A network-based method using a random walk with restart algorithm and screening tests to identify novel genes associated with Menière's disease

AMEND: active module identification using experimental data and network diffusion

BACKGROUND

Molecular interaction networks have become an important tool in providing context to the results of various omics experiments. For example, by integrating transcriptomic data and protein-protein interaction (PPI) networks, one can better understand how the altered expression of several genes are related with one another. The challenge then becomes how to determine, in the context of the interaction network, the subset(s) of genes that best captures the main mechanisms underlying the experimental conditions. Different algorithms have been developed to address this challenge, each with specific biological questions in mind. One emerging area of interest is to determine which genes are equivalently or inversely changed between different experiments. The equivalent change index (ECI) is a recently proposed metric that measures the extent to which a gene is equivalently or inversely regulated between two experiments. The goal of this work is to develop an algorithm that makes use of the ECI and powerful network analysis techniques to identify a connected subset of genes that are highly relevant to the experimental conditions.

RESULTS

To address the above goal, we developed a method called Active Module identification using Experimental data and Network Diffusion (AMEND). The AMEND algorithm is designed to find a subset of connected genes in a PPI network that have large experimental values. It makes use of random walk with restart to create gene weights, and a heuristic solution to the Maximum-weight Connected Subgraph problem using these weights. This is performed iteratively until an optimal subnetwork (i.e., active module) is found. AMEND was compared to two current methods, NetCore and DOMINO, using two gene expression datasets.

CONCLUSION

The AMEND algorithm is an effective, fast, and easy-to-use method for identifying network-based active modules. It returned connected subnetworks with the largest median ECI by magnitude, capturing distinct but related functional groups of genes. Code is freely available at https://github.com/samboyd0/AMEND .

Network medicine: an approach to complex kidney disease phenotypes

Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders.

PWN: enhanced random walk on a warped network for disease target prioritization

BACKGROUND

Extracting meaningful information from unbiased high-throughput data has been a challenge in diverse areas. Specifically, in the early stages of drug discovery, a considerable amount of data was generated to understand disease biology when identifying disease targets. Several random walk-based approaches have been applied to solve this problem, but they still have limitations. Therefore, we suggest a new method that enhances the effectiveness of high-throughput data analysis with random walks.

RESULTS

We developed a new random walk-based algorithm named prioritization with a warped network (PWN), which employs a warped network to achieve enhanced performance. Network warping is based on both internal and external features: graph curvature and prior knowledge.

CONCLUSIONS

We showed that these compositive features synergistically increased the resulting performance when applied to random walk algorithms, which led to PWN consistently achieving the best performance among several other known methods. Furthermore, we performed subsequent experiments to analyze the characteristics of PWN.

Serum/glucocorticoid-inducible kinase 1 deficiency induces NLRP3 inflammasome activation and autoinflammation of macrophages in a murine endolymphatic hydrops model

Ménière's disease, a multifactorial disorder of the inner ear, is characterized by severe vertigo episodes and hearing loss. Although the role of immune responses in Ménière's disease has been proposed, the precise mechanisms remain undefined. Here, we show that downregulation of serum/glucocorticoid-inducible kinase 1 is associated with activation of NLRP3 inflammasome in vestibular-resident macrophage-like cells from Ménière's disease patients. Serum/glucocorticoid-inducible kinase 1 depletion markedly enhances IL-1β production which leads to the damage of inner ear hair cells and vestibular nerve. Mechanistically, serum/glucocorticoid-inducible kinase 1 binds to the PYD domain of NLRP3 and phosphorylates it at Serine 5, thereby interfering inflammasome assembly. Sgk^-/- mice show aggravated audiovestibular symptoms and enhanced inflammasome activation in lipopolysaccharide-induced endolymphatic hydrops model, which is ameliorated by blocking NLRP3. Pharmacological inhibition of serum/glucocorticoid-inducible kinase 1 increases the disease severity in vivo. Our studies demonstrate that serum/glucocorticoid-inducible kinase 1 functions as a physiologic inhibitor of NLRP3 inflammasome activation and maintains inner ear immune homeostasis, reciprocally participating in models of Ménière's disease pathogenesis.

Protective effect of anakinra on audiovestibular function in a murine model of endolymphatic hydrops

Introduction

Ménière's disease (MD), a common disease in the inner ear, is characterized by an increase in endolymph in the cochlear duct and vestibular labyrinth. The pathophysiology of the condition appears to be the immune response. Studies have shown that basal levels of the IL-1β increased in some MD patients.

