Systems biology approaches and tools for analysis of interactomes and multi-target drugs

The psychoactive effects of Bryophyllum pinnatum (Lam.) Oken leaves in young zebrafish

Bryophyllum pinnatum (Lam.) Oken (BP) is a plant that is used worldwide to treat inflammation, infections, anxiety, restlessness, and sleep disorders. While it is known that BP leaves are rich in flavonoids, the extent of the beneficial and toxic effects of its crude extracts remains unclear. Although some neurobehavioral studies using leaf extracts have been conducted, none has examined the effects of water-extracted leaf samples. The zebrafish is a powerful animal model used to gain insights into the efficacy and toxicity profiles of this plant due to its high fecundity, external development, and ease of performing behavioral assays. In this study, we performed behavioral testing after acute exposure to different concentrations of aqueous extract from leaves of B. pinnatum (LABP) on larval zebrafish, investigating light/dark preference, thigmotaxis, and locomotor activity parameters under both normal and stressed conditions. LABP demonstrated dose-and time-dependent biphasic effects on larval behavior. Acute exposure (25 min) to 500 mg/L LABP resulted in decreased locomotor activity. Exposure to 300 mg/L LABP during the sleep cycle decreased dark avoidance and thigmotaxis while increasing swimming velocity. After sleep deprivation, the group treated with 100 mg/L LABP showed decreased dark avoidance and increased velocity. After a heating stressor, the 30 mg/L and 300 mg/L LABP-treated groups showed decreased dark avoidance. These results suggest both anxiolytic and psychoactive effects of LABP in a dose-dependent manner in a larval zebrafish model. These findings provide a better understanding of the mechanisms underlying relevant behavioral effects, consequently supporting the safe and effective use of LABP for the treatment of mood disorders.

Systems biology analysis of lung fibrosis-related genes in the bleomycin mouse model

Tissue fibrosis is a major driver of pathology in aging and is involved in numerous age-related diseases. The lungs are particularly susceptible to fibrotic pathology which is currently difficult to treat. The mouse bleomycin-induced fibrosis model was developed to investigate lung fibrosis and widely used over the years. However, a systematic analysis of the accumulated results has not been performed. We undertook a comprehensive data mining and subsequent manual curation, resulting in a collection of 213 genes (available at the TiRe database, www.tiredb.org ), which when manipulated had a clear impact on bleomycin-induced lung fibrosis. Our meta-analysis highlights the age component in pulmonary fibrosis and strong links of related genes with longevity. The results support the validity of the bleomycin model to human pathology and suggest the importance of a multi-target therapeutic strategy for pulmonary fibrosis treatment.

Integrated multiomics analysis identifies molecular landscape perturbations during hyperammonemia in skeletal muscle and myotubes

Ammonia is a cytotoxic molecule generated during normal cellular functions. Dysregulated ammonia metabolism, which is evident in many chronic diseases such as liver cirrhosis, heart failure, and chronic obstructive pulmonary disease, initiates a hyperammonemic stress response in tissues including skeletal muscle and in myotubes. Perturbations in levels of specific regulatory molecules have been reported, but the global responses to hyperammonemia are unclear. In this study, we used a multiomics approach to vertically integrate unbiased data generated using an assay for transposase-accessible chromatin with high-throughput sequencing, RNA-Seq, and proteomics. We then horizontally integrated these data across different models of hyperammonemia, including myotubes and mouse and human muscle tissues. Changes in chromatin accessibility and/or expression of genes resulted in distinct clusters of temporal molecular changes including transient, persistent, and delayed responses during hyperammonemia in myotubes. Known responses to hyperammonemia, including mitochondrial and oxidative dysfunction, protein homeostasis disruption, and oxidative stress pathway activation, were enriched in our datasets. During hyperammonemia, pathways that impact skeletal muscle structure and function that were consistently enriched were those that contribute to mitochondrial dysfunction, oxidative stress, and senescence. We made several novel observations, including an enrichment in antiapoptotic B-cell leukemia/lymphoma 2 family protein expression, increased calcium flux, and increased protein glycosylation in myotubes and muscle tissue upon hyperammonemia. Critical molecules in these pathways were validated experimentally. Human skeletal muscle from patients with cirrhosis displayed similar responses, establishing translational relevance. These data demonstrate complex molecular interactions during adaptive and maladaptive responses during the cellular stress response to hyperammonemia.

Differential Analysis of Serum Principal Components Treated with Compound Sophora Decoction and Related Compounds Based on High-Resolution Mass Spectrometry (HRMS)

OBJECTIVE

To compare the differences in the serum principal components in ulcerative colitis- (UC-) induced rats, treated with compound Sophora decoction, matrine, oxymatrine monomer mixture, and indirubin monomer, and to provide a modern scientific basis for elucidating the clinical efficacy of compound Sophora decoction for the treatment of UC.

