Sequence variation in G-protein-coupled receptors: analysis of single nucleotide polymorphisms

In vivo functional profiling and structural characterisation of the human Glp1r A316T variant

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are effective therapies for type 2 diabetes (T2D) and obesity, yet patient responses are variable. Variation in the human Glp1r gene might be directly linked to therapeutic responses. A naturally occurring missense variant, A316T, protects against T2D and cardiovascular disease. Here, we have generated and characterised a human Glp1r A316T mouse model. Human Glp1r A316T/A316T mice displayed lower fasting blood glucose versus wildtype littermates, even under metabolic stress, and exhibited alterations in islet cytoarchitecture and α/β identity under a high-fat, high-sucrose diet. This was however associated with blunted responses to GLP-1RAs in vivo. Further investigations in rodent and human β-cell models demonstrated that human Glp1r A316T exhibits characteristics of constitutive activation but dampened GLP-1RA responses. Results are further supported by cryo-EM analyses and molecular dynamics simulations of GLP-1R A316T structure, collectively demonstrating that the A316T variant governs basal GLP-1R activity and pharmacological responses to GLP-1R-targeting therapies.

G protein-coupled receptor (GPCR) pharmacogenomics

The field of pharmacogenetics, the investigation of the influence of one or more sequence variants on drug response phenotypes, is a special case of pharmacogenomics, a discipline that takes a genome-wide approach. Massively parallel, next generation sequencing (NGS), has allowed pharmacogenetics to be subsumed by pharmacogenomics with respect to the identification of variants associated with responders and non-responders, optimal drug response, and adverse drug reactions. A plethora of rare and common naturally-occurring GPCR variants must be considered in the context of signals from across the genome. Many fundamentals of pharmacogenetics were established for G protein-coupled receptor (GPCR) genes because they are primary targets for a large number of therapeutic drugs. Functional studies, demonstrating likely-pathogenic and pathogenic GPCR variants, have been integral to establishing models used for in silico analysis. Variants in GPCR genes include both coding and non-coding single nucleotide variants and insertion or deletions (indels) that affect cell surface expression (trafficking, dimerization, and desensitization/downregulation), ligand binding and G protein coupling, and variants that result in alternate splicing encoding isoforms/variable expression. As the breadth of data on the GPCR genome increases, we may expect an increase in the use of drug labels that note variants that significantly impact the clinical use of GPCR-targeting agents. We discuss the implications of GPCR pharmacogenomic data derived from the genomes available from individuals who have been well-phenotyped for receptor structure and function and receptor-ligand interactions, and the potential benefits to patients of optimized drug selection. Examples discussed include the renin-angiotensin system in SARS-CoV-2 (COVID-19) infection, the probable role of chemokine receptors in the cytokine storm, and potential protease activating receptor (PAR) interventions. Resources dedicated to GPCRs, including publicly available computational tools, are also discussed.

Single-Nucleotide Polymorphisms of TAS2R46 Affect the Receptor Downstream Calcium Regulation in Histamine-Challenged Cells

Bitter taste receptors (TAS2Rs) expressed in extraoral tissues represent a whole-body sensory system, whose role and mechanisms could be of interest for the identification of new therapeutic targets. It is known that TAS2R46s in pre-contracted airway smooth muscle cells increase mitochondrial calcium uptake, leading to bronchodilation, and that several SNPs have been identified in its gene sequence. There are very few reports on the structure-function analysis of TAS2Rs. Thus, we delved into the subject by using mutagenesis and in silico studies. We generated a cellular model that expresses native TAS2R46 to evaluate the influence of the four most common SNPs on calcium fluxes following the activation of the receptor by its specific ligand absinthin. Then, docking studies were conducted to correlate the calcium flux results to the structural mutation. The analysed SNPs differently modulate the TAS2R46 signal cascade according to the altered protein domain. In particular, the SNP in the sixth transmembrane domain of the receptors did not modulate calcium homeostasis, while the SNPs in the sequence coding for the fourth transmembrane domain completely abolished the mitochondrial calcium uptake. In conclusion, these results indicate the fourth transmembrane domain of TAS2R46 is critical for the intrinsic receptor activity.

Leucine-rich repeat receptor-like kinase II phylogenetics reveals five main clades throughout the plant kingdom

Receptor-like kinases (RLKs) represent the largest group of cell surface receptors in plants. The monophyletic leucine-rich repeat (LRR)-RLK subfamily II is considered to contain the somatic embryogenesis receptor kinases (SERKs) and NSP-interacting kinases known to be involved in developmental processes and cellular immunity in plants. There are only a few published studies on the phylogenetics of LRR-RLKII; unfortunately these suffer from poor taxon/gene sampling. Hence, it is not clear how many and what main clades this family contains, let alone what structure-function relationships exist. We used 1342 protein sequences annotated as 'SERK' and 'SERK-like' plus related sequences in order to estimate phylogeny within the LRR-RLKII clade, using the nematode protein kinase Pelle as an outgroup. We reconstruct five main clades (LRR-RLKII 1-5), in each of which the main pattern of land plant relationships re-occurs, confirming previous hypotheses that duplication events happened in this gene subfamily prior to divergence among land plant lineages. We show that domain structures and intron-exon boundaries within the five clades are well conserved in evolution. Furthermore, phylogenetic patterns based on the separate LRR and kinase parts of LRR-RLKs are incongruent: whereas the LRR part supports a LRR-RLKII 2/3 sister group relationship, the kinase part supports clades 1/2. We infer that the kinase part includes few 'radical' amino acid changes compared with the LRR part. Finally, our results confirm that amino acids involved in each LRR-RLKII-receptor complex interaction are located at N-capping residues, and that the short amino acid motifs of this interaction domain are highly conserved throughout evolution within the five LRR-RLKII clades.

