Functional characterization of BRCA1 gene variants by mini-gene splicing assay

Mutational screening of the breast cancer susceptibility gene BRCA1 leads to the identification of numerous pathogenic variants such as frameshift and nonsense variants, as well as large genomic rearrangements. The screening moreover identifies a large number of variants, for example, missense, silent, and intron variants, which are classified as variants of unknown clinical significance owing to the lack of causal evidence. Variants of unknown clinical significance can potentially have an impact on splicing and therefore functional examinations are warranted to classify whether these variants are pathogenic or benign. Here we validate a mini-gene splicing assay by comparing the results of 24 variants with previously published data from RT-PCR analysis on RNA from blood samples/lymphoblastoid cell lines. The analysis showed an overall concordance of 100%. In addition, we investigated 13 BRCA1 variants of unknown clinical significance or putative variants affecting splicing by in silico analysis and mini-gene splicing assay. Both the in silico analysis and mini-gene splicing assay classified six BRCA1 variants as pathogenic (c.80+1G>A, c.132C>T (p.=), c.213−1G>A, c.670+1delG, c.4185+1G>A, and c.5075−1G>C), whereas six BRCA1 variants were classified as neutral (c.-19-22_-19-21dupAT, c.302−15C>G, c.547+14delG, c.4676−20A>G, c.4987−21G>T, and c.5278−14C>G) and one BRCA1 variant remained unclassified (c.670+16G>A). In conclusion, our study emphasizes that in silico analysis and mini-gene splicing assays are important for the classification of variants, especially if no RNA is available from the patient. This knowledge is crucial for proper genetic counseling of patients and their family members.     

Life insurance: genomic stratification and risk classification

With the development and increasing accessibility of new genomic tools such as next-generation sequencing, genome-wide association studies, and genomic stratification models, the debate on genetic discrimination in the context of life insurance became even more complex, requiring a review of current practices and the exploration of new scenarios. In this perspective, a multidisciplinary group of international experts representing different interests revisited the genetics and life insurance debate during a 2-day symposium ‘Life insurance: breast cancer research and genetic risk prediction seminar'' held in Quebec City, Canada on 24 and 25 September 2012. Having reviewed the current legal, social, and ethical issues on the use of genomic information in the context of life insurance, the Expert Group identified four main questions: (1) Have recent developments in genomics and related sciences changed the contours of the genetics and life insurance debate? (2) Are genomic results obtained in a research context relevant for life insurance underwriting? (3) Should predictive risk assessment and risk stratification models based on genomic data also be used for life insurance underwriting? (4) What positive actions could stakeholders in the debate take to alleviate concerns over the use of genomic information by life insurance underwriters? This paper presents a summary of the discussions and the specific action items recommended by the Expert Group.Access to genetic information by life insurers has been a topic of discussion for many years.¹ The possibility of using genetic data to underwrite an applicant''s insurance policy has given rise to concerns about the emergence of ‘genetic discrimination''. Genetic discrimination in the field of life insurance is not necessarily illegal in that in insurance underwriting questions about health, family history of disease, or genetic information may constitute legal exceptions to antidiscrimination legislation.^{2, 3} Nevertheless, the expression ‘genetic discrimination'' has acquired public notoriety⁴ and we will use more neutral language in this paper.Countries including Canada, the United States, Russia, and Japan⁵ have chosen not to adopt laws specifically prohibiting access to genetic data for underwriting by life insurers.⁶ In these countries, life insurance underwriters treat genetic data like other types of medical or lifestyle data. However, a growing number of countries such as Belgium, France, and Norway⁵ have chosen to adopt laws to prevent or limit insurers'' access to genetic data for life insurance underwriting. Other countries including Finland and the United Kingdom have developed voluntary arrangements with the industry (ie moratoria) with similar objectives.⁷Life insurance is a private contract between the policy-holder and the insurer. Its principal role is to provide financial security to the beneficiaries in the event of the insured''s death.⁸ Because of this important role, life insurance is often required, or strongly recommended for those seeking loans to acquire primary social goods, like housing or cars.⁹ In Europe, a consequence of the advent of the welfare state is that private insurance has increasingly played a complementary and supplementary role to social insurance by offering additional security and protection to the population. Thus, in this region, insurance is often considered as a social good that allows individuals to live a comfortable life and as a tool to promote social integration.¹⁰ In other regions of the world, this social role of life insurance is also recognized to a lesser extent. Given this social role, equitable access to life insurance is perceived as a sensitive issue and cases of denial looked upon negatively in popular media. Although documented incidents of denial or of increased premiums on the basis of genetic information have remained limited to the context of a few relatively well known, highly penetrant, familial, adult-onset, genetic conditions,¹¹ they have nevertheless generated significant public concern. Fear that insurers will have access to genetic information generated in a clinical or research setting for use in underwriting has been reported by several studies as a reason for non-participation in genetic research or recommended clinical genetic testing.^{12, 13, 14}The clinical utility of genetic testing for monogenic disorders such as Huntington disease, and hereditary forms of cancer are well established.¹⁵ However, genomic risk profiles based on the known common susceptibility variants have limited utility in risk prediction at the individual level, although they could be used for risk stratification in prevention programmes in populations.¹⁶ Today, a new era of genomic research has made it increasingly affordable to scan the entire genome of an individual. Researchers and physicians can interpret these data together with medical and lifestyle information in the form of sophisticated risk prediction models.¹⁷ Moreover, improvement in computing technologies coupled with the Internet make predictive information increasingly available, whether through direct-to-consumer marketing of genetic tests, genetic data sharing online communities, or international research database projects. Given these important technological and scientific changes, and their impact on various stakeholders. The term ‘stakeholders'' is used in this text to refer to the following groups of individuals: actuaries (person who computes insurance risk and premium rates based on statistical data), academic researchers, community representatives, ethics committees, genetic counsellors, genomic researchers, human rights experts, insurers, governmental representatives, non-governmental organisations, patient representatives, physicians, policy makers, popular media, reinsurers (company in charge of calculating the risk and premium amount for insuring a particular customer), research participants, and underwriters (company or person in charge of calculating the risk involved in providing insurance for a particular customer and to decide how much should be paid for the premium). This list is not meant to be exhaustive as relevant new groups may emerge as this topic further develops in the coming years. A multidisciplinary group of international experts representing different interests (hereinafter ‘the Expert Group'') revisited the genetics and life insurance debate. The following text presents a summary of the issues discussed and the ‘Action Items'' agreed upon by the Expert Group at the ‘Life Insurance, Risk Stratification, and Personalized Medicine Symposium''.     