Methods

Here, we used a murine model of endolymphatic hydrops (EH) to study the effect of anakinra on auditory and vestibular function. Mice were intraperitoneal injected with anakinra or saline before LPS by postauricular injection. Weight and disease severity were measured, histologic changes in auditory were assessed, and inflammation state was evaluated.

Results

We found that anakinra therapy reduced LPS-induced EH, alleviated LPS-induced hearing loss and vestibular dysfunction, and inhibited the expression of the inflammatory cytokines and macrophage infiltration in the cochlea of mice. We further demonstrated that anakinra ameliorated the disorganization and degeneration of myelin sheath, and reduced the neuron damage in cochlea of EH mice.

Discussion

Consequently, anakinra contributes to a promising therapeutic approach to MD, by restricting EH, alleviating auditory and vestibular function, inhibiting inflammation of the inner ear and protecting the cochlear nerve. Further investigations are needed to assess the potential therapeutic benefits of anakinra in patients with MD.

Mining comorbidities of opioid use disorder from FDA adverse event reporting system and patient electronic health records

BACKGROUND

Opioid use disorder (OUD) has become an urgent health problem. People with OUD often experience comorbid medical conditions. Systematical approaches to identifying co-occurring conditions of OUD can facilitate a deeper understanding of OUD mechanisms and drug discovery. This study presents an integrated approach combining data mining, network construction and ranking, and hypothesis-driven case-control studies using patient electronic health records (EHRs).

METHODS

First, we mined comorbidities from the US Food and Drug Administration Adverse Event Reporting System (FAERS) of 12 million unique case reports using frequent pattern-growth algorithm. The performance of OUD comorbidity mining was measured by precision and recall using manually curated known OUD comorbidities. We then constructed a disease comorbidity network using mined association rules and further prioritized OUD comorbidities. Last, novel OUD comorbidities were independently tested using EHRs of 75 million unique patients.

RESULTS

The OUD comorbidities from association rules mining achieves a precision of 38.7% and a recall of 78.2 Based on the mined rules, the global DCN was constructed with 1916 nodes and 32,175 edges. The network-based OUD ranking result shows that 43 of 55 known OUD comorbidities were in the first decile with a precision of 78.2%. Hypothyroidism and type 2 diabetes were two top-ranked novel OUD comorbidities identified by data mining and network ranking algorithms. Based on EHR-based case-control studies, we showed that patients with OUD had significantly increased risk for hyperthyroidism (AOR = 1.46, 95% CI 1.43-1.49, p value < 0.001), hypothyroidism (AOR = 1.45, 95% CI 1.42-1.48, p value < 0.001), type 2-diabetes (AOR = 1.28, 95% CI 1.26-1.29, p value < 0.001), compared with individuals without OUD.

CONCLUSION

Our study developed an integrated approach for identifying and validating novel OUD comorbidities from health records of 87 million unique patients (12 million for discovery and 75 million for validation), which can offer new opportunities for OUD mechanism understanding, drug discovery, and multi-component service delivery for co-occurring medical conditions among patients with OUD.

Network analysis of atherosclerotic genes elucidates druggable targets

Background

Atherosclerosis is one of the major causes of cardiovascular disease. It is characterized by the accumulation of atherosclerotic plaque in arteries under the influence of inflammatory responses, proliferation of smooth muscle cell, accumulation of modified low density lipoprotein. The pathophysiology of atherosclerosis involves the interplay of a number of genes and metabolic pathways. In traditional translation method, only a limited number of genes and pathways can be studied at once. However, the new paradigm of network medicine can be explored to study the interaction of a large array of genes and their functional partners and their connections with the concerned disease pathogenesis. Thus, in our study we employed a branch of network medicine, gene network analysis as a tool to identify the most crucial genes and the miRNAs that regulate these genes at the post transcriptional level responsible for pathogenesis of atherosclerosis.

Result

From NCBI database 988 atherosclerotic genes were retrieved. The protein–protein interaction using STRING database resulted in 22,693 PPI interactions among 872 nodes (genes) at different confidence score. The cluster analysis of the 872 genes using MCODE, a plug-in of Cytoscape software revealed a total of 18 clusters, the topological parameter and gene ontology analysis facilitated in the selection of four influential genes viz., AGT, LPL, ITGB2, IRS1 from cluster 3. Further, the miRNAs (miR-26, miR-27, and miR-29 families) targeting these genes were obtained by employing MIENTURNET webtool.

