Benchmarking selected computational gene network growing tools in context of virus-host interactions

Integrative Analysis of Potential Biomarkers Involved in the Progression of Papillary Thyroid Cancer

This study aims to explore key prognostic and diagnostic biomarkers involved in the pathogenesis of papillary thyroid cancer (PTC) which is one of the most common endocrine cancers and whose occurrence is rapidly increasing. Papillary thyroid cancer datasets containing normal and tumor samples were collected from Gene Expression Omnibus. Protein-protein interaction (PPI) network for common upregulated differentially expressed genes (DEGs) was constructed, and hub genes were studied. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed to identify the vital biological behaviors and pathways involved in PTC. PPI network analysis demonstrated the interaction between 134 common upregulated DEGs, and top 15 pivotal genes with highest degree of connectivity were retrieved. Three of the hub genes (DPP4, ITGA2, FN1) were linked to the prognosis of PTC patients and considered clinically relevant core genes via survival analysis. We suggest that the identification of key genes associated with PTC development help us in understanding molecular mechanisms related to disease. These genes could also be considered the diagnostic biomarkers or as therapeutic targets in the future treatment for PTC.

Classifying Integrated Signature Molecules in Macrophages of Rheumatoid Arthritis, Osteoarthritis, and Periodontal Disease: An Omics-Based Study

Rheumatoid arthritis (RA), osteoarthritis (OA), and periodontal disease (PD) are chronic inflammatory diseases that are globally prevalent, and pose a public health concern. The search for a potential mechanism linking PD to RA and OA continues, as it could play a significant role in disease prevention and treatment. Recent studies have linked RA, OA, and PD to Porphyromonas gingivalis (PG), a periodontal bacterium, through a similar dysregulation in an inflammatory mechanism. This study aimed to identify potential gene signatures that could assist in early diagnosis as well as gain insight into the molecular mechanisms of these diseases. The expression data sets with the series IDs GSE97779, GSE123492, and GSE24897 for macrophages of RA, OA synovium, and PG stimulated macrophages (PG-SM), respectively, were retrieved and screened for differentially expressed genes (DEGs). The 72 common DEGs among RA, OA, and PG-SM were further subjected to gene–gene correlation analysis. A GeneMANIA interaction network of the 47 highly correlated DEGs comprises 53 nodes and 271 edges. Network centrality analysis identified 15 hub genes, 6 of which are DEGs (API5, ATE1, CCNG1, EHD1, RIN2, and STK39). Additionally, two significantly up-regulated non-hub genes (IER3 and RGS16) showed interactions with hub genes. Functional enrichment analysis of the genes showed that “apoptotic regulation” and “inflammasomes” were among the major pathways. These eight genes can serve as important signatures/targets, and provide new insights into the molecular mechanism of PG-induced RA, OA, and PD.

Network Theoretical Approach to Explore Factors Affecting Signal Propagation and Stability in Dementia’s Protein-Protein Interaction Network

Dementia—a syndrome affecting human cognition—is a major public health concern given to its rising prevalence worldwide. Though multiple research studies have analyzed disorders such as Alzheimer’s disease and Frontotemporal dementia using a systems biology approach, a similar approach to dementia syndrome as a whole is required. In this study, we try to find the high-impact core regulating processes and factors involved in dementia’s protein–protein interaction network. We also explore various aspects related to its stability and signal propagation. Using gene interaction databases such as STRING and GeneMANIA, a principal dementia network (PDN) consisting of 881 genes and 59,085 interactions was achieved. It was assortative in nature with hierarchical, scale-free topology enriched in various gene ontology (GO) categories and KEGG pathways, such as negative and positive regulation of apoptotic processes, macroautophagy, aging, response to drug, protein binding, etc. Using a clustering algorithm (Louvain method of modularity maximization) iteratively, we found a number of communities at different levels of hierarchy in PDN consisting of 95 “motif-localized hubs”, out of which, 7 were present at deepest level and hence were key regulators (KRs) of PDN (HSP90AA1, HSP90AB1, EGFR, FYN, JUN, CELF2 and CTNNA3). In order to explore aspects of network’s resilience, a knockout (of motif-localized hubs) experiment was carried out. It changed the network’s topology from a hierarchal scale-free topology to scale-free, where independent clusters exhibited greater control. Additionally, network experiments on interaction of druggable genome and motif-localized hubs were carried out where UBC, EGFR, APP, CTNNB1, NTRK1, FN1, HSP90AA1, MDM2, VCP, CTNNA1 and GRB2 were identified as hubs in the resultant network (RN). We finally concluded that stability and resilience of PDN highly relies on motif-localized hubs (especially those present at deeper levels), making them important therapeutic intervention candidates. HSP90AA1, involved in heat shock response (and its master regulator, i.e., HSF1), and EGFR are most important genes in pathology of dementia apart from KRs, given their presence as KRs as well as hubs in RN.

Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models

Bone is the most common site of distant metastasis from malignant tumors, with the highest prevalence observed in breast and prostate cancers. Such bone metastases (BM) cause many painful skeletal-related events, such as severe bone pain, pathological fractures, spinal cord compression, and hypercalcemia, with adverse effects on life quality. Many bone-targeting agents developed based on the current understanding of BM onset's molecular mechanisms dull these adverse effects. However, only a few studies investigated potential predictors of high risk for developing BM, despite such knowledge being critical for early interventions to prevent or delay BM. This work proposes a computational network-based pipeline that incorporates a ML/DL component to predict BM development. Based on the proposed pipeline we constructed several machine learning models. The deep neural network (DNN) model exhibited the highest prediction accuracy (AUC of 92.11%) using the top 34 featured genes ranked by betweenness centrality scores. We further used an entirely separate, "external" TCGA dataset to evaluate the robustness of this DNN model and achieved sensitivity of 85%, specificity of 80%, positive predictive value of 78.10%, negative predictive value of 80%, and AUC of 85.78%. The result shows the models' way of learning allowed it to zoom in on the featured genes that provide the added benefit of the model displaying generic capabilities, that is, to predict BM for samples from different primary sites. Furthermore, existing experimental evidence provides confidence that about 50% of the 34 hub genes have BM-related functionality, which suggests that these common genetic markers provide vital insight about BM drivers. These findings may prompt the transformation of such a method into an artificial intelligence (AI) diagnostic tool and direct us towards mechanisms that underlie metastasis to bone events.

Fat regulatory mechanisms of pine nut oil based on protein interaction network analysis

BACKGROUND

Pine nut oil (PNO), a standardized and well-defined extract of Pinus koraiensis (Korean pine), has beneficial effects on wound healing, inflammatory diseases, and cancer. However, the explanation for the mechanism by which PNO reduces body fat remains uncertain. We performed a protein-protein interaction network (PPIN) analysis to explore the genes associated with pinolenic acid using the MEDILINE database from PubChem and PubMed. It was concluded through the PPIN analysis that PNO was involved in a neutral lipid biosynthetic process.

PURPOSE

This study evaluated the effects of PNO predicted by the network analysis of fat accumulation in chronic obesity mouse models established by feeding a high fat diet (HFD) to C57BL/6J mice and explored potential mechanisms.

METHODS

HFD mice were fed only HFD or HFD with PNO at 822 and 1644 mg/kg. After an oral administration of 7 weeks, several body weight and body fat-related parameters were examined, including the following: adipose weight, adipocyte size, serum lipid profiles, adipocyte expression of PPAR-γ, sterol regulatory element binding protein (SREBP)-1c, lipoprotein lipase (LPL) and leptin.

RESULTS

We showed that oral administration of PNO to HFD mice reduces body fat weight, fat in tissue, white adipose tissue weight, and adipocyte size. The serum cholesterol was improved in the HFD mice treated with PNO. Additionally, PNO has significantly attenuated the HFD-induced changes in the adipose tissue expression of PPAR-γ, SREBP-1c, LPL, and leptin.

CONCLUSIONS

The findings from this study based on the PPIN analysis suggest that PNO has potential as drug to reduce body fat through fat regulatory mechanisms by PPAR-γ and SREBP-1c.

Understanding the genetics of systemic lupus erythematosus using Bayesian statistics and gene network analysis

The publication of genetic epidemiology meta-analyses has increased rapidly, but it has been suggested that many of the statistically significant results are false positive. In addition, most such meta-analyses have been redundant, duplicate, and erroneous, leading to research waste. In addition, since most claimed candidate gene associations were false-positives, correctly interpreting the published results is important. In this review, we emphasize the importance of interpreting the results of genetic epidemiology meta-analyses using Bayesian statistics and gene network analysis, which could be applied in other diseases.

A dual controllability analysis of influenza virus-host protein-protein interaction networks for antiviral drug target discovery

BACKGROUND

Host factors of influenza virus replication are often found in key topological positions within protein-protein interaction networks. This work explores how protein states can be manipulated through controllability analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here, we complete a two-part controllability analysis of two protein networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. In this context, controllability analyses aim to identify key regulating host factors of the infected cell's progression. This knowledge can be utilized in further biological analysis to understand disease dynamics and isolate proteins for study as drug target candidates.

