The role of noise and positive feedback in the onset of autosomal dominant diseases

Background

Autosomal dominant (AD) diseases result when a single mutant or non-functioning gene is present on an autosomal chromosome. These diseases often do not emerge at birth. There are presently two prevailing theories explaining the expression of AD diseases. One explanation originates from the Knudson two-hit theory of hereditary cancers, where loss of heterozygosity or occurrence of somatic mutations impairs the function of the wild-type copy. While these somatic second hits may be sufficient for stable disease states, it is often difficult to determine if their occurrence necessarily marks the initiation of disease progression. A more direct consequence of a heterozygous genetic background is haploinsufficiency, referring to a lack of sufficient gene function due to reduced wild-type gene copy number; however, haploinsufficiency can involve a variety of additional mechanisms, such as noise in gene expression or protein levels, injury and second hit mutations in other genes. In this study, we explore the possible contribution to the onset of autosomal dominant diseases from intrinsic factors, such as those determined by the structure of the molecular networks governing normal cellular physiology.

Results

First, simple models of single gene insufficiency using the positive feedback loops that may be derived from a three-component network were studied by computer simulation using Bionet software. The network structure is shown to affect the dynamics considerably; some networks are relatively stable even when large stochastic variations in are present, while others exhibit switch-like dynamics. In the latter cases, once the network switches over to the disease state it remains in that state permanently. Model pathways for two autosomal dominant diseases, AD polycystic kidney disease and mature onset diabetes of youth (MODY) were simulated and the results are compared to known disease characteristics.

Conclusions

By identifying the intrinsic mechanisms involved in the onset of AD diseases, it may be possible to better assess risk factors as well as lead to potential new drug targets. To illustrate the applicability of this study of pathway dynamics, we simulated the primary pathways involved in two autosomal dominant diseases, Polycystic Kidney Disease (PKD) and mature onset diabetes of youth (MODY). Simulations demonstrate that some of the primary disease characteristics are consistent with the positive feedback - stochastic variation theory presented here. This has implications for new drug targets to control these diseases by blocking the positive feedback loop in the relevant pathways.     

Chapter 15: Disease Gene Prioritization

Disease-causing aberrations in the normal function of a gene define that gene as a disease gene. Proving a causal link between a gene and a disease experimentally is expensive and time-consuming. Comprehensive prioritization of candidate genes prior to experimental testing drastically reduces the associated costs. Computational gene prioritization is based on various pieces of correlative evidence that associate each gene with the given disease and suggest possible causal links. A fair amount of this evidence comes from high-throughput experimentation. Thus, well-developed methods are necessary to reliably deal with the quantity of information at hand. Existing gene prioritization techniques already significantly improve the outcomes of targeted experimental studies. Faster and more reliable techniques that account for novel data types are necessary for the development of new diagnostics, treatments, and cure for many diseases.

This article is part of the “Translational Bioinformatics" collection for PLOS Computational Biology.

What to Learn in This Chapter

• Identification of specific disease genes is complicated by gene pleiotropy, polygenic nature of many diseases, varied influence of environmental factors, and overlying genome variation.
• Gene prioritization is the process of assigning likelihood of gene involvement in generating a disease phenotype. This approach narrows down, and arranges in the order of likelihood in disease involvement, the set of genes to be tested experimentally.
• The gene “priority" in disease is assigned by considering a set of relevant features such as gene expression and function, pathway involvement, and mutation effects.
• In general, disease genes tend to 1) interact with other disease genes, 2) harbor functionally deleterious mutations, 3) code for proteins localizing to the affected biological compartment (pathway, cellular space, or tissue), 4) have distinct sequence properties such as longer length and a higher number of exons, 5) have more orthologues and fewer paralogues.
• Data sources (directly experimental, extracted from knowledge-bases, or text-mining based) and mathematical/computational models used for gene prioritization vary widely.

     

Why are some genetic diseases common?

