Getting connected: analysis and principles of biological networks

Network Analyses of the Differential Expression of Heat Shock Proteins in Glioma

Heat shock protein (HSP) is a family of highly conserved protein, which exists widely in various organisms and has a variety of important physiological functions. Currently, there is no systematic analysis of HSPs in human glioma. The aim of this study was to investigate the characteristics of HSPs through constructing protein-protein interaction network (PPIN) considering the expression level of HSPs in glioma. After the identification of the differentially expressed HSPs in glioma tissues, a specific PPIN was constructed and found that there were many interactions between the differentially expressed HSPs in glioma. Subcellular localization analysis shows that HSPs and their interacting proteins distribute from the cell membrane to the nucleus in a multilayer structure. By functional enrichment analysis, gene ontology analysis, and Kyoto Encyclopedia of Genes and Genomes pathway analysis, the potential function of HSPs and two meaningful enrichment pathways was revealed. In addition, nine HSPs (DNAJA4, DNAJC6, DNAJC12, HSPA6, HSP90B1, DNAJB1, DNAJB6, DNAJC10, and SERPINH1) are prognostic markers for human brain glioma. These analyses provide a full view of HSPs about their expression, biological process, as well as clinical significance in glioma.

Turning genome-wide association study findings into opportunities for drug repositioning

Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored.

The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations.

Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.

MorbiNet: multimorbidity networks in adult general population. Analysis of type 2 diabetes mellitus comorbidity

Multimorbidity has great impact on health care. We constructed multimorbidity networks in the general population, extracted subnets focused on common chronic conditions and analysed type 2 diabetes mellitus (T2DM) comorbidity network. We used electronic records from 3,135,948 adult people in Catalonia, Spain (539,909 with T2DM), with at least 2 coexistent chronic conditions within the study period (2006-2017). We constructed networks from odds-ratio estimates adjusted by age and sex and considered connections with OR > 1.2 and p-value < 1e-5. Directed networks and trajectories were derived from temporal associations. Interactive networks are freely available in a website with the option to customize characteristics and subnets. The more connected conditions in T2DM undirected network were: complicated hypertension and atherosclerosis/peripheral vascular disease (degree: 32), cholecystitis/cholelithiasis, retinopathy and peripheral neuritis/neuropathy (degree: 31). T2DM has moderate number of connections and centrality but is associated with conditions with high scores in the multimorbidity network (neuropathy, anaemia and digestive diseases), and severe conditions with poor prognosis. The strongest associations from T2DM directed networks were to retinopathy (OR: 23.8), glomerulonephritis/nephrosis (OR: 3.4), peripheral neuritis/neuropathy (OR: 2.7) and pancreas cancer (OR: 2.4). Temporal associations showed the relevance of retinopathy in the progression to complicated hypertension, cerebrovascular disease, ischemic heart disease and organ failure.

KRCC1: A potential therapeutic target in ovarian cancer

Using a systems biology approach to prioritize potential points of intervention in ovarian cancer, we identified the lysine rich coiled-coil 1 (KRCC1), as a potential target. High-grade serous ovarian cancer patient tumors and cells express significantly higher levels of KRCC1 which correlates with poor overall survival and chemoresistance. We demonstrate that KRCC1 is predominantly present in the chromatin-bound nuclear fraction, interacts with HDAC1, HDAC2, and with the serine-threonine phosphatase PP1CC. Silencing KRCC1 inhibits cellular plasticity, invasive properties, and potentiates apoptosis resulting in reduced tumor growth. These phenotypes are associated with increased acetylation of histones and with increased phosphorylation of H2AX and CHK1, suggesting the modulation of transcription and DNA damage that may be mediated by the action of HDAC and PP1CC, respectively. Hence, we address an urgent need to develop new targets in cancer.

Plants are intelligent, here's how

HYPOTHESES

The drive to survive is a biological universal. Intelligent behaviour is usually recognized when individual organisms including plants, in the face of fiercely competitive or adverse, real-world circumstances, change their behaviour to improve their probability of survival.

SCOPE

This article explains the potential relationship of intelligence to adaptability and emphasizes the need to recognize individual variation in intelligence showing it to be goal directed and thus being purposeful. Intelligent behaviour in single cells and microbes is frequently reported. Individual variation might be underpinned by a novel learning mechanism, described here in detail. The requirements for real-world circumstances are outlined, and the relationship to organic selection is indicated together with niche construction as a good example of intentional behaviour that should improve survival. Adaptability is important in crop development but the term may be complex incorporating numerous behavioural traits some of which are indicated.

CONCLUSION

There is real biological benefit to regarding plants as intelligent both from the fundamental issue of understanding plant life but also from providing a direction for fundamental future research and in crop breeding.

Reconstruction of Protein-Protein Interaction Networks Using Homology-Based Search: Application to the Autophagy Pathway of Aging in Podospora anserina

The integration of the available experimental data represents a main problem in systems biology. In particular, in medical sciences, many new data became available, but often data are incomplete and of different quality and quantity. Here, we describe a method for the automatic derivation of protein-protein interaction networks based on homology search, which is applicable to arbitrary pathways and species. We implemented the method as a freely available open-source R package. To demonstrate the application of the method, we consider the autophagy pathway in the filamentous fungus Podospora anserina, which represents an established model organism to unravel the mechanisms of biological aging. Further, we apply network analysis methods to prove the reliability of the network.

Integrative and perturbation-based analysis of the transcriptional dynamics of TGFβ/BMP system components in transition from embryonic stem cells to neural progenitors

Cooperative actions of extrinsic signals and cell‐intrinsic transcription factors alter gene regulatory networks enabling cells to respond appropriately to environmental cues. Signaling by transforming growth factor type β (TGFβ) family ligands (eg, bone morphogenetic proteins [BMPs] and Activin/Nodal) exerts cell‐type specific and context‐dependent transcriptional changes, thereby steering cellular transitions throughout embryogenesis. Little is known about coordinated regulation and transcriptional interplay of the TGFβ system. To understand intrafamily transcriptional regulation as part of this system's actions during development, we selected 95 of its components and investigated their mRNA‐expression dynamics, gene‐gene interactions, and single‐cell expression heterogeneity in mouse embryonic stem cells transiting to neural progenitors. Interrogation at 24 hour intervals identified four types of temporal gene transcription profiles that capture all stages, that is, pluripotency, epiblast formation, and neural commitment. Then, between each stage we performed esiRNA‐based perturbation of each individual component and documented the effect on steady‐state mRNA levels of the remaining 94 components. This exposed an intricate system of multilevel regulation whereby the majority of gene‐gene interactions display a marked cell‐stage specific behavior. Furthermore, single‐cell RNA‐profiling at individual stages demonstrated the presence of detailed co‐expression modules and subpopulations showing stable co‐expression modules such as that of the core pluripotency genes at all stages. Our combinatorial experimental approach demonstrates how intrinsically complex transcriptional regulation within a given pathway is during cell fate/state transitions.

