Targeted proteomics and metabolomics for biomarker discovery in abdominal aortic aneurysm and post-EVAR sac volume

BACKGROUND AND AIMS: Abdominal aortic aneurysm (AAA) patients undergo uniform surveillance programs both leading up to, and following surgery. Circulating biomarkers could play a pivotal role in individualizing surveillance. We applied a multi-omics approach to identify relevant biomarkers and gain pathophysiological insights. MATERIALS AND METHODS: In this cross-sectional study, 108 AAA patients and 200 post-endovascular aneurysm repair (post-EVAR) patients were separately investigated. We performed partial least squares regression and ingenuity pathway analysis on circulating concentrations of 96 proteins (92 Olink Cardiovascular-III panel, 4 ELISA-assays) and 199 metabolites (measured by LC-TQMS), and their associations with CT-based AAA/sac volume. RESULTS: The median (25th-75th percentile) maximal diameter was 50.0 mm (46.0, 53.0) in the AAA group, and 55.4 mm (45.0, 64.2) in the post-EVAR group. Correcting for clinical characteristics in AAA patients, the aneurysm volume Z-score differed 0.068 (95 %CI: (0.042, 0.093)), 0.066 (0.047, 0.085) and -0.051 (-0.064, -0.038) per Z-score valine, leucine and uPA, respectively. After correcting for clinical characteristics and orthogonalization in the post-EVAR group, the sac volume Z-score differed 0.049 (0.034, 0.063) per Z-score TIMP-4, -0.050 (-0.064, -0.037) per Z-score LDL-receptor, -0.051 (-0.062, -0.040) per Z-score 1-OG/2-OG and -0.056 (-0.066, -0.045) per Z-score 1-LG/2-LG. CONCLUSIONS: The branched-chain amino acids and uPA were related to AAA volume. For post-EVAR patients, LDL-receptor, monoacylglycerols and TIMP-4 are potential biomarkers for sac volume. Additionally, distinct markers for sac change were identified.</p

Targeted proteomics and metabolomics for biomarker discovery in abdominal aortic aneurysm and post-EVAR sac volume

BACKGROUND AND AIMS: Abdominal aortic aneurysm (AAA) patients undergo uniform surveillance programs both leading up to, and following surgery. Circulating biomarkers could play a pivotal role in individualizing surveillance. We applied a multi-omics approach to identify relevant biomarkers and gain pathophysiological insights. MATERIALS AND METHODS: In this cross-sectional study, 108 AAA patients and 200 post-endovascular aneurysm repair (post-EVAR) patients were separately investigated. We performed partial least squares regression and ingenuity pathway analysis on circulating concentrations of 96 proteins (92 Olink Cardiovascular-III panel, 4 ELISA-assays) and 199 metabolites (measured by LC-TQMS), and their associations with CT-based AAA/sac volume. RESULTS: The median (25th-75th percentile) maximal diameter was 50.0 mm (46.0, 53.0) in the AAA group, and 55.4 mm (45.0, 64.2) in the post-EVAR group. Correcting for clinical characteristics in AAA patients, the aneurysm volume Z-score differed 0.068 (95 %CI: (0.042, 0.093)), 0.066 (0.047, 0.085) and -0.051 (-0.064, -0.038) per Z-score valine, leucine and uPA, respectively. After correcting for clinical characteristics and orthogonalization in the post-EVAR group, the sac volume Z-score differed 0.049 (0.034, 0.063) per Z-score TIMP-4, -0.050 (-0.064, -0.037) per Z-score LDL-receptor, -0.051 (-0.062, -0.040) per Z-score 1-OG/2-OG and -0.056 (-0.066, -0.045) per Z-score 1-LG/2-LG. CONCLUSIONS: The branched-chain amino acids and uPA were related to AAA volume. For post-EVAR patients, LDL-receptor, monoacylglycerols and TIMP-4 are potential biomarkers for sac volume. Additionally, distinct markers for sac change were identified.</p

