In Silico profiling of deleterious amino acid substitutions of potential pathological importance in haemophlia A and haemophlia B

<p>Abstract</p> <p>Background</p> <p>In this study, instead of current biochemical methods, the effects of deleterious amino acid substitutions in <it>F8 and F9 </it>gene upon protein structure and function were assayed by means of computational methods and information from the databases. Deleterious substitutions of <it>F8 and F9 </it>are responsible for Haemophilia A and Haemophilia B which is the most common genetic disease of coagulation disorders in blood. Yet, distinguishing deleterious variants of <it>F8 and F9 </it>from the massive amount of nonfunctional variants that occur within a single genome is a significant challenge.</p> <p>Methods</p> <p>We performed an <it>in silico </it>analysis of deleterious mutations and their protein structure changes in order to analyze the correlation between mutation and disease. Deleterious nsSNPs were categorized based on empirical based and support vector machine based methods to predict the impact on protein functions. Furthermore, we modeled mutant proteins and compared them with the native protein for analysis of protein structure stability.</p> <p>Results</p> <p>Out of 510 nsSNPs in <it>F8</it>, 378 nsSNPs (74%) were predicted to be 'intolerant' by SIFT, 371 nsSNPs (73%) were predicted to be 'damaging' by PolyPhen and 445 nsSNPs (87%) as 'less stable' by I-Mutant2.0. In <it>F9</it>, 129 nsSNPs (78%) were predicted to be intolerant by SIFT, 131 nsSNPs (79%) were predicted to be damaging by PolyPhen and 150 nsSNPs (90%) as less stable by I-Mutant2.0. Overall, we found that I-Mutant which emphasizes support vector machine based method outperformed SIFT and PolyPhen in prediction of deleterious nsSNPs in both <it>F8 </it>and <it>F9</it>.</p> <p>Conclusions</p> <p>The models built in this work would be appropriate for predicting the deleterious amino acid substitutions and their functions in gene regulation which would be useful for further genotype-phenotype researches as well as the pharmacogenetics studies. These <it>in silico </it>tools, despite being helpful in providing information about the nature of mutations, may also function as a first-pass filter to determine the substitutions worth pursuing for further experimental research in other coagulation disorder causing genes.</p

Genetic counselling and adult polycystic kidney disease: patients' knowledge, perceptions and understanding

Adult Polycystic Kidney Disease (APKD) is a genetic disease transmitted in an autosomal dominant fashion. There is no cure. Treatment is of the symptoms as they appear usually in adulthood. Patients affected by APKD may receive genetic counselling from renal physicians. The aims of genetic counselling can be described through paradigms which reflect the current understanding of genetics and knowledge of the illnesses. The availability of new diagnostic techniques creates a new paradigm concerned with the ethical issues of genetic testing and counselling. An investigation into patients' knowledge, perceptions and understanding of genetic counselling was undertaken at the Renal Unit of Glasgow Royal Infirmary, prior to the establishment of a screening and counselling service for those at risk for APKD. The main findings of the study were: the majority of patients had received some genetic counselling from renal physicians; the majority of patients had relatively good knowledge of the symptoms of and treatments for APKD; nevertheless patients believed that the two most important items to be included in genetic counselling were information about the symptoms and the treatment of APKD; patients did not fully understand the genetic inheritance of APKD; they described the risk of transmission of APKD (50-50) as a medium risk; almost all patients recommended that their at risk relatives and their children be tested for APKD; prior to the availability of prenatal diagnosis, patients thought that their children should be tested between the ages of 16 and 20. A secondary study, including spouses of those with APKD and also haemophiliacs and their spouses, found that respondents favoured prenatal testing without termination of pregnancy and that both diseases were rated as being of medium severity. These findings raise ethical issues for those giving genetic counselling, and have implications for the content of genetic counselling

Characterisation Of Adeno-Associated Virus DNA contaminants and Development of a P5 promoter replacement, high purity Adeno-Associated Virus production system