Prioritizing candidate diseases-related metabolites based on literature and functional similarity

Background

As the terminal products of cellular regulatory process, functional related metabolites have a close relationship with complex diseases, and are often associated with the same or similar diseases. Therefore, identification of disease related metabolites play a critical role in understanding comprehensively pathogenesis of disease, aiming at improving the clinical medicine. Considering that a large number of metabolic markers of diseases need to be explored, we propose a computational model to identify potential disease-related metabolites based on functional relationships and scores of referred literatures between metabolites. First, obtaining associations between metabolites and diseases from the Human Metabolome database, we calculate the similarities of metabolites based on modified recommendation strategy of collaborative filtering utilizing the similarities between diseases. Next, a disease-associated metabolite network (DMN) is built with similarities between metabolites as weight. To improve the ability of identifying disease-related metabolites, we introduce scores of text mining from the existing database of chemicals and proteins into DMN and build a new disease-associated metabolite network (FLDMN) by fusing functional associations and scores of literatures. Finally, we utilize random walking with restart (RWR) in this network to predict candidate metabolites related to diseases.

Results

We construct the disease-associated metabolite network and its improved network (FLDMN) with 245 diseases, 587 metabolites and 28,715 disease-metabolite associations. Subsequently, we extract training sets and testing sets from two different versions of the Human Metabolome database and assess the performance of DMN and FLDMN on 19 diseases, respectively. As a result, the average AUC (area under the receiver operating characteristic curve) of DMN is 64.35%. As a further improved network, FLDMN is proven to be successful in predicting potential metabolic signatures for 19 diseases with an average AUC value of 76.03%.

Conclusion

In this paper, a computational model is proposed for exploring metabolite-disease pairs and has good performance in predicting potential metabolites related to diseases through adequate validation. This result suggests that integrating literature and functional associations can be an effective way to construct disease associated metabolite network for prioritizing candidate diseases-related metabolites.

Complex interaction networks of cytokines after transarterial chemotherapy in patients with hepatocellular carcinoma

Treating hepatocellular carcinoma with transarterial chemoembolization (TACE) induces both local inflammation in the tumor microenvironment as well as systemic inflammation. We analyzed serum cytokine response to TACE to evaluate this. Serum samples obtained from 203 HCC patients treated with TACE were analyzed for inflammatory cytokines including interleukin (IL)-1β, IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12, IL-13, IL-17, IL-22, TNF-α, IFN-γ, and C-reactive protein (CRP) levels. Cytokine concentrations were measured at day 0 (D0, baseline, n = 203), day3 (D3, n = 156), day7 (D7, n = 147), and day 60 (D60, n = 115) after TACE. Network analysis of the cytokines was performed to understand their interactive relationship. After TACE, IL-1β, -6,-9, -12, and -22 increased by D60. IL-2, -5, -10, -17A and INF-γ decreased by D60, and IL-4, -13 and TNF-α revealed stable concentration. D0 network revealed that IL-2, -4, -5, and -10 formed a module. D3 network had the highest clustering coefficient and average degree that revealed similar pattern as CRP. D7 network revealed that IL-6, -9 and CRP were isolated from the network. D60 network had the lower network heterogeneity and lower clustering coefficient, network diameter, shortest path and characteristic path length. Degree correlation revealed that assortative network turned to disassortative network by D60 indicating that the network gained scale free feature. D60 cytokine network retained inflammatory function and these parameters indicated that the systemic inflammation induced by TACE appeared to be attenuated by D60. IL-9 at D3 and D7 seemed to be related to anti-tumor effect and IL-6 at D7 and D60, and IL-22 at D60 was related to regenerative but not pro- or anti- inflammatory function. Median survival month of patient group with high and low values of cytokine with P-values were as follows: D0 CRP, 9.5 and 54.2 months (P<0.0001); D0 IL-2, 39.9 and 56.1 months (P = 0.0084); D3 CRP, 31.3 and 55.1 months (P = 0.0056); D7 CRP, 28.7 and 50.7 months (P = 0.0065), respectively. TACE is associated with systemic inflammation which appears to peak at Day 3 and resolve by D60. Among the tested cytokines, IL-6 and IL-22 appear to play a regenerative role.

Applications of molecular networks in biomedicine

Due to the large interdependence between the molecular components of living systems, many phenomena, including those related to pathologies, cannot be explained in terms of a single gene or a small number of genes. Molecular networks, representing different types of relationships between molecular entities, embody these large sets of interdependences in a framework that allow their mining from a systemic point of view to obtain information. These networks, often generated from high-throughput omics datasets, are used to study the complex phenomena of human pathologies from a systemic point of view. Complementing the reductionist approach of molecular biology, based on the detailed study of a small number of genes, systemic approaches to human diseases consider that these are better reflected in large and intricate networks of relationships between genes. These networks, and not the single genes, provide both better markers for diagnosing diseases and targets for treating them. Network approaches are being used to gain insight into the molecular basis of complex diseases and interpret the large datasets associated with them, such as genomic variants. Network formalism is also suitable for integrating large, heterogeneous and multilevel datasets associated with diseases from the molecular level to organismal and epidemiological scales. Many of these approaches are available to nonexpert users through standard software packages.

A system view and analysis of essential hypertension

OBJECTIVES

The goal of this study was to investigate genes associated with essential hypertension from a system perspective, making use of bioinformatic tools to gain insights that are not evident when focusing at a detail-based resolution.

METHODS

Using various databases (pathways, Genome Wide Association Studies, knockouts etc.), we compiled a set of about 200 genes that play a major role in hypertension and identified the interactions between them. This enabled us to create a protein-protein interaction network graph, from which we identified key elements, based on graph centrality analysis. Enriched gene regulatory elements (transcription factors and microRNAs) were extracted by motif finding techniques and knowledge-based tools.

RESULTS

We found that the network is composed of modules associated with functions such as water retention, endothelial vasoconstriction, sympathetic activity and others. We identified the transcription factor SP1 and the two microRNAs miR27 (a and b) and miR548c-3p that seem to play a major role in regulating the network as they exert their control over several modules and are not restricted to specific functions. We also noticed that genes involved in metabolic diseases (e.g. insulin) are central to the network.

CONCLUSION

We view the blood-pressure regulation mechanism as a system-of-systems, composed of several contributing subsystems and pathways rather than a single module. The system is regulated by distributed elements. Understanding this mode of action can lead to a more precise treatment and drug target discovery. Our analysis suggests that insulin plays a primary role in hypertension, highlighting the tight link between essential hypertension and diseases associated with the metabolic syndrome.

Identification of co-evolving temporal networks

BACKGROUND

Biological networks describes the mechanisms which govern cellular functions. Temporal networks show how these networks evolve over time. Studying the temporal progression of network topologies is of utmost importance since it uncovers how a network evolves and how it resists to external stimuli and internal variations. Two temporal networks have co-evolving subnetworks if the evolving topologies of these subnetworks remain similar to each other as the network topology evolves over a period of time. In this paper, we consider the problem of identifying co-evolving subnetworks given a pair of temporal networks, which aim to capture the evolution of molecules and their interactions over time. Although this problem shares some characteristics of the well-known network alignment problems, it differs from existing network alignment formulations as it seeks a mapping of the two network topologies that is invariant to temporal evolution of the given networks. This is a computationally challenging problem as it requires capturing not only similar topologies between two networks but also their similar evolution patterns.

RESULTS

We present an efficient algorithm, Tempo, for solving identifying co-evolving subnetworks with two given temporal networks. We formally prove the correctness of our method. We experimentally demonstrate that Tempo scales efficiently with the size of network as well as the number of time points, and generates statistically significant alignments-even when evolution rates of given networks are high. Our results on a human aging dataset demonstrate that Tempo identifies novel genes contributing to the progression of Alzheimer's, Huntington's and Type II diabetes, while existing methods fail to do so.

