EgoNet: identification of human disease ego-network modules

The effect of two facets of physicians' environmental stress on patients' compliance with COVID-19 guidelines: moderating roles of two types of ego network

Drawing upon the Conservation of Resources Theory, this study seeks to examine the association between two dimensions of environmental stress experienced by physicians and patients' adherence to COVID-19 guidelines, within the context of a social network framework. A third-wave longitudinal study was employed to gather 439 valid data points in China. Social network analysis and structural equation model were used to test the conceptual model. The results reveal the pivotal role of physicians' environmental stress related to their work and family contexts in influencing patients' adherence to COVID-19 guidelines through the mediation of physicians' information sharing. The ego networks of physicians, encompassing both advice-seeking and friendship ties, were observed to negatively moderate the relationship between stress and resource depletion. Broadly, our study shows the importance of understanding physicians' stress caused by the working and family environments, as these factorsnot only impact the psychological well-being of physicians but also significantly affect patients' compliance with COVID-19 guidelines. In addition, the work offers a framework for understanding the impact of the ego advice-seeking network and the ego friend network.

Genomic Analysis of Abnormal DNAM Methylation in Parathyroid Tumors

BACKGROUND

Parathyroid tumors are common endocrine neoplasias associated with primary hyperparathyroidism. Although numerous studies have studied the subject, the predictive value of gene biomarkers nevertheless remains low.

METHODS

In this study, we performed genomic analysis of abnormal DNA methylation in parathyroid tumors. After data preprocessing, differentially methylated genes were extracted from patients with parathyroid tumors by using t-tests.

RESULTS

After refinement of the basic differential methylation, 28241 unique CpGs (634 genes) were identified to be methylated. The methylated genes were primarily involved in 7 GO terms, and the top 3 terms were associated with cyst morphogenesis, ion transport, and GTPase signal. Following pathway enrichment analyses, a total of 10 significant pathways were enriched; notably, the top 3 pathways were cholinergic synapses, glutamatergic synapses, and oxytocin signaling pathways. Based on PPIN and ego-net analysis, 67 ego genes were found which could completely separate the diseased group from the normal group. The 10 most prominent genes included POLA1, FAM155 B, AMMECR1, THOC2, CCND1, CLDN11, IDS, TST, RBPJ, and GNA11. SVM analysis confirmed that this grouping approach was precise.

CONCLUSIONS

This research provides useful data to further explore novel genes and pathways as therapeutic targets for parathyroid tumors.

DNLC: differential network local consistency analysis

BACKGROUND

The biological network is highly dynamic. Functional relations between genes can be activated or deactivated depending on the biological conditions. On the genome-scale network, subnetworks that gain or lose local expression consistency may shed light on the regulatory mechanisms related to the changing biological conditions, such as disease status or tissue developmental stages.

RESULTS

In this study, we develop a new method to select genes and modules on the existing biological network, in which local expression consistency changes significantly between clinical conditions. The method is called DNLC: Differential Network Local Consistency. In simulations, our algorithm detected artificially created local consistency changes effectively. We applied the method on two publicly available datasets, and the method detected novel genes and network modules that were biologically plausible.

CONCLUSIONS

The new method is effective in finding modules in which the gene expression consistency change between clinical conditions. It is a useful tool that complements traditional differential expression analyses to make discoveries from gene expression data. The R package is available at https://cran.r-project.org/web/packages/DNLC.

An ego network analysis approach identified important biomarkers with an association to progression and metastasis of gastric cancer

BACKGROUND

Gastric cancer (GC) is the fifth most common cancer type worldwide. The aim of this study was to identify gastric-related therapeutic indicators on the basis of the ego network analysis.

MATERIAL AND METHODS

The microarray data related to GC was downloaded from ArrayExpress database. All human protein-protein interaction (PPI) networks were downloaded from the STRING database. Ego genes were identified on the basis of PPI networks and the gene expression in GC, and then co-expression networks (ego networks) were constructed using these ego genes. On the basis of ego networks, the optimal GO terms and genes were predicted by affinity predictions and cold read predictions. Finally, the predicted genes as effective biomarkers for GC were verified by the bioinformatics analysis.

RESULTS

The differential expression networks were conducted and comprised of 365 edges and 232 nodes, which resulted in 218 ego genes. Although there was no significant difference in the expression of top ten ego genes among different groups of GC samples, it was eventually confirmed that top three optimal GO terms with highest cool read values were translational termination (cool read value = 0.987), translational elongation (cool read value = 0.986), and macromolecular complex disassembly (cool read value = 0.985) and top five optimal genes were UBA52, RPS27A, MAPK1, UBC, and UBB. UBA52, RPS27A, and MAPK1 were verified by the bioinformatics analysis to be related to the progression and metastasis of GC.

