The Biomolecular Interaction Network Database and related tools 2005 update

Bioinformatics Resources for Cancer Research with an Emphasis on Gene Function and Structure Prediction Tools

The immensely popular fields of cancer research and bioinformatics overlap in many different areas, e.g. large data repositories that allow for users to analyze data from many experiments (data handling, databases), pattern mining, microarray data analysis, and interpretation of proteomics data. There are many newly available resources in these areas that may be unfamiliar to most cancer researchers wanting to incorporate bioinformatics tools and analyses into their work, and also to bioinformaticians looking for real data to develop and test algorithms. This review reveals the interdependence of cancer research and bioinformatics, and highlight the most appropriate and useful resources available to cancer researchers. These include not only public databases, but general and specific bioinformatics tools which can be useful to the cancer researcher. The primary foci are function and structure prediction tools of protein genes. The result is a useful reference to cancer researchers and bioinformaticians studying cancer alike.

MicroRNA profiling in the dentate gyrus in epileptic rats: The role of miR-187-3p

This study aimed to explore the role of aberrant miRNA expression in epilepsy and to identify more potential genes associated with epileptogenesis.The miRNA expression profile of GSE49850, which included 20 samples from the rat epileptic dentate gyrus at 7, 14, 30, and 90 days after electrical stimulation and 20 additional samples from sham time-matched controls, was downloaded from the Gene Expression Omnibus database. The significantly differentially expressed miRNAs were identified in stimulated samples at each time point compared to time-matched controls, respectively. The target genes of consistently differentially expressed miRNAs were screened from miRDB and microRNA.org databases, followed by Gene Ontology (GO) and pathway enrichment analysis and regulatory network construction. The overlapping target genes for consistently differentially expressed miRNAs were also identified from these 2 databases. Furthermore, the potential binding sites of miRNAs and their target genes were analyzed.Rno-miR-187-3p was consistently downregulated in stimulated groups compared with time-matched controls. The predicted target genes of rno-miR-187-3p were enriched in different GO terms and pathways. In addition, 7 overlapping target genes of rno-miR-187-3p were identified, including NFS1, PAQR4, CAND1, DCLK1, PRKAR2A, AKAP3, and KCNK10. These 7 overlapping target genes were determined to have a different number of matched binding sites with rno-miR-187-3p.Our study suggests that miR-187-3p may play an important role in epilepsy development and progression via regulating numerous target genes, such as NFS1, CAND1, DCLK1, AKAP3, and KCNK10. Determining the underlying mechanism of the role of miR-187-3p in epilepsy may make it a potential therapeutic option.

Systems biology-based discovery of a potential Atg4B agonist (Flubendazole) that induces autophagy in breast cancer

The aim of this study was to explore the autophagy-related protein 4B(ATG4B) and its targeted candidate agonist in triple-negative breast cancer (TNBC) therapy. In this study, the identification of Atg4B as a novel breast cancer target for screening candidate small molecular agonists was performed by phylogenetic analysis, network construction, molecular modelling, molecular docking and molecular dynamics (MD) simulation. In vitro, MTT assay, electron microscopy, western blot and ROS measurement were used for validating the efficacy of the candidate compounds. We used the phylogenetic analysis of Atg4B and constructed their protein-protein interaction (PPI) network. Also, we screened target compounds of Atg4 proteins from Drugbank and ZINC. Flubendazole was validated for its anti-proliferative efficacy in MDA-MB-231 cells. Further MD simulation results supported the stable interaction between Flubendazole and Atg4B. Moreover, Flubendazole induced autophagy and increased ROS production. In conclusion, in silico analysis and experimental validation together demonstrate that Flubendazole can target Atg4B in MDA-MB-231 cells and induce autophagy, which may shed light on the exploration of this compound as a potential new Atg4B targeted drug for future TNBC therapy.

Challenges in structural approaches to cell modeling

Computational modeling is essential for structural characterization of biomolecular mechanisms across the broad spectrum of scales. Adequate understanding of biomolecular mechanisms inherently involves our ability to model them. Structural modeling of individual biomolecules and their interactions has been rapidly progressing. However, in terms of the broader picture, the focus is shifting toward larger systems, up to the level of a cell. Such modeling involves a more dynamic and realistic representation of the interactomes in vivo, in a crowded cellular environment, as well as membranes and membrane proteins, and other cellular components. Structural modeling of a cell complements computational approaches to cellular mechanisms based on differential equations, graph models, and other techniques to model biological networks, imaging data, etc. Structural modeling along with other computational and experimental approaches will provide a fundamental understanding of life at the molecular level and lead to important applications to biology and medicine. A cross section of diverse approaches presented in this review illustrates the developing shift from the structural modeling of individual molecules to that of cell biology. Studies in several related areas are covered: biological networks; automated construction of three-dimensional cell models using experimental data; modeling of protein complexes; prediction of non-specific and transient protein interactions; thermodynamic and kinetic effects of crowding; cellular membrane modeling; and modeling of chromosomes. The review presents an expert opinion on the current state-of-the-art in these various aspects of structural modeling in cellular biology, and the prospects of future developments in this emerging field.

Multi-omics landscapes of colorectal cancer subtypes discriminated by an individualized prognostic signature for 5-fluorouracil-based chemotherapy

Until recently, few prognostic signatures for colorectal cancer (CRC) patients receiving 5-fluorouracil (5-FU)-based chemotherapy could be used in clinical practice. Here, using transcriptional profiles for a panel of cancer cell lines and three cohorts of CRC patients, we developed a prognostic signature based on within-sample relative expression orderings (REOs) of six gene pairs for stage II-III CRC patients receiving 5-FU-based chemotherapy. This REO-based signature had the unique advantage of being insensitive to experimental batch effects and free of the impractical data normalization requirement. After stratifying 184 CRC samples with multi-omics data from The Cancer Genome Atlas into two prognostic groups using the REO-based signature, we further revealed that patients with high recurrence risk were characterized by frequent gene copy number aberrations reducing 5-FU efficacy and DNA methylation aberrations inducing distinct transcriptional alternations to confer 5-FU resistance. In contrast, patients with low recurrence risk exhibited deficient mismatch repair and carried frequent gene mutations suppressing cell adhesion. These results reveal the multi-omics landscapes determining prognoses of stage II-III CRC patients receiving 5-FU-based chemotherapy.