CONCLUSIONS

Studying temporal networks in general and human aging specifically using Tempo enables us to identify age related genes from non age related genes successfully. More importantly, Tempo takes the network alignment problem one huge step forward by moving beyond the classical static network models.

Natural Product Target Network Reveals Potential for Cancer Combination Therapies

A body of research demonstrates examples of in vitro and in vivo synergy between natural products and anti-neoplastic drugs for some cancers. However, the underlying biological mechanisms are still elusive. To better understand biological entities targeted by natural products and therefore provide rational evidence for future novel combination therapies for cancer treatment, we assess the targetable space of natural products using public domain compound-target information. When considering pathways from the Reactome database targeted by natural products, we found an increase in coverage of 61% (725 pathways), relative to pathways covered by FDA approved cancer drugs collected in the Cancer Targetome, a resource for evidence-based drug-target interactions. Not only is the coverage of pathways targeted by compounds increased when we include natural products, but coverage of targets within those pathways is also increased. Furthermore, we examined the distribution of cancer driver genes across pathways to assess relevance of natural products to critical cancer therapeutic space. We found 24 pathways enriched for cancer drivers that had no available cancer drug interactions at a potentially clinically relevant binding affinity threshold of < 100nM that had at least one natural product interaction at that same binding threshold. Assessment of network context highlighted the fact that natural products show target family groupings both distinct from and in common with cancer drugs, strengthening the complementary potential for natural products in the cancer therapeutic space. In conclusion, our study provides a foundation for developing novel cancer treatment with the combination of drugs and natural products.

Adapting machine-learning algorithms to design gene circuits

BACKGROUND

Gene circuits are important in many aspects of biology, and perform a wide variety of different functions. For example, some circuits oscillate (e.g. the cell cycle), some are bistable (e.g. as cells differentiate), some respond sharply to environmental signals (e.g. ultrasensitivity), and some pattern multicellular tissues (e.g. Turing's model). Often, one starts from a given circuit, and using simulations, asks what functions it can perform. Here we want to do the opposite: starting from a prescribed function, can we find a circuit that executes this function? Whilst simple in principle, this task is challenging from a computational perspective, since gene circuit models are complex systems with many parameters. In this work, we adapted machine-learning algorithms to significantly accelerate gene circuit discovery.

RESULTS

We use gradient-descent optimization algorithms from machine learning to rapidly screen and design gene circuits. With this approach, we found that we could rapidly design circuits capable of executing a range of different functions, including those that: (1) recapitulate important in vivo phenomena, such as oscillators, and (2) perform complex tasks for synthetic biology, such as counting noisy biological events.

CONCLUSIONS

Our computational pipeline will facilitate the systematic study of natural circuits in a range of contexts, and allow the automatic design of circuits for synthetic biology. Our method can be readily applied to biological networks of any type and size, and is provided as an open-source and easy-to-use python module, GeneNet.

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.

Gene expression changes associated with the evolutionary loss of a metabolic trait: lack of lipogenesis in parasitoids

Background

Trait loss is a pervasive phenomenon in evolution, yet the underlying molecular causes have been identified in only a handful of cases. Most of these cases involve loss-of-function mutations in one or more trait-specific genes. However, in parasitoid insects the evolutionary loss of a metabolic trait is not associated with gene decay. Parasitoids have lost the ability to convert dietary sugars into fatty acids. Earlier research suggests that lack of lipogenesis in the parasitoid wasp Nasonia vitripennis is caused by changes in gene regulation.

Results

We compared transcriptomic responses to sugar-feeding in the non-lipogenic parasitoid species Nasonia vitripennis and the lipogenic Drosophila melanogaster. Both species adjusted their metabolism within 4 hours after sugar-feeding, but there were sharp differences between the expression profiles of the two species, especially in the carbohydrate and lipid metabolic pathways. Several genes coding for key enzymes in acetyl-CoA metabolism, such as malonyl-CoA decarboxylase (mcd) and HMG-CoA synthase (hmgs) differed in expression between the two species. Their combined action likely blocks lipogenesis in the parasitoid species. Network-based analysis showed connectivity of genes to be negatively correlated to the fold change of gene expression. Furthermore, genes involved in the fatty acid metabolic pathway were more connected than the set of genes of all metabolic pathways combined.

Conclusion

High connectivity of lipogenesis genes is indicative of pleiotropic effects and could explain the absence of gene degradation. We conclude that modification of expression levels of only a few little-connected genes, such as mcd, is sufficient to enable complete loss of lipogenesis in N. vitripennis.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5673-6) contains supplementary material, which is available to authorized users.

New lncRNA annotation reveals extensive functional divergence of the transcriptome in maize

Long non-coding RNAs (lncRNAs), whose sequences are approximately 200 bp or longer and unlikely to encode proteins, may play an important role in eukaryotic gene regulation. Although the latest maize (Zea mays L.) reference genome provides an essential genomic resource, genome-wide annotations of maize lncRNAs have not been updated. Here, we report on a large transcriptomic dataset collected from 749 RNA sequencing experiments across different tissues and stages of the maize reference inbred B73 line and 60 from its wild relative teosinte. We identified 18,165 high-confidence lncRNAs in maize, of which 6,873 are conserved between maize and teosinte. We uncovered distinct genomic characteristics of conserved lncRNAs, non-conserved lncRNAs, and protein-coding transcripts. Intriguingly, Shannon entropy analysis showed that conserved lncRNAs are likely to be expressed similarly to protein-coding transcripts. Co-expression network analysis revealed significant variation in the degree of co-expression. Furthermore, selection analysis indicated that conserved lncRNAs are more likely than non-conserved lncRNAs to be located in regions subject to recent selection, indicating evolutionary differentiation. Our results provide the latest genome-wide annotation and analysis of maize lncRNAs and uncover potential functional divergence between protein-coding, conserved lncRNA, and non-conserved lncRNA genes, demonstrating the high complexity of the maize transcriptome.

miRNet-Functional Analysis and Visual Exploration of miRNA-Target Interactions in a Network Context

To gain functional insights into microRNAs (miRNAs), researchers usually look for pathways or biological processes that are overrepresented in their target genes. The interpretation is often complicated by the fact that a single miRNA can target many genes and multiple miRNAs can regulate a single gene. Here we introduce miRNet ( www.mirnet.ca ), an easy-to-use web-based tool designed for creation, customization, visual exploration and functional interpretation of miRNA-target interaction networks. By integrating multiple high-quality miRNA-target data sources and advanced statistical methods into a powerful network visualization system, miRNet allows researchers to easily navigate the complex landscape of miRNA-target interactions to obtain deep biological insights. This tutorial provides a step-by-step protocol on how to use miRNet to create miRNA-target networks for visual exploration and functional analysis from different types of data inputs.

