Visualisation of quadratic discriminant analysis and its application in exploration of microbial interactions

Background: When comparing diseased and non-diseased patients in order to discriminate between the aspects associated with the specific disease, it is often observed that the diseased patients have more variability than the non-diseased patients. In such cases Quadratic discriminant analysis is required which is based on the estimation of different covariance structures for the different groups. Having different covariance matrices means the Canonical variate transformation cannot be used to obtain a visual representation of the discrimination and group separation. Results: In this paper an alternative method is proposed: combining the different transformations for the different groups into a single representation of the sample points with classification regions. In order to associate the differences in variables with group discrimination, a biplot is produced which include information on the variables, samples and their relationship

Respiratory microbes present in the nasopharynx of children hospitalised with suspected pulmonary tuberculosis in Cape Town, South Africa

Background: Lower respiratory tract infection in children is increasingly thought to be polymicrobial in origin. Children with symptoms suggestive of pulmonary tuberculosis (PTB) may have tuberculosis, other respiratory tract infections or co-infection with Mycobacterium tuberculosis and other pathogens. We aimed to identify the presence of potential respiratory pathogens in nasopharyngeal (NP) samples from children with suspected PTB. Method: NP samples collected from consecutive children presenting with suspected PTB at Red Cross Children’s Hospital (Cape Town, South Africa) were tested by multiplex real-time RT-PCR. Mycobacterial liquid culture and Xpert MTB/RIF was performed on 2 induced sputa obtained from each participant. Children were categorised as definite-TB (culture or qPCR [Xpert MTB/RIF] confirmed), unlikely-TB (improvement of symptoms without TB treatment on follow-up) and unconfirmed-TB (all other children). Results: Amongst 214 children with a median age of 36 months (interquartile range, [IQR] 19–66 months), 34 (16 %) had definite-TB, 86 (40 %) had unconfirmed-TB and 94 (44 %) were classified as unlikely-TB. Moraxella catarrhalis (64 %), Streptococcus pneumoniae (42 %), Haemophilus influenzae spp (29 %) and Staphylococcus aureus (22 %) were the most common bacteria detected in NP samples. Other bacteria detected included Mycoplasma pneumoniae (9 %), Bordetella pertussis (7 %) and Chlamydophila pneumoniae (4 %). The most common viruses detected included metapneumovirus (19 %), rhinovirus (15 %), influenza virus C (9 %), adenovirus (7 %), cytomegalovirus (7 %) and coronavirus O43 (5.6 %). Both bacteria and viruses were detected in 73, 55 and 56 % of the definite, unconfirmed and unlikely-TB groups, respectively. There were no significant differences in the distribution of respiratory microbes between children with and without TB. Using quadratic discriminant analysis, human metapneumovirus, C. pneumoniae, coronavirus 043, influenza virus C virus, rhinovirus and cytomegalovirus best discriminated children with definite-TB from the other groups of children. Conclusions: A broad range of potential respiratory pathogens was detected in children with suspected TB. There was no clear association between TB categorisation and detection of a specific pathogen. Further work is needed to explore potential pathogen interactions and their role in the pathogenesis of PTB

Integration and visualisation of clinical-omics datasets for medical knowledge discovery

In recent decades, the rise of various omics fields has flooded life sciences with unprecedented amounts of high-throughput data, which have transformed the way biomedical research is conducted. This trend will only intensify in the coming decades, as the cost of data acquisition will continue to decrease. Therefore, there is a pressing need to find novel ways to turn this ocean of raw data into waves of information and finally distil those into drops of translational medical knowledge. This is particularly challenging because of the incredible richness of these datasets, the humbling complexity of biological systems and the growing abundance of clinical metadata, which makes the integration of disparate data sources even more difficult. Data integration has proven to be a promising avenue for knowledge discovery in biomedical research. Multi-omics studies allow us to examine a biological problem through different lenses using more than one analytical platform. These studies not only present tremendous opportunities for the deep and systematic understanding of health and disease, but they also pose new statistical and computational challenges. The work presented in this thesis aims to alleviate this problem with a novel pipeline for omics data integration. Modern omics datasets are extremely feature rich and in multi-omics studies this complexity is compounded by a second or even third dataset. However, many of these features might be completely irrelevant to the studied biological problem or redundant in the context of others. Therefore, in this thesis, clinical metadata driven feature selection is proposed as a viable option for narrowing down the focus of analyses in biomedical research. Our visual cortex has been fine-tuned through millions of years to become an outstanding pattern recognition machine. To leverage this incredible resource of the human brain, we need to develop advanced visualisation software that enables researchers to explore these vast biological datasets through illuminating charts and interactivity. Accordingly, a substantial portion of this PhD was dedicated to implementing truly novel visualisation methods for multi-omics studies.Open Acces

