CINNA: an R/CRAN package to decipher Central Informative Nodes in Network Analysis

Hubs and Bottlenecks in Protein-Protein Interaction Networks

Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.

Aberrant pace of cortical neuron development in brain organoids from patients with 22q11.2 deletion syndrome and schizophrenia

Adults and children afflicted with the 22q11.2 deletion syndrome (22q11.2DS) exhibit cognitive, social, and emotional impairments, and are at significantly heightened risk for schizophrenia (SCZ). The impact of this deletion on early human brain development, however, has remained unclear. Here we harness organoid models of the developing human cerebral cortex, cultivated from subjects with 22q11.2DS and SCZ, as well as unaffected control samples, to identify cell-type-specific developmental abnormalities arising from this genomic lesion. Leveraging single-cell RNA-sequencing in conjunction with experimental validation, we find that the loss of genes within the 22q11.2 locus leads to a delayed development of cortical neurons. This compromised development was reflected in an elevated proportion of actively proliferating neural progenitor cells, coupled with a decreased fraction of more mature neurons. Furthermore, we identify perturbed molecular imprints linked to neuronal maturation, observe the presence of sparser neurites, and note a blunted amplitude in glutamate-induced Ca2+ transients. The aberrant transcription program underlying impaired development contains molecular signatures significantly enriched in neuropsychiatric genetic liability. MicroRNA profiling and target gene investigation suggest that microRNA dysregulation may drive perturbations of genes governing the pace at which maturation unfolds. Using protein-protein interaction network analysis we define complementary effects stemming from additional genes residing within the deleted locus. Our study uncovers reproducible neurodevelopmental and molecular alterations due to 22q11.2 deletions. These findings have the potential to facilitate disease modeling and promote the pursuit of therapeutic interventions.

Role of molecular mimicry in the SARS-CoV-2-human interactome for pathogenesis of cardiovascular diseases: An update to ImitateDB

Mimicry of host proteins is a strategy employed by pathogens to hijack host functions. Domain and motif mimicry was explored in the experimental and predicted SARS-CoV-2-human interactome. The host first interactor proteins were also added to capture the continuum of the interactions. The domains and motifs of the proteins were annotated using NCBI CD Search and ScanProsite, respectively. Host and pathogen proteins with a common host interactor and similar domain/motif constitute a mimicry pair indicating global structural similarity (domain mimicry pair; DMP) or local sequence similarity (motif mimicry pair; MMP). 593 DMPs and 7,02,472 MMPs were determined. AAA, DEXDc and Macro domains were frequent among DMPs whereas glycosylation, myristoylation and RGD motifs were abundant among MMP. The proteins involved in mimicry were visualised as a SARS-CoV-2 mimicry interaction network. The host proteins were enriched in multiple CVD pathways indicating the role of mimicry in COVID-19 associated CVDs. Bridging nodes were identified as potential drug targets. Approved antihypertensive and anti-inflammatory drugs are proposed for repurposing against COVID-19 associated CVDs. The SARS-CoV-2 mimicry data has been updated in ImitateDB (http://imitatedb.sblab-nsit.net/SARSCoV2Mimicry). Determination of key mechanisms, proteins, pathways, drug targets and repurposing candidates is critical for developing therapeutics for SARS CoV-2 associated CVDs.

From time-series transcriptomics to gene regulatory networks: A review on inference methods

Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data.

Centrality Analysis of Protein-Protein Interaction Networks Using R

Proteins are structural and functional components of cells. They interact with each other to drive specific cellular functions. The physical and functional protein interactions are an important feature of cellular organization and regulation. Protein interactions are represented as a network or a graph in which proteins are nodes, and interactions between them are edges. Perturbations in the network affecting essential or central proteins can have pathological consequences. Network or graph theory is a branch of mathematics that provides a conceptual framework to decipher topologically important proteins in the network. These concepts are known as centrality measures. This chapter introduces various centrality metrics and provides a stepwise protocol to quantify protein's strategic positions in the network using an R programming language.

Network Science and Group Fusion Similarity-Based Searching to Explore the Chemical Space of Antiparasitic Peptides

