Interaction networks for systems biology

Drug Repositioning with GraphSAGE and Clustering Constraints Based on Drug and Disease Networks

The understanding of therapeutic properties is important in drug repositioning and drug discovery. However, chemical or clinical trials are expensive and inefficient to characterize the therapeutic properties of drugs. Recently, artificial intelligence (AI)-assisted algorithms have received extensive attention for discovering the potential therapeutic properties of drugs and speeding up drug development. In this study, we propose a new method based on GraphSAGE and clustering constraints (DRGCC) to investigate the potential therapeutic properties of drugs for drug repositioning. First, the drug structure features and disease symptom features are extracted. Second, the drug–drug interaction network and disease similarity network are constructed according to the drug–gene and disease–gene relationships. Matrix factorization is adopted to extract the clustering features of networks. Then, all the features are fed to the GraphSAGE to predict new associations between existing drugs and diseases. Benchmark comparisons on two different datasets show that our method has reliable predictive performance and outperforms other six competing. We have also conducted case studies on existing drugs and diseases and aimed to predict drugs that may be effective for the novel coronavirus disease 2019 (COVID-19). Among the predicted anti-COVID-19 drug candidates, some drugs are being clinically studied by pharmacologists, and their binding sites to COVID-19-related protein receptors have been found via the molecular docking technology.

MicrobioLink: An Integrated Computational Pipeline to Infer Functional Effects of Microbiome-Host Interactions

Microbiome–host interactions play significant roles in health and in various diseases including autoimmune disorders. Uncovering these inter-kingdom cross-talks propels our understanding of disease pathogenesis and provides useful leads on potential therapeutic targets. Despite the biological significance of microbe–host interactions, there is a big gap in understanding the downstream effects of these interactions on host processes. Computational methods are expected to fill this gap by generating, integrating, and prioritizing predictions—as experimental detection remains challenging due to feasibility issues. Here, we present MicrobioLink, a computational pipeline to integrate predicted interactions between microbial and host proteins together with host molecular networks. Using the concept of network diffusion, MicrobioLink can analyse how microbial proteins in a certain context are influencing cellular processes by modulating gene or protein expression. We demonstrated the applicability of the pipeline using a case study. We used gut metaproteomic data from Crohn’s disease patients and healthy controls to uncover the mechanisms by which the microbial proteins can modulate host genes which belong to biological processes implicated in disease pathogenesis. MicrobioLink, which is agnostic of the microbial protein sources (bacterial, viral, etc.), is freely available on GitHub.

Unraveling the role of salt-sensitivity genes in obesity with integrated network biology and co-expression analysis

Obesity is a multifactorial disease caused by complex interactions between genes and dietary factors. Salt-rich diet is related to the development and progression of several chronic diseases including obesity. However, the molecular basis of how salt sensitivity genes (SSG) contribute to adiposity in obesity patients remains unexplored. In this study, we used the microarray expression data of visceral adipose tissue samples and constructed a complex protein-interaction network of salt sensitivity genes and their co-expressed genes to trace the molecular pathways connected to obesity. The Salt Sensitivity Protein Interaction Network (SSPIN) of 2691 differentially expressed genes and their 15474 interactions has shown that adipose tissues are enriched with the expression of 23 SSGs, 16 hubs and 84 bottlenecks (p = 2.52 x 10-16) involved in diverse molecular pathways connected to adiposity. Fifteen of these 23 SSGs along with 8 other SSGs showed a co-expression with enriched obesity-related genes (r ≥ 0.8). These SSGs and their co-expression partners are involved in diverse metabolic pathways including adipogenesis, adipocytokine signaling pathway, renin-angiotensin system, etc. This study concludes that SSGs could act as molecular signatures for tracing the basis of adipogenesis among obese patients. Integrated network centered methods may accelerate the identification of new molecular targets from the complex obesity genomics data.

Targeted interplay between bacterial pathogens and host autophagy

Due to the critical role played by autophagy in pathogen clearance, pathogens have developed diverse strategies to subvert it. Despite previous key findings of bacteria-autophagy interplay, asystems-level insight into selective targeting by the host and autophagy modulation by the pathogens is lacking. We predicted potential interactions between human autophagy proteins and effector proteins from 56 pathogenic bacterial species by identifying bacterial proteins predicted to have recognition motifs for selective autophagy receptors SQSTM1/p62, CALCOCO2/NDP52 and MAP1LC3/LC3. Using structure-based interaction prediction, we identified bacterial proteins capable to modify core autophagy components. Our analysis revealed that autophagy receptors in general potentially target mostly genus-specific proteins, and not those present in multiple genera. The complementarity between the predicted SQSTM1/p62 and CALCOCO2/NDP52 targets, which has been shown for Salmonella, Listeria and Shigella, could be observed across other pathogens. This complementarity potentially leaves the host more susceptible to chronic infections upon the mutation of autophagy receptors. Proteins derived from enterotoxigenic and non-toxigenic Bacillus outer membrane vesicles indicated that autophagy targets pathogenic proteins rather than non-pathogenic ones. We also observed apathogen-specific pattern as to which autophagy phase could be modulated by specific genera. We found intriguing examples of bacterial proteins that could modulate autophagy, and in turn being targeted by autophagy as ahost defense mechanism. We confirmed experimentally an interplay between a Salmonella protease, YhjJ and autophagy. Our comparative meta-analysis points out key commonalities and differences in how pathogens could affect autophagy and how autophagy potentially recognizes these pathogenic effectors. Abbreviations: ATG5: autophagy related 5; CALCOCO2/NDP52: calcium binding and coiled-coil domain 2; GST: glutathione S-transferase; LIR: MAP1LC3/LC3-interacting region; MAP1LC3/LC3: microtubule associated protein 1 light chain 3 alpha; OMV: outer membrane vesicles; SQSTM1/p62: sequestosome 1; SCV: Salmonella containing vesicle; TECPR1: tectonin beta-propeller repeat containing 1; YhjJ: hypothetical zinc-protease.

