| 1. |
- Rosendal, Ebba, et al.
(författare)
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Serine Protease Inhibitors Restrict Host Susceptibility to SARS-CoV-2 Infections
- 2022
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Ingår i: mBio. - : American Society for Microbiology. - 2161-2129 .- 2150-7511. ; 13:3
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Tidskriftsartikel (refereegranskat)abstract
- The coronavirus disease 2019, COVID-19, is a complex disease with a wide range of symptoms from asymptomatic infections to severe acute respiratory syndrome with lethal outcome. Individual factors such as age, sex, and comorbidities increase the risk for severe infections, but other aspects, such as genetic variations, are also likely to affect the susceptibility to SARS-CoV-2 infection and disease severity. Here, we used a human 3D lung cell model based on primary cells derived from multiple donors to identity host factors that regulate SARS-CoV-2 infection. With a transcriptomics-based approach, we found that less susceptible donors show a higher expression level of serine protease inhibitors SERPINA1, SERPINE1, and SERPINE2, identifying variation in cellular serpin levels as restricting host factors for SARS-CoV-2 infection. We pinpoint their antiviral mechanism of action to inhibition of the cellular serine protease, TMPRSS2, thereby preventing cleavage of the viral spike protein and TMPRSS2-mediated entry into the target cells. By means of single-cell RNA sequencing, we further locate the expression of the individual serpins to basal, ciliated, club, and goblet cells. Our results add to the importance of genetic variations as determinants for SARS-CoV-2 susceptibility and suggest that genetic deficiencies of cellular serpins might represent risk factors for severe COVID-19. Our study further highlights TMPRSS2 as a promising target for antiviral intervention and opens the door for the usage of locally administered serpins as a treatment against COVID-19.
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| 2. |
- Bengtsson, Anders A., et al.
(författare)
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Metabolic Profiling of Systemic Lupus Erythematosus and Comparison with Primary Sjögren’s Syndrome and Systemic Sclerosis
- 2016
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 11:7
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Tidskriftsartikel (refereegranskat)abstract
- Systemic lupus erythematosus (SLE) is a chronic inflammatory autoimmune disease which can affect most organ systems including skin, joints and the kidney. Clinically, SLE is a heterogeneous disease and shares features of several other rheumatic diseases, in particular primary Sjögrens syndrome (pSS) and systemic sclerosis (SSc), why it is difficult to diag- nose The pathogenesis of SLE is not completely understood, partly due to the heterogeneity of the disease. This study demonstrates that metabolomics can be used as a tool for improved diagnosis of SLE compared to other similar autoimmune diseases. We observed differences in metabolic profiles with a classification specificity above 67% in the comparison of SLE with pSS, SSc and a matched group of healthy individuals. Selected metabolites were also significantly different between studied diseases. Biochemical pathway analysis was conducted to gain understanding of underlying pathways involved in the SLE pathogenesis. We found an increased oxidative activity in SLE, supported by increased xanthine oxidase activity and an increased turnover in the urea cycle. The most discriminatory metabolite observed was tryptophan, with decreased levels in SLE patients compared to control groups. Changes of tryptophan levels were related to changes in the activity of the aromatic amino acid decarboxylase (AADC) and/or to activation of the kynurenine pathway.
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| 3. |
- Eriksson, Lennart, et al.
(författare)
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Editorial
- 2007
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Ingår i: Journal of Chemometrics. - : John Wiley & Sons, Ltd. - 0886-9383 .- 1099-128X. ; 21:10-11, s. 397-
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Tidskriftsartikel (populärvet., debatt m.m.)abstract
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| 4. |
- Eriksson, Lennart, et al.
(författare)
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Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm)
- 2004
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Ingår i: Analytical and Bioanalytical Chemistry. - : Springer Science and Business Media LLC. - 1618-2642 .- 1618-2650. ; 380:3, s. 419-29
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Tidskriftsartikel (refereegranskat)abstract
- This article describes the applicability of multivariate projection techniques, such as principal-component analysis (PCA) and partial least-squares (PLS) projections to latent structures, to the large-volume high-density data structures obtained within genomics, proteomics, and metabonomics. PCA and PLS, and their extensions, derive their usefulness from their ability to analyze data with many, noisy, collinear, and even incomplete variables in both X and Y. Three examples are used as illustrations: the first example is a genomics data set and involves modeling of microarray data of cell cycle-regulated genes in the microorganism Saccharomyces cerevisiae. The second example contains NMR-metabonomics data, measured on urine samples of male rats treated with either of the drugs chloroquine or amiodarone. The third and last data set describes sequence-function classification studies in a set of G-protein-coupled receptors using hierarchical PCA.