Specificity of broad protein interaction surfaces for proteins with multiple binding partners

Analysis of protein-protein interaction networks has revealed the presence of proteins with multiple interaction ligand proteins, such as hub proteins. For such proteins, multiple ligands would be predicted as interacting partners when predicting all-to-all protein-protein interactions (PPIs). In this work, to obtain a better understanding of PPI mechanisms, we focused on protein interaction surfaces, which differ between protein pairs. We then performed rigid-body docking to obtain information of interfaces of a set of decoy structures, which include many possible interaction surfaces between a certain protein pair. Then, we investigated the specificity of sets of decoy interactions between true binding partners in each case of alpha-chymotrypsin, actin, and cyclin-dependent kinase 2 as test proteins having multiple true binding partners. To observe differences in interaction surfaces of docking decoys, we introduced broad interaction profiles (BIPs), generated by assembling interaction profiles of decoys for each protein pair. After cluster analysis, the specificity of BIPs of true binding partners was observed for each receptor. We used two types of BIPs: those involved in amino acid sequences (BIP-seqs) and those involved in the compositions of interacting amino acid residue pairs (BIP-AAs). The specificity of a BIP was defined as the number of group members including all true binding partners. We found that BIP-AA cases were more specific than BIP-seq cases. These results indicated that the composition of interacting amino acid residue pairs was sufficient for determining the properties of protein interaction surfaces.

Review on proteomics for food authentication

Consumers have the right to know what is in the food they are eating. Accordingly, European and global food regulations require that the provenance of the food can be guaranteed from farm to fork. Many different instrumental techniques have been proposed for food authentication. Although traditional methods are still being used, new approaches such as genomics, proteomics, and metabolomics are helping to complement existing methodologies for verifying the claims made about certain food products. During the last decade, proteomics (the large-scale analysis of proteins in a particular biological system at a particular time) has been applied to different research areas within food technology. Since proteins can be used as markers for many properties of a food, even indicating processes to which the food has been subjected, they can provide further evidence of the foods labeling claim. This review is a comprehensive and updated overview of the applications, drawbacks, advantages, and challenges of proteomics for food authentication in the assessment of the foods compliance with labeling regulations and policies.

SIGNIFICANCE

This review paper provides a comprehensive and critical overview of the application of proteomics approaches to determine the authenticity of several food products updating the performances and current limitations of the applied techniques in both laboratory and industrial environments.

Systems Biology of Circadian Rhythms: An Outlook

The circadian system in higher organisms temporally orchestrates rhythmic changes in a vast number of genes and gene products in different organs. Complex interactions between these components, both within and among cells, ultimately lead to rhythmic behavior and physiology. Identifying the plethora of circadian targets and mapping their interactions with one another is therefore essential to comprehend the molecular mechanisms of circadian regulation. The emergence of new technology for unbiased identification of biomolecules and for mapping interactions at the genome-wide scale is offering powerful tools to decipher the regulatory networks underpinning circadian rhythms. In this review, the authors discuss the potential application of these genome-wide approaches in the study of circadian rhythms.

Reconstruction and Application of Protein-Protein Interaction Network

The protein-protein interaction network (PIN) is a useful tool for systematic investigation of the complex biological activities in the cell. With the increasing interests on the proteome-wide interaction networks, PINs have been reconstructed for many species, including virus, bacteria, plants, animals, and humans. With the development of biological techniques, the reconstruction methods of PIN are further improved. PIN has gradually penetrated many fields in biological research. In this work we systematically reviewed the development of PIN in the past fifteen years, with respect to its reconstruction and application of function annotation, subsystem investigation, evolution analysis, hub protein analysis, and regulation mechanism analysis. Due to the significant role of PIN in the in-depth exploration of biological process mechanisms, PIN will be preferred by more and more researchers for the systematic study of the protein systems in various kinds of organisms.

Penicimenolides A-F, Resorcylic Acid Lactones from Penicillium sp., isolated from the Rhizosphere Soil of Panax notoginseng

Five new 12-membered resorcylic acid lactone derivatives, penicimenolides A-E (1-5), one new ring-opened resorcylic acid lactone derivative penicimenolide F (6), and six known biogenetically related derivatives (7-12) were isolated from the culture broth of a strain of Penicillium sp. (NO. SYP-F-7919), a fungus obtained from the rhizosphere soil of Panax notoginseng collected from the Yunnan province of China. Their structures were elucidated by extensive NMR analyses, a modified Mosher's method, chemical derivatization and single crystal X-ray diffraction analysis. Compounds 2-4 exhibited potent cytotoxicity against the U937 and MCF-7 tumour cell lines and showed moderate cytotoxic activity against the SH-SY5Y and SW480 tumour cell lines. The substitution of an acetyloxy or 2-hydroxypropionyloxy group at C-7 significantly increased the cytotoxic activity of the resorcylic acid lactone derivatives. Subsequently, the possible mechanism of compound 2 against MCF-7 cells was preliminarily investigated by in silico analysis and experimental validation, indicating compound 2 may act as a potential MEK/ERK inhibitor. Moreover, proteomics analysis was performed to explore compound 2-regulated concrete mechanism underlying MEK/ERK pathway, which is still need further study in the future. In addition, compounds 2-4 and 7 exhibited a significant inhibitory effect on NO production induced by LPS.

Computational prediction and experimental validation of a novel synthesized pan-PIM inhibitor PI003 and its apoptosis-inducing mechanisms in cervical cancer

PIM protein family, short-lived serine/threonine kinases (PIM1, PIM2 and PIM3), are weak oncogenes but contribute to tumorigenesis as cancer targets. Thus, design of a novel pan-PIM inhibitor is still a challenge for current cancer drug discovery. Herein, we used a Naïve Bayesian model to construct the PIM network and identified Bad and Hsp90 to interact with PIMs. Then, we screened a series of candidate small-molecule compounds targeting PIMs, and subsequently synthesized a novel small-molecule compound PI003 with remarkable anti-proliferative activities in cervical cancer cells. Moreover, we found that PI003 induced apoptosis via the death-receptor and mitochondrial pathways by targeting PIMs and affecting Bad and Hsp90. Combined with microRNA microarray analyses, we demonstrated that some microRNAs such as miR-1296 and miR-1299 could affect PIM1-STAT3 pathway in PI003-induced apoptosis. Finally, we reported that PI003 had remarkable anti-tumor activity and apoptosis-inducing effect in in vivo mouse model. In conclusion, these results demonstrate that PI003, as a novel synthesized pan-PIM inhibitor, induces the death-receptor and mitochondrial apoptosis involved in microRNA regulation, and also possessed remarkable anti-tumor activity and apoptosis-inducing effect in vivo. Thus, these findings would shed light on discovering more potential new small-molecule pan-PIM inhibitors in future cervical cancer therapy.

Genes Caught In Flagranti: Integrating Renal Transcriptional Profiles With Genotypes and Phenotypes

In the past decade, population genetics has gained tremendous success in identifying genetic variations that are statistically relevant to renal diseases and kidney function. However, it is challenging to interpret the functional relevance of the genetic variations found by population genetics studies. In this review, we discuss studies that integrate multiple levels of data, especially transcriptome profiles and phenotype data, to assign functional roles of genetic variations involved in kidney function. Furthermore, we introduce state-of-the-art machine learning algorithms, Bayesian networks, support vector machines, and Gaussian process regression, which have been applied successfully to integrating genetic, regulatory, and clinical information to predict clinical outcomes. These methods are likely to be deployed successfully in the nephrology field in the near future.

