Genotator: a disease-agnostic tool for genetic annotation of disease

A molecular view of amyotrophic lateral sclerosis through the lens of interaction network modules

Background

Despite the discovery of familial cases with mutations in Cu/Zn-superoxide dismutase (SOD1), Guanine nucleotide exchange C9orf72, TAR DNA-binding protein 43 (TARDBP) and RNA-binding protein FUS as well as a number of other genes linked to Amyotrophic Lateral Sclerosis (ALS), the etiology and molecular pathogenesis of this devastating disease is still not understood. As proteins do not act alone, conducting an analysis of ALS at the system level may provide new insights into the molecular biology of ALS and put it into relationship to other neurological diseases.

Methods

A set of ALS-associated genes/proteins were collected from publicly available databases and text mining of scientific literature. We used these as seed proteins to build protein-protein interaction (PPI) networks serving as a scaffold for further analyses. From the collection of networks, a set of core modules enriched in seed proteins were identified. The molecular biology of the core modules was investigated, as were their associations to other diseases. To assess the core modules’ ability to describe unknown or less well-studied ALS biology, they were queried for proteins more recently associated to ALS and not involved in the primary analysis.

Results

We describe a set of 26 ALS core modules enriched in ALS-associated proteins. We show that these ALS core modules not only capture most of the current knowledge about ALS, but they also allow us to suggest biological interdependencies. In addition, new associations of ALS networks with other neurodegenerative diseases, e.g. Alzheimer’s, Huntington’s and Parkinson’s disease were found. A follow-up analysis of 140 ALS-associated proteins identified since 2014 reveals a significant overrepresentation of new ALS proteins in these 26 disease modules.

Conclusions

Using protein-protein interaction networks offers a relevant approach for broadening the understanding of the biological context of known ALS-associated genes. Using a bottom-up approach for the analysis of protein-protein interaction networks is a useful method to avoid bias caused by over-connected proteins. Our ALS-enriched modules cover most known biological functions associated with ALS. The presence of recently identified ALS-associated proteins in the core modules highlights the potential for using these as a scaffold for identification of novel ALS disease mechanisms.

A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions

Drug repositioning is the process of identifying novel therapeutic potentials for existing drugs and discovering therapies for untreated diseases. Drug repositioning, therefore, plays an important role in optimizing the pre-clinical process of developing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repositioning relies on data for existing drugs and diseases the enormous growth of publicly available large-scale biological, biomedical, and electronic health-related data along with the high-performance computing capabilities have accelerated the development of computational drug repositioning approaches. Multidisciplinary researchers and scientists have carried out numerous attempts, with different degrees of efficiency and success, to computationally study the potential of repositioning drugs to identify alternative drug indications. This study reviews recent advancements in the field of computational drug repositioning. First, we highlight different drug repositioning strategies and provide an overview of frequently used resources. Second, we summarize computational approaches that are extensively used in drug repositioning studies. Third, we present different computing and experimental models to validate computational methods. Fourth, we address prospective opportunities, including a few target areas. Finally, we discuss challenges and limitations encountered in computational drug repositioning and conclude with an outline of further research directions.

A centrality based multi-objective approach to disease gene association

Disease Gene Association finds genes that are involved in the presentation of a given genetic disease. We present a hybrid approach which implements a multi-objective genetic algorithm, where input consists of centrality measures based on various relational biological evidence types merged into a complex network. Multiple objective settings and parameters are studied including the development of a new exchange methodology, safe dealer-based crossover. Successful results with respect to breast cancer and Parkinson's disease compared to previous techniques and popular known databases are shown. In addition, the newly developed methodology is also successfully applied to Alzheimer's disease, further demonstrating its flexibility. Across all three case studies the strongest results were produced by the shortest path-based measures stress and betweenness, either in a single objective parameter setting or when used in conjunction in a multi-objective environment. The new crossover technique achieved the best results when applied to Alzheimer's disease.

Rare ABCA7 variants in 2 German families with Alzheimer disease

Objective

The aim of this study was to identify variants associated with familial late-onset Alzheimer disease (AD) using whole-genome sequencing.

Methods

Several families with an autosomal dominant inheritance pattern of AD were analyzed by whole-genome sequencing. Variants were prioritized for rare, likely pathogenic variants in genes already known to be associated with AD and confirmed by Sanger sequencing using standard protocols.

Results

We identified 2 rare ABCA7 variants (rs143718918 and rs538591288) with varying penetrance in 2 independent German AD families, respectively. The single nucleotide variant (SNV) rs143718918 causes a missense mutation, and the deletion rs538591288 causes a frameshift mutation of ABCA7. Both variants have previously been reported in larger cohorts but with incomplete segregation information. ABCA7 is one of more than 20 AD risk loci that have so far been identified by genome-wide association studies, and both common and rare variants of ABCA7 have previously been described in different populations with higher frequencies in AD cases than in controls and varying penetrance. Furthermore, ABCA7 is known to be involved in several AD-relevant pathways.

Conclusions

We conclude that both SNVs might contribute to the development of AD in the examined family members. Together with previous findings, our data confirm ABCA7 as one of the most relevant AD risk genes.

