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- Toro-Dominguez, D, et al.
(författare)
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Differential Treatments Based on Drug-induced Gene Expression Signatures and Longitudinal Systemic Lupus Erythematosus Stratification
- 2019
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 15502-
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Tidskriftsartikel (refereegranskat)abstract
- Systemic lupus erythematosus (SLE) is a heterogeneous disease with unpredictable patterns of activity. Patients with similar activity levels may have different prognosis and molecular abnormalities. In this study, we aimed to measure the main differences in drug-induced gene expression signatures across SLE patients and to evaluate the potential for clinical data to build a machine learning classifier able to predict the SLE subset for individual patients. SLE transcriptomic data from two cohorts were compared with drug-induced gene signatures from the CLUE database to compute a connectivity score that reflects the capability of a drug to revert the patient signatures. Patient stratification based on drug connectivity scores revealed robust clusters of SLE patients identical to the clusters previously obtained through longitudinal gene expression data, implying that differential treatment depends on the cluster to which patients belongs. The best drug candidates found, mTOR inhibitors or those reducing oxidative stress, showed stronger cluster specificity. We report that drug patterns for reverting disease gene expression follow the cell-specificity of the disease clusters. We used 2 cohorts to train and test a logistic regression model that we employed to classify patients from 3 independent cohorts into the SLE subsets and provide a clinically useful model to predict subset assignment and drug efficacy.
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- Wang, Zhaoming, et al.
(författare)
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Imputation and subset-based association analysis across different cancer types identifies multiple independent risk loci in the TERT-CLPTM1L region on chromosome 5p15.33
- 2014
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Ingår i: Human Molecular Genetics. - : Oxford University Press (OUP). - 0964-6906 .- 1460-2083. ; 23:24, s. 6616-6633
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Tidskriftsartikel (refereegranskat)abstract
- Genome-wide association studies (GWAS) have mapped risk alleles for at least 10 distinct cancers to a small region of 63 000 bp on chromosome 5p15.33. This region harbors the TERT and CLPTM1L genes; the former encodes the catalytic subunit of telomerase reverse transcriptase and the latter may play a role in apoptosis. To investigate further the genetic architecture of common susceptibility alleles in this region, we conducted an agnostic subset-based meta-analysis (association analysis based on subsets) across six distinct cancers in 34 248 cases and 45 036 controls. Based on sequential conditional analysis, we identified as many as six independent risk loci marked by common single-nucleotide polymorphisms: five in the TERT gene (Region 1: rs7726159, P = 2.10 × 10(-39); Region 3: rs2853677, P = 3.30 × 10(-36) and PConditional = 2.36 × 10(-8); Region 4: rs2736098, P = 3.87 × 10(-12) and PConditional = 5.19 × 10(-6), Region 5: rs13172201, P = 0.041 and PConditional = 2.04 × 10(-6); and Region 6: rs10069690, P = 7.49 × 10(-15) and PConditional = 5.35 × 10(-7)) and one in the neighboring CLPTM1L gene (Region 2: rs451360; P = 1.90 × 10(-18) and PConditional = 7.06 × 10(-16)). Between three and five cancers mapped to each independent locus with both risk-enhancing and protective effects. Allele-specific effects on DNA methylation were seen for a subset of risk loci, indicating that methylation and subsequent effects on gene expression may contribute to the biology of risk variants on 5p15.33. Our results provide strong support for extensive pleiotropy across this region of 5p15.33, to an extent not previously observed in other cancer susceptibility loci.
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- Beal, Jacob, et al.
(författare)
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Robust estimation of bacterial cell count from optical density
- 2020
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Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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- Lopez-Dominguez, R, et al.
(författare)
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Transcription Factor Activity Inference in Systemic Lupus Erythematosus
- 2021
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Ingår i: Life (Basel, Switzerland). - : MDPI AG. - 2075-1729. ; 11:4
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Tidskriftsartikel (refereegranskat)abstract
- Background: Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease with diverse clinical manifestations. Although most of the SLE-associated loci are located in regulatory regions, there is a lack of global information about transcription factor (TFs) activities, the mode of regulation of the TFs, or the cell or sample-specific regulatory circuits. The aim of this work is to decipher TFs implicated in SLE. Methods: In order to decipher regulatory mechanisms in SLE, we have inferred TF activities from transcriptomic data for almost all human TFs, defined clusters of SLE patients based on the estimated TF activities and analyzed the differential activity patterns among SLE and healthy samples in two different cohorts. The Transcription Factor activity matrix was used to stratify SLE patients and define sets of TFs with statistically significant differential activity among the disease and control samples. Results: TF activities were able to identify two main subgroups of patients characterized by distinct neutrophil-to-lymphocyte ratio (NLR), with consistent patterns in two independent datasets—one from pediatric patients and other from adults. Furthermore, after contrasting all subgroups of patients and controls, we obtained a significant and robust list of 14 TFs implicated in the dysregulation of SLE by different mechanisms and pathways. Among them, well-known regulators of SLE, such as STAT or IRF, were found, but others suggest new pathways that might have important roles in SLE. Conclusions: These results provide a foundation to comprehend the regulatory mechanism underlying SLE and the established regulatory factors behind SLE heterogeneity that could be potential therapeutic targets.
