Biological network analysis: insights into structure and functions

Modeling cell populations metabolism and competition under maximum power constraints

Ecological interactions are fundamental at the cellular scale, addressing the possibility of a description of cellular systems that uses language and principles of ecology. In this work, we use a minimal ecological approach that encompasses growth, adaptation and survival of cell populations to model cell metabolisms and competition under energetic constraints. As a proof-of-concept, we apply this general formulation to study the dynamics of the onset of a specific blood cancer-called Multiple Myeloma. We show that a minimal model describing antagonist cell populations competing for limited resources, as regulated by microenvironmental factors and internal cellular structures, reproduces patterns of Multiple Myeloma evolution, due to the uncontrolled proliferation of cancerous plasma cells within the bone marrow. The model is characterized by a class of regime shifts to more dissipative states for selectively advantaged malignant plasma cells, reflecting a breakdown of self-regulation in the bone marrow. The transition times obtained from the simulations range from years to decades consistently with clinical observations of survival times of patients. This irreversible dynamical behavior represents a possible description of the incurable nature of myelomas based on the ecological interactions between plasma cells and the microenvironment, embedded in a larger complex system. The use of ATP equivalent energy units in defining stocks and flows is a key to constructing an ecological model which reproduces the onset of myelomas as transitions between states of a system which reflects the energetics of plasma cells. This work provides a basis to construct more complex models representing myelomas, which can be compared with model ecosystems.

BioTAGME: A Comprehensive Platform for Biological Knowledge Network Analysis

The inference of novel knowledge and new hypotheses from the current literature analysis is crucial in making new scientific discoveries. In bio-medicine, given the enormous amount of literature and knowledge bases available, the automatic gain of knowledge concerning relationships among biological elements, in the form of semantically related terms (or entities), is rising novel research challenges and corresponding applications. In this regard, we propose BioTAGME, a system that combines an entity-annotation framework based on Wikipedia corpus (i.e., TAGME tool) with a network-based inference methodology (i.e., DT-Hybrid). This integration aims to create an extensive Knowledge Graph modeling relations among biological terms and phrases extracted from titles and abstracts of papers available in PubMed. The framework consists of a back-end and a front-end. The back-end is entirely implemented in Scala and runs on top of a Spark cluster that distributes the computing effort among several machines. The front-end is released through the Laravel framework, connected with the Neo4j graph database to store the knowledge graph.

NETME: on-the-fly knowledge network construction from biomedical literature

BACKGROUND

The rapidly increasing biological literature is a key resource to automatically extract and gain knowledge concerning biological elements and their relations. Knowledge Networks are helpful tools in the context of biological knowledge discovery and modeling.

RESULTS

We introduce a novel system called NETME, which, starting from a set of full-texts obtained from PubMed, through an easy-to-use web interface, interactively extracts biological elements from ontological databases and then synthesizes a network inferring relations among such elements. The results clearly show that our tool is capable of inferring comprehensive and reliable biological networks.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s41109-021-00435-x.

Construction and analysis of the protein-protein interaction network for the detoxification enzymes of the silkworm, Bombyx mori

Detoxification enzymes are necessary for insects to metabolize toxic substances and maintain physiological activities. Cytochromes P450 (CYPs), glutathione S-transferases (GSTs), and carboxylesterase (CarEs) are the main detoxification enzymes in insects. In addition, UDP-glucosyltransferase and ATP-binding cassette transporter also participate in the process of material metabolism. This study collected proteins related to detoxification in the silkworm, Bombyx mori (Lepidoptera: Bombycidae). And we performed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on these proteins to understand their biological function. We constructed the protein-protein interaction network for the silkworm's detoxification enzymes and analyzed the network's topological properties. We found that BGIBMGA014046-TA, BGIBMGA003221-TA, BGIBMGA011092-TA, BGIBMGA000074-TA, and LOC732976 are the essential proteins in the network. These proteins are primarily involved in the process of ribosome biogenesis and may be related to protein synthesis. We integrated GO, KEGG, and network analysis and found that ribosome-associated protein and GSTs played a vital role in the detoxification process.

