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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.

Coronavirus disease-2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is evolving across the world and new treatments are urgently needed as with vaccines to prevent the illness and stem the contagion. The virus affects not only the lungs but also other tissues, thus lending support to the idea that COVID-19 is a systemic disease. The current vaccine and treatment development strategies ought to consider such systems medicine perspectives rather than a narrower focus on the lung infection only. COVID-19 is associated with elevated levels of the inflammatory cytokines such as interleukin-6 (IL-6), IL-10, and interferon-gamma (IFN-γ). Elevated levels of cytokines and the cytokine storm have been linked to fatal disease. This suggests new therapeutic strategies through blocking the cytokine storm. IL-6 is one of the major cytokines associated with the cytokine storm. IL-6 is also known to display pleiotropic/diverse pathophysiological effects. We suggest the blockage of IL-6 signaling and its downstream mediators such as Janus kinases (JAKs), and signal transducer and activators of transcription (STATs) offer potential hope for the treatment of severe cases of COVID-19. Thus, repurposing of already approved IL-6-JAK-STAT signaling inhibitors as well as other anti-inflammatory drugs, including dexamethasone, is under development for severe COVID-19 cases. We conclude this expert review by highlighting the potential role of precision herbal medicines, for example, the Cannabis sativa, provided that omics technologies can be utilized to build a robust scientific evidence base on their clinical safety and efficacy. Precision herbal medicine buttressed by omics systems science would also help identify new molecular targets for drug discovery against COVID-19.

Background Characterizing breast cancer progression and aggressiveness relies on categorical descriptions of tumor stage and grade. Interpreting these categorical descriptions is challenging because stage convolutes the size and spread of the tumor and no consensus exists to define high/low grade tumors. Methods We address this challenge of heterogeneity in patient‐specific cancer samples by adapting and applying several tools originally created for understanding heterogeneity and phenotype development in single cells (specifically, single‐cell topological data analysis and Wanderlust) to create a continuous metric describing breast cancer progression using bulk RNA‐seq samples from individual patient tumors. We also created a linear regression‐based method to predict tumor aggressiveness in vivo from bulk RNA‐seq data. Results We found that breast cancer proceeds along three convergent phenotype trajectories: luminal, HER2‐enriched, and basal‐like. Furthermore, 31 296 genes (for luminal cancers), 17 827 genes (for HER2‐enriched), and 18 505 genes (for basal‐like) are dynamically differentially expressed during breast cancer progression. Across progression trajectories, our results show that expression of genes related to ADP‐ribosylation decreased as tumors progressed (while PARP1 and PARP2 increased or remained stable), suggesting the potential for a differential response to PARP inhibitors based on cancer progression. Additionally, we developed a 132‐gene expression regression equation to predict mitotic index and a 23‐gene expression regression equation to predict growth rate from a single breast cancer biopsy. Conclusion Our results suggest that breast cancer dynamically changes during disease progression, and growth rate of the cancer cells is associated with distinct transcriptional profiles., Breast cancer is a continuous disease and RNA expression dynamically changes with disease progression. Additionally, cellular growth rate is encoded in RNA expression.

Background System medicine approaches have played a pivotal role in identifying novel disease networks especially in miRNA research. It is no wonder that miRNAs are implicated in multiple clinical conditions, allowing us to establish the hubs and nodes for network models of Alzheimer’s Disease (AD). AD is an age-related, progressive, irreversible, and multifactorial neurodegenerative disorder characterized by cognitive and memory impairment and is the most common cause of dementia in older adults. Worldwide, around 50 million people have dementia, and there are nearly 10 million new cases every year. δ-secretase, also known as asparagine endopeptidase (AEP) or legumain (LGMN), is a lysosomal cysteine protease that cleaves peptide bonds C-terminally to asparagine residues in both amyloid precursor protein (APP) and tau, mediating the amyloid-β and tau pathology in AD. The patient's miRNA expression was found to be deregulated in the brain, extracellular fluid, blood plasma, and serum. Methods Protein-Protein Interaction (PPI) networks of LGMN or δ-secretase were constructed using the Genemania database. Network Analyzer, a Cytoscape plugin, analyzed the network topological properties of LGMN. miRNAs related to Alzheimer’s were extracted from the HMDD (Human microRNA Disease Database) and experimentally verified miRNA-gene interaction was obtained by searching miRWalk. Starbase v2.0 and miRanda were used for screening miRNA of LGMN genes. Moreover, to understand the regulatory mechanism in AD, we have screened major transcription factors of LGMN targeted genes using the Network Analyst 3.0, TRRUST (v2.0) server, and ENCODE. The Genotype-Tissue Expression (GTEx) and BEST tool were used to investigate the expression pattern of the LGMN gene. In parallel, we performed in-silico drug designing of the novel inhibitor scaffold of δ-secretase as powerful therapeutic targets by using the concept of scaffolds and frameworks. In this context, this study also aimed at identifying effective small molecule inhibitors targeting δ-secretase. Results Among the 16 experimentally verified miRNAs, Network analysis of the LGMN and its associated miRNA identify novel hsa-miRNA-106a-5p and hsa-miRNA-34a-5p being more expressed in the brain. Our in silico high throughput screening, followed by XP docking revealed Oprea1 as the lead. Molecular dynamic simulations of the δ-secretase-docked complex have been carried out for a time period of 200 ns and revealed that Root Mean Square Deviation (RMSD) of the protein Cα-backbone with respect to its starting position increased to 1.20 A for the first 25 ns of the trajectory and then became stable around 0.6 A in the last 170 ns course of the simulation. The radius of gyration (RGYR) reveals that compactness was maintained till the end of simulations. Conclusion Network analysis of LGMN associated miRNAs lead to the identification of two novel miRNAs, being highly expressed in the brain. This study also lead to the identification and expression of 10 Transcription factors associated with LGMN. Expression Heatmap results show high and continuous expression of LGMN in most of the regions of the brain, especially in the frontal cortex. Further, in silico drug analysis led us to the identification of Oprea1 which could be taken for further investigation to explore its potential for AD therapy.

Metabolic reprogramming is a key feature of Autosomal Dominant Polycystic Kidney Disease (ADPKD) characterized by changes in cellular pathways occurring in response to the pathological cell conditions. In ADPKD, a broad range of dysregulated pathways have been found. The studies supporting alterations in cell metabolism have shown that the metabolic preference for abnormal cystic growth is to utilize aerobic glycolysis, increasing glutamine uptake and reducing oxidative phosphorylation, consequently resulting in ADPKD cells shifting their energy to alternative energetic pathways. The mechanism behind the role of the polycystin proteins and how it leads to disease remains unclear, despite the identification of numerous signaling pathways. The integration of computational data analysis that accompanies experimental findings was pivotal in the identification of metabolic reprogramming in ADPKD. Here, we summarize the important results and argue that their exploitation may give further insights into the regulative mechanisms driving metabolic reprogramming in ADPKD. The aim of this review is to provide a comprehensive overview on metabolic focused studies and potential targets for treatment, and to propose that computational approaches could be instrumental in advancing this field of research.

BACKGROUND The polycystic ovary syndrome (PCOS) has genetic, epigenetic, metabolic and reproductive aspects, while its complex pathophysiology has not been conclusively deciphered. AIM The goal of this research was to screen the gene/gene products associated with PCOS and to predict any possible interactions with the highest possible fidelity. MATERIALS AND METHODS STRING v10.5 database and a confidence level of 0.7 were used. RESULTS A highly interconnected network of 48 nodes was created, where insulin (INS) appears to be the major hub. INS upstream and downstream defects were analysed and revealed that only the kisspeptin- and glucagon-coding genes were upstream of INS. CONCLUSION A metabolic dominance was inferred and discussed herein with its implications in puberty, obesity, infertility and cardiovascular function. This study, thus, may contribute to the resolution of a scientific conflict between the USA and EU definitions of the syndrome and/or provide a new P4 medicine approach.

