From DNA to RNA to disease and back: the 'central dogma' of regulatory disease variation

Translational proteomics and phosphoproteomics: Tissue to extracellular vesicles

We are currently experiencing a rapidly developing era in terms of translational and clinical medical sciences. The relatively mature state of nucleic acid examination has significantly improved our understanding of disease mechanism and therapeutic potential of personalized treatment, but misses a large portion of phenotypic disease information. Proteins, in particular phosphorylation events that regulates many cellular functions, could provide real-time information for disease onset, progression and treatment efficacy. The technical advances in liquid chromatography and mass spectrometry have realized large-scale and unbiased proteome and phosphoproteome analyses with disease relevant samples such as tissues. However, tissue biopsy still has multiple shortcomings, such as invasiveness of sample collection, potential health risk for patients, difficulty in protein preservation and extreme heterogeneity. Recently, extracellular vesicles (EVs) have offered a great promise as a unique source of protein biomarkers for non-invasive liquid biopsy. Membranous EVs provide stable preservation of internal proteins and especially labile phosphoproteins, which is essential for effective routine biomarker detection. To aid efficient EV proteomic and phosphoproteomic analyses, recent developments showcase clinically-friendly EV techniques, facilitating diagnostic and therapeutic applications. Ultimately, we envision that with streamlined sample preparation from tissues and EVs proteomics and phosphoproteomics analysis will become routine in clinical settings.

The prediction of virus mutation using neural networks and rough set techniques

Viral evolution remains to be a main obstacle in the effectiveness of antiviral treatments. The ability to predict this evolution will help in the early detection of drug-resistant strains and will potentially facilitate the design of more efficient antiviral treatments. Various tools has been utilized in genome studies to achieve this goal. One of these tools is machine learning, which facilitates the study of structure-activity relationships, secondary and tertiary structure evolution prediction, and sequence error correction. This work proposes a novel machine learning technique for the prediction of the possible point mutations that appear on alignments of primary RNA sequence structure. It predicts the genotype of each nucleotide in the RNA sequence, and proves that a nucleotide in an RNA sequence changes based on the other nucleotides in the sequence. Neural networks technique is utilized in order to predict new strains, then a rough set theory based algorithm is introduced to extract these point mutation patterns. This algorithm is applied on a number of aligned RNA isolates time-series species of the Newcastle virus. Two different data sets from two sources are used in the validation of these techniques. The results show that the accuracy of this technique in predicting the nucleotides in the new generation is as high as 75 %. The mutation rules are visualized for the analysis of the correlation between different nucleotides in the same RNA sequence.

DNA-protein π-interactions in nature: abundance, structure, composition and strength of contacts between aromatic amino acids and DNA nucleobases or deoxyribose sugar

Four hundred twenty-eight high-resolution DNA-protein complexes were chosen for a bioinformatics study. Although 164 crystal structures (38% of those searched) contained no interactions, 574 discrete π-contacts between the aromatic amino acids and the DNA nucleobases or deoxyribose were identified using strict criteria, including visual inspection. The abundance and structure of the interactions were determined by unequivocally classifying the contacts as either π-π stacking, π-π T-shaped or sugar-π contacts. Three hundred forty-four nucleobase-amino acid π-π contacts (60% of all interactions identified) were identified in 175 of the crystal structures searched. Unprecedented in the literature, 230 DNA-protein sugar-π contacts (40% of all interactions identified) were identified in 137 crystal structures, which involve C-H···π and/or lone-pair···π interactions, contain any amino acid and can be classified according to sugar atoms involved. Both π-π and sugar-π interactions display a range of relative monomer orientations and therefore interaction energies (up to -50 (-70) kJ mol(-1) for neutral (charged) interactions as determined using quantum chemical calculations). In general, DNA-protein π-interactions are more prevalent than perhaps currently accepted and the role of such interactions in many biological processes may yet to be uncovered.

