Multi–omic analysis of signalling factors in inflammatory comorbidities

Background

Inflammation is a core element of many different, systemic and chronic diseases that usually involve an important autoimmune component. The clinical phase of inflammatory diseases is often the culmination of a long series of pathologic events that started years before. The systemic characteristics and related mechanisms could be investigated through the multi–omic comparative analysis of many inflammatory diseases. Therefore, it is important to use molecular data to study the genesis of the diseases. Here we propose a new methodology to study the relationships between inflammatory diseases and signalling molecules whose dysregulation at molecular levels could lead to systemic pathological events observed in inflammatory diseases.

Results

We first perform an exploratory analysis of gene expression data of a number of diseases that involve a strong inflammatory component. The comparison of gene expression between disease and healthy samples reveals the importance of members of gene families coding for signalling factors. Next, we focus on interested signalling gene families and a subset of inflammation related diseases with multi–omic features including both gene expression and DNA methylation. We introduce a phylogenetic–based multi–omic method to study the relationships between multi–omic features of inflammation related diseases by integrating gene expression, DNA methylation through sequence based phylogeny of the signalling gene families. The models of adaptations between gene expression and DNA methylation can be inferred from pre–estimated evolutionary relationship of a gene family. Members of the gene family whose expression or methylation levels significantly deviate from the model are considered as the potential disease associated genes.

Conclusions

Applying the methodology to four gene families (the chemokine receptor family, the TNF receptor family, the TGF– β gene family, the IL–17 gene family) in nine inflammation related diseases, we identify disease associated genes which exhibit significant dysregulation in gene expression or DNA methylation in the inflammation related diseases, which provides clues for functional associations between the diseases.

     

Progress and promise in understanding the genetic basis of common diseases

Susceptibility to common human diseases is influenced by both genetic and environmental factors. The explosive growth of genetic data, and the knowledge that it is generating, are transforming our biological understanding of these diseases. In this review, we describe the technological and analytical advances that have enabled genome-wide association studies to be successful in identifying a large number of genetic variants robustly associated with common disease. We examine the biological insights that these genetic associations are beginning to produce, from functional mechanisms involving individual genes to biological pathways linking associated genes, and the identification of functional annotations, some of which are cell-type-specific, enriched in disease associations. Although most efforts have focused on identifying and interpreting genetic variants that are irrefutably associated with disease, it is increasingly clear that—even at large sample sizes—these represent only the tip of the iceberg of genetic signal, motivating polygenic analyses that consider the effects of genetic variants throughout the genome, including modest effects that are not individually statistically significant. As data from an increasingly large number of diseases and traits are analysed, pleiotropic effects (defined as genetic loci affecting multiple phenotypes) can help integrate our biological understanding. Looking forward, the next generation of population-scale data resources, linking genomic information with health outcomes, will lead to another step-change in our ability to understand, and treat, common diseases.     

Genetic Identification Is Critical for the Diagnosis of Parkinsonism: A Chinese Pedigree with Early Onset of Parkinsonism

Background

A number of hereditary neurological diseases display indistinguishable features at the early disease stage. Parkinsonian symptoms can be found in numerous diseases, making it difficult to get a definitive early diagnosis of primary causes for patients with onset of parkinsonism. The accurate and early diagnosis of the causes of parkinsonian patients is important for effective treatments of these patients.

Methods

We have identified a Chinese family (82 family members over four generations with 21 affected individuals) that manifested the characterized symptoms of parkinsonism and was initially diagnosed as Parkinson’s disease. We followed up with the family for two years, during which we carried out clinical observations, Positron Emission Tomography-Computed Tomography neuroimaging analysis, and exome sequencing to correctly diagnose the case.