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| 5. |
- Gottfries, Johan, et al.
(författare)
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On the impact of uncorrelated variation in regression mathematics
- 2008
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Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 22:11-12, s. 565-70
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Tidskriftsartikel (refereegranskat)abstract
- The objective of the present study is to investigate if, and if so, how uncorrelated variation relates to regression mathematics as exemplified by partial least squares (PLS) methodology. In contrast to previous methods, orthogonal partial least squares (OPLS) method requires a multi-focus, in the sense that in parallel to calculation of correlation it requires an analysis of orthogonal variation, i.e. the uncorrelated structure in a comprehensive way. Subsequent to the estimation of the correlation is the remaining orthogonal variation, i.e. uncorrelated data, divided into uncorrelated structure and stochastic noise by the OPLS component. Thus, it appears obvious that it is of interest to understand how the uncorrelated variation can influence the interpretation of the regression model. We have scrutinized three examples that pinpoint additional value from OPLS regarding the modelling of the orthogonal, i.e. uncorrelated, variation in regression mathematics. In agreement with the results, we conclude that uncorrelated variations do impact interpretations of regression analyses output and provides not only opportunities by OPLS but also an obligation for the user to maximize benefit from OPLS.
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| 6. |
- Idborg, Helena, et al.
(författare)
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STRATIFICATION OF SLE PATIENTS FOR IMPROVED DIAGNOSIS AND TREATMENT
- 2013
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Ingår i: Annals of the Rheumatic Diseases. - : BMJ. - 0003-4967 .- 1468-2060. ; 72, s. A80-A80
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background. Systemic autoimmune diseases (SAIDs) affect about 2% of the population in Western countries. Sufficient diagnostic criteria are lacking due to the heterogeneity within diagnostic categories and apparent overlap regarding symptoms and patterns of autoantibodies between different diagnoses. Systemic lupus erythematosus (SLE) is regarded as a prototype for SAIDs and we hypothesise that subgroups of patients with SLE may have different pathogenesis and should consequently be subject to different treatment strategies.Objectives. Our goal is to find new biomarkers to be used for the identification of more homogenous patient populations for clinical trials and to identify sub-groups of patients with high risk of for example cardiovascular events.Methods. In this study we have utilised 320 SLE patients from the Karolinska lupus cohort and 320 age and gender matched controls. The SLE cohort was characterised based on clinical, genetic and serological data and combined by multivariate data analysis in a systems biology approach to study possible subgroups. A pilot study was designed to verify and investigate suggested subgroups of SLE. Two main subgroups were defined: One group was defined as having SSA and SSB antibodies and a negative lupus anticoagulant test (LAC), i.e., a “Sjögren-like” group. The other group was defined as being negative for SSA and SSB antibodies but positive in the LAC test.i.e. an “APS-like” group. EDTA-plasma from selected patients in these two groups and controls were analysed using a mass spectrometry (MS) based proteomic and metabolomic approach. Pathway analysis was then performed on the obtained data.Results. Our pilot study showed that differences in levels of proteins and metabolites could separate disease groups from population controls. The profile/pattern of involved factors in the complement system supported a division of SLE in two major subgroups, although each individual factor was not significantly different between subgroups. Complement factor 2 (C2) and membrane attack complex (MAC) were analysed in the entire cohort with complementary methods and C2 verifies our results while the levels of MAC did not differ between SLE subgroups. The generated metabolomics data clearly separated SLE patients from controls in both gas chromatography (GC)-MS and liquid chromatography (LC)-MS data. We found for example that tryptophan was lower in the SLE patients compared to controls.Conclusions. Our systems biology approach may lead to a better understanding of the disease and its pathogenesis, and assigning patients into subgroups will result in improved diagnosis and better outcome measures of SLE.
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| 7. |
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| 8. |
- Jonsson, Pär, et al.