A comprehensive understanding of the microbiome-host relationship in respiratory diseases can be elucidated by exploring the landscape of virome-bacteriome-host metabolome data through unsupervised 'multi-omics' approaches. Here, we describe how the composition and function of airway and gut virome and bacteriome may contribute to pathogen establishment and propagation in airway districts and how the virome-bacteriome communities may react to respiratory diseases. A new systems medicine approach, including the characterization of respiratory and gut microbiome, may be crucial to demonstrate the likelihood and odds of respiratory disease pathophysiology, opening new avenues to the discovery of a chain of causation for key bacteria and viruses in disease severity.

[Background]: The gut microbiome and iron status are known to play a role in the pathophysiology of non-alcoholic fatty liver disease (NAFLD), although their complex interaction remains unclear., [Results]: Here, we applied an integrative systems medicine approach (faecal metagenomics, plasma and urine metabolomics, hepatic transcriptomics) in 2 well-characterised human cohorts of subjects with obesity (discovery n = 49 and validation n = 628) and an independent cohort formed by both individuals with and without obesity (n = 130), combined with in vitro and animal models. Serum ferritin levels, as a markers of liver iron stores, were positively associated with liver fat accumulation in parallel with lower gut microbial gene richness, composition and functionality. Specifically, ferritin had strong negative associations with the Pasteurellaceae, Leuconostocaceae and Micrococcaea families. It also had consistent negative associations with several Veillonella, Bifidobacterium and Lactobacillus species, but positive associations with Bacteroides and Prevotella spp. Notably, the ferritin-associated bacterial families had a strong correlation with iron-related liver genes. In addition, several bacterial functions related to iron metabolism (transport, chelation, heme and siderophore biosynthesis) and NAFLD (fatty acid and glutathione biosynthesis) were also associated with the host serum ferritin levels. This iron-related microbiome signature was linked to a transcriptomic and metabolomic signature associated to the degree of liver fat accumulation through hepatic glucose metabolism. In particular, we found a consistent association among serum ferritin, Pasteurellaceae and Micrococcacea families, bacterial functions involved in histidine transport, the host circulating histidine levels and the liver expression of GYS2 and SEC24B. Serum ferritin was also related to bacterial glycine transporters, the host glycine serum levels and the liver expression of glycine transporters. The transcriptomic findings were replicated in human primary hepatocytes, where iron supplementation also led to triglycerides accumulation and induced the expression of lipid and iron metabolism genes in synergy with palmitic acid. We further explored the direct impact of the microbiome on iron metabolism and liver fact accumulation through transplantation of faecal microbiota into recipient’s mice. In line with the results in humans, transplantation from ‘high ferritin donors’ resulted in alterations in several genes related to iron metabolism and fatty acid accumulation in recipient’s mice., [Conclusions]: Altogether, a significant interplay among the gut microbiome, iron status and liver fat accumulation is revealed, with potential significance for target therapies., This work was supported by EU-FP7 FLORINASH (Health-F2-2009-241913) to R.B., M.F., J.M.F.R., E.H. and J.K.N. Infrastructure support was provided by the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre (BRC). L.H. was in receipt of an MRC Intermediate Research Fellowship in Data Science (grant number MR/L01632X/1, UK Med-Bio). This work was also partly supported by funding to M.-E.D. (EU METACARDIS under agreement HEALTH-F4-2012-305312, Neuron II under agreement 291840 and the MRC MR/M501797/1) and by grants from the French National Research Agency (ANR-10-LABX-46 [European Genomics Institute for Diabetes]), from the National Center for Precision Diabetic Medicine – PreciDIAB, which is jointly supported by the French National Agency for Research (ANR-18-IBHU-0001), by the European Union (FEDER), by the Hauts-de-France Regional Council (Agreement 20001891/NP0025517) and by the European Metropolis of Lille (MEL, Agreement 2019_ESR_11) and by Isite ULNE (R-002-20-TALENT-DUMAS), also jointly funded by ANR (ANR-16-IDEX-0004-ULNE) the Hauts-de-France Regional Council (Agreement 20002045) and by the European Metropolis of Lille (MEL). J.M.-P. is funded by the Miguel Servet Program from the Instituto de Salud Carlos III (ISCIII CP18/00009), co-funded by the European Social Fund ‘Investing in your future’. María Arnoriaga Rodríguez is funded by a predoctoral Río Hortega contract (CM19/00190, co-funded by European Social Fund ‘Investing in your future’) from the Instituto de Salud Carlos III, Spain. This work was supported by grants to AM from the Spanish Ministry of Science and Innovation (PID2019-105969GB-I00) and Generalitat Valenciana (project Prometeo/2018/133).

Neurodegenerative diseases (NDDs) comprise a broad range of progressive neurological disorders with multifactorial etiology contributing to disease pathophysiology. Evidence of the microbiome involvement in the gut-brain axis urges the interest in understanding metabolic interactions between the microbiota and host physiology in NDDs. Systems Biology offers a holistic integrative approach to study the interplay between the different biologic systems as part of a whole, and may elucidate the host–microbiome interactions in NDDs. We reviewed direct and indirect pathways through which the microbiota can modulate the bidirectional communication of the gut-brain axis, and explored the evidence of microbial dysbiosis in Alzheimer’s and Parkinson’s diseases. As the gut microbiota being strongly affected by diet, the potential approaches to targeting the human microbiota through diet for the stimulation of neuroprotective microbial-metabolites secretion were described. We explored the potential of Genome-scale metabolic models (GEMs) to infer microbe-microbe and host-microbe interactions and to identify the microbiome contribution to disease development or prevention. Finally, a systemic approach based on GEMs and ‘omics integration, that would allow the design of sustainable personalized anti-inflammatory diets in NDDs prevention, through the modulation of gut microbiota was described.

Gene set enrichment analysis (GSEA) is a powerful tool to associate a disease phenotype to a group of genes/proteins. GSEA attributes a specific weight to each gene/protein in the input list that depends on a metric of choice, which is usually represented by quantitative expression data. However, expression data are not always available. Here, GSEA based on betweenness centrality of a protein–protein interaction (PPI) network is described and applied to two cases, where an expression metric is missing. First, personalized PPI networks were generated from genes displaying alterations (assessed by array comparative genomic hybridization and whole exome sequencing) in four probands bearing a 16p13.11 microdeletion in common and several other point variants. Patients showed disease phenotypes linked to neurodevelopment. All networks were assembled around a cluster of first interactors of altered genes with high betweenness centrality. All four clusters included genes known to be involved in neurodevelopmental disorders with different centrality. Moreover, the GSEA results pointed out to the evidence of “cell cycle” among enriched pathways. Second, a large interaction network obtained by merging proteomics studies on three neurodegenerative disorders was analyzed from the topological point of view. We observed that most central proteins are often linked to Parkinson’s disease. The selection of these proteins improved the specificity of GSEA, with “Metabolism of amino acids and derivatives” and “Cellular response to stress or external stimuli” as top-ranked enriched pathways. In conclusion, betweenness centrality revealed to be a suitable metric for GSEA. Thus, centrality-based GSEA represents an opportunity for precision medicine and network medicine.