Identification of Novel Pathways in Plant Lectin-Induced Cancer Cell Apoptosis

Plant lectins have been investigated to elucidate their complicated mechanisms due to their remarkable anticancer activities. Although plant lectins seems promising as a potential anticancer agent for further preclinical and clinical uses, further research is still urgently needed and should include more focus on molecular mechanisms. Herein, a Naïve Bayesian model was developed to predict the protein-protein interaction (PPI), and thus construct the global human PPI network. Moreover, multiple sources of biological data, such as smallest shared biological process (SSBP), domain-domain interaction (DDI), gene co-expression profiles and cross-species interolog mapping were integrated to build the core apoptotic PPI network. In addition, we further modified it into a plant lectin-induced apoptotic cell death context. Then, we identified 22 apoptotic hub proteins in mesothelioma cells according to their different microarray expressions. Subsequently, we used combinational methods to predict microRNAs (miRNAs) which could negatively regulate the abovementioned hub proteins. Together, we demonstrated the ability of our Naïve Bayesian model-based network for identifying novel plant lectin-treated cancer cell apoptotic pathways. These findings may provide new clues concerning plant lectins as potential apoptotic inducers for cancer drug discovery.

A new multi-scale method to reveal hierarchical modular structures in biological networks

Biological networks are effective tools for studying molecular interactions.

Integrative bioinformatics and proteomics-based discovery of an eEF2K inhibitor (cefatrizine) with ER stress modulation in breast cancer cells

eEF2K, a unique calcium/calmodulin-dependent protein kinase, may regulate ER stress in cancer.

Application of Gene Expression Trajectories Initiated from ErbB Receptor Activation Highlights the Dynamics of Divergent Promoter Usage

Understanding how cells use complex transcriptional programs to alter their fate in response to specific stimuli is an important question in biology. For the MCF-7 human breast cancer cell line, we applied gene expression trajectory models to identify the genes involved in driving cell fate transitions. We modified trajectory models to account for the scenario where cells were exposed to different stimuli, in this case epidermal growth factor and heregulin, to arrive at different cell fates, i.e. proliferation and differentiation respectively. Using genome-wide CAGE time series data collected from the FANTOM5 consortium, we identified the sets of promoters that were involved in the transition of MCF-7 cells to their specific fates versus those with expression changes that were generic to both stimuli. Of the 1,552 promoters identified, 1,091 had stimulus-specific expression while 461 promoters had generic expression profiles over the time course surveyed. Many of these stimulus-specific promoters mapped to key regulators of the ERK (extracellular signal-regulated kinases) signaling pathway such as FHL2 (four and a half LIM domains 2). We observed that in general, generic promoters peaked in their expression early on in the time course, while stimulus-specific promoters tended to show activation of their expression at a later stage. The genes that mapped to stimulus-specific promoters were enriched for pathways that control focal adhesion, p53 signaling and MAPK signaling while generic promoters were enriched for cell death, transcription and the cell cycle. We identified 162 genes that were controlled by an alternative promoter during the time course where a subset of 37 genes had separate promoters that were classified as stimulus-specific and generic. The results of our study highlighted the degree of complexity involved in regulating a cell fate transition where multiple promoters mapping to the same gene can demonstrate quite divergent expression profiles.

FGF8 promotes colorectal cancer growth and metastasis by activating YAP1

Colorectal cancer (CRC) is a major cause of cancer-related death worldwide. The poor prognosis of CRC is mainly due to uncontrolled tumor growth and distant metastases. In this study, we found that the level of FGF8 was elevated in the great majority of CRC cases and high FGF8 expression was significantly correlated with lymph nodes metastasis and worse overall survival. Functional studies showed that FGF8 can induce a more aggressive phenotype displaying epithelial-to-mesenchymal transition (EMT) and enhanced invasion and growth in CRC cells. Consistent with this, FGF8 can also promote tumor growth and metastasis in mouse models. Bioinformatics and pathological analysis suggested that YAP1 is a potential downstream target of FGF8 in CRC cells. Molecular validation demonstrated that FGF8 fully induced nuclear localization of YAP1 and enhanced transcriptional outcomes such as the expression of CTGF and CYR61, while decreasing YAP1 expression impeded FGF-8–induced cell growth, EMT, migration and invasion, revealing that YAP1 is required for FGF8-mediated CRC growth and metastasis. Taken together, these results demonstrate that FGF8 contributes to the proliferative and metastatic capacity of CRC cells and may represent a novel candidate for intervention in tumor growth and metastasis formation.

Reveal genes functionally associated with ACADS by a network study

Establishing a systematic network is aimed at finding essential human gene-gene/gene-disease pathway by means of network inter-connecting patterns and functional annotation analysis. In the present study, we have analyzed functional gene interactions of short-chain acyl-coenzyme A dehydrogenase gene (ACADS). ACADS plays a vital role in free fatty acid β-oxidation and regulates energy homeostasis. Modules of highly inter-connected genes in disease-specific ACADS network are derived by integrating gene function and protein interaction data. Among the 8 genes in ACADS web retrieved from both STRING and GeneMANIA, ACADS is effectively conjoined with 4 genes including HAHDA, HADHB, ECHS1 and ACAT1. The functional analysis is done via ontological briefing and candidate disease identification. We observed that the highly efficient-interlinked genes connected with ACADS are HAHDA, HADHB, ECHS1 and ACAT1. Interestingly, the ontological aspect of genes in the ACADS network reveals that ACADS, HAHDA and HADHB play equally vital roles in fatty acid metabolism. The gene ACAT1 together with ACADS indulges in ketone metabolism. Our computational gene web analysis also predicts potential candidate disease recognition, thus indicating the involvement of ACADS, HAHDA, HADHB, ECHS1 and ACAT1 not only with lipid metabolism but also with infant death syndrome, skeletal myopathy, acute hepatic encephalopathy, Reye-like syndrome, episodic ketosis, and metabolic acidosis. The current study presents a comprehensible layout of ACADS network, its functional strategies and candidate disease approach associated with ACADS network.

Computational analysis of protein interaction networks for infectious diseases

Infectious diseases caused by pathogens, including viruses, bacteria and parasites, pose a serious threat to human health worldwide. Frequent changes in the pattern of infection mechanisms and the emergence of multidrug-resistant strains among pathogens have weakened the current treatment regimen. This necessitates the development of new therapeutic interventions to prevent and control such diseases. To cater to the need, analysis of protein interaction networks (PINs) has gained importance as one of the promising strategies. The present review aims to discuss various computational approaches to analyse the PINs in context to infectious diseases. Topology and modularity analysis of the network with their biological relevance, and the scenario till date about host–pathogen and intra-pathogenic protein interaction studies were delineated. This would provide useful insights to the research community, thereby enabling them to design novel biomedicine against such infectious diseases.