Identification of key regulatory genes connected to NF-κB family of proteins in visceral adipose tissues using gene expression and weighted protein interaction network

Obesity is connected to the activation of chronic inflammatory pathways in both adipocytes and macrophages located in adipose tissues. The nuclear factor (NF)-κB is a central molecule involved in inflammatory pathways linked to the pathology of different complex metabolic disorders. Investigating the gene expression data in the adipose tissue would potentially unravel disease relevant gene interactions. The present study is aimed at creating a signature molecular network and at prioritizing the potential biomarkers interacting with NF-κB family of proteins in obesity using system biology approaches. The dataset GSE88837 associated with obesity was downloaded from Gene Expression Omnibus (GEO) database. Statistical analysis represented the differential expression of a total of 2650 genes in adipose tissues (p = <0.05). Using concepts like correlation, semantic similarity, and theoretical graph parameters we narrowed down genes to a network of 23 genes strongly connected with NF-κB family with higher significance. Functional enrichment analysis revealed 21 of 23 target genes of NF-κB were found to have a critical role in the pathophysiology of obesity. Interestingly, GEM and PPP1R13L were predicted as novel genes which may act as potential target or biomarkers of obesity as they occur with other 21 target genes with known obesity relationship. Our study concludes that NF-κB and prioritized target genes regulate the inflammation in adipose tissues through several molecular signaling pathways like NF-κB, PI3K-Akt, glucocorticoid receptor regulatory network, angiogenesis and cytokine pathways. This integrated system biology approaches can be applied for elucidating functional protein interaction networks of NF-κB protein family in different complex diseases. Our integrative and network-based approach for finding therapeutic targets in genomic data could accelerate the identification of novel drug targets for obesity.

Information Loss in Network Pharmacology

With the advent of increasing computational power and large-scale data acquisition, network analysis has become an attractive tool to study the organisation of complex systems and the interrelation of their constituent entities in various scientific domains. In many cases, relations only occur between entities of two different subsets, thereby forming a bipartite network. Often, the analysis of such bipartite networks involves the consideration of its two monopartite projections in order to focus on each entity subset individually as a means to deduce properties of the underlying original network. Although it is broadly acknowledged that this type of projection is not lossless, the inherent limitations of their interpretability are rarely discussed. In this work, we introduce two approaches for measuring the information loss associated with bipartite network projection. Application to two structurally distinct cases in network pharmacology, namely, drug-target and disease-gene bipartite networks, confirms that the major determinant of information loss is the degree of vertices omitted during the monopartite projection.

Dark Proteome Database: Studies on Dark Proteins

The dark proteome, as we define it, is the part of the proteome where 3D structure has not been observed either by homology modeling or by experimental characterization in the protein universe. From the 550.116 proteins available in Swiss-Prot (as of July 2016), 43.2% of the eukarya universe and 49.2% of the virus universe are part of the dark proteome. In bacteria and archaea, the percentage of the dark proteome presence is significantly less, at 12.6% and 13.3% respectively. In this work, we present a necessary step to complete the dark proteome picture by introducing the map of the dark proteome in the human and in other model organisms of special importance to mankind. The most significant result is that around 40% to 50% of the proteome of these organisms are still in the dark, where the higher percentages belong to higher eukaryotes (mouse and human organisms). Due to the amount of darkness present in the human organism being more than 50%, deeper studies were made, including the identification of ‘dark’ genes that are responsible for the production of so-called dark proteins, as well as the identification of the ‘dark’ tissues where dark proteins are over represented, namely, the heart, cervical mucosa, and natural killer cells. This is a step forward in the direction of gaining a deeper knowledge of the human dark proteome.

Bioluminescence Resonance Energy Transfer as a Method to Study Protein-Protein Interactions: Application to G Protein Coupled Receptor Biology

The bioluminescence resonance energy transfer (BRET) approach involves resonance energy transfer between a light-emitting enzyme and fluorescent acceptors. The major advantage of this technique over biochemical methods is that protein-protein interactions (PPI) can be monitored without disrupting the natural environment, frequently altered by detergents and membrane preparations. Thus, it is considered as one of the most versatile technique for studying molecular interactions in living cells at "physiological" expression levels. BRET analysis has been applied to study many transmembrane receptor classes including G-protein coupled receptors (GPCR). It is well established that these receptors may function as dimeric/oligomeric forms and interact with multiple effectors to transduce the signal. Therefore, they are considered as attractive targets to identify PPI modulators. In this review, we present an overview of the different BRET systems developed up to now and their relevance to identify inhibitors/modulators of protein⁻protein interaction. Then, we introduce the different classes of agents that have been recently developed to target PPI, and provide some examples illustrating the use of BRET-based assays to identify and characterize innovative PPI modulators in the field of GPCRs biology. Finally, we discuss the main advantages and the limits of BRET approach to characterize PPI modulators.

The amyloid interactome: Exploring protein aggregation

Protein-protein interactions are the quintessence of physiological activities, but also participate in pathological conditions. Amyloid formation, an abnormal protein-protein interaction process, is a widespread phenomenon in divergent proteins and peptides, resulting in a variety of aggregation disorders. The complexity of the mechanisms underlying amyloid formation/amyloidogenicity is a matter of great scientific interest, since their revelation will provide important insight on principles governing protein misfolding, self-assembly and aggregation. The implication of more than one protein in the progression of different aggregation disorders, together with the cited synergistic occurrence between amyloidogenic proteins, highlights the necessity for a more universal approach, during the study of these proteins. In an attempt to address this pivotal need we constructed and analyzed the human amyloid interactome, a protein-protein interaction network of amyloidogenic proteins and their experimentally verified interactors. This network assembled known interconnections between well-characterized amyloidogenic proteins and proteins related to amyloid fibril formation. The consecutive extended computational analysis revealed significant topological characteristics and unraveled the functional roles of all constituent elements. This study introduces a detailed protein map of amyloidogenicity that will aid immensely towards separate intervention strategies, specifically targeting sub-networks of significant nodes, in an attempt to design possible novel therapeutics for aggregation disorders.

