Prioritization of candidate disease genes by enlarging the seed set and fusing information of the network topology and gene expression

A Novel Strategy for Decoding and Validating the Combination Principles of Huanglian Jiedu Decoction From Multi-Scale Perspective

Traditional Chinese medicine (TCM) formulas treat complex diseases through combined botanical drugs which follow specific compatibility rules to reduce toxicity and increase efficiency. "Jun, Chen, Zuo and Shi" is one of most used compatibility rules in the combination of botanical drugs. However, due to the deficiency of traditional research methods, the quantified theoretical basis of herbal compatibility including principles of "Jun, Chen, Zuo and Shi" are still unclear. Network pharmacology is a new strategy based on system biology and multi-disciplines, which can systematically and comprehensively observe the intervention of drugs on disease networks, and is especially suitable for the research of TCM in the treatment of complex diseases. In this study, we systematically decoded the "Jun, Chen, Zuo and Shi" rules of Huanglian Jiedu Decoction (HJD) in the treatment of diseases for the first time. This interpretation method considered three levels of data. The data in the first level mainly depicts the characteristics of each component in single botanical drug of HJD, include the physical and chemical properties of component, ADME properties and functional enrichment analysis of component targets. The second level data is the characterization of component-target-protein (C-T-P) network in the whole protein-protein interaction (PPI) network, mainly include the characterization of degree and key communities in C-T-P network. The third level data is the characterization of intervention propagation properties of HJD in the treatment of different complex diseases, mainly include target coverage of pathogenic genes and propagation coefficient of intervention effect between target proteins and pathogenic genes. Finally, our method was validated by metabolic data, which could be used to detect the components absorbed into blood. This research shows the scientific basis of "Jun-Chen-Zuo-Shi" from a multi-dimensional perspective, and provides a good methodological reference for the subsequent interpretation of key components and speculation mechanism of the formula.

Advances in the Prediction of Protein Subcellular Locations with Machine Learning

Background:

Revealing the subcellular location of a newly discovered protein can bring insight into their function and guide research at the cellular level. The experimental methods currently used to identify the protein subcellular locations are both time-consuming and expensive. Thus, it is highly desired to develop computational methods for efficiently and effectively identifying the protein subcellular locations. Especially, the rapidly increasing number of protein sequences entering the genome databases has called for the development of automated analysis methods.

Methods:

In this review, we will describe the recent advances in predicting the protein subcellular locations with machine learning from the following aspects: i) Protein subcellular location benchmark dataset construction, ii) Protein feature representation and feature descriptors, iii) Common machine learning algorithms, iv) Cross-validation test methods and assessment metrics, v) Web servers.

Result & Conclusion:

Concomitant with a large number of protein sequences generated by highthroughput technologies, four future directions for predicting protein subcellular locations with machine learning should be paid attention. One direction is the selection of novel and effective features (e.g., statistics, physical-chemical, evolutional) from the sequences and structures of proteins. Another is the feature fusion strategy. The third is the design of a powerful predictor and the fourth one is the protein multiple location sites prediction.

Loss of function mutation in the P2X7, a ligand-gated ion channel gene associated with hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is an inherited heart failure condition, mostly found to have genetic abnormalities, and is a leading cause of sudden death in young adults. Whole exome sequencing should be given consideration as a molecular diagnostic tool to identify disease-causing mutation/s. In this study, a HCM family with multiple affected members having history of sudden death were subjected to exome sequencing along with unaffected members. Quality passed variants obtained were filtered for rarity (MAF > 0.5%), evolutionary conservation, pathogenic prediction, and segregation in affected members after removing shared variants present in unaffected members. Only one non-synonymous mutation (p. Glu186Lys or E186K) in exon 6 of P2X7 gene segregated in HCM-affected individuals which was absent in unaffected family members and 100 clinically evaluated controls. The site of the mutation is highly conserved and led to complete loss of function which is in close vicinity to ATP-binding site-forming residues, affecting ATP binding, channel gating, or both. Mutations in candidate genes which were not segregated define clinical heterogeneity within affected members. P2X7 gene is highly expressed in the heart and shows direct interaction with major candidate genes for HCM. Our results reveal a significant putative HCM causative gene, P2X7, for the first time and show that germ-line mutations in P2X7 may cause a defective phenotype, suggesting purinergic receptor involvement in heart failure mediated through arrhythmias which need further investigations to be targeted for therapeutic interventions.