Results

During the two-year follow-up period, we performed comprehensive medical history collection, physical examination, and structural and functional neuroimaging studies of this Chinese family. We found that the patient exhibited progressive deteriorated parkinsonism with Parkinson disease-like neuropathology and also had a good response to the initial levodopa treatment. However, exome sequencing identified a missense mutation, N279K, in exon 10 of MAPT gene, verifying that the early parkinsonian symptoms in this family are caused by the genetic mutation for hereditary frontotemporal lobar dementia.

Conclusions

For the inherited parkinsonian patients who even show the neuropathology similar to that in Parkinson’s disease and have initial response to levodopa treatment, genetic identification of the molecular basis for the disease is still required for defining the early diagnosis and correct treatment.     

Gene gravity-like algorithm for disease gene prediction based on phenotype-specific network

Background

Polygenic diseases are usually caused by the dysfunction of multiple genes. Unravelling such disease genes is crucial to fully understand the genetic landscape of diseases on molecular level. With the advent of ‘omic’ data era, network-based methods have prominently boosted disease gene discovery. However, how to make better use of different types of data for the prediction of disease genes remains a challenge.

Results

In this study, we improved the performance of disease gene prediction by integrating the similarity of disease phenotype, biological function and network topology. First, for each phenotype, a phenotype-specific network was specially constructed by mapping phenotype similarity information of given phenotype onto the protein-protein interaction (PPI) network. Then, we developed a gene gravity-like algorithm, to score candidate genes based on not only topological similarity but also functional similarity. We tested the proposed network and algorithm by conducting leave-one-out and leave-10%-out cross validation and compared them with state-of-art algorithms. The results showed a preference to phenotype-specific network as well as gene gravity-like algorithm. At last, we tested the predicting capacity of proposed algorithms by test gene set derived from the DisGeNET database. Also, potential disease genes of three polygenic diseases, obesity, prostate cancer and lung cancer, were predicted by proposed methods. We found that the predicted disease genes are highly consistent with literature and database evidence.

Conclusions

The good performance of phenotype-specific networks indicates that phenotype similarity information has positive effect on the prediction of disease genes. The proposed gene gravity-like algorithm outperforms the algorithm of Random Walk with Restart (RWR), implicating its predicting capacity by combing topological similarity with functional similarity. Our work will give an insight to the discovery of disease genes by fusing multiple similarities of genes and diseases.

     

Persistence of accuracy of genome-wide breeding values over generations when including a polygenic effect

Background

When estimating marker effects in genomic selection, estimates of marker effects may simply act as a proxy for pedigree, i.e. their effect may partially be attributed to their association with superior parents and not be linked to any causative QTL. Hence, these markers mainly explain polygenic effects rather than QTL effects. However, if a polygenic effect is included in a Bayesian model, it is expected that the estimated effect of these markers will be more persistent over generations without having to re-estimate the marker effects every generation and will result in increased accuracy and reduced bias.

Methods

Genomic selection using the Bayesian method, ''BayesB'' was evaluated for different marker densities when a polygenic effect is included (GWpEBV) and not included (GWEBV) in the model. Linkage disequilibrium and a mutation drift balance were obtained by simulating a population with a Ne of 100 over 1,000 generations.

Results

Accuracy of selection was slightly higher for the model including a polygenic effect than for the model not including a polygenic effect whatever the marker density. The accuracy decreased in later generations, and this reduction was stronger for lower marker densities. However, no significant difference in accuracy was observed between the two models. The linear regression of TBV on GWEBV and GWpEBV was used as a measure of bias. The regression coefficient was more stable over generations when a polygenic effect was included in the model, and was always between 0.98 and 1.00 for the highest marker density. The regression coefficient decreased more quickly with decreasing marker density.

Conclusions

Including a polygenic effect had no impact on the selection accuracy, but showed reduced bias, which is especially important when estimates of genome-wide markers are used to estimate breeding values over more than one generation.     