Mapping genotype-phenotype associations of nsSNPs in coiled-coil oligomerization domains of the human proteome

We assessed the impact of disease mutations (DMs) versus polymorphisms (PYs) in coiled-coil (CC) domains in UniProt by modeling the structural and functional impact of variants in silico with the CC prediction program Multicoil. The structural impact of variants was evaluated with respect to three main metrics: the oligomerization score-to determine whether the variant is stabilizing or destabilizing-the oligomerization state, and the register-specific score. The functional impact was queried indirectly in several ways. First, we examined marginally stable CCs that were either stabilized or destabilized by the variant. Second, we looked for variants that altered the register of the wild-type CC near wild-type irregularities of likely functional importance, such as skips and stammers. Third, we searched for variants that altered the oligomerization state of the CC. DMs tended to be more destabilizing than PYs; but interestingly, PYs were more frequently associated with predicted changes in the oligomerization state. The functional impact was also queried by testing the association of CC variants with multiple phenotypes, that is, pleiotropy. Mutations in CC regions of proteins cause 155 different phenotypes and are more frequently associated with pleiotropy than proteins in general. Importantly, the CC region itself often encodes the pleiotropy.

Polymorphic Variation in FFA Receptors: Functions and Consequences

Overfeeding of fat can cause various metabolic disorders including obesity and type 2 diabetes (T2D). Diet provided free fatty acids (FFAs) are not only essential nutrients, but they are also recognized as signaling molecules, which stimulate various important biological functions. Recently, several G protein-coupled receptors (GPCRs), including FFA1-4, have been identified as receptors of FFAs by various physiological and pharmacological studies. FFAs exert physiological functions through these FFA receptors (FFARs) depending on carbon chain length and degree of unsaturation. Functional analyses have revealed that several important metabolic processes, such as peptide hormone secretion, cell maturation and nerve activities, are regulated by FFARs and thereby FFARs contribute to the energy homeostasis through these physiological functions. Hence, FFARs are expected to be promising pharmacological targets for metabolic disorders since imbalances in energy homeostasis lead to metabolic disorders. In human, it is established that different responses of individuals to endogenous ligands and chemical drugs may be due to differences in the ability of such ligands to activate nucleotide polymorphic variants of receptors. However, the clear links between genetic variations that are involved in metabolic disorders and polymorphisms receptors have been relatively difficult to assess. In this review, I summarize current literature describing physiological functions of FFARs and genetic variations of those receptors to discuss the potential of FFARs as drug targets for metabolic disorders.

Cysteinyl Leukotrienes Pathway Genes, Atopic Asthma and Drug Response: From Population Isolates to Large Genome-Wide Association Studies

Genetic variants associated with asthma pathogenesis and altered response to drug therapy are discussed. Many studies implicate polymorphisms in genes encoding the enzymes responsible for leukotriene synthesis and intracellular signaling through activation of seven transmembrane domain receptors, such as the cysteinyl leukotriene 1 (CYSLTR1) and 2 (CYSLTR2) receptors. The leukotrienes are polyunsaturated lipoxygenated eicosatetraenoic acids that exhibit a wide range of pharmacological and physiological actions. Of the three enzymes involved in the formation of the leukotrienes, arachidonate 5 lipoxygenase 5 (ALOX5), leukotriene C4 synthase (LTC4S), and leukotriene hydrolase (LTA4H) are all polymorphic. These polymorphisms often result in variable production of the CysLTs (LTC4, LTD4, and LTE4) and LTB4. Variable number tandem repeat sequences located in the Sp1-binding motif within the promotor region of the ALOX5 gene are associated with leukotriene burden and bronchoconstriction independent of asthma risk. A 444A > C SNP polymorphism in the LTC4S gene, encoding an enzyme required for the formation of a glutathione adduct at the C-6 position of the arachidonic acid backbone, is associated with severe asthma and altered response to the CYSLTR1 receptor antagonist zafirlukast. Genetic variability in the CysLT pathway may contribute additively or synergistically to altered drug responses. The 601 A > G variant of the CYSLTR2 gene, encoding the Met201Val CYSLTR2 receptor variant, is associated with atopic asthma in the general European population, where it is present at a frequency of ∼2.6%. The variant was originally found in the founder population of Tristan da Cunha, a remote island in the South Atlantic, in which the prevalence of atopy is approximately 45% and the prevalence of asthma is 36%. In vitro work showed that the atopy-associated Met201Val variant was inactivating with respect to ligand binding, Ca²⁺ flux and inositol phosphate generation. In addition, the CYSLTR1 gene, located at Xq13-21.1, has been associated with atopic asthma. The activating Gly300Ser CYSLTR1 variant is discussed. In addition to genetic loci, risk for asthma may be influenced by environmental factors such as smoking. The contribution of CysLT pathway gene sequence variants to atopic asthma is discussed in the context of other genes and environmental influences known to influence asthma.