Grade-specific diagnostic and prognostic biomarkers in breast cancer

An integrative approach is presented to identify grade-specific biomarkers for breast cancer. Grade-specific molecular interaction networks were constructed with differentially expressed genes (DEGs) of cancer grade 1, 2, and 3. We observed that the molecular network of grade3 is predominantly associated with cancer-specific processes. Among the top ten connected DEGs in the grade3, the increase in the expression of UBE2C and CCNB2 genes was statistically significant across different grades. Along with UBE2C and CCNB2 genes, the CDK1, KIF2C, NDC80, and CCNB2 genes are also profoundly expressed in different grades and reduce the patient's survival. Gene set enrichment analysis of these six genes reconfirms their role in metastatic phenotype. Moreover, the coexpression network shows a strong association of these six genes promotes cancer specific biological processes and possibly drives cancer from lower to a higher grade. Collectively the identified genes can act as potential biomarkers for breast cancer diagnosis and prognosis.

Deciphering the Potential Pharmaceutical Mechanism of GUI-ZHI-FU-LING-WAN on Systemic Sclerosis based on Systems Biology Approaches

Systemic sclerosis (SSc; scleroderma) is a complicated idiopathic connective tissue disease with seldom effective treatment. GUI-ZHI-FU-LING-WAN (GFW) is a classic Traditional Chinese Medicine (TCM) formula widely used for the treatment of SSc. However, the mechanism of how the GFW affects SSc remains unclear. In this study, the system biology approach was utilized to analyze herb compounds and related targets to get the general information of GFW. The KEGG enrichment analysis of 1645 related targets suggested that the formula is involved in the VEGF signaling pathway, the Toll-like receptor signaling pathway, etc. Quantitative and qualitative analysis of the relationship among the 3 subsets (formula targets, drug targets and disease genes) showed that the formula targets overlapped with 38.0% drug targets and 26.0% proteins encoded by disease genes. Through the analysis of SSc related microarray statistics from the GEO database, we also validated the consistent expression behavior among the 3 subsets before and after treatment. To further reveal the mechanism of prescription, we constructed a network among 3 subsets and decomposed it into 24 modules to decipher how GFW interfere in the progress of SSc. The modules indicated that the intervention may come into effect through following pathogenic processes: vasculopathy, immune dysregulation and tissue fibrosis. Vitro experiments confirmed that GFW could suppress the proliferation of fibroblasts and decrease the Th1 cytokine (TNF-α, MIP-2 and IL-6) expression for lipopolysaccharide (LPS) and bleomycin (BLM) stimulation in macrophages, which is consistent with previous conclusion that GFW is able to relieve SSc. The systems biology approach provides a new insight for deepening understanding about TCM.

Gene regulatory network inference and analysis of multidrug-resistant Pseudomonas aeruginosa

BACKGROUND

Healthcare-associated infections caused by bacteria such as Pseudomonas aeruginosa are a major public health problem worldwide. Gene regulatory networks (GRN) computationally represent interactions among regulatory genes and their targets. They are an important approach to help understand bacterial behaviour and to provide novel ways of overcoming scientific challenges, including the identification of potential therapeutic targets and the development of new drugs.

OBJECTIVES

The goal of this study was to reconstruct the multidrug-resistant (MDR) P. aeruginosa GRN and to analyse its topological properties.

METHODS

The methodology used in this study was based on gene orthology inference using the reciprocal best hit method. We used the genome of P. aeruginosa CCBH4851 as the basis of the reconstruction process. This MDR strain is representative of the sequence type 277, which was involved in an endemic outbreak in Brazil.

FINDINGS

We obtained a network with a larger number of regulatory genes, target genes and interactions as compared to the previously reported network. Topological analysis results are in accordance with the complex network representation of biological processes.

MAIN CONCLUSIONS

The properties of the network were consistent with the biological features of P. aeruginosa. To the best of our knowledge, the P. aeruginosa GRN presented here is the most complete version available to date.

A Small RNA-Mediated Regulatory Network in Arabidopsis thaliana Demonstrates Connectivity Between phasiRNA Regulatory Modules and Extensive Co-Regulation of Transcription by miRNAs and phasiRNAs

Gene regulation involves the orchestrated action of multiple regulators to fine-tune the expression of genes. Hierarchical interactions and co-regulation among regulators are commonly observed in biological systems, leading to complex regulatory networks. Small RNA (sRNAs) have been shown to be important regulators of gene expression due to their involvement in multiple cellular processes. In plants, microRNA (miRNAs) and phased small interfering RNAs (phasiRNAs) correspond to two well-characterized types of sRNAs involved in the regulation of posttranscriptional gene expression, although information about their targets and interactions with other gene expression regulators is limited. We describe an extended sRNA-mediated regulatory network in Arabidopsis thaliana that provides a reference frame to understand sRNA biogenesis and activity at the genome-wide level. This regulatory network combines a comprehensive evaluation of phasiRNA production and sRNA targets supported by degradome data. The network includes ~17% of genes in the A. thaliana genome, representing ~50% annotated gene ontology (GO) functional categories. Approximately 14% of genes with GO annotations corresponding to regulation of gene expression were found to be under sRNA control. The unbiased bioinformatic approach used to produce the network was able to detect 107 PHAS loci (regions of phasiRNA production), 5,047 active phasiRNAs (~70% of which were non-canonical), and reconstruct 17 regulatory modules resulting from complex regulatory interactions between different sRNA-regulatory pathways. Known regulatory modules like miR173-TAS-PPR/TPR and miR390-TAS3-ARF/F-box were faithfully reconstructed and expanded, illustrating the accuracy and sensitivity of the methods and providing confidence for the validity of findings of previously unrecognized modules. The network presented here includes a 2X increase in the number of identified PHAS loci, a large complement (~70%) of non-canonical phasiRNAs, and the most comprehensive evaluation of sRNA cleavage activity in A. thaliana to date. Structural analysis showed similarities to networks of other biological systems and demonstrated connectivity between phasiRNA regulatory modules with extensive co-regulation of transcripts by miRNAs and phasiRNAs. The described regulatory network provides a reference that will facilitate global analyses of individual plant regulatory programs such as those that control homeostasis, development, and responses to biotic and abiotic environmental changes.

Opposite T3 Response of ACTG1-FOS Subnetwork Differentiate Tailfin Fate in Xenopus Tadpole and Post-hatching Axolotl

Amphibian post-embryonic development and Thyroid Hormones (TH) signaling are deeply and intimately connected. In anuran amphibians, TH induce the spectacular and complex process known as metamorphosis. In paedomorphic salamanders, at similar development time, raising levels of TH fail to induce proper metamorphosis, as many "larval" tissues (e.g., gills, tailfin) are maintained. Why does the same evolutionary conserved signaling pathway leads to alternative phenotypes? We used a combination of developmental endocrinology, functional genomics and network biology to compare the transcriptional response of tailfin to TH, in the post-hatching paedormorphic Axolotl salamander and Xenopus tadpoles. We also provide a technological framework that efficiently reduces large lists of regulated genes down to a few genes of interest, which is well-suited to dissect endocrine regulations. We first show that Axolotl tailfin undergoes a strong and robust TH-dependent transcriptional response at post embryonic transition, despite the lack of visible anatomical changes. We next show that Fos and Actg1, which structure a single and dense subnetwork of cellular sensors and regulators, display opposite regulation between the two species. We finally show that TH treatments and natural variations of TH levels follow similar transcriptional dynamics. We suggest that, at the molecular level, tailfin fate correlates with the alternative transcriptional states of an fos-actg1 sub-network, which also includes transcription factors and regulators of cell fate. We propose that this subnetwork is one of the molecular switches governing the initiation of distinct TH responses, with transcriptional programs conducting alternative tailfin fate (maintenance vs. resorption) 2 weeks post-hatching.