(författare)
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A strategy for modelling dynamic responses in metabolic samples characterized by GC/MS
- 2006
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Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 2:3, s. 135-143
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Tidskriftsartikel (refereegranskat)abstract
- A multivariate strategy for studying the metabolic response over time in urinary GC/MS data is presented and exemplified by a study of drug-induced liver toxicity in the rat. The strategy includes the generation of representative data through hierarchical multivariate curve resolution (H-MCR), highlighting the importance of obtaining resolved metabolite profiles for quantification and identification of exogenous (drug related) and endogenous compounds (potential biomarkers) and for allowing reliable comparisons of multiple samples through multivariate projections. Batch modelling was used to monitor and characterize the normal (control) metabolic variation over time as well as to map the dynamic response of the drug treated animals in relation to the control. In this way treatment related metabolic responses over time could be detected and classified as being drug related or being potential biomarkers. In summary the proposed strategy uses the relatively high sensitivity and reproducibility of GC/MS in combination with efficient multivariate curve resolution and data analysis to discover individual markers of drug metabolism and drug toxicity. The presented results imply that the strategy can be of great value in drug toxicity studies for classifying metabolic markers in relation to their dynamic responses as well as for biomarker identification.
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| 9. |
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| 10. |
- Jonsson, Pär, et al.
(författare)
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Predictive metabolite profiling applying hierarchical multivariate curve resolution to GC-MS data : a potential tool for multi-parametric diagnosis
- 2006
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Ingår i: Journal of Proteome Research. - : American Chemical Society. - 1535-3893 .- 1535-3907. ; 5:6, s. 1407-1414
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Tidskriftsartikel (refereegranskat)abstract
- A method for predictive metabolite profiling based on resolution of GC-MS data followed by multivariate data analysis is presented and applied to three different biofluid data sets (rat urine, aspen leaf extracts, and human blood plasma). Hierarchical multivariate curve resolution (H-MCR) was used to simultaneously resolve the GC-MS data into pure profiles, describing the relative metabolite concentrations between samples, for multivariate analysis. Here, we present an extension of the H-MCR method allowing treatment of independent samples according to processing parameters estimated from a set of training samples. Predictions or inclusion of the new samples, based on their metabolite profiles, into an existing model could then be carried out, which is a requirement for a working application within, e.g., clinical diagnosis. Apart from allowing treatment and prediction of independent samples the proposed method also reduces the time for the curve resolution process since only a subset of representative samples have to be processed while the remaining samples can be treated according to the obtained processing parameters. The time required for resolving the 30 training samples in the rat urine example was approximately 13 h, while the treatment of the 30 test samples according to the training parameters required only approximately 30 s per sample (approximately 15 min in total). In addition, the presented results show that the suggested approach works for describing metabolic changes in different biofluids, indicating that this is a general approach for high-throughput predictive metabolite profiling, which could have important applications in areas such as plant functional genomics, drug toxicity, treatment efficacy and early disease diagnosis.
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| 11. |
- Lundstedt, Torbjörn, et al.
(författare)
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Editorial
- 2006
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Ingår i: Journal of Chemometrics. - : John Wiley & Sons, Ltd. - 0886-9383 .- 1099-128X. ; 20:8-10, s. 323-324
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Tidskriftsartikel (populärvet., debatt m.m.)
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| 12. |
- Madsen, Rasmus Kirkegaard, 1979-, et al.
(författare)
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Diagnostic properties of metabolic perturbations in rheumatoid arthritis
- 2011
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Ingår i: Arthritis Research & Therapy. - : Springer Science and Business Media LLC. - 1478-6362 .- 1478-6354. ; 13:1, s. R19-
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Tidskriftsartikel (refereegranskat)abstract
- INTRODUCTION: The aim of the study was to assess the feasibility of diagnosing early rheumatoid arthritis (RA) by measuring selected metabolic biomarkers. METHODS: We compared the metabolic profile of patients with RA with those of healthy controls and patients with psoriatic arthritis (PsoA). The metabolites were measured using two different chromatography-mass spectrometry platforms, thereby giving a broad overview of serum metabolites. The metabolic profiles of patient and control groups were compared using multivariate statistical analysis. The findings were validated in a follow-up study of RA patients and healthy volunteers. RESULTS: RA patients were diagnosed with a sensitivity of 93 % and a specificity of 70 % in a validation study using detection of 52 metabolites. Patients with RA or PsoA could be distinguished with a sensitivity of 90 % and a specificity of 94 %. Glyceric acid, D-ribofuranoise and hypoxanthine were increased in RA patients, whereas histidine, threonic acid, methionine, cholesterol, asparagine and threonine were all decreased when compared with healthy controls. CONCLUSIONS: Metabolite profiling (metabolomics) is a potentially useful technique for diagnosing RA. The predictive value was irrespective of the presence of antibodies against cyclic citrullinated peptides (ACPA).
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| 13. |
- Orikiiriza, Judy, et al.
