Enhancing the Prioritization of Disease-Causing Genes through Tissue Specific Protein Interaction Networks

The prioritization of candidate disease-causing genes is a fundamental challenge in the post-genomic era. Current state of the art methods exploit a protein-protein interaction (PPI) network for this task. They are based on the observation that genes causing phenotypically-similar diseases tend to lie close to one another in a PPI network. However, to date, these methods have used a static picture of human PPIs, while diseases impact specific tissues in which the PPI networks may be dramatically different. Here, for the first time, we perform a large-scale assessment of the contribution of tissue-specific information to gene prioritization. By integrating tissue-specific gene expression data with PPI information, we construct tissue-specific PPI networks for 60 tissues and investigate their prioritization power. We find that tissue-specific PPI networks considerably improve the prioritization results compared to those obtained using a generic PPI network. Furthermore, they allow predicting novel disease-tissue associations, pointing to sub-clinical tissue effects that may escape early detection.     

Highly consistent patterns for inherited human diseases at the molecular level

Over 1600 mammalian genes are known to cause an inherited disorder, when subjected to one or more mutations. These disease genes represent a unique resource for the identification and quantification of relationships between phenotypic attributes of a disease and the molecular features of the associated disease genes, including their ascribed annotated functional classes and expression patterns. Such analyses can provide a more global perspective and a deeper understanding of the probable causes underlying human hereditary diseases. In this perspective and critical view of disease genomics, we present a comparative analysis of genes reported to cause inherited diseases in humans in terms of their causative effects on physiology, their genetics and inheritance modes, the functional processes they are involved in and their expression profiles across a wide spectrum of tissues. Our analysis reveals that there are more extensive correlations between these attributes of genetic disease genes than previously appreciated. For instance, the functional pattern of genes causing dominant and recessive diseases is markedly different. Also, the function of the genes and their expression correlate with the type of disease they cause when mutated. The results further indicate that a comparative genomics approach for the analysis of genes linked to human genetic diseases will facilitate the elucidation of the underlying molecular and cellular mechanisms.     

Tissue-specific splicing of disordered segments that embed binding motifs rewires protein interaction networks

Alternative inclusion of exons increases the functional diversity of proteins. Among alternatively spliced exons, tissue-specific exons play a critical role in maintaining tissue identity. This raises the question of how tissue-specific protein-coding exons influence protein function. Here we investigate the structural, functional, interaction, and evolutionary properties of constitutive, tissue-specific, and other alternative exons in human. We find that tissue-specific protein segments often contain disordered regions, are enriched in posttranslational modification sites, and frequently embed conserved binding motifs. Furthermore, genes containing tissue-specific exons tend to occupy central positions in interaction networks and display distinct interaction partners in the respective tissues, and are enriched in signaling, development, and disease genes. Based on these findings, we propose that tissue-specific inclusion of disordered segments that contain binding motifs rewires interaction networks and signaling pathways. In this way, tissue-specific splicing may contribute to functional versatility of proteins and increases the diversity of interaction networks across tissues.     

Are transient protein-protein interactions more dispensable?

Protein-protein interactions (PPIs) are key drivers of cell function and evolution. While it is widely assumed that most permanent PPIs are important for cellular function, it remains unclear whether transient PPIs are equally important. Here, we estimate and compare dispensable content among transient PPIs and permanent PPIs in human. Starting with a human reference interactome mapped by experiments, we construct a human structural interactome by building three-dimensional structural models for PPIs, and then distinguish transient PPIs from permanent PPIs using several structural and biophysical properties. We map common mutations from healthy individuals and disease-causing mutations onto the structural interactome, and perform structure-based calculations of the probabilities for common mutations (assumed to be neutral) and disease mutations (assumed to be mildly deleterious) to disrupt transient PPIs and permanent PPIs. Using Bayes’ theorem we estimate that a similarly small fraction (<~20%) of both transient and permanent PPIs are completely dispensable, i.e., effectively neutral upon disruption. Hence, transient and permanent interactions are subject to similarly strong selective constraints in the human interactome.     