Peptide fingerprinting and predictive modelling of fermented milk : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Food Technology at Massey University, Palmerston North Campus, New Zealand

Fermented milk products are valued by consumers and the food industry for their nutritional properties, pleasant taste, and texture. Consumer demands and expectations for such products are constantly changing. Understanding how consumers perceive the sensory characteristics of food and the relationship these characteristics have with the chemical components of food can provide insight that can enable food researchers and manufacturers to develop food products that are tailored to provide enhanced sensory qualities. Establishing techniques that allow for in-silico prediction or correlation of sensory qualities can enable a more rapid approach that would aim to enable researchers to meet the demands of consumers. This research firstly explored mass spectrometric techniques for the rapid fingerprinting of milk and fermented milk products, using Matrix-Assisted Laser Desorption Ionisation - Time-of-Flight Mass Spectrometry (MALDI-TOF MS) and Rapid Evaporative Ionisation Mass Spectrometry (REIMS), two technologies that require minimal sample preparation and can rapidly generate a fingerprint of a food’s chemical components. Peptide fingerprints obtained by MALDI-TOF MS and analysed by principal component analysis were effective at discriminating the two fermented milk and milk samples. Supervised discrimination of low molecular weight fingerprints obtained via REIMS and MALDI-TOF MS proved less effective but demonstrated some potential and could be used alongside other analyses in future studies. These techniques were explored with a view to establishing a technique that could provide rapid insights into a food’s chemical composition, and which could also effectively discriminate the chemical components of the product. Such techniques could be used for rapid screening of products and can provide insight into the chemical components that are driving the variation in different products, which may be reflective of the differences in sensory characteristics. Next, peptide fingerprinting and predictive modelling were investigated in milk fermented with various bacterial combinations, including probiotic cultures. Fingerprinting was performed on samples collected at each hour of fermentation. Predictive modelling techniques, using both regression and classification approaches, were trialled in order to predict the change in signal intensity throughout fermentation. This aimed to understand if peptides could be predicted throughout fermentation, with a view to enable the targeted prediction of desirable peptides, or other relevant components, which may impart favourable sensory qualities in the final product. Regression techniques were somewhat effective for predicting the signal intensity of individual m/z ions throughout fermentation. Most of the ions did not follow a linear relationship, and, as such, a multiple linear regression model was unable to model most of the ions. Using a generalised additive model, a non-linear approach, improved the performance in most cases and could predict the signal intensity of individual ions throughout fermentation. However, the model was unable to correctly predict all cases. Classification techniques were effective for predicting the general direction of the signal intensity between start and end fermentation times. Five classification techniques were trialled, with each model providing accurate predictions for the increase or decrease of signal intensity between early and late fermentation times. Lastly, consumer panellists were recruited to evaluate the change in important sensory characteristics throughout the fermentation of milk prepared using two different starter cultures. This aimed to understand if consumer responses to such products could be correlated with instrumental analysis, in order to predict the consumer responses from instrumental data. Consumers perceived significant differences in bitterness and flavour intensity between fermented milk samples at different fermentation time points. There were significant correlations between peptide fingerprints and the consumer rankings for the sensory attributes in each fermented milk product. XGBoost regression could predict consumer responses with reasonable accuracy. This thesis explored the fermentation of milk using specific bacteria and fermentation processes. To validate this work, further products could be explored, in addition to different processing parameters. Furthermore, a more in-depth analysis of the chemical components of the products could be investigated and analysed with additional sensory evaluation to further explore and confirm the findings