(författare)
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Lipid response patterns in acute phase paediatric Plasmodium falciparum malaria
- 2017
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Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 13:4
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Several studies have observed serum lipid changes during malaria infection in humans. All of them were focused at analysis of lipoproteins, not specific lipid molecules. The aim of our study was to identify novel patterns of lipid species in malaria infected patients using lipidomics profiling, to enhance diagnosis of malaria and to evaluate biochemical pathways activated during parasite infection.Methods: Using a multivariate characterization approach, 60 samples were representatively selected, 20 from each category (mild, severe and controls) of the 690 study participants between age of 0.5–6 years. Lipids from patient’s plasma were extracted with chloroform/methanol mixture and subjected to lipid profiling with application of the LCMS-QTOF method.Results: We observed a structured plasma lipid response among the malaria-infected patients as compared to healthy controls, demonstrated by higher levels of a majority of plasma lipids with the exception of even-chain length lysophosphatidylcholines and triglycerides with lower mass and higher saturation of the fatty acid chains. An inverse lipid profile relationship was observed when plasma lipids were correlated to parasitaemia.Conclusions: This study demonstrates how mapping the full physiological lipid response in plasma from malaria-infected individuals can be used to understand biochemical processes during infection. It also gives insights to how the levels of these molecules relate to acute immune responses.
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| 14. |
- Pinto, Rui Climaco, et al.
(författare)
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Advantages of orthogonal inspection in chemometrics
- 2012
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Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 26:6, s. 231-235
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Tidskriftsartikel (refereegranskat)abstract
- The demand for chemometrics tools and concepts to study complex problems in modern biology and medicine has prompted chemometricians to shift their focus away from a traditional emphasis on model predictive capacity toward optimizing information exchange via model interpretation for biological validation. The interpretation of projection-based latent variable models is not straightforward because of its confounding of different systematic variations in the model components. Over the last 15?years, this has spurred the development of orthogonal-based methods that are capable of separating the correlated variation (to Y) from the noncorrelated (orthogonal to Y) variations in a single model. Here, we aim to provide a conceptual explanation of the advantages of orthogonal variation inspection in the context of Partial Least Squares (PLS) in multivariate classification and calibration. We propose that by inspecting the orthogonal variation, both model interpretation and information quality are improved by enhancement of the resulting level of knowledge. Although the predictive capacity of PLS using orthogonal methods may be identical to that of PLS alone, the combined result can be superior when it comes to the model interpretation. By discussing theory and examples, several new advantages revealed by inspection of orthogonal variation are highlighted. Copyright (c) 2012 John Wiley & Sons, Ltd.
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| 15. |
- Stenlund, Hans, et al.
(författare)
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Unlocking Interpretation in Near Infrared Multivariate Calibrations by Orthogonal Partial Least Squares
- 2009
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Ingår i: Analytical Chemistry. ; 81:1, s. 203-9
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Tidskriftsartikel (refereegranskat)abstract
- Near infrared spectroscopy (NIR) was developed primarily for applications such as the quantitative determination of nutrients in the agricultural and food industries. Examples include the determination of water, protein, and fat within complex samples such as grain and milk. Because of its useful properties, NIR analysis has spread to other areas such as chemistry and pharmaceutical production. NIR spectra consist of infrared overtones and combinations thereof, making interpretation of the results complicated. It can be very difficult to assign peaks to known constituents in the sample. Thus, multivariate analysis (MVA) has been crucial in translating spectral data into information, mainly for predictive purposes. Orthogonal partial least squares (OPLS), a new MVA method, has prediction and modeling properties similar to those of other MVA techniques, e.g., partial least squares (PLS), a method with a long history of use for the analysis of NIR data. OPLS provides an intrinsic algorithmic improvement for the interpretation of NIR data. In this report, four sets of NIR data were analyzed to demonstrate the improved interpretation provided by OPLS. The first two sets included simulated data to demonstrate the overall principles; the third set comprised a statistically replicated design of experiments (DoE), to demonstrate how instrumental difference could be accurately visualized and correctly attributed to Wood’s anomaly phenomena; the fourth set was chosen to challenge the MVA by using data relating to powder mixing, a crucial step in the pharmaceutical industry prior to tabletting. Improved interpretation by OPLS was demonstrated for all four examples, as compared to alternative MVA approaches. It is expected that OPLS will be used mostly in applications where improved interpretation is crucial; one such area is process analytical technology (PAT). PAT involves fewer independent samples, i.e., batches, than would be associated with agricultural applications; in addition, the Food and Drug Administration (FDA) demands “process understanding” in PAT. Both these issues make OPLS the ideal tool for a multitude of NIR calibrations. In conclusion, OPLS leads to better interpretation of spectrometry data (e.g., NIR) and improved understanding facilitates cross-scientific communication. Such improved knowledge will decrease risk, with respect to both accuracy and precision, when using NIR for PAT applications.
