To a future of genetic medicine

In Silico Analysis Identified Putative Pathogenic Missense nsSNPs in Human SLITRK1 Gene

Human DNA contains several variations, which can affect the structure and normal functioning of a protein. These variations could be single nucleotide polymorphisms (SNPs) or insertion-deletions (InDels). SNPs, as opposed to InDels, are more commonly present in DNA and may cause genetic disorders. In the current study, several bioinformatic tools were used to prioritize the pathogenic variants in the SLITRK1 gene. Out of all of the variants, 16 were commonly predicted to be pathogenic by these tools. All the variants had very low frequency, i.e., <0.0001 in the global population. The secondary structure of all filtered variants was predicted, but no structural change was observed at the site of variation in any variant. Protein stability analysis of these variants was then performed, which determined a decrease in protein stability of 10 of the variants. Amino acid conservation analysis revealed that all the amino acids were highly conserved, indicating their structural and functional importance. Protein 3D structure of wildtype SLITRK1 and all of its variants was predicted using I-TASSER, and the effect of variation on 3D structure of the protein was observed using the Missense3D tool, which presented the probable structural loss in three variants, i.e., Asn529Lys, Leu496Pro and Leu94Phe. The wildtype SLITRK1 protein and these three variants were independently docked with their close interactor protein PTPRD, and remarkable differences were observed in the docking sites of normal and variants, which will ultimately affect the functional activity of the SLITRK1 protein. Previous studies have shown that mutations in SLITRK1 are involved in Tourette syndrome. The present study may assist a molecular geneticist in interpreting the variant pathogenicity in research as well as diagnostic setup.

Transmission of polycystic ovary syndrome susceptibility single-nucleotide polymorphisms and their association with phenotype changes in offspring

STUDY QUESTION

Does the inheritance of polycystic ovary syndrome (PCOS) susceptibility single-nucleotide polymorphism affect the phenotype of offspring?

SUMMARY ANSWER

Male offspring who inherit PCOS-related genetic variations from PCOS mothers were more susceptible to developing the metabolic abnormality in their later life.

WHAT IS KNOWN ALREADY

Genetic factors are considered the major etiology of PCOS. Previous studies have highlighted that offspring of women with PCOS had an increased risk of the same disease or PCOS-like symptoms.

STUDY DESIGN, SIZE, DURATION

The study involved 172 children born to women with PCOS and 529 children born to non-PCOS women. All offspring were conceived by assisted reproductive technologies.

PARTICIPANTS/MATERIALS, SETTING, METHODS

The offspring ranged from 1 to 8 years old. Metabolic phenotype analyses were performed in offspring aged from 2 to 8 (N = 619). Sanger sequencing, TaqMan and Sequenom MassARRAY were used to sequence the samples.

MAIN RESULTS AND THE ROLE OF CHANCE

In male offspring, the fasting insulin (FINS) (P = 0.037) homeostasis model assessment of insulin resistance (HOMA-IR) (P = 0.038) and the homeostasis model assessment of pancreatic beta-cell function (HOMA-β) (P = 0.038) levels were higher in offspring of PCOS mothers compared to controls. In female offspring, PCOS offspring had a significantly higher anti-Müllerian hormone levels (P = 0.001) compared to those from control mothers. In male offspring of PCOS mothers, subjects with a T allele at rs2349415 in the gene FSHR had higher FINS (P = 0.023), HOMA-IR (P = 0.030) and HOMA-β levels (P = 0.013) than those in the homozygous CC group. The same increased trend in FINS, HOMA-IR and HOMA-β levels could be found in the CC and TC group in rs2268361 located in gene FSHR compared to the TT group (P = 0.029, P = 0.030, P = 0.046, respectively). As for rs10818854 in the DENND1A gene, the AA and AG group had a higher FINS (P = 0.037) and HOMA-β (P = 0.008) levels than the homozygous CC group.

LIMITATIONS, REASONS FOR CAUTION

Firstly, the offspring may be too young to see any phenotype changes. Secondly, this study only analyzed the differences of genotype frequency using the dominant model instead of all three models due to the limited sample size of the homozygous model. The results, therefore, should be replicated and performed in a larger sample size population. Thirdly, environmental impacts cannot be ruled out.

WIDER IMPLICATIONS OF THE FINDINGS

The findings presented in this thesis add to our understanding the changes in offspring born to PCOS women and remind us to consider early intervention to avoid more severe effects.

STUDY FUNDING/COMPETING INTEREST(S)

This study was supported by the National Key Research and Development Program of China 2017YFC1001000 (to Z.-J.C.), the National Natural Science Foundation of China 81430029 (to Z.-J.C.), 81622021 and 31571548 (to H.Z.), the National Natural Science Foundation of Shandong Province JQ201816 (to H.Z.) and Shandong Provincial Key Research and Development Program 2017G006036 (to L.-L.C.) and 2018YFJH0504 (to Z.-J.C.). There are no conflicts of interest to declare.

TRIAL REGISTRATION NUMBER

N/A.