k-core genes underpin structural features of breast cancer

Gene co-expression networks (GCNs) have been developed as relevant analytical tools for the study of the gene expression patterns behind complex phenotypes. Determining the association between structure and function in GCNs is a current challenge in biomedical research. Several structural differences between GCNs of breast cancer and healthy phenotypes have been reported. In a previous study, using co-expression multilayer networks, we have shown that there are abrupt differences in the connectivity patterns of the GCN of basal-like breast cancer between top co-expressed gene-pairs and the remaining gene-pairs. Here, we compared the top-100,000 interactions networks for the four breast cancer phenotypes (Luminal-A, Luminal-B, Her2+ and Basal), in terms of structural properties. For this purpose, we used the graph-theoretical k-core of a network (maximal sub-network with nodes of degree at least k). We developed a comprehensive analysis of the network k-core ([Formula: see text]) structures in cancer, and its relationship with biological functions. We found that in the Top-100,000-edges networks, the majority of interactions in breast cancer networks are intra-chromosome, meanwhile inter-chromosome interactions serve as connecting bridges between clusters. Moreover, core genes in the healthy network are strongly associated with processes such as metabolism and cell cycle. In breast cancer, only the core of Luminal A is related to those processes, and genes in its core are over-expressed. The intersection of the core nodes in all subtypes of cancer is composed only by genes in the chr8q24.3 region. This region has been observed to be highly amplified in several cancers before, and its appearance in the intersection of the four breast cancer k-cores, may suggest that local co-expression is a conserved phenomenon in cancer. Considering the many intricacies associated with these phenomena and the vast amount of research in epigenomic regulation which is currently undergoing, there is a need for further research on the epigenomic effects on the structure and function of gene co-expression networks in cancer.

Network Analysis of Local Gene Regulators in Arabidopsis thaliana under Spaceflight Stress

Spaceflight microgravity affects normal plant growth in several ways. The transcriptional dataset of the plant model organism Arabidopsis thaliana grown in the international space station is mined using graph-theoretic network analysis approaches to identify significant gene transcriptions in microgravity essential for the plant’s survival and growth in altered environments. The photosynthesis process is critical for the survival of the plants in spaceflight under different environmentally stressful conditions such as lower levels of gravity, lesser oxygen availability, low atmospheric pressure, and the presence of cosmic radiation. Lasso regression method is used for gene regulatory network inferencing from gene expressions of four different ecotypes of Arabidopsis in spaceflight microgravity related to the photosynthetic process. The individual behavior of hub-genes and stress response genes in the photosynthetic process and their impact on the whole network is analyzed. Logistic regression on centrality measures computed from the networks, including average shortest path, betweenness centrality, closeness centrality, and eccentricity, and the HITS algorithm is used to rank genes and identify interactor or target genes from the networks. Through the hub and authority gene interactions, several biological processes associated with photosynthesis and carbon fixation genes are identified. The altered conditions in spaceflight have made all the ecotypes of Arabidopsis sensitive to dehydration-and-salt stress. The oxidative and heat-shock stress-response genes regulate the photosynthesis genes that are involved in the oxidation-reduction process in spaceflight microgravity, enabling the plant to adapt successfully to the spaceflight environment.

Construction and analysis of the protein-protein interaction network for the olfactory system of the silkworm Bombyx mori

Olfaction plays an essential role in feeding and information exchange in insects. Previous studies on the olfaction of silkworms have provided a wealth of information about genes and proteins, yet, most studies have only focused on a single gene or protein related to the insect's olfaction. The aim of the current study is to determine key proteins in the olfactory system of the silkworm, and further understand protein-protein interactions (PPIs) in the olfactory system of Lepidoptera. To achieve this goal, we integrated Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and network analyses. Furthermore, we selected 585 olfactory-related proteins and constructed a (PPI) network for the olfactory system of the silkworm. Network analysis led to the identification of several key proteins, including GSTz1, LOC733095, BGIBMGA002169-TA, BGIBMGA010939-TA, GSTs2, GSTd2, Or-2, and BGIBMGA013255-TA. A comprehensive evaluation of the proteins showed that glutathione S-transferases (GSTs) had the highest ranking. GSTs also had the highest enrichment levels in GO and KEGG. In conclusion, our analysis showed that key nodes in the biological network had a significant impact on the network, and the key proteins identified via network analysis could serve as new research targets to determine their functions in olfaction.