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| 16. |
- Surowiec, Izabella, et al.
(författare)
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Joint and unique multiblock analysis of biological data : multiomics malaria study
- 2019
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Ingår i: Faraday discussions. - Cambridge : Royal Society of Chemistry. - 1359-6640 .- 1364-5498. ; 218, s. 268-283
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Tidskriftsartikel (refereegranskat)abstract
- Modern profiling technologies enable obtaining large amounts of data which can be later used for comprehensive understanding of the studied system. Proper evaluation of such data is challenging, and cannot be faced by bare analysis of separate datasets. Integrated approaches are necessary, because only data integration allows finding correlation trends common for all studied data sets and revealing hidden structures not known a priori. This improves understanding and interpretation of the complex systems. Joint and Unique MultiBlock Analysis (JUMBA) is an analysis method based on the OnPLS-algorithm that decomposes a set of matrices into joint parts containing variation shared with other connected matrices and variation that is unique for each single matrix. Mapping unique variation is important from a data integration perspective, since it certainly cannot be expected that all variation co-varies. In this work we used JUMBA for integrated analysis of lipidomic, metabolomic and oxylipin datasets obtained from profiling of plasma samples from children infected with P. falciparum malaria. P. falciparum is one of the primary contributors to childhood mortality and obstetric complications in the developing world, what makes development of the new diagnostic and prognostic tools, as well as better understanding of the disease, of utmost importance. In presented work JUMBA made it possible to detect already known trends related to disease progression, but also to discover new structures in the data connected to food intake and personal differences in metabolism. By separating the variation in each data set into joint and unique, JUMBA reduced complexity of the analysis, facilitated detection of samples and variables corresponding to specific structures across multiple datasets and by doing this enabled fast interpretation of the studied system. All this makes JUMBA a perfect choice for multiblock analysis of systems biology data.
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| 17. |
- Surowiec, Izabella, et al.
(författare)
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Metabolic signature profiling as a diagnostic and prognostic tool in paediatric Plasmodium falciparum malaria
- 2015
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Ingår i: Open Forum Infectious Diseases. - : Oxford University Press. - 2328-8957. ; 2:2
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Tidskriftsartikel (refereegranskat)abstract
- Background: Accuracy in malaria diagnosis and staging is vital in order to reduce mortality and post infectious sequelae. Herein we present a metabolomics approach to diagnostic staging of malaria infection, specifically Plasmodium falciparum infection in children. Methods: A group of 421 patients between six months and six years of age with mild and severe states of malaria with age-matched controls were included in the study, 107, 192 and 122 individuals respectively. A multivariate design was used as basis for representative selection of twenty patients in each category. Patient plasma was subjected to Gas Chromatography-Mass Spectrometry analysis and a full metabolite profile was produced from each patient. In addition, a proof-of-concept model was tested in a Plasmodium berghei in-vivo model where metabolic profiles were discernible over time of infection. Results: A two-component principal component analysis (PCA) revealed that the patients could be separated into disease categories according to metabolite profiles, independently of any clinical information. Furthermore, two sub-groups could be identified in the mild malaria cohort who we believe represent patients with divergent prognoses. Conclusion: Metabolite signature profiling could be used both for decision support in disease staging and prognostication.
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| 18. |
- Surowiec, Izabella, et al.
