A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization

Identification of New Toxicity Mechanisms in Drug-Induced Liver Injury through Systems Pharmacology

Among adverse drug reactions, drug-induced liver injury presents particular challenges because of its complexity, and the underlying mechanisms are still not completely characterized. Our knowledge of the topic is limited and based on the assumption that a drug acts on one molecular target. We have leveraged drug polypharmacology, i.e., the ability of a drug to bind multiple targets and thus perturb several biological processes, to develop a systems pharmacology platform that integrates all drug–target interactions. Our analysis sheds light on the molecular mechanisms of drugs involved in drug-induced liver injury and provides new hypotheses to study this phenomenon.

A new grid- and modularity-based layout algorithm for complex biological networks

The visualization of biological networks is critically important to aid researchers in understanding complex biological systems and arouses interest in designing efficient layout algorithms to draw biological networks according to their topology structures, especially for those networks with potential modules. The algorithms of grid layout series have an advantage in generating compact layouts with overlap-free nodes compared to force-directed; however, extant grid layout algorithms have difficulty in drawing modular networks and often generate layouts of high visual complexity when applied to networks with dense or clustered connectivity structure. To specifically assist the study of modular networks, we propose a grid- and modularity-based layout algorithm (GML) that consists of three stages: network preprocessing, module layout and grid optimization. The algorithm can draw complex biological networks with or without predefined modules based on the grid layout algorithm. It also outperforms other existing grid-based algorithms in the measurement of computation performance, ratio of edge-edge/node-edge crossings, relative edge lengths, and connectivity F-measures. GML helps users to gain insight into the network global characteristics through module layout, as well as to discern network details with grid optimization. GML has been developed as a VisANT plugin (https://hscz.github.io/Biological-Network-Visualization/) and is freely available to the research community.

Mapping Collaborative Relations among Canada's Chronic Disease Prevention Organizations

In the field of chronic disease prevention (CDP), collaborations between organizations provide a vital framework for intersectoral engagement and exchanges of knowledge, expertise and resources. However, little is known about how the structures of preventive health systems actually articulate with CDP capacity and outcomes. Drawing upon data from the Public Health Organizational Capacity Study - a repeat census of all public health organizations in Canada - we used social network analysis to map and examine interorganizational collaborative relationships in the Canadian preventive health system. The network of relationships obtained through our study shows that provincial boundaries remain a major factor influencing collaborative patterns. Not only are collaborations scarce on the interprovincial level but they are also mostly limited to links with federal and multi-provincial organizations. Given this finding, federal or multi-provincial organizations that occupy central bridging positions in the Canadian CDP collaborative structure should serve as key players for shaping CDP practices in the country.

Integrative systems analysis of diet-induced obesity identified a critical transition in the transcriptomes of the murine liver and epididymal white adipose tissue

BACKGROUND

It is well known that high-fat diet (HFD) can cause immune system-related pathological alterations after a significant body weight gain. The mechanisms of the delayed pathological alterations during the development of diet-induced obesity (DIO) are not fully understood.

METHODS

To elucidate the mechanisms underlying DIO development, we analyzed time-course microarray data obtained from a previous study. First, differentially expressed genes (DEGs) were identified at each time point by comparing the hepatic transcriptome of mice fed HFD with that of mice fed normal diet. Next, we clustered the union of DEGs and identified annotations related to each cluster. Finally, we constructed an 'integrated obesity-associated gene regulatory network (GRN) in murine liver'. We analyzed the epididymal white adipose tissue (eWAT) transcriptome usig the same procedure.

RESULTS

Based on time-course microarray data, we found that the genes associated with immune responses were upregulated with an oscillating expression pattern between weeks 2 and 8, relatively downregulated between weeks 12 and 16, and eventually upregulated after week 20 in the liver of the mice fed HFD. The genes associated with immune responses were also upregulated at late stage, in the eWAT of the mice fed HFD. These results suggested that a critical transition occurred in the immune system-related transcriptomes of the liver and eWAT around week 16 of the DIO development, and this may be associated with the delayed pathological alterations. The GRN analysis suggested that Maff may be a key transcription factor for the immune system-related critical transition thatoccurred at week 16. We found that transcription factors associated with immune responses were centrally located in the integrated obesity-associated GRN in the liver.

CONCLUSIONS

In this study, systems analysis identified regulatory network modules underlying the delayed immune system-related pathological changes during the development of DIO and could suggest possible therapeutic targets.

Comprehensive metabolome analyses reveal N-acetylcysteine-responsive accumulation of kynurenine in systemic lupus erythematosus: implications for activation of the mechanistic target of rapamycin

Systemic lupus erythematosus (SLE) patients exhibit depletion of the intracellular antioxidant glutathione and downstream activation of the metabolic sensor, mechanistic target of rapamycin (mTOR). Since reversal of glutathione depletion by the amino acid precursor, N-acetylcysteine (NAC), is therapeutic in SLE, its mechanism of impact on the metabolome was examined within the context of a double-blind placebo-controlled trial. Quantitative metabolome profiling of peripheral blood lymphocytes (PBL) was performed in 36 SLE patients and 42 healthy controls matched for age, gender, and ethnicity of patients using mass spectrometry that covers all major metabolic pathways. mTOR activity was assessed by western blot and flow cytometry. Metabolome changes in lupus PBL affected 27 of 80 KEGG pathways at FDR p < 0.05 with most prominent impact on the pentose phosphate pathway (PPP). While cysteine was depleted, cystine, kynurenine, cytosine, and dCTP were the most increased metabolites. Area under the receiver operating characteristic curve (AUC) logistic regression approach identified kynurenine (AUC = 0.859), dCTP (AUC = 0.762), and methionine sulfoxide (AUC = 0.708), as top predictors of SLE. Kynurenine was the top predictor of NAC effect in SLE (AUC = 0.851). NAC treatment significantly reduced kynurenine levels relative to placebo in vivo (raw p = 2.8 × 10^-7, FDR corrected p = 6.6 × 10^-5). Kynurenine stimulated mTOR activity in healthy control PBL in vitro. Metabolome changes in lupus PBL reveal a dominant impact on the PPP that reflect greater demand for nucleotides and oxidative stress. The PPP-connected and NAC-responsive accumulation of kynurenine and its stimulation of mTOR are identified as novel metabolic checkpoints in lupus pathogenesis.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Genetic variants and their interactions in disease risk prediction - machine learning and network perspectives

A central challenge in systems biology and medical genetics is to understand how interactions among genetic loci contribute to complex phenotypic traits and human diseases. While most studies have so far relied on statistical modeling and association testing procedures, machine learning and predictive modeling approaches are increasingly being applied to mining genotype-phenotype relationships, also among those associations that do not necessarily meet statistical significance at the level of individual variants, yet still contributing to the combined predictive power at the level of variant panels. Network-based analysis of genetic variants and their interaction partners is another emerging trend by which to explore how sub-network level features contribute to complex disease processes and related phenotypes. In this review, we describe the basic concepts and algorithms behind machine learning-based genetic feature selection approaches, their potential benefits and limitations in genome-wide setting, and how physical or genetic interaction networks could be used as a priori information for providing improved predictive power and mechanistic insights into the disease networks. These developments are geared toward explaining a part of the missing heritability, and when combined with individual genomic profiling, such systems medicine approaches may also provide a principled means for tailoring personalized treatment strategies in the future.