Network spectra for drug-target identification in complex diseases: new guns against old foes

The fundamental understanding of altered complex molecular interactions in a diseased condition is the key to its cure. The overall functioning of these molecules is kind of jugglers play in the cell orchestra and to anticipate these relationships among the molecules is one of the greatest challenges in modern biology and medicine. Network science turned out to be providing a successful and simple platform to understand complex interactions among healthy and diseased tissues. Furthermore, much information about the structure and dynamics of a network is concealed in the eigenvalues of its adjacency matrix. In this review, we illustrate rapid advancements in the field of network science in combination with spectral graph theory that enables us to uncover the complexities of various diseases. Interpretations laid by network science approach have solicited insights into molecular relationships and have reported novel drug targets and biomarkers in various complex diseases.

Minipuberty and Sexual Dimorphism in the Infant Human Thymus

AIRE expression in thymus is downregulated by estrogen after puberty, what probably renders women more susceptible to autoimmune disorders. Here we investigated the effects of minipuberty on male and female infant human thymic tissue in order to verify if this initial transient increase in sex hormones - along the first six months of life - could affect thymic transcriptional network regulation and AIRE expression. Gene co-expression network analysis for differentially expressed genes and miRNA-target analysis revealed sex differences in thymic tissue during minipuberty, but such differences were not detected in the thymic tissue of infants aged 7-18 months, i.e. the non-puberty group. AIRE expression was essentially the same in both sexes in minipuberty and in non-puberty groups, as assessed by genomic and immunohistochemical assays. However, AIRE-interactors networks showed several differences in all groups regarding gene-gene expression correlation. Therefore, minipuberty and genomic mechanisms interact in shaping thymic sexual dimorphism along the first six months of life.

Network community-based model reduction for vortical flows

A network community-based reduced-order model is developed to capture key interactions among coherent structures in high-dimensional unsteady vortical flows. The present approach is data-inspired and founded on network-theoretic techniques to identify important vortical communities that are comprised of vortical elements that share similar dynamical behavior. The overall interaction-based physics of the high-dimensional flow field is distilled into the vortical community centroids, considerably reducing the system dimension. Taking advantage of these vortical interactions, the proposed methodology is applied to formulate reduced-order models for the inter-community dynamics of vortical flows, and predict lift and drag forces on bodies in wake flows. We demonstrate the capabilities of these models by accurately capturing the macroscopic dynamics of a collection of discrete point vortices, and the complex unsteady aerodynamic forces on a circular cylinder and an airfoil with a Gurney flap. The present formulation is found to be robust against simulated experimental noise and turbulence due to its integrating nature of the system reduction.

Extended local similarity analysis (eLSA) reveals unique associations between bacterial community structure and odor emission during pig carcasses decomposition

Soil burial and composting methods have been widely used for the disposal of pig carcasses. The relationship between bacterial community structure and odor emission was examined using extended local similarity analysis (eLSA) during the degradation of pig carcasses in soil and compost. In soil, Hyphomicrobium, Niastella, Rhodanobacter, Polaromonas, Dokdonella and Mesorhizobium were associated with the emission of sulfur-containing odors such as hydrogen sulfide, methyl mercaptan and dimethyl disulfide. Sphingomonas, Rhodanobacter, Mesorhizobium, Dokdonella, Leucobacter and Truepera were associated with the emission of nitrogen-containing odors including ammonia and trimetylamine. In compost, however, Carnobacteriaceae, Lachnospiaceae and Clostridiales were highly correlated with the emission of sulfur-containing odors, while Rumincoccaceae was associated with the emission of nitrogen-containing odors. The emission of organic acids was closely related to Massilia, Sphaerobacter and Bradyrhizobiaceae in soil, but to Actinobacteria, Sporacetigenium, Micromonosporaceae and Solirubrobacteriales in compost. This study suggests that network analysis using eLSA is a useful strategy for exploring the mechanisms of odor emission during biodegradation of pig carcasses.

Phenotype-loci associations in networks of patients with rare disorders: application to assist in the diagnosis of novel clinical cases

Copy number variations (CNVs) are genomic structural variations (deletions, duplications, or translocations) that represent the 4.8-9.5% of human genome variation in healthy individuals. In some cases, CNVs can also lead to disease, being the etiology of many known rare genetic/genomic disorders. Despite the last advances in genomic sequencing and diagnosis, the pathological effects of many rare genetic variations remain unresolved, largely due to the low number of patients available for these cases, making it difficult to identify consistent patterns of genotype-phenotype relationships. We aimed to improve the identification of statistically consistent genotype-phenotype relationships by integrating all the genetic and clinical data of thousands of patients with rare genomic disorders (obtained from the DECIPHER database) into a phenotype-patient-genotype tripartite network. Then we assessed how our network approach could help in the characterization and diagnosis of novel cases in clinical genetics. The systematic approach implemented in this work is able to better define the relationships between phenotypes and specific loci, by exploiting large-scale association networks of phenotypes and genotypes in thousands of rare disease patients. The application of the described methodology facilitated the diagnosis of novel clinical cases, ranking phenotypes by locus specificity and reporting putative new clinical features that may suggest additional clinical follow-ups. In this work, the proof of concept developed over a set of novel clinical cases demonstrates that this network-based methodology might help improve the precision of patient clinical records and the characterization of rare syndromes.

Networked-oscillator-based modeling and control of unsteady wake flows

A networked-oscillator-based analysis is performed to examine and control the transfer of kinetic energy for periodic bluff body flows. The dynamics of energy fluctuations in the flow field are described by a set of oscillators defined by conjugate pairs of spatial proper orthogonal decomposition (POD) modes. To extract the network of interactions among oscillators, impulse responses of the oscillators to amplitude and phase perturbations are tracked. Tracking small energy inputs and using linear regression, a networked-oscillator model is constructed that reveals energy exchange among the modes. The model captures the nonlinear interactions among the modal oscillators through a linear approximation. A large collection of system responses is aggregated to capture the general network structure of oscillator interactions. The present networked-oscillator model describes the modal perturbation dynamics more accurately than the empirical Galerkin reduced-order model. The linear network model for nonlinear dynamics is subsequently utilized to design a model-based feedback controller. The controller suppresses the modal amplitudes that result in wake unsteadiness leading to drag reduction. The strength of the proposed approach is demonstrated for a canonical example of two-dimensional unsteady flow over a circular cylinder. The present formulation enables the characterization of modal interactions to control fundamental energy transfers in unsteady bluff body flows.

The protein-protein interaction network and clinical significance of heat-shock proteins in esophageal squamous cell carcinoma

Heat-shock proteins (HSPs), one of the evolutionarily conserved protein families, are widely found in various organisms, and play important physiological functions. Nevertheless, HSPs have not been systematically analyzed in esophageal squamous cell carcinoma (ESCC). In this study, we applied the protein-protein interaction (PPI) network methodology to explore the characteristics of HSPs, and integrate their expression in ESCC. First, differentially expressed HSPs in ESCC were identified from our previous RNA-seq data. By constructing a specific PPI network, we found differentially expressed HSPs interacted with hundreds of neighboring proteins. Subcellular localization analyses demonstrated that HSPs and their interacting proteins distributed in multiple layers, from membrane to nucleus. Functional enrichment annotation analyses revealed known and potential functions for HSPs. KEGG pathway analyses identified four significant enrichment pathways. Moreover, three HSPs (DNAJC5B, HSPA1B, and HSPH1) could serve as promising targets for prognostic prediction in ESCC, suggesting these HSPs might play a significant role in the development of ESCC. These multiple bioinformatics analyses have provided a comprehensive view of the roles of heat-shock proteins in esophageal squamous cell carcinoma.