Comparative in silico profiling of epigenetic modifiers in human tissues

The technology of tissue differentiation from human pluripotent stem cells has attracted attention as a useful resource for regenerative medicine, disease modeling and drug development. Recent studies have suggested various key factors and specific culture methods to improve the successful tissue differentiation and efficient generation of human induced pluripotent stem cells. Among these methods, epigenetic regulation and epigenetic signatures are regarded as an important hurdle to overcome during reprogramming and differentiation. Thus, in this study, we developed an in silico epigenetic panel and performed a comparative analysis of epigenetic modifiers in the RNA-seq results of 32 human tissues. We demonstrated that an in silico epigenetic panel can identify epigenetic modifiers in order to overcome epigenetic barriers to tissue-specific differentiation.     

Differential metabolic and multi-tissue transcriptomic responses to fructose consumption among genetically diverse mice

Understanding how individuals react differently to the same treatment is a major concern in precision medicine. Metabolic challenges such as the one posed by high fructose intake are important determinants of disease mechanisms. We embarked on studies to determine how fructose affects differential metabolic dysfunctions across genetically dissimilar mice, namely, C57BL/6 J (B6), DBA/2 J (DBA) and FVB/NJ (FVB), by integrating physiological and gene regulatory mechanisms. We report that fructose has strain-specific effects, involving tissue-specific gene regulatory cascades in hypothalamus, liver, and white adipose tissues. DBA mice showed the largest numbers of genes associated with adiposity, congruent with their highest susceptibility to adiposity gain and glucose intolerance across the three tissues. In contrast, B6 and FVB mainly exhibited cholesterol phenotypes, accompanying the largest number of adipose genes correlating with total cholesterol in B6, and liver genes correlating with LDL in FVB mice. Tissue-specific network modeling predicted strain-and tissue-specific regulators such as Fgf21 (DBA) and Lss (B6), which were subsequently validated in primary hepatocytes. Strain-specific fructose-responsive genes revealed susceptibility for human diseases such that genes in liver and adipose tissue in DBA showed strong enrichment for human type 2 diabetes and obesity traits. Liver and adipose genes in FVB were mostly related to lipid traits, and liver and adipose genes in B6 showed relevance to most cardiometabolic traits tested. Our results show that fructose induces gene regulatory pathways that are tissue specific and dependent on the genetic make-up, which may underlie interindividual variability in cardiometabolic responses to high fructose consumption.     

Comparative Analysis of Protein-Protein Interactions in Cancer-Associated Genes 25

Protein-protein interactions (PPIs) have been widely studied to understand the bi-ological processes or molecular functions associated with different disease systems like cancer. While focused studies on individual cancers have generated valuable in-formation, global and comparative analysis of datasets from different cancer types has not been done. In this work, we carried out bioinformatic analysis of PPIs corresponding to differentially expressed genes from microarrays of various tumor tissues (belonging to bladder, colon, kidney and thyroid cancers) and compared their associated biological processes and molecular functions (based on Gene On-tology terms). We identified a set of processes or functions that are common to all these cancers, as well as those that are specific to only one or partial cancer types. Similarly, protein interaction networks in nucleic acid metabolism were compared to identify the common/specific clusters of proteins across different cancer types. Our results provide a basis for further experimental investigations to study protein interaction networks associated with cancer. The methodology developed in this work can also be applied to study similar disease systems.     

EvoTol: a protein-sequence based evolutionary intolerance framework for disease-gene prioritization

Methods to interpret personal genome sequences are increasingly required. Here, we report a novel framework (EvoTol) to identify disease-causing genes using patient sequence data from within protein coding-regions. EvoTol quantifies a gene''s intolerance to mutation using evolutionary conservation of protein sequences and can incorporate tissue-specific gene expression data. We apply this framework to the analysis of whole-exome sequence data in epilepsy and congenital heart disease, and demonstrate EvoTol''s ability to identify known disease-causing genes is unmatched by competing methods. Application of EvoTol to the human interactome revealed networks enriched for genes intolerant to protein sequence variation, informing novel polygenic contributions to human disease.     

