Metabolomics in systems medicine: an overview of methods and applications

Patient-derived metabolomics offers valuable insights into the metabolic phenotype underlying diseases with a strong metabolic component. Thus, these data sets will be pivotal to the implementation of personalized medicine strategies in health and disease. However, to take full advantage of such data sets, they must be integrated with other omics within a coherent pathophysiological framework to enable improved diagnostics, to identify therapeutic interventions, and to accurately stratify patients. Herein, we provide an overview of the state-of-the-art data analysis and modeling approaches applicable to metabolomics data and of their potential for systems medicine

Proteomics Studies of Subjects with Alzheimer’s Disease and Chronic Pain

Alzheimer’s disease (AD) is a neurodegenerative disease and the major cause of dementia, affecting more than 50 million people worldwide. Chronic pain is long-lasting, persistent pain that affects more than 1.5 billion of the world population. Overlapping and heterogenous symptoms of AD and chronic pain conditions complicate their diagnosis, emphasizing the need for more specific biomarkers to improve the diagnosis and understand the disease mechanisms. To characterize disease pathology of AD, we measured the protein changes in the temporal neocortex region of the brain of AD subjects using mass spectrometry (MS). We found proteins involved in exo-endocytic and extracellular vesicle functions displaying altered levels in the AD brain, potentially resulting in neuronal dysfunction and cell death in AD. To detect novel biomarkers for AD, we used MS to analyze cerebrospinal fluid (CSF) of AD patients and found decreased levels of eight proteins compared to controls, potentially indicating abnormal activity of complement system in AD. By integrating new proteomics markers with absolute levels of Aβ42, total tau (t-tau) and p-tau in CSF, we improved the prediction accuracy from 83% to 92% of early diagnosis of AD. We found increased levels of chitinase-3-like protein 1 (CH3L1) and decreased levels of neurosecretory protein VGF (VGF) in AD compared to controls. By exploring the CSF proteome of neuropathic pain patients before and after successful spinal cord stimulation (SCS) treatment, we found altered levels of twelve proteins, involved in neuroprotection, synaptic plasticity, nociceptive signaling and immune regulation. To detect biomarkers for diagnosing a chronic pain state known as fibromyalgia (FM), we analyzed the CSF of FM patients using MS. We found altered levels of four proteins, representing novel biomarkers for diagnosing FM. These proteins are involved in inflammatory mechanisms, energy metabolism and neuropeptide signaling. Finally, to facilitate fast and robust large-scale omics data handling, we developed an e-infrastructure. We demonstrated that the e-infrastructure provides high scalability, flexibility and it can be applied in virtually any fields including proteomics. This thesis demonstrates that proteomics is a promising approach for gaining deeper insight into mechanisms of nervous system disorders and find biomarkers for diagnosis of such diseases

Identification of genes involved in T-cell differentiation

Background: T-cells are involved in many immune functions. Each function is carried out by specific sub-set of T-cells. All T-cell sub-types originate from one stem cell and the fate of each cell is dictated by its pattern of gene expression. The pattern of gene expression is the direct outcome of genetic regulatory network which can be visualized as a network containing nodes (genes) with edges (interaction) between them.  Simulation of the dynamics of gene regulatory networks reveals several attributes of not only the network itself but also the pattern of gene expression of different developmental or differentiation processes. Since gene regulatory networks often include thousands of genes, the network has to shrink or contain only a small subset of possible states of the large networks can be explored through the simulation. Therefore, different methods are needed to extract information from gene regulatory network. Methods: Central T-cell Network (the main gene regulatory network in T-cells) is used to start the simulation with customized random Boolean networks. Because of large scale of the network an initiative approach was used to reduce the number of possible states which were needed to be explored. Graph theory was used to find the attractors. GO analysis was used to find information in attractors. Clustering methods were applied on attractors in order to find groups of interesting gene states. Finally, data mining and microarray data analysis were utilized to verify the simulation system. Results: Forty experiments resulted in 833 attractors (with period 2 or 4). GO analysis (performed on most frequent attractors) resulted in no significance in T-cell differentiation processes. Clustering methods classified each type of attractors to exactly two different clusters. The simulated gene expression divided the genes into 3 groups, and GO analysis did not show significance in any differentiation process. The result of gene expression ratio of CD4+ and CD8+ cells showed a significant difference between the microarray data experiments and simulated gene expression ratios. Finally, the result of data mining suggested that CD4+ cells were located in one of the clustered attractors. Conclusion: A new environment was developed to simulate the dynamics of the gene regulatory network in T-cells. A novel approach was used to reduce the state space and in finding attractors. The resulting attractors were analyzed by several experiments. Although the genes involved in differentiation processes were distributed sporadically on the attractor clusters, CD4+ related genes were clustered in one group. This indicates the usability of the system for distinguishing different cell types. The result also indicates that the system can be used not only for T-cells but also for any biological network. A conclusion can be drawn that this new system is applicable for different networks but more experiments with different parameters are needed to verify the simulation system. Keywords: T-cells differentiation, immunology, random Boolean networks, genes involved in T-cell differentiatio