METHODS

The serum samples of rats from each group were obtained after drug administration, and the serum principal components of each group were analyzed by high-resolution mass spectrometry. Agilent Eclipse XDB C18 chromatographic column (100 mm × 2.1 mm, 3.5 m) was used for separation. The mobile phase was water (A) and methanol (B) (0.1% formic acid) gradient elution, 0-3 min (B: 20%-40%), 3-10 min (B: 40%-54%), 10-25 min (B: 54%), 25-35 min (B: 54%-70%), 35-45 min (B: 70%-80%), 45-50 min (B: 80%), 50-60 min (B: 80%-100%), 70-72 min (B: 100%-20%), and 72-77 min (B: 20%); flow rate, 300 μL/min; column temperature, 40°C; and injection volume, 10 μL. ESI source was selected and scanned in the positive and negative ion modes. The scanning range was 70-1500 m/z; ion-source gas 1 (GS1): 55 psi; ion-source gas 2 (GS2): 60 psi; CUR: 30 psi; ion-source temperature (TEM): 550°C; ion-source voltage (ISVF) : 5500 V/-4500 V; decluster voltage (DP): 100 V; collision energy (CE): 35 V/-35 V; collision energy gain (CES) : 15 V/-15 V; and data acquisition mode: IDA. After the data from each group were imported into MarkView 1.3, the molecular weights and retention times of different substances were obtained and qualitatively analyzed by ChemSpider and PeakView 2.0.

RESULTS

In the negative ion mode, 26 differential compounds were identified in the compound Sophora decoction group (FFKST) compared to the model group (M), and 18 differential compounds were identified in the matrine and oxymatrine group (KST) compared to the model group (M). In the positive ion mode, 11 and 7 differential compounds were identified in the compound Sophora decoction group (FFKST) and the matrine and oxymatrine group (KST) compared to the model group (M), respectively. The responses of all compounds in each group were compared with each other. As the different principal component substances in the indirubin group (DYH) displayed little correlation with other groups, the different components in this group were not researched thoroughly.

CONCLUSION

By comparing the differences in the serum principal components from each administration group, we found that the FFKST group exhibited enhanced synthesis of the serum principal components; however, the compound doses of matrine and oxymatrine monomers did not exhibit the same changes in the serum principal components of UC-induced rats. Finally, the traditional Chinese medicine compound is more advantageous than monomers.

The Effect of Glutamatergic Modulators on Extracellular Glutamate: How Does this Information Contribute to the Discovery of Novel Antidepressants?

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The complexity of glutamatergic signaling challenges glutamate modulator usage.

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Functional biomarkers are needed to understand the MOA of glutamate modulators.

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Evaluating drug effect on EAATs' kinetics may add to antidepressant discovery.

Background

In the search for new antidepressants, clinical researchers have been using drugs that simultaneously modulate multiple targets. During preclinical and clinical trials, the glutamatergic modulators riluzole and ketamine have received particular attention. Glutamatergic agents have a modulatory effect on synaptic transmission, so they can act on both neurons and astrocytes. In addition to influencing the quantity of glutamate released, these modulators can also affect the expression, localization, and functionality of glutamate-binding sites.

Objective

This review discusses the complexity of the glutamatergic system, the ambiguity of data regarding glutamate levels in patients with depression, as well as the mechanisms of action for riluzole and ketamine, which includes their relation to the physiology of glutamatergic transmission. The principal aim is to contribute to the development of novel glutamatergic antidepressant medications whilst emphasizing the need for innovative approaches that evaluate their effects on extracellular glutamate.

Methods

Literature was obtained via PubMed by searching the term depression in combination with each of the following terms: riluzole, ketamine, and glutamate. The search was restricted to full-text articles published in English between 1985 and 2018 relating to both the modulatory mechanisms of glutamatergic-binding proteins and the antidepressant actions of these medicines. Articles about mechanisms associated with synaptic plasticity and antidepressant effects were excluded.

Results

Although experimental data relates glutamatergic signaling to the pathophysiology of major depression and bipolar disorder, the role of glutamate—as well as its extracellular concentration in patients with said disorders—is still unclear. Riluzole's antidepressant action is ascribed to its capacity to reduce glutamate levels in the synaptic cleft, and ketamine's effect has been associated with increased extracellular glutamate levels.

Conclusions

The strategy of using glutamatergic modulators as therapeutic agents requires a better understanding of the role of glutamate in the pathophysiology of depression. Gaining such understanding is a challenge because it entails evaluating different targets as well as the effects of these modulators on the kinetics of glutamate uptake. Essentially, glutamate transport is a dynamic process and, currently, it is still necessary to develop new approaches to assay glutamate in the synaptic cleft. ORCID: 0000-0002-3358-6939.