(författare)
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Multivariate strategy for the sample selection and integration of multi-batch data in metabolomics
- 2017
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Ingår i: Metabolomics. - : SPRINGER. - 1573-3882 .- 1573-3890. ; 13:10
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Tidskriftsartikel (refereegranskat)abstract
- Introduction Availability of large cohorts of samples with related metadata provides scientists with extensive material for studies. At the same time, recent development of modern high-throughput 'omics' technologies, including metabolomics, has resulted in the potential for analysis of large sample sizes. Representative subset selection becomes critical for selection of samples from bigger cohorts and their division into analytical batches. This especially holds true when relative quantification of compound levels is used.Objectives We present a multivariate strategy for representative sample selection and integration of results from multi-batch experiments in metabolomics.Methods Multivariate characterization was applied for design of experiment based sample selection and subsequent subdivision into four analytical batches which were analyzed on different days by metabolomics profiling using gas-chromatography time-of-flight mass spectrometry (GC-TOFMS). For each batch OPLS-DA (R) was used and its p(corr) vectors were averaged to obtain combined metabolic profile. Jackknifed standard errors were used to calculate confidence intervals for each metabolite in the average p(corr) profile.Results A combined, representative metabolic profile describing differences between systemic lupus erythematosus (SLE) patients and controls was obtained and used for elucidation of metabolic pathways that could be disturbed in SLE.Conclusion Design of experiment based representative sample selection ensured diversity and minimized bias that could be introduced at this step. Combined metabolic profile enabled unified analysis and interpretation.
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| 19. |
- Surowiec, Izabella, et al.
(författare)
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Quantification of run order effect on chromatography : mass spectrometry profiling data
- 2018
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Ingår i: Journal of Chromatography A. - : Elsevier BV. - 0021-9673 .- 1873-3778. ; 1568, s. 229-234
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Tidskriftsartikel (refereegranskat)abstract
- Chromatographic systems coupled with mass spectrometry detection are widely used in biological studies investigating how levels of biomolecules respond to different internal and external stimuli. Such changes are normally expected to be of low magnitude and therefore all experimental factors that can influence the analysis need to be understood and minimized. Run order effect is commonly observed and constitutes a major challenge in chromatography-mass spectrometry based profiling studies that needs to be addressed before the biological evaluation of measured data is made. So far there is no established consensus, metric or method that quickly estimates the size of this effect. In this paper we demonstrate how orthogonal projections to latent structures (OPLS®) can be used for objective quantification of the run order effect in profiling studies. The quantification metric is expressed as the amount of variation in the experimental data that is correlated to the run order. One of the primary advantages with this approach is that it provides a fast way of quantifying run-order effect for all detected features, not only internal standards. Results obtained from quantification of run order effect as provided by the OPLS can be used in the evaluation of data normalization, support the optimization of analytical protocols and identification of compounds highly influenced by instrumental drift. The application of OPLS for quantification of run order is demonstrated on experimental data from plasma profiling performed on three analytical platforms: GCMS metabolomics, LCMS metabolomics and LCMS lipidomics.
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| 20. |
- Surowiec, Izabella, et al.
(författare)
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The oxylipin and endocannabidome responses in acute phase Plasmodium falciparum malaria in children
- 2017
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Ingår i: Malaria Journal. - : BIOMED CENTRAL LTD. - 1475-2875. ; 16
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Tidskriftsartikel (refereegranskat)abstract
- Background: Oxylipins and endocannabinoids are low molecular weight bioactive lipids that are crucial for initiation and resolution of inflammation during microbial infections. Metabolic complications in malaria are recognized contributors to severe and fatal malaria, but the impact of malaria infection on the production of small lipid derived signalling molecules is unknown. Knowledge of immunoregulatory patterns of these molecules in malaria is of great value for better understanding of the disease and improvement of treatment regimes, since the action of these classes of molecules is directly connected to the inflammatory response of the organism.Methods: Detection of oxylipins and endocannabinoids from plasma samples from forty children with uncomplicated and severe malaria as well as twenty controls was done after solid phase extraction followed by chromatography mass spectrometry analysis. The stable isotope dilution method was used for compound quantification. Data analysis was done with multivariate (principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA (R)) and univariate approaches (receiver operating characteristic (ROC) curves, t tests, correlation analysis).Results: Forty different oxylipin and thirteen endocannabinoid metabolites were detected in the studied samples, with one oxylipin (thromboxane B2, TXB2) in significantly lower levels and four endocannabinoids (OEA, PEA, DEA and EPEA) at significantly higher levels in infected individuals as compared to controls according to t test analysis with Bonferroni correction. Three oxylipins (13-HODE, 9-HODE and 13-oxo-ODE) were higher in severe compared to uncomplicated malaria cases according to the results from multivariate analysis. Observed changes in oxylipin levels can be connected to activation of cytochrome P450 (CYP) and 5-lipoxygenase (5-LOX) metabolic pathways in malaria infected individuals compared to controls, and related to increased levels of all linoleic acid oxylipins in severe patients compared to uncomplicated ones. The endocannabinoids were extremely responsive to malaria infection with majority of this class of molecules found at higher levels in infected individuals compared to controls.Conclusions: It was possible to detect oxylipin and endocannabinoid molecules that can be potential biomarkers for differentiation between malaria infected individuals and controls and between different classes of malaria. Metabolic pathways that could be targeted towards an adjunctive therapy in the treatment of malaria were also pinpointed.