Functional Analysis of OMICs Data and Small Molecule Compounds in an Integrated "Knowledge-Based" Platform

Analysis of NGS and other sequencing data, gene variants, gene expression, proteomics, and other high-throughput (OMICs) data is challenging because of its biological complexity and high level of technical and biological noise. One way to deal with both problems is to perform analysis with a high fidelity annotated knowledgebase of protein interactions, pathways, and functional ontologies. This knowledgebase has to be structured in a computer-readable format and must include software tools for managing experimental data, analysis, and reporting. Here, we present MetaCore™ and Key Pathway Advisor (KPA), an integrated platform for functional data analysis. On the content side, MetaCore and KPA encompass a comprehensive database of molecular interactions of different types, pathways, network models, and ten functional ontologies covering human, mouse, and rat genes. The analytical toolkit includes tools for gene/protein list enrichment analysis, statistical "interactome" tool for the identification of over- and under-connected proteins in the dataset, and a biological network analysis module made up of network generation algorithms and filters. The suite also features Advanced Search, an application for combinatorial search of the database content, as well as a Java-based tool called Pathway Map Creator for drawing and editing custom pathway maps. Applications of MetaCore and KPA include molecular mode of action of disease research, identification of potential biomarkers and drug targets, pathway hypothesis generation, analysis of biological effects for novel small molecule compounds and clinical applications (analysis of large cohorts of patients, and translational and personalized medicine).

An Integrative Analysis of Preeclampsia Based on the Construction of an Extended Composite Network Featuring Protein-Protein Physical Interactions and Transcriptional Relationships

Preeclampsia (PE) is a pregnancy disorder defined by hypertension and proteinuria. This disease remains a major cause of maternal and fetal morbidity and mortality. Defective placentation is generally described as being at the root of the disease. The characterization of the transcriptome signature of the preeclamptic placenta has allowed to identify differentially expressed genes (DEGs). However, we still lack a detailed knowledge on how these DEGs impact the function of the placenta. The tools of network biology offer a methodology to explore complex diseases at a systems level. In this study we performed a cross-platform meta-analysis of seven publically available gene expression datasets comparing non-pathological and preeclamptic placentas. Using the rank product algorithm we identified a total of 369 DEGs consistently modified in PE. The DEGs were used as seeds to build both an extended physical protein-protein interactions network and a transcription factors regulatory network. Topological and clustering analysis was conducted to analyze the connectivity properties of the networks. Finally both networks were merged into a composite network which presents an integrated view of the regulatory pathways involved in preeclampsia and the crosstalk between them. This network is a useful tool to explore the relationship between the DEGs and enable hypothesis generation for functional experimentation.

Analysis of dermal papilla cell interactome using STRING database to profile the ex vivo hair growth inhibition effect of a vinca alkaloid drug, colchicine

Dermal papillae (DPs) control the formation of hair shafts. In clinical settings, colchicine (CLC) induces patients' hair shedding. Compared to the control, the ex vivo hair fiber elongation of organ cultured vibrissa hair follicles (HFs) declined significantly after seven days of CLC treatment. The cultured DP cells (DPCs) were used as the experimental model to study the influence of CLC on the protein dynamics of DPs. CLC could alter the morphology and down-regulate the expression of alkaline phosphatase (ALP), the marker of DPC activity, and induce IκBα phosphorylation of DPCs. The proteomic results showed that CLC modulated the expression patterns (fold>2) of 24 identified proteins, seven down-regulated and 17 up-regulated. Most of these proteins were presumably associated with protein turnover, metabolism, structure and signal transduction. Protein-protein interactions (PPI) among these proteins, established by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, revealed that they participate in protein metabolic process, translation, and energy production. Furthermore, ubiquitin C (UbC) was predicted to be the controlling hub, suggesting the involvement of ubiquitin-proteasome system in modulating the pathogenic effect of CLC on DPC.

Complexity and Modularity of MAPK Signaling Networks

Signaling through mitogen-activated protein kinase (MAPK) cascades is a conserved and fundamental process in all eukaryotes. This chapter reviews recent progress made in the identification of components of MAPK signaling networks using novel large scale experimental methods. It also presents recent landmarks in the computational modeling and simulation of the dynamics of MAPK signaling modules. The in vitro MAPK signaling network reconstructed from predicted phosphorylation events is dense, supporting the hypothesis of a combinatorial control of transcription through selective phosphorylation of sets of transcription factors. Despite the fact that additional co-factors and scaffold proteins may regulate the dynamics of signal transduction in vivo, the complexity of MAPK signaling networks supports a new model that departs significantly from that of the classical definition of a MAPK cascade.

Equal opportunity for low-degree network nodes: a PageRank-based method for protein target identification in metabolic graphs

Biological network data, such as metabolic-, signaling- or physical interaction graphs of proteins are increasingly available in public repositories for important species. Tools for the quantitative analysis of these networks are being developed today. Protein network-based drug target identification methods usually return protein hubs with large degrees in the networks as potentially important targets. Some known, important protein targets, however, are not hubs at all, and perturbing protein hubs in these networks may have several unwanted physiological effects, due to their interaction with numerous partners. Here, we show a novel method applicable in networks with directed edges (such as metabolic networks) that compensates for the low degree (non-hub) vertices in the network, and identifies important nodes, regardless of their hub properties. Our method computes the PageRank for the nodes of the network, and divides the PageRank by the in-degree (i.e., the number of incoming edges) of the node. This quotient is the same in all nodes in an undirected graph (even for large- and low-degree nodes, that is, for hubs and non-hubs as well), but may differ significantly from node to node in directed graphs. We suggest to assign importance to non-hub nodes with large PageRank/in-degree quotient. Consequently, our method gives high scores to nodes with large PageRank, relative to their degrees: therefore non-hub important nodes can easily be identified in large networks. We demonstrate that these relatively high PageRank scores have biological relevance: the method correctly finds numerous already validated drug targets in distinct organisms (Mycobacterium tuberculosis, Plasmodium falciparum and MRSA Staphylococcus aureus), and consequently, it may suggest new possible protein targets as well. Additionally, our scoring method was not chosen arbitrarily: its value for all nodes of all undirected graphs is constant; therefore its high value captures importance in the directed edge structure of the graph.