Protein-protein interaction analysis to identify biomarker networks for endometriosis

The identification of biomarkers and their interaction network involved in the processes of endometriosis is a critical step in understanding the underlying mechanisms of the disease. The aim of the present study was to construct biomarker networks of endometriosis that integrated human protein-protein interactions and known disease-causing genes. Endometriosis-associated genes were extracted from Genotator and DisGeNet and biomarker network and pathway analyses were constructed using atBioNet. Of 100 input genes, 96 were strongly mapped to six major modules. The majority of the pathways in the first module were associated with the proliferation of cancer cells, the enriched pathways in module B were associated with the immune system and infectious diseases, module C included pathways related to immune and metastasis, the enriched pathways in module D were associated with inflammatory processes, and the majority of the pathways in module E were related to replication and repair. The present approach identified known and potential biomarkers in endometriosis. The identified biomarker networks are highly enriched in biological pathways associated with endometriosis, which may provide further insight into the molecular mechanisms underlying endometriosis.

Biological networks in Parkinson's disease: an insight into the epigenetic mechanisms associated with this disease

BACKGROUND

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorders in the world. Studying PD from systems biology perspective involving genes and their regulators might provide deeper insights into the complex molecular interactions associated with this disease.

RESULT

We have studied gene co-expression network obtained from a PD-specific microarray data. The co-expression network identified 11 hub genes, of which eight genes are not previously known to be associated with PD. Further study on the functionality of these eight novel hub genes revealed that these genes play important roles in several neurodegenerative diseases. Furthermore, we have studied the tissue-specific expression and histone modification patterns of the novel hub genes. Most of these genes possess several histone modification sites those are already known to be associated with neurodegenerative diseases. Regulatory network namely mTF-miRNA-gene-gTF involves microRNA Transcription Factor (mTF), microRNA (miRNA), gene and gene Transcription Factor (gTF). Whereas long noncoding RNA (lncRNA) mediated regulatory network involves miRNA, gene, mTF and lncRNA. mTF-miRNA-gene-gTF regulatory network identified a novel feed-forward loop. lncRNA-mediated regulatory network identified novel lncRNAs of PD and revealed the two-way regulatory pattern of PD-specific miRNAs where miRNAs can be regulated by both the TFs and lncRNAs. SNP analysis of the most significant genes of the co-expression network identified 20 SNPs. These SNPs are present in the 3' UTR of known PD genes and are controlled by those miRNAs which are also involved in PD.

CONCLUSION

Our study identified eight novel hub genes which can be considered as possible candidates for future biomarker identification studies for PD. The two regulatory networks studied in our work provide a detailed overview of the cellular regulatory mechanisms where the non-coding RNAs namely miRNA and lncRNA, can act as epigenetic regulators of PD. SNPs identified in our study can be helpful for identifying PD at an earlier stage. Overall, this study may impart a better comprehension of the complex molecular interactions associated with PD from systems biology perspective.

Arete - candidate gene prioritization using biological network topology with additional evidence types

BACKGROUND

Refinement of candidate gene lists to select the most promising candidates for further experimental verification remains an essential step between high-throughput exploratory analysis and the discovery of specific causal genes. Given the qualitative and semantic complexity of biological data, successfully addressing this challenge requires development of flexible and interoperable solutions for making the best possible use of the largest possible fraction of all available data.

RESULTS

We have developed an easily accessible framework that links two established network-based gene prioritization approaches with a supporting isolation forest-based integrative ranking method. The defining feature of the method is that both topological information of the biological networks and additional sources of evidence can be considered at the same time. The implementation was realized as an app extension for the Cytoscape graph analysis suite, and therefore can further benefit from the synergy with other analysis methods available as part of this system.

CONCLUSIONS

We provide efficient reference implementations of two popular gene prioritization algorithms - DIAMOnD and random walk with restart for the Cytoscape system. An extension of those methods was also developed that allows outputs of these algorithms to be combined with additional data. To demonstrate the utility of our software, we present two example disease gene prioritization application cases and show how our tool can be used to evaluate these different approaches.

Searching for convergent pathways in autism spectrum disorders: insights from human brain transcriptome studies

Autism spectrum disorder (ASD) is one of the most heritable neuropsychiatric conditions. The complex genetic landscape of the disorder includes both common and rare variants at hundreds of genetic loci. This marked heterogeneity has thus far hampered efforts to develop genetic diagnostic panels and targeted pharmacological therapies. Here, we give an overview of the current literature on the genetic basis of ASD, and review recent human brain transcriptome studies and their role in identifying convergent pathways downstream of the heterogeneous genetic variants. We also discuss emerging evidence on the involvement of non-coding genomic regions and non-coding RNAs in ASD.

Autism genes are selectively targeted by environmental pollutants including pesticides, heavy metals, bisphenol A, phthalates and many others in food, cosmetics or household products

The increasing incidence of autism suggests a major environmental influence. Epidemiology has implicated many candidates and genetics many susceptibility genes. Gene/environment interactions in autism were analysed using 206 autism susceptibility genes (ASG's) from the Autworks database to interrogate ∼1 million chemical/gene interactions in the comparative toxicogenomics database. Any bias towards ASG's was statistically determined for each chemical. Many suspect compounds identified in epidemiology, including tetrachlorodibenzodioxin, pesticides, particulate matter, benzo(a)pyrene, heavy metals, valproate, acetaminophen, SSRI's, cocaine, bisphenol A, phthalates, polyhalogenated biphenyls, flame retardants, diesel constituents, terbutaline and oxytocin, inter alia showed a significant degree of bias towards ASG's, as did relevant endogenous agents (retinoids, sex steroids, thyroxine, melatonin, folate, dopamine, serotonin). Numerous other suspected endocrine disruptors (over 100) selectively targeted ASG's including paraquat, atrazine and other pesticides not yet studied in autism and many compounds used in food, cosmetics or household products, including tretinoin, soy phytoestrogens, aspartame, titanium dioxide and sodium fluoride. Autism polymorphisms influence the sensitivity to some of these chemicals and these same genes play an important role in barrier function and control of respiratory cilia sweeping particulate matter from the airways. Pesticides, heavy metals and pollutants also disrupt barrier and/or ciliary function, which is regulated by sex steroids and by bitter/sweet taste receptors. Further epidemiological studies and neurodevelopmental and behavioural research is warranted to determine the relevance of large number of suspect candidates whose addition to the environment, household, food and cosmetics might be fuelling the autism epidemic in a gene-dependent manner.