Antimicrobial peptides (AMPs) have appeared as promising compounds to treat a wide range of diseases. Their clinical potentialities reside in the wide range of mechanisms they can use for both killing microbes and modulating immune responses. However, the hugeness of the AMPs' chemical space (AMPCS), represented by more than 1065 unique sequences, has represented a big challenge for the discovery of new promising therapeutic peptides and for the identification of common structural motifs. Here, we introduce network science and a similarity searching approach to discover new promising AMPs, specifically antiparasitic peptides (APPs). We exploited the network-based representation of APPs' chemical space (APPCS) to retrieve valuable information by using three network types: chemical space (CSN), half-space proximal (HSPN), and metadata (METN). Some centrality measures were applied to identify in each network the most important and nonredundant peptides. Then, these central peptides were considered as queries (Qs) in group fusion similarity-based searches against a comprehensive collection of known AMPs, stored in the graph database StarPepDB, to propose new potential APPs. The performance of the resulting multiquery similarity-based search models (mQSSMs) was evaluated in five benchmarking data sets of APP/non-APPs. The predictions performed by the best mQSSM showed a strong-to-very-strong performance since their external Matthews correlation coefficient (MCC) values ranged from 0.834 to 0.965. Outstanding MCC values (>0.85) were attained by the mQSSM with 219 Qs from both networks CSN and HSPN with 0.5 as similarity threshold in external data sets. Then, the performance of our best mQSSM was compared with the APPs prediction servers AMPDiscover and AMPFun. The proposed model showed its relevance by outperforming state-of-the-art machine learning models to predict APPs. After applying the best mQSSM and additional filters on the non-APP space from StarPepDB, 95 AMPs were repurposed as potential APP hits. Due to the high sequence diversity of these peptides, different computational approaches were applied to identify relevant motifs for searching and designing new APPs. Lastly, we identified 11 promising APP lead candidates by using our best mQSSMs together with diversity-based network analyses, and 24 web servers for activity/toxicity and drug-like properties. These results support that network-based similarity searches can be an effective and reliable strategy to identify APPs. The proposed models and pipeline are freely available through the StarPep toolbox software at http://mobiosd-hub.com/starpep.

Identification of familial networks reveals sex-specific density dependence in the dispersal and reproductive success of an endangered ungulate

Density is an important demographic parameter that is commonly overlooked in studies of wild populations. Here, we examined the effects of variable spatially explicit density on a range of demographic parameters in a wild population of a cryptic ungulate, boreal woodland caribou (Rangifer tarandus caribou). Using non-invasive genetic sampling, we applied spatial capture–recapture methods with landscape covariates to estimate the density of boreal woodland caribou across a 108,806 km2 study area. We then created a familial network from the reconstructed parent–offspring relationships to determine whether spatial density influenced sex-specific individual reproductive success, female pregnancy status, and dispersal distance. We found that animal density varied greatly in response to land cover types and disturbance; animal density was most influenced by landscape composition and distance to roads varying from 0 in areas with >20% deciduous cover to 270 caribou per 1,000 km2 in areas presenting contiguous older coniferous cover. We found that both male and female reproductive success varied with density, with males showing a higher probability of having offspring in higher-density areas, and the opposite for females. No differences were found in female pregnancy rates occurring in high- and low-density areas. Dispersal distances varied with density, with offspring moving shorter distances when parents were found in higher-density areas. Familial networks showed lower-closeness centrality and lower-degree centrality for females in higher-density areas, indicating that females found in higher-density areas tend to be less broadly associated with animals across the range. Although high-density areas do reflect good-quality caribou habitat, the observed decreased closeness and degree centrality measures, dispersal rates, and lower female recruitment rates suggest that remnant habitat patches across the landscape may create population sinks.

Network Analysis for Better Understanding the Complex Psycho-Biological Mechanisms behind Fibromyalgia Syndrome

The aim of this study was to assess potential associations between sensory, cognitive, health-related, and physical variables in women with fibromyalgia syndrome (FMS) using a network analysis for better understanding the complexity of psycho-biological mechanisms. Demographic, clinical, pressure pain threshold (PPT), health-related, physical, and psychological/cognitive variables were collected in 126 women with FMS. A network analysis was conducted to quantify the adjusted correlations between the modeled variables and to assess the centrality indices (i.e., the degree of connection with other symptoms in the network and the importance in the system modeled as a network. This model showed several local associations between the variables. Multiple positive correlations between PPTs were observed, being the strongest weight between PPTs over the knee and tibialis anterior (ρ: 0.28). Catastrophism was associated with higher hypervigilance (ρ: 0.23) and lower health-related EuroQol-5D (ρ: −0.24). The most central variables were PPT over the tibialis anterior (the highest strength centrality), hand grip (the highest harmonic centrality) and Time Up and Go (the highest betweenness centrality). This study, applying network analysis to understand the complex mechanisms of women with FMS, supports a model where sensory-related, psychological/cognitive, health-related, and physical variables are connected. Implications of the current findings, e.g., developing treatments targeting these mechanisms, are discussed.