Structural modeling of G-protein coupled receptors: An overview on automatic web-servers

Despite the significant efforts and discoveries during the last few years in G protein-coupled receptor (GPCR) expression and crystallization, the receptors with known structures to date are limited only to a small fraction of human GPCRs. The lack of experimental three-dimensional structures of the receptors represents a strong limitation that hampers a deep understanding of their function. Computational techniques are thus a valid alternative strategy to model three-dimensional structures. Indeed, recent advances in the field, together with extraordinary developments in crystallography, in particular due to its ability to capture GPCRs in different activation states, have led to encouraging results in the generation of accurate models. This, prompted the community of modelers to render their methods publicly available through dedicated databases and web-servers. Here, we present an extensive overview on these services, focusing on their advantages, drawbacks and their role in successful applications. Future challenges in the field of GPCR modeling, such as the predictions of long loop regions and the modeling of receptor activation states are presented as well.

Computational approaches to study the effects of small genomic variations

Advances in DNA sequencing technologies have led to an avalanche-like increase in the number of gene sequences deposited in public databases over the last decade as well as the detection of an enormous number of previously unseen nucleotide variants therein. Given the size and complex nature of the genome-wide sequence variation data, as well as the rate of data generation, experimental characterization of the disease association of each of these variations or their effects on protein structure/function would be costly, laborious, time-consuming, and essentially impossible. Thus, in silico methods to predict the functional effects of sequence variations are constantly being developed. In this review, we summarize the major computational approaches and tools that are aimed at the prediction of the functional effect of mutations, and describe the state-of-the-art databases that can be used to obtain information about mutation significance. We also discuss future directions in this highly competitive field.

Functional characterization of two single nucleotide polymorphisms of acyl-coenzyme A:cholesterol acyltransferase 2

BACKGROUND

Acyl-coenzyme A:cholesterol acyltransferase 2 (ACAT2) plays a critical role in the formation of cholesteryl esters from cholesterol and fatty acids, and is a potential target for treating hypercholesterolemia. We recently reported the significant effects of two human ACAT2 gene polymorphisms, 41A>G (Glu(14)Gly, rs9658625) and 734C>T (Thr(254)Ile, rs2272296), on plasma lipid levels and coronary artery disease susceptibility in a case-control association study. In the present study, we evaluated the possible biological influence of the two polymorphism using two approaches.

METHODS

In the first approach, the functional impact of the two polymorphisms was predicted in-silico using available web-based software, and in the second approach, the varying functions of the two polymorphisms were characterized in in vitro experiments, using ACAT2-deficient AC-29 cells.

RESULTS

Our results show that the enzymatic activity of mutant Glu(14)Gly is approximately two times higher than wildtype, and that this increase is primarily due to the increased expression and/or stability of the mutant ACAT2 protein.

CONCLUSIONS

These results suggest that the genetic variation at Glu(14)Gly is functionally important and may contribute to ACAT2 protein expression and stability.

Widespread macromolecular interaction perturbations in human genetic disorders

How disease-associated mutations impair protein activities in the context of biological networks remains mostly undetermined. Although a few renowned alleles are well characterized, functional information is missing for over 100,000 disease-associated variants. Here we functionally profile several thousand missense mutations across a spectrum of Mendelian disorders using various interaction assays. The majority of disease-associated alleles exhibit wild-type chaperone binding profiles, suggesting they preserve protein folding or stability. While common variants from healthy individuals rarely affect interactions, two-thirds of disease-associated alleles perturb protein-protein interactions, with half corresponding to "edgetic" alleles affecting only a subset of interactions while leaving most other interactions unperturbed. With transcription factors, many alleles that leave protein-protein interactions intact affect DNA binding. Different mutations in the same gene leading to different interaction profiles often result in distinct disease phenotypes. Thus disease-associated alleles that perturb distinct protein activities rather than grossly affecting folding and stability are relatively widespread.

Assessment of the predictive accuracy of five in silico prediction tools, alone or in combination, and two metaservers to classify long QT syndrome gene mutations

Background

Long QT syndrome (LQTS) is an autosomal dominant condition predisposing to sudden death from malignant arrhythmia. Genetic testing identifies many missense single nucleotide variants of uncertain pathogenicity. Establishing genetic pathogenicity is an essential prerequisite to family cascade screening. Many laboratories use in silico prediction tools, either alone or in combination, or metaservers, in order to predict pathogenicity; however, their accuracy in the context of LQTS is unknown. We evaluated the accuracy of five in silico programs and two metaservers in the analysis of LQTS 1–3 gene variants.

Methods

The in silico tools SIFT, PolyPhen-2, PROVEAN, SNPs&GO and SNAP, either alone or in all possible combinations, and the metaservers Meta-SNP and PredictSNP, were tested on 312 KCNQ1, KCNH2 and SCN5A gene variants that have previously been characterised by either in vitro or co-segregation studies as either “pathogenic” (283) or “benign” (29). The accuracy, sensitivity, specificity and Matthews Correlation Coefficient (MCC) were calculated to determine the best combination of in silico tools for each LQTS gene, and when all genes are combined.