Various processes (selection, mutation, migration and genetic dirft) are known to determine the frequency of genetic disease in human populations, but so far it has proved almost impossible to decide to what extent each is responsible for the presence of a particular genetic disease. The techniques of gene and haplotype analysis offer new hope in addressing this issue, and we review relevant studies of three haemoglobinopathies: sickle cell anaemia, and and thalassaemia. We show how for each disease it is possible to recognize a pattern of regionally specific mutations, found in association with one or a few haplotypes, that is best explained as the result of selection; other patterns are due to population migration and genetic drift. However, we caution that such conclusions can be drawn in special circumstances only. In the case of the haemoglobinopathies it is possible because a selective agent (malaria) was already suspected, and the investigations could be carried out in relatively genetically homogenous populations whose migratory histories are known. Moreover, some data reviewed here suggest that gene conversion and the haplotype composition of a population may affect the frequency of a mutation, making interpretation of gene frequencies difficult on the basis of standard population genetics theory. Hence attempts to use the same approaches with other genetic diseases are likely to be frustrated by a lack of suitably untrammelled populations and by difficulties accounting for poorly understood genetic processes. We conclude that although this combination of molecular and population genetics is successful when applied to the study of haemoglobinopathies, it may not be so easy to apply it to the study of other genetic diseases.     

Personalisierte Medizin – Vision oder Fiktion? Beispiel: Altersdiabetes

Typical civilization diseases, such as type II diabetes, are common, complex in the underlying pathogenic mechanisms, heterogenous in the phenotype and multifactorial due to a wide variety of possible combinations of disease susceptibility or protective genes in different relevant tissues and negative or positive environmental factors. This is in sharp contrast to classical inherited diseases, such as Chorea Huntington, which are often caused by complete loss‐ or gain‐of‐function mutations in a single gene. The causative polymorphisms of susceptibility genes, however, are characterized by relative subtle alterations in the function of the corresponding gene product, which per se do not support the pathogenesis, by high frequency, high expenditure for their identification and rather low predictive value. Consequently, the reliable and early diagnosis of civilization diseases depends on the individual determination of all (or as many as possible) polymorphisms of each susceptibility gene together with the corresponding gene products and the metabolites emerging thereof.     

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.

     

Microsatellite polymorphism in the human heme oxygenase-1 gene promoter and its application in association studies with Alzheimer and Parkinson disease

Oxidative stress has been suggested to be involved in the pathogenesis of neurodegenerative diseases, such as Alzheimer disease (AD) and Parkinson disease (PD). Heme oxygenase-1 (HO-1), a key enzyme in heme catabolism, also functions as an antioxidant enzyme. Here, we show that a (GT)_n repeat in the human HO-1 gene promoter region is highly polymorphic, although no particular alleles are associated with AD or PD. This newly identified genetic marker should allow us to study the possible involvement of HO-1 in certain human diseases. Received: 5 November 1996 / Accepted: 18 February 1997     

Shared mechanisms among neurodegenerative diseases: from genetic factors to gene networks

Neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis are pressing health concerns in modern societies for which effective therapies are still lacking. Recent high-throughput genomic technologies have enabled genome-scale, multidimensional investigations to facilitate a better understanding of the underlying mechanisms and the identification of novel targets. Here we review the molecular insights gained through such studies, and compare the similarities and differences between neurodegenerative diseases revealed by systems genomics and gene network modelling approaches. We focus specifically on the shared mechanisms at multiple molecular scales ranging from genetic factors to gene expression to network-level features of gene regulation, and whenever possible also point out mechanisms that distinguish one disease from another. Our review sets the stage for similar genomewide inspection in the future on shared/distinct features of neurodegenerative diseases at the levels of cellular, proteomic or epigenomic signatures, and how these features may interact to determine the progression and treatment response of different diseases afflicting the same individual.     

Gene profiling of the erythro- and megakaryoblastic leukaemias induced by the Graffi murine retrovirus

Background

Many common diseases arise from an interaction between environmental and genetic factors. Our knowledge regarding environment and gene interactions is growing, but frameworks to build an association between gene-environment interactions and disease using preexisting, publicly available data has been lacking. Integrating freely-available environment-gene interaction and disease phenotype data would allow hypothesis generation for potential environmental associations to disease.