Predicting DNA methylation level across human tissues

Differences in methylation across tissues are critical to cell differentiation and are key to understanding the role of epigenetics in complex diseases. In this investigation, we found that locus-specific methylation differences between tissues are highly consistent across individuals. We developed a novel statistical model to predict locus-specific methylation in target tissue based on methylation in surrogate tissue. The method was evaluated in publicly available data and in two studies using the latest IlluminaBeadChips: a childhood asthma study with methylation measured in both peripheral blood leukocytes (PBL) and lymphoblastoid cell lines; and a study of postoperative atrial fibrillation with methylation in PBL, atrium and artery. We found that our method can greatly improve accuracy of cross-tissue prediction at CpG sites that are variable in the target tissue [R(2) increases from 0.38 (original R(2) between tissues) to 0.89 for PBL-to-artery prediction; from 0.39 to 0.95 for PBL-to-atrium; and from 0.81 to 0.98 for lymphoblastoid cell line-to-PBL based on cross-validation, and confirmed using cross-study prediction]. An extended model with multiple CpGs further improved performance. Our results suggest that large-scale epidemiology studies using easy-to-access surrogate tissues (e.g. blood) could be recalibrated to improve understanding of epigenetics in hard-to-access tissues (e.g. atrium) and might enable non-invasive disease screening using epigenetic profiles.

Non-coding RNAs in homeostasis, disease and stress responses: an evolutionary perspective

Cells and organisms are subject to challenges and perturbations in their environment and physiology in all stages of life. The molecular response to such changes, including insulting conditions such as pathogen infections, involves coordinated modulation of gene expression programmes and has not only homeostatic but also ecological and evolutionary importance. Although attention has been primarily focused on signalling pathways and protein networks, non-coding RNAs (ncRNAs), which comprise a significant output of the genomes of prokaryotes and especially eukaryotes, are increasingly implicated in the molecular mechanisms of these responses. Long and short ncRNAs not only regulate development and cell physiology, they are also involved in disease states, including cancers, in host-pathogen interactions, and in a variety of stress responses. Indeed, regulatory RNAs are part of genetically encoded response networks and also underpin epigenetic processes, which are emerging as key mechanisms of adaptation and transgenerational inheritance. Here we present the growing evidence that ncRNAs are intrinsically involved in cellular and organismal adaptation processes, in both robustness and protection to stresses, as well as in mechanisms generating evolutionary change.

Genetics of human gene expression

A steadily growing number of studies have identified and characterized expression quantitative trait loci (eQTLs) in human cell-lines, primary cells, and tissues. This class of variation has been shown to play a role in complex traits, including disease. Here, we discuss how eQTLs have the potential to accelerate discovery of disease genes and functional mechanisms underlying complex traits. We discuss how context-specificity of eQTLs is being characterized at an unprecedented scale and breadth, and how this both informs on the intricacy of human genome function, and has important ramifications for elucidating function of genetic variants of interest, particularly for those contributing to disease.

Significant strength of charged DNA-protein π-π interactions: a preliminary study of cytosine

The present work characterized the preferred gas-phase structure and optimum interaction energy of both parallel stacked and perpendicular T-shaped dimers between cytosine (C), as a representative nucleobase, and aspartic/glutamic acid (DE), aspartate/glutamate (DE(-)) or arginine (R(+)), using detailed M06-2X/6-31+G(d,p) potential energy surface scans as a function of the relative monomer orientation. Through comparison to previous literature on the π-π interactions between the DNA nucleobases and the aromatic amino acid residues, this work will allow for comparisons between DNA-protein interactions involving aromatic and acyclic R-side chains, as well as comparisons of the relative geometric dependence and magnitude of π-π (C:DE), πcation-π (C:R(+)), and πanion-π (C:DE(-)) interactions. Our results show that the preferred relative monomer orientation is highly dependent on the monomer composition and charge, and is dictated by electrostatic-driven interactions. More importantly, for the first time, we report that the π-π interactions between cytosine and (neutral) aspartic/glutamic acid are up to approximately -40 kJ mol(-1), while the πcation-π or πanion-π interactions between cytosine and arginine or aspartate/glutamate are up to approximately -90 and -99 kJ mol(-1), respectively. An extensive investigation of the effects of the computational methodology implemented, including comparisons to detailed CCSD(T)/CBS potential energy surfaces and interaction energies, supports the use of M06-2X, as well as ωB97X-D, to study DNA-protein π-π interactions of varying composition and charge. Most importantly, the CCSD(T)/CBS results verify the strong nature of these DNA-protein π-π interactions, as well as the unique nature of the πcation-π and πanion-π counterparts. Therefore, our results emphasize that a wide variety of different types of noncovalent interactions between both cyclic and acyclic π-containing components can significantly contribute to the stability of DNA-protein complexes and likely play a larger role in biology than currently accepted.