Target-mediated clearance and bio-distribution of a monoclonal antibody against the Kunitz–type protease inhibitor 2 domain of Tissue Factor Pathway Inhibitor

Introduction

A humanised monoclonal antibody, concizumab, that binds with high affinity to the Kunitz-type protease inhibitor (KPI) 2 domain of human tissue factor pathway inhibitor (TFPI) is in clinical development. It promotes coagulation by neutralising the inhibitory function of TFPI and may provide a subcutaneous prophylaxis option for patients with haemophilia. We aimed to study biodistribution and pharmacokinetics (PK) of concizumab.

Materials and Methods

Blockage of cellular TFPI by concizumab was measured by tissue factor/Factor VIIa-mediated Factor X activation on human EA.hy926 cells. Biodistribution of concizumab was analysed in rabbits by immunohistology, and the PK was measured in rabbits and rats.

Results and Conclusions

Concizumab bound to cell surface TFPI on EA.hy926 cells and neutralised TFPI inhibition of Factor X activation. The antibody cross-reacted with rabbit TFPI, but not with rat TFPI, allowing for comparative PK studies. PK data in rats described a log-linear profile typical for a non-binding antibody, whereas PK data in rabbits revealed a non-linear, dose-dependent profile, consistent with a target-mediated clearance mechanism. Immunohistology in rabbits during target-saturation showed localisation of the antibody on the endothelium of the microvasculature in several organs. We observed a marked co-localisation with endogenous rabbit TFPI, but a negligible sub-endothelial build-up. Concizumab binds and neutralises the inhibitory effect of cell surface-bound TFPI. The PK profile observed in rabbits is consistent with a TFPI-mediated drug disposition. Double immunofluorescence shows co-localisation of the antibody with TFPI on the endothelium of the microvasculature and points to this TFPI as a putative target involved in the clearance mechanism.     