Methods

We integrated publicly available disease-specific gene expression microarray data and curated chemical-gene interaction data to systematically predict environmental chemicals associated with disease. We derived chemical-gene signatures for 1,338 chemical/environmental chemicals from the Comparative Toxicogenomics Database (CTD). We associated these chemical-gene signatures with differentially expressed genes from datasets found in the Gene Expression Omnibus (GEO) through an enrichment test.

Results

We were able to verify our analytic method by accurately identifying chemicals applied to samples and cell lines. Furthermore, we were able to predict known and novel environmental associations with prostate, lung, and breast cancers, such as estradiol and bisphenol A.

Conclusions

We have developed a scalable and statistical method to identify possible environmental associations with disease using publicly available data and have validated some of the associations in the literature.     

PHD fingers in human diseases: disorders arising from misinterpreting epigenetic marks

Histone covalent modifications regulate many, if not all, DNA-templated processes, including gene expression and DNA damage response. The biological consequences of histone modifications are mediated partially by evolutionarily conserved "reader/effector" modules that bind to histone marks in a modification- and context-specific fashion and subsequently enact chromatin changes or recruit other proteins to do so. Recently, the Plant Homeodomain (PHD) finger has emerged as a class of specialized "reader" modules that, in some instances, recognize the methylation status of histone lysine residues, such as histone H3 lysine 4 (H3K4). While mutations in catalytic enzymes that mediate the addition or removal of histone modifications (i.e., "writers" and "erasers") are already known to be involved in various human diseases, mutations in the modification-specific "reader" proteins are only beginning to be recognized as contributing to human diseases. For instance, point mutations, deletions or chromosomal translocations that target PHD fingers encoded by many genes (such as recombination activating gene 2 (RAG2), Inhibitor of Growth (ING), nuclear receptor-binding SET domain-containing 1 (NSD1) and Alpha Thalassaemia and Mental Retardation Syndrome, X-linked (ATRX)) have been associated with a wide range of human pathologies including immunological disorders, cancers, and neurological diseases. In this review, we will discuss the structural features of PHD fingers as well as the diseases for which direct mutation or dysregulation of the PHD finger has been reported. We propose that misinterpretation of the epigenetic marks may serve as a general mechanism for human diseases of this category. Determining the regulatory roles of histone covalent modifications in the context of human disease will allow for a more thorough understanding of normal and pathological development, and may provide innovative therapeutic strategies wherein "chromatin readers" stand as potential drug targets.     

RNA interference in infectious tropical diseases

Introduction of double-stranded RNA (dsRNA) into some cells or organisms results in degradation of its homologous mRNA, a process called RNA interference (RNAi). The dsRNAs are processed into short interfering RNAs (siRNAs) that subsequently bind to the RNA-induced silencing complex (RISC), causing degradation of target mRNAs. Because of this sequence-specific ability to silence target genes, RNAi has been extensively used to study gene functions and has the potential to control disease pathogens or vectors. With this promise of RNAi to control pathogens and vectors, this paper reviews the current status of RNAi in protozoans, animal parasitic helminths and disease-transmitting vectors, such as insects. Many pathogens and vectors cause severe parasitic diseases in tropical regions and it is difficult to control once the host has been invaded. Intracellularly, RNAi can be highly effective in impeding parasitic development and proliferation within the host. To fully realize its potential as a means to control tropical diseases, appropriate delivery methods for RNAi should be developed, and possible off-target effects should be minimized for specific gene suppression. RNAi can also be utilized to reduce vector competence to interfere with disease transmission, as genes critical for pathogenesis of tropical diseases are knockdowned via RNAi.     