Stress dynamically regulates co-expression networks of glucocorticoid receptor-dependent MDD and SCZ risk genes

Early-life adversity is an important risk factor for major depressive disorder (MDD) and schizophrenia (SCZ) that interacts with genetic factors to confer disease risk through mechanisms that are still insufficiently understood. One downstream effect of early-life adversity is the activation of glucocorticoid receptor (GR)-dependent gene networks that drive acute and long-term adaptive behavioral and cellular responses to stress. We have previously shown that genetic variants that moderate GR-induced gene transcription (GR-response eSNPs) are significantly enriched among risk variants from genome-wide association studies (GWASs) for MDD and SCZ. Here, we show that the 63 transcripts regulated by these disease-associated functional genetic variants form a tight glucocorticoid-responsive co-expression network (termed GCN). We hypothesized that changes in the correlation structure of this GCN may contribute to early-life adversity-associated disease risk. Therefore, we analyzed the effects of different qualities of social support and stress throughout life on GCN formation across distinct brain regions using a translational mouse model. We observed that different qualities of social experience substantially affect GCN structure in a highly brain region-specific manner. GCN changes were predominantly found in two functionally interconnected regions, the ventral hippocampus and the hypothalamus, two brain regions previously shown to be of relevance for the stress response, as well as psychiatric disorders. Overall, our results support the hypothesis that a subset of genetic variants may contribute to risk for MDD and SCZ by altering circuit-level effects of early and adult social experiences on GCN formation and structure.

Analyzing of Molecular Networks for Human Diseases and Drug Discovery

Molecular networks represent the interactions and relations of genes/proteins, and also encode molecular mechanisms of biological processes, development and diseases. Among the molecular networks, protein-protein Interaction Networks (PINs) have become effective platforms for uncovering the molecular mechanisms of diseases and drug discovery. PINs have been constructed for various organisms and utilized to solve many biological problems. In human, most proteins present their complex functions by interactions with other proteins, and the sum of these interactions represents the human protein interactome. Especially in the research on human disease and drugs, as an emerging tool, the PIN provides a platform to systematically explore the molecular complexities of specific diseases and the references for drug design. In this review, we summarized the commonly used approaches to aid disease research and drug discovery with PINs, including the network topological analysis, identification of novel pathways, drug targets and sub-network biomarkers for diseases. With the development of bioinformatic techniques and biological networks, PINs will play an increasingly important role in human disease research and drug discovery.

In vitro toxicity testing of cigarette smoke based on the air-liquid interface exposure: A review

Cigarette smoke is a complex aerosol comprising particulate phase and gaseous vapour phase. The air-liquid interface exposure provides a possible technical means to implement whole smoke exposure for the assessment of tobacco products. In this review, the research progress in the in vitro toxicity testing of cigarette smoke based on the air-liquid interface exposure is summarized. The contents presented involve mainly cytotoxicity, genotoxicity, oxidative stress, inflammation, systems toxicology, 3D culture and cigarette smoke dosimetry related to cigarette smoke, as well as the assessment of electronic cigarette aerosol. Prospect of the application of the air-liquid interface exposure method in assessing the biological effects of tobacco smoke is discussed.

Circuit-wide Transcriptional Profiling Reveals Brain Region-Specific Gene Networks Regulating Depression Susceptibility

Depression is a complex, heterogeneous disorder and a leading contributor to the global burden of disease. Most previous research has focused on individual brain regions and genes contributing to depression. However, emerging evidence in humans and animal models suggests that dysregulated circuit function and gene expression across multiple brain regions drive depressive phenotypes. Here, we performed RNA sequencing on four brain regions from control animals and those susceptible or resilient to chronic social defeat stress at multiple time points. We employed an integrative network biology approach to identify transcriptional networks and key driver genes that regulate susceptibility to depressive-like symptoms. Further, we validated in vivo several key drivers and their associated transcriptional networks that regulate depression susceptibility and confirmed their functional significance at the levels of gene transcription, synaptic regulation, and behavior. Our study reveals novel transcriptional networks that control stress susceptibility and offers fundamentally new leads for antidepressant drug discovery.