Exploring the raison d'etre behind metric selection in network analysis: a systematic review

Network analysis is a useful tool to analyse the interactions and structure of graphs that represent the relationships among entities, such as sectors within an urban system. Connecting entities in this way is vital in understanding the complexity of the modern world, and how to navigate these complexities during an event. However, the field of network analysis has grown rapidly since the 1970s to produce a vast array of available metrics that describe different graph properties. This diversity allows network analysis to be applied across myriad research domains and contexts, however widespread applications have produced polysemic metrics. Challenges arise in identifying which method of network analysis to adopt, which metrics to choose, and how many are suitable. This paper undertakes a structured review of literature to provide clarity on raison d'etre behind metric selection and suggests a way forward for applied network analysis. It is essential that future studies explicitly report the rationale behind metric choice and describe how the mathematics relates to target concepts and themes. An exploratory metric analysis is an important step in identifying the most important metrics and understanding redundant ones. Finally, where applicable, one should select an optimal number of metrics that describe the network both locally and globally, so as to understand the interactions and structure as holistically as possible.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s41109-022-00476-w.

Functional feeds marginally alter immune expression and microbiota of Atlantic salmon (Salmo salar) gut, gill, and skin mucosa though evidence of tissue-specific signatures and host-microbe coadaptation remain

BACKGROUND

Mucosal surfaces of fish provide cardinal defense against environmental pathogens and toxins, yet these external mucosae are also responsible for maintaining and regulating beneficial microbiota. To better our understanding of interactions between host, diet, and microbiota in finfish and how those interactions may vary across mucosal tissue, we used an integrative approach to characterize and compare immune biomarkers and microbiota across three mucosal tissues (skin, gill, and gut) in Atlantic salmon receiving a control diet or diets supplemented with mannan-oligosaccharides, coconut oil, or both. Dietary impacts on mucosal immunity were further evaluated by experimental ectoparasitic sea lice (Lepeophtheirus salmonis) challenge.

RESULTS

Fish grew to a final size of 646.5 g ± 35.8 during the 12-week trial, with no dietary effects on growth or sea lice resistance. Bacterial richness differed among the three tissues with the highest richness detected in the gill, followed by skin, then gut, although dietary effects on richness were only detected within skin and gill. Shannon diversity was reduced in the gut compared to skin and gill but was not influenced by diet. Microbiota communities clustered separately by tissue, with dietary impacts on phylogenetic composition only detected in the skin, although skin and gill communities showed greater overlap compared to the gut according to overall composition, differential abundance, and covariance networks. Inferred metagenomic functions revealed preliminary evidence for tissue-specific host-microbiota coadaptation, as putative microbiota functions showed ties to the physiology of each tissue. Immune gene expression profiles displayed tissue-specific signatures, yet dietary effects were also detected within each tissue and peripheral blood leukocytes. Procrustes analysis comparing sample-matched multivariate variation in microbiota composition to that of immune expression profiles indicated a highly significant correlation between datasets.

CONCLUSIONS

Diets supplemented with functional ingredients, namely mannan-oligosaccharide, coconut oil, or a both, resulted in no difference in Atlantic salmon growth or resistance to sea lice infection. However, at the molecular level, functional ingredients caused physiologically relevant changes to mucosal microbiota and host immune expression. Putative tissue-specific metagenomic functions and the high correlation between expression profiles and microbiota composition suggest host and microbiota are interdependent and coadapted in a tissue-specific manner.

Identifying large scale interaction atlases using probabilistic graphs and external knowledge

Introduction:

Reconstruction of gene interaction networks from experimental data provides a deep understanding of the underlying biological mechanisms. The noisy nature of the data and the large size of the network make this a very challenging task. Complex approaches handle the stochastic nature of the data but can only do this for small networks; simpler, linear models generate large networks but with less reliability.

Methods:

We propose a divide-and-conquer approach using probabilistic graph representations and external knowledge. We cluster the experimental data and learn an interaction network for each cluster, which are merged using the interaction network for the representative genes selected for each cluster.

Results:

We generated an interaction atlas for 337 human pathways yielding a network of 11,454 genes with 17,777 edges. Simulated gene expression data from this atlas formed the basis for reconstruction. Based on the area under the curve of the precision-recall curve, the proposed approach outperformed the baseline (random classifier) by ∼15-fold and conventional methods by ∼5–17-fold. The performance of the proposed workflow is significantly linked to the accuracy of the clustering step that tries to identify the modularity of the underlying biological mechanisms.

Conclusions:

We provide an interaction atlas generation workflow optimizing the algorithm/parameter selection. The proposed approach integrates external knowledge in the reconstruction of the interactome using probabilistic graphs. Network characterization and understanding long-range effects in interaction atlases provide means for comparative analysis with implications in biomarker discovery and therapeutic approaches. The proposed workflow is freely available at http://otulab.unl.edu/atlas.