Machine learning approaches for high-dimensional genome-wide association studies

Formålet med Genome-wide association studies (GWAS) er å finne statistiske sammenhenger mellom genetiske varianter og egenskaper av interesser. De genetiske variantene som forklarer mye av variasjonene i genomfattende genekspresjoner kan medføre konfunderende analyser av kvantitative egenskaper ved ekspresjonsplasseringer (eQTL). For å betrakte konfunderende faktorene, presenterte vi LVREML-metoden i artikkel I, en metode som er konseptuelt analogt med å estimere faste og tilfeldige effekter i Lineære Blandede modeller (LMM). Vi viste at de latente variablene med “Maximum likelihood” alltid kan velges ortogonalt til de kjente faktorene (som genetiske variasjoner). Dette indikerer at “Maximum likelihood” variablene forklarer utvalgsvariansene som ikke allerede er forklart av de genetiske variantene i modellen. For å kartlegge hvilke egenskaper som påvirkes av de identifiserte genetiske variantene, må vi reversere den funksjonelle relasjonen mellom genotyper og egenskaper. I denne sammenhengen er en “multi-trait” metode mer fordelaktige enn å studere egenskapene individuelt. “Multi-trait”-metoden drar nytte av økt kapasitet som følge av å vurdere kovarianser på tvers av egenskaper, og redusert multiple tester, fordi det trengs en enkelt test for å teste for sammenhenger til et sett med egenskaper. I artikkel II analyserte vi ulike maskinlæringsmetoder (Naive Bayes/independent univariate correlation, random forests og support vector machines) for omvendt regresjon i multi-trekk GWAS, ved bruk av genotyper, genuttrykksdata og “groundtruth” transcriptional regulatory networks fra DREAM5 SysGen Challenge og fra en krysning mellom to gjærstammer for å evaluere metoder. I artikkel III utvidet vi metoden ovenfor til å behandle menneskelig data. En viktig forskjell mellom data fra artikkel II og artikkel III er at vi ikke har “Groundtruth” data tilgjengelig for sistnevnte. Vi brukte genotypen og Magnetresonanstomografi (MRI) data hentet fra ADNI databasen. Resultatene fra både artikkel II og artikkel III viste at resultat av genotypeprediksjon varierte på tvers av genetiske varianter. Dette hjulpet med å identifisere genomiske regioner som er assosiert med stort antall egenskaper i høydimensjonale fenotypiske data. Vi observerte også at koeffisientene til maskinlæringsmodeller korrelerte med styrken til assosiasjonene mellom varianter og egenskaper. Resultatene våre viste også at ikke-lineære maskin-læringsmetoder som “random forests” identifiserte genetiske varianter tydeligere enn de lineære metodene. Spesielt observerte vi i artikkel III at “random forests” var i stand til å identifisere enkeltnukleotidpolymorfismer (SNP-er) som var forskjellige fra de som ble identifisert “ridge” og“lasso” regresjonsmetodene. Ytterligere analyse viste at de identifiserte SNP-ene tilhørte gener som tidligere var assosiert med hjernerelaterte lidelser.Genome-wide association studies (GWAS) aim to find statistical associations between genetic variants and traits of interests. The genetic variants that explain a lot of variation in genome-wide gene expression may lead to confounding in expression quantitative trait loci (eQTL) analyses. To account for these confounding factors, in Article I we proposed LVREML, a method conceptually analogous to estimating fixed and random effects in linear mixed models (LMM). We showed that the maximum-likelihood latent variables can always be chosen orthogonal to the known factors (such genetic variants). This indicates that the maximum-likelihood variables explain the sample covariances that is not already explained by the genetic variants in the model. For identifying which traits are effected by the identified genetic variants, we need to reverse the functional relation between genotypes and traits. In this regard, multitrait approaches are more advantageous than studying the traits individually. The multi-trait approaches benefit from increased power from considering cross-trait covariances and reduced multiple testing burden because a single test is needed to test for associations to a set of traits. In Article II, we analyzed various machine learning methods (ridge regression, Naive Bayes/independent univariate correlation, random forests and support vector machines) for reverse regression in multi-trait GWAS, using genotypes, gene expression data and ground-truth transcriptional regulatory networks from the DREAM5 SysGen Challenge and from a cross between two yeast strains to evaluate methods. In Article III, we extended the above approach to human dataset. An important difference between data from Article II and Article III is that we do not have groundtruth data available for the latter. We used the genotype and brain-imaging features extracted from the MRIs obtained from the ADNI database. The results from both Article II and Article III showed that the genotype prediction performance varied across genetic variants. This helped in identifying genomic regions that are associated with high number of traits in high-dimensional phenotypic data. We also observed that the feature coefficients of fitted machine learning models correlated with the strength of association between variants and traits. Our results also showed that non-linear machine learning methods like random forests identified genetic variants distinct from the linear methods. In particular, we observed in Article III that random forest was able to identify single-nueclotide-polymorphisms (SNPs) that were distinct from the ones identified by ridge and lasso regression. Further analysis showed that the identified SNPs belonged to genes previously associated with brain-related disorders.Doktorgradsavhandlin

Epigenetic analysis of Paget's disease of bone identifies differentially methylated loci that predict disease status