Conclusion

Gene network analysis assisted in filtering out the 4 probable influential genes and 3 miRNA families in the pathogenesis of atherosclerosis. These genes, miRNAs can be targeted to restrict the occurrence of atherosclerosis. Given the importance of atherosclerosis, any approach in the understanding the genes involved in its pathogenesis can substantially enhance the health care system.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01195-y.

Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes

Background

Identifying human protein-phenotype relationships has attracted researchers in bioinformatics and biomedical natural language processing due to its importance in uncovering rare and complex diseases. Since experimental validation of protein-phenotype associations is prohibitive, automated tools capable of accurately extracting these associations from the biomedical text are in high demand. However, while the manual annotation of protein-phenotype co-mentions required for training such models is highly resource-consuming, extracting millions of unlabeled co-mentions is straightforward.

Results

In this study, we propose a novel deep semi-supervised ensemble framework that combines deep neural networks, semi-supervised, and ensemble learning for classifying human protein-phenotype co-mentions with the help of unlabeled data. This framework allows the ability to incorporate an extensive collection of unlabeled sentence-level co-mentions of human proteins and phenotypes with a small labeled dataset to enhance overall performance. We develop PPPredSS, a prototype of our proposed semi-supervised framework that combines sophisticated language models, convolutional networks, and recurrent networks. Our experimental results demonstrate that the proposed approach provides a new state-of-the-art performance in classifying human protein-phenotype co-mentions by outperforming other supervised and semi-supervised counterparts. Furthermore, we highlight the utility of PPPredSS in powering a curation assistant system through case studies involving a group of biologists.

Conclusions

This article presents a novel approach for human protein-phenotype co-mention classification based on deep, semi-supervised, and ensemble learning. The insights and findings from this work have implications for biomedical researchers, biocurators, and the text mining community working on biomedical relationship extraction.

Candidate gene prioritization for chronic obstructive pulmonary disease using expression information in protein-protein interaction networks

Background

Identifying or prioritizing genes for chronic obstructive pulmonary disease (COPD), one type of complex disease, is particularly important for its prevention and treatment.

Methods

In this paper, a novel method was proposed to Prioritize genes using Expression information in Protein–protein interaction networks with disease risks transferred between genes (abbreviated as PEP). A weighted COPD PPI network was constructed using expression information and then COPD candidate genes were prioritized based on their corresponding disease risk scores in descending order.

Results

Further analysis demonstrated that the PEP method was robust in prioritizing disease candidate genes, and superior to other existing prioritization methods exploiting either topological or functional information. Top-ranked COPD candidate genes and their significantly enriched functions were verified to be related to COPD. The top 200 candidate genes might be potential disease genes in the diagnosis and treatment of COPD.

Conclusions

The proposed method could provide new insights to the research of prioritizing candidate genes of COPD or other complex diseases with expression information from sequencing or microarray data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-021-01646-9.

Identification of Therapeutic Targets for the Selective Killing of HBV-Positive Hepatocytes

The hepatitis B virus (HBV) infection is a major risk factor for cirrhosis and hepatocellular carcinoma. Most infected individuals become lifelong carriers of HBV as the drugs currently used to treat the patients can only control the disease, thereby achieving functional cure (loss of the hepatitis B surface antigen) but not complete cure (elimination of infected hepatocytes). Therefore, we aimed to identify the target genes for the selective killing of HBV-positive hepatocytes to develop a novel therapy for the treatment of HBV infection. Our strategy was to recognize the conditionally essential genes that are essential for the survival of HBV-positive hepatocytes, but non-essential for the HBV-negative hepatocytes. Using microarray gene expression data curated from the Gene Expression Omnibus database and the known essential genes from the Online GEne Essentiality database, we used two approaches, comprising the random walk with restart algorithm and the support vector machine approach, to determine the potential targets for the selective killing of HBV-positive hepatocytes. The final candidate genes list obtained using these two approaches consisted of 36 target genes, which may be conditionally essential for the cell survival of HBV-positive hepatocytes; however, this requires further experimental validation. Therefore, the genes identified in this study can be used as potential drug targets to develop novel therapeutic strategies for the treatment of HBV, and may ultimately help in achieving the elusive goal of a complete cure for hepatitis B.

