN model, conditioned on raw topological features of the PPI network, is an applicable solution for the PPI prediction problem without requiring often unavailable molecular node attributes. The corresponding scripts are available at https://github.com/semmelweis-pharmacology/ppi_pred.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04598-x.

GPCRs Are Optimal Regulators of Complex Biological Systems and Orchestrate the Interface between Health and Disease

GPCRs arguably represent the most effective current therapeutic targets for a plethora of diseases. GPCRs also possess a pivotal role in the regulation of the physiological balance between healthy and pathological conditions; thus, their importance in systems biology cannot be underestimated. The molecular diversity of GPCR signaling systems is likely to be closely associated with disease-associated changes in organismal tissue complexity and compartmentalization, thus enabling a nuanced GPCR-based capacity to interdict multiple disease pathomechanisms at a systemic level. GPCRs have been long considered as controllers of communication between tissues and cells. This communication involves the ligand-mediated control of cell surface receptors that then direct their stimuli to impact cell physiology. Given the tremendous success of GPCRs as therapeutic targets, considerable focus has been placed on the ability of these therapeutics to modulate diseases by acting at cell surface receptors. In the past decade, however, attention has focused upon how stable multiprotein GPCR superstructures, termed receptorsomes, both at the cell surface membrane and in the intracellular domain dictate and condition long-term GPCR activities associated with the regulation of protein expression patterns, cellular stress responses and DNA integrity management. The ability of these receptorsomes (often in the absence of typical cell surface ligands) to control complex cellular activities implicates them as key controllers of the functional balance between health and disease. A greater understanding of this function of GPCRs is likely to significantly augment our ability to further employ these proteins in a multitude of diseases.

Network pharmacology: curing causal mechanisms instead of treating symptoms

For complex diseases, most drugs are highly ineffective, and the success rate of drug discovery is in constant decline. While low quality, reproducibility issues, and translational irrelevance of most basic and preclinical research have contributed to this, the current organ-centricity of medicine and the 'one disease-one target-one drug' dogma obstruct innovation in the most profound manner. Systems and network medicine and their therapeutic arm, network pharmacology, revolutionize how we define, diagnose, treat, and, ideally, cure diseases. Descriptive disease phenotypes are replaced by endotypes defined by causal, multitarget signaling modules that also explain respective comorbidities. Precise and effective therapeutic intervention is achieved by synergistic multicompound network pharmacology and drug repurposing, obviating the need for drug discovery and speeding up clinical translation.

Drug Repurposing Using Modularity Clustering in Drug-Drug Similarity Networks Based on Drug-Gene Interactions

Drug repurposing is a valuable alternative to traditional drug design based on the assumption that medicines have multiple functions. Computer-based techniques use ever-growing drug databases to uncover new drug repurposing hints, which require further validation with in vitro and in vivo experiments. Indeed, such a scientific undertaking can be particularly effective in the case of rare diseases (resources for developing new drugs are scarce) and new diseases such as COVID-19 (designing new drugs require too much time). This paper introduces a new, completely automated computational drug repurposing pipeline based on drug-gene interaction data. We obtained drug-gene interaction data from an earlier version of DrugBank, built a drug-gene interaction network, and projected it as a drug-drug similarity network (DDSN). We then clustered DDSN by optimizing modularity resolution, used the ATC codes distribution within each cluster to identify potential drug repurposing candidates, and verified repurposing hints with the latest DrugBank ATC codes. Finally, using the best modularity resolution found with our method, we applied our pipeline to the latest DrugBank drug-gene interaction data to generate a comprehensive drug repurposing hint list.

Perspectives on Drug Repurposing

Complex common diseases are a significant burden for our societies and demand not only preventive measures but also more effective, safer, and more affordable treatments. The whole process of the current model of drug discovery and development implies a high investment by the pharmaceutical industry, which ultimately impact in high drug prices. In this sense, drug repurposing would help meet the needs of patients to access useful and novel treatments. Unlike the traditional approach, drug repurposing enters both the preclinical evaluation and clinical trials of the compound of interest faster, budgeting research and development costs, and limiting potential biosafety risks. The participation of government, society, and private investors is needed to secure the funds for experimental design and clinical development of repurposing candidates to have affordable, effective, and safe repurposed drugs. Moreover, extensive advertising of repurposing as a concept in the health community, could reduce prescribing bias when enough clinical evidence exists, which will support the employment of cheaper and more accessible repurposed compounds for common conditions.