Results

The best combination of in silico tools for KCNQ1 is PROVEAN, SNPs&GO and SIFT (accuracy 92.7%, sensitivity 93.1%, specificity 100% and MCC 0.70). The best combination of in silico tools for KCNH2 is SIFT and PROVEAN or PROVEAN, SNPs&GO and SIFT. Both combinations have the same scores for accuracy (91.1%), sensitivity (91.5%), specificity (87.5%) and MCC (0.62). In the case of SCN5A, SNAP and PROVEAN provided the best combination (accuracy 81.4%, sensitivity 86.9%, specificity 50.0%, and MCC 0.32). When all three LQT genes are combined, SIFT, PROVEAN and SNAP is the combination with the best performance (accuracy 82.7%, sensitivity 83.0%, specificity 80.0%, and MCC 0.44). Both metaservers performed better than the single in silico tools; however, they did not perform better than the best performing combination of in silico tools.

Conclusions

The combination of in silico tools with the best performance is gene-dependent. The in silico tools reported here may have some value in assessing variants in the KCNQ1 and KCNH2 genes, but caution should be taken when the analysis is applied to SCN5A gene variants.

Electronic supplementary material

The online version of this article (doi:10.1186/s12881-015-0176-z) contains supplementary material, which is available to authorized users.

Assessment of the predictive accuracy of five in silico prediction tools, alone or in combination, and two metaservers to classify long QT syndrome gene mutations

BACKGROUND

Long QT syndrome (LQTS) is an autosomal dominant condition predisposing to sudden death from malignant arrhythmia. Genetic testing identifies many missense single nucleotide variants of uncertain pathogenicity. Establishing genetic pathogenicity is an essential prerequisite to family cascade screening. Many laboratories use in silico prediction tools, either alone or in combination, or metaservers, in order to predict pathogenicity; however, their accuracy in the context of LQTS is unknown. We evaluated the accuracy of five in silico programs and two metaservers in the analysis of LQTS 1-3 gene variants.

METHODS

The in silico tools SIFT, PolyPhen-2, PROVEAN, SNPs&GO and SNAP, either alone or in all possible combinations, and the metaservers Meta-SNP and PredictSNP, were tested on 312 KCNQ1, KCNH2 and SCN5A gene variants that have previously been characterised by either in vitro or co-segregation studies as either "pathogenic" (283) or "benign" (29). The accuracy, sensitivity, specificity and Matthews Correlation Coefficient (MCC) were calculated to determine the best combination of in silico tools for each LQTS gene, and when all genes are combined.

RESULTS

The best combination of in silico tools for KCNQ1 is PROVEAN, SNPs&GO and SIFT (accuracy 92.7%, sensitivity 93.1%, specificity 100% and MCC 0.70). The best combination of in silico tools for KCNH2 is SIFT and PROVEAN or PROVEAN, SNPs&GO and SIFT. Both combinations have the same scores for accuracy (91.1%), sensitivity (91.5%), specificity (87.5%) and MCC (0.62). In the case of SCN5A, SNAP and PROVEAN provided the best combination (accuracy 81.4%, sensitivity 86.9%, specificity 50.0%, and MCC 0.32). When all three LQT genes are combined, SIFT, PROVEAN and SNAP is the combination with the best performance (accuracy 82.7%, sensitivity 83.0%, specificity 80.0%, and MCC 0.44). Both metaservers performed better than the single in silico tools; however, they did not perform better than the best performing combination of in silico tools.

CONCLUSIONS

The combination of in silico tools with the best performance is gene-dependent. The in silico tools reported here may have some value in assessing variants in the KCNQ1 and KCNH2 genes, but caution should be taken when the analysis is applied to SCN5A gene variants.

Pharmacogenetics of the G protein-coupled receptors

Pharmacogenetics investigates the influence of genetic variants on physiological phenotypes related to drug response and disease, while pharmacogenomics takes a genome-wide approach to advancing this knowledge. Both play an important role in identifying responders and nonresponders to medication, avoiding adverse drug reactions, and optimizing drug dose for the individual. G protein-coupled receptors (GPCRs) are the primary target of therapeutic drugs and have been the focus of these studies. With the advance of genomic technologies, there has been a substantial increase in the inventory of naturally occurring rare and common GPCR variants. These variants include single-nucleotide polymorphisms and insertion or deletions that have potential to alter GPCR expression of function. In vivo and in vitro studies have determined functional roles for many GPCR variants, but genetic association studies that define the physiological impact of the majority of these common variants are still limited. Despite the breadth of pharmacogenetic data available, GPCR variants have not been included in drug labeling and are only occasionally considered in optimizing clinical use of GPCR-targeted agents. In this chapter, pharmacogenetic and genomic studies on GPCR variants are reviewed with respect to a subset of GPCR systems, including the adrenergic, calcium sensing, cysteinyl leukotriene, cannabinoid CB1 and CB2 receptors, and the de-orphanized receptors such as GPR55. The nature of the disruption to receptor function is discussed with respect to regulation of gene expression, expression on the cell surface (affected by receptor trafficking, dimerization, desensitization/downregulation), or perturbation of receptor function (altered ligand binding, G protein coupling, constitutive activity). The large body of experimental data generated on structure and function relationships and receptor-ligand interactions are being harnessed for the in silico functional prediction of naturally occurring GPCR variants. We provide information on online resources dedicated to GPCRs and present applications of publically available computational tools for pharmacogenetic studies of GPCRs. As the breadth of GPCR pharmacogenomic data becomes clearer, the opportunity for routine assessment of GPCR variants to predict disease risk, drug response, and potential adverse drug effects will become possible.