Inferring Novel Autophagy Regulators Based on Transcription Factors and Non-Coding RNAs Coordinated Regulatory Network

Autophagy is a complex cellular digestion process involving multiple regulators. Compared to post-translational autophagy regulators, limited information is now available about transcriptional and post-transcriptional regulators such as transcription factors (TFs) and non-coding RNAs (ncRNAs). In this study, we proposed a computational method to infer novel autophagy-associated TFs, micro RNAs (miRNAs) and long non-coding RNAs (lncRNAs) based on TFs and ncRNAs coordinated regulatory (TNCR) network. First, we constructed a comprehensive TNCR network, including 155 TFs, 681 miRNAs and 1332 lncRNAs. Next, we gathered the known autophagy-associated factors, including TFs, miRNAs and lncRNAs, from public data resources. Then, the random walk with restart (RWR) algorithm was conducted on the TNCR network by using the known autophagy-associated factors as seeds and novel autophagy regulators were finally prioritized. Leave-one-out cross-validation (LOOCV) produced an area under the curve (AUC) of 0.889. In addition, functional analysis of the top 100 ranked regulators, including 55 TFs, 26 miRNAs and 19 lncRNAs, demonstrated that these regulators were significantly enriched in cell death related functions and had significant semantic similarity with autophagy-related Gene Ontology (GO) terms. Finally, extensive literature surveys demonstrated the credibility of the predicted autophagy regulators. In total, we presented a computational method to infer credible autophagy regulators of transcriptional factors and non-coding RNAs, which would improve the understanding of processes of autophagy and cell death and provide potential pharmacological targets to autophagy-related diseases.

DIGNiFI: Discovering causative genes for orphan diseases using protein-protein interaction networks

BACKGROUND

An orphan disease is any disease that affects a small percentage of the population. Orphan diseases are a great burden to patients and society, and most of them are genetic in origin. Unfortunately, our current understanding of the genes responsible for inherited orphan diseases is still quite limited. Developing effective computational algorithms to discover disease-causing genes would help unveil disease mechanisms and may enable better diagnosis and treatment.

RESULTS

We have developed a novel method, named as DIGNiFI (Disease causIng GeNe FInder), which uses Protein-Protein Interaction (PPI) network-based features to discover and rank candidate disease-causing genes. Specifically, our approach computes topologically similar genes by taking into account both local and global connected paths in PPI networks via Direct Neighbors and Local Random Walks, respectively. Furthermore, since genes with similar phenotypes tend to be functionally related, we have integrated PPI data with gene ontology (GO) annotations and protein complex data to further improve the performance of this approach. Results of 128 orphan diseases with 1184 known disease genes collected from the Orphanet show that our proposed methods outperform existing state-of-the-art methods for discovering candidate disease-causing genes. We also show that further performance improvement can be achieved when enriching the human-curated PPI network data with text-mined interactions from the biomedical literature. Finally, we demonstrate the utility of our approach by applying our method to identifying novel candidate genes for a set of four inherited retinal dystrophies. In this study, we found the top predictions for these retinal dystrophies consistent with literature reports and online databases of other retinal dystrophies.

CONCLUSIONS

Our method successfully prioritizes orphan-disease-causative genes. This method has great potential to benefit the field of orphan disease research, where resources are scarce and greatly needed.

D-Map: Random Walking on Gene Network Inference Maps Towards differential Avenue Discovery

Differential rewiring of cellular interaction networks between disease and healthy state is of great importance. Through a systems level approach, malfunctioned mechanisms that are absent in the normal cases, may enlighten the key-players in terms of genes and their interaction chains related to disease. We have developed D-Map, a publicly available user-friendly web application, capable of generating and manipulating advanced differential networks by combining state-of-the-art inference reconstruction methods with random walk simulations. The inputs are expression profiles obtained from the Gene Expression Omnibus and a gene list under investigation. Differential networks may be visualized and interpreted through the use of D-Map interface, where display of the disease, the normal and the common state can be performed, interactively. A case study scenario concerning Alzheimer's disease, as well as breast, lung, and bladder cancer was conducted in order to demonstrate the usefulness of the proposed methodology to different disease types. Findings were consistent with the current bibliography, and the provided interaction lists may be further explored towards novel biological insights of the investigated diseases. The DMap web-application is available at: http://bioserver-3.bioacademy.gr/Bioserver/DMap/index.php.

m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks

As the most prevalent mammalian mRNA epigenetic modification, N6-methyladenosine (m⁶A) has been shown to possess important post-transcriptional regulatory functions. However, the regulatory mechanisms and functional circuits of m⁶A are still largely elusive. To help unveil the regulatory circuitry mediated by mRNA m⁶A methylation, we develop here m⁶A-Driver, an algorithm for predicting m⁶A-driven genes and associated networks, whose functional interactions are likely to be actively modulated by m⁶A methylation under a specific condition. Specifically, m⁶A-Driver integrates the PPI network and the predicted differential m⁶A methylation sites from methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data using a Random Walk with Restart (RWR) algorithm and then builds a consensus m⁶A-driven network of m⁶A-driven genes. To evaluate the performance, we applied m⁶A-Driver to build the context-specific m⁶A-driven networks for 4 known m⁶A (de)methylases, i.e., FTO, METTL3, METTL14 and WTAP. Our results suggest that m⁶A-Driver can robustly and efficiently identify m⁶A-driven genes that are functionally more enriched and associated with higher degree of differential expression than differential m⁶A methylated genes. Pathway analysis of the constructed context-specific m⁶A-driven gene networks further revealed the regulatory circuitry underlying the dynamic interplays between the methyltransferases and demethylase at the epitranscriptomic layer of gene regulation.