Longitudinal colonisation by Streptococcus pneumoniae and nasopharyngeal microbial interactions in health and disease: a South African birth cohort study

Streptococcus pneumoniae (the pneumococcus) is the most common cause of childhood pneumonia. Nasopharyngeal (NP) colonization by the pneumococcus is a necessary first step in the pathogenesis of pneumonia and yet the dynamic nature of pneumococcal colonization remains incompletely understood. In children, asymptomatic colonization of the nasopharynx by the pneumococcus is common and also serves as a reservoir for person-to-person transmission. We aimed to investigate in detail, the dynamics of pneumococcal nasopharyngeal carriage over the first year of life, in a cohort of South African children, particularly after implementation of the 13-valent pneumococcal conjugate vaccine (PCV-13). The study will further elucidate the interaction of S. pneumoniae with other respiratory pathogens and how such interactions may contribute development of severe disease

Exploring ambient mass spectrometry capacities for rapid detection and phenotyping of pseudomonas aeruginosa infection in cystic fibrosis patients

Cystic fibrosis (CF) patients mostly succumb to respiratory failure, usually caused by Pseudomonas aeruginosa infection. Early, but not chronic, infection can be eradicated, mandating timely detection. However, current methods are insufficiently sensitive and unsuitable for children. This thesis explores the potential of novel ambient mass spectrometric (MS) tools for the phenotyping of P. aeruginosa and its detection in non-invasive samples. The hypotheses are that bacterial expression evolves as infection progresses and that the composition of host body fluids is impacted by P. aeruginosa acquisition. Rapid evaporative ionisation MS (REIMS) was applied to clinical isolate cultures including common CF pathogens. The detected metabolic profiles differentiated between species and led to the identification of virulence-associated metabolites in P. aeruginosa: quorum sensing molecules and rhamnolipids. Repeated analysis revealed differences between P. aeruginosa strains that allowed isolate classification. Although classification according to genetically-defined types was not achieved, REIMS potentially provides a finer resolution on quickly evolving virulence features. Exploration of intra-species disparity showed a higher metabolic diversity in chronic respiratory than in acute infections, attributed to lengthy and site-specific adaptation. The bacterial population was more disparate between than within CF patients. Variations in virulence factor levels between early and chronic isolates provided insight into P. aeruginosa‘s metabolism and adaptation, supporting the first hypothesis. These findings may support future clinical strategies. REIMS and desorption electrospray ionisation MS (DESI-MS) were evaluated as direct-from-sample diagnostic tools using sputum as a reference, and urine and skin secretions as easily accessible samples. REIMS could not detect P. aeruginosa in sputum and urine pellets, nor could DESI-MS on skin secretions. Nevertheless, the rich sputum metabolic profile and skin lipidome contained potentially highly relevant information about host physiology that may assist clinicians in the future. Although the second hypothesis was not verified, the data reported will generate future clinically relevant hypotheses.Open Acces

Développement d'outils NIR et de méthodes pour monitorer des produits de lyophilisation