Gene Prioritization by Integrated Analysis of Protein Structural and Network Topological Properties for the Protein-Protein Interaction Network of Neurological Disorders

Neurological disorders are known to show similar phenotypic manifestations like anxiety, depression, and cognitive impairment. There is a need to identify shared genetic markers and molecular pathways in these diseases, which lead to such comorbid conditions. Our study aims to prioritize novel genetic markers that might increase the susceptibility of patients affected with one neurological disorder to other diseases with similar manifestations. Identification of pathways involving common candidate markers will help in the development of improved diagnosis and treatments strategies for patients affected with neurological disorders. This systems biology study for the first time integratively uses 3D-structural protein interface descriptors and network topological properties that characterize proteins in a neurological protein interaction network, to aid the identification of genes that are previously not known to be shared between these diseases. Results of protein prioritization by machine learning have identified known as well as new genetic markers which might have direct or indirect involvement in several neurological disorders. Important gene hubs have also been identified that provide an evidence for shared molecular pathways in the neurological disease network.

Sex Biased Gene Expression Profiling of Human Brains at Major Developmental Stages

There are many differences in brain structure and function between males and females. However, how these differences were manifested during development and maintained through adulthood are still unclear. Here we present a time series analyses of genome-wide transcription profiles of the human brain, and we identified genes showing sex biased expression at major developmental stages (prenatal time, early childhood, puberty time and adulthood). We observed a great number of genes (>2,000 genes) showing between-sex expression divergence at all developmental stages with the greatest number (4,164 genes) at puberty time. However, there are little overlap of sex-biased genes among the major developmental stages, an indication of dynamic expression regulation of the sex-biased genes in the brain during development. Notably, the male biased genes are highly enriched for genes involved in neurological and psychiatric disorders like schizophrenia, bipolar disorder, Alzheimer’s disease and autism, while no such pattern was seen for the female-biased genes, suggesting that the differences in brain disorder susceptibility between males and females are likely rooted from the sex-biased gene expression regulation during brain development. Collectively, these analyses reveal an important role of sex biased genes in brain development and neurodevelopmental disorders.

Specific Regional and Age-Related Small Noncoding RNA Expression Patterns Within Superior Temporal Gyrus of Typical Human Brains Are Less Distinct in Autism Brains

Small noncoding RNAs play a critical role in regulating messenger RNA throughout brain development and when altered could have profound effects leading to disorders such as autism spectrum disorders (ASD). We assessed small noncoding RNAs, including microRNA and small nucleolar RNA, in superior temporal sulcus association cortex and primary auditory cortex in typical and ASD brains from early childhood to adulthood. Typical small noncoding RNA expression profiles were less distinct in ASD, both between regions and changes with age. Typical micro-RNA coexpression associations were absent in ASD brains. miR-132, miR-103, and miR-320 micro-RNAs were dysregulated in ASD and have previously been associated with autism spectrum disorders. These diminished region- and age-related micro-RNA expression profiles are in line with previously reported findings of attenuated messenger RNA and long noncoding RNA in ASD brain. This study demonstrates alterations in superior temporal sulcus in ASD, a region implicated in social impairment, and is the first to demonstrate molecular alterations in the primary auditory cortex.

Genetic investigation of sudden unexpected death in epilepsy cohort by panel target resequencing

Sudden unexpected death in epilepsy (SUDEP) is defined as the abrupt, no traumatic, witnessed or unwitnessed death, occurring in benign circumstances, in an individual with epilepsy, with or without evidence for a seizure and excluding documented status epilepticus (seizure duration ≥ 30 min or seizures without recovery), and in which postmortem examination does not reveal a cause of death. Although the physiopathological mechanisms that underlie SUDEP remain to be clarified, the genetic background has been described to play a role in this disorder. Pathogenic variants in genes associated with epilepsy and encoding cardiac ion channels could explain the SUDEP phenotype. To test this we use the next-generation sequencing technology to sequence a cohort of SUDEP cases and its translation into clinical and forensic fields. A panel target resequencing was used to study 14 SUDEP cases from both postmortem (2 cases) and from living patients (12 cases). Genes already associated with SUDEP and also candidate genes had been investigated. Overall, 24 rare genetic variants were identified in 13 SUDEP cases. Four cases showed rare variants with complete segregation in the SCN1A, FBN1, HCN1, SCN4A, and EFHC1 genes, and one case with a rare variant in KCNQ1 gene showed incomplete pattern of inheritance. In four cases, rare variants were detected in CACNA1A, SCN11A and SCN10A, and KCNQ1 genes, but familial segregation was not possible due to lack of DNA from relatives. Finally, in the four remaining cases, the rare variants did not segregate in the family. This study confirms the link between epilepsy, sudden death, and cardiac disease. In addition, we identified new potential candidate genes for SUDEP: FBN1, HCN1, SCN4A, EFHC1, CACNA1A, SCN11A, and SCN10A. Further confirmation in larger cohorts will be necessary especially if genetic screening for SUDEP is applied to forensic and clinical medicine. Nevertheless, this study supports the emerging concept of a genetically determined cardiocerebral channelopathy.