Network-Based Analysis of Fatal Comorbidities of COVID-19 and Potential Therapeutics

COVID-19 is a highly contagious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The case-fatality rate is significantly higher in older patients and those with diabetes, cancer or cardiovascular disorders. The human proteins, angiotensin-converting enzyme 2 (ACE2), transmembrane protease serine 2 (TMPRSS2) and basigin (BSG), are involved in high-confidence host-pathogen interactions with SARS-CoV-2 proteins. We considered these three proteins as seed nodes and applied the random walk with restart method on the human interactome to construct a protein-protein interaction sub-network, which captures the effects of viral invasion. We found that 'Insulin resistance', 'AGE-RAGE signaling in diabetic complications' and 'adipocytokine signaling' were the common pathways associated with diabetes, cancer and cardiovascular disorders. The association of these critical pathways with aging and its related diseases explains the molecular basis of COVID-19 fatality. We further identified drugs that have effects on these proteins/pathways based on gene expression studies. We particularly focused on drugs that significantly downregulate ACE2 along with other critical proteins identified by the network-based approach. Among them, COL-3 had earlier shown activity against acute lung injury and acute respiratory distress, while entinostat and mocetinostat have been investigated for non-small-cell lung cancer. We propose that these drugs can be repurposed for COVID-19.

Network-Based Analysis of Fatal Comorbidities of COVID-19 and Potential Therapeutics

COVID-19 is a highly contagious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The case-fatality rate is significantly higher in older patients and those with diabetes, cancer or cardiovascular disorders. The human proteins, angiotensin-converting enzyme 2 (ACE2), transmembrane protease serine 2 (TMPRSS2) and basigin (BSG), are involved in high-confidence host-pathogen interactions with SARS-CoV-2 proteins. We considered these three proteins as seed nodes and applied the random walk with restart method on the human interactome to construct a protein-protein interaction sub-network, which captures the effects of viral invasion. We found that 'Insulin resistance', 'AGE-RAGE signaling in diabetic complications' and 'adipocytokine signaling' were the common pathways associated with diabetes, cancer and cardiovascular disorders. The association of these critical pathways with aging and its related diseases explains the molecular basis of COVID-19 fatality. We further identified drugs that have effects on these proteins/pathways based on gene expression studies. We particularly focused on drugs that significantly downregulate ACE2 along with other critical proteins identified by the network-based approach. Among them, COL-3 had earlier shown activity against acute lung injury and acute respiratory distress, while entinostat and mocetinostat have been investigated for non-small-cell lung cancer. We propose that these drugs can be repurposed for COVID-19.

Identification of Potential Meniere's Disease Targets in the Adult Stria Vascularis

The stria vascularis generates the endocochlear potential and is involved in processes that underlie ionic homeostasis in the cochlear endolymph, both which play essential roles in hearing. The histological hallmark of Meniere's disease (MD) is endolymphatic hydrops, which refers to the bulging or expansion of the scala media, which is the endolymph-containing compartment of the cochlea. This histologic hallmark suggests that processes that disrupt ion homeostasis or potentially endocochlear potential may underlie MD. While treatments exist for vestibular symptoms related to MD, effective therapies for hearing fluctuation and hearing loss seen in MD remain elusive. Understanding the potential cell types involved in MD may inform the creation of disease mouse models and provide insight into underlying mechanisms and potential therapeutic targets. For these reasons, we compare published datasets related to MD in humans with our previously published adult mouse stria vascularis single-cell and single-nucleus RNA-Seq datasets to implicate potentially involved stria vascularis (SV) cell types in MD. Finally, we provide support for these implicated cell types by demonstrating co-expression of select candidate genes for MD within SV cell types.

Identification of Latent Oncogenes with a Network Embedding Method and Random Forest

Oncogene is a special type of genes, which can promote the tumor initiation. Good study on oncogenes is helpful for understanding the cause of cancers. Experimental techniques in early time are quite popular in detecting oncogenes. However, their defects become more and more evident in recent years, such as high cost and long time. The newly proposed computational methods provide an alternative way to study oncogenes, which can provide useful clues for further investigations on candidate genes. Considering the limitations of some previous computational methods, such as lack of learning procedures and terming genes as individual subjects, a novel computational method was proposed in this study. The method adopted the features derived from multiple protein networks, viewing proteins in a system level. A classic machine learning algorithm, random forest, was applied on these features to capture the essential characteristic of oncogenes, thereby building the prediction model. All genes except validated oncogenes were ranked with a measurement yielded by the prediction model. Top genes were quite different from potential oncogenes discovered by previous methods, and they can be confirmed to become novel oncogenes. It was indicated that the newly identified genes can be essential supplements for previous results.

Rare Variants of Putative Candidate Genes Associated With Sporadic Meniere's Disease in East Asian Population

Objectives: The cause of Meniere's disease (MD) is unclear but likely involves genetic and environmental factors. The aim of this study was to investigate the genetic basis underlying MD by screening putative candidate genes for MD.