Network Medicine-Based Unbiased Disease Modules for Drug and Diagnostic Target Identification in ROSopathies

Most diseases are defined by a symptom, not a mechanism. Consequently, therapies remain symptomatic. In reverse, many potential disease mechanisms remain in arbitrary search for clinical relevance. Reactive oxygen species (ROS) are such an example. It is an attractive hypothesis that dysregulation of ROS can become a disease trigger. Indeed, elevated ROS levels of various biomarkers have been correlated with almost every disease, yet after decades of research without any therapeutic application. We here present a first systematic, non-hypothesis-based approach to transform this field as a proof of concept for biomedical research in general. We selected as seed proteins 9 families with 42 members of clinically researched ROS-generating enzymes, ROS-metabolizing enzymes or ROS targets. Applying an unbiased network medicine approach, their first neighbours were connected, and, based on a stringent subnet participation degree (SPD) of 0.4, hub nodes excluded. This resulted in 12 distinct human interactome-based ROS signalling modules, while 8 proteins remaining unconnected. This ROSome is in sharp contrast to commonly used highly curated and integrated KEGG, HMDB or WikiPathways. These latter serve more as mind maps of possible ROS signalling events but may lack important interactions and often do not take different cellular and subcellular localization into account. Moreover, novel non-ROS-related proteins were part of these forming functional hybrids, such as the NOX5/sGC, NOX1,2/NOS2, NRF2/ENC-1 and MPO/SP-A modules. Thus, ROS sources are not interchangeable but associated with distinct disease processes or not at all. Module members represent leads for precision diagnostics to stratify patients with specific ROSopathies for precision intervention. The upper panel shows the classical approach to generate hypotheses for a role of ROS in a given disease by focusing on ROS levels and to some degree the ROS type or metabolite. Low levels are considered physiological; higher amounts are thought to cause a redox imbalance, oxidative stress and eventually disease. The source of ROS is less relevant; there is also ROS-induced ROS formation, i.e. by secondary sources (see upwards arrow). The non-hypothesis-based network medicine approach uses genetically or otherwise validated risk genes to construct disease-relevant signalling modules, which will contain also ROS targets. Not all ROS sources will be relevant for a given disease; some may not be disease relevant at all. The three examples show (from left to right) the disease-relevant appearance of an unphysiological ROS modifier/toxifier protein, ROS target or ROS source.

Redirecting drug repositioning to discover innovative cosmeceuticals

Skin appearance is essential for self-esteem and quality of life; consequently, skin care products represent a huge market. In particular, cosmeceuticals constitute a hybrid category of skin care formulations, at the interphase of cosmetics and pharmaceuticals, rationally designed to target (patho) physiological mechanisms aiming to enhance skin health and appearance. Cosmeceuticals are marketed as anti-ageing, anti-wrinkle, hair regrowth, skin whitening and wound healing agents with special emphasis on scar-free healing. An overview on recent cutting-edge advances concerning the discovery and development of enhanced performance cosmeceuticals by drug repositioning approaches is presented here. In this context, we propose "target repositioning," a new term, to highlight that druggable protein targets implicated in multiple diseases (hubs in the diseasome) can be exploited to accelerate the discovery of molecularly targeted cosmeceuticals that can promote skin health as an added benefit, which is a novel concept not described before. In this direction, emphasis is placed on the role of mouse models, for often untreatable skin diseases, as well as recent breakthroughs on monogenic rare skin syndromes, in promoting compound repositioning to innovative cosmeceuticals.