Comparison of Family History and SNPs for Predicting Risk of Complex Disease

The clinical utility of family history and genetic tests is generally well understood for simple Mendelian disorders and rare subforms of complex diseases that are directly attributable to highly penetrant genetic variants. However, little is presently known regarding the performance of these methods in situations where disease susceptibility depends on the cumulative contribution of multiple genetic factors of moderate or low penetrance. Using quantitative genetic theory, we develop a model for studying the predictive ability of family history and single nucleotide polymorphism (SNP)–based methods for assessing risk of polygenic disorders. We show that family history is most useful for highly common, heritable conditions (e.g., coronary artery disease), where it explains roughly 20%–30% of disease heritability, on par with the most successful SNP models based on associations discovered to date. In contrast, we find that for diseases of moderate or low frequency (e.g., Crohn disease) family history accounts for less than 4% of disease heritability, substantially lagging behind SNPs in almost all cases. These results indicate that, for a broad range of diseases, already identified SNP associations may be better predictors of risk than their family history–based counterparts, despite the large fraction of missing heritability that remains to be explained. Our model illustrates the difficulty of using either family history or SNPs for standalone disease prediction. On the other hand, we show that, unlike family history, SNP–based tests can reveal extreme likelihood ratios for a relatively large percentage of individuals, thus providing potentially valuable adjunctive evidence in a differential diagnosis.     

Genome-wide association study as a method to analyze the genome architecture in polygenic diseases,with the example of multiple sclerosis

Genome-wide association study (GWAS) provides a powerful tool for investigating the genetic architecture of human polygenic diseases and is generally used to identify the genetic factors of disease susceptibility, clinical phenotypes, and treatment response. The differences in allele frequencies of single nucleotide polymorphisms (SNPs) distributed throughout the genome are analyzed with a microarray technique or other technologies that allow simultaneous genotyping at several tens of thousands to several millions of SNPs per sample. Owing to its power to find out highly reliable differences between patients and controls, GWAS became a common approach to identification of the genetic susceptibility factors in complex diseases of a polygenic nature. Using multiple sclerosis (MS) as a prototype complex disease, the review considers the main achievements and challenges of using GWAS to identify the genes involved in the disease and, therefore, to better understand the pathogenetic molecular mechanisms and genetic risk factors.     

Neurological diseases caused by ion-channel mutations

During the past decade, mutations in several ion-channel genes have been shown to cause inherited neurological diseases. This is not surprising given the large number of different ion channels and their prominent role in signal processing. Biophysical studies of mutant ion channels in vitro allow detailed investigations of the basic mechanism underlying these 'channelopathies'. A full understanding of these diseases, however, requires knowing the roles these channels play in their cellular and systemic context. Differences in this context often cause different phenotypes in humans and mice. The situation is further complicated by the developmental effects and other secondary effects that might result from ion-channel mutations. Recent studies have described the different thresholds to which ion-channel function must be decreased in order to cause disease.     

Computational identification of residues that modulate voltage sensitivity of voltage-gated potassium channels

Background  

Studies of the structure-function relationship in proteins for which no 3D structure is available are often based on inspection of multiple sequence alignments. Many functionally important residues of proteins can be identified because they are conserved during evolution. However, residues that vary can also be critically important if their variation is responsible for diversity of protein function and improved phenotypes. If too few sequences are studied, the support for hypotheses on the role of a given residue will be weak, but analysis of large multiple alignments is too complex for simple inspection. When a large body of sequence and functional data are available for a protein family, mature data mining tools, such as machine learning, can be applied to extract information more easily, sensitively and reliably. We have undertaken such an analysis of voltage-gated potassium channels, a transmembrane protein family whose members play indispensable roles in electrically excitable cells.     