Kaposi's sarcoma: a computational approach through protein-protein interaction and gene regulatory networks analysis

Interactomic data for Kaposi's Sarcoma Associated Herpes virus (KSHV)-the causative agent of vascular origin tumor called Kaposi's sarcoma-is relatively modest to date. The objective of this study was to assign functions to the previously uncharacterized ORFs in the virus using computational approaches and subsequently fit them to the host interactome landscape on protein, gene, and cellular level. On the basis of expression data, predicted RNA interference data, reported experimental data, and sequence based functional annotation we also tried to hypothesize the ORFs role in lytic and latent cycle during viral infection. We studied 17 previously uncharacterized ORFs in KSHV and the host-virus interplay seems to work in three major functional pathways-cell division, transport, metabolic and enzymatic in general. Studying the host-virus crosstalk for lytic phase predicts ORF 10 and ORF 11 as a predicted virus hub whereas PCNA is predicted as a host hub. On the other hand, ORF31 has been predicted as a latent phase inducible protein. KSHV invests a lion's share of its coding potential to suppress host immune response; various inflammatory mediators such as IFN-γ, TNF, IL-6, and IL-8 are negatively regulated by the ORFs while Il-10 secretion is stimulated in contrast. Although, like any other computational prediction, the study requires further validation, keeping into account the reproducibility and vast sample size of the systems biology approach the study allows us to propose an integrated network for host-virus interaction with good confidence. We hope that the study, in the long run, would help us identify effective dug against potential molecular targets.

IT Future of Medicine: from molecular analysis to clinical diagnosis and improved treatment

The IT Future of Medicine (ITFoM, http://www.itfom.eu/) initiative will produce computational models of individuals to enable the prediction of their future health risks, progression of diseases and selection and efficacy of treatments while minimising side effects. To be able to move our health care system to treat patients as individuals rather than as members of larger, divergent groups, the ITFoM initiative, proposes to integrate molecular, physiological and anatomical data of every person in 'virtual patient' models. The establishment of such 'virtual patient' models is now possible due to the enormous progress in analytical techniques, particularly in the '-omics' technology areas and in imaging, as well as in sensor technologies, but also due to the immense developments in the ICT field. As one of six Future and Emerging Technologies (FET) Flagship Pilot Projects funded by the European Commission, ITFoM with more than 150 academic and industrial partners from 34 countries, will foster the development in functional genomics and computer technologies to generate 'virtual patient' models to make them available for clinical application. The increase in the capacity of next generation sequencing systems will enable the high-throughput analysis of a large number of individuals generating huge amounts of genome, epigenome and transcriptome data, but making it feasible to apply deep sequencing in the clinic to characterise not only the patient's genome, but also individual samples, for example, from tumours. The genome profile will be integrated with proteome and metabolome information generated via new powerful chromatography, mass spectrometry and nuclear magnetic resonance techniques. The individualised model will not only enable the analysis of the current situation, but will allow the prediction of the response of the patient to different therapy options or intolerance for certain drugs.

HINT: High-quality protein interactomes and their applications in understanding human disease

Background

A global map of protein-protein interactions in cellular systems provides key insights into the workings of an organism. A repository of well-validated high-quality protein-protein interactions can be used in both large- and small-scale studies to generate and validate a wide range of functional hypotheses.

Results

We develop HINT (http://hint.yulab.org) - a database of high-quality protein-protein interactomes for human, Saccharomyces cerevisiae, Schizosaccharomyces pombe, and Oryza sativa. These were collected from several databases and filtered both systematically and manually to remove low-quality/erroneous interactions. The resulting datasets are classified by type (binary physical interactions vs. co-complex associations) and data source (high-throughput systematic setups vs. literature-curated small-scale experiments). We find strong sociological sampling biases in literature-curated datasets of small-scale interactions. An interactome without such sampling biases was used to understand network properties of human disease-genes - hubs are unlikely to cause disease, but if they do, they usually cause multiple disorders.

Conclusions

HINT is of significant interest to researchers in all fields of biology as it addresses the ubiquitous need of having a repository of high-quality protein-protein interactions. These datasets can be utilized to generate specific hypotheses about specific proteins and/or pathways, as well as analyzing global properties of cellular networks. HINT will be regularly updated and all versions will be tracked.

Analyzing the homeostasis of signaling proteins by a combination of Western blot and fluorescence correlation spectroscopy

The determination of intracellular protein concentrations is a prerequisite for understanding protein interaction networks in systems biology. Today, protein quantification is based either on mass spectrometry, which requires large cell numbers and sophisticated measurement protocols, or on quantitative Western blotting, which requires the expression and purification of a recombinant protein as a reference. Here, we present a method that uses a transiently expressed fluorescent fusion protein of the protein-of-interest as an easily accessible reference in small volumes of crude cell lysates. The concentration of the fusion protein is determined by fluorescence correlation spectroscopy, and this concentration is used to calibrate the intensity of bands on a Western blot. We applied this method to address cellular protein homeostasis by determining the concentrations of the plasma membrane-located transmembrane scaffolding protein LAT and soluble signaling proteins in naïve T cells and transformed T-cell lymphoma (Jurkat) cells (with the latter having nine times the volume of the former). Strikingly, the protein numbers of soluble proteins scaled with the cell volume, whereas that of the transmembrane protein LAT scaled with the membrane surface. This leads to significantly different stoichiometries of signaling proteins in transformed and naïve cells in concentration ranges that may translate directly into differences in complex formation.