Microglia and myeloperoxidase: a deadly partnership in neurodegenerative disease

The role of inflammation in Alzheimer's disease, Parkinson's disease, and multiple sclerosis has recently come under increased scrutiny. Associated with these inflammatory responses are tumor necrosis factor-alpha (TNF-alpha) and reactive oxygen species (ROS), both believed to be derived from brain microglia. In addition to the above, the presence of myeloperoxidase (MPO) in these diseased brains has been reported by a number of investigators. However, the possible role of MPO and enzymatically inactive MPO (iMPO) as the "choreographers" of the destruction done by TNF-alpha and ROS is not generally recognized. Previously, our laboratory has reported that MPO/iMPO enhance macrophage generation of ROS and expression of proinflammatory cytokine genes as well as gene products. Recent studies in our laboratory indicate that the same response occurs with microglia. A paradigm is presented for the perpetuation of inflammation associated with neurodegenerative diseases. This model describes the unrecognized consequences of the stimulation of microglia by MPO or iMPO. Both MPO and iMPO and/or its receptor may represent new therapeutic targets for the treatment of these diseases.     

New findings showing how DNA methylation influences diseases

In 1975, Holliday and Pugh as well as Riggs independently hypothesized that DNA methylation in eukaryotes could act as a hereditary regulation mechanism that influences gene expression and cell differentiation. Interest in the study of epigenetic processes has been inspired by their reversibility as well as their potentially preventable or treatable consequences. Recently, we have begun to understand that the features of DNA methylation are not the same for all cells.Major differences have been found between differentiated cells and stem cells.Methylation influences various pathologies, and it is very important to improve the understanding of the pathogenic mechanisms. Epigenetic modifications may take place throughout life and have been related to cancer, brain aging, memory disturbances, changes in synaptic plasticity, and neurodegenerative diseases,such as Parkinson's disease and Huntington's disease. DNA methylation also has a very important role in tumor biology. Many oncogenes are activated by mutations in carcinogenesis. However, many genes with tumor-suppressor functions are "silenced" by the methylation of CpG sites in some of their regions.Moreover, the role of epigenetic alterations has been demonstrated in neurological diseases. In neuronal precursors, many genes associated with development and differentiation are silenced by CpG methylation. In addition,recent studies show that DNA methylation can also influence diseases that do not appear to be related to the environment, such as IgA nephropathy, thus affecting,the expression of some genes involved in the T-cell receptor signaling. In conclusion, DNA methylation provides a whole series of fundamental information for the cell to regulate gene expression, including how and when the genes are read, and it does not depend on the DNA sequence.     

Computational gene knockout reveals transdisease-transgene association structure

Genome-wide association studies for a variety of diseases are identifying increasing numbers of candidate genes. Now we are confronted with the fact that some genes are common candidates across diseases. Thus there is a strong need to develop a hypothesis formulation methodology to comprehend multifaceted associations between genes and diseases. We have developed a computational method for building transdisease-transgene association structure. By introducing the basic rationale underlying the gene knockout approach as an information processing procedure to a network constructed on the basis of hyperlinks between disease and gene pages listed in the Online Mendelian Inheritance in Man (OMIM) database, relations of genes with diseases are computationally quantified. We did successively eliminate gene pages (called "computational gene knockout" in this paper) expected to contribute to metabolic syndrome, and catalogued each association with various disease pages. We thereby apply a co-clustering method to the gene-disease relations to obtain an association structure by classifying diseases and genes simultaneously. Observing an association structure between over 100 diseases and their related genes, we then found that the structure revealed gene classes that were commonly associated with diseases as well as gene classes that were selectively associated with a specific disease class.     

Effects of interleukin-1beta polymorphisms on brain function and behavior in healthy and psychiatric disease conditions

