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- Brynedal, B, et al.
(författare)
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Molecular signature of methotrexate response among rheumatoid arthritis patients
- 2023
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Ingår i: Frontiers in medicine. - : Frontiers Media SA. - 2296-858X. ; 10, s. 1146353-
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Tidskriftsartikel (refereegranskat)abstract
- Methotrexate (MTX) is the first line treatment for rheumatoid arthritis (RA), but failure of satisfying treatment response occurs in a significant proportion of patients. Here we present a longitudinal multi-omics study aimed at detecting molecular and cellular processes in peripheral blood associated with a successful methotrexate treatment of rheumatoid arthritis.MethodsEighty newly diagnosed patients with RA underwent clinical assessment and donated blood before initiation of MTX, and 3 months into treatment. Flow cytometry was used to describe cell types and presence of activation markers in peripheral blood, the expression of 51 proteins was measured in serum or plasma, and RNA sequencing was performed in peripheral blood mononuclear cells (PBMC). Response to treatment after 3 months was determined using the EULAR response criteria. We assessed the changes in biological phenotypes during treatment, and whether these changes differed between responders and non-responders with regression analysis. By using measurements from baseline, we also tried to find biomarkers of future MTX response or, alternatively, to predict MTX response.ResultsAmong the MTX responders, (Good or Moderate according to EULAR treatment response classification, n = 60, 75%), we observed changes in 29 partly overlapping cell types proportions, levels of 13 proteins and expression of 38 genes during treatment. These changes were in most cases suppressions that were stronger among responders compared to non-responders. Within responders to treatment, we observed a suppression of FOXP3 gene expression, reduction of immunoglobulin gene expression and suppression of genes involved in cell proliferation. The proportion of many HLA-DR expressing T-cell populations were suppressed in all patients irrespective of clinical response, and the proportion of many IL21R+ T-cells were reduced exclusively in non-responders. Using only the baseline measurements we could not detect any biomarkers or prediction models that could predict response to MTX.ConclusionWe conclude that a deep molecular and cellular phenotyping of peripheral blood cells in RA patients treated with methotrexate can reveal previously not recognized differences between responders and non-responders during 3 months of treatment with MTX. This may contribute to the understanding of MTX mode of action and explain non-responsiveness to MTX therapy.
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- Diaz-Gallo, LM, et al.
(författare)
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Systematic approach demonstrates enrichment of multiple interactions between non-HLA risk variants and HLA-DRB1 risk alleles in rheumatoid arthritis
- 2018
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Ingår i: Annals of the rheumatic diseases. - : BMJ. - 1468-2060 .- 0003-4967. ; 77:10, s. 1454-1462
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Tidskriftsartikel (refereegranskat)abstract
- In anti-citrullinated protein antibody positive rheumatoid arthritis (ACPA-positive RA), a particular subset of HLA-DRB1 alleles, called shared epitope (SE) alleles, is a highly influential genetic risk factor. Here, we investigated whether non-HLA single nucleotide polymorphisms (SNP), conferring low disease risk on their own, interact with SE alleles more frequently than expected by chance and if such genetic interactions influence the HLA-DRB1 SE effect concerning risk to ACPA-positive RA.MethodsWe computed the attributable proportion (AP) due to additive interaction at genome-wide level for two independent ACPA-positive RA cohorts: the Swedish epidemiological investigation of rheumatoid arthritis (EIRA) and the North American rheumatoid arthritis consortium (NARAC). Then, we tested for differences in the AP p value distributions observed for two groups of SNPs, non-associated and associated with disease. We also evaluated whether the SNPs in interaction with HLA-DRB1 were cis-eQTLs in the SE alleles context in peripheral blood mononuclear cells from patients with ACPA-positive RA (SE-eQTLs).ResultsWe found a strong enrichment of significant interactions (AP p<0.05) between the HLA-DRB1 SE alleles and the group of SNPs associated with ACPA-positive RA in both cohorts (Kolmogorov-Smirnov test D=0.35 for EIRA and D=0.25 for NARAC, p<2.2e-16 for both). Interestingly, 564 out of 1492 SNPs in consistent interaction for both cohorts were significant SE-eQTLs. Finally, we observed that the effect size of HLA-DRB1 SE alleles for disease decreases from 5.2 to 2.5 after removal of the risk alleles of the two top interacting SNPs (rs2476601 and rs10739581).ConclusionOur data demonstrate that there are massive genetic interactions between the HLA-DRB1 SE alleles and non-HLA genetic variants in ACPA-positive RA.