Classifying variants of CDKN2A using computational and laboratory studies

Variants in the CDKN2A tumor suppressor are associated with Familial Melanoma (FM), although for many variants the linkage is weak. The effects of missense variants on protein function and pathogenicity are often unclear. Multiple methods (e.g., laboratory, computational, epidemiological) have been developed to analyze whether a missense variant is pathogenic or not. It is not yet clear how to integrate these data types into a strategy for variant classification. We studied 51 CDKN2A missense variants using a cell cycle arrest assay. There was a continuum of results ranging from full wild-type effect through partial activity to complete loss of arrest. A reproducible decrease of 30% of cell cycle arrest activity correlated with FM association. We analyzed missense CDKN2A germline variants using a Bayesian method to combine multiple data types and derive a probability of pathogenicity. When equal to or more than two data types could be evaluated with this method, 22 of 25 FM-associated variants and 8 of 15 variants of uncertain significance were classified as likely pathogenic with >95% probability. The other 10 variants were classified as uncertain (probability 5-95%). For most variants, there were insufficient data to draw a conclusion. The Bayesian model appears to be a sound method of classifying missense variants in cancer susceptibility genes.

Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed

Multiple algorithms are used to predict the impact of missense mutations on protein structure and function using algorithm-generated sequence alignments or manually curated alignments. We compared the accuracy with native alignment of SIFT, Align-GVGD, PolyPhen-2, and Xvar when generating functionality predictions of well-characterized missense mutations (n = 267) within the BRCA1, MSH2, MLH1, and TP53 genes. We also evaluated the impact of the alignment employed on predictions from these algorithms (except Xvar) when supplied the same four alignments including alignments automatically generated by (1) SIFT, (2) Polyphen-2, (3) Uniprot, and (4) a manually curated alignment tuned for Align-GVGD. Alignments differ in sequence composition and evolutionary depth. Data-based receiver operating characteristic curves employing the native alignment for each algorithm result in area under the curve of 78-79% for all four algorithms. Predictions from the PolyPhen-2 algorithm were least dependent on the alignment employed. In contrast, Align-GVGD predicts all variants neutral when provided alignments with a large number of sequences. Of note, algorithms make different predictions of variants even when provided the same alignment and do not necessarily perform best using their own alignment. Thus, researchers should consider optimizing both the algorithm and sequence alignment employed in missense prediction.

Synthesis and binding studies of novel diethynyl-pyridine amides with genomic promoter DNA G-quadruplexes

Herein, we report the design, synthesis and biophysical evaluation of novel 1,2,3-triazole-linked diethynyl-pyridine amides and trisubstituted diethynyl-pyridine amides as promising G-quadruplex binding ligands. We have used a Cu(I)-catalysed azide-alkyne cycloaddition click reaction to prepare the 1,2,3-triazole-linked diethynyl-pyridine amides. The G-quadruplex DNA binding properties of the ligands have been examined by using a Förster resonance energy transfer (FRET) melting assay and surface plasmon resonance (SPR) experiments. The investigated compounds are conformationally flexible, having free rotation around the triple bond, and exhibit enhanced G-quadruplex binding stabilisation and specificity between intramolecular promoter G-quadruplex DNA motifs compared to the first generation of diaryl-ethynyl amides (J. Am. Chem. Soc. 2008, 130, 15950-15956). The ligands show versatility in molecular recognition and promising G-quadruplex discrimination with 2-50-fold selectivity exhibited between different intramolecular promoter G-quadruplexes. Circular dichroism (CD) spectroscopic analysis suggested that at higher concentration these ligands disrupt the c-kit2 G-quadruplex structure. The studies validate the design concept of the 1,3-diethynyl-pyridine-based scaffold and demonstrate that these ligands exhibit not only significant selectivity over duplex DNA but also variation in G-quadruplex interaction properties based on small chemical changes in the scaffold, leading to unprecedented differential recognition of different DNA G-quadruplex sequences.

Structural imperatives impose diverse evolutionary constraints on helical membrane proteins

The amino acid sequences of transmembrane regions of helical membrane proteins are highly constrained, diverging at slower rates than their extramembrane regions and than water-soluble proteins. Moreover, helical membrane proteins seem to fall into fewer families than water-soluble proteins. The reason for the differential restrictions on sequence remains unexplained. Here, we show that the evolution of transmembrane regions is slowed by a previously unrecognized structural constraint: Transmembrane regions bury more residues than extramembrane regions and soluble proteins. This fundamental feature of membrane protein structure is an important contributor to the differences in evolutionary rate and to an increased susceptibility of the transmembrane regions to disease-causing single-nucleotide polymorphisms.