CONCLUSIONS

An ego network analysis approach is a very effective method for screening GC and the screened genes might be biomarkers for GC diagnosis and treatment.

High throughput analysis to identify key gene molecules that inhibit adipogenic differentiation and promote osteogenic differentiation of human mesenchymal stem cells

The present study investigated the key genes, which cause switch from adipogenic to osteogenic differentiation of human mesenchymal stem cells (hMSCs). The transcriptomic profile of hMSCs samples were collected from Array Express database. Differential expression network was constructed by calculating the Pearson's correlation coefficient and ranked according to their topological features. The top 5% genes with degree ≥2 were selected as ego genes. Following the KEGG pathway enrichment analysis and the relevant miRNAs prediction, the miRNA-mRNA-pathway networks were constructed by combining the miRNA-mRNA pairs and mRNA-pathway pairs together. In total, we obtained 84, 119, 94 and 97 ego-genes in B, BI, BT and BTI groups, and DLGAP5, DLGAP5, NUSAP1 and NDC80 were the ego-genes with the highest z-score of each group, respectively. Beginning from each ego-gene, we identified 2 significant ego-modules with gene size ≥4 in group BI, and the ego-genes were PBK and NCOA3, respectively. Through KEGG pathway analysis, we found that most of the pathways enriched by ego-genes were associated with gene replication and repair, and cell proliferation. According to the miRNA prediction results, we found that some of the predicted miRNAs have been validated to be the regulatory miRNAs of these corresponding mRNAs. Finally we constructed a miRNA-mRNA-pathway network by integrating the miRNA-mRNA and mRNA-pathway pairs together. The constructed network gives us a more comprehensive understanding of the mechanism of osteogenic differentiation of hMSCs.

A global analysis of CNVs in diverse yak populations using whole-genome resequencing

Background

Genomic structural variation represents a source for genetic and phenotypic variation, which may be subject to selection during the environmental adaptation and population differentiation. Here, we described a genome-wide analysis of copy number variations (CNVs) in 16 populations of yak based on genome resequencing data and CNV-based cluster analyses of these populations.

Results

In total, we identified 51,461 CNV events and defined 3174 copy number variation regions (CNVRs) that covered 163.8 Mb (6.2%) of yak genome with more “loss” events than both “gain” and “both” events, and we confirmed 31 CNVRs in 36 selected yaks using quantitative PCR. Of the total 163.8 Mb CNVR coverage, a 10.8 Mb region of high-confidence CNVRs directly overlapped with the 52.9 Mb of segmental duplications, and we confirmed their uneven distributions across chromosomes. Furthermore, functional annotation indicated that the CNVR-harbored genes have a considerable variety of molecular functions, including immune response, glucose metabolism, and sensory perception. Notably, some of the identified CNVR-harbored genes associated with adaptation to hypoxia (e.g., DCC, MRPS28, GSTCD, MOGAT2, DEXI, CIITA, and SMYD1). Additionally, cluster analysis, based on either individuals or populations, showed that the CNV clustering was divided into two origins, indicating that some yak CNVs are likely to arisen independently in different populations and contribute to population difference.

Conclusions

Collectively, the results of the present study advanced our understanding of CNV as an important type of genomic structural variation in yak, and provide a useful genomic resource to facilitate further research on yak evolution and breeding.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5451-5) contains supplementary material, which is available to authorized users.

Systemic tracking of diagnostic function modules for post-menopausal osteoporosis in a differential co-expression network view

Post-menopausal osteoporosis is one of the most common bone diseases in women. The aim of the present study was to predict the diagnostic function modules from a differential co-expression gene network in order to enhance the current understanding of the biological processes and to promote the early prevention and intervention of post-menopausal osteoporosis. The diagnostic function modules were extracted from a differential co-expression network by the established protein-protein interaction (PPI) network analysis. First, significant genes were identified from the differential co-expression network, which were regarded as seed genes. Starting from the seed genes, the sub-networks in this disease, referred to as diagnostic function modules, were exhaustively searched and prioritized through a snowball sampling strategy to identify genes to accurately predict clinical outcomes. In addition, crucial function inference was performed for each diagnostic function module. Based on the microarray and PPI data, the differential co-expression network was constructed, which contained 1,607 genes and 4,197 interactions. A total of 110 seed genes were identified, and nine diagnostic modules that accurately distinguished post-menopausal osteoporosis from healthy controls were screened out from these seed genes. The diagnostic modules may be associated with five functional pathways with emphasis on metabolism. A total of nine diagnostic functional modules screened in the present study may be considered as potential targets for predicting the clinical outcomes of post-menopausal osteoporosis, and may contribute to the early diagnosis and therapy of osteoporosis.