Highlights • Aging and neurodegenerative diseases are characterized by complex and multifactorial aetiology and challenges regarding treatments efficacy and costs persist. • Oxygen-ozone (O2-O3) therapy is a non-invasive, non-pharmacological, low cost and no-side effect procedure based on antioxidant capacity of O3. • A plethora of scientific evidence supports other crucial properties of O3 in physiopathological processes. • Nrf2 is one of the principal molecular signalling on which O3 exercises its antioxidant/anti-apoptotic/pro-autophagy effects, involved in aging and in neurodegenerative disorders. • This review provides a consistent rationale to implement future clinical studies to apply the O2-O3 treatment along with other antioxidants (polyphenols, mushrooms) in aging and in neurodegeneration., Systems medicine is founded on a mechanism-based approach and identifies in this way specific therapeutic targets. This approach has been applied for the transcription factor nuclear factor (erythroid-derived 2)–like 2 (Nrf2). Nrf2 plays a central role in different pathologies including neurodegenerative disorders (NDs), which are characterized by common pathogenetic features. We here present wide scientific background indicating how a natural bioactive molecule with antioxidant/anti-apoptotic and pro-autophagy properties such as the ozone (O3) can represent a potential new strategy to delay neurodegeneration. Our hypothesis is based on different evidence demonstrating the interaction between O3 and Nrf2 system. Through a meta-analytic approach, we found a significant modulation of O3 on endogenous antioxidant-Nrf2 (p

The prevalence of non-alcoholic fatty liver disease (NAFLD) continues to increase dramatically, and there is no approved medication for its treatment. Recently, we predicted the underlying molecular mechanisms involved in the progression of NAFLD using network analysis and identified metabolic cofactors that might be beneficial as supplements to decrease human liver fat. Here, we first assessed the tolerability of the combined metabolic cofactors including l-serine, N-acetyl-l-cysteine (NAC), nicotinamide riboside (NR), and l-carnitine by performing a 7-day rat toxicology study. Second, we performed a human calibration study by supplementing combined metabolic cofactors and a control study to study the kinetics of these metabolites in the plasma of healthy subjects with and without supplementation. We measured clinical parameters and observed no immediate side effects. Next, we generated plasma metabolomics and inflammatory protein markers data to reveal the acute changes associated with the supplementation of the metabolic cofactors. We also integrated metabolomics data using personalized genome-scale metabolic modeling and observed that such supplementation significantly affects the global human lipid, amino acid, and antioxidant metabolism. Finally, we predicted blood concentrations of these compounds during daily long-term supplementation by generating an ordinary differential equation model and liver concentrations of serine by generating a pharmacokinetic model and finally adjusted the doses of individual metabolic cofactors for future human clinical trials.

Stress activates a complex network of hormones known as the hypothalamic–pituitary–adrenal (HPA) axis. The HPA axis is dysregulated in chronic stress and psychiatric disorders, but the origin of this dysregulation is unclear and cannot be explained by current HPA models. To address this, we developed a mathematical model for the HPA axis that incorporates changes in the total functional mass of the HPA hormone‐secreting glands. The mass changes are caused by HPA hormones which act as growth factors for the glands in the axis. We find that the HPA axis shows the property of dynamical compensation, where gland masses adjust over weeks to buffer variation in physiological parameters. These mass changes explain the experimental findings on dysregulation of cortisol and ACTH dynamics in alcoholism, anorexia, and postpartum. Dysregulation occurs for a wide range of parameters and is exacerbated by impaired glucocorticoid receptor (GR) feedback, providing an explanation for the implication of GR in mood disorders. These findings suggest that gland‐mass dynamics may play an important role in the pathophysiology of stress‐related disorders., Prolonged activation of the HPA axis leads to dysregulation and has clinical consequences. This study presents a mechanism for HPA dysregulation based on the effect of HPA hormones acting as growth factors for their downstream glands.

Systems biology offers great promise for the interpretation of genetic variation from functional standpoint. Sequence variation analysis holds a potential to inform systems biology by highlighting genes and their pathways. Genomic sequencing enables extensive amount of somatic mutations in tumors. Cancer genomic research aims to identify all genes associated to cancer and involvements of these genes in initiation of cancer and progression; however, this endeavor is characterized by numerous challenges like intricacy of biological networks affected by local mutation spectrum and huge amount of other passenger mutations. To date, development of multiple refined computational methods for screening cancer-driven mutations and pathways on the basis of their network information and biological pathways. These approaches can be classified into (1) network-based, (2) mutation frequency-based, (3) functional impact-based, (4) data integration-based, and (5) structural genomic-based methods. In silico modeling may pinpoint the effects caused by the missense mutations in order to deal with disease morphology. Hence, analyzing the mutations affecting a protein with the help of computational approaches and methods may intimate recognition of its functional importance and understanding the molecular mechanism of cancer progression. Here, we summarize some of the major computational methods and approaches that are useful in mutational studies, along with their recent advances and existing limitations. Nevertheless, better estimation of genetic variant association and genomics data interpretation with cancer or other disorders still requires significant efforts and improvement.

The group of inborn errors of metabolism (IEM) displays a marked heterogeneity and IEM can affect virtually all functions and organs of the human organism; however, IEM share that their associated proteins function in metabolism. Most proteins carry out cellular functions by interacting with other proteins, and thus are organized in biological networks. Therefore, diseases are rarely the consequence of single gene mutations but of the perturbations caused in the related cellular network. Systematic approaches that integrate multi-omics and database information into biological networks have successfully expanded our knowledge of complex disorders but network-based strategies have been rarely applied to study IEM. We analyzed IEM on a proteome scale and found that IEM-associated proteins are organized as a network of linked modules within the human interactome of protein interactions, the IEM interactome. Certain IEM disease groups formed self-contained disease modules, which were highly interlinked. On the other hand, we observed disease modules consisting of proteins from many different disease groups in the IEM interactome. Moreover, we explored the overlap between IEM and non-IEM disease genes and applied network medicine approaches to investigate shared biological pathways, clinical signs and symptoms, and links to drug targets. The provided resources may help to elucidate the molecular mechanisms underlying new IEM, to uncover the significance of disease-associated mutations, to identify new biomarkers, and to develop novel therapeutic strategies. Electronic supplementary material The online version of this article (10.1007/s10545-018-0140-0) contains supplementary material, which is available to authorized users.

Summary Obesity, characterized by expansion and metabolic dysregulation of white adipose tissue (WAT), has reached pandemic proportions and acts as a primer for a wide range of metabolic disorders. Remodeling of WAT lipidome in obesity and associated comorbidities can explain disease etiology and provide valuable diagnostic and prognostic markers. To support understanding of WAT lipidome remodeling at the molecular level, we provide in-depth lipidomics profiling of human subcutaneous and visceral WAT of lean and obese individuals. We generate a human WAT reference lipidome by performing tissue-tailored preanalytical and analytical workflows, which allow accurate identification and semi-absolute quantification of 1,636 and 737 lipid molecular species, respectively. Deep lipidomic profiling allows identification of main lipid (sub)classes undergoing depot-/phenotype-specific remodeling. Previously unanticipated diversity of WAT ceramides is now uncovered. AdipoAtlas reference lipidome serves as a data-rich resource for the development of WAT-specific high-throughput methods and as a scaffold for systems medicine data integration., Graphical abstract, Highlights AdipoAtlas provides a reference lipidome of human white adipose tissue 1,636 and 737 lipids were identified and quantified by tissue tailored LC-MS lipidomics AdipoAtlas demonstrates prominent differences between subcutaneous and visceral tissue depots Obesity leads to the remodeling of sphingo-, ether-, and neutral lipid metabolism, Lange et al. report a reference lipidome of human white adipose tissue by providing qualitative and quantitative analyses of lipid compositions in lean and obese states for subcutaneous and visceral depots. Several key events of lipidome remodeling in obesity are identified within the metabolism of sphingo-, ether-, and neutral lipids.