Methods: Sixty-eight patients who met the diagnostic criteria for MD of the Barany Society were included. We performed targeted gene sequencing using next generation sequencing (NGS) panel composed of 45 MD-associated genes. We identified the rare variants causing non-synonymous amino acid changes, stop codons, and insertions/deletions in the coding regions, and excluded the common variants with minor allele frequency >0.01 in public databases. The pathogenicity of the identified variants was analyzed by various predictive tools and protein structural modeling.

Results: The average read depth for the targeted regions was 1446.3-fold, and 99.4% of the targeted regions were covered by 20 or more reads, achieving the high quality of the sequencing. After variant filtering, annotation, and interpretation, we identified a total of 15 rare heterozygous variants in 12 (17.6%) sporadic patients. Among them, four variants were detected in familial MD genes (DTNA, FAM136A, DPT), and the remaining 11 in MD-associated genes (PTPN22, NFKB1, CXCL10, TLR2, MTHFR, SLC44A2, NOS3, NOTCH2). Three patients had the variants in two or more genes. All variants were not detected in our healthy controls (n = 100). No significant differences were observed between patients with and without a genetic variant in terms of sex, mean age of onset, bilaterality, the type of MD, and hearing threshold at diagnosis.

Conclusions: Our study identified rare variants of putative candidate genes in some of MD patients. The genes were related to the formation of inner ear structures, the immune-associated process, or systemic hemostasis derangement, suggesting the multiple genetic predispositions in the development of MD.

The exploration of disease-specific gene regulatory networks in esophageal carcinoma and stomach adenocarcinoma

BACKGROUND

Feed-forward loops (FFLs), consisting of miRNAs, transcription factors (TFs) and their common target genes, have been validated to be important for the initialization and development of complex diseases, including cancer. Esophageal Carcinoma (ESCA) and Stomach Adenocarcinoma (STAD) are two types of malignant tumors in the digestive tract. Understanding common and distinct molecular mechanisms of ESCA and STAD is extremely crucial.

RESULTS

In this paper, we presented a computational framework to explore common and distinct FFLs, and molecular biomarkers for ESCA and STAD. We identified FFLs by combining regulation pairs and RNA-seq data. Then we constructed disease-specific co-expression networks based on the FFLs identified. We also used random walk with restart (RWR) on disease-specific co-expression networks to prioritize candidate molecules. We identified 148 and 242 FFLs for these two types of cancer, respectively. And we found that one TF, E2F3 was related to ESCA, two genes, DTNA and KCNMA1 were related to STAD, while one TF ESR1 and one gene KIT were associated with both of the two types of cancer.

CONCLUSIONS

This proposed computational framework predicted disease-related biomolecules effectively and discovered the correlation between two types of cancers, which helped develop the diagnostic and therapeutic strategies of Esophageal Carcinoma and Stomach Adenocarcinoma.

A network-based approach reveals novel invasion and Maurer's clefts-related proteins in Plasmodium falciparum

Malaria continues to be a major concern in developing countries despite continuous efforts to find a cure for the disease. Understanding the pathogenesis mechanism is necessary to identify more effective drug targets against malaria. Many years of experimental research have generated a large amount of data for the malarial parasite, Plasmodium falciparum. These data are useful to understand the importance of certain parasite proteins, but it often remains unclear how these proteins come together, interact with other proteins and carry out their function. Identification of all proteins involved in pathogenesis is an important step towards understanding the molecular mechanism of pathogenesis. In this study, dynamic stage-specific protein-protein interaction networks were created based on gene expression data during the parasite's intra-erythrocytic stages and static protein-protein interaction data. Using previously known proteins of a biological event as seed proteins, the random walk with restart (RWR) method was used on the dynamic protein-protein interaction networks to identify novel proteins related to that event. Two screening procedures namely, permutation test and GO enrichment test were performed to increase the reliability of the RWR predictions. The proposed method was first validated on Plasmodium falciparum proteins related to invasion, where it could reproduce the existing knowledge from a small set of seed proteins. It was then used to identify novel Maurer's clefts resident proteins, where it could identify 152 parasite proteins. We show that the current approach can annotate conserved proteins with unknown function. The predicted proteins can help build a mechanistic model for disease pathogenesis, which will be useful in identifying new drug targets.