Characteristics of electrocardiographic repolarization in acute myocardial infarction complicated by ventricular fibrillation

Background and PurposeSome de- and re-polarization patterns can reflect an increased risk of ventricular tachyarrhythmias. We studied whether some electrocardiographic (ECG) patterns are able to predict the development of ventricular fibrillation (VF) during acute myocardial infarction (MI).MethodsWe compared the patterns of ST-T segment of 78 patients who developed VF during acute MI (patient with VF) vs 170 comparable patients with acute MI but with no VF complications.ResultsOf the VF group, 47 developed out-of-hospital VF and 31 developed VF after their admission to the hospital. A steep downsloping ST segment toward a negative T wave with or without a short, flat, or rising portion at the initial portion was observed in 69.2% of the 78 patients: 61.3% in patients with pre-VF and 74.5% in patients with post-VF, vs 9.4% of patients who did not develop VF (P < .0001). In 90.6% of the latter, a typical upward-concave or convex “ischemic” pattern of the ST segment was observed. Thus, the characteristic ST-T patterns were highly associated with VF with a specificity greater than 90%.ConclusionsA steep downsloping ST segment may characterize the ECGs of patients who develop VF during acute MI.     

Highly neurotoxic monomeric α-helical prion protein

Prion diseases are infectious and belong to the group of protein misfolding neurodegenerative diseases. In these diseases, neuronal dysfunction and death are caused by the neuronal toxicity of a particular misfolded form of their cognate protein. The ability to specifically target the toxic protein conformer or the neuronal death pathway would provide powerful therapeutic approaches to these diseases. The neurotoxic forms of the prion protein (PrP) have yet to be defined but there is evidence suggesting that at least some of them differ from infectious PrP (PrP(Sc)). Herein, without making an assumption about size or conformation, we searched for toxic forms of recombinant PrP after dilution refolding, size fractionation, and systematic biological testing of all fractions. We found that the PrP species most neurotoxic in vitro and in vivo (toxic PrP, TPrP) is a monomeric, highly α-helical form of PrP. TPrP caused autophagy, apoptosis, and a molecular signature remarkably similar to that observed in the brains of prion-infected animals. Interestingly, highly α-helical intermediates have been described for other amyloidogenic proteins but their biological significance remains to be established. We provide unique experimental evidence that a monomeric α-helical form of an amyloidogenic protein represents a cytotoxic species. Although toxic PrP has yet to be purified from prion-infected brains, TPrP might be the equivalent of one highly neurotoxic PrP species generated during prion replication. Because TPrP is a misfolded, highly neurotoxic form of PrP reproducing several features of prion-induced neuronal death, it constitutes a useful model to study PrP-induced neurodegenerative mechanisms.     

Longitudinal study of variation in body mass index in middle-aged UK females

The importance of changing patterns of obesity in society and its implications for public health are well recognized. However, the adult life course of body mass index (BMI) changes in individuals over time is largely unknown and has mostly been extrapolated from cross-sectional studies. The present study examines individual specific variation of BMI during a 15-year follow-up period in a community-based sample of UK females. We attempted to establish whether there is a common, generalized pattern which captures variation in BMI over time. The participants of this study belong to a prospective population cohort of British women studied intensively since 1989: the Chingford Study. The sample originally consisted of 1,003 women aged 45-68 years, who were assessed annually for BMI during follow-up period. Polynomial regression models were used to assess longitudinal BMI variation. We observed a great stability in individual BMI variation during the follow-up period, reflected by high correlations between the baseline BMI and follow-up BMI 10 and 15 years later (r = 0.876, N = 810, and r = 0.824, N = 638, respectively). We also found that three different major age-related patterns in BMI could be clearly identified: no change in 30.6% in 58% it increased and in 11.4% it decreased with age. Thus, our data suggest that individual age-related changes in BMI are very different. Therefore, simply combining all individuals into groups by any other criteria (age, sex, etc.) and overlooking the distinctive patterns of BMI change may lead to biased inferences in epidemiologic and etiologic research of the future.