Pathways of topological rank analysis (PoTRA): a novel method to detect pathways involved in hepatocellular carcinoma

Complex diseases such as cancer are usually the result of a combination of environmental factors and one or several biological pathways consisting of sets of genes. Each biological pathway exerts its function by delivering signaling through the gene network. Theoretically, a pathway is supposed to have a robust topological structure under normal physiological conditions. However, the pathway's topological structure could be altered under some pathological condition. It is well known that a normal biological network includes a small number of well-connected hub nodes and a large number of nodes that are non-hubs. In addition, it is reported that the loss of connectivity is a common topological trait of cancer networks, which is an assumption of our method. Hence, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal or the distribution of topological ranks of genes might be altered. Based on this, we propose a new PageRank-based method called Pathways of Topological Rank Analysis (PoTRA) to detect pathways involved in cancer. We use PageRank to measure the relative topological ranks of genes in each biological pathway, then select hub genes for each pathway, and use Fisher's exact test to test if the number of hub genes in each pathway is altered from normal to cancer. Alternatively, if the distribution of topological ranks of gene in a pathway is altered between normal and cancer, this pathway might also be involved in cancer. Hence, we use the Kolmogorov-Smirnov test to detect pathways that have an altered distribution of topological ranks of genes between two phenotypes. We apply PoTRA to study hepatocellular carcinoma (HCC) and several subtypes of HCC. Very interestingly, we discover that all significant pathways in HCC are cancer-associated generally, while several significant pathways in subtypes of HCC are HCC subtype-associated specifically. In conclusion, PoTRA is a new approach to explore and discover pathways involved in cancer. PoTRA can be used as a complement to other existing methods to broaden our understanding of the biological mechanisms behind cancer at the system-level.

SparseNCA: Sparse Network Component Analysis for Recovering Transcription Factor Activities with Incomplete Prior Information

Network component analysis (NCA) is an important method for inferring transcriptional regulatory networks (TRNs) and recovering transcription factor activities (TFAs) using gene expression data, and the prior information about the connectivity matrix. The algorithms currently available crucially depend on the completeness of this prior information. However, inaccuracies in the measurement process may render incompleteness in the available knowledge about the connectivity matrix. Hence, computationally efficient algorithms are needed to overcome the possible incompleteness in the available data. We present a sparse network component analysis algorithm (sparseNCA), which incorporates the effect of incompleteness in the estimation of TRNs by imposing an additional sparsity constraint using the norm, which results in a greater estimation accuracy. In order to improve the computational efficiency, an iterative re-weighted method is proposed for the NCA problem which not only promotes sparsity but is hundreds of times faster than the norm based solution. The performance of sparseNCA is rigorously compared to that of FastNCA and NINCA using synthetic data as well as real data. It is shown that sparseNCA outperforms the existing state-of-the-art algorithms both in terms of estimation accuracy and consistency with the added advantage of low computational complexity. The performance of sparseNCA compared to its predecessors is particularly pronounced in case of incomplete prior information about the sparsity of the network. Subnetwork analysis is performed on the E.coli data which reiterates the superior consistency of the proposed algorithm.

Common Variable Immunodeficiency Non-Infectious Disease Endotypes Redefined Using Unbiased Network Clustering in Large Electronic Datasets

Common variable immunodeficiency (CVID) is increasingly recognized for its association with autoimmune and inflammatory complications. Despite recent advances in immunophenotypic and genetic discovery, clinical care of CVID remains limited by our inability to accurately model risk for non-infectious disease development. Herein, we demonstrate the utility of unbiased network clustering as a novel method to analyze inter-relationships between non-infectious disease outcomes in CVID using databases at the United States Immunodeficiency Network (USIDNET), the centralized immunodeficiency registry of the United States, and Partners, a tertiary care network in Boston, MA, USA, with a shared electronic medical record amenable to natural language processing. Immunophenotypes were comparable in terms of native antibody deficiencies, low titer response to pneumococcus, and B cell maturation arrest. However, recorded non-infectious disease outcomes were more substantial in the Partners cohort across the spectrum of lymphoproliferation, cytopenias, autoimmunity, atopy, and malignancy. Using unbiased network clustering to analyze 34 non-infectious disease outcomes in the Partners cohort, we further identified unique patterns of lymphoproliferative (two clusters), autoimmune (two clusters), and atopic (one cluster) disease that were defined as CVID non-infectious endotypes according to discrete and non-overlapping immunophenotypes. Markers were both previously described {high serum IgE in the atopic cluster [odds ratio (OR) 6.5] and low class-switched memory B cells in the total lymphoproliferative cluster (OR 9.2)} and novel [low serum C3 in the total lymphoproliferative cluster (OR 5.1)]. Mortality risk in the Partners cohort was significantly associated with individual non-infectious disease outcomes as well as lymphoproliferative cluster 2, specifically (OR 5.9). In contrast, unbiased network clustering failed to associate known comorbidities in the adult USIDNET cohort. Together, these data suggest that unbiased network clustering can be used in CVID to redefine non-infectious disease inter-relationships; however, applicability may be limited to datasets well annotated through mechanisms such as natural language processing. The lymphoproliferative, autoimmune, and atopic Partners CVID endotypes herein described can be used moving forward to streamline genetic and biomarker discovery and to facilitate early screening and intervention in CVID patients at highest risk for autoimmune and inflammatory progression.

The association of hormone signalling genes, transcription and changes in shoot anatomy during moso bamboo growth

Moso bamboo is a large, woody bamboo with the highest ecological, economic and cultural value of all the bamboo types and accounts for up to 70% of the total area of bamboo grown. However, the spatiotemporal variation role of moso bamboo shoot during growth period is still unclear. We found that the bamboo shoot growth can be divided into three distinct periods, including winter growth, early growth and late growth based on gene expression and anatomy. In the early growth period, lateral buds germinated from the top of the bamboo joint in the shoot tip. Intercalary meristems grew vigorously during the winter growth period and early growth period, but in the late growth period, mitosis in the intercalary meristems decreased. The expression of cell cycle-associated genes and the quantity of differentially expressed genes were higher in early growth than those in late growth, appearing to be influenced by hormonal concentrations. Gene expression analysis indicates that hormone signalling genes play key roles in shoot growth, while auxin signalling genes play a central role. In situ hybridization analyses illustrate how auxin signalling genes regulate apical dominance, meristem maintenance and lateral bud development. Our study provides a vivid picture of the dynamic changes in anatomy and gene expression during shoot growth in moso bamboo, and how hormone signalling-associated genes participate in moso bamboo shoot growth.