Graph theoretic network analysis reveals protein pathways underlying cell death following neurotropic viral infection

Complex protein networks underlie any cellular function. Certain proteins play a pivotal role in many network configurations, disruption of whose expression proves fatal to the cell. An efficient method to tease out such key proteins in a network is still unavailable. Here, we used graph-theoretic measures on protein-protein interaction data (interactome) to extract biophysically relevant information about individual protein regulation and network properties such as formation of function specific modules (sub-networks) of proteins. We took 5 major proteins that are involved in neuronal apoptosis post Chandipura Virus (CHPV) infection as seed proteins in a database to create a meta-network of immediately interacting proteins (1^st order network). Graph theoretic measures were employed to rank the proteins in terms of their connectivity and the degree upto which they can be organized into smaller modules (hubs). We repeated the analysis on 2^nd order interactome that includes proteins connected directly with proteins of 1^st order. FADD and Casp-3 were connected maximally to other proteins in both analyses, thus indicating their importance in neuronal apoptosis. Thus, our analysis provides a blueprint for the detection and validation of protein networks disrupted by viral infections.

Systematic synergy modeling: understanding drug synergy from a systems biology perspective

Owing to drug synergy effects, drug combinations have become a new trend in combating complex diseases like cancer, HIV and cardiovascular diseases. However, conventional synergy quantification methods often depend on experimental dose–response data which are quite resource-demanding. In addition, these methods are unable to interpret the explicit synergy mechanism. In this review, we give representative examples of how systems biology modeling offers strategies toward better understanding of drug synergy, including the protein-protein interaction (PPI) network-based methods, pathway dynamic simulations, synergy network motif recognitions, integrative drug feature calculations, and “omic”-supported analyses. Although partially successful in drug synergy exploration and interpretation, more efforts should be put on a holistic understanding of drug-disease interactions, considering integrative pharmacology and toxicology factors. With a comprehensive and deep insight into the mechanism of drug synergy, systems biology opens a novel avenue for rational design of effective drug combinations.

Analysis of miRNA, mRNA, and TF interactions through network-based methods

Recent findings have elucidated that the regulation of messenger RNA (mRNA) levels is due to the synergistic and antagonist actions of transcription factors (TFs) and microRNAs (miRNAs). Mutual interactions among these molecules are easily modeled and analyzed using graphs whose nodes are molecules, and directed edges represent the associations among them. In particular, small subgraphs having three nodes also referred to as feed-forward loops (FFLs) or regulatory loops play a crucial role in many different diseases, such as cancer. Available technological platforms enable the investigation of only a single aspect of these mechanisms, e.g., the quantification of levels of mRNA or miRNA. Consequently, there exist different data sources for investigating some aspects of this problem, e.g., miRNA-mRNA or TF-mRNA associations. The comprehensive analysis is made possible only by the integration and the analysis of these data sources. Currently, the interest of researchers in this area is growing, the number of projects is increasing, and the number of challenges and issues for computer scientists is considerable. The need for an introductive survey from a computer science point of view consequently arises. This survey starts by discussing general concepts related to production of data. Then, main existing approaches of analysis are presented and discussed. Future improvements and challenges are also discussed.

Proteomics, metabolomics, and protein interactomics in the characterization of the molecular features of major depressive disorder

Omics technologies emerged as complementary strategies to genomics in the attempt to understand human illnesses. In general, proteomics technologies emerged earlier than those of metabolomics for major depressive disorder (MDD) research, but both are driven by the identification of proteins and/or metabolites that can delineate a comprehensive characterization of MDD's molecular mechanisms, as well as lead to the identification of biomarker candidates of all types—prognosis, diagnosis, treatment, and patient stratification. Also, one can explore protein and metabolite interactomes in order to pinpoint additional molecules associated with the disease that had not been picked up initially. Here, results and methodological aspects of MDD research using proteomics, metabolomics, and protein interactomics are reviewed, focusing on human samples.

Drug repositioning by applying 'expression profiles' generated by integrating chemical structure similarity and gene semantic similarity

Drug repositioning, also known as drug repurposing or reprofiling, is the process of finding new indications for established drugs. Because drug repositioning can reduce costs and enhance the efficiency of drug development, it is of paramount importance in medical research. Here, we present a systematic computational method to identify potential novel indications for a given drug. This method utilizes some prior knowledge such as 3D drug chemical structure information, drug-target interactions and gene semantic similarity information. Its prediction is based on another form of 'expression profile', which contains scores ranging from -1 to 1, reflecting the consensus response scores (CRSs) between each drug of 965 and 1560 proteins. The CRS integrates chemical structure similarity and gene semantic similarity information. We define the degree of similarity between two drugs as the absolute value of their correlation coefficients. Finally, we establish a drug similarity network (DSN) and obtain 33 modules of drugs with similar modes of action, determining their common indications. Using these modules, we predict new indications for 143 drugs and identify previously unknown indications for 42 drugs without ATC codes. This method overcomes the instability of gene expression profiling derived from experiments due to experimental conditions, and predicts indications for a new compound feasibly, requiring only the 3D structure of the compound. In addition, the high literature validation rate of 71.8% also suggests that our method has the potential to discover novel drug indications for existing drugs.