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- Lindblom, J., et al.
(författare)
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TRANSCRIPTOME PROFILING AND AUTOIMMUNITY-RELATED SEROLOGICAL MARKERS IDENTIFY TP53 AND C3AR AS DRUG TARGETS IN NEUROPSYCHIATRIC SYSTEMIC LUPUS ERYTHEMATOSUS
- 2022
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Ingår i: Annals of the Rheumatic Diseases. - : HighWire Press. - 0003-4967 .- 1468-2060. ; 81:Suppl. 1, s. 326-326
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Involvement of the nervous system is a common but poorly understood manifestation of systemic lupus erythematosus (SLE), termed neuropsychiatric SLE (NPSLE). Although studies have reported varying prevalence estimates [1], NPSLE affects at least 20% of patients with SLE within the first years of the disease course [2]. The management of neuropsychiatric SLE (NPSLE) is poorly optimised and specific treatment is lacking.ObjectivesThe aim of this study was to investigate expression quantitative trait loci (eQTLs), the transcriptome, and autoimmunity-related cytokines and autoantibodies in patients with central nervous system (CNS) lupus to gain insights into underlying genetics and biologic mechanisms towards identification of novel drug targets.MethodsWe analysed differentially expressed genes (DEGs), pathways and their druggability via the Drug Gene Interaction database (DGIdb) [3] in active CNS lupus (n=26) versus healthy controls (HC; n=497), and eQTLs in active or past CNS lupus (n=53), based on validated (identified in two independent SLE populations) DEGs in SLE (n=350) versus HC (n=497), in whole blood collected within the frame of the European PRECISESADS consortium [4]. CNS lupus was defined according to SLE Disease Activity Index 2000 (SLEDAI-2K) [5] CNS items or by CNS manifestations such as chorea, acute confusional state, transverse myelitis, aseptic meningitis, and optic neuritis in the absence of predisposing conditions unrelated to SLE. Genome-wide RNA-sequencing and genotyping was previously performed by Illumina assays, and serum levels of 17 cytokines were analysed using a Luminex assay and ELISA [4].ResultsAmong 5631 significant and validated DEGs in active CNS patients compared with HC, 1922 unique DEGs were tagged to 21 and 176 significant KEGG [6] and Reactome [7] pathways, respectively. Pathways included “Interferon signalling”, “TNF signalling” and “Toll-like Receptor Cascades”. The pathways included 29 of 59 DEGs with a |fold change (FC)| > 1.5, 6 genes from 14 significant cis-eQTLs and 10 genes from 22 trans-eQTLs, and 2 genes from 8 cytokines that differed significantly between active CNS lupus and HC. These genes could be targeted by 496 different drugs, with the Bruton tyrosine kinase (BTK) inhibitor ibrutinib and the anti-CD20 B cell depleting monoclonal rituximab with ability to interfere with tumour protein P53 (TP53) activity, and a complement C3a Receptor (C3aR) antagonist being of particular interest.ConclusionIntegrated multilevel omics analysis revealed a set of enriched pathways of potential interest for future drug investigation in CNS lupus, including BTK and C3aR inhibition, and B cell depletion.References[1]Unterman A, Nolte JE, Boaz M, Abady M, Shoenfeld Y, Zandman-Goddard G. Neuropsychiatric syndromes in systemic lupus erythematosus: a meta-analysis. Semin Arthritis Rheum. 2011 Aug; 41(1):1-11[2]Hanly JG, Urowitz MB, Su L, Bae SC, Gordon C, Wallace DJ, et al. Prospective analysis of neuropsychiatric events in an international disease inception cohort of patients with systemic lupus erythematosus. Ann Rheum Dis. 2010 Mar; 69(3):529-535[3]Wagner AH, Coffman AC, Ainscough BJ, Spies NC, Skidmore ZL, Campbell KM, et al. DGIdb 2.0: mining clinically relevant drug-gene interactions. Nucleic Acids Res. 2016 Jan 4; 44(D1):D1036-1044[4]Barturen G, Babaei S, Català-Moll F, Martínez-Bueno M, Makowska Z, Martorell-Marugán J, et al. Integrative Analysis Reveals a Molecular Stratification of Systemic Autoimmune Diseases. Arthritis Rheumatol. 2021 Jun; 73(6):1073-1085[5]Gladman DD, Ibanez D, Urowitz MB. Systemic lupus erythematosus disease activity index 2000. J Rheumatol. 2002 Feb; 29(2):288-291[6]Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017 Jan 4; 45(D1):D353-d361[7]Jassal B, Matthews L, Viteri G, Gong C, Lorente P, Fabregat A, et al. The reactome pathway knowledgebase. Nucleic Acids Res. 2020 Jan 8; 48(D1):D498-d503AcknowledgementsThe PRECISESADS clinical consortiumDisclosure of InterestsNone declared
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- Parodis, Ioannis, 1981-, et al.