Patterns of cis regulatory variation in diverse human populations

The genetic basis of gene expression variation has long been studied with the aim to understand the landscape of regulatory variants, but also more recently to assist in the interpretation and elucidation of disease signals. To date, many studies have looked in specific tissues and population-based samples, but there has been limited assessment of the degree of inter-population variability in regulatory variation. We analyzed genome-wide gene expression in lymphoblastoid cell lines from a total of 726 individuals from 8 global populations from the HapMap3 project and correlated gene expression levels with HapMap3 SNPs located in cis to the genes. We describe the influence of ancestry on gene expression levels within and between these diverse human populations and uncover a non-negligible impact on global patterns of gene expression. We further dissect the specific functional pathways differentiated between populations. We also identify 5,691 expression quantitative trait loci (eQTLs) after controlling for both non-genetic factors and population admixture and observe that half of the cis-eQTLs are replicated in one or more of the populations. We highlight patterns of eQTL-sharing between populations, which are partially determined by population genetic relatedness, and discover significant sharing of eQTL effects between Asians, European-admixed, and African subpopulations. Specifically, we observe that both the effect size and the direction of effect for eQTLs are highly conserved across populations. We observe an increasing proximity of eQTLs toward the transcription start site as sharing of eQTLs among populations increases, highlighting that variants close to TSS have stronger effects and therefore are more likely to be detected across a wider panel of populations. Together these results offer a unique picture and resource of the degree of differentiation among human populations in functional regulatory variation and provide an estimate for the transferability of complex trait variants across populations.

Using gene expression to investigate the genetic basis of complex disorders

The identification of complex disease susceptibility loci through genome-wide association studies (GWAS) has recently become possible and is now a method of choice for investigating the genetic basis of complex traits. The number of results from such studies is constantly increasing but the challenge lying forward is to identify the biological context in which these statistically significant candidate variants act. Regulatory variation plays an important role in shaping phenotypic differences among individuals and thus is very likely to also influence disease susceptibility. As such, integrating gene expression data and other disease relevant intermediate phenotypes with GWAS results could potentially help prioritize fine-mapping efforts and provide a shortcut to disease biology. Combining these different levels of information in a meaningful way is however not trivial. In the present review, we outline the several approaches that have been explored so far in this sense and their achievements. We also discuss the limitations of the methods and how upcoming technological developments could help circumvent these limitations. Overall, such efforts will be very helpful in understanding initially regulatory effects on disease and disease etiology in general.

A simple principle concerning the robustness of protein complex activity to changes in gene expression

Background

The functions of a eukaryotic cell are largely performed by multi-subunit protein complexes that act as molecular machines or information processing modules in cellular networks. An important problem in systems biology is to understand how, in general, these molecular machines respond to perturbations.

Results

In yeast, genes that inhibit growth when their expression is reduced are strongly enriched amongst the subunits of multi-subunit protein complexes. This applies to both the core and peripheral subunits of protein complexes, and the subunits of each complex normally have the same loss-of-function phenotypes. In contrast, genes that inhibit growth when their expression is increased are not enriched amongst the core or peripheral subunits of protein complexes, and the behaviour of one subunit of a complex is not predictive for the other subunits with respect to over-expression phenotypes.