Identification of genes involved in T-cell differentiation

Background: T-cells are involved in many immune functions. Each function is carried out by specific sub-set of T-cells. All T-cell sub-types originate from one stem cell and the fate of each cell is dictated by its pattern of gene expression. The pattern of gene expression is the direct outcome of genetic regulatory network which can be visualized as a network containing nodes (genes) with edges (interaction) between them.  Simulation of the dynamics of gene regulatory networks reveals several attributes of not only the network itself but also the pattern of gene expression of different developmental or differentiation processes. Since gene regulatory networks often include thousands of genes, the network has to shrink or contain only a small subset of possible states of the large networks can be explored through the simulation. Therefore, different methods are needed to extract information from gene regulatory network. Methods: Central T-cell Network (the main gene regulatory network in T-cells) is used to start the simulation with customized random Boolean networks. Because of large scale of the network an initiative approach was used to reduce the number of possible states which were needed to be explored. Graph theory was used to find the attractors. GO analysis was used to find information in attractors. Clustering methods were applied on attractors in order to find groups of interesting gene states. Finally, data mining and microarray data analysis were utilized to verify the simulation system. Results: Forty experiments resulted in 833 attractors (with period 2 or 4). GO analysis (performed on most frequent attractors) resulted in no significance in T-cell differentiation processes. Clustering methods classified each type of attractors to exactly two different clusters. The simulated gene expression divided the genes into 3 groups, and GO analysis did not show significance in any differentiation process. The result of gene expression ratio of CD4+ and CD8+ cells showed a significant difference between the microarray data experiments and simulated gene expression ratios. Finally, the result of data mining suggested that CD4+ cells were located in one of the clustered attractors. Conclusion: A new environment was developed to simulate the dynamics of the gene regulatory network in T-cells. A novel approach was used to reduce the state space and in finding attractors. The resulting attractors were analyzed by several experiments. Although the genes involved in differentiation processes were distributed sporadically on the attractor clusters, CD4+ related genes were clustered in one group. This indicates the usability of the system for distinguishing different cell types. The result also indicates that the system can be used not only for T-cells but also for any biological network. A conclusion can be drawn that this new system is applicable for different networks but more experiments with different parameters are needed to verify the simulation system. Keywords: T-cells differentiation, immunology, random Boolean networks, genes involved in T-cell differentiatio

Evaluation of polarity switching for untargeted lipidomics using liquid chromatography coupled to high resolution mass spectrometry