Rectifying cancer drug discovery through network pharmacology

In spite of the expensive preclinical testing, the consistent failure to translate many promising targeted drugs from the laboratory bench to the clinic raises the question of whether the single-pathway drug-discovery strategies offer the correct perspective. As revealed by network biology, cancers harbor robust biological networks that are inherently resistant to changes, such as those induced by drugs with very narrow mechanisms of action. Therefore, network pharmacology strategies, the treatment of cancer by modulating more than one target, are needed. Different promiscuous approaches targeting multiple avenues within cancer-associated networks, such as the pleiotropic natural products, are emerging. Nevertheless, there is a long way before such 'proof-of-concept strategies' can be successfully applied in the clinical setting. This article provides a perspective on the current challenges in drug discovery, the reasons for high failure rates and how network pharmacology can aid the successful design of agents against cancer.

Metabolomic and network analysis of pharmacotherapies for sensorineural hearing loss

HYPOTHESIS

Different pharmacotherapies for sensorineural hearing loss (SNHL) are interconnected in metabolic networks with molecular hubs.

BACKGROUND

Sensorineural hearing loss is the most common sensory deficit worldwide. Dozens of drugs have shown efficacy against SNHL in animal studies and a few in human studies. Analyzing metabolic networks that interconnect these drugs will point to and prioritize development of new pharmacotherapies for human SNHL.

METHODS

Drugs that have shown efficacy in treating mammalian SNHL were identified through PubMed literature searches. The drugs were analyzed using the metabolomic analysis and the "grow-tool function" in ingenuity pathway analysis (IPA). The top 3 most interconnected molecules and drugs (i.e., the hubs) within the generated networks were considered important targets for the treatment of SNHL.

RESULTS

A total of 70 drugs were investigated with IPA. The metabolomic analysis revealed 2 statistically significant networks (Networks 1 and 2). A network analysis using the "grow-tool function" generated one statistically significant network (Network 3). Hubs of these networks were as follows: P38 mitogen-activated protein kinases (P38 MAPK), p42/p44 MAP kinase (ERK1/2) and glutathione for Network 1; protein kinase B (Akt), nuclear factor kappa B (NFkB) and ERK for Network 2; and dexamethasone, tretinoin, and cyclosporin A for Network 3.

CONCLUSION

Metabolomic and network analysis of the existing pharmacotherapies for SNHL has pointed to and prioritized a number of potential novel targets for treatment of SNHL.

Proteomics for systems toxicology

Current toxicology studies frequently lack measurements at molecular resolution to enable a more mechanism-based and predictive toxicological assessment. Recently, a systems toxicology assessment framework has been proposed, which combines conventional toxicological assessment strategies with system-wide measurement methods and computational analysis approaches from the field of systems biology. Proteomic measurements are an integral component of this integrative strategy because protein alterations closely mirror biological effects, such as biological stress responses or global tissue alterations. Here, we provide an overview of the technical foundations and highlight select applications of proteomics for systems toxicology studies. With a focus on mass spectrometry-based proteomics, we summarize the experimental methods for quantitative proteomics and describe the computational approaches used to derive biological/mechanistic insights from these datasets. To illustrate how proteomics has been successfully employed to address mechanistic questions in toxicology, we summarized several case studies. Overall, we provide the technical and conceptual foundation for the integration of proteomic measurements in a more comprehensive systems toxicology assessment framework. We conclude that, owing to the critical importance of protein-level measurements and recent technological advances, proteomics will be an integral part of integrative systems toxicology approaches in the future.

Cytotoxicity and modes of action of five Cameroonian medicinal plants against multi-factorial drug resistance of tumor cells

ETHNOPHARMACOLOGICAL RELEVANCE

Beilschmiedia acuta Kosterm, Clausena anisata (Willd) Hook, Fagara tessmannii Engl., Newbouldia laevis Seem., and Polyscias fulva (Hiern) Harms. are medicinal plants used in Cameroonian traditional medicine in the treatment of various types of cancers. The present study aims at investigating 11 methanolic extracts from the above Cameroonian medicinal plants on a panel of human cancer cell lines, including various drug-resistant phenotypes. Possible modes of action were analyzed for two extracts from Beilschmiedia acuta and Polyscia fulva and alpha-hederin, the representative constituent of Polyscia fulva.

MATERIALS AND METHODS

Cytotoxicity was determined using a resazurin assay. Cell cycle, apoptosis, mitochondrial membrane potential (MMP), and reactive oxygen species (ROS) were measured by flow cytometry. Cellular response to alpha-hederin was investigated by a mRNA microarray approach.