A high level of Interleukin-1beta (IL1B), a key mediator of inflammation, is expressed in the brain, particularly in the hippocampus, which plays a pivotal role in memory and mood regulation. In the brain, IL1B exerts a myriad of effects such as neuronal proliferation, differentiation, apoptosis, and long-term potentiation. Considering its pleiotropic effects in the brain, IL1B has been implicated in the pathogenesis of various psychiatric disorders as well as cognitive function in normal individuals. Thus, IL1B has been considered a candidate gene for the study of psychiatric diseases as well as brain function in normal individuals. The polymorphisms of IL1B have been described in relation to various expression levels in response to stimulation. This review describes previous studies on the genetic effects of IL1B, which relate it to psychiatric diseases such as major depressive disorder, bipolar disorder, schizophrenia, and Alzheimer’s disease, as well as cognitive function in normal individuals. Although many reports have indicated a possible role of the genetic effects of IL1B or its phenotypes in psychiatric diseases, some reports were unable to confirm these findings. IL1B release is mediated by an inflammatory response or psychological stress, leading to a cascade of immune reactions involving numerous immune components. To further explore the genetic effects of IL1B on mental diseases and brain function, gene–gene and gene–environment interactions should also be considered.     

Rise of the planet

Despite early failures, somatic gene therapy has recently shown renewed promise. Howy suggests that the day may come when germline gene therapy needs also to be reconsidered.EMBO reports (2013) 14, 1; doi:10.1038/embor.2012.194Human gene therapy has a short but chequered history. The recognition, in the 1980s, that many human diseases were caused by recessive single gene mutations, led inevitably to the idea that such defects could be corrected by the same technology that facilitates the creation of transgenic animals.At an early stage, scientists and clinicians explicitly eschewed the idea of making genetic modifications to the germline, even in the case of fatal diseases. This was partly to assuage public concern that such technology could be misused for eugenic purposes widely considered unethical and tainted by association with the Nazi genocide. More prosaically, the techniques for engineering changes in the genome were so novel that their safety could not be assured. Even if such hurdles could be overcome, the unpredictable effects upon subsequent generations were considered a sufficient reason to observe a self-imposed moratorium on any such ‘playing with evolution''. Germline manipulation of the human genome remains largely a taboo subject, of greater interest to Hollywood than Bethesda [1]. Indeed, editing even plant and animal genomes remains highly controversial and tightly restricted, if not completely prohibited, in many jurisdictions.Three decades later, it is time to reassess the issue in the light of our greatly expanded knowledge of genetics, and the rather limited success of non-germline approaches to genetic therapy to date.With the route blocked to germline transgenesis, the era of somatic gene therapy was born. In outline, the method seeks to replace a defective gene in those cells whose function is compromised, thus overcoming the deficiency. Numerous variations on this theme seek to target the gene or its expression to specified cell types, control its integration into the genome, incorporate molecular ‘safety triggers'' or limit the immune response to the modified cells. The experimental nature of somatic gene therapy dictated that initial trials were conducted only in cases of severely debilitating and inevitably fatal diseases. The inherent difficulties of targeting tissues within the body also restricted it, at first, to cell types where an ex vivo approach could be employed, such as blood. But even this approach to treat diseases such as severe combined immunodeficiency met with only limited success. Therapeutic benefits were seen, but were typically temporary, and some patients succumbed to serious or even fatal side-effects, for example, through the oncogenic effects of random insertions in the genome.These tragic outcomes cast a long shadow over subsequent trials. For a long time, almost the only diseases for which such an approach was contemplated were end-stage cancers, where the risk of novel neoplasms can be considered a side issue, as for radiotherapy or treatment with genotoxic drugs. Unfortunately, gene therapy for cancer, even when cleverly targeted, suffers the same methodological flaw as these older, cruder therapies, namely the practical impossibility of zapping every single tumour cell, including quiescent progenitor-type cells that may be the primary reservoir of disease. Some remissions have, however, been reported.A few brave attempts to develop the field are now under way, but fundamental safety and efficacy problems remain. Even when a non-immunogenic delivery system can be employed, a replacement gene typically elicits an immune reaction against what is, to the patient''s immune system, a foreign gene product. To be permanently effective, any such therapy requires at least a partial disabling of the recipient''s immune system, thus replacing one disease with another. The oncogenic risk associated with random insertions, as well as the common problem of transgene silencing can, in theory, be overcome by the use of targeted insertion systems based on site-specific recombinases. This has proven to be a powerful tool for creating transgenic animals in research. However, to be effective, it requires a specific landing pad in the recipient genome, and thus implies the prior use of germline genetic manipulation. In the future, it should be possible to use customized recombinases with enhanced specificity to target only one or a few ‘benign'' insertion sites in the human genome, permissive for transgene expression. But this has not yet been achieved. In weighing the ethical objections against germline gene therapy, we need to take account of the persisting problems with its somatic cousin.In the case of recessive disorders, preimplantation diagnosis offers a simple and safe alternative approach, although many people have ethical or religious objections to this procedure as well. But what if it were to be ascertained that making a specific alteration to the human genome could protect against Alzheimer disease or malaria? What if adding just a few additional copies of a tumour suppressor gene such as p53 could provide lifetime resistance to most common cancers? Preimplantation diagnosis is clearly useless for diseases acquired through somatic mutation or via epigenetic errors during development. Would it be ethical to withhold prophylactic germline ‘therapy'' if it could ensure the alleviation of suffering on a massive scale? Germline manipulation is already marching towards approval in the UK for mitochondrial DNA disorders [2]. To some this is the thin end of the wedge: to others it is a chink of light in a dark landscape.At some point in the future, humanity will have to face such questions. I believe that the continuing rapid progress in elucidating the underlying basis of disease must lead to feasible preventative strategies based on genetic technologies that essentially exist already. We need to be ready with answers.     