Mutation analysis in congenital Long QT Syndrome--a case with missense mutations in KCNQ1 and SCN5A

Long QT Syndrome (LQTS) is a cardiac disease characterized by a prolonged QT interval on a surface electrocardiogram (ECG) and by clinical symptoms such as seizures, syncope, and cardiac sudden death. At present, causal mutations of LQTS have been identified in five cardiac ion-channel genes. Because a causal mutation is usually unique to a specific family and can be located in any region of any of these five genes, a mutation analysis effort may require screening of the complete coding regions of each of these genes. The causative nature of a detected mutation can then be determined either by family history or by functional studies, such as the electrophysiological signature of the mutation. Here we describe a mutation analysis of an LQTS patient who carries two heterozygous missense mutations in two different LQTS genes. The first mutation identified, A572D in SCN5A, was not linked with clinical LQTS features in the two other mutation carriers in the family; neither was it identified in 90 healthy controls. Therefore, this mutation most likely has either a mild effect on cardiac ion-channel function or represents a very rare polymorphism. The second mutation, V254M in KCNQ1, co-segregated with higher QT intervals and symptoms in other family members, and was previously reported in another LQTS family. Because the clinical LQTS symptoms are most pronounced in the proband, a combined effect of both mutations cannot be excluded, although no functional data are available to support such an hypothesis. We conclude that, for newly presented LQTS cases, a mutation analysis strategy should routinely screen the complete coding region of all LQTS genes, followed by an evaluation of the identified mutation(s) in conjunction with family or functional data.     

A genomic view of the NOD-like receptor family in teleost fish: identification of a novel NLR subfamily in zebrafish

Background

The mammalian heme peroxidases (MHPs) are a medically important group of enzymes. Included in this group are myeloperoxidase, eosinophil peroxidase, lactoperoxidase, and thyroid peroxidase. These enzymes are associated with such diverse diseases as asthma, Alzheimer's disease and inflammatory vascular disease. Despite much effort to elucidate a clearer understanding of the function of the 4 major groups of this multigene family, we still do not have a clear understanding of their relationships to each other.

Results

Sufficient signal exists for the resolution of the evolutionary relationships of this family of enzymes. We demonstrate, using a root mean squared deviation statistic, how the removal of the fastest evolving sites aids in the minimisation of the effect of long branch attraction and the generation of a highly supported phylogeny. Based on this phylogeny we have pinpointed the amino acid positions that have most likely contributed to the diverse functions of these enzymes. Many of these residues are in close proximity to sites implicated in protein misfolding, loss of function or disease.

Conclusion

Our analysis of all available genomic sequence data for the MHPs from all available completed mammalian genomes, involved sophisticated methods of phylogeny reconstruction and data treatment. Our study has (i) fully resolved the phylogeny of the MHPs and the subsequent pattern of gene duplication, and (ii), we have detected amino acids under positive selection that have most likely contributed to the observed functional shifts in each type of MHP.     

Ranking non-synonymous single nucleotide polymorphisms based on disease concepts

As the number of non-synonymous single nucleotide polymorphisms (nsSNPs) identified through whole-exome/whole-genome sequencing programs increases, researchers and clinicians are becoming increasingly reliant upon computational prediction algorithms designed to prioritize potential functional variants for further study. A large proportion of existing prediction algorithms are ‘disease agnostic’ but are nevertheless quite capable of predicting when a mutation is likely to be deleterious. However, most clinical and research applications of these algorithms relate to specific diseases and would therefore benefit from an approach that discriminates between functional variants specifically related to that disease from those which are not. In a whole-exome/whole-genome sequencing context, such an approach could substantially reduce the number of false positive candidate mutations. Here, we test this postulate by incorporating a disease-specific weighting scheme into the Functional Analysis through Hidden Markov Models (FATHMM) algorithm. When compared to traditional prediction algorithms, we observed an overall reduction in the number of false positives identified using a disease-specific approach to functional prediction across 17 distinct disease concepts/categories. Our results illustrate the potential benefits of making disease-specific predictions when prioritizing candidate variants in relation to specific diseases. A web-based implementation of our algorithm is available at http://fathmm.biocompute.org.uk.     