RESULTS

Prescreening of extracts (40µg/mL) showed that three of eleven plant extracts inhibited proliferation of CCRF-CEM cells by more than 50%, i.e. BAL (73.65%), the bark extract of Beilschmiedia acuta (78.67%) and PFR (68.72%). Subsequent investigations revealed IC50 values below or around 30µg/mL of BAL and PFR in 10 cell lines, including drug-resistant models, i.e. P-glycoprotein-overexpressing CEM/ADR5000, breast cancer resistance protein-transfected MDA-MB-231-BCRP, TP53 knockout cells (HCT116 p53(-/-)), and mutation-activated epidermal growth factor receptor-transfected U87MG.ΔEGFR cells. IC50 values below 5µg/mL of BAL were obtained for HCT116 (p53(-/-)) cells. IC50 values below 10µM of alpha-hederin were found for sensitive CCRF-CEM and multidrug-resistant CEM/ADR5000 cells. The BAL and PFR extracts induced cell cycle arrest between G0/G1 and S phases. PFR-induced apoptosis was associated with increased ROS generation and MMP breakdown. Microarray-based cluster analysis revealed a gene expression profile that predicted cellular response to alpha-hederin.

CONCLUSION

BAL, PFL and alpha-hederin, an exemplarily taken constituent of Beilschmiedia acuta and Polyscia fulva extracts revealed cytotoxicity towards cancer cell lines. Hence, Beilschmiedia acuta and Polyscia fulva may be valuable to develop drugs against otherwise drug-resistant cancer cells.

Biomarkers in inflammatory bowel diseases: Current status and proteomics identification strategies

Unambiguous diagnosis of the two main forms of inflammatory bowel diseases (IBD): Ulcerative colitis (UC) and Crohn’s disease (CD), represents a challenge in the early stages of the diseases. The diagnosis may be established several years after the debut of symptoms. Hence, protein biomarkers for early and accurate diagnostic could help clinicians improve treatment of the individual patients. Moreover, the biomarkers could aid physicians to predict disease courses and in this way, identify patients in need of intensive treatment. Patients with low risk of disease flares may avoid treatment with medications with the concomitant risk of adverse events. In addition, identification of disease and course specific biomarker profiles can be used to identify biological pathways involved in the disease development and treatment. Knowledge of disease mechanisms in general can lead to improved future development of preventive and treatment strategies. Thus, the clinical use of a panel of biomarkers represents a diagnostic and prognostic tool of potentially great value. The technological development in recent years within proteomic research (determination and quantification of the complete protein content) has made the discovery of novel biomarkers feasible. Several IBD-associated protein biomarkers are known, but none have been successfully implemented in daily use to distinguish CD and UC patients. The intestinal tissue remains an obvious place to search for novel biomarkers, which blood, urine or stool later can be screened for. When considering the protein complexity encountered in intestinal biopsy-samples and the recent development within the field of mass spectrometry driven quantitative proteomics, a more thorough and accurate biomarker discovery endeavor could today be performed than ever before. In this review, we report the current status of the proteomics IBD biomarkers and discuss various emerging proteomic strategies for identifying and characterizing novel biomarkers, as well as suggesting future targets for analysis.

VNP: Interactive Visual Network Pharmacology of Diseases, Targets, and Drugs

In drug discovery, promiscuous targets, multifactorial diseases, and "dirty" drugs construct complex network relationships. Network pharmacology 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 target selection and drug design. Visual network pharmacology (VNP) is developed to visualize network pharmacology of targets, diseases, and drugs with a graph network by using disease, target or drug names, chemical structures, or protein sequence. To our knowledge, VNP is the first free interactive VNP server that should be very helpful for systems pharmacology research. VNP is freely available at http://cadd.whu.edu.cn/ditad/vnpsearch.

Network understanding of herb medicine via rapid identification of ingredient-target interactions

Today, herb medicines have become the major source for discovery of novel agents in countermining diseases. However, many of them are largely under-explored in pharmacology due to the limitation of current experimental approaches. Therefore, we proposed a computational framework in this study for network understanding of herb pharmacology via rapid identification of putative ingredient-target interactions in human structural proteome level. A marketing anti-cancer herb medicine in China, Yadanzi (Brucea javanica), was chosen for mechanistic study. Total 7,119 ingredient-target interactions were identified for thirteen Yadanzi active ingredients. Among them, about 29.5% were estimated to have better binding affinity than their corresponding marketing drug-target interactions. Further Bioinformatics analyses suggest that simultaneous manipulation of multiple proteins in the MAPK signaling pathway and the phosphorylation process of anti-apoptosis may largely answer for Yadanzi against non-small cell lung cancers. In summary, our strategy provides an efficient however economic solution for systematic understanding of herbs' power.

Has discovery-based cancer research been a bust?