Algorithms for modeling global and context-specific functional relationship networks

Functional genomics has enormous potential to facilitate our understanding of normal and disease-specific physiology. In the past decade, intensive research efforts have been focused on modeling functional relationship networks, which summarize the probability of gene co-functionality relationships. Such modeling can be based on either expression data only or heterogeneous data integration. Numerous methods have been deployed to infer the functional relationship networks, while most of them target the global (non-context-specific) functional relationship networks. However, it is expected that functional relationships consistently reprogram under different tissues or biological processes. Thus, advanced methods have been developed targeting tissue-specific or developmental stage-specific networks. This article brings together the state-of-the-art functional relationship network modeling methods, emphasizes the need for heterogeneous genomic data integration and context-specific network modeling and outlines future directions for functional relationship networks.

Systems biology network-based discovery of a small molecule activator BL-AD008 targeting AMPK/ZIPK and inducing apoptosis in cervical cancer

The aim of this study was to discover a small molecule activator BL-AD008 targeting AMPK/ZIPK and inducing apoptosis in cervical cancer. In this study, we systematically constructed the global protein-protein interaction (PPI) network and predicted apoptosis-related protein connections by the Naïve Bayesian model. Then, we identified some classical apoptotic PPIs and other previously unrecognized PPIs between apoptotic kinases, such as AMPK and ZIPK. Subsequently, we screened a series of candidate compounds targeting AMPK/ZIPK, synthesized some compounds and eventually discovered a novel dual-target activator (BL-AD008). Moreover, we found BL-AD008 bear remarkable anti-proliferative activities toward cervical cancer cells and could induce apoptosis by death-receptor and mitochondrial pathways. Additionally, we found that BL-AD008-induced apoptosis was affected by the combination of AMPK and ZIPK. Then, we found that BL-AD008 bear its anti-tumor activities and induced apoptosis by targeting AMPK/ZIPK in vivo. In conclusion, these results demonstrate the ability of systems biology network to identify some key apoptotic kinase targets AMPK and ZIPK; thus providing a dual-target small molecule activator (BL-AD008) as a potential new apoptosis-modulating drug in future cervical cancer therapy.

A review on computational systems biology of pathogen-host interactions

Pathogens manipulate the cellular mechanisms of host organisms via pathogen-host interactions (PHIs) in order to take advantage of the capabilities of host cells, leading to infections. The crucial role of these interspecies molecular interactions in initiating and sustaining infections necessitates a thorough understanding of the corresponding mechanisms. Unlike the traditional approach of considering the host or pathogen separately, a systems-level approach, considering the PHI system as a whole is indispensable to elucidate the mechanisms of infection. Following the technological advances in the post-genomic era, PHI data have been produced in large-scale within the last decade. Systems biology-based methods for the inference and analysis of PHI regulatory, metabolic, and protein-protein networks to shed light on infection mechanisms are gaining increasing demand thanks to the availability of omics data. The knowledge derived from the PHIs may largely contribute to the identification of new and more efficient therapeutics to prevent or cure infections. There are recent efforts for the detailed documentation of these experimentally verified PHI data through Web-based databases. Despite these advances in data archiving, there are still large amounts of PHI data in the biomedical literature yet to be discovered, and novel text mining methods are in development to unearth such hidden data. Here, we review a collection of recent studies on computational systems biology of PHIs with a special focus on the methods for the inference and analysis of PHI networks, covering also the Web-based databases and text-mining efforts to unravel the data hidden in the literature.

Rational design of protein–protein interaction inhibitors

Low molecular weight compound competing for the binding of the p53 tumor suppressor to the MDM2 oncoprotein.

Polygonatum odoratum lectin induces apoptosis and autophagy via targeting EGFR-mediated Ras-Raf-MEK-ERK pathway in human MCF-7 breast cancer cells

Polygonatum odoratum lectin (POL), a mannose-binding GNA-related lectin, has been reported to display remarkable anti-proliferative and apoptosis-inducing activities toward a variety of cancer cells; however, the precise molecular mechanisms by which POL induces cancer cell death are still elusive. In the current study, we found that POL could induce both apoptosis and autophagy in human MCF-7 breast cancer cells. Subsequently, we found that POL induced MCF-7 cell apoptosis via the mitochondrial pathway. Additionally, we also found that POL induces MCF-7 cell apoptosis via EGFR-mediated Ras-Raf-MEK-ERK pathway, suggesting that POL may be a potential EGFR inhibitor. Finally, we used proteomics analyses for exploring more possible POL-induced pathways with EGFR, Ras, Raf, MEK and ERK, some of which were consistent with our in silico network prediction. Taken together, these results demonstrate that POL induces MCF-7 cell apoptosis and autophagy via targeting EGFR-mediated Ras-Raf-MEK-ERK signaling pathway, which would provide a new clue for exploiting POL as a potential anti-neoplastic drug for future cancer therapy.

Text mining in cancer gene and pathway prioritization

Prioritization of cancer implicated genes has received growing attention as an effective way to reduce wet lab cost by computational analysis that ranks candidate genes according to the likelihood that experimental verifications will succeed. A multitude of gene prioritization tools have been developed, each integrating different data sources covering gene sequences, differential expressions, function annotations, gene regulations, protein domains, protein interactions, and pathways. This review places existing gene prioritization tools against the backdrop of an integrative Omic hierarchy view toward cancer and focuses on the analysis of their text mining components. We explain the relatively slow progress of text mining in gene prioritization, identify several challenges to current text mining methods, and highlight a few directions where more effective text mining algorithms may improve the overall prioritization task and where prioritizing the pathways may be more desirable than prioritizing only genes.

Integration strategy is a key step in network-based analysis and dramatically affects network topological properties and inferring outcomes

An increasing number of experiments have been designed to detect intracellular and intercellular molecular interactions. Based on these molecular interactions (especially protein interactions), molecular networks have been built for using in several typical applications, such as the discovery of new disease genes and the identification of drug targets and molecular complexes. Because the data are incomplete and a considerable number of false-positive interactions exist, protein interactions from different sources are commonly integrated in network analyses to build a stable molecular network. Although various types of integration strategies are being applied in current studies, the topological properties of the networks from these different integration strategies, especially typical applications based on these network integration strategies, have not been rigorously evaluated. In this paper, systematic analyses were performed to evaluate 11 frequently used methods using two types of integration strategies: empirical and machine learning methods. The topological properties of the networks of these different integration strategies were found to significantly differ. Moreover, these networks were found to dramatically affect the outcomes of typical applications, such as disease gene predictions, drug target detections, and molecular complex identifications. The analysis presented in this paper could provide an important basis for future network-based biological researches.