In Silico Tools and Approaches for the Prediction of Functional and Structural Effects of Single-Nucleotide Polymorphisms on Proteins: An Expert Review

Single-nucleotide polymorphisms (SNPs) are single-base variants that contribute to human biological variation and pathogenesis of many human diseases. Among all SNP types, nonsynonymous single-nucleotide polymorphisms (nsSNPs) can alter many structural, biochemical, and functional features of a protein such as folding characteristics, charge distribution, stability, dynamics, and interactions with other proteins/nucleotides. These modifications in the protein structure can lead nsSNPs to be closely associated with many multifactorial diseases such as cancer, diabetes, and neurodegenerative diseases. Predicting structural and functional effects of nsSNPs with experimental approaches can be time-consuming and costly; hence, computational prediction tools and algorithms are being widely and increasingly utilized in biology and medical research. This expert review examines the in silico tools and algorithms for the prediction of functional or structural effects of SNP variants, in addition to the description of the phenotypic effects of nsSNPs on protein structure, association between pathogenicity of variants, and functional or structural features of disease-associated variants. Finally, case studies investigating the functional and structural effects of nsSNPs on selected protein structures are highlighted. We conclude that creating a consistent workflow with a combination of in silico approaches or tools should be considered to increase the performance, accuracy, and precision of the biological and clinical predictions made in silico.

Glucocorticoid Receptor Polymorphisms in Children Undergoing Congenital Heart Surgery with Cardiopulmonary Bypass

We conducted a candidate gene association study to test the hypothesis that different gene polymorphisms will be associated with corticosteroid responsiveness and study outcomes among children undergoing congenital heart surgery. This is a prospective observational cohort study at a large, tertiary pediatric cardiac center on children undergoing corrective or palliative congenital heart surgery. A total of 83 children were enrolled. DNA was isolated for three polymorphisms of interest namely N363 (rs56149945) and 9β (rs6198) associated with increased sensitivity to corticosteroids and Bcl I (rs41423247) associated with decreased sensitivity to corticosteroids. Duration of inotropic use, low cardiac output scores (LCOS), and vasoactive inotrope scores were examined in relation to these three polymorphisms. Using Kaplan-Meier analysis, heterozygous individuals showed longer transcriptional intermediary factor (TIF) compared with wild type for N363 polymorphism ( p  = 0.05). In multivariable Cox regression, heterozygous alleles for 9β polymorphism showed significantly shorter TIF compared with wild type (hazard ratio = 2.04 [1.08-3.87], p  = 0.03). The relationship between lower LCOS scores and alleles groups was significant for 9β heterozygous polymorphism only (1.5 [1-2.2], p  = 0.01) in comparison to wild type and homozygous. The presence of heterozygote alleles for the increased corticosteroid sensitivity is associated with longer TIF compared with wild type. Conversely, the presence of heterozygous alleles for the decreased sensitivity to corticosteroids is associated with shorter TIF compared with wild type.

Oral submucous fibrosis: A global challenge. Rising incidence, risk factors, management, and research priorities

Oral submucous fibrosis is a potentially malignant disorder of the oral cavity, with a high rate of malignant transformation. It is very common among habitual areca nut chewers. The pathogenesis of oral submucous fibrosis is not well established, but it is believed to be a disease of multifactorial origin, including areca nut chewing, ingestion of chilies, genetic factors, immunologic processes and nutritional deficiencies. Genetically susceptible individuals when exposed to areca nut chewing develop this disease over a variable period of time. Oral submucous fibrosis is considered to be a disease of collagen metabolism. Several genetic factors are reported but there is no consensus about the exact mechanism of disease initiation. Variations in histopathological presentation are noted among oral submucous fibrosis patients with habitual areca nut chewing in different forms and other additive agents, eg betel quid, pan masala and gutkha, together with a variety of tobacco habits. The role of epigenetic modifications, such as miRNA regulation, and DNA methylation is also being reported as part of the pathogenesis of oral submucous fibrosis. A combined approach, including analysis of genetic and epigenetic regulations with different habits, might be helpful to better understand the contributory factors and pathogenesis of this serious disorder.

Computational analysis for the determination of deleterious nsSNPs in human MTHFR gene

Methylenetetrahydrofolate reductase (MTHFR) is a key enzyme involved in folate metabolism and plays a central role in DNA methylation and biosynthesis. MTHFR mutations may alter the cellular folate supply which in turn affects nucleic acid synthesis, DNA methylation and chromosomal damage. The identification of number of SNPs in the human genome growing nowadays and hence, the evaluation of functional & structural consequences of these SNPs is very laborious by means of experimental analysis. Therefore, in the present study, recently developed various computational algorithms have been used which can predict the functional and structural consequences of the SNPs. Various computational tools like SIFT, PolyPhen2, PROVEAN, SNAP2, nsSNPAnalyzer, SNPs&GO, PhD-SNP, PMut, I-Mutant, iPTREE-STAB and MUpro were used to predict most deleterious SNPs. Additionally, ConSurf was used to find amino acids conservation and NCBI conserved domain search tool to find conserved domains in MTHFR. Post translational modification sites were predicted using ModPred. SPARKS-X was used to generate 3D structure of the native and mutant MTHFR protein, ModRefiner for further refinement, Varify3D and RAMPAGE to validate structure. Ligand binding sites were predicted using FTsite, RaptorX binding and COACH. Three SNPs i.e. R157Q, L323P and W500C predicted the most deleterious in all the tools used for functional and stability analysis. Moreover, both residues R157, L323 and W500 were predicted highly conserved, buried and structural residues by ConSurf. Post translational modification sites were also predicted at R157 and W500. The ligand binding sites were predicted at R157, L323 and W500.