Hepatoblastoma is the most common malignant pediatric liver cancer. Histological evaluation of tumor biopsies is used to distinguish among the different subtypes of hepatoblastoma, with fetal and embryonal representing the two main epithelial components. With frequent CTNNB1 mutations, hepatoblastoma is a Wnt/β-catenin-driven malignancy. Considering that Wnt activation has been associated with tumor metabolic reprogramming, we characterized the metabolic profile of cells from hepatoblastoma and compared it to cells from hepatocellular carcinoma. First, we demonstrated that glucose transporter GLUT3 is a direct TCF4/β-catenin target gene. RNA sequencing enabled to identify molecular and metabolic features specific to hepatoblastoma and revealed that several glycolytic enzymes are overexpressed in embryonal-like compared to fetal-like tumor cells. This led us to implement successfully three biomarkers to distinguish embryonal from fetal components by immunohistochemistry from a large panel of human hepatoblastoma samples. Functional analyses demonstrated that embryonal-like hepatoblastoma cells are highly glycolytic and sensitive to hexokinase-1 silencing. Altogether, our findings reveal a new, metabolic classification of human hepatoblastoma, with potential future implications for patients' diagnosis and treatment.

In the age of big data, genomics and clinical research have reached a crossroads. A wealth of data is being generated, but it is becoming increasingly complicated to analyze these data to extract meaningful results. The ability to understand biological systems holistically has unprecedented potential to transform how cancers are treated. Recent major advances leading biomedical research towards “systems medicine” have been fueled by high-throughput platforms, such as microarrays and next-generation sequencing, which can capture vast amounts of data in different genomic spaces. Unfortunately, because of high dimensionality and complex relationships among these data, inferring comprehensive and useful biological models has proven computationally and statistically challenging. However, novel bioinformatic methods for data integration of cancer genomic datasets have been developed. In this review, we will describe the applications of various genomic approaches in sarcoma research and introduce bioinformatic methods for data integration. With the continuing evolution of technological and bioinformatic methodologies, the application of big data within clinics and hospitals will ultimately result in significant improvements on how cancers are detected and treated.

Metabolism is highly complex and involves thousands of different connected reactions; it is therefore necessary to use mathematical models for holistic studies. The use of mathematical models in biology is referred to as systems biology. In this review, the principles of systems biology are described, and two different types of mathematical models used for studying metabolism are discussed: kinetic models and genome-scale metabolic models. The use of different omics technologies, including transcriptomics, proteomics, metabolomics, and fluxomics, for studying metabolism is presented. Finally, the application of systems biology for analyzing global regulatory structures, engineering the metabolism of cell factories, and analyzing human diseases is discussed.

Ovarian cancer remains the leading cause of death from a gynecologic malignancy, and treatment of this disease is harder than any other type of female reproductive cancer. Improvements in the diagnosis and development of novel and effective treatment strategies for complex pathophysiologies, such as ovarian cancer, require a better understanding of disease emergence and mechanisms of progression through systems medicine approaches. RNA-level analyses generate new information that can help in understanding the mechanisms behind disease pathogenesis, to identify new biomarkers and therapeutic targets and in new drug discovery. Whole RNA sequencing and coding and non-coding RNA expression array datasets have shed light on the mechanisms underlying disease progression and have identified mRNAs, miRNAs, and lncRNAs involved in ovarian cancer progression. In addition, the results from these analyses indicate that various signalling pathways and biological processes are associated with ovarian cancer. Here, we present a comprehensive literature review on RNA-based ovarian cancer research and highlight the benefits of integrative approaches within the systems biomedicine concept for future ovarian cancer research. We invite the ovarian cancer and systems biomedicine research fields to join forces to achieve the interdisciplinary caliber and rigor required to find real-life solutions to common, devastating, and complex diseases such as ovarian cancer.CAF: cancer-associated fibroblasts; COG: Cluster of Orthologous Groups; DEA: disease enrichment analysis; EOC: epithelial ovarian carcinoma; ESCC: oesophageal squamous cell carcinoma; GSI: gamma secretase inhibitor; GO: Gene Ontology; GSEA: gene set enrichment analyzes; HAS: Hungarian Academy of Sciences; lncRNAs: long non-coding RNAs; MAPK/ERK: mitogen-activated protein kinase/extracellular signal-regulated kinases; NGS: next-generation sequencing; ncRNAs: non-coding RNAs; OvC: ovarian cancer; PI3K/Akt/mTOR: phosphatidylinositol-3-kinase/protein kinase B/mammalian target of rapamycin; RT-PCR: real-time polymerase chain reaction; SNP: single nucleotide polymorphism; TF: transcription factor; TGF-β: transforming growth factor-β.

Systems biology uses mathematical models to analyze large datasets and simulate system behavior. It enables integrative analysis of different types of data and can thereby provide new insight into complex biological systems. Here will be discussed the challenges of using systems medicine for advancing the development of personalized and precision medicine to treat metabolic diseases like insulin resistance, obesity, NAFLD, NASH, and cancer. It will be illustrated how the concept of genome-scale metabolic models can be used for integrative analysis of big data with the objective of identifying novel biomarkers that are foundational for personalized and precision medicine.

Measuring and modeling the integrated behavior of biomolecular-cellular networks is central to systems biology. Over several decades, systems biology has been shaped by quantitative biologists, physicists, mathematicians, and engineers in different ways. However, the basic and applied versions of systems biology are not typically distinguished, which blurs the separate aspirations of the field and its potential for real-world impact. Here, we articulate an engineering approach to systems biology, which applies educational philosophy, engineering design, and predictive models to solve contemporary problems in an age of biomedical Big Data. A concerted effort to train systems bioengineers will provide a versatile workforce capable of tackling the diverse challenges faced by the biotechnological and pharmaceutical sectors in a modern, information-dense economy.

In this study, in terms of systems biology approaches and deep learning method, we proposed a series of strategies for systems medicine design toward TNBC and non-TNBC. For systems biology approach, we constructed candidate genome-wide genetic and epigenetic network (GWGEN) by big data mining technique and identified real GWGEN of TNBC and non-TNBC by corresponding microarray data via system identification and model order selection methods. Core GWGEN of TNBC and non-TNBC were constructed from their corresponding GWGENs and then denoted by KEGG pathways to obtain core signaling pathways of TNBC and non-TNBC, which were compared to find essential carcinogenic biomarkers to bring about multiple cellular dysfunctions including cell proliferation, autophagy, immune response, cell differentiation, apoptosis, metastasis, angiogenesis, and epithelial-mesenchymal transition (EMT). With the help of the drug-target interaction (DTI) model based on deep neural network trained through feature vectors of drug-target databases, we could select candidate drugs for these drug targets. These candidate drugs were still filtered for the toxicity by LD50 and for regulation ability by connectively Map (CMap) as potential drugs, and then these potential drugs are combined as potential multiple-molecule drugs, i.e., resveratrol, sirolimus, prednisolone for TNBC and resveratrol, sirolimus, carbamazepine, verapamil for non-TNBC.

Autoimmune blistering diseases (AIBDs) of the skin are characterized by autoantibodies against different intra-/extracellular structures within the epidermis and at the basement membrane zone (BMZ). Binding of the antibodies to their target antigen leads to inflammation at the respective binding site and degradation of these structures, resulting in the separation of the affected skin layers. Clinically, blistering, erythema and lesions of the skin and/or mucous membranes can be observed. Based on the localization of the autoantigen, AIBDs can be divided into pemphigus (intra-epidermal blistering diseases) and pemphigoid diseases (sub-epidermal blistering diseases), respectively. Although autoantigens have been extensively characterized, the underlying causes that trigger the diseases are still poorly understood. Besides the environment, genetic factors seem to play an important role in a predisposition to AIBDs. Here, we review currently known genetic and immunological mechanisms that contribute to the pathogenesis of AIBDs. Among the most commonly encountered genetic predispositions for AIBDs are the HLA gene region, and deleterious mutations of key genes for the immune system. Particularly, HLA class II genes such as the HLA-DR and HLA-DQ alleles have been shown to be prevalent in patients. This has prompted further epidemiological studies as well as unbiased Omics approaches on the transcriptome, microbiome, and proteome level to elucidate common and individual genetic risk factors as well as the molecular pathways that lead to the pathogenesis of AIBDs.

Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clinical predictor based on genomic data needs to be easily interpretable to drive hypothesis-driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks. netDx meets the above criteria and particularly excels at data integration and model interpretability. As a machine learning method, netDx demonstrates consistently excellent performance in a cancer survival benchmark across four cancer types by integrating up to six genomic and clinical data types. In these tests, netDx has significantly higher average performance than most other machine-learning approaches across most cancer types and its best model outperforms all other methods for two cancer types. In comparison to traditional machine learning-based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space. When patient similarity is defined by pathway-level gene expression, netDx identifies biological pathways important for outcome prediction, as demonstrated in diverse data sets of breast cancer and asthma. Thus, netDx can serve both as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a software complete implementation of netDx along with sample files and automation workflows in R.

The liver and kidney in mammals play central roles in protecting the organism from xenobiotics and are at high risk of xenobiotic‐induced injury. Xenobiotic‐induced tissue injury has been extensively studied from both classical histopathological and biochemical perspectives. Here, we introduce a machine‐learning approach to analyze toxicological response. Unsupervised characterization of physiological and histological changes in a large toxicogenomic dataset revealed nine discrete toxin‐induced disease states, some of which correspond to known pathology, but others were novel. Analysis of dynamics revealed transitions between disease states at constant toxin exposure, mostly toward decreased pathology, implying induction of tolerance. Tolerance correlated with induction of known xenobiotic defense genes and decrease of novel ferroptosis sensitivity biomarkers, suggesting ferroptosis as a druggable driver of tissue pathophysiology. Lastly, mechanism of body weight decrease, a known primary marker for xenobiotic toxicity, was investigated. Combined analysis of food consumption, body weight, and molecular biomarkers indicated that organ injury promotes cachexia by whole‐body signaling through Gdf15 and Igf1, suggesting strategies for therapeutic intervention that may be broadly relevant to human disease.

Biologically data-driven networks have become powerful analytical tools that handle massive, heterogeneous datasets generated from biomedical fields. Protein-protein interaction networks can identify the most relevant structures directly tied to biological functions. Functional enrichments can then be performed based on these structural aspects of gene relationships for the study of channelopathies. Channelopathies refer to a complex group of disorders resulting from dysfunctional ion channels with distinct polygenic manifestations. This study presents a semi-automatic workflow using protein-protein interaction networks that can identify the most relevant genes and their biological processes and pathways in channelopathies to better understand their etiopathogenesis. In addition, the clinical manifestations that are strongly associated with these genes are also identified as the most characteristic in this complex group of diseases. This research provides a systems biology approach to extract information from interaction networks of gene expression. We show how large-scale computational integration of heterogeneous datasets, PPI network analyses, functional databases and published literature may support the detection and assessment of possible potential therapeutic targets in the disease. Applying our workflow makes it feasible to spot the most relevant genes and unknown relationships in channelopathies and shows its potential as a first-step approach to identify both genes and functional interactions in clinical-knowledge scenarios of target diseases., This work was supported by funds from MINECO-FEDER (TIN2016–81041-R to E.R.), European Human Brain Project SGA2 (H2020-RIA 785907 to M.J.S.), Junta de Andalucía (BIO-302 to F.J.E.) and MEIC (Systems Medicine Excellence Network, SAF2015–70270-REDT to F.J.E.).

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.

In the field of life sciences, although system biology and "precision medicine" introduce some complex scientifific methods and techniques, it is still based on the "analysis-reconstruction" of reductionist theory as a whole. Adaptability of complex system increase system behaviour uncertainty as well as the difficulties of precise identifification and control. It also put systems biology research into trouble. To grasp the behaviour and characteristics of organism fundamentally, systems biology has to abandon the "analysis-reconstruction" concept. In accordance with the guidelines of complexity science, systems biology should build organism model from holistic level, just like the Chinese medicine did in dealing with human body and disease. When we study the living body from the holistic level, we will fifind the adaptability of complex system is not the obstacle that increases the diffificulty of problem solving. It is the "exceptional", "right-hand man" that helping us to deal with the complexity of life more effectively.

Computer simulation is now becoming a central scientific paradigm of systems biology and a basic tool for the theoretical study and understanding of the complex mechanisms of living systems. The increase in the number and complexity of these models leads to the need for their collaborative development, reuse of models, their verification, and the description of computational experiments and their results. Ontological modeling is used to develop formats for knowledge-oriented mathematical modeling of biological systems. In this sense, ontology associated with the entire set of formats that support research in systems biology, in particular, computer modeling of biological systems and processes, can be regarded as a first approximation to the ontology of systems biology. This review summarizes the features of the subject area (bioinformatics, systems biology, and biomedicine), the main motivation for the development of ontologies and the most important examples of ontological modeling and semantic analysis at different levels of the hierarchy of knowledge: molecular genetic, cellular, tissue, organs, and the body. Bioinformatics and systems biology is an excellent ground for testing the technologies and efficient use of ontological modeling. Several dozens of verified basic reference ontologies now represent a source of knowledge for the integration and development of more complex domain models aimed at addressing specific issues in biomedicine and biotechnology. Further formalization and ontological accumulation of knowledge and the use of formal methods of analysis can take the entire cycle of research in systems biology to a new technological level.

Cancer cell metabolism has received increasing attention. Despite a boost in the application of clinical metabolic profiling (CMP) in cancer patients, a meta‐analysis has not been performed. The primary goal of this study was to assess whether public accessibility of metabolomics data and identification and reporting of metabolites were sufficient to assess which metabolites were consistently altered in cancer patients. We therefore retrospectively curated data from CMP studies in cancer patients published during 5 recent years and used an established vote‐counting method to perform a semiquantitative meta‐analysis of metabolites in tumor tissue and blood. This analysis confirmed well‐known increases in glycolytic metabolites, but also unveiled unprecedented changes in other metabolites such as ketone bodies and amino acids (histidine, tryptophan). However, this study also highlighted that insufficient public accessibility of metabolomics data, and inadequate metabolite identification and reporting hamper the discovery potential of meta‐analyses of CMP studies, calling for improved standardization of metabolomics studies. ![][1] The results of clinical metabolic profiling studies in cancer were curated and a meta‐analysis was subsequently performed to identify deregulated metabolites in blood, tissue, and urine. EMBO Mol Med (2016) 8: 1134–1142 [1]: /embed/graphic-1.gif