An Understudied Dimension: Why Age Needs to Be Considered When Studying Epigenetic-Environment Interactions

We live in a complex chemical environment where there are an estimated 350 000 chemical compounds or mixtures commercially produced. A strong body of literature shows that there are time points during early development when an organism’s epigenome is particularly sensitive to chemicals in its environment. What is less understood is how gene-environment and epigenetic-environment interactions change with age. This question is bidirectional: (1) how do chemicals in the environment affect the aging process and (2) how does aging affect an organism’s response to its chemical environment? The study of gene-environment interactions with age is especially important because, in many parts of the world, older individuals are a large and rapidly growing proportion of the population and because aging is a process universal to most of the animal kingdom. Epigenetics has emerged as a crucial framework for studying aging as epigenetic pathways, often triggered by environmental stimuli, have been shown to be essential regulators of the aging process. In this perspective article, we delineate the connection between aging, epigenetics, and environmental exposures. We discuss why it is essential to consider age when researching how an organism interacts with its environment. We describe recent advances in understanding how the chemical environment affects aging and the gap in research on how age affects an organism’s response to the environment. Finally, we highlight how model organisms and network approaches can help fill this crucial gap. Taken together, systemic changes that occur in the epigenome with age indicate that adult organisms cannot be treated as a homogeneous population and that there are discrete mechanisms modulating the aging epigenome that we do not yet understand.

Stability Analysis of Biological Networks' Diffusion State

Computational knowledge acquired from noisy networks is not reliable and the network topology determines the reliability. Protein-protein interaction networks have uncertain topologies and noise that contain false positive and false negative edges at high rates. In this study, we analyze effects of the existing mutations in a network topology to the diffusion state of that network. To evaluate the sensitivity of the diffusion state, we derive the fitness measures based on the mathematically defined stability of a network. Searching for an influential set of edges in a network is a difficult problem. We handle the computational challenge by developing a novel metaheuristic optimization method and we find influential mutations time-efficiently. Our experiments, conducted on both synthetic and real networks from public databases, demonstrated that our method obtained better results than competitors for all types of network topologies. This is the first-time that the diffusion has been evaluated under topological mutations. Our analysis identifies significant biological results about the stability of biological - synthetic networks and diffusion state. In this manner, mutations in protein-protein interaction network topologies have a significant influence on the diffusion state of the network. Network stability is more affected by the network model than the network size.

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.

EcoToxModules: Custom Gene Sets to Organize and Analyze Toxicogenomics Data from Ecological Species

Traditional results from toxicogenomics studies are complex lists of significantly impacted genes or gene sets, which are challenging to synthesize down to actionable results with a clear interpretation. Here, we defined two sets of 21 custom gene sets, called the functional and statistical EcoToxModules, in fathead minnow (Pimephales promelas) to (1) re-cast predefined molecular pathways into a toxicological framework and (2) provide a data-driven, unsupervised grouping of genes impacted by exposure to environmental contaminants. The functional EcoToxModules were identified by re-organizing KEGG pathways into biological processes that are more relevant to ecotoxicology based on the input from expert scientists and regulators. The statistical EcoToxModules were identified using co-expression analysis of publicly available microarray data (n = 303 profiles) measured in livers of fathead minnows after exposure to 38 different conditions. Potential applications of the EcoToxModules were demonstrated with two case studies that represent exposure to a pure chemical and to environmental wastewater samples. In comparisons to differential expression and gene set analysis, we found that EcoToxModule responses were consistent with these traditional results. Additionally, they were easier to visualize and quantitatively compare across different conditions, which facilitated drawing conclusions about the relative toxicity of the exposures within each case study.