Untargeted lipidomics using liquid chromatography high-resolution mass spectrometry (LC-HRMS) was performed using polarity switching, and in positive and negative polarity separately on the same set of serum samples, and the performances of the methods were evaluated.& nbsp;Polarity switching causes an increase in the cycle time of the HRMS measurements (1.18 s/cycle vs 0.27 s/ cycle), resulting in fewer data points across chromatographic peaks. The coefficient of variation (CV) was on average lower for the added isotopically labelled standards in pooled samples (QC) and patient samples using separate polarities (QC = 5.6%, samples = 12.5%) compared to polarity switching (QC = 8.5%, samples = 13.4%), but the difference was not statistically significant. For the endogenous features measured in the QCs polarity switching resulted in on average significantly higher CVs (3.80 (p = 4.25e-30) and 3.3 percentage points (p = 6.84e-40), for positive and negative modes, respectively) however still acceptable for an untargeted method (mean CVs of 17.9% and 12.2% in positive and negative modes respectively). A slightly larger number of endogenous features were detected using the separate polarities, but the large majority of features (> 95%) were detected with both methodologies. The overlap of features detected in both positive and negative polarities was low (4.1%) demonstrating the importance of using both polarities for untargeted lipidomics. When investigating the effects of a treatment on multiple sclerosis patients it was found that both methodologies gave highly similar biological results, further confirming the applicability of polarity switching

Increased Release of Apolipoprotein E in Extracellular Vesicles Following Amyloid-β Protofibril Exposure of Neuroglial Co-Cultures

Extracellular vesicles (EVs), including exosomes and larger microvesicles, have been implicated to play a role in several conditions, including Alzheimer's disease (AD). Since the EV content mirrors the intracellular environment, it could contribute with important information about ongoing pathological processes and may be a useful source for biomarkers, reflecting the disease progression. The aim of the present study was to analyze the protein content of EVs specifically released from a mixed co-culture of primary astrocytes, neurons, and oligodendrocytes treated with synthetic amyloid-beta (A beta(42)) protofibrils. The EV isolation was performed by ultracentrifugation and validated by transmission electron microscopy. Mass spectrometry analysis of the EV content revealed a total of 807 unique proteins, of which five displayed altered levels in A beta(42) protofibril exposed cultures. The most prominent protein was apolipoprotein E (apoE), and by western blot analysis we could confirm a threefold increase of apoE in EVs from A beta(42) protofibril exposed cells, compared to unexposed cells. Moreover, immunoprecipitation studies demonstrated that apoE was primarily situated inside the EVs, whereas immunocytochemistry indicated that the EVs most likely derived from the astrocytes and the neurons in the culture. The identified A beta-induced sorting of apoE into EVs from cultured neuroglial cells suggests a possible role for intercellular transfer of apoE in AD pathology and encourage future studies to fully elucidate the clinical relevance of this event

Metabolic drift in the aging nervous system is reflected in human cerebrospinal fluid

Chronic diseases affecting the central nervous system (CNS) like Alzheimer's or Parkinson's disease typically develop with advanced chronological age. Yet, aging at the metabolic level has been explored only sporadically in humans using biofluids in close proximity to the CNS such as the cerebrospinal fluid (CSF). We have used an untargeted liquid chromatography high-resolution mass spectrometry (LC-HRMS) based metabolomics approach to measure the levels of metabolites in the CSF of non-neurological control subjects in the age of 20 up to 74. Using a random forest-based feature selection strategy, we extracted 69 features that were strongly related to age (page < 0.001, rage = 0.762, R2Boruta age = 0.764). Combining an in-house library of known substances with in silico chemical classification and functional semantic annotation we successfully assigned putative annotations to 59 out of the 69 CSF metabolites. We found alterations in metabolites related to the Cytochrome P450 system, perturbations in the tryptophan and kynurenine pathways, metabolites associated with cellular energy (NAD+, ADP), mitochondrial and ribosomal metabolisms, neurological dysfunction, and an increase of adverse microbial metabolites. Taken together our results point at a key role for metabolites found in CSF related to the Cytochrome P450 system as most often associated with metabolic aging