Functional features and protein network of human sperm-egg interaction

Elucidation of the sperm-egg interaction at the molecular level is one of the unresolved problems in sexual reproduction, and understanding the molecular mechanism is crucial in solving problems in infertility and failed in vitro fertilization (IVF). Many molecular interactions in the form of protein-protein interactions (PPIs) mediate the sperm-egg membrane interaction. Due to the complexity of the problem such as difficulties in analyzing in vivo membrane PPIs, many efforts have failed to comprehensively elucidate the fusion mechanism and the molecular interactions that mediate sperm-egg membrane fusion. The main purpose of this study was to reveal possible protein interactions and associated molecular function during sperm-egg interaction using a protein interaction network approach. Different databases have been used to construct the human sperm-egg interaction network. The constructed network revealed new interactions. These included CD151 and CD9 in human oocyte that interact with CD49 in sperm, and CD49 and ITGA4 in sperm that interact with CD63 and CD81, respectively, in the oocyte. These results showed that the different integrins in sperm may be involved in human sperm-egg interaction. It was also suggested that sperm ADAM2 plays a role as a protein candidate involved in sperm-egg membrane interaction by interacting with CD9 in the oocyte. Interleukin-4 receptor activity, receptor signaling protein tyrosine kinase activity, and manganese ion transmembrane transport activity are the major molecular functions in sperm-egg interaction protein network. The disease association analysis indicated that sperm-egg interaction defects are also reflected in other disease networks such as cardiovascular, hematological, and breast cancer diseases. By analyzing the network, we identified the major molecular functions and disease association genes in sperm-egg interaction protein. Further experimental studies will be required to confirm the significance of these new computationally resolved interactions and the genetic links between sperm-egg interaction abnormalities and the associated disease.

ATF3 and extracellular matrix-related genes associated with the process of chronic obstructive pulmonary

INTRODUCTION

Chronic obstructive pulmonary disease (COPD) is a major public health problem worldwide and is proved to be the number three cause of death in globally. The objective of this study was to explore the molecular mechanism of the progression of COPD.

METHODS

Using the GSE1650 affymetrix microarray data accessible from Gene Expression Omnibus database, we first identified the differentially expressed genes (DEGs) between 18 COPD samples and 12 normal samples, followed by the GO / KEGG pathway analysis and gene interaction networks analysis of the DEGs. Our study identified 134 DEGs which involved in regulation of immune response, vesicle transport system, growth regulator and extracellular matrix (ECM)-related pathways.

RESULTS

Gene interaction networks analysis showed that the sub-network involved by activating transcription factor-3 (ATF3) was the most significant sub-network in gene interaction networks. Furthermore, the investigation of extracellular matrix-related genes showed that genes like collagen and insulin-like growth factor binding protein could clearly distinguish the COPD and normal control.

CONCLUSIONS

The genes regulated by ATF3 transcriptional activator as well as ECM-related genes may play an important role in the process of COPD. Our study provides a comprehensive bioinformatics analysis of genes and pathways which may be involved in the progression of COPD.

Modeling dynamic functional relationship networks and application to ex vivo human erythroid differentiation

MOTIVATION

Functional relationship networks, which summarize the probability of co-functionality between any two genes in the genome, could complement the reductionist focus of modern biology for understanding diverse biological processes in an organism. One major limitation of the current networks is that they are static, while one might expect functional relationships to consistently reprogram during the differentiation of a cell lineage. To address this potential limitation, we developed a novel algorithm that leverages both differentiation stage-specific expression data and large-scale heterogeneous functional genomic data to model such dynamic changes. We then applied this algorithm to the time-course RNA-Seq data we collected for ex vivo human erythroid cell differentiation.

RESULTS

Through computational cross-validation and literature validation, we show that the resulting networks correctly predict the (de)-activated functional connections between genes during erythropoiesis. We identified known critical genes, such as HBD and GATA1, and functional connections during erythropoiesis using these dynamic networks, while the traditional static network was not able to provide such information. Furthermore, by comparing the static and the dynamic networks, we identified novel genes (such as OSBP2 and PDZK1IP1) that are potential drivers of erythroid cell differentiation. This novel method of modeling dynamic networks is applicable to other differentiation processes where time-course genome-scale expression data are available, and should assist in generating greater understanding of the functional dynamics at play across the genome during development.

AVAILABILITY AND IMPLEMENTATION

The network described in this article is available at http://guanlab.ccmb.med.umich.edu/stageSpecificNetwork.

In silico analysis and experimental validation of azelastine hydrochloride (N4) targeting sodium taurocholate co-transporting polypeptide (NTCP) in HBV therapy

OBJECTIVES

The aim of this study was to explore sodium taurocholate co-transporting polypeptide (NTCP) exerting its function with hepatitis B virus (HBV) and its targeted candidate compounds, in HBV therapy.

MATERIALS AND METHODS

Identification of NTCP as a novel HBV target for screening candidate small molecules, was used by phylogenetic analysis, network construction, molecular modelling, molecular docking and molecular dynamics (MD) simulation. In vitro virological examination, q-PCR, western blotting and cytotoxicity studies were used for validating efficacy of the candidate compound.

RESULTS

We used the phylogenetic analysis of NTCP and constructed its protein-protein network. Also, we screened compounds from Drugbank and ZINC, among which five were validated for their authentication in HepG 2.2.15 cells. Then, we selected compound N4 (azelastine hydrochloride) as the most potent of them. This showed good inhibitory activity against HBsAg (IC50 = 7.5 μm) and HBeAg (IC50 = 3.7 μm), as well as high SI value (SI = 4.68). Further MD simulation results supported good interaction between compound N4 and NTCP.

CONCLUSIONS

In silico analysis and experimental validation together demonstrated that compound N4 can target NTCP in HepG2.2.15 cells, which may shed light on exploring it as a potential anti-HBV drug.

Plant systems biology: insights, advances and challenges

Plants dwelling at the base of biological food chain are of fundamental significance in providing solutions to some of the most daunting ecological and environmental problems faced by our planet. The reductionist views of molecular biology provide only a partial understanding to the phenotypic knowledge of plants. Systems biology offers a comprehensive view of plant systems, by employing a holistic approach integrating the molecular data at various hierarchical levels. In this review, we discuss the basics of systems biology including the various 'omics' approaches and their integration, the modeling aspects and the tools needed for the plant systems research. A particular emphasis is given to the recent analytical advances, updated published examples of plant systems biology studies and the future trends.

AKT pathway genes define 5 prognostic subgroups in glioblastoma

Activity of GFR/PI3K/AKT pathway inhibitors in glioblastoma clinical trials has not been robust. We hypothesized variations in the pathway between tumors contribute to poor response. We clustered GBM based on AKT pathway genes and discovered new subtypes then characterized their clinical and molecular features. There are at least 5 GBM AKT subtypes having distinct DNA copy number alterations, enrichment in oncogenes and tumor suppressor genes and patterns of expression for PI3K/AKT/mTOR signaling components. Gene Ontology terms indicate a different cell of origin or dominant phenotype for each subgroup. Evidence suggests one subtype is very sensitive to BCNU or CCNU (median survival 5.8 vs. 1.5 years; BCNU/CCNU vs other treatments; respectively). AKT subtyping advances previous approaches by revealing additional subgroups with unique clinical and molecular features. Evidence indicates it is a predictive marker for response to BCNU or CCNU and PI3K/AKT/mTOR pathway inhibitors. We anticipate Akt subtyping may help stratify patients for clinical trials and augment discovery of class-specific therapeutic targets.