Ultraviolet solar flare signatures in the framework of complex network

Studying natural phenomena via the complex network approach makes it possible to quantify the time-evolving structures with too many elements and achieve a deeper understanding of interactions among the components of a system. In this sense, solar flare as a complex system with the chaotic behavior could be better characterized by the network parameters. Here, we employed an unsupervised network-based method to recognize the position and occurrence time of the solar flares by using the ultraviolet emission (1600 Å) recorded by the Atmospheric Imaging Assembly on board Solar Dynamics Observatory. Three different regions, the flaring active regions, the non-flaring active regions, and the quiet-Sun regions, were considered to study the variations of the network parameters in the presence and absence of flaring phases in various datasets over time intervals of several hours. The whole parts of the selected datasets were partitioned into sub-windows to construct networks based on computing the Pearson correlation between time series of the region of interest and intensities. Analyzing the network parameters such as the clustering coefficient, degree centrality, characteristic length, and PageRank verified that flare triggering has an influence on the network parameters around the flare occurrence time and close to the location of flaring. It was found that the values of the clustering coefficient and characteristic length approach those obtained for the corresponding random network in the flaring phase. These findings could be used for detecting the occurrence times and locations of the region at ultraviolet images.

Identifying Protein-metabolite Networks Associated with COPD Phenotypes

Chronic obstructive pulmonary disease (COPD) is a disease in which airflow obstruction in the lung makes it difficult for patients to breathe. Although COPD occurs predominantly in smokers, there are still deficits in our understanding of the additional risk factors in smokers. To gain a deeper understanding of the COPD molecular signatures, we used Sparse Multiple Canonical Correlation Network (SmCCNet), a recently developed tool that uses sparse multiple canonical correlation analysis, to integrate proteomic and metabolomic data from the blood of 1008 participants of the COPDGene study to identify novel protein-metabolite networks associated with lung function and emphysema. Our aim was to integrate -omic data through SmCCNet to build interpretable networks that could assist in the discovery of novel biomarkers that may have been overlooked in alternative biomarker discovery methods. We found a protein-metabolite network consisting of 13 proteins and 7 metabolites which had a -0.34 correlation (p-value = 2.5 × 10^-28) to lung function. We also found a network of 13 proteins and 10 metabolites that had a -0.27 correlation (p-value = 2.6 × 10^-17) to percent emphysema. Protein-metabolite networks can provide additional information on the progression of COPD that complements single biomarker or single -omic analyses.

Post genome-wide association analysis: dissecting computational pathway/network-based approaches

Over thousands of genetic associations to diseases have been identified by genome-wide association studies (GWASs), which conceptually is a single-marker-based approach. There are potentially many uses of these identified variants, including a better understanding of the pathogenesis of diseases, new leads for studying underlying risk prediction and clinical prediction of treatment. However, because of inadequate power, GWAS might miss disease genes and/or pathways with weak genetic or strong epistatic effects. Driven by the need to extract useful information from GWAS summary statistics, post-GWAS approaches (PGAs) were introduced. Here, we dissect and discuss advances made in pathway/network-based PGAs, with a particular focus on protein-protein interaction networks that leverage GWAS summary statistics by combining effects of multiple loci, subnetworks or pathways to detect genetic signals associated with complex diseases. We conclude with a discussion of research areas where further work on summary statistic-based methods is needed.