Endophytic fungi as biocontrol agents: elucidating mechanisms in disease suppression

ABSTRACT

Background

Fungal endophytes occur ubiquitously in plants and are being increasingly studied for their ability to support plant health and protect the host from diseases. Using endophytes in disease control provides potential advantages compared to other biocontrol agents since they colonise the plant internally and thereby stay protected from environmental stresses and fluctuations. A thorough understanding of their mechanisms is required in their mutualistic association with plants; both to optimise their efficacy and for registration as plant protection products.     

NOD-like receptors and inflammation

The nucleotide-binding and oligomerization domain, leucine-rich repeat (also known as NOD-like receptors, both abbreviated to NLR) family of intracellular pathogen recognition receptors are increasingly being recognized to play a pivotal role in the pathogenesis of a number of rare monogenic diseases, as well as some more common polygenic conditions. Bacterial wall constituents and other cellular stressor molecules are recognized by a range of NLRs, which leads to activation of the innate immune response and upregulation of key proinflammatory pathways, such as IL-1β production and translocation of nuclear factor-κB to the nucleus. These signalling pathways are increasingly being targeted as potential sites for new therapies. This review discusses the role played by NLRs in a variety of inflammatory diseases and describes the remarkable success to date of these therapeutic agents in treating some of the disorders associated with aberrant NLR function.     

Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases

Background

Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult.

Principal Findings

We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell.

Conclusions

For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases.

Availability

The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download.     

An open state of a voltage-gated sodium channel involving a π-helix and conserved pore-facing asparagine

Voltage-gated sodium (Nav) channels play critical roles in propagating action potentials and otherwise manipulating ionic gradients in excitable cells. These channels open in response to membrane depolarization, selectively permeating sodium ions until rapidly inactivating. Structural characterization of the gating cycle in this channel family has proved challenging, particularly due to the transient nature of the open state. A structure from the bacterium Magnetococcus marinus Nav (NavMs) was initially proposed to be open, based on its pore diameter and voltage-sensor conformation. However, the functional annotation of this model, and the structural details of the open state, remain disputed. In this work, we used molecular modeling and simulations to test possible open-state models of NavMs. The full-length experimental structure, termed here the α-model, was consistently dehydrated at the activation gate, indicating an inability to conduct ions. Based on a spontaneous transition observed in extended simulations, and sequence/structure comparison to other Nav channels, we built an alternative π-model featuring a helix transition and the rotation of a conserved asparagine residue into the activation gate. Pore hydration, ion permeation, and state-dependent drug binding in this model were consistent with an open functional state. This work thus offers both a functional annotation of the full-length NavMs structure and a detailed model for a stable Nav open state, with potential conservation in diverse ion-channel families.     

Laplacian normalization and bi-random walks on heterogeneous networks for predicting lncRNA-disease associations

Background

Evidences have increasingly indicated that lncRNAs (long non-coding RNAs) are deeply involved in important biological regulation processes leading to various human complex diseases. Experimental investigations of these disease associated lncRNAs are slow with high costs. Computational methods to infer potential associations between lncRNAs and diseases have become an effective prior-pinpointing approach to the experimental verification.

Results

In this study, we develop a novel method for the prediction of lncRNA-disease associations using bi-random walks on a network merging the similarities of lncRNAs and diseases. Particularly, this method applies a Laplacian technique to normalize the lncRNA similarity matrix and the disease similarity matrix before the construction of the lncRNA similarity network and disease similarity network. The two networks are then connected via existing lncRNA-disease associations. After that, bi-random walks are applied on the heterogeneous network to predict the potential associations between the lncRNAs and the diseases. Experimental results demonstrate that the performance of our method is highly comparable to or better than the state-of-the-art methods for predicting lncRNA-disease associations. Our analyses on three cancer data sets (breast cancer, lung cancer, and liver cancer) also indicate the usefulness of our method in practical applications.

Conclusions

Our proposed method, including the construction of the lncRNA similarity network and disease similarity network and the bi-random walks algorithm on the heterogeneous network, could be used for prediction of potential associations between the lncRNAs and the diseases.