An application of competitive reporter monitored amplification (CMA) for rapid detection of single nucleotide polymorphisms (SNPs)

Single nucleotide polymorphisms (SNPs) are essential parameters in molecular diagnostics and can be used for the early detection and clinical prognosis in various diseases. Available methods for SNP detection are still labor-intensive and require a complex laboratory infrastructure, which are not suitable for the usage in resource-limited settings. Thus, there is an urgent need for a simple, reliable and rapid approach. In this paper we modified the previously developed competitive reporter monitored amplification (CMA) technique for the detection of resistance mediating SNPs in Mycobacterium tuberculosis complex (MTBC) strains. As a proof-of-principle for the application of the CMA-based SNP assay in routine molecular tuberculosis diagnostic, we show that the assay recognizes resistance mediating SNPs for rifampicin, isoniazid and ethambutol from either isolated DNA or heat inactivated M. tuberculosis cell cultures. The CMA-based SNP assay can identify the most prevalent resistance mediating mutations in the genes rpoB, katG, embB, and the promotor region of inhA within one hour.

Automated Purification of DNA from Large Samples: A Study of Effectiveness and Labor Efficiency

Investigations into the underlying genetic contributions to human disease are transitioning from small family-based traditional linkage analyses to large population-based studies designed to identify genetic factors in more complex and common diseases that have the greatest impact on human health. These types of studies have driven the need for larger numbers of samples for analysis and more efficient and effective methods for DNA purification, especially for large samples that provide sufficient quantities of DNA for extensive analysis. The Autopure LS™ Nucleic Acid Purification Instrument, by Gentra Systems, Inc., a platform capable of high-throughput sample purification from large samples, was developed to meet the demands of these large studies. This article presents data demonstrating the equivalency of DNA purified using the Autopure LS automated instrument and the manual method based on the same purification process. In addition, we present data demonstrating the in-lab time savings realized by automating the purification process.

Human Nutrition and Genetic Variation

Human genetic variation is a determinant of nutrient efficacy and of tolerances and intolerances and has the potential to influence nutrient intake values (NIVs). Knowledge derived from the comprehensive identification of human genetic variation offers the potential to predict the physiological and pathological consequences of individual genetic differences and prevent and/or manage adverse outcomes through diet. Nutrients and genomes interact reciprocally; genomes confer differences in nutrient utilization, whereas nutrients effectively modify genome expression, stability, and viability. Understanding the interactions that occur among human genes, including all genetic variants thereof, and environmental exposures is enabling the development of genotype-specific nutritional regimens that prevent disease and promote wellness for individuals and populations throughout the life cycle. Genomic technologies may provide new criteria for establishing NIVs. The impact of a gene variant on NIVs will be dependent on its penetrance and prevalence within a population. Recent experiences indicate that few gene variants are anticipated to be sufficiently penetrant to affect average requirement (AR) values to a greater degree than environmental factors. If highly penetrant gene variants are identified that affect nutrient requirements, the prevalence of the variant in that country or region will determine the feasibility and necessity of deriving more than one AR or upper limit (UL) for affected genetic subgroups.

Multivariate models from RNA-Seq SNVs yield candidate molecular targets for biomarker discovery: SNV-DA

BACKGROUND

It has recently been shown that significant and accurate single nucleotide variants (SNVs) can be reliably called from RNA-Seq data. These may provide another source of features for multivariate predictive modeling of disease phenotype for the prioritization of candidate biomarkers. The continuous nature of SNV allele fraction features allows the concurrent investigation of several genomic phenomena, including allele specific expression, clonal expansion and/or deletion, and copy number variation.

RESULTS

The proposed software pipeline and package, SNV Discriminant Analysis (SNV-DA), was applied on two RNA-Seq datasets with varying sample sizes sequenced at different depths: a dataset containing primary tumors from twenty patients with different disease outcomes in lung adenocarcinoma and a larger dataset of primary tumors representing two major breast cancer subtypes, estrogen receptor positive and triple negative. Predictive models were generated using the machine learning algorithm, sparse projections to latent structures discriminant analysis. Training sets composed of RNA-Seq SNV features limited to genomic regions of origin (e.g. exonic or intronic) and/or RNA-editing sites were shown to produce models with accurate predictive performances, were discriminant towards true label groupings, and were able to produce SNV rankings significantly different from than univariate tests. Furthermore, the utility of the proposed methodology is supported by its comparable performance to traditional models as well as the enrichment of selected SNVs located in genes previously associated with cancer and genes showing allele-specific expression. As proof of concept, we highlight the discovery of a previously unannotated intergenic locus that is associated with epigenetic regulatory marks in cancer and whose significant allele-specific expression is correlated with ER+ status; hereafter named ER+ associated hotspot (ERPAHS).