An inherent dysfunction in soluble guanylyl cyclase is present in the airway of severe asthmatics and is associated with aberrant redox enzyme expression and compromised NO-cGMP signaling

A subset of asthmatics develop a severe form of the disease whose etiology involves airway inflammation along with inherent drivers that remain ill-defined. To address this, we studied human airway smooth muscle cells (HASMC), whose relaxation drives airway bronchodilation and whose dysfunction contributes to airway obstruction and hypersensitivity in severe asthma. Because HASMC relaxation can be driven by the NO-soluble guanylyl cyclase (sGC)-cGMP signaling pathway, we questioned if HASMC from severe asthma donors might possess inherent defects in their sGC or in redox enzymes that support sGC function. We analyzed HASMC primary lines derived from 17 severe asthma and 16 normal donors and corresponding lung tissue samples regarding sGC activation by NO or by pharmacologic agonists, and also determined expression levels of sGC α1 and β1 subunits, supporting redox enzymes, and related proteins. We found a majority of the severe asthma donor HASMC (12/17) and lung samples primarily expressed a dysfunctional sGC that was NO-unresponsive and had low heterodimer content and high Hsp90 association. This sGC phenotype correlated with lower expression levels of the supporting redox enzymes cytochrome b5 reductase, catalase, and thioredoxin-1, and higher expression of heme oxygenases 1 and 2. Together, our work reveals that severe asthmatics are predisposed toward defective NO-sGC-cGMP signaling in their airway smooth muscle due to an inherent sGC dysfunction, which in turn is associated with inherent changes in the cell redox enzymes that impact sGC maturation and function.

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The etiology of severe asthma involves airway inflammation and inherent drivers that remain ill-defined.

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Airway smooth muscle cells of severe asthmatics display a NO-unresponsive and dysfunctional sGC which persists in culture.

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Their inherent sGC dysfunction is associated with low CYB5R3 expression and altered expression of other redox enzymes.

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That airway sGC dysfunction and redox enzyme changes cluster within severe asthma is unexpected and may help guide therapy.

Bone marrow-derived myeloid progenitors in the leptomeninges of adult mice

Although the bone marrow contains most hematopoietic activity during adulthood, hematopoietic stem and progenitor cells can be recovered from various extramedullary sites. Cells with hematopoietic progenitor properties have even been reported in the adult brain under steady-state conditions, but their nature and localization remain insufficiently defined. Here, we describe a heterogeneous population of myeloid progenitors in the leptomeninges of adult C57BL/6 mice. This cell pool included common myeloid, granulocyte/macrophage, and megakaryocyte/erythrocyte progenitors. Accordingly, it gave rise to all major myelo-erythroid lineages in clonogenic culture assays. Brain-associated progenitors persisted after tissue perfusion and were partially inaccessible to intravenous antibodies, suggesting their localization behind continuous blood vessel endothelium such as the blood-arachnoid barrier. Flt3^Cre lineage tracing and bone marrow transplantation showed that the precursors were derived from adult hematopoietic stem cells and were most likely continuously replaced via cell trafficking. Importantly, their occurrence was tied to the immunologic state of the central nervous system (CNS) and was diminished in the context of neuroinflammation and ischemic stroke. Our findings confirm the presence of myeloid progenitors at the meningeal border of the brain and lay the foundation to unravel their possible functions in CNS surveillance and local immune cell production.

Integrating Phenotypic Search and Phosphoproteomic Profiling of Active Kinases for Optimization of Drug Mixtures for RCC Treatment

Combined application of multiple therapeutic agents presents the possibility of enhanced efficacy and reduced development of resistance. Definition of the most appropriate combination for any given disease phenotype is challenged by the vast number of theoretically possible combinations of drugs and doses, making extensive empirical testing a virtually impossible task. We have used the streamlined-feedback system control (s-FSC) technique, a phenotypic approach, which converges to optimized drug combinations (ODC) within a few experimental steps. Phosphoproteomics analysis coupled to kinase activity analysis using the novel INKA (integrative inferred kinase activity) pipeline was performed to evaluate ODC mechanisms in a panel of renal cell carcinoma (RCC) cell lines. We identified different ODC with up to 95% effectivity for each RCC cell line, with low doses (ED_5-25) of individual drugs. Global phosphoproteomics analysis demonstrated inhibition of relevant kinases, and targeting remaining active kinases with additional compounds improved efficacy. In addition, we identified a common RCC ODC, based on kinase activity data, to be effective in all RCC cell lines under study. Combining s-FSC with a phosphoproteomic profiling approach provides valuable insight in targetable kinase activity and allows for the identification of superior drug combinations for the treatment of RCC.

