Biobanking from the patient perspective

Biobanks and biobanking research plays an increasingly important role in healthcare research and delivery as health systems become more patient-centred and medicine becomes more personalised. There is also growing acceptance and appreciation of the value that patients, patient advocacy organisations and the public can bring as stakeholders in biobanking and more generally in research. Therefore, the importance of active, early and sustained engagement and involvement of patient and public representatives in biobanks will become increasingly relevant. Organising and facilitating patient and public involvement in biobanking takes considerable time and effort for all stakeholders involved. Therefore, for any biobank operator considering involving patients and the public in their biobanking activities, consideration of best practices, current guidance, ethical issues and evaluation of involvement will be important. In this article, we demonstrate that patients are much more than donors to biobanks—they are collaborators at the heart of biobanking with an important voice to identify perspective, which can be an extremely valuable resource for all biobanks to utilise. The case studies herein provide examples of good practice of patient involvement in biobanking as well as outcomes from these practices, and lessons learned. Our aim is to provide useful insights from these efforts and potential future strategies for the multiple stakeholders that work with patients and the public involved in biobank-based research

Artificial Neural Network Inference (ANNI): A Study on Gene-Gene Interaction for Biomarkers in Childhood Sarcomas

Objective: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI). Method: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model. Results: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing’s sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen. Conclusions: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas

A simpler method of preprocessing MALDI-TOF MS data for differential biomarker analysis: stem cell and melanoma cancer studies

<p>Abstract</p> <p>Introduction</p> <p>Raw spectral data from matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) with MS profiling techniques usually contains complex information not readily providing biological insight into disease. The association of identified features within raw data to a known peptide is extremely difficult. Data preprocessing to remove uncertainty characteristics in the data is normally required before performing any further analysis. This study proposes an alternative yet simple solution to preprocess raw MALDI-TOF-MS data for identification of candidate marker ions. Two in-house MALDI-TOF-MS data sets from two different sample sources (melanoma serum and cord blood plasma) are used in our study.</p> <p>Method</p> <p>Raw MS spectral profiles were preprocessed using the proposed approach to identify peak regions in the spectra. The preprocessed data was then analysed using bespoke machine learning algorithms for data reduction and ion selection. Using the selected ions, an ANN-based predictive model was constructed to examine the predictive power of these ions for classification.</p> <p>Results</p> <p>Our model identified 10 candidate marker ions for both data sets. These ion panels achieved over 90% classification accuracy on blind validation data. Receiver operating characteristics analysis was performed and the area under the curve for melanoma and cord blood classifiers was 0.991 and 0.986, respectively.</p> <p>Conclusion</p> <p>The results suggest that our data preprocessing technique removes unwanted characteristics of the raw data, while preserving the predictive components of the data. Ion identification analysis can be carried out using MALDI-TOF-MS data with the proposed data preprocessing technique coupled with bespoke algorithms for data reduction and ion selection.</p

A food safety control low mass-range proteomics platform for the detection of illicit treatments in veal calves by MALDI-TOF-MS serum profiling

International audiencePerformance enhancing agents (PEAs) are illegally used in cattle and other meat producing species to increase food conversion and lean meat production. Due to the very short breeding cycle, veal calves represent the meat producing bovine category mostly subjected to illicit treatments. These chemical agents are difficult to detect by conventional analytical approaches due to the employment of synergistic formulations at very low dosage and given the use of uncharacterised novel compounds. Such a scenario has fostered a strong interest in the discovery of functional molecular biomarkers for the detection of growth promoting agents in meat producing species. A multivariate MALDI-TOF-MS proteomics platform has been developed using bovine serum samples. Analytical performances have been thoroughly evaluated in order to enable reproducible profiles from 10 μl sera samples. We propose univariate and multivariate discrimination models capable to identify calves undergoing illicit treatments. In particular, we found a strong discrimination power associated with a polypeptide fragment from β2-glycoprotein-I. We provide a fundamental proof of concept in the potential application of MALDI-TOF-MS proteomics profiling in the food safety control

N_LyST: a simple and rapid screening test for Lynch Syndrome

Aims: We sought to use PCR followed by high-resolution melting (HRM) analysis to develop a single closed-tube screening panel to screen for Lynch Syndrome. This comprises tests for microsatellite instability (MSI), MLH1 methylation promoter and BRAF mutation.Methods:For MSI-testing, 5 mononucleotide markers (BAT25, BAT26, BCAT25, MYB, EWSR1) were developed. In addition, primers were designed to interrogate Region C of the MLH1 promoter for methylation (using bisulphite-modified DNA) and to test for mutations in codon 600 of BRAF. Two separate cohorts from Nottingham (n = 99, 46 with MSI, 53 being microsatellite stable (MSS)) and Edinburgh (n=88, 45 MSI, 43 MSS). Results:All the cases (n=187) were blind tested for MSI and all were correctly characterised by our panel. The MLH1 promoter and BRAF were tested only in the Nottingham cohort. Successful blinded analysis was performed on the MLH1 promoter in 97 cases. All MSS cases showed a pattern of non-methylation whilst 41/44 cases with MSI showed full methylation. The three cases with MSI and a non-methylated pattern had aberrations in MSH2 and MSH6 expression. BRAF mutation was detected in 61% of MSI cases and 11% of MSS cases. Finally, 12 cases were blind screened by using the whole panel as a single test. Of these, 5 were identified as MSS, 4 as MSI/non-LS and 3 as MSI/possible LS. These results were concordant with the previous data.Conclusion: We describe the Nottingham Lynch Syndrome Test (N_LyST). This is a quick simple cheap method for screening for Lynch Syndrome