Building a hierarchical organization of protein complexes out of protein association data

Organizing experimentally determined protein associations as a hierarchy can be a good approach to elucidating the content of protein complexes and the modularity of subcomplexes. Several challenges exist. First, intrinsically sticky proteins, such as chaperones, are often falsely assigned to many functionally unrelated complexes. Second, the reported collections of proteins may not be true "complexes" in the sense that they bind together and perform a joint cellular function. Third, due to imperfect sensitivity of protein detection methods, both false positive and false negative assignments of a protein to complexes may occur. We mitigate the first issue by down-weighting sticky proteins by their occurrence frequencies. We approach the other two problems by merging nearly identical complexes and by constructing a directed acyclic graph (DAG) based on the relationship of partial inclusion. The constructed DAG, within which smaller complexes form parts of the larger, can reveal how different complexes are joined. By merging almost identical complexes one can deemphasize the influence of false positives, while allowing false negatives to be rescued by other nearly identical association data. We investigate several protein weighting schemes and compare their corresponding DAGs using yeast and human complexes. We find that the scheme incorporating weights based on information flow in the network of direct protein-protein interactions produces biologically most meaningful DAGs. In either yeast or human, isolated nodes form a large proportion of the final hierarchy. While most connected components encompass very few nodes, the largest one for each species contains a sizable portion of all nodes. By considering examples of subgraphs composed of nodes containing a specified protein, we illustrate that the graphs' topological features can correctly suggest the biological roles of protein complexes. The input data, final results and the source code are available at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbpmn/ProteinComplexDAG/.

Network-based identification of key proteins involved in apoptosis and cell cycle regulation

OBJECTIVES

Cancer cells differ from normal body cells in their ability to divide indefinitely and to evade programmed cell death. Crosstalk between apoptosis and cell cycle processes promotes balance between proliferation and death, and limits population growth and survival of cells. However, intricate relationships between them and how they are able to manipulate the fate of cancer cells still remain to be clarified. Identification of key factors involved in both apoptosis and cell cycle regulation may help to address this problem.

MATERIALS AND METHODS

Identification of such key proteins was carried out, using a series of bioinformatics methods, such as network construction and key protein identification.

RESULTS

In this study, we computationally constructed human apoptotic/cell cycle-related protein-protein interactions (PPIs) networks from five experimentally supported protein interaction databases, and further integrated these high-throughput data sets into a Naïve Bayesian model to predict protein functional connections. On the basis of modified apoptotic/cell cycle related PPI networks, we calculated and ranked all protein members involved in apoptosis and cell cycle regulation. Our results not only identified some already known key proteins such as p53, Rb, Myc and Src but also found that the proteasome, Cullin family members, kinases and transcriptional repressors play important roles in regulating apoptosis and the cell cycle. Furthermore, we found that the top 100 proteins ranked by PeC were enriched in some pathways such as those of cancer, the proteasome, the cell cycle and Wnt signalling.

CONCLUSIONS

We constructed the global human apoptotic/cell cycle related PPI network based on five online databases, and a Naïve Bayesian model. In addition, we systematically identified apoptotic/cell cycle related key proteins in cancer cells. These findings may uncover intricate relationships between apoptosis and cell cycle processes and thus provide further new clues towards future anticancer drug discovery.

The domain landscape of virus-host interactomes

Viral infections result in millions of deaths in the world today. A thorough analysis of virus-host interactomes may reveal insights into viral infection and pathogenic strategies. In this study, we presented a landscape of virus-host interactomes based on protein domain interaction. Compared to the analysis at protein level, this domain-domain interactome provided a unique abstraction of protein-protein interactome. Through comparisons among DNA, RNA, and retrotranscribing viruses, we identified a core of human domains, that viruses used to hijack the cellular machinery and evade the immune system, which might be promising antiviral drug targets. We showed that viruses preferentially interacted with host hub and bottleneck domains, and the degree and betweenness centrality among three categories of viruses are significantly different. Further analysis at functional level highlighted that different viruses perturbed the host cellular molecular network by common and unique strategies. Most importantly, we creatively proposed a viral disease network among viral domains, human domains and the corresponding diseases, which uncovered several unknown virus-disease relationships that needed further verification. Overall, it is expected that the findings will help to deeply understand the viral infection and contribute to the development of antiviral therapy.

Identification of novel kinase inhibitors by targeting a kinase-related apoptotic protein-protein interaction network in HeLa cells

OBJECTIVES

Protein kinases orchestrate activation of signalling cascades in response to extra- and intracellular stimuli for regulation of cell proliferation. They are directly involved in a variety of diseases, particularly cancers. Systems biology approaches have become increasingly important in understanding regulatory frameworks in cancer, and thus may facilitate future anti-cancer discoveries. Moreover, it has been suggested and confirmed that high-throughput virtual screening provides a novel, effective way to reveal small molecule protein kinase inhibitors. Accordingly, we aimed to identify kinase targets and novel kinase inhibitors.

MATERIALS AND METHODS

A series of bioinformatics methods, such as network construction, molecular docking and microarray analyses were performed.

RESULTS

In this study, we computationally constructed the appropriate global human protein-protein interaction network with data from online databases, and then modified it into a kinase-related apoptotic protein-protein interaction network. Subsequently, we identified several kinases as potential drug targets according to their differential expression observed by microarray analyses. Then, we predicted relevant microRNAs, which could target the above-mentioned kinases. Ultimately, we virtually screened a number of small molecule natural products from Traditional Chinese Medicine (TCM)@Taiwan database and identified a number of compounds that are able to target polo-like kinase 1, cyclin-dependent kinase 1 and cyclin-dependent kinase 2 in HeLa cervical carcinoma cells.

CONCLUSIONS

Taken together, all these findings might hopefully facilitate discovery of new kinase inhibitors that could be promising candidates for anti-cancer drug development.