An integrated network of microRNA and gene expression in ovarian cancer

Background

Ovarian cancer is a deadly female reproductive cancer. Understanding the biological mechanisms underlying ovarian cancer could help lead to quicker and more accurate diagnosis and more effective treatments. Both changes in microRNA(miRNA) expression and miRNA/mRNA dysregulation have been associated with ovarian cancer. With the availability of whole-genome miRNA and mRNA sequencing we now have new potentials to study these associations. In this study, we performed a comprehensive analysis of miRNA and mRNA expression in ovarian cancer using an integrative network approach combined with association analysis.

Results

We developed an integrative approach to construct a network that illustrates the complex interplay among miRNA and gene expression from a systems perspective. Our method is composed of expanding networks from eQTL associations, building network associations in eQTL analysis, and then combine the networks into an integrated network. This integrated network takes account of miRNA expression quantitative trait loci (eQTL) associations, miRNAs and their targets, protein-protein interactions, co-expressions among miRNAs and genes respectively. Applied to the ovarian cancer data set from The Cancer Genome Atlas (TCGA), we created an integrated network with 167 nodes containing 108 miRNA-target interactions and 145 from protein-protein interactions, starting from 44 initial eQTLs. This integrated network encompassed 26 genes and 14 miRNAs associated with cancer. In particular, 11 genes and 12 miRNAs in the integrated network are associated with ovarian cancer.

Conclusion

We demonstrated an integrated network approach that integrates multiple data sources at a systems level. We applied this approach to the TCGA ovarian cancer dataset, and constructed a network that provided a more inclusive view of miRNA and gene expression in ovarian cancer. This network included four separate types of interactions among miRNAs and genes. Simply analyzing each interaction component in isolation, such as the eQTL associations, the miRNA-target interactions or the protein-protein interactions, would create a much more limited network than the integrated one.

A bidirectional drug repositioning approach for Parkinson's disease through network-based inference

Parkinson's Disease (PD) is one of the most prevailing neurodegenerative disorders. Novel computational approaches are required to find new ways of using the existing drugs or drug repositioning, as currently there exists no cure for PD. We proposed a new bidirectional drug repositioning method that consists of Top-down and Bottom-up approaches and finally gives information about significant repositioning drug candidates. This method takes into account of the topological significance of drugs in the tripartite Indication-drug-target network (IDTN) as well the significance of their targets in the PD-specific protein-protein interaction network (PPIN). 9 non-Parkinsonian drugs have been proposed as the significant repositioning candidates for PD. In order to find out the efficiency of the repositioning candidates we introduced a parameter called the On-target ratio (OTR). The average OTR value of final repositioning candidates has been found to be higher than that of known PD specific drugs.

Construction and analysis of the protein-protein interaction networks based on gene expression profiles of Parkinson's disease

Background

Parkinson's Disease (PD) is one of the most prevailing neurodegenerative diseases. Improving diagnoses and treatments of this disease is essential, as currently there exists no cure for this disease. Microarray and proteomics data have revealed abnormal expression of several genes and proteins responsible for PD. Nevertheless, few studies have been reported involving PD-specific protein-protein interactions.

Results

Microarray based gene expression data and protein-protein interaction (PPI) databases were combined to construct the PPI networks of differentially expressed (DE) genes in post mortem brain tissue samples of patients with Parkinson's disease. Samples were collected from the substantia nigra and the frontal cerebral cortex. From the microarray data, two sets of DE genes were selected by 2-tailed t-tests and Significance Analysis of Microarrays (SAM), run separately to construct two Query-Query PPI (QQPPI) networks. Several topological properties of these networks were studied. Nodes with High Connectivity (hubs) and High Betweenness Low Connectivity (bottlenecks) were identified to be the most significant nodes of the networks. Three and four-cliques were identified in the QQPPI networks. These cliques contain most of the topologically significant nodes of the networks which form core functional modules consisting of tightly knitted sub-networks. Hitherto unreported 37 PD disease markers were identified based on their topological significance in the networks. Of these 37 markers, eight were significantly involved in the core functional modules and showed significant change in co-expression levels. Four (ARRB2, STX1A, TFRC and MARCKS) out of the 37 markers were found to be associated with several neurotransmitters including dopamine.

Conclusion

This study represents a novel investigation of the PPI networks for PD, a complex disease. 37 proteins identified in our study can be considered as PD network biomarkers. These network biomarkers may provide as potential therapeutic targets for PD applications development.