Relationship between changes in hearing function and volumes of endolymphatic hydrops after endolymphatic sac drainage

Background: Endolymphatic sac drainage (ELSD) may have a positive effect on endolymphatic hydrops (EH) and may help to preserve inner ear function. However, the relationship between changes in EH volumes and hearing function after ELSD has not been described. Objectives: We aimed to reveal the factors related to changes in hearing and EH following ELSD. Material and Methods: Twenty-one patients who received ELSD were enrolled. Pure tone audiometry and 3-T magnetic resonance imaging (MRI) 4 h after intravenous injection of gadolinium enhancement were performed just before surgery and 2 years later. To characterize the endolymphatic space (ELS), we measured the volume of the total fluid (TFS) and ELS and calculated the ratio of ELS to TFS (ELS ratio). Results: The ELS ratio of the patients who showed hearing improvement was 18.5 ± 11.4% before surgery and 23.9 ± 14.3% after. For those with no change, it was 29.7 ± 10.8% before and 29.4 ± 9.5% after, and in patients with worsened hearing function it was 22.7 ± 7.5% before and 27.2 ± 13.4% after. Conclusion: We found no correlation between the changes in hearing function and the volume of EH after ELSD.

Inferring novel genes related to colorectal cancer via random walk with restart algorithm

Colorectal cancer (CRC) is the third most common type of cancer. In recent decades, genomic analysis has played an increasingly important role in understanding the molecular mechanisms of CRC. However, its pathogenesis has not been fully uncovered. Identification of genes related to CRC as complete as possible is an important way to investigate its pathogenesis. Therefore, we proposed a new computational method for the identification of novel CRC-associated genes. The proposed method is based on existing proven CRC-associated genes, human protein-protein interaction networks, and random walk with restart algorithm. The utility of the method is indicated by comparing it to the methods based on Guilt-by-association or shortest path algorithm. Using the proposed method, we successfully identified 298 novel CRC-associated genes. Previous studies have validated the involvement of the majority of these 298 novel genes in CRC-associated biological processes, thus suggesting the efficacy and accuracy of our method.

Candidate gene prioritization for non-communicable diseases based on functional information: Case studies

Candidate gene prioritization for complex non-communicable diseases is essential to understanding the mechanism and developing better means for diagnosing and treating these diseases. Many methods have been developed to prioritize candidate genes in protein-protein interaction (PPI) networks. Integrating functional information/similarity into disease-related PPI networks could improve the performance of prioritization. In this study, a candidate gene prioritization method was proposed for non-communicable diseases considering disease risks transferred between genes in weighted disease PPI networks with weights for nodes and edges based on functional information. Here, three types of non-communicable diseases with pathobiological similarity, Type 2 diabetes (T2D), coronary artery disease (CAD) and dilated cardiomyopathy (DCM), were used as case studies. Literature review and pathway enrichment analysis of top-ranked genes demonstrated the effectiveness of our method. Better performance was achieved after comparing our method with other existing methods. Pathobiological similarity among these three diseases was further investigated for common top-ranked genes to reveal their pathogenesis.

Prediction of Drug Combinations with a Network Embedding Method

Aim and Objective:

There are several diseases having a complicated mechanism. For such complicated diseases, a single drug cannot treat them very well because these diseases always involve several targets and single targeted drugs cannot modulate these targets simultaneously. Drug combination is an effective way to treat such diseases. However, determination of effective drug combinations is time- and cost-consuming via traditional methods. It is urgent to build quick and cheap methods in this regard. Designing effective computational methods incorporating advanced computational techniques to predict drug combinations is an alternative and feasible way.

Method:

In this study, we proposed a novel network embedding method, which can extract topological features of each drug combination from a drug network that was constructed using chemical-chemical interaction information retrieved from STITCH. These topological features were combined with individual features of drug combination reported in one previous study. Several advanced computational methods were employed to construct an effective prediction model, such as synthetic minority oversampling technique (SMOTE) that was used to tackle imbalanced dataset, minimum redundancy maximum relevance (mRMR) and incremental feature selection (IFS) methods that were adopted to analyze features and extract optimal features for building an optimal support machine vector (SVM) classifier.

Results and Conclusion:

The constructed optimal SVM classifier yielded an MCC of 0.806, which is superior to the classifier only using individual features with or without SMOTE. The performance of the classifier can be improved by combining the topological features and essential features of a drug combination.