TulsiPIN: An Interologous Protein Interactome of Ocimum tenuiflorum

Ocimum tenuiflorum, commonly known as holy basil or tulsi, is globally recognized for its multitude of medicinal properties. However, a comprehensive study revealing the complex interplay among its constituent proteins at subcellular level is still lacking. To bridge this gap, in this work, a genome-scale interologous protein-protein interaction (PPI) network, TulsiPIN, is developed using 36 template plants, which consists of 13 660 nodes and 327 409 binary interactions. A high confidence network, hc-TulsiPIN, consisting of 7719 nodes having 95 532 interactions is inferred using domain-domain interaction information along with interolog-based statistics, and its reliability is assessed using pathway enrichment, functional homogeneity, and protein colocalization of PPIs. Examination of topological features revealed that hc-TulsiPIN possesses conventional properties, like small-world, scale-free, and modular architecture. A total of 1625 vital proteins are predicted by statistically evaluating hc-TulsiPIN with two ensembles of corresponding random networks, each consisting of 10 000 realizations of Erdoős-Rényi and Barabási-Albert models. Also, numerous regulatory proteins like transcription factors, transcription regulators, and protein kinases are profiled. Using 36 guide genes participating in 9 secondary metabolite biosynthetic pathways, a subnetwork consisting of 171 proteins and 612 interactions was constructed, and 127 of these proteins could be successfully characterized. Detailed information of TulsiPIN is available at https://cuhpcbbtulsipin.shinyapps.io/tulsipin_v0/ .

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.

Computational Approaches in Theranostics: Mining and Predicting Cancer Data

The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and artificial intelligence, and (2) advances in imaging technology and probe development. Computational modelling and simulation are systematic and cost-effective tools able to identify important temporal/spatial patterns (and relationships), characterize distinct molecular features of cancer states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance. These approaches have provided invaluable insights for improving the experimental design of therapeutic delivery systems and for increasing the translational value of the results obtained from early and preclinical studies. The big question is: Could cancer theranostics be determined and controlled in silico? This review describes the recent progress in the development of computational models and methods used to facilitate research on the molecular basis of cancer and on the respective diagnosis and optimized treatment, with particular emphasis on the design and optimization of theranostic systems. The current role of computational approaches is providing innovative, incremental, and complementary data-driven solutions for the prediction, simplification, and characterization of cancer and intrinsic mechanisms, and to promote new data-intensive, accurate diagnostics and therapeutics.

Computational Analysis of High-Dimensional Mass Cytometry Data from Clinical Tissue Samples

The advent of mass cytometry has resulted in the generation of high-dimensional, single-cell expression data sets from clinical samples. These data sets cannot be effectively analyzed using traditional approaches. Instead, new approaches using dimensionality reduction and network analysis techniques have been implemented to assess these data. Here, detailed methods are described for analyzing immune cell expression from clinical samples using network analyses. Specifically, details are given for performing SCAFFoLD and CITRUS analyses. The methods described will use immune cell tumor infiltrate as an example.

NeVOmics: An Enrichment Tool for Gene Ontology and Functional Network Analysis and Visualization of Data from OMICs Technologies

The increasing number of OMICs studies demands bioinformatic tools that aid in the analysis of large sets of genes or proteins to understand their roles in the cell and establish functional networks and pathways. In the last decade, over-representation or enrichment tools have played a successful role in the functional analysis of large gene/protein lists, which is evidenced by thousands of publications citing these tools. However, in most cases the results of these analyses are long lists of biological terms associated to proteins that are difficult to digest and interpret. Here we present NeVOmics, Network-based Visualization for Omics, a functional enrichment analysis tool that identifies statistically over-represented biological terms within a given gene/protein set. This tool provides a hypergeometric distribution test to calculate significantly enriched biological terms, and facilitates analysis on cluster distribution and relationship of proteins to processes and pathways. NeVOmics is adapted to use updated information from the two main annotation databases: Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG). NeVOmics compares favorably to other Gene Ontology and enrichment tools regarding coverage in the identification of biological terms. NeVOmics can also build different network-based graphical representations from the enrichment results, which makes it an integrative tool that greatly facilitates interpretation of results obtained by OMICs approaches. NeVOmics is freely accessible at https://github.com/bioinfproject/bioinfo/.

SyNDI: synchronous network data integration framework

BACKGROUND

Systems biology takes a holistic approach by handling biomolecules and their interactions as big systems. Network based approach has emerged as a natural way to model these systems with the idea of representing biomolecules as nodes and their interactions as edges. Very often the input data come from various sorts of omics analyses. Those resulting networks sometimes describe a wide range of aspects, for example different experiment conditions, species, tissue types, stimulating factors, mutants, or simply distinct interaction features of the same network produced by different algorithms. For these scenarios, synchronous visualization of more than one distinct network is an excellent mean to explore all the relevant networks efficiently. In addition, complementary analysis methods are needed and they should work in a workflow manner in order to gain maximal biological insights.