Identification of differentially expressed microRNAs across the developing human brain

We present a spatio-temporal assessment of microRNA (miRNA) expression throughout early human brain development. We assessed the prefrontal cortex, hippocampus and cerebellum of 18 normal human donor brains spanning infancy through adolescence by RNA-seq. We discovered differentially expressed miRNAs and broad miRNA patterns across both temporal and spatial dimensions, and between male and female prefrontal cortex. Putative target genes of the differentially expressed miRNAs were identified, which demonstrated functional enrichment for transcription regulation, synaptogenesis and other basic intracellular processes. Sex-biased miRNAs also targeted genes related to Wnt and transforming growth factor-beta pathways. The differentially expressed miRNA targets were highly enriched for gene sets related to autism, schizophrenia, bipolar disorder and depression, but not neurodegenerative diseases, epilepsy or other adult-onset psychiatric diseases. Our results suggest critical roles for the identified miRNAs in transcriptional networks of the developing human brain and neurodevelopmental disorders.

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.

A literature search tool for intelligent extraction of disease-associated genes

OBJECTIVE

To extract disorder-associated genes from the scientific literature in PubMed with greater sensitivity for literature-based support than existing methods.

METHODS

We developed a PubMed query to retrieve disorder-related, original research articles. Then we applied a rule-based text-mining algorithm with keyword matching to extract target disorders, genes with significant results, and the type of study described by the article.

RESULTS

We compared our resulting candidate disorder genes and supporting references with existing databases. We demonstrated that our candidate gene set covers nearly all genes in manually curated databases, and that the references supporting the disorder-gene link are more extensive and accurate than other general purpose gene-to-disorder association databases.

CONCLUSIONS

We implemented a novel publication search tool to find target articles, specifically focused on links between disorders and genotypes. Through comparison against gold-standard manually updated gene-disorder databases and comparison with automated databases of similar functionality we show that our tool can search through the entirety of PubMed to extract the main gene findings for human diseases rapidly and accurately.

Prioritization of candidate genes for periodontitis using multiple computational tools

BACKGROUND

Both genetic and environmental factors contribute to the development of periodontitis. Genetic studies identified a variety of candidate genes for periodontitis. The aim of the present study is to identify the most promising candidate genes for periodontitis using an integrative gene ranking method.

METHODS

Seed genes that were confirmed to be associated with periodontitis were identified using text mining. Three types of candidate genes were then extracted from different resources (expression profiles, genome-wide association studies). Combining the seed genes, four freely available bioinformatics tools (ToppGene, DIR, Endeavour, and GPEC) were integrated for prioritization of candidate genes. Candidate genes that identified with at least three programs and ranked in the top 20 by each program were considered the most promising.

RESULTS

Prioritization analysis resulted in 21 promising genes involved or potentially involved in periodontitis. Among them, IL18 (interleukin 18), CD44 (CD44 molecule), CXCL1 (chemokine [CXC motif] ligand 1), IL6ST (interleukin 6 signal transducer), MMP3 (matrix metallopeptidase 3), MMP7, CCR1 (chemokine [C-C motif] receptor 1), MMP13, and TLR9 (Toll-like receptor 9) had been associated with periodontitis. However, the roles of other genes, such as CSF3 (colony stimulating factor 3 receptor), CD40, TNFSF14 (tumor necrosis factor receptor superfamily, member 14), IFNB1 (interferon-β1), TIRAP (toll-interleukin 1 receptor domain containing adaptor protein), IL2RA (interleukin 2 receptor α), ETS1 (v-ets avian erythroblastosis virus E26 oncogene homolog 1), GADD45B (growth arrest and DNA-damage-inducible 45 β), BIRC3 (baculoviral IAP repeat containing 3), VAV1 (vav 1 guanine nucleotide exchange factor), COL5A1 (collagen, type V, α1), and C3 (complement component 3), have not been investigated thoroughly in the process of periodontitis. These genes are mainly involved in bacterial infection, immune response, and inflammatory reaction, suggesting that further characterizing their roles in periodontitis will be important.

CONCLUSIONS

A combination of computational tools will be useful in mining candidate genes for periodontitis. These theoretical results provide new clues for experimental biologists to plan targeted experiments.

Identification of key regulators for the migration and invasion of rheumatoid synoviocytes through a systems approach

Rheumatoid synoviocytes, which consist of fibroblast-like synoviocytes (FLSs) and synovial macrophages (SMs), are crucial for the progression of rheumatoid arthritis (RA). Particularly, FLSs of RA patients (RA-FLSs) exhibit invasive characteristics reminiscent of cancer cells, destroying cartilage and bone. RA-FLSs and SMs originate differently from mesenchymal and myeloid cells, respectively, but share many pathologic functions. However, the molecular signatures and biological networks representing the distinct and shared features of the two cell types are unknown. We performed global transcriptome profiling of FLSs and SMs obtained from RA and osteoarthritis patients. By comparing the transcriptomes, we identified distinct molecular signatures and cellular processes defining invasiveness of RA-FLSs and proinflammatory properties of RA-SMs, respectively. Interestingly, under the interleukin-1β (IL-1β)-stimulated condition, the RA-FLSs newly acquired proinflammatory signature dominant in RA-SMs without losing invasive properties. We next reconstructed a network model that delineates the shared, RA-FLS-dominant (invasive), and RA-SM-dominant (inflammatory) processes. From the network model, we selected 13 genes, including periostin, osteoblast-specific factor (POSTN) and twist basic helix-loop-helix transcription factor 1 (TWIST1), as key regulator candidates responsible for FLS invasiveness. Of note, POSTN and TWIST1 expressions were elevated in independent RA-FLSs and further instigated by IL-1β. Functional assays demonstrated the requirement of POSTN and TWIST1 for migration and invasion of RA-FLSs stimulated with IL-1β. Together, our systems approach to rheumatoid synovitis provides a basis for identifying key regulators responsible for pathological features of RA-FLSs and -SMs, demonstrating how a certain type of cells acquires functional redundancy under chronic inflammatory conditions.