The functional complexity of human gut microbiota and its relationship with host physiology and environmental modulating factors, offers the opportunity to investigate (i) the host and microbiota role in organism-environment relationship; (ii) the individual functional diversity and response to environmental stimuli (exposome); (iii) the host genome and microbiota metagenomes' modifications by diet-mediated epigenomic controls (nutriepigenomics); and (iv) the genotype-phenotype “trajectories” under physiological and disease constraints. Systems biology-based approaches aim at integrating biological data at cellular, tissue and organ organization levels, using computational modeling to interpret diseases' physiopathological mechanisms (i.e., onset and progression). Proteomics improves the existing gene models by profiling molecular phenotypes at protein abundance level, by analyzing post-translational modifications and protein–protein interactions and providing specific pathway information, hence contributing to functional molecular networks. Transcriptomics and metabolomics may determine host ad microbiota changes induced by food ingredients at molecular level, complementing functional genomics and proteomics data. Since foodomics is an -omic wide methodology may feed back all integrative data to foster the omics-based systems medicine field. Hence, coupled to ecological genomics of gut microbial communities, foodomics may highlight health benefits from nutrients, dissecting diet-induced gut microbiota eubiosis mechanisms and significantly contributing to understand and prevent complex disease phenotypes. Biological significance Besides transcriptomics and proteomics there is a growing interest in applying metabolic profiling to food science for the development of functional foods. Indeed, one of the biggest challenges of modern nutrition is to propose a healthy diet to populations worldwide, intrinsically respecting the high inter-individual variability, driven by complex host/nutrients/microbiota/environment interactions. Therefore, metabolic profiling can assist at various levels for the development of functional foods, starting from screening for food composition to identification of new biomarkers to trace food intake. This current approach can support diet intervention strategies, epidemiological studies, and controlling of metabolic disorders worldwide spreading, hence ensuring healthy aging. With high-throughput molecular technologies driving foodomics, studying bidirectional interactions of host-microbial co-metabolism, innate immune development, dysfunctional nutrient absorption and processing, complex signaling pathways involved in nutritional metabolism, is now likely. In all cases, as microbiome pipeline efforts continue, it is possible that enhanced standardized protocols can be developed, which may lead to new testable biological and clinical hypotheses. This Review provides a comprehensive update on the current state-of-the-art of the integrated -omics route in food, microbiota and host co-metabolism studies, which may revolutionize the design of new dietary intervention strategies.

In systems biology, researchers aim to understand complex biological systems as a whole, which is often achieved by mathematical modelling and the analyses of high-throughput data. In this review, we give an overview of medical applications of systems biology approaches with special focus on host–pathogen interactions. After introducing general ideas of systems biology, we focus on (1) the detection of putative biomarkers for improved diagnosis and support of therapeutic decisions, (2) network modelling for the identification of regulatory interactions between cellular molecules to reveal putative drug targets and (3) module discovery for the detection of phenotype-specific modules in molecular interaction networks. Biomarker detection applies supervised machine learning methods utilizing high-throughput data (e.g. single nucleotide polymorphism (SNP) detection, RNA-seq, proteomics) and clinical data. We demonstrate structural analysis of molecular networks, especially by identification of disease modules as a novel strategy, and discuss possible applications to host–pathogen interactions. Pioneering work was done to predict molecular host–pathogen interactions networks based on dual RNA-seq data. However, currently this network modelling is restricted to a small number of genes. With increasing number and quality of databases and data repositories, the prediction of large-scale networks will also be feasible that can used for multidimensional diagnosis and decision support for prevention and therapy of diseases. Finally, we outline further perspective issues such as support of personalized medicine with high-throughput data and generation of multiscale host–pathogen interaction models.

Introduction: Pharmacological manipulation of signalling pathways is becoming an increasingly important avenue for the rational clinical management of disease but is hampered by a lack of technologies that allow the generation of comprehensive descriptions of cellular signalling.Areas covered: Herein, the authors discuss the potential of peptide array-based kinome profiling for evaluating cellular signalling in the context of drug discovery.Expert commentary: Genomic and proteomic approaches have been of significant value to our elucidation of the molecular mechanisms that govern physiology. However, an equally, if not more important goal, is to define those proteins that participate in signalling pathways that ultimately control cell fate, especially kinases. Traditional genetic and biochemical approaches can certainly provide answers here, but for technical and practical reasons, are typically pursued one gene or pathway at a time. A more comprehensive approach is one in which peptide arrays of kinase-specific substrates are incubated with cell lysates and P-33-ATP generating comprehensive descriptions, or where arrays are interrogated with phosphospecific antibodies. Both approaches allow analysis of cellular signalling without a priori assumptions to possibly influenced pathways.

Current colorectal cancer (CRC) treatment guidelines are primarily based on clinical features, such as cancer stage and grade. However, outcomes may be improved using molecular treatment guidelines. Potentially useful biomarkers include driver mutations and somatically inherited alterations, signaling proteins (their expression levels and (post) translational modifications), mRNAs, micro‐RNAs and long noncoding RNAs. Moving to an integrated system is potentially very relevant. To implement such an integrated system: we focus on an important region of the signaling network, immediately above the G1‐S restriction point, and discuss the reconstruction of a Molecular Interaction Map and interrogating it with a dynamic mathematical model. Extensive model pretraining achieved satisfactory, validated, performance. The model helps to propose future target combination priorities, and restricts drastically the number of drugs to be finally tested at a cellular, in vivo, and clinical‐trial level. Our model allows for the inclusion of the unique molecular profiles of each individual patient's tumor. While existing clinical guidelines are well established, dynamic modeling may be used for future targeted combination therapies, which may progressively become part of clinical practice within the near future. WIREs Syst Biol Med 2016, 8:314–336. doi: 10.1002/wsbm.1342 This article is categorized under: 1Biological Mechanisms > Cell Signaling2Analytical and Computational Methods > Computational Methods3Translational, Genomic, and Systems Medicine > Translational Medicine

Phenotyping of the CYP450 enzyme activities contributes to personalized medicine, but the past phenotyping approaches have followed a piecemeal strategy measuring single enzyme activities in vivo. A barrier to phenotyping of populations in rural and remote areas is the limited time and resources for sample collection. The CEIBA cocktail approach allows metabolic capacity estimation of multiple CYP450 enzymes in a single sample analysis, but the attendant sample collection schemes for applications in diverse global settings are yet to be optimized. The present study aimed to select an optimal matrix to simultaneously analyze CYP450 enzyme activities so as to simplify the sampling schemes in the phenotyping protocol to enhance its throughput and feasibility in native populations or in remote and underserviced geographies and social contexts. We evaluated 13 Ecuadorian healthy volunteers for CYP1A2, CYP2C9, CYP2C19, and CYP2D6 genotypes and their metabolic phenotypes, including CYP3A4, in plasma and urine after administering one reduced dose of caffeine, losartan, omeprazole, and dextromethorphan. Pharmacokinetic analyses were performed, and the correlation between AUC parent/AUC metabolite and the ratio between concentrations of probe drugs and their corresponding metabolites at timepoints ranging from 0 to 12 hours post-dose were analyzed. A single sampling timepoint, 4 hours post-dose in plasma, was identified as optimal to reflect the metabolic activity of the attendant CYP450 enzymes. This study optimizes the CEIBA multiplexed phenotyping approach and offers new ways forward for integrated drug metabolism analyses, in the pursuit of global personalized medicine applications in resource-limited regions, be they in developed or developing countries.

Oral health and systems medicine are intimately related but have remained, sadly, as isolated knowledge communities for decades. Are there veritable connector knowledge domains that can usefully link them together on the critical path to biomarker research and “one health”? In this context, it is noteworthy that bacteria form surface-attached communities on most biological surfaces, including the oral cavity. Biofilm-forming bacteria contribute to periodontal diseases and recent evidences point to roles of these bacteria in systemic diseases as well, with cardiovascular diseases, obesity, and cancer as notable examples. Interestingly, the combined mass of microorganisms such as bacteria are so large that when we combine all plants and animals on earth, the total biomass of bacteria is still bigger. They literally do colonize everywhere, not only soil and water but our skin, digestive tract, and even oral cavity are colonized by bacteria. Hence efforts to delineate biofilm formation mechanisms of oral bacteria and microorganisms and the development of small molecules to inhibit biofilm formation in the oral cavity is very timely for both diagnostics and therapeutics. Research on biofilms can benefit both oral and systems medicine. Here, we examine, review, and synthesize new knowledge on the current understanding of oral biofilm formation, the small molecule targets that can inhibit biofilm formation in the mouth. We suggest new directions for both oral and systems medicine, using various omics technologies such as SILAC and RNAseq, that could yield deeper insights, biomarkers, and molecular targets to design small molecules that selectively aim at eradication of pathogenic oral bacteria. Ultimately, devising new ways to control and eradicate bacteria in biofilms will open up novel diagnostic and therapeutic avenues for oral and systemic diseases alike.