Viruses and interactomes in translation

A decade of high-throughput screenings for intraviral and virus-host protein-protein interactions led to the accumulation of data and to the development of theories on laws governing interactome organization for many viruses. We present here a computational analysis of intraviral protein networks (EBV, FLUAV, HCV, HSV-1, KSHV, SARS-CoV, VACV, and VZV) and virus-host protein networks (DENV, EBV, FLUAV, HCV, and VACV) from up-to-date interaction data, using various mathematical approaches. If intraviral networks seem to behave similarly, they are clearly different from the human interactome. Viral proteins target highly central human proteins, which are precisely the Achilles' heel of the human interactome. The intrinsic structural disorder is a distinctive feature of viral hubs in virus-host interactomes. Overlaps between virus-host data sets identify a core of human proteins involved in the cellular response to viral infection and in the viral capacity to hijack the cell machinery for viral replication. Host proteins that are strongly targeted by a virus seem to be particularly attractive for other viruses. Such protein-protein interaction networks and their analysis represent a powerful resource from a therapeutic perspective.

Ethylene-responsive element-binding factor 5, ERF5, is involved in chitin-induced innate immunity response

Our recent work demonstrated that chitin treatment modulated the expression of 118 transcription factor (TF) genes in Arabidopsis. To investigate the potential roles of these TF in chitin signaling and plant defense, we initiated an interaction study among these TF proteins, as well as two chitin-activated mitogen-activated protein kinases (MPK3 and MPK6), using a yeast two-hybrid system. This study revealed interactions among the following proteins: three ethylene-responsive element-binding factors (ERF), five WRKY transcription factors, one scarecrow-like (SCL), and the two MPK, in addition to many other interactions, reflecting a complex TF interaction network. Most of these interactions were subsequently validated by other methods, such as pull-down and in planta bimolecular fluorescence complementation assays. The key node ERF5 was shown to interact with multiple proteins in the network, such as ERF6, ERF8, and SCL13, as well as MPK3 and MPK6. Interestingly, ERF5 appeared to negatively regulate chitin signaling and plant defense against the fungal pathogen Alternaria brassicicola and positively regulate salicylic acid signaling and plant defense against the bacterial pathogen Pseudomonas syringae pv. tomato DC3000. Therefore, ERF5 may play an important role in plant innate immunity, likely through coordinating chitin and other defense pathways in plants in response to different pathogens.

Setting Up a Bioluminescence Resonance Energy Transfer High throughput Screening Assay to Search for Protein/Protein Interaction Inhibitors in Mammalian Cells

Each step of the cell life and its response or adaptation to its environment are mediated by a network of protein/protein interactions termed "interactome." Our knowledge of this network keeps growing due to the development of sensitive techniques devoted to study these interactions. The bioluminescence resonance energy transfer (BRET) technique was primarily developed to allow the dynamic monitoring of protein/protein interactions (PPI) in living cells, and has widely been used to study receptor activation by intra- or extra-molecular conformational changes within receptors and activated complexes in mammal cells. Some interactions are described as crucial in human pathological processes, and a new class of drugs targeting them has recently emerged. The BRET method is well suited to identify inhibitors of PPI and here is described why and how to set up and optimize a high throughput screening assay based on BRET to search for such inhibitory compounds. The different parameters to take into account when developing such BRET assays in mammal cells are reviewed to give general guidelines: considerations on the targeted interaction, choice of BRET version, inducibility of the interaction, kinetic of the monitored interaction, and of the BRET reading, influence of substrate concentration, number of cells and medium composition used on the Z' factor, and expected interferences from colored or fluorescent compounds.

Investigating a macromolecular complex: the toolkit of methods

Structural biologists studying macromolecular complexes spend considerable effort doing strictly "non-structural" work: investigating the physiological relevance and biochemical properties of a complex, preparing homogeneous samples for structural analysis, and experimentally validating structure-based hypotheses regarding function or mechanism. Familiarity with the diverse perspectives and techniques available for studying complexes helps in the critical assessment of non-structural data, expedites the pre-structural characterization of a complex and facilitates the investigation of function. Here we survey the approaches and techniques used to study macromolecular complexes from various viewpoints, including genetics, cell and molecular biology, biochemistry/biophysics, structural biology, and systems biology/bioinformatics. The aim of this overview is to heighten awareness of the diversity of perspectives and experimental tools available for investigating complexes and of their usefulness for the structural biologist.

Functional synergies yet distinct modulators affected by genetic alterations in common human cancers

An important general concern in cancer research is how diverse genetic alterations and regulatory pathways can produce common signaling outcomes. In this study, we report the construction of cancer models that combine unique regulation and common signaling. We compared and functionally analyzed sets of genetic alterations, including somatic sequence mutations and copy number changes, in breast, colon, and pancreatic cancer and glioblastoma that had been determined previously by global exon sequencing and SNP (single nucleotide polymorphism) array analyses in multiple patients. The genes affected by the different types of alterations were mostly unique in each cancer type, affected different pathways, and were connected with different transcription factors, ligands, and receptors. In our model, we show that distinct amplifications, deletions, and sequence alterations in each cancer resulted in common signaling pathways and transcription regulation. In functional clustering, the impact of the type of alteration was more pronounced than the impact of the kind of cancer. Several pathways such as TGF-β/SMAD signaling and PI3K (phosphoinositide 3-kinase) signaling were defined as synergistic (affected by different alterations in all four cancer types). Despite large differences at the genetic level, all data sets interacted with a common group of 65 "universal cancer genes" (UCG) comprising a concise network focused on proliferation/apoptosis balance and angiogenesis. Using unique nodal regulators ("overconnected" genes), UCGs, and synergistic pathways, the cancer models that we built could combine common signaling with unique regulation. Our findings provide a novel integrated perspective on the complex signaling and regulatory networks that underlie common human cancers.