RESULTS

Both topological and controllability analyses provide evidence of wide-reaching network effects stemming from the addition of viral-host protein interactions. Virus interacting and driver host proteins are significant both topologically and in controllability, therefore playing important roles in cell behavior during infection. Functional analysis finds overlap of results with previous siRNA studies of host factors involved in influenza replication, NF-kB pathway and infection relevance, and roles as interferon regulating genes. 24 proteins are identified as holding regulatory roles specific to the infected cell by measures of topology, controllability, and functional role. These proteins are recommended for further study as potential antiviral drug targets.

CONCLUSIONS

Seasonal outbreaks of influenza A virus are a major cause of illness and death around the world each year with a constant threat of pandemic infection. This research aims to increase the efficiency of antiviral drug target discovery using existing protein-protein interaction data and network analysis methods. These results are beneficial to future studies of influenza virus, both experimental and computational, and provide evidence that the combination of topology and controllability analyses may be valuable for future efforts in drug target discovery.

Grade-specific diagnostic and prognostic biomarkers in breast cancer

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

Network-Guided Discovery of Influenza Virus Replication Host Factors

Integrating virus-host interactions with host protein-protein interactions, we have created a method using these established network practices to identify host factors (i.e., proteins) that are likely candidates for antiviral drug targeting. We demonstrate that interaction cascades between host proteins that directly interact with viral proteins and host factors that are important to influenza virus replication are enriched for signaling and immune processes. Additionally, we show that host proteins that interact with viral proteins are in network locations of power. Finally, we demonstrate a new network methodology to predict novel host factors and validate predictions with an siRNA screen. Our results show that integrating virus-host proteins interactions is useful in the identification of antiviral drug target candidates.

The positions of host factors required for viral replication within a human protein-protein interaction (PPI) network can be exploited to identify drug targets that are robust to drug-mediated selective pressure. Host factors can physically interact with viral proteins, be a component of virus-regulated pathways (where proteins do not interact with viral proteins), or be required for viral replication but unregulated by viruses. Here, we demonstrate a method of combining human PPI networks with virus-host PPI data to improve antiviral drug discovery for influenza viruses by identifying target host proteins. Analysis shows that influenza virus proteins physically interact with host proteins in network positions significant for information flow, even after the removal of known abundance-degree bias within PPI data. We have isolated a subnetwork of the human PPI network that connects virus-interacting host proteins to host factors that are important for influenza virus replication without physically interacting with viral proteins. The subnetwork is enriched for signaling and immune processes distinct from those associated with virus-interacting proteins. Selecting proteins based on subnetwork topology, we performed an siRNA screen to determine whether the subnetwork was enriched for virus replication host factors and whether network position within the subnetwork offers an advantage in prioritization of drug targets to control influenza virus replication. We found that the subnetwork is highly enriched for target host proteins—more so than the set of host factors that physically interact with viral proteins. Our findings demonstrate that network positions are a powerful predictor to guide antiviral drug candidate prioritization.

Inter-Species Host Gene Expression Differences in Response to Human and Avian Influenza A Virus Strains

Low pathogenic avian influenza (LPAI) viruses are a source of sporadic human infections and could also contribute to future pandemic outbreaks but little is known about inter-species differences in the host responses to these viruses. Here, we studied host gene expression signatures of cell lines from three species (human, chicken, and canine) in response to six different viruses (H1N1/WSN, H5N2/F59, H5N2/F118, H5N2/F189, H5N3 and H9N2). Comprehensive microarray probe set re-annotation and ortholog mapping of the host genes was necessary to allow comparison over extended functionally annotated gene sets and orthologous pathways. The annotations are made available to the community for commonly used microarray chips. We observe a strong tendency of the response being cell type- rather than virus-specific. In chicken cells, we found up-regulation of host factors inducing virus infectivity (e.g., oxysterol binding protein like 1A (OSBPL1A) and Rho GTPase activating protein 21 (ARHGAP21)) while reducing apoptosis (e.g., mitochondrial ribosomal protein S27 (MRPS27)) and increasing cell proliferation (e.g., COP9 signalosome subunit 2 (COPS2)). On the other hand, increased antiviral, pro-apoptotic and inflammatory signatures have been identified in human cells while cell cycle and metabolic pathways were down-regulated. This signature describes how low pathogenic avian influenza (LPAI) viruses are being tolerated and shed from chicken but potentially causing cellular disruption in mammalian cells.