Protein-Protein Interaction Network Analysis for a Biomarker Panel Related to Human Esophageal Adenocarcinoma

Background: Esophageal adenocarcinoma (EAC) is one of the mostlethal cancers in the world with a very poor prognosis. Identification of molecular diagnostic methods is an important goal. Since protein-protein interaction (PPI) network analysis is a suitable method for molecular assessment, in the present research a PPI network related to EAC was targeted. Material and Method: Cytoscape software and its applications including STRING DB, Cluster ONE and ClueGO were applied to analyze the PPI network. Result: Among 182 EAC-related proteins which were identified, 129 were included in a main connected component. Proteins based on centrality analysis of characteristics such as degree, betweenness, closeness and stress were screened and key nodes were introduced. Two clusters were determined of which only one was significant statistically. Gene ontology revealed 50 terms in three groups associated with EAC. Conclusion: The findings indicate nine crucial proteins could form a candidate biomarker panel for EAC. Furthermore, an important cluster with 27 proteins related to the disease was identified. Gene ontology analysis of this cluster showed main related terms to closely correspond with those for colorectal cancer.

Gene network analysis: from heart development to cardiac therapy

Networks offer a flexible framework to represent and analyse the complex interactions between components of cellular systems. In particular gene networks inferred from expression data can support the identification of novel hypotheses on regulatory processes. In this review we focus on the use of gene network analysis in the study of heart development. Understanding heart development will promote the elucidation of the aetiology of congenital heart disease and thus possibly improve diagnostics. Moreover, it will help to establish cardiac therapies. For example, understanding cardiac differentiation during development will help to guide stem cell differentiation required for cardiac tissue engineering or to enhance endogenous repair mechanisms. We introduce different methodological frameworks to infer networks from expression data such as Boolean and Bayesian networks. Then we present currently available temporal expression data in heart development and discuss the use of network-based approaches in published studies. Collectively, our literature-based analysis indicates that gene network analysis constitutes a promising opportunity to infer therapy-relevant regulatory processes in heart development. However, the use of network-based approaches has so far been limited by the small amount of samples in available datasets. Thus, we propose to acquire high-resolution temporal expression data to improve the mathematical descriptions of regulatory processes obtained with gene network inference methodologies. Especially probabilistic methods that accommodate the intrinsic variability of biological systems have the potential to contribute to a deeper understanding of heart development.

Structure of the Transcriptional Regulatory Network Correlates with Regulatory Divergence in Drosophila

Transcriptional control of gene expression is regulated by biochemical interactions between cis-regulatory DNA sequences and trans-acting factors that form complex regulatory networks. Genetic changes affecting both cis- and trans-acting sequences in these networks have been shown to alter patterns of gene expression as well as higher-order organismal phenotypes. Here, we investigate how the structure of these regulatory networks relates to patterns of polymorphism and divergence in gene expression. To do this, we compared a transcriptional regulatory network inferred for Drosophila melanogaster to differences in gene regulation observed between two strains of D. melanogaster as well as between two pairs of closely related species: Drosophila sechellia and Drosophila simulans, and D. simulans and D. melanogaster. We found that the number of transcription factors predicted to directly regulate a gene ("in-degree") was negatively correlated with divergence in both gene expression (mRNA abundance) and cis-regulation. This observation suggests that the number of transcription factors directly regulating a gene's expression affects the conservation of cis-regulation and gene expression over evolutionary time. We also tested the hypothesis that transcription factors regulating more target genes (higher "out-degree") are less likely to evolve changes in their cis-regulation and expression (presumably due to increased pleiotropy), but found little support for this predicted relationship. Taken together, these data show how the architecture of regulatory networks can influence regulatory evolution.

Network analyses elucidate the role of SMYD3 in esophageal squamous cell carcinoma

SMYD3 is a member of the SET and myeloid-Nervy-DEAF-1 (MYND) domain-containing protein family of methyltransferases, which are known to play critical roles in carcinogenesis. Expression of SMYD3 is elevated in various cancers, including esophageal squamous cell carcinoma (ESCC), and is correlated with the survival time of patients with ESCC. Here, we dissect gene expression data, from a previously described KYSE150 ESCC cell line in which SMYD3 had been knocked down, by integration with the protein-protein interaction (PPI) network, to find the new potential biological roles of SMYD3 and subsequent target genes. By construction of a specific PPI network, differentially expressed genes (DEGs), following SMYD3 knockdown, were identified as interacting with thousands of neighboring proteins. Enrichment analyses from the DAVID Functional Annotation Chart found significant Gene Ontology (GO) terms associated with transcription activities, which were closely related to SMYD3 function. For example, YAP1 and GATA3 might be a target gene for SMYD3 to regulate transcription. Enrichment annotation of the total DEG PPI network by GO 'Biological Process' generated a connected functional map and found 532 significant terms, including known and potential biological roles of SMYD3 protein, such as expression regulation, signal transduction, cell cycle, cell metastasis, and invasion. Subcellular localization analyses found that DEGs and their interacting proteins were distributed in multiple layers, which might reflect the intricate biological processes at the spatial level. Our analysis of the PPI network has provided important clues for future detection of the biological roles and mechanisms, as well as the target genes of SMYD3.

Two faces of competition: target-mediated reverse signalling in microRNA and mitogen-activated protein kinase regulatory networks

Biomolecular regulatory networks are organised around hubs, which can interact with a large number of targets. These targets compete with each other for access to their common hubs, but whether the effect of this competition would be significant in magnitude and in function is not clear. In this review, the authors discuss recent in vivo studies that analysed the system level retroactive effects induced by target competition in microRNA and mitogen-activated protein kinase regulatory networks. The results of these studies suggest that downstream targets can regulate the overall state of their upstream regulators, and thus cannot be ignored in analysing biomolecular networks.

Network analysis and cross species comparison of protein-protein interaction networks of human, mouse and rat cytochrome P450 proteins that degrade xenobiotics

Cytochrome P450 (CYP) enzymes that degrade xenobiotics play a critical role in the metabolism and biotransformation of drugs and xenobiotics in humans as well as experimental animal models such as mouse and rat. These proteins function as a network collectively as well as independently. Though there are several reports on the organization, regulation and functionality of various CYP enzymes at the molecular level, the understanding of organization and functionality of these proteins at the holistic level remain unclear. The objective of this study is to understand the organization and functionality of xenobiotic degrading CYP enzymes of human, mouse and rat using network theory approaches and to study species differences that exist among them at the holistic level. For our analysis, a protein-protein interaction (PPI) network for CYP enzymes of human, mouse and rat was constructed using the STRING database. Topology, centrality, modularity and robustness analyses were performed for our predicted CYP PPI networks that were then validated by comparison with randomly generated network models. Network centrality analyses of CYP PPI networks reveal the central/hub proteins in the network. Modular analysis of the CYP PPI networks of human, mouse and rat resulted in functional clusters. These clusters were subjected to ontology and pathway enrichment analysis. The analyses show that the cluster of the human CYP PPI network is enriched with pathways principally related to xenobiotic/drug metabolism. Endo-xenobiotic crosstalk dominated in mouse and rat CYP PPI networks, and they were highly enriched with endogenous metabolic and signaling pathways. Thus, cross-species comparisons and analyses of human, mouse and rat CYP PPI networks gave insights about species differences that existed at the holistic level. More investigations from both reductionist and holistic perspectives can help understand CYP metabolism and species extrapolation in a much better way.