CONCLUSION

The use of models from RNA-Seq SNVs to identify and prioritize candidate molecular targets for biomarker discovery is supported by the ability of the proposed method to produce significantly accurate predictive models that are discriminant towards true label groupings. Importantly, the proposed methodology allows investigation of mutations outside of exonic regions and identification of interesting expressed loci not included in traditional gene annotations. An implementation of the proposed methodology is provided that allows the user to specify SNV filtering criteria and cross-validation design during model creation and evaluation.

Multiple detection of single nucleotide polymorphism by microarray-based resonance light scattering assay with enlarged gold nanoparticle probes

A gold nanoparticle enlargement assisted DNA microarray-based RLS assay has been developed for multiplexed detection of single nucleotide polymorphism with high sensitivity.

Functional and Structural Consequences of Damaging Single Nucleotide Polymorphisms in Human Prostate Cancer Predisposition Gene RNASEL

A commonly diagnosed cancer, prostate cancer (PrCa), is being regulated by the gene RNASEL previously known as PRCA1 codes for ribonuclease L which is an integral part of interferon regulated system that mediates antiviral and antiproliferative role of the interferons. Both somatic and germline mutations have been implicated to cause prostate cancer. With an array of available Single Nucleotide Polymorphism data on dbSNP this study is designed to sort out functional SNPs in RNASEL by implementing different authentic computational tools such as SIFT, PolyPhen, SNPs&GO, Fathmm, ConSurf, UTRScan, PDBsum, Tm-Align, I-Mutant, and Project HOPE for functional and structural assessment, solvent accessibility, molecular dynamics, and energy minimization study. Among 794 RNASEL SNP entries 124 SNPs were found nonsynonymous from which SIFT predicted 13 nsSNPs as nontolerable whereas PolyPhen-2 predicted 28. SNPs found on the 3' and 5' UTR were also assessed. By analyzing six tools having different perspectives an aggregate result was produced where nine nsSNPs were found to be most likely to exert deleterious effect. 3D models of mutated proteins were generated to determine the functional and structural effect of the mutations on ribonuclease L. The initial findings were reinforced by the results from I-Mutant and Project HOPE as these tools predicted significant structural and functional instability of the mutated proteins. Expasy-ProSit tool defined the mutations to be situated in the functional domains of the protein. Considering previous analysis this study revealed a conclusive result deducing the available SNP data on the database by identifying the most damaging three nsSNP rs151296858 (G59S), rs145415894 (A276V), and rs35896902 (R592H). As such studies involving polymorphisms of RNASEL were none to be found, the results of the current study would certainly be helpful in future prospects concerning prostate cancer in males.

DBDiaSNP: An Open-Source Knowledgebase of Genetic Polymorphisms and Resistance Genes Related to Diarrheal Pathogens

Diarrhea is a highly common infection among children, responsible for significant morbidity and mortality rate worldwide. After pneumonia, diarrhea remains the second leading cause of neonatal deaths. Numerous viral, bacterial, and parasitic enteric pathogens are associated with diarrhea. With increasing antibiotic resistance among enteric pathogens, there is an urgent need for global surveillance of the mutations and resistance genes primarily responsible for resistance to antibiotic treatment. Single Nucleotide Polymorphisms are important in this regard as they have a vast potential to be utilized as molecular diagnostics for gene-disease or pharmacogenomics association studies linking genotype to phenotype. DBDiaSNP is a comprehensive repository of mutations and resistance genes among various diarrheal pathogens and hosts to advance breakthroughs that will find applications from development of sequence-based diagnostic tools to drug discovery. It contains information about 946 mutations and 326 resistance genes compiled from literature and various web resources. As of March 2015, it houses various pathogen genes and the mutations responsible for antibiotic resistance. The pathogens include, for example, DEC (Diarrheagenic E.coli), Salmonella spp., Campylobacter spp., Shigella spp., Clostridium difficile, Aeromonas spp., Helicobacter pylori, Entamoeba histolytica, Vibrio cholera, and viruses. It also includes mutations from hosts (e.g., humans, pigs, others) that render them either susceptible or resistant to a certain type of diarrhea. DBDiaSNP is therefore intended as an integrated open access database for researchers and clinicians working on diarrheal diseases. Additionally, we note that the DBDiaSNP is one of the first antibiotic resistance databases for the diarrheal pathogens covering mutations and resistance genes that have clinical relevance from a broad range of pathogens and hosts. For future translational research involving integrative biology and global health, the database offers veritable potentials, particularly for developing countries and worldwide monitoring and personalized effective treatment of pathogens associated with diarrhea. The database is accessible on the public domain at http://www.juit.ac.in/attachments/dbdiasnp/ .