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- Diaz-Gallo, LM, et al.
(författare)
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Understanding interactions between risk factors, and assessing the utility of the additive and multiplicative models through simulations
- 2021
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Ingår i: PloS one. - : Public Library of Science (PLoS). - 1932-6203. ; 16:4, s. e0250282-
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Tidskriftsartikel (refereegranskat)abstract
- Understanding the genetic background of complex diseases requires the expansion of studies beyond univariate associations. Therefore, it is important to use interaction assessments of risk factors in order to discover whether, and how genetic risk variants act together on disease development. The principle of interaction analysis is to explore the magnitude of the combined effect of risk factors on disease causation. In this study, we use simulations to investigate different scenarios of causation to show how the magnitude of the effect of two risk factors interact. We mainly focus on the two most commonly used interaction models, the additive and multiplicative risk scales, since there is often confusion regarding their use and interpretation. Our results show that the combined effect is multiplicative when two risk factors are involved in the same chain of events, an interaction called synergism. Synergism is often described as a deviation from additivity, which is a broader term. Our results also confirm that it is often relevant to estimate additive effect relationships, because they correspond to independent risk factors at low disease prevalence. Importantly, we evaluate the threshold of more than two required risk factors for disease causation, called the multifactorial threshold model. We found a simple mathematical relationship (square root) between the threshold and an additive-to-multiplicative linear effect scale (AMLES), where 0 corresponds to an additive effect and 1 to a multiplicative. We propose AMLES as a metric that could be used to test different effects relationships at the same time, given that it can simultaneously reveal additive, multiplicative and intermediate risk effects relationships. Finally, the utility of our simulation study was demonstrated using real data by analyzing and interpreting gene-gene interaction odds ratios from a rheumatoid arthritis case-control cohort.
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- Mendez, D. Y., et al.
(författare)
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Characterization of data-driven clusters in diabetes-free adults and their utility for risk stratification of type 2 diabetes
- 2022
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Ingår i: Bmc Medicine. - : Springer Science and Business Media LLC. - 1741-7015. ; 20:1
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Tidskriftsartikel (refereegranskat)abstract
- Background The prevention of type 2 diabetes is challenging due to the variable effects of risk factors at an individual level. Data-driven methods could be useful to detect more homogeneous groups based on risk factor variability. The aim of this study was to derive characteristic phenotypes using cluster analysis of common risk factors and to assess their utility to stratify the risk of type 2 diabetes. Methods Data on 7317 diabetes-free adults from Sweden were used in the main analysis and on 2332 diabetes-free adults from Mexico for external validation. Clusters were based on sex, family history of diabetes, educational attainment, fasting blood glucose and insulin levels, estimated insulin resistance and beta-cell function, systolic and diastolic blood pressure, and BMI. The risk of type 2 diabetes was assessed using Cox proportional hazards models. The predictive accuracy and long-term stability of the clusters were then compared to different definitions of prediabetes. Results Six risk phenotypes were identified independently in both cohorts: very low-risk (VLR), low-risk low beta-cell function (LRLB), low-risk high beta-cell function (LRHB), high-risk high blood pressure (HRHBP), high-risk beta-cell failure (HRBF), and high-risk insulin-resistant (HRIR). Compared to the LRHB cluster, the VLR and LRLB clusters showed a lower risk, while the HRHBP, HRBF, and HRIR clusters showed a higher risk of developing type 2 diabetes. The high-risk clusters, as a group, had a better predictive accuracy than prediabetes and adequate stability after 20 years. Conclusions Phenotypes derived using cluster analysis were useful in stratifying the risk of type 2 diabetes among diabetes-free adults in two independent cohorts. These results could be used to develop more precise public health interventions.