Gene therapy is the transfer of nucleic acids for therapeutic benefit, a process potentially achieved through several delivery methods. One of the most common gene therapy delivery modalities is adeno associated virus (AAV). The availability of a system to produce large quantities of AAV, and of diverse AAV serotypes, that can infect different cell types makes AAV very attractive for widespread clinical implementation. Viruses like AAV that are manipulated to become highly useful delivery tools rely on complex biological systems for production. It is imperative that these systems are well characterised and designed to be highly safe and efficacious. This PhD thesis investigated nucleic acid contamination of AAV preparations, looking at the production system as a source, and the problems that this may pose for AAV infected cells. Specific regions of DNA from AAV producer plasmids were incorporated into virions more abundantly. These were found adjacent to the outside of the inverted terminal repeats (ITRs) that flank the expression cassette, and on the plasmid containing the replication and capsid genes, directly upstream of the P5 promoter which drives expression of the replication proteins, REP78 and REP68. Contaminant sequences were found to be transferred into transduced cells during AAV infection, persistent within cells and transcriptionally active. Investigation of P5 upstream contaminants showed potential for protein to be produced persistently from contaminant sequences both in vitro and in vivo. Finally, precise sequence regions causing DNA incorporation upstream of the P5 promoter were examined. This information was used to produce alternative production 3 reagents that, in their first iteration, provided proof of principle of P5-related contaminant removal from the final AAV product, and in additional iterations resulted in a modified P5 promoter (P5-HS). This promoter limited upstream contamination thereby resulting in higher purity AAV, whilst retaining vector yields equivalent to the original AAV production system

The High-Throughput Analyses Era: Are We Ready for the Data Struggle?

Recent and rapid technological advances in molecular sciences have dramatically increased the ability to carry out high-throughput studies characterized by big data production. This, in turn, led to the consequent negative effect of highlighting the presence of a gap between data yield and their analysis. Indeed, big data management is becoming an increasingly important aspect of many fields of molecular research including the study of human diseases. Now, the challenge is to identify, within the huge amount of data obtained, that which is of clinical relevance. In this context, issues related to data interpretation, sharing and storage need to be assessed and standardized. Once this is achieved, the integration of data from different -omic approaches will improve the diagnosis, monitoring and therapy of diseases by allowing the identification of novel, potentially actionably biomarkers in view of personalized medicine

A bioinformatics toolkit: in silico tools and online resources for investigating genetic variation

With the advent of large-scale next-generation sequencing initiatives, there is an increasing importance to interpret and understand the potential phenotypic influence of identified genetic variation and its significance in the human genome. Bioinformatics analyses can provide useful information to assist with variant interpretation. This review provides an overview of tools/resources currently available, and how they can help predict the impact of genetic variation at the deoxyribonucleic acid, ribonucleic acid, and protein level

Haematalogical investigations in children

The haematology laboratory is able to perform a number of tests to help establish the cause of illness in children. The full blood count (FBC, also known as a complete blood count, CBC) is one of the most basic blood tests performed on children attending hospital or a primary care clinic. All doctors should therefore have an understanding of how the test is performed, possible pitfalls, be able to interpret results and know when more specialised testing or advice is required. Other haematological investigations in routine use include coagulation screens, blood film examination, reticulocyte counts and methods for estimation of iron stores and detection of abnormal haemoglobins. This section will focus on these basic tests and simple algorithms for the subsequent investigation and differential diagnosis of the commonest haemato-logical abnormalities encountered in general paediatric practice. The reader is referred to Chapter 15 for an account of the clinical presentation and management of primary haematological disorders in children

Clinical molecular genetics in the UK c.1975-c.2000

seminar transcriptChaired by Professor Martin Bobrow and introduced by Professor Bob Williamson, this Witness Seminar included geneticists from a broad range of research and clinical specialities. Discussions of molecular research into haemoglobin disorders, and the development of probes for related genes in the 1970s, included particular acknowledgment of Southern blotting as a critical tool for such research. Also noted was a landmark conference in Crete in 1978 that emphasized the special significance of research work on thalassaemia, as well as providing fruitful networking opportunities for scientists from around the world. Similarly, in 1982, a key course at Leiden University introduced molecular techniques to geneticists from across Europe. In that same year the first prenatal diagnosis by chorionic villus sampling was published, and the emotional aspects of such genetic diagnoses for patients, families and clinicians were frequently discussed during the seminar. Other issues, including the funding of research, and especially the role of patient support groups; the establishment and growth of professional interest groups and bodies such as the Clinical Molecular Genetics Society; and the development of national genetics

Characterization of Sex-Based Dna Methylation Signatures in the Airways During Early Life.