Lipidomic analysis of plasma samples from women with polycystic ovary syndrome

Abstract Polycystic ovary syndrome (PCOS) is a common disorder affecting between 5 and 18 % of females of reproductive age and can be diagnosed based on a combination of clinical, ultrasound and biochemical features, none of which on its own is diagnostic. A lipidomic approach using liquid chromatography coupled with accurate mass high-resolution mass-spectrometry (LCHRMS) was used to investigate if there were any differences in plasma lipidomic profiles in women with PCOS compared with control women at different stages of menstrual cycle. Plasma samples from 40 women with PCOS and 40 controls aged between 18 and 40 years were analysed in combination with multivariate statistical analyses. Multivariate data analysis (LASSO regression and OPLSDA) of the sample lipidomics datasets showed a weak prediction model for PCOS versus control samples from the follicular and mid-cycle phases of the menstrual cycle, but a stronger model (specificity 85 % and sensitivity 95 %) for PCOS versus the luteal phase menstrual cycle controls. The PCOS vs luteal phase model showed increased levels of plasma triglycerides and sphingomyelins and decreased levels of lysophosphatidylcholines and phosphatidylethanolamines in PCOS women compared with controls. Lipid biomarkers of PCOS were tentatively identified which may be useful in distinguishing PCOS from controls especially when performed during the menstrual cycle luteal phase

Role of Haptoglobin in Polycystic Ovary Syndrome (PCOS), Obesity and Disorders of Glucose Tolerance in Premenopausal Women

alleles of the haptoglobin α–chain polymorphism reduce the anti-oxidant properties and increase the pro-inflammatory actions of this acute-phase protein in a gene-dosage fashion. We hypothesized that the haptoglobin polymorphism might contribute to the increased oxidative stress and low-grade chronic inflammation frequently associated with polycystic ovary syndrome, obesity, and abnormalities of glucose tolerance.<0.001), yet no association was found between obesity and haptoglobin genotypes. No differences were observed in haptoglobin levels or genotype frequencies depending on glucose tolerance. Fifty percent of the variation in serum haptoglobin concentrations was explained by the variability in serum C-reactive protein concentrations, BMI, insulin sensitivity and haptoglobin genotypes. alleles suggests that the anti-oxidant and anti-inflammatory properties of haptoglobin may be reduced in these patients

A Computational Model of the Ionic Currents, Ca2+ Dynamics and Action Potentials Underlying Contraction of Isolated Uterine Smooth Muscle

Uterine contractions during labor are discretely regulated by rhythmic action potentials (AP) of varying duration and form that serve to determine calcium-dependent force production. We have employed a computational biology approach to develop a fuller understanding of the complexity of excitation-contraction (E-C) coupling of uterine smooth muscle cells (USMC). Our overall aim is to establish a mathematical platform of sufficient biophysical detail to quantitatively describe known uterine E-C coupling parameters and thereby inform future empirical investigations of physiological and pathophysiological mechanisms governing normal and dysfunctional labors. From published and unpublished data we construct mathematical models for fourteen ionic currents of USMCs: currents (L- and T-type), current, an hyperpolarization-activated current, three voltage-gated currents, two -activated current, -activated current, non-specific cation current, - exchanger, - pump and background current. The magnitudes and kinetics of each current system in a spindle shaped single cell with a specified surface area∶volume ratio is described by differential equations, in terms of maximal conductances, electrochemical gradient, voltage-dependent activation/inactivation gating variables and temporal changes in intracellular computed from known fluxes. These quantifications are validated by the reconstruction of the individual experimental ionic currents obtained under voltage-clamp. Phasic contraction is modeled in relation to the time constant of changing . This integrated model is validated by its reconstruction of the different USMC AP configurations (spikes, plateau and bursts of spikes), the change from bursting to plateau type AP produced by estradiol and of simultaneous experimental recordings of spontaneous AP, and phasic force. In summary, our advanced mathematical model provides a powerful tool to investigate the physiological ionic mechanisms underlying the genesis of uterine electrical E-C coupling of labor and parturition. This will furnish the evolution of descriptive and predictive quantitative models of myometrial electrogenesis at the whole cell and tissue levels