The completion of the human genome sequence sparked optimism about prospects for new anticancer drug development, but clinical progress over the last decade has proven slower than expected. Here it is proposed that unrealistically high expectations of first-generation discovery-based diagnostics have contributed to this problem. Hypothesis-based single-molecule tests (e.g., mutation screening of KRAS, EGFR, BRAF or KIT genes) continue to change clinical practice incrementally, whereas first-generation multiplex assays--such as gene expression profiling and proteomics--have identified few high-impact therapeutic targets despite numerous correlations with prognosis. To move forward, second-generation multiplex diagnostics should be based not on statistical patterns/associations alone, but on clinically interpretable ('high-signal-to-noise') data such as change-of-function mutations, gene amplifications, recurrent chromosomal anomalies, and abnormal phosphorylation profiles of ERK or mTOR signaling cascades.

Genomics, Other “Omic” Technologies, Personalized Medicine, and Additional Biotechnology-Related Techniques

The products resulting for biotechnologies continue to grow at an exponential rate, and the expectations are that an even greater percentage of drug development will be in the area of the biologics. In 2011, worldwide there were over 800 new biotech drugs and treatments in development including 23 antisense, 64 cell therapy, 50 gene therapy, 300 monoclonal antibodies, 78 recombinant proteins, and 298 vaccines (PhRMA 2012). Pharmaceutical biotechnology techniques are at the core of most methodologies used today for drug discovery and development of both biologics and small molecules. While recombinant DNA technology and hybridoma techniques were the major methods utilized in pharmaceutical biotechnology through most of its historical timeline, our ever-widening understanding of human cellular function and disease processes and a wealth of additional and innovative biotechnologies have been, and will continue to be, developed in order to harvest the information found in the human genome. These technological advances will provide a better understanding of the relationship between genetics and biological function, unravel the underlying causes of disease, explore the association of genomic variation and drug response, enhance pharmaceutical research, and fuel the discovery and development of new and novel biopharmaceuticals. These revolutionary technologies and additional biotechnology-related techniques are improving the very competitive and costly process of drug development of new medicinal agents, diagnostics, and medical devices. Some of the technologies and techniques described in this chapter are both well established and commonly used applications of biotechnology producing potential therapeutic products now in development including clinical trials. New techniques are emerging at a rapid and unprecedented pace and their full impact on the future of molecular medicine has yet to be imagined.

Proteomics and metabolomics in inflammatory bowel disease

Genome-wide studies in inflammatory bowel disease (IBD) have allowed us to understand Crohn's disease and ulcerative colitis as forms of related autoinflammatory disorders that arise from a multitude of pathogenic origins. Proteomics and metabolomics are the offspring of genomics that possess unprecedented possibilities to characterize unknown pathogenic pathways. It has been about a decade since proteomics was first applied to IBD, and 5 years for metabolomics. These techniques have yielded novel and potentially important findings, but turning these results into beneficial patient outcomes remains challenging. This review recounts the history and context of clinical IBD developments before and after proteomics and metabolomics IBD in this field, discusses the challenges in consolidating high complexity data with physiological understanding, and provides an outlook on the emerging principles that will help interface the bioanalytical laboratory with IBD prognosis.

Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction

Background

Previous studies have noted that drug targets appear to be associated with higher-degree or higher-centrality proteins in interaction networks. These studies explicitly or tacitly make choices of different source databases, data integration strategies, representation of proteins and complexes, and data reliability assumptions. Here we examined how the use of different data integration and representation techniques, or different notions of reliability, may affect the efficacy of degree and centrality as features in drug target prediction.

Results

Fifty percent of drug targets have a degree of less than nine, and ninety-five percent have a degree of less than ninety. We found that drug targets are over-represented in higher degree bins – this relationship is only seen for the consolidated interactome and it is not dependent on n-ary interaction data or its representation. Degree acts as a weak predictive feature for drug-target status and using more reliable subsets of the data does not increase this performance. However, performance does increase if only cancer-related drug targets are considered. We also note that a protein’s membership in pathway records can act as a predictive feature that is better than degree and that high-centrality may be an indicator of a drug that is more likely to be withdrawn.

Conclusions

These results show that protein interaction data integration and cleaning is an important consideration when incorporating network properties as predictive features for drug-target status. The provided scripts and data sets offer a starting point for further studies and cross-comparison of methods.