A plasma lipid signature predicts incident coronary artery disease

Background: Dyslipidemia is a hallmark of cardiovascular disease but is characterized by crude measurements of triglycerides, HDL- and LDL cholesterol. Lipidomics enables more detailed measurements of plasma lipids, which may help improve risk stratification and understand the pathophysiology of cardiovascular disease. Methods: Lipidomics was used to measure 184 lipids in plasma samples from the Malmö Diet and Cancer – Cardiovascular Cohort (N = 3865), taken at baseline examination. During an average follow-up time of 20.3 years, 536 participants developed coronary artery disease (CAD). Least absolute shrinkage and selection operator (LASSO) were applied to Cox proportional hazards models in order to identify plasma lipids that predict CAD. Results: Eight plasma lipids improved prediction of future CAD on top of traditional cardiovascular risk factors. Principal component analysis of CAD-associated lipids revealed one principal component (PC2) that was associated with risk of future CAD (HR per SD increment =1.46, C·I = 1.35–1.48, P < 0.001). The risk increase for being in the highest quartile of PC2 (HR = 2.33, P < 0.001) was higher than being in the top quartile of systolic blood pressure. Addition of PC2 to traditional risk factors achieved an improvement (2%) in the area under the ROC-curve for CAD events occurring within 10 (P = 0.03), 15 (P = 0.003) and 20 (P = 0.001) years of follow-up respectively. Conclusions: A lipid pattern improve CAD prediction above traditional risk factors, highlighting that conventional lipid-measures insufficiently describe dyslipidemia that is present years before CAD. Identifying this hidden dyslipidemia may help motivate lifestyle and pharmacological interventions early enough to reach a substantial reduction in absolute risk

Targeted metabolomics of CSF in healthy individuals and patients with secondary progressive multiple sclerosis using high-resolution mass spectrometry

Introduction: Standardized commercial kits enable targeted metabolomics analysis and may thus provide an attractive complement to the more explorative approaches. The kits are typically developed for triple quadrupole mass spectrometers using serum and plasma. Objectives: Here we measure the concentrations of preselected metabolites in cerebrospinal fluid (CSF) using a kit developed for high-resolution mass spectrometry (HRMS). Secondarily, the study aimed to investigate metabolite alterations in patients with secondary progressive multiple sclerosis (SPMS) compared to controls. Methods: We performed targeted metabolomics in human CSF on twelve SPMS patients and twelve age and sex-matched healthy controls using the Absolute IDQ-p400 kit (Biocrates Life Sciences AG) developed for HRMS. The extracts were analysed using two methods; liquid chromatography-mass spectrometry (LC-HRMS) and flow injection analysis-MS (FIA-HRMS). Results: Out of 408 targeted metabolites, 196 (48%) were detected above limit of detection and 35 were absolutely quantified. Metabolites analyzed using LC-HRMS had a median coefficient of variation (CV) of 3% and 2.5% between reinjections the same day and after prolonged storage, respectively. The corresponding results for the FIA-HRMS were a median CV of 27% and 21%, respectively. We found significantly (p &lt; 0.05) elevated levels of glycine, asymmetric dimethylarginine (ADMA), glycerophospholipid PC-O (34:0) and sum of hexoses in SPMS patients compared to controls. Conclusion: The Absolute IDQ-p400 kit could successfully be used for quantifying targeted metabolites in the CSF. Metabolites quantified using LC-HRMS showed superior reproducibility compared to FIA-HRMS

Disease phenotype prediction in multiple sclerosis

Progressive multiple sclerosis (PMS) is currently diagnosed retrospectively. Here, we work toward a set of biomarkers that could assist in early diagnosis of PMS. A selection of cerebrospinal fluid metabolites (n = 15) was shown to differentiate between PMS and its preceding phenotype in an independent cohort (AUC = 0.93). Complementing the classifier with conformal prediction showed that highly confident predictions could be made, and that three out of eight patients devel-oping PMS within three years of sample collection were predicted as PMS at that time point. Finally, this methodology was applied to PMS patients as part of a clin-ical trial for intrathecal treatment with rituximab. The methodology showed that 68% of the patients decreased their similarity to the PMS phenotype one year af-ter treatment. In conclusion, the inclusion of confidence predictors contributes with more information compared to traditional machine learning, and this information is relevant for disease monitoring