Non-canonical chemical feedback self-limits nitric oxide-cyclic GMP signaling in health and disease

Nitric oxide (NO)-cyclic GMP (cGMP) signaling is a vasoprotective pathway therapeutically targeted, for example, in pulmonary hypertension. Its dysregulation in disease is incompletely understood. Here we show in pulmonary artery endothelial cells that feedback inhibition by NO of the NO receptor, the cGMP forming soluble guanylate cyclase (sGC), may contribute to this. Both endogenous NO from endothelial NO synthase and exogenous NO from NO donor compounds decreased sGC protein and activity. This effect was not mediated by cGMP as the NO-independent sGC stimulator, or direct activation of cGMP-dependent protein kinase did not mimic it. Thiol-sensitive mechanisms were also not involved as the thiol-reducing agent N-acetyl-L-cysteine did not prevent this feedback. Instead, both in-vitro and in-vivo and in health and acute respiratory lung disease, chronically elevated NO led to the inactivation and degradation of sGC while leaving the heme-free isoform, apo-sGC, intact or even increasing its levels. Thus, NO regulates sGC in a bimodal manner, acutely stimulating and chronically inhibiting, as part of self-limiting direct feedback that is cGMP independent. In high NO disease conditions, this is aggravated but can be functionally recovered in a mechanism-based manner by apo-sGC activators that re-establish cGMP formation.

A new computational drug repurposing method using established disease-drug pair knowledge

MOTIVATION

Drug repurposing is a potential alternative to the classical drug discovery pipeline. Repurposing involves finding novel indications for already approved drugs. In this work, we present a novel machine learning-based method for drug repurposing. This method explores the anti-similarity between drugs and a disease to uncover new uses for the drugs. More specifically, our proposed method takes into account three sources of information: (i) large-scale gene expression profiles corresponding to human cell lines treated with small molecules, (ii) gene expression profile of a human disease and (iii) the known relationship between Food and Drug Administration (FDA)-approved drugs and diseases. Using these data, our proposed method learns a similarity metric through a supervised machine learning-based algorithm such that a disease and its associated FDA-approved drugs have smaller distance than the other disease-drug pairs.

RESULTS

We validated our framework by showing that the proposed method incorporating distance metric learning technique can retrieve FDA-approved drugs for their approved indications. Once validated, we used our approach to identify a few strong candidates for repurposing.

AVAILABILITY AND IMPLEMENTATION

The R scripts are available on demand from the authors.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Molecular networks in Network Medicine: Development and applications

Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.

Soluble Guanylate Cyclase Agonists Induce Bronchodilation in Human Small Airways

The soluble guanylyl cyclase (sGC)-cyclic guanosine monophosphate signaling pathway evokes vascular smooth muscle relaxation; whether this pathway mediates airway smooth muscle relaxation remains controversial. We posit that sGC activators are equi-effective as β-agonists in reversing contractile agonist-induced airway smooth muscle shortening. To provide clarity, we tested the efficacy of sGC stimulator and activator drugs, BAY 41-2272 and BAY 60-2270, respectively, in reversing bronchoconstriction of human small airways using human precision-cut lung slices (hPCLS). Both BAY drugs reversed carbachol-induced bronchoconstriction to a maximal degree comparable to that of formoterol. Moreover, the sGC drugs remained effective bronchodilators despite formoterol-induced desensitization of the airways. Analysis of the hPCLS after their activation by sGC or β2-adrenergic receptor agonist showed distinct cyclic nucleotide accumulation in the hPCLS. Collectively, these data suggest that cAMP and cyclic guanosine monophosphate pathways are equi-effective for reversing carbachol-induced bronchoconstriction in the human airway via separate and distinct second messenger pathways. This should open the door for future studies to test whether sGC-targeted drugs alone or in combination can serve as effective bronchodilators in asthma and chronic obstructive pulmonary disease.