RESULTS

In order to address the aforementioned needs, we have developed a Synchronous Network Data Integration (SyNDI) framework. This framework contains SyncVis, a Cytoscape application for user-friendly synchronous and simultaneous visualization of multiple biological networks, and it is seamlessly integrated with other bioinformatics tools via the Galaxy platform. We demonstrated the functionality and usability of the framework with three biological examples - we analyzed the distinct connectivity of plasma metabolites in networks associated with high or low latent cardiovascular disease risk; deeper insights were obtained from a few similar inflammatory response pathways in Staphylococcus aureus infection common to human and mouse; and regulatory motifs which have not been reported associated with transcriptional adaptations of Mycobacterium tuberculosis were identified.

CONCLUSIONS

Our SyNDI framework couples synchronous network visualization seamlessly with additional bioinformatics tools. The user can easily tailor the framework for his/her needs by adding new tools and datasets to the Galaxy platform.

Systems, variation, individuality and plant hormones

Inter-individual variation in plants and particularly in hormone content, figures strongly in evolution and behaviour. Homo sapiens and Arabidopsis exhibit similar and substantial phenotypic and molecular variation. Whereas there is a very substantial degree of hormone variation in mankind, reports of inter-individual variation in plant hormone content are virtually absent but are likely to be as large if not larger than that in mankind. Reasons for this absence are discussed. Using an example of inter-individual variation in ethylene content in ripening, the article shows how biological time is compressed by hormones. It further resolves an old issue of very wide hormone dose response that result directly from negative regulation in hormone (and light) transduction. Negative regulation is used because of inter-individual variability in hormone synthesis, receptors and ancillary proteins, a consequence of substantial genomic and environmental variation. Somatic mosaics have been reported for several plant tissues and these too contribute to tissue variation and wide variation in hormone response. The article concludes by examining what variation exists in gravitropic responses. There are multiple sensing systems of gravity vectors and multiple routes towards curvature. These are an aspect of the need for reliability in both inter-individual variation and unpredictable environments. Plant hormone inter-individuality is a new area for research and is likely to change appreciation of the mechanisms that underpin individual behaviour.

Bioinformatics-based tools in drug discovery: the cartography from single gene to integrative biological networks

Originally developed for the analysis of biological sequences, bioinformatics has advanced into one of the most widely recognized domains in the scientific community. Despite this technological evolution, there is still an urgent need for nontoxic and efficient drugs. The onus now falls on the 'omics domain to meet this need by implementing bioinformatics techniques that will allow for the introduction of pioneering approaches in the rational drug design process. Here, we categorize an updated list of informatics tools and explore the capabilities of integrative bioinformatics in disease control. We believe that our review will serve as a comprehensive guide toward bioinformatics-oriented disease and drug discovery research.

Unveiling network-based functional features through integration of gene expression into protein networks

Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype-phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers.

Inflammatory Bowel Disease Meets Systems Biology: A Multi-Omics Challenge and Frontier

The inflammatory bowel disease (IBD) is a systemic disease that is characterized by the inflammation of the gastrointestinal tract. It includes ulcerative colitis and the Crohn's disease. Presently, IBD is one of the most investigated common complex human disorders, although its causes remain unclear. Multi-omics mechanisms involving genomic, transcriptomic, proteomic, and epigenomic variations, not to forget the miRNome, together with environmental contributions, result in an impairment of the immune system in persons with IBD. Such interactions at multiple levels of biology and in concert with the environment constitute the actual engine of this complex disease, demanding a multifactorial and multi-omics perspective to better understand the root causes of IBD. This expert analysis reviews and examines the latest literature and underscores, from the perspective of systems biology, the value of multi-omics technologies as opportunities to unravel the "IBD integrome." We anticipate that multi-omics research will accelerate the new discoveries and insights on IBD in the near future. It shall also pave the way for early diagnosis and help clinicians and families with IBD to forecast and make informed decisions about the prognosis and, possibly, personalized therapeutics in the future.