Spatial familial networks to infer demographic structure of wild populations

In social species, reproductive success and rates of dispersal vary among individuals resulting in spatially structured populations. Network analyses of familial relationships may provide insights on how these parameters influence population-level demographic patterns. These methods, however, have rarely been applied to genetically derived pedigree data from wild populations.Here, we use parent-offspring relationships to construct familial networks from polygamous boreal woodland caribou (Rangifer tarandus caribou) in Saskatchewan, Canada, to inform recovery efforts. We collected samples from 933 individuals at 15 variable microsatellite loci along with caribou-specific primers for sex identification. Using network measures, we assess the contribution of individual caribou to the population with several centrality measures and then determine which measures are best suited to inform on the population demographic structure. We investigate the centrality of individuals from eighteen different local areas, along with the entire population.We found substantial differences in centrality of individuals in different local areas, that in turn contributed differently to the full network, highlighting the importance of analyzing networks at different scales. The full network revealed that boreal caribou in Saskatchewan form a complex, interconnected familial network, as the removal of edges with high betweenness did not result in distinct subgroups. Alpha, betweenness, and eccentricity centrality were the most informative measures to characterize the population demographic structure and for spatially identifying areas of highest fitness levels and family cohesion across the range. We found varied levels of dispersal, fitness, and cohesion in family groups. Synthesis and applications: Our results demonstrate the value of different network measures in assessing genetically derived familial networks. The spatial application of the familial networks identified individuals presenting different fitness levels, short- and long-distance dispersing ability across the range in support of population monitoring and recovery efforts.

Integrated Value of Influence: An Integrative Method for the Identification of the Most Influential Nodes within Networks

Biological systems are composed of highly complex networks, and decoding the functional significance of individual network components is critical for understanding healthy and diseased states. Several algorithms have been designed to identify the most influential regulatory points within a network. However, current methods do not address all the topological dimensions of a network or correct for inherent positional biases, which limits their applicability. To overcome this computational deficit, we undertook a statistical assessment of 200 real-world and simulated networks to decipher associations between centrality measures and developed an algorithm termed Integrated Value of Influence (IVI), which integrates the most important and commonly used network centrality measures in an unbiased way. When compared against 12 other contemporary influential node identification methods on ten different networks, the IVI algorithm outperformed all other assessed methods. Using this versatile method, network researchers can now identify the most influential network nodes.

Molecular Network Analysis in Rabies Pathogenesis Using Cooperative Game Theory

Background and Purpose

Recently, many researchers from different fields of science have been used networks to analyze complex relational big data. The identification of which nodes are more important than the others, known as centrality analysis, is a key issue in biological network analysis. Although, several centralities have been introduced degree, closeness, and betweenness centralities are the most popular. These centralities are based on the individual position of each node and the cooperation and synergies between nodes have been ignored.

Objectives

Since in many cases, the network function is a consequence of cooperation and interaction between nodes, classical centralities were extended to a group of nodes instead of only individual nodes using cooperative game theory concepts. In this study, we analyze the protein interaction network inferred in rabies disease and rank gene products based on group centrality measurements to identify the novel gene candidates.

Materials and Methods

For this purpose, we used a game-theoretic approach at three scenarios, where the power of a coalition of genes assessed using different criteria including the neighbors of genes in the network, and predefined importance of the genes in its neighborhood. The Shapley value of such a game was considered as a new centrality. In this study, we analyze the network of gene products implicates rabies. The network has 1059 nodes and 8844 edges and centrality analysis was performed using CINNA package in R software.

Results

Based on three scenarios, we selected genes among the highest Shapley value that had low ranking from classical centralities. The enrichment analysis among the selected genes in scenario 1 indicates important pathways in rabies pathogenesis. Pair-wise correlation analysis reveals that changing the weights of nodes at different scenarios can significantly affect the results of ranking genes in the network.

Conclusion

A prior knowledge about the disease and the topology of the network, enable us to design an appropriate game and consequently infer some biological important nodes (genes) in the network. Obviously, a single centrality cannot capture all significant features embedded in the network.

CatbNet: A Multi Network Analyzer for Comparing and Analyzing the Topology of Biological Networks

Background

Complexity and dynamicity of biological events is a reason to use comprehen-sive and holistic approaches to deal with their difficulty. Currently with advances in omics data genera-tion, network-based approaches are used frequently in different areas of computational biology and bio-informatics to solve problems in a systematic way. Also, there are many applications and tools for net-work data analysis and manipulation which their goal is to facilitate the way of improving our under-standings of inter/intra cellular interactions.

Methods

In this article, we introduce CatbNet, a multi network analyzer application which is prepared for network comparison objectives.

Result and Conclusion

CatbNet uses many topological features of networks to compare their structure and foundations. One of the most prominent properties of this application is classified network analysis in which groups of networks are compared with each other.