When social computing meets soft computing: opportunities and insights

The characteristics of the massive social media data, diverse mobile sensing devices as well as the highly complex and dynamic user’s social behavioral patterns have led to the generation of huge amounts of high dimension, uncertain, imprecision and noisy data from social networks. Thanks to the emerging soft computing techniques which unlike the conventional hard computing. It is widely used for coping with the tolerant of imprecision, uncertainty, partial truth, and approximation. One of the most important and promising applications is social network analysis (SNA) that is the process of investigating social structures and relevant properties through the use of network and graph theories. This paper aims to survey various SNA approaches using soft computing techniques such as fuzzy logic, formal concept analysis, rough sets theory and soft set theory. In addition, the relevant software packages about SNA are clearly summarized.

Screening feature modules and pathways in glioma using EgoNet

Background

To investigate differential egonetwork modules and pathways in glioma using EgoNet algorithm.

Methodology

Based on microarray data, EgoNet algorithm mainly comprised three stages: construction of differential co-expression network (DCN); EgoNet algorithm used to identify candidate ego-network modules based on the increased classification accuracy; statistical significance for candidate modules using random permutation testing. After that, pathway enrichment analysis for differential ego-network modules was implemented to illuminate the biological processes.

Results

We obtained 109 ego genes. From every ego gene, we progressively grew the ego-networks by levels; we extracted 109 ego-networks and the mean node size in an ego-network was 6. By setting the classification accuracy threshold at 0.90 and the count of nodes in an ego-network module at 10, we extracted 8 candidate ego-network modules. After random permutation test with 1000 times, 5 modules including module 59, 72, 78, 86, and 90 were identified to be significant. Of note, the genes of module 90 and 86 were enriched in the pathway of resolution of sister chromatid cohesion and mitotic prometaphase, respectively.

Conclusion

The identified modules and their corresponding ego genes might be beneficial in revealing the pathology underlying glioma and give insight for future research of glioma.

Detection of Significant Pneumococcal Meningitis Biomarkers by Ego Network

OBJECTIVE

To identify significant biomarkers for detection of pneumococcal meningitis based on ego network.

METHODS

Based on the gene expression data of pneumococcal meningitis and global protein-protein interactions (PPIs) data recruited from open access databases, the authors constructed a differential co-expression network (DCN) to identify pneumococcal meningitis biomarkers in a network view. Here EgoNet algorithm was employed to screen the significant ego networks that could accurately distinguish pneumococcal meningitis from healthy controls, by sequentially seeking ego genes, searching candidate ego networks, refinement of candidate ego networks and significance analysis to identify ego networks. Finally, the functional inference of the ego networks was performed to identify significant pathways for pneumococcal meningitis.

RESULTS

By differential co-expression analysis, the authors constructed the DCN that covered 1809 genes and 3689 interactions. From the DCN, a total of 90 ego genes were identified. Starting from these ego genes, three significant ego networks (Module 19, Module 70 and Module 71) that could predict clinical outcomes for pneumococcal meningitis were identified by EgoNet algorithm, and the corresponding ego genes were GMNN, MAD2L1 and TPX2, respectively. Pathway analysis showed that these three ego networks were related to CDT1 association with the CDC6:ORC:origin complex, inactivation of APC/C via direct inhibition of the APC/C complex pathway, and DNA strand elongation, respectively.

CONCLUSIONS

The authors successfully screened three significant ego modules which could accurately predict the clinical outcomes for pneumococcal meningitis and might play important roles in host response to pathogen infection in pneumococcal meningitis.