Design of enzyme-interfaced DNA logic operations (AND, OR and INHIBIT) with an assaying application for single-base mismatch

We devised AND, OR and INHIBIT logic gates.

Single nucleotide variations: biological impact and theoretical interpretation

Genome-wide association studies (GWAS) and whole-exome sequencing (WES) generate massive amounts of genomic variant information, and a major challenge is to identify which variations drive disease or contribute to phenotypic traits. Because the majority of known disease-causing mutations are exonic non-synonymous single nucleotide variations (nsSNVs), most studies focus on whether these nsSNVs affect protein function. Computational studies show that the impact of nsSNVs on protein function reflects sequence homology and structural information and predict the impact through statistical methods, machine learning techniques, or models of protein evolution. Here, we review impact prediction methods and discuss their underlying principles, their advantages and limitations, and how they compare to and complement one another. Finally, we present current applications and future directions for these methods in biological research and medical genetics.

Amplified single base-pair mismatch detection via aggregation of exonuclease-sheared gold nanoparticles

Single nucleotide polymorphism (SNP) detection is important for early diagnosis, clinical prognostics, and disease prevention, and a rapid and sensitive low-cost SNP detection assay would be valuable for resource-limited clinical settings. We present a simple platform that enables sensitive, naked-eye detection of SNPs with minimal reagent and equipment requirements at room temperature within 15 min. SNP detection is performed in a single tube with one set of DNA probe-modified gold nanoparticles (AuNPs), a single exonuclease (Exo III), and the target in question. Exo III’s apurinic endonucleolytic activity differentially processes hybrid duplexes between the AuNP-bound probe and DNA targets that are perfectly matched or contain a single-base mismatch. For perfectly matched targets, Exo III’s exonuclease activity facilitates a process of target recycling that rapidly shears DNA probes from the particles, generating an AuNP aggregation-induced color change, whereas no such change occurs for mismatched targets. This color change is easily observed with as little as 2 nM of target, 100-fold lower than the target concentration required for reliable naked eye observation with unmodified AuNPs in well-optimized reaction conditions. We further demonstrate that this system can effectively discriminate a range of different mismatches.

Distilling pathophysiology from complex disease genetics

Technologies for genome-wide sequence interrogation have dramatically improved our ability to identify loci associated with complex human disease. However, a chasm remains between correlations and causality that stems, in part, from a limiting theoretical framework derived from Mendelian genetics and an incomplete understanding of disease physiology. Here we propose a set of criteria, akin to Koch's postulates for infectious disease, for assigning causality between genetic variants and human disease phenotypes.

The epilepsy phenome/genome project

BACKGROUND

Epilepsy is a common neurological disorder that affects approximately 50 million people worldwide. Both risk of epilepsy and response to treatment partly depend on genetic factors, and gene identification is a promising approach to target new prediction, treatment, and prevention strategies. However, despite significant progress in the identification of genes causing epilepsy in families with a Mendelian inheritance pattern, there is relatively little known about the genetic factors responsible for common forms of epilepsy and so-called epileptic encephalopathies. Study design The Epilepsy Phenome/Genome Project (EPGP) is a multi-institutional, retrospective phenotype-genotype study designed to gather and analyze detailed phenotypic information and DNA samples on 5250 participants, including probands with specific forms of epilepsy and, in a subset, parents of probands who do not have epilepsy.

RESULTS

EPGP is being executed in four phases: study initiation, pilot, study expansion/establishment, and close-out. This article discusses a number of key challenges and solutions encountered during the first three phases of the project, including those related to (1) study initiation and management, (2) recruitment and phenotyping, and (3) data validation. The study has now enrolled 4223 participants.

CONCLUSIONS

EPGP has demonstrated the value of organizing a large network into cores with specific roles, managed by a strong Administrative Core that utilizes frequent communication and a collaborative model with tools such as study timelines and performance-payment models. The study also highlights the critical importance of an effective informatics system, highly structured recruitment methods, and expert data review.

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.

DNA repair and cytotoxic drugs: the potential role of RAD51 in clinical outcome of non-small-cell lung cancer patients

Many of the cytotoxic drugs used in the treatment of non-small-cell lung carcinoma patients can interfere with DNA activity and the definition of an individual DNA repair profile could be a key strategy to achieve better response to chemotherapeutic treatment. Although DNA repair mechanisms are important factors in the prevention of carcinogenesis, these molecular pathways are also involved in therapy response. RAD51 is a crucial element in DNA repair by homologous recombination and has been shown to interfere with the prognosis of patients treated with chemoradiotherapy. There is increasing evidence that genetic polymorphisms in repair enzymes can influence DNA repair capacity and, consequently, affect chemotherapy efficacy. We conducted this review to show the possible influence of the RAD51 genetic variants in damage repair capacity and treatment response in non-small-cell lung carcinoma patients.

Is type I alpha 2 collagen gene responsible for intracranial aneurysm in Northeast China?