Comparison of strong cation exchange and SDS-PAGE fractionation for analysis of multiprotein complexes

Affinity purification of protein complexes followed by identification using liquid chromatography/mass spectrometry (LC-MS/MS) is a robust method to study the fundamental process of protein interaction. Although affinity isolation reduces the complexity of the sample, fractionation prior to LC-MS/MS analysis is still necessary to maximize protein coverage. In this study, we compared the protein coverage obtained via LC-MS/MS analysis of protein complexes prefractionated using two commonly employed methods, SDS-PAGE and strong cation exchange chromatography (SCX). The two complexes analyzed focused on the nuclear proteins Bmi-1 and GATA3 that were expressed within the cells at low and high levels, respectively. Prefractionation of the complexes at the peptide level using SCX consistently resulted in the identification of approximately 3-fold more proteins compared to separation at the protein level using SDS-PAGE. The increase in the number of identified proteins was especially pronounced for the Bmi-1 complex, where the target protein was expressed at a low level. The data show that prefractionation of affinity isolated protein complexes using SCX prior to LC-MS/MS analysis significantly increases the number of identified proteins and individual protein coverage, particularly for target proteins expressed at low levels.

Protein-protein interaction map is a key gateway into liver regeneration

Recent studies indicate that the process of liver regeneration involves multiple signaling pathways and a variety of genes, cytokines and growth factors. Protein-protein interactions (PPIs) play a role in nearly all events that take place within the cell and PPI maps should be helpful in further understanding the process of liver regeneration. In this review, we discuss recent progress in understanding the PPIs that occur during liver regeneration especially those in the transforming growth factor β signaling pathways. We believe the use of large-scale PPI maps for integrating the information already known about the liver regeneration is a useful approach in understanding liver regeneration from the standpoint of systems biology.

Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation

Background

Several protein-protein interaction studies have been performed for the yeast Saccharomyces cerevisiae using different high-throughput experimental techniques. All these results are collected in the BioGRID database and the SGD database provide detailed annotation of the different proteins. Despite the value of BioGRID for studying protein-protein interactions, there is a need for manual curation of these interactions in order to remove false positives.

Results

Here we describe an annotated reconstruction of the protein-protein interactions around four key nutrient-sensing and metabolic regulatory signal transduction pathways (STP) operating in Saccharomyces cerevisiae. The reconstructed STP network includes a full protein-protein interaction network including the key nodes Snf1, Tor1, Hog1 and Pka1. The network includes a total of 623 structural open reading frames (ORFs) and 779 protein-protein interactions. A number of proteins were identified having interactions with more than one of the protein kinases. The fully reconstructed interaction network includes all the information available in separate databases for all the proteins included in the network (nodes) and for all the interactions between them (edges). The annotated information is readily available utilizing the functionalities of network modelling tools such as Cytoscape and CellDesigner.

Conclusions

The reported fully annotated interaction model serves as a platform for integrated systems biology studies of nutrient sensing and regulation in S. cerevisiae. Furthermore, we propose this annotated reconstruction as a first step towards generation of an extensive annotated protein-protein interaction network of signal transduction and metabolic regulation in this yeast.

Profiling of protein interaction networks of protein complexes using affinity purification and quantitative mass spectrometry

Protein-protein interactions are important for nearly all biological processes, and it is known that aberrant protein-protein interactions can lead to human disease and cancer. Recent evidence has suggested that protein interaction interfaces describe a new class of attractive targets for drug development. Full characterization of protein interaction networks of protein complexes and their dynamics in response to various cellular cues will provide essential information for us to understand how protein complexes work together in cells to maintain cell viability and normal homeostasis. Affinity purification coupled with quantitative mass spectrometry has become the primary method for studying in vivo protein interactions of protein complexes and whole organism proteomes. Recent developments in sample preparation and affinity purification strategies allow the capture, identification, and quantification of protein interactions of protein complexes that are stable, dynamic, transient, and/or weak. Current efforts have mainly focused on generating reliable, reproducible, and high confidence protein interaction data sets for functional characterization. The availability of increasing amounts of information on protein interactions in eukaryotic systems and new bioinformatics tools allow functional analysis of quantitative protein interaction data to unravel the biological significance of the identified protein interactions. Existing studies in this area have laid a solid foundation toward generating a complete map of in vivo protein interaction networks of protein complexes in cells or tissues.

A structural network associated with the kallikrein-kinin and renin-angiotensin systems

The kallikrein-kinin and renin-angiotensin (KKS-RAS) systems represent two highly regulated proteolytic systems that are involved in several physiological and pathological processes. Although their protein-protein interactions can be studied using experimental approaches, it is difficult to differentiate between direct physical interactions and functional associations, which do not involve direct atomic contacts between macromolecules. This information can be obtained from an atomic-resolution characterization of the protein interfaces. As a result of this, various three-dimensional-based protein-protein interaction databases have become available. To gain insight into the multilayered interaction of the KKS-RAS systems, we present a protein network that is built up on three-dimensional domain-domain interactions. The essential domains that link these systems are as follows: Cystatin, Peptidase_C1, Thyroglobulin_1, Insulin, CIMR (Cation-independent mannose-6-phosphate receptor repeat), fn2 (Fibronectin type II domain), fn1 (Fibronectin type I domain), EGF, Trypsin, and Serpin. We found that the CIMR domain is located at the core of the network, thus connecting both systems. From the latter, all domain interactors up to level 4 were retrieved, thus displaying a more comprehensive representation of the KKS-RAS structural network.