Identification of gene expression changes from colitis to CRC in the mouse CAC model

A connection between colorectal carcinogenesis and inflammation is well known, but the underlying molecular mechanisms have not been elucidated. Chemically induced colitis-associated cancer (CAC) is an outstanding mouse model for studying the link between inflammation and cancer. Additionally, the CAC model is used for examining novel diagnostic, prognostic, and predictive markers for use in clinical practice. Here, a CAC model was established in less than 100 days using azoxymethane (AOM) with dextran sulfate sodium salt (DSS) in BALB/c mice. We examined the mRNA expression profiles of three groups: control untreated mice (K), DSS-induced chronic colitis mice (D), and AOM/DSS-induced CAC (AD) mice. We identified 6301 differentially expressed genes (DEGs) among the three groups, including 93 persistently upregulated genes and 139 persistently downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that the most persistent DEGs were significantly enriched in metabolic or inflammatory components in the tumor microenvironment. Furthermore, several associated DEGs were identified as potential DEGs by protein-protein interaction (PPI) network analysis. We selected 14 key genes from the DEGs and potential DEGs for further quantitative real-time PCR (qPCR) verification. Six persistently upregulated, 3 persistently downregulated DEGs, and the other 3 genes showed results consistent with the microarray data. We demonstrated the regulation of 12 key genes specifically involved in Wnt signaling, cytokine and cytokine receptor interactions, homeostasis, and tumor-associated metabolism during colitis-associated CRC. Our results suggest that a close relationship between metabolic and inflammatory mediators of the tumor microenvironment is present in CAC.

Aberrant gene expression in mucosa adjacent to tumor reveals a molecular crosstalk in colon cancer

Background

A colorectal tumor is not an isolated entity growing in a restricted location of the body. The patient’s gut environment constitutes the framework where the tumor evolves and this relationship promotes and includes a complex and tight correlation of the tumor with inflammation, blood vessels formation, nutrition, and gut microbiome composition. The tumor influence in the environment could both promote an anti-tumor or a pro-tumor response.

Methods

A set of 98 paired adjacent mucosa and tumor tissues from colorectal cancer (CRC) patients and 50 colon mucosa from healthy donors (246 samples in total) were included in this work. RNA extracted from each sample was hybridized in Affymetrix chips Human Genome U219. Functional relationships between genes were inferred by means of systems biology using both transcriptional regulation networks (ARACNe algorithm) and protein-protein interaction networks (BIANA software).

Results

Here we report a transcriptomic analysis revealing a number of genes activated in adjacent mucosa from CRC patients, not activated in mucosa from healthy donors. A functional analysis of these genes suggested that this active reaction of the adjacent mucosa was related to the presence of the tumor. Transcriptional and protein-interaction networks were used to further elucidate this response of normal gut in front of the tumor, revealing a crosstalk between proteins secreted by the tumor and receptors activated in the adjacent colon tissue; and vice versa. Remarkably, Slit family of proteins activated ROBO receptors in tumor whereas tumor-secreted proteins transduced a cellular signal finally activating AP-1 in adjacent tissue.

Conclusions

The systems-level approach provides new insights into the micro-ecology of colorectal tumorogenesis. Disrupting this intricate molecular network of cell-cell communication and pro-inflammatory microenvironment could be a therapeutic target in CRC patients.

Detection of type 2 diabetes related modules and genes based on epigenetic networks

Background

Type 2 diabetes (T2D) is one of the most common chronic metabolic diseases characterized by insulin resistance and the decrease of insulin secretion. Genetic variation can only explain part of the heritability of T2D, so there need new methods to detect the susceptibility genes of the disease. Epigenetics could establish the interface between the environmental factor and the T2D Pathological mechanism.

Results

Based on the network theory and by combining epigenetic characteristics with human interactome, the weighted human DNA methylation network (WMPN) was constructed, and a T2D-related subnetwork (TMSN) was obtained through T2D-related differentially methylated genes. It is found that TMSN had a T2D specific network structure that non-fatal metabolic disease causing genes were often located in the topological and functional periphery of network. Combined with chromatin modifications, the weighted chromatin modification network (WCPN) was built, and a T2D-related chromatin modification pattern subnetwork was obtained by the TMSN gene set. TCSN had a densely connected network community, indicating that TMSN and TCSN could represent a collection of T2D-related epigenetic dysregulated sub-pathways. Using the cumulative hypergeometric test, 24 interplay modules of DNA methylation and chromatin modifications were identified. By the analysis of gene expression in human T2D islet tissue, it is found that there existed genes with the variant expression level caused by the aberrant DNA methylation and (or) chromatin modifications, which might affect and promote the development of T2D.

Conclusions

Here we have detected the potential interplay modules of DNA methylation and chromatin modifications for T2D. The study of T2D epigenetic networks provides a new way for understanding the pathogenic mechanism of T2D caused by epigenetic disorders.

Prediction of heterotrimeric protein complexes by two-phase learning using neighboring kernels

Background

Protein complexes play important roles in biological systems such as gene regulatory networks and metabolic pathways. Most methods for predicting protein complexes try to find protein complexes with size more than three. It, however, is known that protein complexes with smaller sizes occupy a large part of whole complexes for several species. In our previous work, we developed a method with several feature space mappings and the domain composition kernel for prediction of heterodimeric protein complexes, which outperforms existing methods.

Results

We propose methods for prediction of heterotrimeric protein complexes by extending techniques in the previous work on the basis of the idea that most heterotrimeric protein complexes are not likely to share the same protein with each other. We make use of the discriminant function in support vector machines (SVMs), and design novel feature space mappings for the second phase. As the second classifier, we examine SVMs and relevance vector machines (RVMs). We perform 10-fold cross-validation computational experiments. The results suggest that our proposed two-phase methods and SVM with the extended features outperform the existing method NWE, which was reported to outperform other existing methods such as MCL, MCODE, DPClus, CMC, COACH, RRW, and PPSampler for prediction of heterotrimeric protein complexes.

Conclusions

We propose two-phase prediction methods with the extended features, the domain composition kernel, SVMs and RVMs. The two-phase method with the extended features and the domain composition kernel using SVM as the second classifier is particularly useful for prediction of heterotrimeric protein complexes.

Characterizing and controlling the inflammatory network during influenza A virus infection

To gain insights into the pathogenesis of influenza A virus (IAV) infections, this study focused on characterizing the inflammatory network and identifying key proteins by combining high-throughput data and computational techniques. We constructed the cell-specific normal and inflammatory networks for H5N1 and H1N1 infections through integrating high-throughput data. We demonstrated that better discrimination between normal and inflammatory networks by network entropy than by other topological metrics. Moreover, we identified different dynamical interactions among TLR2, IL-1β, IL10 and NFκB between normal and inflammatory networks using optimization algorithm. In particular, good robustness and multistability of inflammatory sub-networks were discovered. Furthermore, we identified a complex, TNFSF10/HDAC4/HDAC5, which may play important roles in controlling inflammation, and demonstrated that changes in network entropy of this complex negatively correlated to those of three proteins: TNFα, NFκB and COX-2. These findings provide significant hypotheses for further exploring the molecular mechanisms of infectious diseases and developing control strategies.