In this study, we investigated whether a single nucleotide polymorphism (rs42524 G > C) in the type I alpha 2 collagen gene was associated with sporadic ruptured intracranial aneurysm or its clinical characteristics in patients from Northeast China. Genotyping of the rs42524 G > C polymorphism was carried out using a polymerase chain reaction-restriction fragment length polymorphism assay. The data showed that the frequency of the rs42524 GC + CC genotype was significantly higher than the GG genotype among intracranial aneurysm patients whose Hunt and Hess grading scale was > 3. In addition, the rs42524 G > C genotype was found to have a statistically significant association with intracranial aneurysm risk. These findings indicate that the type I alpha 2 collagen gene gene may be involved in a predisposition to intracranial aneurysm in the Northeast Chinese population. Crucially, the rs42524 C allele may be an important risk factor for increased severity of the condition in patients with ruptured intracranial aneurysms.

Ovarian cancer and DNA repair: DNA ligase IV as a potential key

Ovarian cancer (OC) is the sixth most common cancer and the seventh cause of death from cancer in women. The etiology and the ovarian carcinogenesis still need clarification although ovulation may be determinant due to its carcinogenic role in ovarian surface epithelium. The link between ovarian carcinogenesis and DNA repair is well established and it became clear that alterations in DNA damage response may affect the risk to develop OC. Polymorphisms are variations in the DNA sequence that exist in normal individuals of a population and are capable to change, among other mechanisms, the balance between DNA damage and cellular response. Consequently, genetic variability of the host has a great role in the development, progression and consequent prognosis of the oncologic patient as well as in treatment response. Standard treatment for OC patients is based on cytoreductive surgery, followed by chemotherapy with a platinum agent and a taxane. Although 80% of the patients respond to the first-line therapy, the development of resistance is common although the mechanisms underlying therapy failure remain mostly unknown. Because of their role in oncology, enzymes involved in the DNA repair pathways, like DNA Ligase IV (LIG4), became attractive study targets. It has been reported that variations in LIG4 activity can lead to a hyper-sensitivity to DNA damage, deregulation of repair and apoptosis mechanisms, affecting the susceptibility to cancer development and therapy response. To overcome resistance mechanisms, several investigations have been made and the strategy to target crucial molecular pathways, such as DNA repair, became one of the important areas in clinical oncology. This review aims to elucidate the link between DNA repair and OC, namely which concerns the role of LIG4 enzyme, and how genetic polymorphisms in LIG4 gene can modulate the activity of the enzyme and affect the ovarian carcinogenesis and treatment response. Moreover, we try to understand how LIG4 inhibition can be a potential contributor for the development of new cancer treatment strategies.

Management of retinal vascular diseases: a patient-centric approach

Retinal vascular diseases are a leading cause of blindness in the Western world. Advancement in the clinical management of these diseases has been fast-paced, with new treatments becoming available as well as license extensions of existing treatments. Vascular endothelial growth factor (VEGF) has been implicated in certain retinal vascular diseases, including wet age-related macular degeneration (AMD), diabetic macular oedema (DMO), and retinal vein occlusion (RVO). Treatment of wet AMD and visual impairment due to either DMO or macular oedema secondary to RVO with an anti-VEGF on an as needed basis, rather than a fixed schedule, allows an individualised treatment approach; providing treatment when patients are most likely to benefit from it, while minimising the number of unnecessary intravitreal injections. Thus, an individualised treatment regimen reduces the chances of over-treatment and under-treatment, optimising both the risk/benefit profile of the treatment and the efficient use of NHS resource. Streamlining of treatment for patients with wet AMD and visual impairment due to either DMO or macular oedema secondary to RVO, by using one treatment with similar posology across all three diseases, may help to minimise burden of clinic capacity and complexity and hence optimise patient outcomes. Informed treatment decisions and efficient clinic throughput are important for optimal patient outcomes in the fast-changing field of retinal vascular diseases.

Paying for personalized care: cancer biomarkers and comparative effectiveness

Genomic-based diagnostics can play a key role in creating a more efficient healthcare system by directing patients toward beneficial therapies and away from therapies that pose substantial risk or are unlikely to improve outcomes for the patient. We outline how the value provided by diagnostics is closely linked to a range of factors including magnitude of health outcome improvement, avoiding adverse effect, diagnostic parameters, process of care, resource utilization, and costs. Comparative effectiveness approaches to evidence generation, including health outcome measurements, quality of life, economic analyses, decision modeling, and pragmatic clinical trials, can be used to provide stakeholders with a range of information to inform treatment, guidelines, coverage, and reimbursement decisions. Evidence of comparative effectiveness can also help support value-based reimbursement of cancer biomarkers and treatment strategies as means of paying for personalized medicine.

SNP calling using genotype model selection on high-throughput sequencing data

MOTIVATION

A review of the available single nucleotide polymorphism (SNP) calling procedures for Illumina high-throughput sequencing (HTS) platform data reveals that most rely mainly on base-calling and mapping qualities as sources of error when calling SNPs. Thus, errors not involved in base-calling or alignment, such as those in genomic sample preparation, are not accounted for.