The genetic basis of complex diseases involves alterations on multiple genes. Unraveling the interplay between these genetic factors is key to the discovery of new biomarkers and treatments. In 2014, we introduced GUILDify, a web server that searches for genes associated to diseases, finds novel disease genes applying various network-based prioritization algorithms and proposes candidate drugs. Here, we present GUILDify v2.0, a major update and improvement of the original method, where we have included protein interaction data for seven species and 22 human tissues and incorporated the disease-gene associations from DisGeNET. To infer potential disease relationships associated with multi-morbidities, we introduced a novel feature for estimating the genetic and functional overlap of two diseases using the top-ranking genes and the associated enrichment of biological functions and pathways (as defined by GO and Reactome). The analysis of this overlap helps to identify the mechanistic role of genes and protein-protein interactions in comorbidities. Finally, we provided an R package, guildifyR, to facilitate programmatic access to GUILDify v2.0 (http://sbi.upf.edu/guildify2). The authors received support from the following:ISCIII–FEDER (PI13/00082, CP10/00524, CPII16/00026); IMI-JU under grants agreements no. 116030(TransQST) and no. 777365 (eTRANSAFE), re-sources of which are composed of financial contribu-tion from the EU-FP7 (FP7/2007- 2013) and EFPIAcompanies in kind contribution; the EU H2020Programme 2014–2020 under grant agreementsno. 634143 (MedBioinformatics) and no. 676559(Elixir-Excelerate); the Spanish Ministry of Economy(MINECO) (BIO2017-85329-R, RYC-2015-17519);and“Unidad de Excelencia María de Maeztu,”fundedby the Spanish Ministry of Economy (ref.: MDM-2014-0370). The Research Programme on BiomedicalInformatics is a member of the Spanish NationalBioinformatics Institute, PRB2-ISCIII, and is supportedby Grant PT13/0001/0023 of the PE I+D+i 2013-2016funded by ISCIII and FEDER

Receptor tyrosine kinases (RTKs) are cell membrane proteins that provide cells with the ability to sense proteins in their environments. Many RTKs are essential to development and organ growth. Derangement of RTKs-by mutation or by overexpression-is central to several developmental and adult disorders including cancer, short stature, and vascular pathologies. The mechanism of action of RTKs is complex and is regulated by contextual components, including the existence of multiple competing ligands and receptors in many families, the intracellular location of the RTK, the dynamic and cell-specific coexpression of other RTKs, and the commonality of downstream signaling pathways. This means that both the state of the cell and the microenvironment outside the cell play a role, which makes sense given the pivotal location of RTKs as the nexus linking the extracellular milieu to intracellular signaling and modification of cell behavior. In this review, we describe these different contextual components through the lens of systems biology, in which both computational modeling and experimental "omics" approaches have been used to better understand RTK networks. The complexity of these networks is such that using these systems biology approaches is necessary to get a handle on the mechanisms of pathology and the design of therapeutics targeting RTKs. In particular, we describe in detail three concrete examples (involving ErbB3, VEGFR2, and AXL) that illustrate how systems approaches can reveal key mechanistic and therapeutic insights. This article is categorized under: Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Therapeutic Methods.

An important goal of systems medicine is to study disease in the context of genetic and environmental perturbations to the human interactome network. For diseases with both genetic and infectious contributors, a key postulate is that similar perturbations of the human interactome by either disease mutations or pathogens can have similar disease consequences. This postulate has so far only been tested for a few viral species at the level of whole proteins. Here, we expand the scope of viral species examined, and test this postulate more rigorously at the higher resolution of protein domains. Focusing on diseases with both genetic and viral contributors, we found significant convergent perturbation of the human domain-resolved interactome by endogenous genetic mutations and exogenous viral proteins inducing similar disease phenotypes. Pan-cancer, pan-oncovirus analysis further revealed that domains of human oncoproteins either physically targeted or structurally mimicked by oncoviruses are enriched for cancer driver rather than passenger mutations, suggesting convergent targeting of cancer driver pathways by diverse oncoviruses. Our study provides a framework for high-resolution, network-based comparison of various disease factors, both genetic and environmental, in terms of their impacts on the human interactome., Author summary Cellular function and behaviour are driven by highly coordinated biomolecular interaction networks. A prime example is the protein-protein interaction network, often simply referred to as the “interactome”. Recent advances in systems biology have spawned the view of human disease as a manifestation of genetic and environmental perturbations to the human interactome, a key postulate being that similar perturbation patterns lead to similar disease phenotypes. Here, we took a structural systems biology approach to compare mutation-induced and virus-induced perturbations of the human interactome in diseases with both genetic and viral contributors. Specifically, we constructed a domain-resolved human-virus protein interactome and characterized the distribution of genetic disease mutations with respect to human domains either physically targeted or structurally mimicked by virus. Overall, we found significant convergent perturbation of the human domain-resolved interactome by viruses and mutations inducing similar disease phenotypes. Structure-guided, integrated analysis of host genetic variation and host-pathogen protein interaction data may help elucidate the molecular mechanisms of infection and reveal its connections to genetic diseases such as cancer, autoimmunity, and neurodegeneration. On a broader note, our finding implies that similar perturbations of the human interactome at the domain level can have similar phenotypic consequences, regardless of the source of perturbation.

Neurology research and clinical practice are transforming toward postgenomics integrative biology. One such example is the study of human brain metabolism that is highly sophisticated due to reactions occurring in and between the astrocytes and neurons. Because of the inherent difficulty of performing experimental studies in human brain, metabolic network modeling has grown in importance to decipher the contribution of brain metabolite kinetics to human health and disease. Multiomics system science-driven metabolic models, using genome-scale and transcriptomics Big Data, offer the promise of new insights on metabolic networks in human brain. Added to this, the availability of omics technologies in both developed and developing world, neurology research, and clinical practice ought to be repositioned with a view to systems medicine. In this expert analysis, we present a critical and in-depth overview of the basic tenets of human brain metabolism, together with the most recent metabolic modeling strategies and computational studies of brain in health and neurological diseases. Human genome-scale metabolic models developed in a both global and brain-specific manner and multiomics synthesis of knowledge are highlighted in particular. We conclude by underscoring the value of multiomics modeling for metabolic diseases and computational investigations of the brain networks, with a view to unlocking the pathophysiology of Alzheimer's disease, Parkinson's disease, migraine, stroke, epilepsy, and multiple sclerosis, among other neurological disorders of importance for global health.