(författare)
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DRUG REPURPOSING FOR TREATING LUPUS NEPHRITIS BASED ON TRANSCRIPTOME PROFILING AND AUTOIMMUNITY-RELATED SEROLOGICAL MARKERS
- 2022
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Ingår i: Annals of the Rheumatic Diseases. - : HighWire Press. - 0003-4967 .- 1468-2060. ; 81:Suppl. 1, s. 326-326
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Lupus nephritis (LN) is one of the most severe organ manifestations of systemic lupus erythematosus (SLE) and constitutes an important cause of morbidity and death among patients with SLE [1]. The associated renal injury, and ultimately damage, is the result of an immune-mediated process which involves leukocytes, immune complexes, complement and cytokines [2].ObjectivesLupus nephritis (LN) is one of the most severe organ manifestations of systemic lupus erythematosus (SLE) and constitutes an important cause of morbidity and death among patients with SLE [1]. The associated renal injury, and ultimately damage, is the result of an immune-mediated process which involves leukocytes, immune complexes, complement and cytokines [2].MethodsWe analysed differentially expressed genes (DEGs), pathways and their druggability via the Drug Gene Interaction database (DGIdb) [3] in active LN (n=41) versus healthy controls (HC; n=497), and eQTLs in active or past LN (n=87), based on validated (identified in two independent SLE populations) DEGs in SLE (n=350) vs HC (n=497), in whole blood collected within the frame of the European PRECISESADS consortium [4]. Genome-wide RNA-sequencing and genotyping was previously performed by Illumina assays, and serum levels of 17 cytokines and 18 autoantibodies were analysed using a Luminex assay, ELISA, IDS-iSYS and SPAPLUS analyser [4].ResultsA total of 6 869 significant and validated DEGs were identified in active LN patients compared with HC. Of these, 1010 validated DEGs were tagged to 34 KEGG pathways including 24 DEGs with a |fold change (FC)| > 1.5, genes of 18 cis-eQTLs and 3 trans-eQTLs, and 1 gene from cytokines that differed significantly between active LN and HC. Moreover, 2446 validated DEGs were tagged to 216 Reactome pathways included 85 DEGs with a |FC| > 1.5, genes of 21 cis-eQTLs and 5 trans-eQTLs, and 1 gene from cytokines that differed significantly between active LN and HC. These genes could be targeted by 203 different drugs, with the proteasome inhibitor bortezomib interfering with cathepsin B (CTSB) regulation and cyclophosphamide interfering with the regulation of tumour necrosis factor receptor superfamily member 1A (TNFRSF1A) being of particular interest.ConclusionIntegrated multilevel omics analysis in LN revealed a set of enriched pathways of potential interest for future drug investigation. A prospect for proteasome inhibition was implicated.References[1]Croca SC, Rodrigues T, Isenberg DA. Assessment of a lupus nephritis cohort over a 30-year period. Rheumatology (Oxford). 2011 Aug; 50(8):1424-1430.[2]Anders HJ, Saxena R, Zhao MH, Parodis I, Salmon JE, Mohan C. Lupus nephritis. Nat Rev Dis Primers. 2020 Jan 23; 6(1):7.[3]Wagner AH, Coffman AC, Ainscough BJ, Spies NC, Skidmore ZL, Campbell KM, et al. DGIdb 2.0: mining clinically relevant drug-gene interactions. Nucleic Acids Res. 2016 Jan 4; 44(D1):D1036-1044.[4]Barturen G, Babaei S, Català-Moll F, Martínez-Bueno M, Makowska Z, Martorell-Marugán J, et al. Integrative Analysis Reveals a Molecular Stratification of Systemic Autoimmune Diseases. Arthritis Rheumatol. 2021 Jun; 73(6):1073-1085.[5]Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017 Jan 4; 45(D1):D353-d361.[6]Jassal B, Matthews L, Viteri G, Gong C, Lorente P, Fabregat A, et al. The reactome pathway knowledgebase. Nucleic Acids Res. 2020 Jan 8; 48(D1):D498-d503.AcknowledgementsThe PRECISESADS Clinical ConsortiumDisclosure of InterestsNone declared
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| 20. |
- Toro-Dominguez, D, et al.
(författare)
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A survey of gene expression meta-analysis: methods and applications
- 2021
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Ingår i: Briefings in bioinformatics. - : Oxford University Press (OUP). - 1477-4054 .- 1467-5463. ; 22:2, s. 1694-1705
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Tidskriftsartikel (refereegranskat)abstract
- The increasing use of high-throughput gene expression quantification technologies over the last two decades and the fact that most of the published studies are stored in public databases has triggered an explosion of studies available through public repositories. All this information offers an invaluable resource for reuse to generate new knowledge and scientific findings. In this context, great interest has been focused on meta-analysis methods to integrate and jointly analyze different gene expression datasets. In this work, we describe the main steps in the gene expression meta-analysis, from data preparation to the state-of-the art statistical methods. We also analyze the main types of applications and problems that can be approached in gene expression meta-analysis studies and provide a comparative overview of the available software and bioinformatics tools. Moreover, a practical guide for choosing the most appropriate method in each case is also provided.
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