Contribution of genome-wide association studies to scientific research: a pragmatic approach to evaluate their impact

The factual value of genome-wide association studies (GWAS) for the understanding of multifactorial diseases is a matter of intense debate. Practical consequences for the development of more effective therapies do not seem to be around the corner. Here we propose a pragmatic and objective evaluation of how much new biology is arising from these studies, with particular attention to the information that can help prioritize therapeutic targets. We chose multiple sclerosis (MS) as a paradigm disease and assumed that, in pre-GWAS candidate-gene studies, the knowledge behind the choice of each gene reflected the understanding of the disease prior to the advent of GWAS. Importantly, this knowledge was based mainly on non-genetic, phenotypic grounds. We performed single-gene and pathway-oriented comparisons of old and new knowledge in MS by confronting an unbiased list of candidate genes in pre-GWAS association studies with those genes exceeding the genome-wide significance threshold in GWAS published from 2007 on. At the single gene level, the majority (94 out of 125) of GWAS-discovered variants had never been contemplated as plausible candidates in pre-GWAS association studies. The 31 genes that were present in both pre- and post-GWAS lists may be of particular interest in that they represent disease-associated variants whose pathogenetic relevance is supported at the phenotypic level (i.e. the phenotypic information that steered their selection as candidate genes in pre-GWAS association studies). As such they represent attractive therapeutic targets. Interestingly, our analysis shows that some of these variants are targets of pharmacologically active compounds, including drugs that are already registered for human use. Compared with the above single-gene analysis, at the pathway level GWAS results appear more coherent with previous knowledge, reinforcing some of the current views on MS pathogenesis and related therapeutic research. This study presents a pragmatic approach that helps interpret and exploit GWAS knowledge.

Systems biology as a comparative approach to understand complex gene expression in neurological diseases

Systems biology interdisciplinary approaches have become an essential analytical tool that may yield novel and powerful insights about the nature of human health and disease. Complex disorders are known to be caused by the combination of genetic, environmental, immunological or neurological factors. Thus, to understand such disorders, it becomes necessary to address the study of this complexity from a novel perspective. Here, we present a review of integrative approaches that help to understand the underlying biological processes involved in the etiopathogenesis of neurological diseases, for example, those related to autism and autism spectrum disorders (ASD) endophenotypes. Furthermore, we highlight the role of systems biology in the discovery of new biomarkers or therapeutic targets in complex disorders, a key step in the development of personalized medicine, and we demonstrate the role of systems approaches in the design of classifiers that can shorten the time for behavioral diagnosis of autism.

Genetic architecture of Wistar-Kyoto rat and spontaneously hypertensive rat substrains from different sources

The spontaneously hypertensive rat (SHR) has been widely used as a model for studies of hypertension and attention deficit/hyperactivity disorder. The inbred Wistar-Kyoto (WKY) rat, derived from the same ancestral outbred Wistar rat as the SHR, are normotensive and have been used as the closest genetic control for the SHR, although the WKY has also been used as a model for depression. Notably, however, substantial behavioral and genetic differences among the WKY substrains, usually from the different vendors and breeders, have been observed. These differences have often been overlooked in prior studies, leading to inconsistent and even contradictory findings. The complicated breeding history of the SHR and WKY rats and the lack of a comprehensive understanding of the genetic background of different commercial substrains make the selection of control rats a daunting task, even for researchers who are mindful of their genetic heterogeneity. In this study, we examined the genetic relationship of 16 commonly used WKY and SHR rat substrains using genome-wide SNP genotyping data. Our results confirmed a large genetic divergence and complex relationships among the SHR and WKY substrains. This understanding, although incomplete without the genome sequence, provides useful guidance in selecting substrains and helps to interpret previous reports when the source of the animals was known. Moreover, we found two closely related, yet distinct WKY substrains that may provide novel opportunities in modeling psychiatric disorders.

Web scraping technologies in an API world

Web services are the de facto standard in biomedical data integration. However, there are data integration scenarios that cannot be fully covered by Web services. A number of Web databases and tools do not support Web services, and existing Web services do not cover for all possible user data demands. As a consequence, Web data scraping, one of the oldest techniques for extracting Web contents, is still in position to offer a valid and valuable service to a wide range of bioinformatics applications, ranging from simple extraction robots to online meta-servers. This article reviews existing scraping frameworks and tools, identifying their strengths and limitations in terms of extraction capabilities. The main focus is set on showing how straightforward it is today to set up a data scraping pipeline, with minimal programming effort, and answer a number of practical needs. For exemplification purposes, we introduce a biomedical data extraction scenario where the desired data sources, well-known in clinical microbiology and similar domains, do not offer programmatic interfaces yet. Moreover, we describe the operation of WhichGenes and PathJam, two bioinformatics meta-servers that use scraping as means to cope with gene set enrichment analysis.

Neurocarta: aggregating and sharing disease-gene relations for the neurosciences

BACKGROUND

Understanding the genetic basis of diseases is key to the development of better diagnoses and treatments. Unfortunately, only a small fraction of the existing data linking genes to phenotypes is available through online public resources and, when available, it is scattered across multiple access tools.