Rationally designed tumor-penetrating nanocomplexes

Small interfering RNA (siRNA) therapeutics have broad potential uses in medicine but require safe and effective delivery vehicles to function. An ideal delivery system should encapsulate and protect the siRNA cargo from serum proteins, exhibit target tissue and cell specificity, penetrate the cell surface, and release its cargo in the desired intracellular compartment. One approach to the design of delivery vehicles that meets all of these requirements utilizes the systematic assembly of multiple components that can address each barrier. This rational approach was adopted by Ren et al., who designed novel myristoylated tandem peptides that consist of a tumor-targeting module and a cell-penetrating module, as described in this issue of ACS Nano. These tandem peptides were formulated with siRNAs into nanocomplexes for cell-specific delivery to a variety of tumor cell lines. The correlation of the structural properties of the nanocomplex to cell-type-specific activity via a computational approach identified the valence of the tumor-targeting ligand and overall nanocomplex charge as important parameters for the activity of the formulations. The in vivo gene silencing potency of these peptide-based nanocomplex formulations was demonstrated by Ren et al. in an ovarian cancer model. Tumor-penetrating nanocomplexes carrying a siRNA sequence against a novel oncogene (ID4) led to a significant reduction in tumor burden and an 80% increase in mouse survival. As such, the combination of a systematic approach with computational modeling can be advantageous for improving the delivery and potency of siRNA therapeutics.

Protein interactions: mapping interactome networks to support drug target discovery and selection

Proteins are biomolecular structures that build the microscopic working machinery of any living system. Proteins within the cells and biological systems do not act alone, but rather team up into macromolecular structures enclosing intricate physicochemical dynamic connections to undertake biological functions. A critical step towards unraveling the complex molecular relationships in living systems is the mapping of protein-to-protein physical "interactions". The complete map of protein interactions that can occur in a living organism is called the "interactome". Achieving an adequate atlas of all the protein interactions within a living system should allow to build its interaction network and to identity the "central nodes" that can be critical for the function, the homeostasis, and the movement of such system. Focusing on human studies, the data about the human interactome are most relevant for current biomedical research, because it is clear that the location of the proteins in the interactome network will allow to evaluate their centrality and to redefine the potential value of each protein as a drug target. This chapter presents our current knowledge on the human protein-protein interactome and explains how such knowledge can help us to select adequate targets for drugs.

Role of computational methods in pharmaceutical sciences

Over the past two decades computational methods have eased up the financial and experimental burden of early drug discovery process. The in silico methods have provided support in terms of databases, data mining of large genomes, network analysis, systems biology on the bioinformatics front and structure-activity relationship, similarity analysis, docking, and pharmacophore methods for lead design and optimization. This review highlights some of the applications of bioinformatics and chemoinformatics methods that have enriched the field of drug discovery. In addition, the review also provided insights into the use of free energy perturbation methods for efficiently computing binding energy. These in silico methods are complementary and can be easily integrated into the traditional in vitro and in vivo methods to test pharmacological hypothesis.

Neuroblastoma therapy: what is in the pipeline?

Despite the expansion of knowledge about neuroblastoma (NB) in recent years, the therapeutic outcome for children with a high-risk NB has not significantly improved. Therefore, more effective therapies are needed. This might be achieved by aiming future efforts at recently proposed but not yet developed targets for NB therapy. In this review, we discuss the recently proposed molecular targets that are in clinical trials and, in particular, those that are not yet explored in the clinic. We focus on the selection of these molecular targets for which promising in vitro and in vivo results have been obtained by silencing/inhibiting them. In addition, these selected targets are involved at least in one of the NB tumorigenic processes: proliferation, anti-apoptosis, angiogenesis and/or metastasis. In particular, we will review a recently proposed target, the microtubule-associated proteins (MAPs) encoded by doublecortin-like kinase gene (DCLK1). DCLK1-derived MAPs are crucial for proliferation and survival of neuroblasts and are highly expressed not only in NB but also in other tumours such as gliomas. Additionally, we will discuss neuropeptide Y, its Y2 receptor and cathepsin L as examples of targets to decrease angiogenesis and metastasis of NB. Furthermore, we will review the micro-RNAs that have been proposed as therapeutic targets for NB. Detailed investigation of these not yet developed targets as well as exploration of multi-target approaches might be the key to a more effective NB therapy, i.e. increasing specificity, reducing toxicity and avoiding long-term side effects.

From in silico target prediction to multi-target drug design: current databases, methods and applications

Given the tremendous growth of bioactivity databases, the use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. Applications span a wide range, from the 'designed polypharmacology' of compounds to mode-of-action analysis. In this review, we firstly survey databases that can be used for ligand-based target prediction and which have grown tremendously in size in the past. We furthermore outline methods for target prediction that exist, both based on the knowledge of bioactivities from the ligand side and methods that can be applied in situations when a protein structure is known. Applications of successful in silico target identification attempts are discussed in detail, which were based partly or in whole on computational target predictions in the first instance. This includes the authors' own experience using target prediction tools, in this case considering phenotypic antibacterial screens and the analysis of high-throughput screening data. Finally, we will conclude with the prospective application of databases to not only predict, retrospectively, the protein targets of a small molecule, but also how to design ligands with desired polypharmacology in a prospective manner.