Three-Dimensional Magnetic Resonance Imaging Reveals the Relationship Between the Control of Vertigo and Decreases in Endolymphatic Hydrops After Endolymphatic Sac Drainage With Steroids for Meniere's Disease

Meniere's disease is a common disease, that presents with recurrent vertigo and cochlear symptoms. The pathology of Meniere's disease was first reported to involve endolymphatic hydrops in 1938. The endolymphatic sac is thought to have a role to keep the hydrostatic pressure and endolymph homeostasis for the inner ear. As a surgery for intractable Meniere's disease, endolymphatic sac drainage with intraendolymphatic sac application of large doses of steroids is performed to control the endolymphatic hydrops and preserve or improve inner ear function. In the present study, to observe the effect of this surgery, we calculated the endolymphatic space size using 3-Tesla magnetic resonance imaging (MRI) 4 h after intravenous injection of gadolinium enhancement at two time points: just before surgery and 2 years after. To reveal the condition of the endolymphatic space, we constructed three-dimensional MR images semi-automatically and fused the three-dimensional images of the total fluid space of inner ear and the endolymphatic space. After fusing the images, we calculated the volume of the total fluid space and endolymphatic space. Two years after surgery, 16 of 20 patients (80.0%) showed relief from vertigo/dizziness and reductions in the ratio of the volume of the endolymphatic size to the total fluid space of inner ear. Endolymphatic sac drainage with intraendolymphatic sac application of large doses of steroids could control vertigo/dizziness and decrease the endolymphatic hydrops. These results indicate that endolymphatic sac drainage is a good treatment option for patients with intractable Meniere's disease. In addition, volumetric measurement of inner ear volume could be useful for confirming the effect of treatments on Meniere's disease.

Integrating Biological Networks for Drug Target Prediction and Prioritization

Computational prediction of the clinical success or failure of a potential drug target for therapeutic use is a challenging problem. Novel network propagation algorithms that integrate heterogeneous biological networks are proving useful for drug target identification and prioritization. These approaches typically utilize a network describing relationships between targets, a method to disseminate the relevant information through the network, and a method to elucidate new associations between targets and diseases. Here, we utilize one such network propagation-based approach, DTINet, which starts with diffusion component analysis of networks of both potential drug targets and diseases. Then an inductive matrix completion algorithm is applied to identify novel disease targets based on their network topological similarities with known disease targets with successfully launched drugs. DTINet performed well as assessed with area under the precision-recall curve (AUPR = 0.88 ± 0.007) and area under the receiver operating characteristic curve (AUROC = 0.86 ± 0.008). These metrics improved when we combined data from multiple networks in the target space but reduced significantly when we used a more conservative method to define negative controls (AUPR = 0.56 ± 0.007, AUROC = 0.57 ± 0.007). We are optimistic that integration of more relevant and cleaner datasets and networks, careful calibration of model parameters, as well as algorithmic improvements will improve prediction accuracy. However, we also recognize that predicting drug targets that are likely to be successful is an extremely challenging problem due to its complex nature and sparsity of known disease targets.

Large-scale mining disease comorbidity relationships from post-market drug adverse events surveillance data

Background

Systems approaches in studying disease relationship have wide applications in biomedical discovery, such as disease mechanism understanding and drug discovery. The FDA Adverse Event Reporting System (FAERS) contains rich information about patient diseases, medications, drug adverse events and demographics of 17 million case reports. Here, we explored this data resource to mine disease comorbidity relationships using association rule mining algorithm and constructed a disease comorbidity network.

Results

We constructed a disease comorbidity network with 1059 disease nodes and 12,608 edges using association rule mining of FAERS (14,157 rules). We evaluated the performance of comorbidity mining from FAERS using known disease comorbidities of multiple sclerosis (MS), psoriasis and obesity that represent rare, moderate and common disease respectively. Comorbidities of MS, obesity and psoriasis obtained from our network achieved precisions of 58.6%, 73.7%, 56.2% and recalls 87.5%, 69.2% and 72.7% separately. We performed comparative analysis of the disease comorbidity network with disease semantic network, disease genetic network and disease treatment network. We showed that (1) disease comorbidity clusters exhibit significantly higher semantic similarity than random network (0.18 vs 0.10); (2) disease comorbidity clusters share significantly more genes (0.46 vs 0.06); and (3) disease comorbidity clusters share significantly more drugs (0.64 vs 0.17). Finally, we demonstrated that the disease comorbidity network has potential in uncovering novel disease relationships using asthma as a case study.

Conclusions

Our study presented the first comprehensive attempt to build a disease comorbidity network from FDA Adverse Event Reporting System. This network shows well correlated with disease semantic similarity, disease genetics and disease treatment, which has great potential in disease genetics prediction and drug discovery.