Network Marker Selection for Untargeted LC-MS Metabolomics Data

Untargeted metabolomics using high-resolution liquid chromatography-mass spectrometry (LC-MS) is becoming one of the major areas of high-throughput biology. Functional analysis, that is, analyzing the data based on metabolic pathways or the genome-scale metabolic network, is critical in feature selection and interpretation of metabolomics data. One of the main challenges in the functional analyses is the lack of the feature identity in the LC-MS data itself. By matching mass-to-charge ratio (m/z) values of the features to theoretical values derived from known metabolites, some features can be matched to one or more known metabolites. When multiple matchings occur, in most cases only one of the matchings can be true. At the same time, some known metabolites are missing in the measurements. Current network/pathway analysis methods ignore the uncertainty in metabolite identification and the missing observations, which could lead to errors in the selection of significant subnetworks/pathways. In this paper, we propose a flexible network feature selection framework that combines metabolomics data with the genome-scale metabolic network. The method adopts a sequential feature screening procedure and machine learning-based criteria to select important subnetworks and identify the optimal feature matching simultaneously. Simulation studies show that the proposed method has a much higher sensitivity than the commonly used maximal matching approach. For demonstration, we apply the method on a cohort of healthy subjects to detect subnetworks associated with the body mass index (BMI). The method identifies several subnetworks that are supported by the current literature, as well as detects some subnetworks with plausible new functional implications. The R code is available at http://web1.sph.emory.edu/users/tyu8/MSS.

Characterizing biomarkers in osteosarcoma metastasis based on an ego-network

OBJECTIVES

To characterize biomarkers that underlie osteosarcoma (OS) metastasis based on an ego-network.

RESULTS

From the microarray data, we obtained 13,326 genes. By combining PPI data and microarray data, 10,520 shared genes were found and constructed into ego-networks. 17 significant ego-networks were identified with p < 0.05. In the pathway enrichment analysis, seven ego-networks were identified with the most significant pathway.

CONCLUSIONS

These significant ego-modules were potential biomarkers that reveal the potential mechanisms in OS metastasis, which may contribute to understanding cancer prognoses and providing new perspectives in the treatment of cancer.

Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis

Osteosarcoma (OS) is the most common primary bone malignancy, but current therapies are far from effective for all patients. A better understanding of the pathological mechanism of OS may help to achieve new treatments for this tumor. Hence, the objective of this study was to investigate ego modules and pathways in OS utilizing EgoNet algorithm and pathway-related analysis, and reveal pathological mechanisms underlying OS. The EgoNet algorithm comprises four steps: constructing background protein-protein interaction (PPI) network (PPIN) based on gene expression data and PPI data; extracting differential expression network (DEN) from the background PPIN; identifying ego genes according to topological features of genes in reweighted DEN; and collecting ego modules using module search by ego gene expansion. Consequently, we obtained 5 ego modules (Modules 2, 3, 4, 5, and 6) in total. After applying the permutation test, all presented statistical significance between OS and normal controls. Finally, pathway enrichment analysis combined with Reactome pathway database was performed to investigate pathways, and Fisher's exact test was conducted to capture ego pathways for OS. The ego pathway for Module 2 was CLEC7A/inflammasome pathway, while for Module 3 a tetrasaccharide linker sequence was required for glycosaminoglycan (GAG) synthesis, and for Module 6 was the Rho GTPase cycle. Interestingly, genes in Modules 4 and 5 were enriched in the same pathway, the 2-LTR circle formation. In conclusion, the ego modules and pathways might be potential biomarkers for OS therapeutic index, and give great insight of the molecular mechanism underlying this tumor.

Investigating ego modules involved in TGFβ3-induced chondrogenesis in mesenchymal stem cells based on ego network

OBJECTIVE

This paper aimed to investigate ego modules for TGFβ3-induced chondrogenesis in mesenchymal stem cells (MSCs) using ego network algorithm.

METHODS

The ego network algorithm comprised three parts, extracting differential expression network (DEN) based on gene expression data and protein-protein interaction (PPI) data; exploring ego genes by reweighting DEN; and searching ego modules by ego gene expansions. Subsequently, permutation test was carried out to evaluate the statistical significance of the ego modules. Finally, pathway enrichment analysis was conducted to investigate ego pathways enriched by the ego modules.

RESULTS

A total of 15 ego genes were obtained from the DEN, such as PSMA4, HNRNPM and WDR77. Starting with each ego genes, 15 candidate modules were gained. When setting the thresholds of the area under the receiver operating characteristics curve (AUC) ≥0.9 and gene size ≥4, three ego modules (Module 3, Module 8 and Module 14) were identified, and all of them had statistical significances between normal and TGFβ3-induced chondrogenesis in MSCs. By mapping module genes to confirmed pathway database, their ego pathways were detected, Cdc20:Phospho-APC/C mediated degradation of Cyclin A for Module 3, Mitotic G1-G1/S phases for Module 8, and mRNA Splicing for Module 14.