Systematic expression profiling analysis mines dys-regulated modules in active tuberculosis based on re-weighted protein-protein interaction network and attract algorithm

About 90% of tuberculosis (TB) patients latently infected with Mycobacterium tuberculosis (Mtb) show no symptoms, yet have a 10% chance in lifetime to progress active TB. Nevertheless, current diagnosis approaches need improvement in efficiency and sensitivity. The objective of this work was to detect potential signatures for active TB to further improve the understanding of the biological roles of functional modules involved in this disease. First, targeted networks of active TB and control groups were established via re-weighting protein-protein interaction (PPI) networks using Pearson's correlation coefficient (PCC). Candidate modules were detected from the targeted networks, and the modules with Jaccard score >0.7 were defined as attractors. After that, identification of dys-regulated modules was conducted from the attractors using attract method, Subsequently, gene oncology (GO) enrichment analyses were implemented for genes in the dys-regulated modules. We obtained 33 and 65 candidate modules from the targeted networks of control and active TB groups, respectively. Overall, 13 attractors were identified. Using the cut-off criteria of false discovery rate <0.05, there were 4 dys-regulated modules (Module 1, 2, 3, and 4). Based on the GO annotation results, genes in Modules 1, 2 and 4 were only involved in translation. Most genes in Module 1, 2 and 4 were associated with ribosomes. Accordingly, these dys-regulated modules might serve as potential biomarkers of active TB, facilitating the development for a more efficient, and sensitive diagnostic assay for active TB.

Microarray and network-based identification of functional modules and pathways of active tuberculosis

Diagnose of active tuberculosis (TB) is challenging and treatment response is also difficult to efficiently monitor. The aim of this study was to use an integrated analysis of microarray and network-based method to the samples from publically available datasets to obtain a diagnostic module set and pathways in active TB. Towards this goal, background protein-protein interactions (PPI) network was generated based on global PPI information and gene expression data, following by identification of differential expression network (DEN) from the background PPI network. Then, ego genes were extracted according to the degree features in DEN. Next, module collection was conducted by ego gene expansion based on EgoNet algorithm. After that, differential expression of modules between active TB and controls was evaluated using random permutation test. Finally, biological significance of differential modules was detected by pathways enrichment analysis based on Reactome database, and Fisher's exact test was implemented to extract differential pathways for active TB. Totally, 47 ego genes and 47 candidate modules were identified from the DEN. By setting the cutoff-criteria of gene size >5 and classification accuracy ≥0.9, 7 ego modules (Module 4, Module 7, Module 9, Module 19, Module 25, Module 38 and Module 43) were extracted, and all of them had the statistical significance between active TB and controls. Then, Fisher's exact test was conducted to capture differential pathways for active TB. Interestingly, genes in Module 4, Module 25, Module 38, and Module 43 were enriched in the same pathway, formation of a pool of free 40S subunits. Significant pathway for Module 7 and Module 9 was eukaryotic translation termination, and for Module 19 was nonsense mediated decay enhanced by the exon junction complex (EJC). Accordingly, differential modules and pathways might be potential biomarkers for treating active TB, and provide valuable clues for better understanding of molecular mechanism of active TB.

Correlation-Based Network Generation, Visualization, and Analysis as a Powerful Tool in Biological Studies: A Case Study in Cancer Cell Metabolism

In the last decade vast data sets are being generated in biological and medical studies. The challenge lies in their summary, complexity reduction, and interpretation. Correlation-based networks and graph-theory based properties of this type of networks can be successfully used during this process. However, the procedure has its pitfalls and requires specific knowledge that often lays beyond classical biology and includes many computational tools and software. Here we introduce one of a series of methods for correlation-based network generation and analysis using freely available software. The pipeline allows the user to control each step of the network generation and provides flexibility in selection of correlation methods and thresholds. The pipeline was implemented on published metabolomics data of a population of human breast carcinoma cell lines MDA-MB-231 under two conditions: normal and hypoxia. The analysis revealed significant differences between the metabolic networks in response to the tested conditions. The network under hypoxia had 1.7 times more significant correlations between metabolites, compared to normal conditions. Unique metabolic interactions were identified which could lead to the identification of improved markers or aid in elucidating the mechanism of regulation between distantly related metabolites induced by the cancer growth.