PRIMOS: an integrated database of reassessed protein-protein interactions providing web-based access to in silico validation of experimentally derived data

Steady improvements in proteomics present a bioinformatic challenge to retrieve, store, and process the accumulating and often redundant amount of information. In particular, a large-scale comparison and analysis of protein-protein interaction (PPI) data requires tools for data interpretation as well as validation. At this juncture, the Protein Interaction and Molecule Search (PRIMOS) platform represents a novel web portal that unifies six primary PPI databases (BIND, Biomolecular Interaction Network Database; DIP, Database of Interacting Proteins; HPRD, Human Protein Reference Database; IntAct; MINT, Molecular Interaction Database; and MIPS, Munich Information Center for Protein Sequences) into a single consistent repository, which currently includes more than 196,700 redundancy-removed PPIs. PRIMOS supports three advanced search strategies centering on disease-relevant PPIs, on inter- and intra-organismal crosstalk relations (e.g., pathogen-host interactions), and on highly connected protein nodes analysis ("hub" identification). The main novelties distinguishing PRIMOS from other secondary PPI databases are the reassessment of known PPIs, and the capacity to validate personal experimental data by our peer-reviewed, homology-based validation. This article focuses on definite PRIMOS use cases (presentation of embedded biological concepts, example applications) to demonstrate its broad functionality and practical value. PRIMOS is publicly available at http://primos.fh-hagenberg.at.

Integrating in silico resources to map a signaling network

The abundance of publicly available life science databases offers a wealth of information that can support interpretation of experimentally derived data and greatly enhance hypothesis generation. Protein interaction and functional networks are not simply new renditions of existing data: they provide the opportunity to gain insights into the specific physical and functional role a protein plays as part of the biological system. In this chapter, we describe different in silico tools that can quickly and conveniently retrieve data from existing data repositories and we discuss how the available tools are best utilized for different purposes. While emphasizing protein-protein interaction databases (e.g., BioGrid and IntAct), we also introduce metasearch platforms such as STRING and GeneMANIA, pathway databases (e.g., BioCarta and Pathway Commons), text mining approaches (e.g., PubMed and Chilibot), and resources for drug-protein interactions, genetic information for model organisms and gene expression information based on microarray data mining. Furthermore, we provide a simple step-by-step protocol for building customized protein-protein interaction networks in Cytoscape, a powerful network assembly and visualization program, integrating data retrieved from these various databases. As we illustrate, generation of composite interaction networks enables investigators to extract significantly more information about a given biological system than utilization of a single database or sole reliance on primary literature.

KBDOCK 2013: a spatial classification of 3D protein domain family interactions

Comparing, classifying and modelling protein structural interactions can enrich our understanding of many biomolecular processes. This contribution describes Kbdock (http://kbdock.loria.fr/), a database system that combines the Pfam domain classification with coordinate data from the PDB to analyse and model 3D domain–domain interactions (DDIs). Kbdock can be queried using Pfam domain identifiers, protein sequences or 3D protein structures. For a given query domain or pair of domains, Kbdock retrieves and displays a non-redundant list of homologous DDIs or domain–peptide interactions in a common coordinate frame. Kbdock may also be used to search for and visualize interactions involving different, but structurally similar, Pfam families. Thus, structural DDI templates may be proposed even when there is little or no sequence similarity to the query domains.

PATRIC, the bacterial bioinformatics database and analysis resource

The Pathosystems Resource Integration Center (PATRIC) is the all-bacterial Bioinformatics Resource Center (BRC) (http://www.patricbrc.org). A joint effort by two of the original National Institute of Allergy and Infectious Diseases-funded BRCs, PATRIC provides researchers with an online resource that stores and integrates a variety of data types [e.g. genomics, transcriptomics, protein-protein interactions (PPIs), three-dimensional protein structures and sequence typing data] and associated metadata. Datatypes are summarized for individual genomes and across taxonomic levels. All genomes in PATRIC, currently more than 10,000, are consistently annotated using RAST, the Rapid Annotations using Subsystems Technology. Summaries of different data types are also provided for individual genes, where comparisons of different annotations are available, and also include available transcriptomic data. PATRIC provides a variety of ways for researchers to find data of interest and a private workspace where they can store both genomic and gene associations, and their own private data. Both private and public data can be analyzed together using a suite of tools to perform comparative genomic or transcriptomic analysis. PATRIC also includes integrated information related to disease and PPIs. All the data and integrated analysis and visualization tools are freely available. This manuscript describes updates to the PATRIC since its initial report in the 2007 NAR Database Issue.

Network-based penalized regression with application to genomic data

Penalized regression approaches are attractive in dealing with high-dimensional data such as arising in high-throughput genomic studies. New methods have been introduced to utilize the network structure of predictors, for example, gene networks, to improve parameter estimation and variable selection. All the existing network-based penalized methods are based on an assumption that parameters, for example, regression coefficients, of neighboring nodes in a network are close in magnitude, which however may not hold. Here we propose a novel penalized regression method based on a weaker prior assumption that the parameters of neighboring nodes in a network are likely to be zero (or non-zero) at the same time, regardless of their specific magnitudes. We propose a novel non-convex penalty function to incorporate this prior, and an algorithm based on difference convex programming. We use simulated data and two breast cancer gene expression datasets to demonstrate the advantages of the proposed methods over some existing methods. Our proposed methods can be applied to more general problems for group variable selection.

Structural and functional characterization of the recombinant death domain from death-associated protein kinase

Death-associated protein kinase (DAPk) is a calcium/calmodulin-regulated Ser/Thr-protein kinase that functions at an important point of integration for cell death signaling pathways. DAPk has a structurally unique multi-domain architecture, including a C-terminally positioned death domain (DD) that is a positive regulator of DAPk activity. In this study, recombinant DAPk-DD was observed to aggregate readily and could not be prepared in sufficient yield for structural analysis. However, DAPk-DD could be obtained as a soluble protein in the form of a translational fusion protein with the B1 domain of streptococcal protein G. In contrast to other DDs that adopt the canonical six amphipathic α-helices arranged in a compact fold, the DAPk-DD was found to possess surprisingly low regular secondary structure content and an absence of a stable globular fold, as determined by circular dichroism (CD), NMR spectroscopy and a temperature-dependent fluorescence assay. Furthermore, we measured the in vitro interaction between extracellular-regulated kinase-2 (ERK2) and various recombinant DAPk-DD constructs. Despite the low level of structural order, the recombinant DAPk-DD retained the ability to interact with ERK2 in a 1∶1 ratio with a K d in the low micromolar range. Only the full-length DAPk-DD could bind ERK2, indicating that the apparent 'D-motif' located in the putative sixth helix of DAPk-DD is not sufficient for ERK2 recognition. CD analysis revealed that binding of DAPk-DD to ERK2 is not accompanied by a significant change in secondary structure. Taken together our data argue that the DAPk-DD, when expressed in isolation, does not adopt a classical DD fold, yet in this state retains the capacity to interact with at least one of its binding partners. The lack of a stable globular structure for the DAPk-DD may reflect either that its folding would be supported by interactions absent in our experimental set-up, or a limitation in the structural bioinformatics assignment of the three-dimensional structure.