RESULTS

A novel method of consensus and SNP calling, Genotype Model Selection (GeMS), is given which accounts for the errors that occur during the preparation of the genomic sample. Simulations and real data analyses indicate that GeMS has the best performance balance of sensitivity and positive predictive value among the tested SNP callers.

AVAILABILITY

The GeMS package can be downloaded from https://sites.google.com/a/bioinformatics.ucr.edu/xinping-cui/home/software or http://computationalbioenergy.org/software.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Developments in obesity genetics in the era of genome-wide association studies

Obesity is an important factor contributing to the global burden of morbidity and mortality. By identifying obesity susceptibility genes, scientists aim to elucidate some of its aetiology. Early studies used candidate gene and genome-wide linkage approaches to search for such genes with limited success. However, the advent of genome-wide association studies (GWAS) has dramatically increased the pace of gene discovery. So far, GWAS have identified at least 50 loci robustly associated with body mass index (BMI), waist-to-hip ratio, body fat percentage and extreme obesity. Some of these have been shown to replicate in non-white populations and in children and adolescents. Furthermore, for some loci interaction studies have shown that the BMI-increasing effect is attenuated in physically active individuals. Despite many successful discoveries, the effect sizes of the established loci are small, and combined they explain only a fraction of the inter-individual variation in BMI. The low predictive value means that their value in mainstream health care is limited. However, as most of these newly established loci were not previously linked to obesity, they may provide new insights into body weight regulation. Continued efforts in gene discovery, using a range of approaches, will be needed to increase our understanding of obesity.

Evaluation of High Resolution Melting analysis as an alternate tool to screen for risk alleles associated with small kidneys in Indian newborns

BACKGROUND

Single nucleotide polymorphisms (SNPs) are the most common forms of sequence variations in the human genome. They contribute to the human phenotypic spectrum and are associated with variations in response to pathogens, drugs and vaccines. Recently, SNPs in three human genes involved in kidney development (RET, PAX2 and ALDH1A2) have been reported to be associated with variation in renal size and function. These known SNPs could potentially be used in the clinic as markers for identifying babies who may have smaller kidneys and permit close follow up for early detection of hypertension and acquired renal dysfunction. The aim of this study was to evaluate the use of High Resolution Melting technique (HRM) as a tool for detecting the known SNPs in these three genes in comparison to sequencing which is the gold standard.

METHODS

High resolution melting analysis was performed on 75 DNA samples that were previously sequenced for the known polymorphisms in RET (rs1800860), PAX2 (rs11190688) and ALDH1A2 (rs7169289) genes. The SNPs were G > A transitions in RET and PAX2 and A > G in ALDH1A2 gene. A blinded assessment was performed on these samples for evaluation of the HRM technique as compared to sequencing.

RESULTS

Each variant had a unique melt curve profile that was reproducible. The shift in melting temperature (Tm) allowed visual discrimination between the homozygous alleles (major and minor) in all three genes. The shape of the melting curve as compared to the major allele homozygous curve allowed the identification of the heterozygotes in each of the three SNPs. For validation, HRM was performed on 25 samples for each of the three SNPs. The results were compared with the sequencing results and 100% correct identification of the samples was obtained for RET, PAX2, and ALDA1H2 gene.

CONCLUSION

High Resolution Melting analysis is a simple, rapid and cost effective technique that could be used in a large population to identify babies with the risk alleles. These high risk children could be followed up for early detection of hypertension and acquired renal dysfunction.

Genome-wide prediction of splice-modifying SNPs in human genes using a new analysis pipeline called AASsites

Background

Some single nucleotide polymorphisms (SNPs) are known to modify the risk of developing certain diseases or the reaction to drugs. Due to next generation sequencing methods the number of known human SNPs has grown. Not all SNPs lead to a modified protein, which may be the origin of a disease. Therefore, the recognition of functional SNPs is needed. Because most SNP annotation tools look for SNPs which lead to an amino acid exchange or a premature stop, we designed a new tool called AASsites which searches for SNPs which modify splicing.

Results

AASsites uses several gene prediction programs and open reading frame prediction to compare the wild type (wt) and the variant gene sequence. The results of the comparison are combined by a handmade rule system to classify a change in splicing as “likely, probable, unlikely”. Having received good results from tests with SNPs known for changing the splicing pattern we checked 80,000 SNPs from the human genome which are located near splice sites for their ability to change the splicing pattern of the gene and hereby result in a different protein. We identified 301 “likely” and 985 “probable” classified SNPs with such characteristics. Within this set 33 SNPs are described in the ssSNP Target database to cause modified splicing.

Conclusions

With AASsites single SNPs can be checked for those causing splice modifications. Screening 80,000 known human SNPs we detected about 1,200 SNPs which probably modify splicing. AASsites is available at http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar using any web browser.