Building Graphs To Describe Dynamics, Kinetics, and Energetics in the d-ALa:d-Lac Ligase VanA

The d-Ala:d-Lac ligase, VanA, plays a critical role in the resistance of vancomycin. Indeed, it is involved in the synthesis of a peptidoglycan precursor, to which vancomycin cannot bind. The reaction catalyzed by VanA requires the opening of the so-called “ω-loop”, so that the substrates can enter the active site. Here, the conformational landscape of VanA is explored by an enhanced sampling approach: the temperature-accelerated molecular dynamics (TAMD). Analysis of the molecular dynamics (MD) and TAMD trajectories recorded on VanA permits a graphical description of the structural and kinetics aspects of the conformational space of VanA, where the internal mobility and various opening modes of the ω-loop play a major role. The other important feature is the correlation of the ω-loop motion with the movements of the opposite domain, defined as containing the residues A149–Q208. Conformational and kinetic clusters have been determined and a path describing the ω-loop opening was extracted from these clusters. The determination of this opening path, as well as the relative importance of hydrogen bonds along the path, permit one to propose some key residue interactions for the kinetics of the ω-loop opening.

Mining large-scale response networks reveals 'topmost activities' in Mycobacterium tuberculosis infection

Mycobacterium tuberculosis owes its high pathogenic potential to its ability to evade host immune responses and thrive inside the macrophage. The outcome of infection is largely determined by the cellular response comprising a multitude of molecular events. The complexity and inter-relatedness in the processes makes it essential to adopt systems approaches to study them. In this work, we construct a comprehensive network of infection-related processes in a human macrophage comprising 1888 proteins and 14,016 interactions. We then compute response networks based on available gene expression profiles corresponding to states of health, disease and drug treatment. We use a novel formulation for mining response networks that has led to identifying highest activities in the cell. Highest activity paths provide mechanistic insights into pathogenesis and response to treatment. The approach used here serves as a generic framework for mining dynamic changes in genome-scale protein interaction networks.

Research prioritization through prediction of future impact on biomedical science: a position paper on inference-analytics

Background

Advances in biotechnology have created “big-data” situations in molecular and cellular biology. Several sophisticated algorithms have been developed that process big data to generate hundreds of biomedical hypotheses (or predictions). The bottleneck to translating this large number of biological hypotheses is that each of them needs to be studied by experimentation for interpreting its functional significance. Even when the predictions are estimated to be very accurate, from a biologist’s perspective, the choice of which of these predictions is to be studied further is made based on factors like availability of reagents and resources and the possibility of formulating some reasonable hypothesis about its biological relevance. When viewed from a global perspective, say from that of a federal funding agency, ideally the choice of which prediction should be studied would be made based on which of them can make the most translational impact.

Results

We propose that algorithms be developed to identify which of the computationally generated hypotheses have potential for high translational impact; this way, funding agencies and scientific community can invest resources and drive the research based on a global view of biomedical impact without being deterred by local view of feasibility. In short, data-analytic algorithms analyze big-data and generate hypotheses; in contrast, the proposed inference-analytic algorithms analyze these hypotheses and rank them by predicted biological impact. We demonstrate this through the development of an algorithm to predict biomedical impact of protein-protein interactions (PPIs) which is estimated by the number of future publications that cite the paper which originally reported the PPI.

Conclusions

This position paper describes a new computational problem that is relevant in the era of big-data and discusses the challenges that exist in studying this problem, highlighting the need for the scientific community to engage in this line of research. The proposed class of algorithms, namely inference-analytic algorithms, is necessary to ensure that resources are invested in translating those computational outcomes that promise maximum biological impact. Application of this concept to predict biomedical impact of PPIs illustrates not only the concept, but also the challenges in designing these algorithms.

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

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