Predicting potentially functional SNPs in drug-response genes

SNPs are known to contribute to variations in drug response and there are more than 14 million polymorphisms spanning the human genome. However, not all of these SNPs are functional. It would be impractical and costly to evaluate every individual SNP for functionality experimentally. Consequently, one of the major challenges for researchers has been to seek out functional SNPs from all the SNPs in the human genome. In silico or bioinformatic methods are economical, less labor intensive, yet powerful approaches to filter out potentially functional SNPs in drug-response genes for further study. This allows researchers to prioritize which SNPs to subsequently evaluate experimentally for drug-response studies, as well as potentially providing insights into possible mechanisms underlying how SNPs may affect drug-response genes.

In silico analysis of missense substitutions using sequence-alignment based methods

Genetic testing for mutations in high-risk cancer susceptibility genes often reveals missense substitutions that are not easily classified as pathogenic or neutral. Among the methods that can help in their classification are computational analyses. Predictions of pathogenic vs. neutral, or the probability that a variant is pathogenic, can be made based on: 1) inferences from evolutionary conservation using protein multiple sequence alignments (PMSAs) of the gene of interest for almost any missense sequence variant; and 2) for many variants, structural features of wild-type and variant proteins. These in silico methods have improved considerably in recent years. In this work, we review and/or make suggestions with respect to: 1) the rationale for using in silico methods to help predict the consequences of missense variants; 2) important aspects of creating PMSAs that are informative for classification; 3) specific features of algorithms that have been used for classification of clinically-observed variants; 4) validation studies demonstrating that computational analyses can have predictive values (PVs) of approximately 75 to 95%; 5) current limitations of data sets and algorithms that need to be addressed to improve the computational classifiers; and 6) how in silico algorithms can be a part of the "integrated analysis" of multiple lines of evidence to help classify variants. We conclude that carefully validated computational algorithms, in the context of other evidence, can be an important tool for classification of missense variants.

Polymorphisms in uncoupling protein, melanocortin 3 receptor, melanocortin 4 receptor, and pro-opiomelanocortin genes and association with production traits in a commercial broiler line

Because avian uncoupling protein (avUCP), melanocortin 3 receptor (MC3R), melanocortin 4 receptor (MC4R), and pro-opiomelanocortin (POMC) genes may be associated with production traits [e.g., BW, weight gain (WG), and feed conversion ratio (FCR)], male and female broilers from an elite broiler line were screened for polymorphisms in these genes. The PCR-restriction fragment length polymorphism (RFLP) tests were developed to type the missense polymorphisms UCPAla118Val, MC4RSer76Leu, MC3R-Met54Leu, and Gly104Ser and POMCPro61Leu. Of 39 single nucleotide polymorphisms identified in all 4 genes, 24/39 were transitions with 11 having a C to T change. Of the 23 polymorphisms in UCP, 17 represented at least 7 haplotypes in this pedigreed broiler line. The UCP Ala-118Val allele was associated with a) high feed efficiency (FE; P = 0.03) and WG (P = 0.053) in selected males, and b) high BW in selected females (P = 0.07) and unselected males (P = 0.015). The UCPVal118Val allele was found in approximately 10% of the birds that were screened. Five silent substitutions, 3 in MC3R and 2 in MC4R, were also identified. Thirteen polymorphisms were identified in the POMC gene representing at least 3 different alleles. A missense Pro61Leu heterozygote was associated with greater BW in females. The heterozygote MC3R Gly104Ser polymorphism was associated with greater FE in selected males (P = 0.03) and greater BW in unselected males (P = 0.007). The MC4R Ser76Leu heterozygote polymorphism was associated with greater BW than the Leu76 homozygote in females (P = 0.05). From these findings, we hypothesize that UCP, MC3R, MC4R and POMC genes may play important roles and could be candidate loci for production traits such as feed conversion and BW in commercial broiler breeding stock.

Genetic evidence and integration of various data sources for classifying uncertain variants into a single model

Genetic testing often results in the finding of a variant whose clinical significance is unknown. A number of different approaches have been employed in the attempt to classify such variants. For some variants, case-control, segregation, family history, or other statistical studies can provide strong evidence of direct association with cancer risk. For most variants, other evidence is available that relates to properties of the protein or gene sequence. In this work we propose a Bayesian method for assessing the likelihood that a variant is pathogenic. We discuss the assessment of prior probability, and how to combine the various sources of data into a statistically valid integrated assessment with a posterior probability of pathogenicity. In particular, we propose the use of a two-component mixture model to integrate these various sources of data and to estimate the parameters related to sensitivity and specificity of specific kinds of evidence. Further, we discuss some of the issues involved in this process and the assumptions that underpin many of the methods used in the evaluation process.

Exhaustive prediction of disease susceptibility to coding base changes in the human genome

Background

Single Nucleotide Polymorphisms (SNPs) are the most abundant form of genomic variation and can cause phenotypic differences between individuals, including diseases. Bases are subject to various levels of selection pressure, reflected in their inter-species conservation.