Powered by methylated RNA immunoprecipitation sequencing (MeRIP-Seq) technology, recent studies have revealed a new mode of post transcriptional regulation mediated by mRNA N6-methyladenosine (m⁶A). Currently, the analysis of m⁶A focuses mostly on prediction of m⁶A sites as well as differential m⁶A methylation, and systematic approach for predicting m⁶A functions is yet to emerge. We develop here m⁶A-Driver, the first network-based approach, to identify m⁶A-driven genes and their associated networks, whose functional interactions are likely to be actively modulated by m⁶A methylation under a specific condition. Our test results showed that m⁶A-Driver can robustly and efficiently identify m⁶A-driven genes that are functionally more enriched and associated with higher degree of differential expression than differential m⁶A methylated genes. m⁶A-Driver is an effective and reliable approach to identify functionally relevant m⁶A-driven genes and networks from MeRIP-Seq data.

A Novel Prioritization Method in Identifying Recurrent Venous Thromboembolism-Related Genes

Identifying the genes involved in venous thromboembolism (VTE) recurrence is important not only for understanding the pathogenesis but also for discovering the therapeutic targets. We proposed a novel prioritization method called Function-Interaction-Pearson (FIP) by creating gene-disease similarity scores to prioritize candidate genes underling VTE. The scores were calculated by integrating and optimizing three types of resources including gene expression, gene ontology and protein-protein interaction. As a result, 124 out of top 200 prioritized candidate genes had been confirmed in literature, among which there were 34 antithrombotic drug targets. Compared with two well-known gene prioritization tools Endeavour and ToppNet, FIP was shown to have better performance. The approach provides a valuable alternative for drug targets discovery and disease therapy.

Prioritisation and network analysis of Crohn's disease susceptibility genes

Recent Genome-Wide Association Studies (GWAS) have revealed numerous Crohn's disease susceptibility genes and a key challenge now is in understanding how risk polymorphisms in associated genes might contribute to development of this disease. For a gene to contribute to disease phenotype, its risk variant will likely adversely communicate with a variety of other gene products to result in dysregulation of common signaling pathways. A vital challenge is to elucidate pathways of potentially greatest influence on pathological behaviour, in a manner recognizing how multiple relevant genes may yield integrative effect. In this work we apply mathematical analysis of networks involving the list of recently described Crohn's susceptibility genes, to prioritise pathways in relation to their potential development of this disease. Prioritisation was performed by applying a text mining and a diffusion based method (GRAIL, GPEC). Prospective biological significance of the resulting prioritised list of proteins is highlighted by changes in their gene expression levels in Crohn's patients intestinal tissue in comparison with healthy donors.

A novel method for gathering and prioritizing disease candidate genes based on construction of a set of disease-related MeSH® terms

BACKGROUND

Understanding the molecular mechanisms involved in disease is critical for the development of more effective and individualized strategies for prevention and treatment. The amount of disease-related literature, including new genetic information on the molecular mechanisms of disease, is rapidly increasing. Extracting beneficial information from literature can be facilitated by computational methods such as the knowledge-discovery approach. Several methods for mining gene-disease relationships using computational methods have been developed, however, there has been a lack of research evaluating specific disease candidate genes.

RESULTS

We present a novel method for gathering and prioritizing specific disease candidate genes. Our approach involved the construction of a set of Medical Subject Headings (MeSH) terms for the effective retrieval of publications related to a disease candidate gene. Information regarding the relationships between genes and publications was obtained from the gene2pubmed database. The set of genes was prioritized using a "weighted literature score" based on the number of publications and weighted by the number of genes occurring in a publication. Using our method for the disease states of pain and Alzheimer's disease, a total of 1101 pain candidate genes and 2810 Alzheimer's disease candidate genes were gathered and prioritized. The precision was 0.30 and the recall was 0.89 in the case study of pain. The precision was 0.04 and the recall was 0.6 in the case study of Alzheimer's disease. The precision-recall curve indicated that the performance of our method was superior to that of other publicly available tools.

CONCLUSIONS

Our method, which involved the use of a set of MeSH terms related to disease candidate genes and a novel weighted literature score, improved the accuracy of gathering and prioritizing candidate genes by focusing on a specific disease.