Bioinformatics in bioinorganic chemistry

Bioinformatics is a central discipline in modern life sciences aimed at describing the complex properties of living organisms starting from large-scale data sets of cellular constituents such as genes and proteins. In order for this wealth of information to provide useful biological knowledge, databases and software tools for data collection, analysis and interpretation need to be developed. In this paper, we review recent advances in the design and implementation of bioinformatics resources devoted to the study of metals in biological systems, a research field traditionally at the heart of bioinorganic chemistry. We show how metalloproteomes can be extracted from genome sequences, how structural properties can be related to function, how databases can be implemented, and how hints on interactions can be obtained from bioinformatics.

A reliable measure of similarity based on dependency for short time series: an application to gene expression networks

Background

Microarray techniques have become an important tool to the investigation of genetic relationships and the assignment of different phenotypes. Since microarrays are still very expensive, most of the experiments are performed with small samples. This paper introduces a method to quantify dependency between data series composed of few sample points. The method is used to construct gene co-expression subnetworks of highly significant edges.

Results

The results shown here are for an adapted subset of a Saccharomyces cerevisiae gene expression data set with low temporal resolution and poor statistics. The method reveals common transcription factors with a high confidence level and allows the construction of subnetworks with high biological relevance that reveals characteristic features of the processes driving the organism adaptations to specific environmental conditions.

Conclusion

Our method allows a reliable and sophisticated analysis of microarray data even under severe constraints. The utilization of systems biology improves the biologists ability to elucidate the mechanisms underlying celular processes and to formulate new hypotheses.

ITM Probe: analyzing information flow in protein networks

Summary: Founded upon diffusion with damping, ITM Probe is an application for modeling information flow in protein interaction networks without prior restriction to the sub-network of interest. Given a context consisting of desired origins and destinations of information, ITM Probe returns the set of most relevant proteins with weights and a graphical representation of the corresponding sub-network. With a click, the user may send the resulting protein list for enrichment analysis to facilitate hypothesis formation or confirmation.

Availability:ITM Probe web service and documentation can be found at www.ncbi.nlm.nih.gov/CBBresearch/qmbp/mn/itm_probe

Contact:yyu@ncbi.nlm.nih.gov

Integrated analysis of co-expressed MAP kinase substrates in Arabidopsis thaliana

MAP kinase (MAPK) signal transduction cascades are conserved eukaryotic pathways that modulate stress responses and developmental processes. In a recent report we have identified novel Arabidopsis MAPKK/MAPK/Substrate signaling pathways using microarrays containing 2,158 unique Arabidopsis proteins. Subsequently, several WRKY and TGA targets phosphorylated by MAPKs were verified in planta. We have also reported that specific MAPKK/MAPK modules expressed in Nicotiana benthamiana induced a cell death phenotype related to the immune response. We have generated a MAPK phosphorylation network based on our protein microarray experimental data. Here we further analyze our network by integrating phosphorylation and gene expression information to identify biologically relevant signaling modules. We have identified 108 phosphorylation events that occur among 96 annotated genes with highly similar pairwise expression profiles. Our analysis brings a new perspective on MAPK signaling by revealing new relationships between components of signaling pathways.

Enabling a systems biology approach to immunology: focus on innate immunity

Immunity is not simply the product of a series of discrete linear signalling pathways; rather it is comprised of a complex set of integrated responses arising from a dynamic network of thousands of molecules subject to multiple influences. Its behaviour often cannot be explained or predicted solely by examining its components. Here, we review recently developed resources for the systems-level investigation of immunity. Although innate immunity is emphasized here, its considerable overlap with adaptive immunity makes many of these resources relevant to both arms of the immune response. We discuss recent studies implementing these approaches and illustrate the potential of systems biology to generate novel insights into the complexities of innate immunity.

Accurate characterization of weak macromolecular interactions by titration of NMR residual dipolar couplings: application to the CD2AP SH3-C:ubiquitin complex

The description of the interactome represents one of key challenges remaining for structural biology. Physiologically important weak interactions, with dissociation constants above 100 μM, are remarkably common, but remain beyond the reach of most of structural biology. NMR spectroscopy, and in particular, residual dipolar couplings (RDCs) provide crucial conformational constraints on intermolecular orientation in molecular complexes, but the combination of free and bound contributions to the measured RDC seriously complicates their exploitation for weakly interacting partners. We develop a robust approach for the determination of weak complexes based on: (i) differential isotopic labeling of the partner proteins facilitating RDC measurement in both partners; (ii) measurement of RDC changes upon titration into different equilibrium mixtures of partially aligned free and complex forms of the proteins; (iii) novel analytical approaches to determine the effective alignment in all equilibrium mixtures; and (iv) extraction of precise RDCs for bound forms of both partner proteins. The approach is demonstrated for the determination of the three-dimensional structure of the weakly interacting CD2AP SH3-C:Ubiquitin complex (K_d = 132 ± 13 μM) and is shown, using cross-validation, to be highly precise. We expect this methodology to extend the remarkable and unique ability of NMR to study weak protein–protein complexes.

Markov clustering versus affinity propagation for the partitioning of protein interaction graphs

Background

Genome scale data on protein interactions are generally represented as large networks, or graphs, where hundreds or thousands of proteins are linked to one another. Since proteins tend to function in groups, or complexes, an important goal has been to reliably identify protein complexes from these graphs. This task is commonly executed using clustering procedures, which aim at detecting densely connected regions within the interaction graphs. There exists a wealth of clustering algorithms, some of which have been applied to this problem. One of the most successful clustering procedures in this context has been the Markov Cluster algorithm (MCL), which was recently shown to outperform a number of other procedures, some of which were specifically designed for partitioning protein interactions graphs. A novel promising clustering procedure termed Affinity Propagation (AP) was recently shown to be particularly effective, and much faster than other methods for a variety of problems, but has not yet been applied to partition protein interaction graphs.