Results

We propose a method that is not dependant on transcription information to score each coding base in the human genome reflecting the disease probability associated with its mutation. Twelve factors likely to be associated with disease alleles were chosen as the input for a support vector machine prediction algorithm. The analysis yielded 83% sensitivity and 84% specificity in segregating disease like alleles as found in the Human Gene Mutation Database from non-disease like alleles as found in the Database of Single Nucleotide Polymorphisms. This algorithm was subsequently applied to each base within all known human genes, exhaustively confirming that interspecies conservation is the strongest factor for disease association. For each gene, the length normalized average disease potential score was calculated. Out of the 30 genes with the highest scores, 21 are directly associated with a disease. In contrast, out of the 30 genes with the lowest scores, only one is associated with a disease as found in published literature. The results strongly suggest that the highest scoring genes are enriched for those that might contribute to disease, if mutated.

Conclusion

This method provides valuable information to researchers to identify sensitive positions in genes that have a high disease probability, enabling them to optimize experimental designs and interpret data emerging from genetic and epidemiological studies.

A novel computational and structural analysis of nsSNPs in CFTR gene

Single Nucleotide Polymorphisms (SNPs) are being intensively studied to understand the biological basis of complex traits and diseases. The Genetics of human phenotype variation could be understood by knowing the functions of SNPs. In this study using computational methods, we analyzed the genetic variations that can alter the expression and function of the CFTR gene responsible candidate for causing cystic fibrosis. We applied an evolutionary perspective to screen the SNPs using a sequence homology-based SIFT tool, which suggested that 17 nsSNPs (44%) were found to be deleterious. The structure-based approach PolyPhen server suggested that 26 nsSNPS (66%) may disrupt protein function and structure. The PupaSuite tool predicted the phenotypic effect of SNPs on the structure and function of the affected protein. Structure analysis was carried out with the major mutation that occurred in the native protein coded by CFTR gene, and which is at amino acid position F508C for nsSNP with id (rs1800093). The amino acid residues in the native and mutant modeled protein were further analyzed for solvent accessibility, secondary structure and stabilizing residues to check the stability of the proteins. The SNPs were further subjected to iHAP analysis to identify htSNPs, and we report potential candidates for future studies on CFTR mutations.

Prediction of functional nonsynonymous single nucleotide polymorphisms in human G-protein-coupled receptors

G-protein-coupled receptors (GPCRs) are found in a wide range of organisms and are central to a cellular signaling network that regulates many basic physiological processes. GPCRs are the focus of a significant amount of current pharmaceutical research because they play a key role in many diseases. In this paper, we predict the functional nonsynonymous single nucleotide polymorphisms (nsSNPs) in human GPCRs by defining optimal attributes and using a decision tree method. The predictive power of each attribute was evaluated. A subset of sequences with optimal attributes was obtained using the decision tree method combined with a genetic search algorithm. The subset contains both sequence-based and structure-based information, and the information for each subset consists of a conservation score, the location of the mutation, the BLOSUM62 substitution matrix score, as well as the hydrophobicity change, the solvent accessibility, and the buried charge. Seven important rules were derived from the decision tree. A total of 166 functional nsSNPs in human GPCRs from the dbSNP have been predicted using the optimal attributes subset.

Sequence-based prioritization of nonsynonymous single-nucleotide polymorphisms for the study of disease mutations

The increasing demand for the identification of genetic variation responsible for common diseases has translated into a need for sophisticated methods for effectively prioritizing mutations occurring in disease-associated genetic regions. In this article, we prioritize candidate nonsynonymous single-nucleotide polymorphisms (nsSNPs) through a bioinformatics approach that takes advantages of a set of improved numeric features derived from protein-sequence information and a new statistical learning model called "multiple selection rule voting" (MSRV). The sequence-based features can maximize the scope of applications of our approach, and the MSRV model can capture subtle characteristics of individual mutations. Systematic validation of the approach demonstrates that this approach is capable of prioritizing causal mutations for both simple monogenic diseases and complex polygenic diseases. Further studies of familial Alzheimer diseases and diabetes show that the approach can enrich mutations underlying these polygenic diseases among the top of candidate mutations. Application of this approach to unclassified mutations suggests that there are 10 suspicious mutations likely to cause diseases, and there is strong support for this in the literature.

Interpreting missense variants: comparing computational methods in human disease genes CDKN2A, MLH1, MSH2, MECP2, and tyrosinase (TYR)

The human genome contains frequent single-basepair variants that may or may not cause genetic disease. To characterize benign vs. pathogenic missense variants, numerous computational algorithms have been developed based on comparative sequence and/or protein structure analysis. We compared computational methods that use evolutionary conservation alone, amino acid (AA) change alone, and a combination of conservation and AA change in predicting the consequences of 254 missense variants in the CDKN2A (n = 92), MLH1 (n = 28), MSH2 (n = 14), MECP2 (n = 30), and tyrosinase (TYR) (n = 90) genes. Variants were validated as either neutral or deleterious by curated locus-specific mutation databases and published functional data. All methods that use evolutionary sequence analysis have comparable overall prediction accuracy (72.9-82.0%). Mutations at codons where the AA is absolutely conserved over a sufficient evolutionary distance (about one-third of variants) had a 91.6 to 96.8% likelihood of being deleterious. Three algorithms (SIFT, PolyPhen, and A-GVGD) that differentiate one variant from another at a given codon did not significantly improve predictive value over conservation score alone using the BLOSUM62 matrix. However, when all four methods were in agreement (62.7% of variants), predictive value improved to 88.1%. These results confirm a high predictive value for methods that use evolutionary sequence conservation, with or without considering protein structural change, to predict the clinical consequences of missense variants. The methods can be generalized across genes that cause different types of genetic disease. The results support the clinical use of computational methods as one tool to help interpret missense variants in genes associated with human genetic disease.