Identifying genetic risk factors for osteoporosis

Over the past decades epidemiological research of so-called "complex" diseases, i.e., common age-related disorders such as cancer, cardiovascular disease, diabetes, and osteoporosis, has identified anthropometric, behavioural, and serum parameters as risk factors. Recently, genetic polymorphisms have gained considerable interest, propelled by the Human Genome Project and its sequela that have identified most genes and uncovered a plethora of polymorphic variants, some of which embody the genetic risk factors. In all fields of complex disease genetics (including osteoporosis) progress in identifying these genetic factors has been hampered by often controversial results. Because of the small effect size for each individual risk polymorphism, this is mostly due to low statistical power and limitations of analytical methods. Genome-wide scanning approaches can be used to find the responsible genes. It is by now clear that linkage analysis is not suitable for this, but genome-wide association analysis has much better possibilities, as is illustrated by successful identification of risk alleles for several complex diseases. Candidate gene association analysis followed by replication and prospective multi-centred meta-analysis, is currently the best way forward to identify genetic markers for complex traits, such as osteoporosis. To accomplish this, we need large (global) collaborative studies using standardized methodology and definitions, to quantify by meta-analysis the subtle effects of the responsible gene variants.     

Evolutionary Signatures amongst Disease Genes Permit Novel Methods for Gene Prioritization and Construction of Informative Gene-Based Networks

Genes involved in the same function tend to have similar evolutionary histories, in that their rates of evolution covary over time. This coevolutionary signature, termed Evolutionary Rate Covariation (ERC), is calculated using only gene sequences from a set of closely related species and has demonstrated potential as a computational tool for inferring functional relationships between genes. To further define applications of ERC, we first established that roughly 55% of genetic diseases posses an ERC signature between their contributing genes. At a false discovery rate of 5% we report 40 such diseases including cancers, developmental disorders and mitochondrial diseases. Given these coevolutionary signatures between disease genes, we then assessed ERC''s ability to prioritize known disease genes out of a list of unrelated candidates. We found that in the presence of an ERC signature, the true disease gene is effectively prioritized to the top 6% of candidates on average. We then apply this strategy to a melanoma-associated region on chromosome 1 and identify MCL1 as a potential causative gene. Furthermore, to gain global insight into disease mechanisms, we used ERC to predict molecular connections between 310 nominally distinct diseases. The resulting “disease map” network associates several diseases with related pathogenic mechanisms and unveils many novel relationships between clinically distinct diseases, such as between Hirschsprung''s disease and melanoma. Taken together, these results demonstrate the utility of molecular evolution as a gene discovery platform and show that evolutionary signatures can be used to build informative gene-based networks.