Drug repurposing for cancer therapy, easier said than done

Drug repurposing for cancer therapy is currently a hot topic of research. Theoretically, in contrast to the known hurdles of developing new molecular entities, the approach of repurposing has several advantages. Mostly, it is said that it is faster, safer, easier, and cheaper. In the real world, however, there are only three repurposed drugs so far, that are listed in widely recognized cancer guidelines, but a large number of them are being studied. Among the many barriers to repurposing cancer drugs, economical-driven are the most important that difficult the clinical development of them. In this review, we provide an overview of the current status of drug repurposing for cancer therapy and the barriers that need to be overcome to realize the benefit of this approach. It means to have repositioned drugs for cancer therapy accepted as standard therapy for cancer indications at low cost.

cGMP-dependent protein kinase I in vascular smooth muscle cells improves ischemic stroke outcome in mice

Recent works highlight the therapeutic potential of targeting cyclic guanosine monophosphate (cGMP)-dependent pathways in the context of brain ischemia/reperfusion injury (IRI). Although cGMP-dependent protein kinase I (cGKI) has emerged as a key mediator of the protective effects of nitric oxide (NO) and cGMP, the mechanisms by which cGKI attenuates IRI remain poorly understood. We used a novel, conditional cGKI knockout mouse model to study its role in cerebral IRI. We assessed neurological deficit, infarct volume, and cerebral perfusion in tamoxifen-inducible vascular smooth muscle cell-specific cGKI knockout mice and control animals. Stroke experiments revealed greater cerebral infarct volume in smooth muscle cell specific cGKI knockout mice (males: 96 ± 16 mm3; females: 93 ± 12 mm3, mean±SD) than in all control groups: wild type (males: 66 ± 19; females: 64 ± 14), cGKI control (males: 65 ± 18; females: 62 ± 14), cGKI control with tamoxifen (males: 70 ± 8; females: 68 ± 10). Our results identify, for the first time, a protective role of cGKI in vascular smooth muscle cells during ischemic stroke injury. Moreover, this protective effect of cGKI was found to be independent of gender and was mediated via improved reperfusion. These results suggest that cGKI in vascular smooth muscle cells should be targeted by therapies designed to protect brain tissue against ischemic stroke.

Reactive Oxygen Comes of Age: Mechanism-Based Therapy of Diabetic End-Organ Damage

Reactive oxygen species (ROS) have been mainly viewed as unwanted by-products of cellular metabolism, oxidative stress, a sign of a cellular redox imbalance, and potential disease mechanisms, such as in diabetes mellitus (DM). Antioxidant therapies, however, have failed to provide clinical benefit. This paradox can be explained by recent discoveries that ROS have mainly essential signaling and metabolic functions and evolutionally conserved physiological enzymatic sources. Disease can occur when ROS accumulate in nonphysiological concentrations, locations, or forms. By focusing on disease-relevant sources and targets of ROS, and leaving ROS physiology intact, precise therapeutic interventions are now possible and are entering clinical trials. Their outcomes are likely to profoundly change our concepts of ROS in DM and in medicine in general.

Drug repurposing in oncology: Compounds, pathways, phenotypes and computational approaches for colorectal cancer

The strategy of using existing drugs originally developed for one disease to treat other indications has found success across medical fields. Such drug repurposing promises faster access of drugs to patients while reducing costs in the long and difficult process of drug development. However, the number of existing drugs and diseases, together with the heterogeneity of patients and diseases, notably including cancers, can make repurposing time consuming and inefficient. The key question we address is how to efficiently repurpose an existing drug to treat a given indication. As drug efficacy remains the main bottleneck for overall success, we discuss the need for machine-learning computational methods in combination with specific phenotypic studies along with mechanistic studies, chemical genetics and omics assays to successfully predict disease-drug pairs. Such a pipeline could be particularly important to cancer patients who face heterogeneous, recurrent and metastatic disease and need fast and personalized treatments. Here we focus on drug repurposing for colorectal cancer and describe selected therapeutics already repositioned for its prevention and/or treatment as well as potential candidates. We consider this review as a selective compilation of approaches and methodologies, and argue how, taken together, they could bring drug repurposing to the next level.