Results

In this work we compare the performance of the Affinity Propagation (AP) and Markov Clustering (MCL) procedures. To this end we derive an unweighted network of protein-protein interactions from a set of 408 protein complexes from S. cervisiae hand curated in-house, and evaluate the performance of the two clustering algorithms in recalling the annotated complexes. In doing so the parameter space of each algorithm is sampled in order to select optimal values for these parameters, and the robustness of the algorithms is assessed by quantifying the level of complex recall as interactions are randomly added or removed to the network to simulate noise. To evaluate the performance on a weighted protein interaction graph, we also apply the two algorithms to the consolidated protein interaction network of S. cerevisiae, derived from genome scale purification experiments and to versions of this network in which varying proportions of the links have been randomly shuffled.

Conclusion

Our analysis shows that the MCL procedure is significantly more tolerant to noise and behaves more robustly than the AP algorithm. The advantage of MCL over AP is dramatic for unweighted protein interaction graphs, as AP displays severe convergence problems on the majority of the unweighted graph versions that we tested, whereas MCL continues to identify meaningful clusters, albeit fewer of them, as the level of noise in the graph increases. MCL thus remains the method of choice for identifying protein complexes from binary interaction networks.

Fast predictions of thermodynamics and kinetics of protein-protein recognition from structures: from molecular design to systems biology

The increasing call for an overall picture of the interactions between the components of a biological system that give rise to the observed function is often summarized by the expression systems biology. Both the interpretative and predictive capabilities of holistic models of biochemical systems, however, depend to a large extent on the level of physico-chemical knowledge of the individual molecular interactions making up the network. This review is focused on the structure-based quantitative characterization of protein-protein interactions, ubiquitous in any biochemical pathway. Recently developed, fast and effective computational methods are reviewed, which allow the assessment of kinetic and thermodynamic features of the association-dissociation processes of protein complexes, both in water soluble and membrane environments. The performance and the accuracy of fast and semi-empirical structure-based methods have reached comparable levels with respect to the classical and more elegant molecular simulations. Nevertheless, the broad accessibility and lower computational cost provide the former methods with the advantageous possibility to perform systems-level analyses including extensive in silico mutagenesis screenings and large-scale structural predictions of multiprotein complexes.

Literature-curated protein interaction datasets

High quality datasets are needed to understand how global and local properties of protein-protein interaction, or “interactome”, networks relate to biological mechanisms, and to guide research on individual proteins. Evaluations of existing curation of protein interaction experiments reported in the literature find that curation can be error prone and possibly of lower quality than commonly assumed.

Functional analysis of OMICs data and small molecule compounds in an integrated "knowledge-based" platform

Analysis of microarray, SNPs, proteomics, and other high-throughput (OMICs) data is challenging because of its biological complexity and high level of technical and biological noise. One way to deal with both problems is to perform analysis with a high-fidelity annotated knowledge base of protein interactions, pathways, and functional ontologies. This knowledge base has to be structured in a computer-readable format and must include software tools for managing experimental data, analysis, and reporting. Here we present MetaDiscovery, an integrated platform for functional data analysis which is being developed at GeneGo for the past 8 years. On the content side, MetaDiscovery encompasses a comprehensive database of protein interactions of different types, pathways, network models and 10 functional ontologies covering human, mouse, and rat proteins. The analytical toolkit includes tools for gene/protein list enrichment analysis, statistical "interactome" tool for identification of over- and under-connected proteins in the data set, and a network module made up of network generation algorithms and filters. The suite also features MetaSearch, an application for combinatorial search of the database content, as well as a Java-based tool called MapEditor for drawing and editing custom pathway maps. Applications of MetaDiscovery include identification of potential biomarkers and drug targets, pathway hypothesis generation, analysis of biological effects for novel small molecule compounds, and clinical applications (analysis of large cohorts of patients and translational and personalized medicine).

Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.

STRING 8--a global view on proteins and their functional interactions in 630 organisms

Functional partnerships between proteins are at the core of complex cellular phenotypes, and the networks formed by interacting proteins provide researchers with crucial scaffolds for modeling, data reduction and annotation. STRING is a database and web resource dedicated to protein–protein interactions, including both physical and functional interactions. It weights and integrates information from numerous sources, including experimental repositories, computational prediction methods and public text collections, thus acting as a meta-database that maps all interaction evidence onto a common set of genomes and proteins. The most important new developments in STRING 8 over previous releases include a URL-based programming interface, which can be used to query STRING from other resources, improved interaction prediction via genomic neighborhood in prokaryotes, and the inclusion of protein structures. Version 8.0 of STRING covers about 2.5 million proteins from 630 organisms, providing the most comprehensive view on protein–protein interactions currently available. STRING can be reached at http://string-db.org/.

InnateDB: facilitating systems-level analyses of the mammalian innate immune response

Although considerable progress has been made in dissecting the signaling pathways involved in the innate immune response, it is now apparent that this response can no longer be productively thought of in terms of simple linear pathways. InnateDB (www.innatedb.ca) has been developed to facilitate systems-level analyses that will provide better insight into the complex networks of pathways and interactions that govern the innate immune response. InnateDB is a publicly available, manually curated, integrative biology database of the human and mouse molecules, experimentally verified interactions and pathways involved in innate immunity, along with centralized annotation on the broader human and mouse interactomes. To date, more than 3500 innate immunity-relevant interactions have been contextually annotated through the review of 1000 plus publications. Integrated into InnateDB are novel bioinformatics resources, including network visualization software, pathway analysis, orthologous interaction network construction and the ability to overlay user-supplied gene expression data in an intuitively displayed molecular interaction network and pathway context, which will enable biologists without a computational background to explore their data in a more systems-oriented manner.