DESCRIPTION

Neurocarta is a knowledgebase that consolidates information on genes and phenotypes across multiple resources and allows tracking and exploring of the associations. The system enables automatic and manual curation of evidence supporting each association, as well as user-enabled entry of their own annotations. Phenotypes are recorded using controlled vocabularies such as the Disease Ontology to facilitate computational inference and linking to external data sources. The gene-to-phenotype associations are filtered by stringent criteria to focus on the annotations most likely to be relevant. Neurocarta is constantly growing and currently holds more than 30,000 lines of evidence linking over 7,000 genes to 2,000 different phenotypes.

CONCLUSIONS

Neurocarta is a one-stop shop for researchers looking for candidate genes for any disorder of interest. In Neurocarta, they can review the evidence linking genes to phenotypes and filter out the evidence they're not interested in. In addition, researchers can enter their own annotations from their experiments and analyze them in the context of existing public annotations. Neurocarta's in-depth annotation of neurodevelopmental disorders makes it a unique resource for neuroscientists working on brain development.

A two-step drug repositioning method based on a protein-protein interaction network of genes shared by two diseases and the similarity of drugs

The present study proposed a two-step drug repositioning method based on a protein-protein interaction (PPI) network of two diseases and the similarity of the drugs prescribed for one of the two. In the proposed method, first, lists of disease related genes were obtained from a meta-database called Genotator. Then genes shared by a pair of diseases were sought. At the first step of the method, if a drug having its target(s) in the PPI network, the drug was deemed a repositioning candidate. Because targets of many drugs are still unknown, the similarities between the prescribed drugs for a specific disease were used to infer repositioning candidates at the second step. As a first attempt, we applied the proposed method to four different types of diseases: hypertension, diabetes mellitus, Crohn disease, and autism. Some repositioning candidates were found both at the first and second steps.

'Omics' approaches to understanding interstitial cystitis/painful bladder syndrome/bladder pain syndrome

Recent efforts in the generation of large genomics, transcriptomics, proteomics, metabolomics and other types of 'omics' data sets have provided an unprecedentedly detailed view of certain diseases, however to date most of this literature has been focused on malignancy and other lethal pathological conditions. Very little intensive work on global profiles has been performed to understand the molecular mechanism of interstitial cystitis/painful bladder syndrome/bladder pain syndrome (IC/PBS/BPS), a chronic lower urinary tract disorder characterized by pelvic pain, urinary urgency and frequency, which can lead to long lasting adverse effects on quality of life. A lack of understanding of molecular mechanism has been a challenge and dilemma for diagnosis and treatment, and has also led to a delay in basic and translational research focused on biomarker and drug discovery, clinical therapy, and preventive strategies against IC/PBS/BPS. This review describes the current state of 'omics' studies and available data sets relevant to IC/PBS/BPS, and presents opportunities for new research directed at understanding the pathogenesis of this complex condition.

Cross-pollination of research findings, although uncommon, may accelerate discovery of human disease genes

Background

Technological leaps in genome sequencing have resulted in a surge in discovery of human disease genes. These discoveries have led to increased clarity on the molecular pathology of disease and have also demonstrated considerable overlap in the genetic roots of human diseases. In light of this large genetic overlap, we tested whether cross-disease research approaches lead to faster, more impactful discoveries.

Methods

We leveraged several gene-disease association databases to calculate a Mutual Citation Score (MCS) for 10,853 pairs of genetically related diseases to measure the frequency of cross-citation between research fields. To assess the importance of cooperative research, we computed an Individual Disease Cooperation Score (ICS) and the average publication rate for each disease.

Results

For all disease pairs with one gene in common, we found that the degree of genetic overlap was a poor predictor of cooperation (r²=0.3198) and that the vast majority of disease pairs (89.56%) never cited previous discoveries of the same gene in a different disease, irrespective of the level of genetic similarity between the diseases. A fraction (0.25%) of the pairs demonstrated cross-citation in greater than 5% of their published genetic discoveries and 0.037% cross-referenced discoveries more than 10% of the time. We found strong positive correlations between ICS and publication rate (r²=0.7931), and an even stronger correlation between the publication rate and the number of cross-referenced diseases (r²=0.8585). These results suggested that cross-disease research may have the potential to yield novel discoveries at a faster pace than singular disease research.

Conclusions

Our findings suggest that the frequency of cross-disease study is low despite the high level of genetic similarity among many human diseases, and that collaborative methods may accelerate and increase the impact of new genetic discoveries. Until we have a better understanding of the taxonomy of human diseases, cross-disease research approaches should become the rule rather than the exception.

Genetic interactions associated with 12-month atrophy in hippocampus and entorhinal cortex in Alzheimer's Disease Neuroimaging Initiative

Missing heritability in late onset Alzheimer disease can be attributed, at least in part, to heterogeneity in disease status and to the lack of statistical analyses exploring genetic interactions. In the current study, we use quantitative intermediate phenotypes derived from magnetic resonance imaging data available from the Alzheimer's Disease Neuroimaging Initiative, and we test for association with gene-gene interactions within biological pathways. Regional brain volumes from the hippocampus (HIP) and entorhinal cortex (EC) were estimated from baseline and 12-month magnetic resonance imaging scans. Approximately 560,000 single nucleotide polymorphisms (SNPs) were available genome-wide. We tested all pairwise SNP-SNP interactions (approximately 151 million) within 212 Kyoto Encyclopedia of Genes and Genomes pathways for association with 12-month regional atrophy rates using linear regression, with sex, APOE ε4 carrier status, age, education, and clinical status as covariates. A total of 109 SNP-SNP interactions were associated with right HIP atrophy, and 125 were associated with right EC atrophy. Enrichment analysis indicated significant SNP-SNP interactions were overrepresented in the calcium signaling and axon guidance pathways for both HIP and EC atrophy and in the ErbB signaling pathway for HIP atrophy.