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- Ringh, MV, et al.
(författare)
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Methylome and transcriptome signature of bronchoalveolar cells from multiple sclerosis patients in relation to smoking
- 2021
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Ingår i: Multiple sclerosis (Houndmills, Basingstoke, England). - : SAGE Publications. - 1477-0970 .- 1352-4585. ; 27:7, s. 1014-1026
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Tidskriftsartikel (refereegranskat)abstract
- Despite compelling evidence that cigarette smoking impacts the risk of developing multiple sclerosis (MS), little is known about smoking-associated changes in the primary exposed lung cells of patients. Objectives: We aimed to examine molecular changes occurring in bronchoalveolar lavage (BAL) cells from MS patients in relation to smoking and in comparison to healthy controls (HCs). Methods: We profiled DNA methylation in BAL cells from female MS ( n = 17) and HC ( n = 22) individuals, using Illumina Infinium EPIC and performed RNA-sequencing in non-smokers. Results: The most prominent changes were found in relation to smoking, with 1376 CpG sites (adjusted P < 0.05) differing between MS smokers and non-smokers. Approximately 30% of the affected genes overlapped with smoking-associated changes in HC, leading to a strong common smoking signature in both MS and HC after gene ontology analysis. Smoking in MS patients resulted in additional discrete changes related to neuronal processes. Methylome and transcriptome analyses in non-smokers suggest that BAL cells from MS patients display very subtle (not reaching adjusted P < 0.05) but concordant changes in genes connected to reduced transcriptional/translational processes and enhanced cellular motility. Conclusions: Our study provides insights into the impact of smoking on lung inflammation and immunopathogenesis of MS.
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- Yoosuf, N, et al.
(författare)
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Early prediction of clinical response to anti-TNF treatment using multi-omics and machine learning in rheumatoid arthritis
- 2022
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Ingår i: Rheumatology (Oxford, England). - : Oxford University Press (OUP). - 1462-0332 .- 1462-0324. ; 61:4, s. 1680-1689
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Tidskriftsartikel (refereegranskat)abstract
- ObjectivesAdvances in immunotherapy by blocking TNF have remarkably improved treatment outcomes for Rheumatoid arthritis (RA) patients. Although treatment specifically targets TNF, the downstream mechanisms of immune suppression are not completely understood. The aim of this study was to detect biomarkers and expression signatures of treatment response to TNF inhibition.MethodsPeripheral blood mononuclear cells (PBMCs) from 39 female patients were collected before anti-TNF treatment initiation (day 0) and after 3 months. The study cohort included patients previously treated with MTX who failed to respond adequately. Response to treatment was defined based on the EULAR criteria and classified 23 patients as responders and 16 as non-responders. We investigated differences in gene expression in PBMCs, the proportion of cell types and cell phenotypes in peripheral blood using flow cytometry and the level of proteins in plasma. Finally, we used machine learning models to predict non-response to anti-TNF treatment.ResultsThe gene expression analysis in baseline samples revealed notably higher expression of the gene EPPK1 in future responders. We detected the suppression of genes and proteins following treatment, including suppressed expression of the T cell inhibitor gene CHI3L1 and its protein YKL-40. The gene expression results were replicated in an independent cohort. Finally, machine learning models mainly based on transcriptomic data showed high predictive utility in classifying non-response to anti-TNF treatment in RA.ConclusionsOur integrative multi-omics analyses identified new biomarkers for the prediction of response, found pathways influenced by treatment and suggested new predictive models of anti-TNF treatment in RA patients.
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