Multi-Level Integration of Environmentally Perturbed Internal Phenotypes Reveals Key Points of Connectivity between Them

The genotype and external phenotype of organisms are linked by so-called internal phenotypes which are influenced by environmental conditions. In this study, we used five existing -omics datasets representing five different layers of internal phenotypes, which were simultaneously measured in dietarily perturbed mice. We performed 10 pair-wise correlation analyses verified with a null model built from randomized data. Subsequently, the inferred networks were merged and literature mined for co-occurrences of identified linked nodes. Densely connected internal phenotypes emerged. Forty-five nodes have links with all other data-types and we denote them “connectivity hubs.” In literature, we found proof of 6% of the 577 connections, suggesting a biological meaning for the observed correlations. The observed connectivities between metabolite and cytokines hubs showed higher numbers of literature hits as compared to the number of literature hits on the connectivities between the microbiota and gene expression internal phenotypes. We conclude that multi-level integrated networks may help to generate hypotheses and to design experiments aiming to further close the gap between genotype and phenotype. We describe and/or hypothesize on the biological relevance of four identified multi-level connectivity hubs.

Frogs model man: In vivo thyroid hormone signaling during development

Thyroid hormone (TH) signaling comprises TH transport across cell membranes, metabolism by deiodinases, and molecular mechanisms of gene regulation. Proper TH signaling is essential for normal perinatal development, most notably for neurogenesis and fetal growth. Knowledge of perinatal TH endocrinology needs improvement to provide better treatments for premature infants and endocrine diseases during gestation and to counteract effects of endocrine disrupting chemicals. Studies in amphibians have provided major insights to understand in vivo mechanisms of TH signaling. The frog model boasts dramatic TH-dependent changes directly observable in free-living tadpoles with precise and easy experimental control of the TH response at developmental stages comparable to fetal stages in mammals. The hormones, their receptors, molecular mechanisms, and developmental roles of TH signaling are conserved to a high degree in humans and amphibians, such that with respect to developmental TH signaling "frogs are just little people that hop." The frog model is exceptionally illustrative of fundamental molecular mechanisms of in vivo TH action involving TH receptors, transcriptional cofactors, and chromatin remodeling. This review highlights the current need, recent successes, and future prospects using amphibians as a model to elucidate molecular mechanisms and functional roles of TH signaling during post-embryonic development.

Integrative approach for the analysis of the proteome-wide response to bismuth drugs in Helicobacter pylori

An integrative metalloproteomic approach to unveil the role of antimicrobial metals in general using bismuth as an example.

Bismuth drugs, despite being clinically used for decades, surprisingly remain in use and effective for the treatment of Helicobacter pylori infection, even for resistant strains when co-administrated with antibiotics. However, the molecular mechanisms underlying the clinically sustained susceptibility of H. pylori to bismuth drugs remain elusive. Herein, we report that integration of in-house metalloproteomics and quantitative proteomics allows comprehensive uncovering of the bismuth-associated proteomes, including 63 bismuth-binding and 119 bismuth-regulated proteins from Helicobacter pylori, with over 60% being annotated with catalytic functions. Through bioinformatics analysis in combination with bioassays, we demonstrated that bismuth drugs disrupted multiple essential pathways in the pathogen, including ROS defence and pH buffering, by binding and functional perturbation of a number of key enzymes. Moreover, we discovered that HpDnaK may serve as a new target of bismuth drugs to inhibit bacterium-host cell adhesion. The integrative approach we report, herein, provides a novel strategy to unveil the molecular mechanisms of antimicrobial metals against pathogens in general. This study sheds light on the design of new types of antimicrobial agents with multiple targets to tackle the current crisis of antimicrobial resistance.

The autophagy interaction network of the aging model Podospora anserina

BACKGROUND

Autophagy is a conserved molecular pathway involved in the degradation and recycling of cellular components. It is active either as response to starvation or molecular damage. Evidence is emerging that autophagy plays a key role in the degradation of damaged cellular components and thereby affects aging and lifespan control. In earlier studies, it was found that autophagy in the aging model Podospora anserina acts as a longevity assurance mechanism. However, only little is known about the individual components controlling autophagy in this aging model. Here, we report a biochemical and bioinformatics study to detect the protein-protein interaction (PPI) network of P. anserina combining experimental and theoretical methods.

RESULTS

We constructed the PPI network of autophagy in P. anserina based on the corresponding networks of yeast and human. We integrated PaATG8 interaction partners identified in an own yeast two-hybrid analysis using ATG8 of P. anserina as bait. Additionally, we included age-dependent transcriptome data. The resulting network consists of 89 proteins involved in 186 interactions. We applied bioinformatics approaches to analyze the network topology and to prove that the network is not random, but exhibits biologically meaningful properties. We identified hub proteins which play an essential role in the network as well as seven putative sub-pathways, and interactions which are likely to be evolutionary conserved amongst species. We confirmed that autophagy-associated genes are significantly often up-regulated and co-expressed during aging of P. anserina.

CONCLUSIONS

With the present study, we provide a comprehensive biological network of the autophagy pathway in P. anserina comprising PPI and gene expression data. It is based on computational prediction as well as experimental data. We identified sub-pathways, important hub proteins, and evolutionary conserved interactions. The network clearly illustrates the relation of autophagy to aging processes and enables further specific studies to understand autophagy and aging in P. anserina as well as in other systems.

Extracting Genes Involved in Disease from a Connected Network of Perturbed Biological Processes

Metabolic disorders such as obesity and diabetes are nowadays regarded as diseases affecting majority of population. These diseases develop gradually over time in an individual. Recently, systematic experiments tracking disease progression are conducted giving a high-throughput complex data. There is a pressing need for developing methods to analyze this complex data to capture the disease mechanism at molecular level. Diseases usually develop through perturbations of biological processes in an organism. In this study, we have tried to capture the interlinking between different biological processes that work together to regulate the disease phenotype. Here, we have considered a temporal microarray data from an experiment conducted to study obesity and diabetes in mice. We have analyzed the data to obtain perturbed biological processes and developed methods to establish link between these perturbed biological processes. We have derived a mathematical formula to score genes and identified a significant set of genes regulating such a complex process network. The methods developed in our study are also applicable to a broad array of data types.

Orthoscape: a cytoscape application for grouping and visualization KEGG based gene networks by taxonomy and homology principles

Background

There are many available software tools for visualization and analysis of biological networks. Among them, Cytoscape (http://cytoscape.org/) is one of the most comprehensive packages, with many plugins and applications which extends its functionality by providing analysis of protein-protein interaction, gene regulatory and gene co-expression networks, metabolic, signaling, neural as well as ecological-type networks including food webs, communities networks etc. Nevertheless, only three plugins tagged ‘network evolution’ found in Cytoscape official app store and in literature. We have developed a new Cytoscape 3.0 application Orthoscape aimed to facilitate evolutionary analysis of gene networks and visualize the results.

Results

Orthoscape aids in analysis of evolutionary information available for gene sets and networks by highlighting: (1) the orthology relationships between genes; (2) the evolutionary origin of gene network components; (3) the evolutionary pressure mode (diversifying or stabilizing, negative or positive selection) of orthologous groups in general and/or branch-oriented mode. The distinctive feature of Orthoscape is the ability to control all data analysis steps via user-friendly interface.

Conclusion

Orthoscape allows its users to analyze gene networks or separated gene sets in the context of evolution. At each step of data analysis, Orthoscape also provides for convenient visualization and data manipulation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1427-5) contains supplementary material, which is available to authorized users.