Human respiratory conditions are largely influenced by the individual\u27s sex resulting in overall higher risk for males. Sex-based respiratory differences are present at birth suggesting a strong genetic component. Our objective was to characterize early life sex-based genomic signatures determined by variable X-chromosome methylation in the airways. We compared male versus female genome-wide DNA methylation in nasal airway samples from newborns and infants aged 1-6 months (N = 12). We analyzed methylation signals across CpG sites mapped to each X-linked gene using an unsupervised classifier (principal components) followed by an internal evaluation and an exhaustive cross-validation. Results were validated in an independent population of children (N = 72) following the same algorithm. X-linked genes with significant sex-based differential methylation in the nasal airway of infants represented only about 50% of the unique protein coding transcripts. X-linked genes without significant sex-based differential methylation included genes with evidence of escaping X-inactivation and female-biased airway expression. These genes showed similar methylation patterns in males and females suggesting unbalanced X-chromosome dosage. In conclusion, we identified that the human airways have already sex-based DNA methylation signatures at birth. These early airway epigenomic marks may determine sex-based respiratory phenotypes and overall predisposition to develop respiratory disorders later in life

Predicting the Development of Anti-Drug Antibodies against Recombinant alpha-Galactosidase A in Male Patients with Classical Fabry Disease

Fabry Disease (FD) is a rare, X-linked, lysosomal storage disease that mainly causes renal, cardiac and cerebral complications. Enzyme replacement therapy (ERT) with recombinant alpha-galactosidase A is available, but approximately 50% of male patients with classical FD develop inhibiting anti-drug antibodies (iADAs) that lead to reduced biochemical responses and an accelerated loss of renal function. Once immunization has occurred, iADAs tend to persist and tolerization is hard to achieve. Here we developed a pre-treatment prediction model for iADA development in FD using existing data from 120 classical male FD patients from three European centers, treated with ERT. We found that nonsense and frameshift mutations in the α-galactosidase A gene (p = 0.05), higher plasma lysoGb3 at baseline (p < 0.001) and agalsidase beta as first treatment (p = 0.006) were significantly associated with iADA development. Prediction performance of a Random Forest model, using multiple variables (AUC-ROC: 0.77) was compared to a logistic regression (LR) model using the three significantly associated variables (AUC-ROC: 0.77). The LR model can be used to determine iADA risk in individual FD patients prior to treatment initiation. This helps to determine in which patients adjusted treatment and/or immunomodulatory regimes may be considered to minimize iADA development risk. View Full-Tex

The Utilisiation of composite Machine Learning models for the Classification of Medical Datasets For Sickle Cell Disease

The increase growth of health information systems has provided a significant way to deliver great change in medical domains. Up to this date, the majority of medical centres and hospitals continue to use manual approaches for determining the correct medication dosage for sickle cell disease. Such methods depend completely on the experience of medical consultants to determine accurate medication dosages, which can be slow to analyse, time consuming and stressful. The aim of this paper is to provide a robust approach to various applications of machine learning in medical domain problems. The initial case study addressed in this paper considers the classification of medication dosage levels for the treatment of sickle cell disease. This study base on different architectures of machine learning in order to maximise accuracy and performance. The leading motivation for such automated dosage analysis is to enable healthcare organisations to provide accurate therapy recommendations based on previous data. The results obtained from a range of models during our experiments have shown that a composite model, comprising a Neural Network learner, trained using the Levenberg-Marquardt algorithm, combined with a Random Forest learner, produced the best results when compared to other models with an Area under the Curve of 0.995