A Comparative Study of Drosophila and Human A-Type Lamins

Nuclear intermediate filament proteins, called lamins, form a meshwork that lines the inner surface of the nuclear envelope. Lamins contain three domains: an N-terminal head, a central rod and a C-terminal tail domain possessing an Ig-fold structural motif. Lamins are classified as either A- or B-type based on structure and expression pattern. The Drosophila genome possesses two genes encoding lamins, Lamin C and lamin Dm₀, which have been designated A- and B-type, respectively, based on their expression profile and structural features. In humans, mutations in the gene encoding A-type lamins are associated with a spectrum of predominantly tissue-specific diseases known as laminopathies. Linking the disease phenotypes to cellular functions of lamins has been a major challenge. Drosophila is being used as a model system to identify the roles of lamins in development. Towards this end, we performed a comparative study of Drosophila and human A-type lamins. Analysis of transgenic flies showed that human lamins localize predictably within the Drosophila nucleus. Consistent with this finding, yeast two-hybrid data demonstrated conservation of partner-protein interactions. Drosophila lacking A-type lamin show nuclear envelope defects similar to those observed with human laminopathies. Expression of mutant forms of the A-type Drosophila lamin modeled after human disease-causing amino acid substitutions revealed an essential role for the N-terminal head and the Ig-fold in larval muscle tissue. This tissue-restricted sensitivity suggests a conserved role for lamins in muscle biology. In conclusion, we show that (1) localization of A-type lamins and protein-partner interactions are conserved between Drosophila and humans, (2) loss of the Drosophila A-type lamin causes nuclear defects and (3) muscle tissue is sensitive to the expression of mutant forms of A-type lamin modeled after those causing disease in humans. These studies provide new insights on the role of lamins in nuclear biology and support Drosophila as a model for studies of human laminopathies involving muscle dysfunction.     

Disease-Aging Network Reveals Significant Roles of Aging Genes in Connecting Genetic Diseases

One of the challenging problems in biology and medicine is exploring the underlying mechanisms of genetic diseases. Recent studies suggest that the relationship between genetic diseases and the aging process is important in understanding the molecular mechanisms of complex diseases. Although some intricate associations have been investigated for a long time, the studies are still in their early stages. In this paper, we construct a human disease-aging network to study the relationship among aging genes and genetic disease genes. Specifically, we integrate human protein-protein interactions (PPIs), disease-gene associations, aging-gene associations, and physiological system–based genetic disease classification information in a single graph-theoretic framework and find that (1) human disease genes are much closer to aging genes than expected by chance; and (2) diseases can be categorized into two types according to their relationships with aging. Type I diseases have their genes significantly close to aging genes, while type II diseases do not. Furthermore, we examine the topological characters of the disease-aging network from a systems perspective. Theoretical results reveal that the genes of type I diseases are in a central position of a PPI network while type II are not; (3) more importantly, we define an asymmetric closeness based on the PPI network to describe relationships between diseases, and find that aging genes make a significant contribution to associations among diseases, especially among type I diseases. In conclusion, the network-based study provides not only evidence for the intricate relationship between the aging process and genetic diseases, but also biological implications for prying into the nature of human diseases.     

Discovering disease-genes by topological features in human protein-protein interaction network

MOTIVATION: Mining the hereditary disease-genes from human genome is one of the most important tasks in bioinformatics research. A variety of sequence features and functional similarities between known human hereditary disease-genes and those not known to be involved in disease have been systematically examined and efficient classifiers have been constructed based on the identified common patterns. The availability of human genome-wide protein-protein interactions (PPIs) provides us with new opportunity for discovering hereditary disease-genes by topological features in PPIs network. RESULTS: This analysis reveals that the hereditary disease-genes ascertained from OMIM in the literature-curated (LC) PPIs network are characterized by a larger degree, tendency to interact with other disease-genes, more common neighbors and quick communication to each other whereas those properties could not be detected from the network identified from high-throughput yeast two-hybrid mapping approach (EXP) and predicted interactions (PDT) PPIs network. KNN classifier based on those features was created and on average gained overall prediction accuracy of 0.76 in cross-validation test. Then the classifier was applied to 5262 genes on human genome and predicted 178 novel disease-genes. Some of the predictions have been validated by biological experiments.     