Paget’s disease of bone (PDB) is characterized by focal increases in disorganized bone remodeling. This study aims to characterize PDB-associated changes in DNA methylation profiles in patients’ blood. Meta-analysis of data from the discovery and cross-validation set, each comprising 116 PDB cases and 130 controls, revealed significant differences in DNA methylation at 14 CpG sites, 4 CpG islands, and 6 gene-body regions. These loci, including two characterized as functional through expression quantitative trait-methylation analysis, were associated with functions related to osteoclast differentiation, mechanical loading, immune function, and viral infection. A multivariate classifier based on discovery samples was found to discriminate PDB cases and controls from the cross-validation with a sensitivity of 0.84, specificity of 0.81, and an area under curve of 92.8%. In conclusion, this study has shown for the first time that epigenetic factors contribute to the pathogenesis of PDB and may offer diagnostic markers for prediction of the disease

Population-based case-control study revealed metabolomic biomarkers of suboptimal health status in Chinese population—potential utility for innovative approach by predictive, preventive, and personalized medicine

Background: Suboptimal health status (SHS) is a subclinical stage of chronic diseases, and the identification of SHS provides an opportunity for the predictive, preventive, and personalized medicine (PPPM) of chronic diseases. Previous studies have reported the associations between metabolic signatures and early signs of chronic diseases. Methods: This study aimed to detect the metabolic biomarkers for the identification of SHS in a case-control study. SHS questionnaire-25 (SHSQ-25) was used in a population-based health survey to measure the SHS levels of participants. The liquid chromatography-mass spectrometry-based untargeted metabolomics analysis was conducted on plasma samples collected from 50 SHS participants and 50 age- and sex-matched healthy controls. Results: After adjusting for the confounders, 24 significantly differential metabolites, such as sphingomyelin, sphingosine, sphinganine, progesterone, pregnanolone, and bilirubin, were identified as the candidate biomarkers for SHS. Pathway analysis revealed that sphingolipid metabolism, taurine metabolism, and steroid hormone biosynthesis are the disturbed metabolic pathways related to SHS. A combination of four metabolic biomarkers (sphingosine, pregnanolone, taurolithocholate sulfate, cervonyl carnitine) can distinguish SHS individuals from the controls with a sensitivity of 94.0%, a specificity of 90.0%, and an area under the receiver operating characteristic curve of 0.977. Conclusion: Plasma metabolites are valuable biomarkers for SHS identification, and meanwhile, SHSQ-25 can be used as an alternative health screening tool in the population-based health survey. SHS-related metabolic disturbances could be detected at the early onset of SHS, and SHS-related metabolites could create a window opportunity for PPPM of chronic diseases

Development and validation of HRCT airway segmentation algorithms

Direct measurements of airway lumen and wall areas are potentially useful as a diagnostic tool and as an aid to understanding the pathophysiology underlying lung disease. Direct measurements can be made from images created by high resolution computer tomography (HRCT) by using computer-based algorithms to segment airways, but current validation techniques cannot adequately establish the accuracy and precision of these algorithms. A detailed review of HRCT airway segmentation algorithms was undertaken, from which three candidate algorithm designs were developed. A custom Windows-based software program was implemented to facilitate multi-modality development and validation of the segmentation algorithms. The performance of the algorithms was examined in clinical HRCT images. A centre-likelihood (CL) ray-casting algorithm was found to be the most suitable algorithm due to its speed and reliability in semi-automatic segmentation and tracking of the airway wall. Several novel refinements were demonstrated to improve the CL algorithm’s robustness in HRCT lung data. The performance of the CL algorithm was then quantified in two-dimensional simulated data to optimise customisable parameters such as edge-detection method, interpolation and number of rays. Novel correction equations to counter the effects of volume averaging and airway orientation angle were derived and demonstrated in three-dimensional simulated data. The optimal CL algorithm was validated with HRCT data using a plastic phantom and a pig lung phantom matched to micro-CT. Accuracy was found to be improved compared to previous studies using similar methods. The volume averaging correction was found to improve precision and accuracy in the plastic phantom but not in the pig lung phantom. When tested in a clinical setting the results of the optimised CL algorithm was in agreement with the results of other measures of lung function. The thesis concludes that the relative contributions of confounders of airway measurement have been quantified in simulated data and the CL algorithm’s performance has been validated in a plastic phantom as well as animal model. This validation protocol has improved the accuracy and precision of measurements made using the CL algorith

Urinary Proteomic Profile of Arterial Stiffness Is Associated With Mortality and Cardiovascular Outcomes