CONCLUSIONS

We have successfully identified three ego modules, evaluated their statistical significances and investigated their functional enriched ego pathways. The findings might provide potential biomarkers and give great insights to reveal molecular mechanism underlying this process.

Detecting subnetwork-level dynamic correlations

MOTIVATION

The biological regulatory system is highly dynamic. The correlations between many functionally related genes change over different biological conditions. Finding dynamic relations on the existing biological network may reveal important regulatory mechanisms. Currently no method is available to detect subnetwork-level dynamic correlations systematically on the genome-scale network. Two major issues hampered the development. The first is gene expression profiling data usually do not contain time course measurements to facilitate the analysis of dynamic relations, which can be partially addressed by using certain genes as indicators of biological conditions. Secondly, it is unclear how to effectively delineate subnetworks, and define dynamic relations between them.

RESULTS

Here we propose a new method named LANDD (Liquid Association for Network Dynamics Detection) to find subnetworks that show substantial dynamic correlations, as defined by subnetwork A is concentrated with Liquid Association scouting genes for subnetwork B. The method produces easily interpretable results because of its focus on subnetworks that tend to comprise functionally related genes. Also, the collective behaviour of genes in a subnetwork is a much more reliable indicator of underlying biological conditions compared to using single genes as indicators. We conducted extensive simulations to validate the method's ability to detect subnetwork-level dynamic correlations. Using a real gene expression dataset and the human protein-protein interaction network, we demonstrate the method links subnetworks of distinct biological processes, with both confirmed relations and plausible new functional implications. We also found signal transduction pathways tend to show extensive dynamic relations with other functional groups.

AVAILABILITY AND IMPLEMENTATION

The R package is available at https://cran.r-project.org/web/packages/LANDD CONTACTS: yunba@pcom.edu, jwlu33@hotmail.com or tianwei.yu@emory.eduSupplementary information: Supplementary data are available at Bioinformatics online.

A network-based approach to enrich gene signatures for the prediction of breast cancer metastases

Despite the multiplicity of the gene expression analysis studies for the identification of genomics based origins of cancerous diseases, the presented gene signatures have generally little overlap. The genes do not function in isolation and therefore a more holistic approach that takes into account the interactions among them is needed. In this study we present a stepwise refinement methodology where starting from some initial set of biomarkers we expand and enrich this set taking into account existing biological information. In particular, we start with a 27 gene signature previously identified as indicative of the presence of circulating tumor cells (CTCs) in peripheral blood of breast cancer patients. We use the manually curated HINT database of protein-protein interactions as the background biological network to locate the network-based similarity of the input genes and how they connect to each other. The result is an enriched connected set of genes that is subsequently expanded to form an even bigger network based on the ability of the surrounding genes to strongly correlate with the phenotypes of a training set of breast cancer patient cases. The induced network is then used as a new gene signature for the classification of breast brain metastases in an independent dataset. The results are encouraging for the validity of this method.

Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network

A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network (interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy (IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.

Distilling a Visual Network of Retinitis Pigmentosa Gene-Protein Interactions to Uncover New Disease Candidates

Background

Retinitis pigmentosa (RP) is a highly heterogeneous genetic visual disorder with more than 70 known causative genes, some of them shared with other non-syndromic retinal dystrophies (e.g. Leber congenital amaurosis, LCA). The identification of RP genes has increased steadily during the last decade, and the 30% of the cases that still remain unassigned will soon decrease after the advent of exome/genome sequencing. A considerable amount of genetic and functional data on single RD genes and mutations has been gathered, but a comprehensive view of the RP genes and their interacting partners is still very fragmentary. This is the main gap that needs to be filled in order to understand how mutations relate to progressive blinding disorders and devise effective therapies.

Methodology

We have built an RP-specific network (RPGeNet) by merging data from different sources: high-throughput data from BioGRID and STRING databases, manually curated data for interactions retrieved from iHOP, as well as interactions filtered out by syntactical parsing from up-to-date abstracts and full-text papers related to the RP research field. The paths emerging when known RP genes were used as baits over the whole interactome have been analysed, and the minimal number of connections among the RP genes and their close neighbors were distilled in order to simplify the search space.

Conclusions

In contrast to the analysis of single isolated genes, finding the networks linking disease genes renders powerful etiopathological insights. We here provide an interactive interface, RPGeNet, for the molecular biologist to explore the network centered on the non-syndromic and syndromic RP and LCA causative genes. By integrating tissue-specific expression levels and phenotypic data on top of that network, a more comprehensive biological view will highlight key molecular players of retinal degeneration and unveil new RP disease candidates.