Prioritization of orphan disease-causing genes using topological feature and GO similarity between proteins in interaction networks

Identification of disease-causing genes among a large number of candidates is a fundamental challenge in human disease studies. However, it is still time-consuming and laborious to determine the real disease-causing genes by biological experiments. With the advances of the high-throughput techniques, a large number of protein-protein interactions have been produced. Therefore, to address this issue, several methods based on protein interaction network have been proposed. In this paper, we propose a shortest path-based algorithm, named SPranker, to prioritize disease-causing genes in protein interaction networks. Considering the fact that diseases with similar phenotypes are generally caused by functionally related genes, we further propose an improved algorithm SPGOranker by integrating the semantic similarity of GO annotations. SPGOranker not only considers the topological similarity between protein pairs in a protein interaction network but also takes their functional similarity into account. The proposed algorithms SPranker and SPGOranker were applied to 1598 known orphan disease-causing genes from 172 orphan diseases and compared with three state-of-the-art approaches, ICN, VS and RWR. The experimental results show that SPranker and SPGOranker outperform ICN, VS, and RWR for the prioritization of orphan disease-causing genes. Importantly, for the case study of severe combined immunodeficiency, SPranker and SPGOranker predict several novel causal genes.     

Isochores and tissue-specificity

The housekeeping (ubiquitously expressed) genes in the mammal genome were shown here to be on average slightly GC-richer than tissue-specific genes. Both housekeeping and tissue-specific genes occupy similar ranges of GC content, but the former tend to concentrate in the upper part of the range. In the human genome, tissue-specific genes show two maxima, GC-poor and GC-rich. The strictly tissue-specific human genes tend to concentrate in the GC-poor region; their distribution is left-skewed and thus reciprocal to the distribution of housekeeping genes. The intermediately tissue-specific genes show an intermediate GC content and the right-skewed distribution. Both in the human and mouse, genes specific for some tissues (e.g., parts of the central nervous system) have a higher average GC content than housekeeping genes. Since they are not transcribed in the germ line (in contrast to housekeeping genes), and therefore have a lower probability of inheritable gene conversion, this finding contradicts the biased gene conversion (BGC) explanation for elevated GC content in the heavy isochores of mammal genome. Genes specific for germ-line tissues (ovary, testes) show a low average GC content, which is also in contradiction to the BGC explanation. Both for the total data set and for the most part of tissues taken separately, a weak positive correlation was found between gene GC content and expression level. The fraction of ubiquitously expressed genes is nearly 1.5-fold higher in the mouse than in the human. This suggests that mouse tissues are comparatively less differentiated (on the molecular level), which can be related to a less pronounced isochoric structure of the mouse genome. In each separate tissue (in both species), tissue-specific genes do not form a clear-cut frequency peak (in contrast to housekeeping genes), but constitute a continuum with a gradually increasing degree of tissue-specificity, which probably reflects the path of cell differentiation and/or an independent use of the same protein in several unrelated tissues.     

Genome-wide detection of tissue-specific alternative splicing in the human transcriptome

We have developed an automated method for discovering tissue-specific regulation of alternative splicing through a genome-wide analysis of expressed sequence tags (ESTs). Using this approach, we have identified 667 tissue-specific alternative splice forms of human genes. We validated our muscle-specific and brain-specific splice forms for known genes. A high fraction (8/10) were reported to have a matching tissue specificity by independent studies in the published literature. The number of tissue-specific alternative splice forms is highest in brain, while eye-retina, muscle, skin, testis and lymph have the greatest enrichment of tissue-specific splicing. Overall, 10-30% of human alternatively spliced genes in our data show evidence of tissue-specific splice forms. Seventy-eight percent of our tissue-specific alternative splices appear to be novel discoveries. We present bioinformatics analysis of several tissue-specific splice forms, including automated protein isoform sequence and domain prediction, showing how our data can provide valuable insights into gene function in different tissues. For example, we have discovered a novel kidney-specific alternative splice form of the WNK1 gene, which appears to specifically disrupt its N-terminal kinase domain and may play a role in PHAII hypertension. Our database greatly expands knowledge of tissue-specific alternative splicing and provides a comprehensive dataset for investigating its functional roles and regulation in different human tissues.