Background The underlying mechanisms of arterial stiffness remain not fully understood. This study aimed to identify a urinary proteomic profile to illuminate its pathogenesis and to determine the prognostic value of the profile for adverse outcomes. Methods and Results We measured aortic stiffness using pulse wave velocity (PWV) and analyzed urinary proteome using capillary electrophoresis coupled with mass spectrometry in 669 randomly recruited Flemish patients (mean age, 50.2 years; 51.1% women). We developed a PWV‐derived urinary proteomic score (PWV‐UP) by modeling PWV with proteomics data at baseline through orthogonal projections to latent structures. PWV‐UP that consisted of 2336 peptides explained the 65% variance of PWV, higher than 36% explained by clinical risk factors. PWV‐UP was significantly associated with PWV (adjusted β=0.73 [95% CI, 0.67–0.79]; P\u3c0.0001). Over 9.2 years (median), 36 participants died, and 75 experienced cardiovascular events. The adjusted hazard ratios (+1 SD) were 1.46 (95% CI, 1.08–1.97) for all‐cause mortality, 2.04 (95% CI, 1.07–3.87) for cardiovascular mortality, and 1.39 (95% CI, 1.11–1.74) for cardiovascular events (P≤0.031). For PWV, the corresponding estimates were 1.25 (95% CI, 0.97–1.60), 1.35 (95% CI, 0.85–2.15), and 1.22 (95% CI, 1.02–1.47), respectively (P≥0.033). Pathway analysis revealed that the peptides in PWV‐UP mostly involved multiple pathways, including collagen turnover, cell adhesion, inflammation, and lipid metabolism. Conclusions PWV‐UP was highly associated with PWV and could be used as a biomarker of arterial stiffness. PWV‐UP, but not PWV, was associated with all‐cause mortality and cardiovascular mortality, implying that PWV‐UP–associated peptides may be multifaceted and involved in diverse pathological processes beyond arterial stiffness

Molecular phenomics and metagenomics of hepatic steatosis in non-diabetic obese women

The role of molecular signals from the microbiome and their coordinated interactions with those from the host in hepatic steatosis – notably in obese patients and as risk factors for insulin resistance and atherosclerosis – needs to be understood. We reveal molecular networks linking gut microbiome and host phenome to hepatic steatosis in a cohort of non diabetic obese women. Steatotic patients had low microbial gene richness and increased genetic potential for processing of dietary lipids and endotoxin biosynthesis (notably from Proteobacteria), hepatic inflammation and dysregulation of aromatic and branched-chain amino acid (AAA and BCAA) metabolism. We demonstrated that faecal microbiota transplants and chronic treatment with phenylacetic acid (PAA), a microbial product of AAA metabolism, successfully trigger steatosis and BCAA metabolism. Molecular phenomic signatures were predictive (AUC = 87%) and consistent with the gut microbiome making an impact on the steatosis phenome (>75% shared variation) and, therefore, actionable via microbiome-based therapies

Novel strategies for the identification of biomarkers of non-Hodgkin lymphoma: evidence from the European Prospective Investigation into Cancer (EPIC)

Non-Hodgkin’s Lymphomas (NHL) represent the eighth most common cancer in Western Europe. Yet despite their widespread prevalence and high mortality rate relatively little is known about the aetiology of these hematological malignancies. Consequently NHL represents an ideal candidate for the discovery of biomarkers lying along the causal pathway. Such biomarkers would allow the improved identification of risk factors and high risk individuals, as well as an enhanced understanding of lymphomageneisis. However, to date there has been little progress in determining validated predictive biomarkers of NHL. This thesis attempts to address some of the issues that have previously hampered the study of NHL through novel strategies of biomarker identification utilising novel methodologies, technologies and statistical techniques. The thesis comprises a nested case-control study within the European Prospective investigation into Cancer (EPIC) cohort and is split into two parts: the ‘validation of biomarkers’ and the ‘integration of biomarkers’. The most exciting finding was the identification of a novel biomarker for Follicular lymphoma based on the t(14;18) translocation which comprises a previously unknown pre-disease condition. Although no other predictive biomarkers were identified this work represents a ‘proof-of-principle’ for the use profile regression in the study of highly dimensional complex datasets, and the possibility of using mass-spectrometry derived metabolic profiles in the study of lymphoma. Part two of the thesis confirmed that the use of the ‘meet-in-the-middle’ approach was a valuable and feasible method for studying the complete causal pathway from risk factor to disease. Together these results highlight potential avenues for further study of NHL and confirm the utility of a number of novel strategies that can aid such work. Additionally it informs on some of the likely challenges that will be involved.Open Acces