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| 21. |
- Torell, Frida, et al.
(författare)
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Cytokine Profiles in Autoantibody Defined Subgroups of Systemic Lupus Erythematosus
- 2019
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 18:3, s. 1208-1217
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this study was to evaluate how the cytokine profiles differed between autoantibody based subgroups of systemic lupus erythematosus (SLE). SLE is a systemic autoimmune disease, characterized by periods of flares (active disease) and remission (inactive disease). The disease can affect many organ systems, e.g., skin, joints, kidneys, heart, and the central nervous system (CNS). SLE patients often have an overproduction of cytokines, e.g., interferons, chemokines, and interleukins. The high cytokine levels are part of the systemic inflammation, which can lead to tissue injury. In the present study, SLE patients were divided into five groups based on their autoantibody profiles. We thus defined these five groups: ANA negative, antiphospholipid (aPL) positive, anti-Sm/anti-RNP positive, Sjögren’s syndrome (SS) antigen A and B positive, and patients positive for more than one type of autoantibodies (other SLE). Cytokines were measured using Mesoscale Discovery (MSD) multiplex analysis. On the basis of the cytokine data, ANA negative patients were the most deviating subgroup, with lower levels of interferon (IFN)-γ, tumor necrosis factor (TNF)-α, interleukin (IL)-12/IL-23p40, and interferon gamma-induced protein (IP)-10. Despite low cytokine levels in the ANA negative group, autoantibody profiles did not discriminate between different cytokine patterns.
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| 22. |
- Wiklund, Susanne, et al.
(författare)
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Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models
- 2008
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Ingår i: Analytical Chemistry. - Columbus, OH : American Chemical Society. - 0003-2700 .- 1520-6882. ; 80:1, s. 115-22
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Tidskriftsartikel (refereegranskat)abstract
- Metabolomics studies generate increasingly complex data tables, which are hard to summarize and visualize without appropriate tools. The use of chemometrics tools, e.g., principal component analysis (PCA), partial least-squares to latent structures (PLS), and orthogonal PLS (OPLS), is therefore of great importance as these include efficient, validated, and robust methods for modeling information-rich chemical and biological data. Here the S-plot is proposed as a tool for visualization and interpretation of multivariate classification models, e.g., OPLS discriminate analysis, having two or more classes. The S-plot visualizes both the covariance and correlation between the metabolites and the modeled class designation. Thereby the S-plot helps identifying statistically significant and potentially biochemically significant metabolites, based both on contributions to the model and their reliability. An extension of the S-plot, the SUS-plot (shared and unique structure), is applied to compare the outcome of multiple classification models compared to a common reference, e.g., control. The used example is a gas chromatography coupled mass spectroscopy based metabolomics study in plant biology where two different transgenic poplar lines are compared to wild type. By using OPLS, an improved visualization and discrimination of interesting metabolites could be demonstrated.
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| 23. |
- Åkesson, Karolina, et al.
(författare)
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Kynurenine pathway is altered in patients with SLE and associated with severe fatigue
- 2018
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Ingår i: Lupus Science and Medicine. - : BMJ Publishing Group Ltd. - 2053-8790. ; 5:1
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Fatigue has been reported as the most disturbing symptom in a majority of patients with SLE. Depression is common and often severe. Together these symptoms cause significant morbidity and affect patients with otherwise relatively mild disease. Tryptophan and its metabolites in the kynurenine pathway are known to be important in several psychiatric conditions, for example, depression, which are often also associated with fatigue. We therefore investigated the kynurenine pathway in patients with SLE and controls.Methods: In a cross-sectional design plasma samples from 132 well-characterised patients with SLE and 30 age-matched and gender-matched population-based controls were analysed by liquid chromatography tandem mass spectrometry to measure the levels of tryptophan and its metabolites kynurenine and quinolinic acid. Fatigue was measured with Fatigue Severity Scale and depression with Hospital Anxiety and Depression Scale. SLE disease activity was assessed with Systemic Lupus Erythematosus Disease Activity Index (SLEDAI).Results: The kynurenine/tryptophan ratio, as a measure of indoleamine 2,3-dioxygenase (IDO) activity, was increased in patients with SLE. Patients with active disease (SLEDAI >= 6) showed lower tryptophan levels compared with controls (54 mu M, SD=19 vs 62 mu M, SD=14, p=0.03), although patients with SLE overall did not differ compared with controls. Patients with SLE had higher levels of tryptophan metabolites kynurenine (966 nM, SD=530) and quinolinic acid (546 nM, SD=480) compared with controls (kynurenine: 712 nM, SD=230, p=0.0001; quinolinic acid: 380 nM, SD=150, p=0.001). Kynurenine, quinolinic acid and the kynurenine/tryptophan ratio correlated weakly with severe fatigue (r(s)=0.34, r(s)=0.28 and r(s)=0.24, respectively) but not with depression.Conclusions: Metabolites in the kynurenine pathway are altered in patients with SLE compared with controls. Interestingly, fatigue correlated weakly with measures of enhanced tryptophan metabolism, while depression did not. Drugs targeting enzymes in the kynurenine pathway, for example, IDO inhibitors or niacin (B12) supplementation, which suppresses IDO activity, merit further investigation as treatments in SLE.