The past decades have witnessed a paradigm shift from the traditional drug discovery shaped around the idea of “one target, one disease” to polypharmacology (multiple targets, one disease). Given the lack of clear-cut boundaries across disease (endo)phenotypes and genetic heterogeneity across patients, a natural extension to the current polypharmacology paradigm is to target common biological pathways involved in diseases via endopharmacology (multiple targets, multiple diseases). In this study, we present proximal pathway enrichment analysis (PxEA) for pinpointing drugs that target common disease pathways towards network endopharmacology. PxEA uses the topology information of the network of interactions between disease genes, pathway genes, drug targets and other proteins to rank drugs by their interactome-based proximity to pathways shared across multiple diseases, providing unprecedented drug repurposing opportunities. Using PxEA, we show that many drugs indicated for autoimmune disorders are not necessarily specific to the condition of interest, but rather target the common biological pathways across these diseases. Finally, we provide high scoring drug repurposing candidates that can target common mechanisms involved in type 2 diabetes and Alzheimer’s disease, two conditions that have recently gained attention due to the increased comorbidity among patients. The authors received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 116030. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. The authors also received support from EU H2020 Programme 2014–2020 under grant agreement No. 676559 (Elixir-Excelerate). E.G. was supported by EU-cofunded Beatriu de Pinós incoming fellowship from the Agency for Management of University and Research Grants (AGAUR) of Government of Catalunya and L.I.F. received support from ISCIII-FEDER (CPII16/00026). H.H.H.W.S. has received funding from from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 777111 (Repotrial). The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), PRB2-ISCIII and is supported by grant PT13/0001/0023, of the PE I+D+i 2013–2016, funded by ISCIII and FEDER. The DCEXS is a “Unidad de Excelencia María de Maeztu”, funded by the MINECO (ref: MDM-2014-0370)

Attempts to develop drugs that address sepsis based on leads developed in animal models have failed. We sought to identify leads based on human data by exploiting a natural experiment: the relative resistance of children to mortality from severe infections and sepsis. Using public datasets, we identified key differences in pathway activity (Pathprint) in blood transcriptome profiles of septic adults and children. To find drugs that could promote beneficial (child) pathways or inhibit harmful (adult) ones, we built an in silico pathway drug network (PDN) using expression correlation between drug, disease, and pathway gene signatures across 58,475 microarrays. Specific pathway clusters from children or adults were assessed for correlation with drug‐based signatures. Validation by literature curation and by direct testing in an endotoxemia model of murine sepsis of the most correlated drug candidates demonstrated that the Pathprint‐PDN methodology is more effective at generating positive drug leads than gene‐level methods (e.g., CMap). Pathway‐centric Pathprint‐PDN is a powerful new way to identify drug candidates for intervention against sepsis and provides direct insight into pathways that may determine survival.

The liver is to date the best example of a sexually dimorphic non-reproductive organ. Over 1000 genes are differentially expressed between sexes indicating that female and male livers are two metabolically distinct organs. The spectrum of liver diseases is broad and is usually prevalent in one or the other sex, with different contributing genetic and environmental factors. It is thus difficult to predict individual’s disease outcomes and treatment options. Systems approaches including mathematical modelling can aid importantly in understanding the multifactorial liver disease etiology leading towards tailored diagnostics, prognostics and therapy. The currently established computational models of hepatic metabolism that have proven to be essential for understanding of non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC) are limited to the description of gender-independent response or reflect solely the response of the males. Herein we present LiverSex, the first sex-based multi-tissue and multi-level liver metabolic computational model. The model was constructed based on in silico liver model SteatoNet and the object-oriented modelling. The crucial factor in adaptation of liver metabolism to the sex is the inclusion of estrogen and androgen receptor responses to respective hormones and the link to sex-differences in growth hormone release. The model was extensively validated on literature data and experimental data obtained from wild type C57BL/6 mice fed with regular chow and western diet. These experimental results show extensive sex-dependent changes and could not be reproduced in silico with the uniform model SteatoNet. LiverSex represents the first large-scale liver metabolic model, which allows a detailed insight into the sex-dependent complex liver pathologies, and how the genetic and environmental factors interact with the sex in disease appearance and progression. We used the model to identify the most important sex-dependent metabolic pathways, which are involved in accumulation of triglycerides representing initial steps of NAFLD. We identified PGC1A, PPARα, FXR, and LXR as regulatory factors that could become important in sex-dependent personalized treatment of NAFLD.

The relationship between metabolism and methylation is considered to be an important aspect of cancer development and drug efficacy. However, it remains poorly defined how to apply this aspect to improve preclinical disease characterization and clinical treatment outcome. Using available molecular information from Kyoto Encyclopedia of Genes and Genomes (KEGG) and literature, we constructed a large-scale knowledge-based metabolic in silico model. For the purpose of model validation, we applied data from the Cancer Cell Line Encyclopedia (CCLE) to investigate computationally the impact of metabolism on chemotherapy efficacy. In our model, different metabolic components such as MAT2A, ATP6V0E1, NNMT involved in methionine cycle correlate with biologically measured chemotherapy outcome (IC50) that are in agreement with findings of independent studies. These proteins are potentially also involved in cellular methylation processes. In addition, several components such as 3,4-dihydoxymandelate, PAPSS2, UPP1 from metabolic pathways involved in the production of purine and pyrimidine correlate with IC50. This study clearly demonstrates that complex computational approaches can reflect findings of biological experiments. This demonstrates their high potential to grasp complex issues within systems medicine such as response prediction, biomarker identification using available data resources.

Microsatellite instability (MSI) leads to accumulation of an excessive number of mutations in the genome, mostly small insertions and deletions. MSI colorectal cancers (CRCs), however, also contain more point mutations than microsatellite-stable (MSS) tumors, yet they have not been as comprehensively studied. To identify candidate driver genes affected by point mutations in MSI CRC, we ranked genes based on mutation significance while correcting for replication timing and gene expression utilizing an algorithm, MutSigCV. Somatic point mutation data from the exome kit-targeted area from 24 exome-sequenced sporadic MSI CRCs and respective normals, and 12 whole-genome-sequenced sporadic MSI CRCs and respective normals were utilized. The top 73 genes were validated in 93 additional MSI CRCs. The MutSigCV ranking identified several well-established MSI CRC driver genes and provided additional evidence for previously proposed CRC candidate genes as well as shortlisted genes that have to our knowledge not been linked to CRC before. Two genes, SMARCB1 and STK38L, were also functionally scrutinized, providing evidence of a tumorigenic role, for SMARCB1 mutations in particular. © 2018 The Authors. Published under the terms of the CC BY 4.0 license

New technologies to generate, store and retrieve medical and research data are inducing a rapid change in clinical and translational research and health care. Systems medicine is the interdisciplinary approach wherein physicians and clinical investigators team up with experts from biology, biostatistics, informatics, mathematics and computational modeling to develop methods to use new and stored data to the benefit of the patient. We here provide a critical assessment of the opportunities and challenges arising out of systems approaches in medicine and from this provide a definition of what systems medicine entails. Based on our analysis of current developments in medicine and healthcare and associated research needs, we emphasize the role of systems medicine as a multilevel and multidisciplinary methodological framework for informed data acquisition and interdisciplinary data analysis to extract previously inaccessible knowledge for the benefit of patients. Creating specialised computer systems to integrate big data from multiple sources may help provide tailored future healthcare. A healthcare system that meets the needs of every individual needs the ability to collect and mine data precisely and efficiently. Olaf Wolkenhauer at Rostock University, Germany and co-workers have explored the requirements for developing this ‘systems medicine’ approach. Interdisciplinary teams should be established specifically to develop software, computational models and integrated datasets for use in healthcare. Being able to harness information from huge medical datasets could dramatically improve our understanding of disease, and allow scientists to examine illnesses from the molecular scale to whole body responses. Systems medicine could also allow doctors to investigate an individual's specific needs, trial treatments through computer models, and analyze data captured on patients' smartphones, to provide safer, more personalised heathcare.

Chronic inflammatory diseases represent an increasing medical burden, yet neither tools to predict the individual disease course nor causal cures are at hand. We discuss opportunities for systems medicine to derive precise, individualized disease models and outline the European consortium SYSCID as part of the roadmap to clinical practice.

The human gut microbiota encompasses a densely populated ecosystem that provides essential functions for host development, immune maturation, and metabolism. Alterations to the gut microbiota have been observed in numerous diseases, including human metabolic diseases such as obesity, type 2 diabetes (T2D), and irritable bowel syndrome, and some animal experiments have suggested causality. However, few studies have validated causality in humans and the underlying mechanisms remain largely to be elucidated. We discuss how systems biology approaches combined with new experimental technologies may disentangle some of the mechanistic details in the complex interactions of diet, microbiota, and host metabolism and may provide testable hypotheses for advancing our current understanding of human-microbiota interaction.