Abstract : The demand to achieve improved drug product quality has been accelerated with the advent of quality by design (QbD) guidance launched by regulatory agencies around the world. This extends to freeze-drying processes, where bio-pharmaceutical products are dried under an extremely controlled environment. Freeze-drying, or lyophilization, is a low-temperature dehydration process that involves multistep transformations making use of the principles of heat & mass transfer. This often renders the process complicated and time-consuming, resulting in large operating costs. Multiple process analytical technology (PAT) tools have been introduced to monitor product quality attributes in batch dried vials, as these tools help in keeping an eye on the product/process to achieve acceptable product quality attributes. Despite significant advances, many topics remain to be addressed. One of them being the impact of spatial variations in the product attributes, thus rendering the accuracy of in-process results obtained from a single point on the vial surface questionable. Another being the aesthetic appearance of the product, specifically collapse inside the products, which is usually assessed by visual inspection. However, relying completely on human input can be fallible and unrealistic in the production environment as thousands of product vials roll out from the freeze-dryers. Failure to detect an aesthetic defect in the finished freeze-dried product cake may put a patient’s life at risk as any defect might be a result of product collapse or meltback affecting the drug safety and efficacy. This project consisted of two main areas of work. 1) Using NIR Chemical Imaging (NIR-CI) and NIR spectroscopy (NIRS) to investigate the spatial variability of moisture on the surface of the vials undergoing drying. Furthermore, it demonstrates the necessity of using multiple measurement points on the vial surface to quantify moisture inside the freeze-drying products. 2) Using NIRS to identify the physical properties of the product, such as normal or collapsed product. This is performed by leveraging the ability of NIRS to exhibit unique spectra relative to the physical characteristics of the product. Two intensities of collapse were induced in the freeze-drying products, and the potential of NIRS in identifying the collapse during the process and in the finished freeze-dried products was demonstrated. Results show the promising nature of the NIR-CI and NIRS in combination with the multivariate data analysis (MVDA) methods to monitor product quality attributes and better understand their variability. Overall, this thesis work presents a detailed investigation about the moisture distribution and collapse inside the freeze-dried products.La demande d'amélioration de la qualité des produits pharmaceutiques a été accélérée avec l'avènement des directives de qualité par la conception (QbD) lancées par les agences de réglementation du monde entier. Cela s'étend aux procédés de lyophilisation, où les produits biopharmaceutiques sont séchés dans un environnement extrêmement contrôlé. La lyophilisation est un de déshydratation à basse température qui implique des transformations en plusieurs étapes utilisant les principe de transfert de chaleur et de masse. Cela rend souvent le procédé compliqué et long, ce qui entraîne des coûts d'exploitation importants. Plusieurs outils de technologie d'analyse de processus (PAT) ont été introduits pour surveiller les attributs de qualité du produit dans des flacons séchés par lots, car ces outils aident à garder un œil sur le produit / procédé pour obtenir des attributs de qualité de produit acceptables. Malgré des avancées significatives, de nombreux sujets restent à traiter. L'un d'eux est l'impact des variations spatiales dans les attributs du produit, rendant ainsi la précision des résultats en cours de procédé obtenus à partir d'un seul point sur la surface du flacon discutable. Un autre est l'aspect esthétique du produit, qui est généralement évalué par une inspection visuelle. Cependant, se fier entièrement à l'apport humain peut être problématique et irréaliste dans l'environnement de production, car des milliers de flacons de produit sortent des lyophilisateurs. Le fait de ne pas détecter un défaut esthétique dans le gâteau de produit lyophilisé fini peut mettre la vie d'un patient en danger, car tout défaut peut être le résultat de l'effondrement du produit (meltback) affectant l'innocuité et l'efficacité du médicament. Ce projet comprenait deux thèmes principaux. 1) Utilisation de l'imagerie chimique NIR (NIR-CI) et de la spectroscopie NIR (NIRS) pour étudier la variabilité spatiale de l'humidité à la surface des flacons en cours de séchage. 2) Utilisation de NIRS pour identifier les propriétés physiques du produit, en tirant parti de la capacité du NIRS à présenter des spectres uniques par rapport aux caractéristiques physiques du produit. Deux intensités d'affaissement ont été induites dans les produits de lyophilisation, et le potentiel du NIRS dans l'identification de l'effondrement pendant le procédé et dans les produits lyophilisés finis a été démontré. Les résultats montrent la nature prometteuse du NIR-CI et du NIRS en combinaison avec les méthodes d'analyse de données multivariées (MVDA) pour surveiller les attributs de qualité des produits et mieux comprendre leur variabilité. Dans l'ensemble, ce travail de thèse présente une étude détaillée de la répartition de l'humidité et de l'effondrement à l'intérieur des produits lyophilisés