Prediction of the deleterious nsSNPs in ABCB transporters

The non-synonymous SNPs (nsSNPs) in coding regions, neutral or deleterious, could lead to the alteration of the function or structure of proteins. We have developed the computational models to analyze the deleterious nsSNPs in the transporters and predict ones in ABCB (ATP-binding cassette B) transporters of interest. The RPLS (ridge partial least square) and LDA (linear discriminant analysis) methods were applied to the problem, by training on a selection of datasets from a specified source, i.e., human transporters. The best combination of datasets and prediction attributes was ascertained. The prediction accuracy of the theoretical RPLS model for the training and testing sets is 84.8% and 80.4%, respectively (LDA: 84.3% and 80.4%), which indicates the models are reasonable and may be helpful for pharmacogenetics studies.

Computational prediction of the effects of non-synonymous single nucleotide polymorphisms in human DNA repair genes

Non-synonymous single nucleotide polymorphisms (nsSNPs) represent common genetic variation that alters encoded amino acids in proteins. All nsSNPs may potentially affect the structure or function of expressed proteins and could therefore have an impact on complex diseases. In an effort to evaluate the phenotypic effect of all known nsSNPs in human DNA repair genes, we have characterized each polymorphism in terms of different functional properties. The properties are computed based on amino acid characteristics (e.g. residue volume change); position-specific phylogenetic information from multiple sequence alignments and from prediction programs such as SIFT (Sorting Intolerant From Tolerant) and PolyPhen (Polymorphism Phenotyping). We provide a comprehensive, updated list of all validated nsSNPs from dbSNP (public database of human single nucleotide polymorphisms at National Center for Biotechnology Information, USA) located in human DNA repair genes. The list includes repair enzymes, genes associated with response to DNA damage as well as genes implicated with genetic instability or sensitivity to DNA damaging agents. Out of a total of 152 genes involved in DNA repair, 95 had validated nsSNPs in them. The fraction of nsSNPs that had high probability of being functionally significant was predicted to be 29.6% and 30.9%, by SIFT and PolyPhen respectively. The resulting list of annotated nsSNPs is available online (http://dna.uio.no/repairSNP), and is an ongoing project that will continue assessing the function of coding SNPs in human DNA repair genes.

Genome-wide isolation of growth and obesity QTL using mouse speed congenic strains

Background

High growth (hg) modifier and background independent quantitative trait loci (QTL) affecting growth, adiposity and carcass composition were previously identified on mouse chromosomes (MMU) 1, 2, 5, 8, 9, 11 and 17. To confirm and further characterize each QTL, two panels of speed congenic strains were developed by introgressing CAST/EiJ (CAST) QTL alleles onto either mutant C57Bl/6J-hg/hg (HG) or wild type C57Bl/6J (B6) genetic backgrounds.

Results

The first speed congenic panel was developed by introgressing four overlapping donor regions spanning MMU2 in its entirety onto both HG and B6 backgrounds, for a total of eight strains. Phenotypic characterization of the MMU2 panel confirmed the segregation of multiple growth and obesity QTL and strongly suggested that a subset of these loci modify the effects of the hg deletion. The second panel consisted of individual donor regions on an HG background for each QTL on MMU1, 5, 8, 9, 11 and 17. Of the six developed strains, five were successfully characterized and displayed significant differences in growth and/or obesity as compared to controls. All five displayed phenotypes similar to those originally attributed to each QTL, however, novel phenotypes were unmasked in several of the strains including sex-specific effects.

Conclusion

The speed congenic strains developed herein constitute an invaluable genomic resource and provide the foundation to identify the specific nature of genetic variation influencing growth and obesity.

Orphan GPCRs and their ligands

Due to their diversity, G-protein-coupled receptors (GPCRs) are major regulators of intercellular interactions. They exert their actions by being activated by a vast array of natural ligands, referred to in this article as "transmitters". Yet each GPCR is highly selective in its ligand recognition. Traditionally, the transmitters were found first and served to characterize the receptors pharmacologically. Since the end of the 1980s, however, it is the GPCRs that are first to be found because they are identified molecularly by homology screening approaches. But the GPCRs found this way suffer of one drawback, they lack their natural transmitters, they are "orphan" GPCRs. Searching for transmitters of orphan GPCRs has given birth to the reverse pharmacology approach that uses orphan GPCRs as targets to identify their transmitters. The most salient successes of the reverse pharmacology approach were the discoveries of 9 novel neuropeptide families. These have enriched our understanding of several important behavioral responses. But the application of reverse pharmacology has also led to some surprising results that question some basic pharmacological concepts. This review aims at describing the history of the orphan GPCRs and their impact on our understanding of biology.