Use of Laplacian Heat Diffusion Algorithm to Infer Novel Genes With Functions Related to Uveitis

Uveitis is the inflammation of the uvea and is a serious eye disease that can cause blindness for middle-aged and young people. However, the pathogenesis of this disease has not been fully uncovered and thus renders difficulties in designing effective treatments. Completely identifying the genes related to this disease can help improve and accelerate the comprehension of uveitis. In this study, a new computational method was developed to infer potential related genes based on validated ones. We employed a large protein–protein interaction network reported in STRING, in which Laplacian heat diffusion algorithm was applied using validated genes as seed nodes. Except for the validated ones, all genes in the network were filtered by three tests, namely, permutation, association, and function tests, which evaluated the genes based on their specialties and associations to uveitis. Results indicated that 59 inferred genes were accessed, several of which were confirmed to be highly related to uveitis by literature review. In addition, the inferred genes were compared with those reported in a previous study, indicating that our reported genes are necessary supplements.

Towards personalized medicine in Ménière's disease

Ménière's disease (MD) represents a heterogeneous group of relatively rare disorders with three core symptoms: episodic vertigo, tinnitus, and sensorineural hearing loss involving 125 to 2,000 Hz frequencies. The majority of cases are considered sporadic, although familial aggregation has been recognized in European and Korean populations, and the search for familial MD genes has been elusive until the last few years. Detailed phenotyping and cluster analyses have found several clinical predictors for different subgroups of patients, which may indicate different mechanisms, including genetic and immune factors. The genes associated with familial MD are COCH, FAM136A, DTNA, PRKCB, SEMA3D, and DPT. At least two mechanisms have been involved in MD: (a) a pro-inflammatory immune response mediated by interleukin-1 beta (IL-1β), tumor necrosis factor alpha (TNFα), and IL-6, and (b) a nuclear factor-kappa B (NF-κB)-mediated inflammation in the carriers of the single-nucleotide variant rs4947296. It is conceivable that microbial antigens trigger inflammation with release of pro-inflammatory cytokines at different sites within the cochlea, such as the endolymphatic sac, the stria vascularis, or the spiral ligament, leading to fluid imbalance with an accumulation of endolymph. Computational integration of clinical and "omics" data eventually should transform the management of MD from "one pill fits all" to precise patient stratification and a personalized approach. This article lays out a proposal for an algorithm for the genetic diagnosis of MD. This approach will facilitate the identification of new molecular targets for individualized treatment, including immunosuppressant and gene therapy, in the near future.

Genes important for otoneurological diagnostic purposes - current status and future prospects

SUMMARY

This review focuses on the current knowledge of the genes responsible for non-syndromic hearing loss that can be useful for otoneurological diagnostic purposes. From among a large number of genes that have been associated with non-syndromic hearing impairment, we selected several best-known genes, including the COCH gene, GJB2, GJB6 and SLC26A4, and we describe their role and effects of mutations and prevalence of mutations in various populations. Next, we focus on genes associated with tinnitus. Important areas for further research include assessment of genes potentially involved in pathophysiology of tinnitus and vertigo, which have traditionally been considered as being of otological aetiology, while advances in neuroimaging techniques have increasingly shifted studies toward neurological correlations.

Cross talk of chromosome instability, CpG island methylator phenotype and mismatch repair in colorectal cancer

Colorectal cancer is a severe cancer associated with a high prevalence and fatality rate. There are three major mechanisms for colorectal cancer: (1) Chromosome instability (CIN), (2) CpG island methylator phenotype (CIMP) and (3) mismatch repair (MMR), of which CIN is the most common type. However, these subtypes are not exclusive and overlap. To investigate their biological mechanisms and cross talk, the gene expression profiles of 585 colorectal cancer patients with CIN, CIMP and MMR status records were collected. By comparing the CIN+ and CIN-samples, CIMP+ and CIMP-samples, MMR+ and MMR-samples with minimal redundancy maximal relevance (mRMR) and incremental feature selection (IFS) methods, the CIN, CIMP and MMR associated genes were selected. Unfortunately, there was little direct overlap among them. To investigate their indirect interactions, downstream genes of CIN, CIMP and MMR were identified using the random walk with restart (RWR) method and a greater overlap of downstream genes was indicated. The common downstream genes were involved in biosynthetic and metabolic pathways. These findings were consistent with the clinical observation of wide range metabolite aberrations in colorectal cancer. To conclude, the present study gave a gene level explanation of CIN, CIMP and MMR, but also showed the network level cross talk of CIN, CIMP and MMR. The common genes of CIN, CIMP and MMR may be useful for cross-subtype general colorectal cancer drug development.