In silico prediction of deleterious single amino acid polymorphisms from amino acid sequence

Molecular cause of human disease retains as one of the most attractive scientific research targets for decades. An effective approach toward this topic is analysis and identification of disease-related amino acid polymorphisms. In this work, we developed a concise and promising deleterious amino acid polymorphism identification method SeqSubPred based on 44 features solely extracted from protein sequence. SeqSubPred achieved surprisingly good predictive ability with accuracy (0.88) and area under receiver operating characteristic (0.94) without resorting to homology or evolution information, which is frequently used in similar methods and usually more complex and time-consuming. SeqSubPred also identified several critical sequence features obtained from random forests model, and these features brought some interesting insights into the factors affecting human disease-related amino acid substitutions. The online version of SeqSubPred method is available at montana.informatics.indiana.edu/cgi-bin/seqmut/seqsubpred.cgi

Current research status, databases and application of single nucleotide polymorphism

Single Nucleotide Polymorphisms (SNPs) are the most frequent form of DNA variation in the genome. SNPs are genetic markers which are bi-allelic in nature and grow at a very fast rate. Current genomic databases contain information on several million SNPs. More than 6 million SNPs have been identified and the information is publicly available through the efforts of the SNP Consortium and others data bases. The NCBI plays a major role in facillating the identification and cataloging of SNPs through creation and maintenance of the public SNP database (dbSNP) by the biomedical community worldwide and stimulate many areas of biological research including the identification of the genetic components of disease. In this review article, we are compiling the existing SNP databases, research status and their application.

An utter refutation of the "fundamental theorem of the HapMap"

The International HapMap Project was proposed in order to quantify linkage disequilibrium (LD) relationships among human DNA polymorphisms in an assortment of populations, in order to facilitate the process of selecting a minimal set of markers that could capture most of the signal from the untyped markers in a genome-wide association study. The central dogma can be summarized by the argument that if a marker is in tight LD with a polymorphism that directly impacts disease risk, as measured by the metric r(2), then one would be able to detect an association between the marker and disease with sample size that was increased by a factor of 1/r(2) over that needed to detect the effect of the functional variant directly. This "fundamental theorem" holds, however, only if one assumes that the LD between loci and the etiological effect of the functional variant are independent of each other, that they are statistically independent of all other etiological factors (in exposure and action), that sampling is prospective, and that the estimates of r(2) are accurate. None of these are standard operating assumptions, however. We describe the ramifications of these implicit assumptions, and provide simple examples in which the effects of a functional variant could be unequivocally detected if it were directly genotyped, even as markers in high LD with the functional variant would never show association with disease, even in infinite sample sizes. Both theoretical and empirical refutation of the central dogma of genome-wide association studies is thus presented.

Physiology, structure, and regulation of the cloned organic anion transporters

1. The transport of negatively charged drugs, xenobiotics, and metabolites by epithelial tissues, particularly the kidney, plays critical roles in controlling their distribution, concentration, and retention in the body. Thus, organic anion transporters (OATs) impact both their therapeutic efficacy and potential toxicity. 2. This review summarizes current knowledge of the properties and functional roles of the cloned OATs, the relationships between transporter structure and function, and those factors that determine the efficacy of transport. Such factors include plasma protein binding of substrates, genetic polymorphisms among the transporters, and regulation of transporter expression. 3. Clearly, much progress has been made in the decade since the first OAT was cloned. However, unresolved questions remain. Several of these issues--drug-drug interactions, functional characterization of newly cloned OATs, tissue differences in expression and function, and details of the nature and consequences of transporter regulation at genomic and intracellular sites--are discussed in the concluding Perspectives section.

Construction of a dense SNP map for bovine chromosome 6 to assist the assembly of the bovine genome sequence

A linkage map was constructed for bovine chromosome 6 (BTA6), using 399 single nucleotide polymorphisms (SNPs) detected primarily from PCR-resequencing. The efficiency of SNP detection was highly dependent on the source of sequence information chosen for primer design (BAC-end sequences, introns or promoters). The SNPs were used to build a linkage map comprising 104 cM on BTA6. The SNP order in the linkage map corresponded very well with radiation hybrid (RH) maps available for BTA6 as well as with expected positions in the human comparative map, but diverged significantly from the current assembly of the bovine genome (Btau_3.1). When performing linkage analysis with the marker order suggested from the Btau_3.1 we observed an expansion of the genetic map from 104 cM to 137 cM, strongly suggesting a reordering of scaffolds in the current version of the bovine genome assembly. The extent of LD on BTA6 was evaluated by calculating the average r(2) for SNP pairs separated by given distances. The decline of LD was rapid with distance, such that r(2) was 0.1 at 100 kb. Our results indicate that linkage mapping will be a valuable source of information for correcting errors in the current bovine assembly. These errors were sufficiently frequent to be of concern for the accuracy of mapping QTL with panels of SNPs whose positions are based on the current assembly.

Point mutation detection by surface plasmon resonance imaging coupled with a temperature scan method in a model system

The detection of point mutations in genes presents clear biological and medical interest. Various methods have been considered. In this paper, we take advantage of surface plasmon resonance imaging, a technique allowing detection of unlabeled DNA hybridization. Coupled with a temperature scan, this approach allows us to determine the presence of single-point mutations in oligonucleotide samples from the analysis of DNA's melting curves in either the homozygous or heterozygous case. Moreover, these experimental data are confirmed in good agreement with numerical calculations.