Time-Resolved Systems Medicine Reveals Viral Infection-Modulating Host Targets

Introduction: Drug-resistant infections are becoming increasingly frequent worldwide, causing hundreds of thousands of deaths annually. This is partly due to the very limited set of protein drug targets known for human-infecting viral genomes. The eleven influenza virus proteins, for instance, exploit host cell factors for replication and suppression of the antiviral immune responses. A systems medicine approach to identify relevant and druggable host factors would dramatically expand therapeutic options. Therapeutic target identification, however, has hitherto relied on static molecular networks, whereas in reality the interactome, in particular during an infection, is subject to constant change. Methods: We developed time-course network enrichment (TiCoNE), an expert-centered approach for discovering temporal response pathways. In the first stage of TiCoNE, time-series expression data is clustered in a human-augmented manner to identify groups of biological entities with coherent temporal responses. Throughout this process, the expert can add, remove, merge, or split temporal patterns. The resulting groups can then be mapped to an interaction network to identify enriched pathways and to analyze cross-talk enrichments and depletions between groups. Finally, temporal response groups of two experiments can be intersected, to identify condition-variant response patterns that represent promising drug-target candidates. Results: We applied TiCoNE to human gene expression data for influenza A virus infection and rhino virus infection, respectively. We then identified coherent temporal response patterns and employed our cross-talk analysis to establish two potential timelines of systems-level host responses for either infection. Next, we compared the two phenotypes and unraveled condition-variant temporal groups interacting on a networks level. The highest-ranking ones we then validated via literature search and wet-lab experiments. This not only confirmed many of our candidates as previously known, but we also identified phospholipid scramblase 1 (encoded by PLSCR1) as a previously not recognized host factor that is essential for influenza A virus infection. Conclusion: With TiCoNE we developed a novel approach for conjointly analyzing molecular networks with time-series expression data and demonstrated its power by identifying temporal drug-targets. We provide proof-of-concept that not only novel targets can be identified using our approach, but also that anti-infective drug target discovery can be enhanced by investigating temporal molecular networks of the host in response to viral infection.

From single drug targets to synergistic network pharmacology in ischemic stroke

Current one drug–one target–one disease approaches in drug discovery have become increasingly inefficient. Network pharmacology defines disease mechanisms as networks best targeted by multiple, synergistic drugs. Using the high unmet medical need indication stroke, we here develop an integrative in silico approach based on a primary target, NADPH oxidase type 4, to identify a mechanistically related cotarget, NO synthase, for network pharmacology. Indeed, we validate both in vivo and in vitro, including humans, that both NOX4 and NOS inhibition is highly synergistic, leading to a significant reduction of infarct volume, direct neuroprotection, and blood–brain-barrier stabilization. This systems medicine approach provides a ground plan to decrease current failure in the field by being implemented in other complex indications.

Drug discovery faces an efficacy crisis to which ineffective mainly single-target and symptom-based rather than mechanistic approaches have contributed. We here explore a mechanism-based disease definition for network pharmacology. Beginning with a primary causal target, we extend this to a second using guilt-by-association analysis. We then validate our prediction and explore synergy using both cellular in vitro and mouse in vivo models. As a disease model we chose ischemic stroke, one of the highest unmet medical need indications in medicine, and reactive oxygen species forming NADPH oxidase type 4 (Nox4) as a primary causal therapeutic target. For network analysis, we use classical protein–protein interactions but also metabolite-dependent interactions. Based on this protein–metabolite network, we conduct a gene ontology-based semantic similarity ranking to find suitable synergistic cotargets for network pharmacology. We identify the nitric oxide synthase (Nos1 to 3) gene family as the closest target to Nox4. Indeed, when combining a NOS and a NOX inhibitor at subthreshold concentrations, we observe pharmacological synergy as evidenced by reduced cell death, reduced infarct size, stabilized blood–brain barrier, reduced reoxygenation-induced leakage, and preserved neuromotor function, all in a supraadditive manner. Thus, protein–metabolite network analysis, for example guilt by association, can predict and pair synergistic mechanistic disease targets for systems medicine-driven network pharmacology. Such approaches may in the future reduce the risk of failure in single-target and symptom-based drug discovery and therapy.