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| 24. |
- Abbasi, Ahtisham Fazeel, et al.
(författare)
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Deep learning architectures for the prediction of YY1-mediated chromatin loops
- 2023
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Ingår i: Bioinformatics research and applications. - : Springer. - 9789819970735 - 9789819970742 ; , s. 72-84
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Konferensbidrag (refereegranskat)abstract
- YY1-mediated chromatin loops play substantial roles in basic biological processes like gene regulation, cell differentiation, and DNA replication. YY1-mediated chromatin loop prediction is important to understand diverse types of biological processes which may lead to the development of new therapeutics for neurological disorders and cancers. Existing deep learning predictors are capable to predict YY1-mediated chromatin loops in two different cell lines however, they showed limited performance for the prediction of YY1-mediated loops in the same cell lines and suffer significant performance deterioration in cross cell line setting. To provide computational predictors capable of performing large-scale analyses of YY1-mediated loop prediction across multiple cell lines, this paper presents two novel deep learning predictors. The two proposed predictors make use of Word2vec, one hot encoding for sequence representation and long short-term memory, and a convolution neural network along with a gradient flow strategy similar to DenseNet architectures. Both of the predictors are evaluated on two different benchmark datasets of two cell lines HCT116 and K562. Overall the proposed predictors outperform existing DEEPYY1 predictor with an average maximum margin of 4.65%, 7.45% in terms of AUROC, and accuracy, across both of the datases over the independent test sets and 5.1%, 3.2% over 5-fold validation. In terms of cross-cell evaluation, the proposed predictors boast maximum performance enhancements of up to 9.5% and 27.1% in terms of AUROC over HCT116 and K562 datasets.
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| 25. |
- Alinaghi, Masoumeh, et al.
(författare)
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Hierarchical time-series analysis of dynamic bioprocess systems
- 2022
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Ingår i: Biotechnology Journal. - : John Wiley & Sons. - 1860-6768 .- 1860-7314. ; 17:12
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Tidskriftsartikel (refereegranskat)abstract
- Background: Monoclonal antibodies (mAbs) are leading types of ‘blockbuster’ biotherapeutics worldwide; they have been successfully used to treat various cancers and chronic inflammatory and autoimmune diseases. Biotherapeutics process development and manufacturing are complicated due to lack of understanding the factors that impact cell productivity and product quality attributes. Understanding complex interactions between cells, media, and process parameters on the molecular level is essential to bring biomanufacturing to the next level. This can be achieved by analyzing cell culture metabolic levels connected to vital process parameters like viable cell density (VCD). However, VCD and metabolic profiles are dynamic parameters and inherently correlated with time, leading to a significant correlation without actual causality. Many time-series methods deal with such issues. However, with metabolic profiling, the number of measured variables vastly exceeds the number of experiments, making most of existing methods ill-suited and hard to interpret. Methods and MajorResults: Here we propose an alternative workflow using hierarchical dimension reduction to visualize and interpret the relation between evolution of metabolic profiles and dynamic process parameters. The first step of proposed method is focused on finding predictive relation between metabolic profiles and process parameter at all time points using OPLS regression. For each time point, the p(corr) obtained from OPLS model is considered as a differential metabogram and is further assessed using principal components analysis (PCA).Conclusions: Compared to traditional batch modeling, applying proposed methodology on metabolic data from Chinese Hamster Ovary (CHO) antibody production characterized the dynamic relation between metabolic profiles and critical process parameters.
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