Tissue-based Alzheimer gene expression markers-comparison of multiple machine learning approaches and investigation of redundancy in small biomarker sets

BACKGROUND

Alzheimer's disease has been known for more than 100 years and the underlying molecular mechanisms are not yet completely understood. The identification of genes involved in the processes in Alzheimer affected brain is an important step towards such an understanding. Genes differentially expressed in diseased and healthy brains are promising candidates.

RESULTS

Based on microarray data we identify potential biomarkers as well as biomarker combinations using three feature selection methods: information gain, mean decrease accuracy of random forest and a wrapper of genetic algorithm and support vector machine (GA/SVM). Information gain and random forest are two commonly used methods. We compare their output to the results obtained from GA/SVM. GA/SVM is rarely used for the analysis of microarray data, but it is able to identify genes capable of classifying tissues into different classes at least as well as the two reference methods.

CONCLUSION

Compared to the other methods, GA/SVM has the advantage of finding small, less redundant sets of genes that, in combination, show superior classification characteristics. The biological significance of the genes and gene pairs is discussed.

Ensemble transcript interaction networks: a case study on Alzheimer's disease

Systems biology techniques are a topic of recent interest within the neurological field. Computational intelligence (CI) addresses this holistic perspective by means of consensus or ensemble techniques ultimately capable of uncovering new and relevant findings. In this paper, we propose the application of a CI approach based on ensemble Bayesian network classifiers and multivariate feature subset selection to induce probabilistic dependences that could match or unveil biological relationships. The research focuses on the analysis of high-throughput Alzheimer's disease (AD) transcript profiling. The analysis is conducted from two perspectives. First, we compare the expression profiles of hippocampus subregion entorhinal cortex (EC) samples of AD patients and controls. Second, we use the ensemble approach to study four types of samples: EC and dentate gyrus (DG) samples from both patients and controls. Results disclose transcript interaction networks with remarkable structures and genes not directly related to AD by previous studies. The ensemble is able to identify a variety of transcripts that play key roles in other neurological pathologies. Classical statistical assessment by means of non-parametric tests confirms the relevance of the majority of the transcripts. The ensemble approach pinpoints key metabolic mechanisms that could lead to new findings in the pathogenesis and development of AD.

Dealing with the Data Deluge: Handling the Multitude Of Chemical Biology Data Sources

Over the last 20 years, there has been an explosion in the amount and type of biological and chemical data that has been made publicly available in a variety of online databases. While this means that vast amounts of information can be found online, there is no guarantee that it can be found easily (or at all). A scientist searching for a specific piece of information is faced with a daunting task - many databases have overlapping content, use their own identifiers and, in some cases, have arcane and unintuitive user interfaces. In this overview, a variety of well known data sources for chemical and biological information are highlighted, focusing on those most useful for chemical biology research. The issue of using multiple data sources together and the associated problems such as identifier disambiguation are highlighted. A brief discussion is then provided on Tripod, a recently developed platform that supports the integration of arbitrary data sources, providing users a simple interface to search across a federated collection of resources.

Systems analysis of inflammatory bowel disease based on comprehensive gene information

Background

The rise of systems biology and availability of highly curated gene and molecular information resources has promoted a comprehensive approach to study disease as the cumulative deleterious function of a collection of individual genes and networks of molecules acting in concert. These "human disease networks" (HDN) have revealed novel candidate genes and pharmaceutical targets for many diseases and identified fundamental HDN features conserved across diseases. A network-based analysis is particularly vital for a study on polygenic diseases where many interactions between molecules should be simultaneously examined and elucidated. We employ a new knowledge driven HDN gene and molecular database systems approach to analyze Inflammatory Bowel Disease (IBD), whose pathogenesis remains largely unknown.

Methods and Results

Based on drug indications for IBD, we determined sibling diseases of mild and severe states of IBD. Approximately 1,000 genes associated with the sibling diseases were retrieved from four databases. After ranking the genes by the frequency of records in the databases, we obtained 250 and 253 genes highly associated with the mild and severe IBD states, respectively. We then calculated functional similarities of these genes with known drug targets and examined and presented their interactions as PPI networks.

Conclusions

The results demonstrate that this knowledge-based systems approach, predicated on functionally similar genes important to sibling diseases is an effective method to identify important components of the IBD human disease network. Our approach elucidates a previously unknown biological distinction between mild and severe IBD states.

A disease annotation study of gene signatures in a breast cancer microarray dataset

Breast cancer is a complex disease with heterogeneity between patients regarding prognosis and treatment response. Recent progress in advanced molecular biology techniques and the development of efficient methods for database mining lead to the discovery of promising novel biomarkers for prognosis and prediction of breast cancer. In this paper, we applied three computational algorithms (RFE-LNW, Lasso and FSMLP) to one microarray dataset for breast cancer and compared the obtained gene signatures with a recently described disease-agnostic tool, the Genotator. We identified a panel of 152 genes as a potential prognostic signature and the ERRFI1 gene as possible biomarker of breast cancer disease.