Conclusion

We propose the principle that the overall activity of a protein complex is in general robust to an increase, but not to a decrease in the expression of its subunits. This means that whereas phenotypes resulting from a decrease in gene expression can be predicted because they cluster on networks of protein complexes, over-expression phenotypes cannot be predicted in this way. We discuss the implications of these findings for understanding how cells are regulated, how they evolve, and how genetic perturbations connect to disease in humans.

Regulatory variation and evolution: implications for disease

In the past few years, it has become apparent that there is a substantial amount of noncoding DNA that contributes to genome function. However, the multidimensionality of noncoding DNA properties does not allow us to readily identify, characterize, and assess the functional impact of mutations, polymorphisms, and interspecific substitutions. In this chapter, we discuss the evolutionary properties of some of the known noncoding genomic elements, namely regulatory regions, and the extensions of this to other potentially functionally important noncoding regions such as conserved noncoding regions. The implications of this analysis for studies looking at molecular phenotypes such as gene expression and whole-organism phenotypes (e.g., disease) are presented in the context of the exploration of noncoding DNA properties. The aim is to take advantage of current and emerging analysis methods for noncoding DNA to elucidate the genetic causes of phenotypic variation.

Copy number variants and genetic traits: closer to the resolution of phenotypic to genotypic variability

A considerable and unanticipated plasticity of the human genome, manifested as inter-individual copy number variation, has been discovered. These structural changes constitute a major source of inter-individual genetic variation that could explain variable penetrance of inherited (Mendelian and polygenic) diseases and variation in the phenotypic expression of aneuploidies and sporadic traits, and might represent a major factor in the aetiology of complex, multifactorial traits. For these reasons, an effort should be made to discover all common and rare copy number variants (CNVs) in the human population. This will also enable systematic exploration of both SNPs and CNVs in association studies to identify the genomic contributors to the common disorders and complex traits.

Population genomics of human gene expression

Genetic variation influences gene expression, and this variation in gene expression can be efficiently mapped to specific genomic regions and variants. Here we have used gene expression profiling of Epstein-Barr virus-transformed lymphoblastoid cell lines of all 270 individuals genotyped in the HapMap Consortium to elucidate the detailed features of genetic variation underlying gene expression variation. We find that gene expression is heritable and that differentiation between populations is in agreement with earlier small-scale studies. A detailed association analysis of over 2.2 million common SNPs per population (5% frequency in HapMap) with gene expression identified at least 1,348 genes with association signals in cis and at least 180 in trans. Replication in at least one independent population was achieved for 37% of cis signals and 15% of trans signals, respectively. Our results strongly support an abundance of cis-regulatory variation in the human genome. Detection of trans effects is limited but suggests that regulatory variation may be the key primary effect contributing to phenotypic variation in humans. We also explore several methodologies that improve the current state of analysis of gene expression variation.

Survey of polymorphic sequence variation in the immediate 5' region of human DNA repair genes

Systematic screens have revealed extensive DNA sequence variation existing in the human population. Studies of the role of polymorphic genetic variants in explaining the association of family history with risk of common disease have generally focused on variants predicted to disrupt protein structure and activity. Recent studies have identified genetic variation in the level of expression of many genes, variation that is potentially biologically relevant in explaining individual variation in disease risk. In a survey of data available for 108 DNA repair genes that have been systematically screened for sequence variation, an average of 3.3 SNPs per gene were found to exist at a variant allele frequency of at least 0.02 in the region 2kb upstream from the 5'-untranslated region. One-third of the genes harbored a SNP with an allele frequency of at least 0.02 within a predicted promotor element. These variants are distributed among promoter elements that average 20 elements per gene. The frequency of polymorphic SNPs in CpG islands was 0.8 per gene, while the frequency of SNPs in the 5'-UTR was 0.7 per gene. The recognition of extensive genetic variation with potential to impact levels of gene expression, and thereby exacerbate the impact of amino acid substitution variants on the activity of proteins, increases the complexity of analyses required to explain the molecular genetic basis for the familial contribution to the sporadic incidence of common disease.