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  1. Article ; Online: Examination of the shared genetic architecture between multiple sclerosis and systemic lupus erythematosus facilitates discovery of novel lupus risk loci.

    Kerns, Sophia / Owen, Katherine A / Schwalbe, Dana / Grammer, Amrie C / Lipsky, Peter E

    Human genetics

    2024  

    Abstract: Systemic Lupus Erythematosus (SLE) is an autoimmune disease with heterogeneous manifestations, including neurological and psychiatric symptoms. Genetic association studies in SLE have been hampered by insufficient sample size and limited power compared ... ...

    Abstract Systemic Lupus Erythematosus (SLE) is an autoimmune disease with heterogeneous manifestations, including neurological and psychiatric symptoms. Genetic association studies in SLE have been hampered by insufficient sample size and limited power compared to many other diseases. Multiple Sclerosis (MS) is a chronic relapsing autoimmune disease of the central nervous system (CNS) that also manifests neurological and immunological features. Here, we identify a method of leveraging large-scale genome wide association studies (GWAS) in MS to identify novel genetic risk loci in SLE. Statistical genetic comparison methods including linkage disequilibrium score regression (LDSC) and cross-phenotype association analysis (CPASSOC) to identify genetic overlap in disease pathophysiology, traditional 2-sample and novel PPI-based mendelian randomization to identify causal associations and Bayesian colocalization were applied to association studies conducted in MS to facilitate discovery in the smaller, more limited datasets available for SLE. Pathway analysis using SNP-to-gene mapping identified biological networks composed of molecular pathways with causal implications for CNS disease in SLE specifically, as well as pathways likely causal of both pathologies, providing key insights for therapeutic selection.
    Language English
    Publishing date 2024-04-12
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 223009-4
    ISSN 1432-1203 ; 0340-6717
    ISSN (online) 1432-1203
    ISSN 0340-6717
    DOI 10.1007/s00439-024-02672-3
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  2. Article ; Online: Classification of COVID-19 Patients into Clinically Relevant Subsets by a Novel Machine Learning Pipeline Using Transcriptomic Features.

    Daamen, Andrea R / Bachali, Prathyusha / Grammer, Amrie C / Lipsky, Peter E

    International journal of molecular sciences

    2023  Volume 24, Issue 5

    Abstract: The persistent impact of the COVID-19 pandemic and heterogeneity in disease manifestations point to a need for innovative approaches to identify drivers of immune pathology and predict whether infected patients will present with mild/moderate or severe ... ...

    Abstract The persistent impact of the COVID-19 pandemic and heterogeneity in disease manifestations point to a need for innovative approaches to identify drivers of immune pathology and predict whether infected patients will present with mild/moderate or severe disease. We have developed a novel iterative machine learning pipeline that utilizes gene enrichment profiles from blood transcriptome data to stratify COVID-19 patients based on disease severity and differentiate severe COVID cases from other patients with acute hypoxic respiratory failure. The pattern of gene module enrichment in COVID-19 patients overall reflected broad cellular expansion and metabolic dysfunction, whereas increased neutrophils, activated B cells, T-cell lymphopenia, and proinflammatory cytokine production were specific to severe COVID patients. Using this pipeline, we also identified small blood gene signatures indicative of COVID-19 diagnosis and severity that could be used as biomarker panels in the clinical setting.
    MeSH term(s) Humans ; COVID-19 ; Transcriptome ; SARS-CoV-2 ; Pandemics ; COVID-19 Testing ; Machine Learning
    Language English
    Publishing date 2023-03-03
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2019364-6
    ISSN 1422-0067 ; 1422-0067 ; 1661-6596
    ISSN (online) 1422-0067
    ISSN 1422-0067 ; 1661-6596
    DOI 10.3390/ijms24054905
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  3. Article ; Online: An interpretable machine learning pipeline based on transcriptomics predicts phenotypes of lupus patients.

    Leventhal, Emily L / Daamen, Andrea R / Grammer, Amrie C / Lipsky, Peter E

    iScience

    2023  Volume 26, Issue 10, Page(s) 108042

    Abstract: Machine learning (ML) has the potential to identify subsets of patients with distinct phenotypes from gene expression data. However, phenotype prediction using ML has often relied on identifying important genes without a systems biology context. To ... ...

    Abstract Machine learning (ML) has the potential to identify subsets of patients with distinct phenotypes from gene expression data. However, phenotype prediction using ML has often relied on identifying important genes without a systems biology context. To address this, we created an interpretable ML approach based on blood transcriptomics to predict phenotype in systemic lupus erythematosus (SLE), a heterogeneous autoimmune disease. We employed a sequential grouped feature importance algorithm to assess the performance of gene sets, including immune and metabolic pathways and cell types, known to be abnormal in SLE in predicting disease activity and organ involvement. Gene sets related to interferon, tumor necrosis factor, the mitoribosome, and T cell activation were the best predictors of phenotype with excellent performance. These results suggest potential relationships between the molecular pathways identified in each model and manifestations of SLE. This ML approach to phenotype prediction can be applied to other diseases and tissues.
    Language English
    Publishing date 2023-09-25
    Publishing country United States
    Document type Journal Article
    ISSN 2589-0042
    ISSN (online) 2589-0042
    DOI 10.1016/j.isci.2023.108042
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  4. Article ; Online: Publisher Correction: Analysis of transcriptomic features reveals molecular endotypes of SLE with clinical implications.

    Hubbard, Erika L / Bachali, Prathyusha / Kingsmore, Kathryn M / He, Yisha / Catalina, Michelle D / Grammer, Amrie C / Lipsky, Peter E

    Genome medicine

    2023  Volume 15, Issue 1, Page(s) 113

    Language English
    Publishing date 2023-12-13
    Publishing country England
    Document type Published Erratum
    ZDB-ID 2484394-5
    ISSN 1756-994X ; 1756-994X
    ISSN (online) 1756-994X
    ISSN 1756-994X
    DOI 10.1186/s13073-023-01251-x
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  5. Article ; Online: Single-cell RNA sequencing analysis reveals the heterogeneity of IL-10 producing regulatory B cells in lupus-prone mice.

    Daamen, Andrea R / Alajoleen, Razan M / Grammer, Amrie C / Luo, Xin M / Lipsky, Peter E

    Frontiers in immunology

    2023  Volume 14, Page(s) 1282770

    Abstract: Introduction: B cells can have both pathogenic and protective roles in autoimmune diseases, including systemic lupus erythematosus (SLE). Deficiencies in the number or immunosuppressive function of IL-10 producing regulatory B cells (Bregs) can cause ... ...

    Abstract Introduction: B cells can have both pathogenic and protective roles in autoimmune diseases, including systemic lupus erythematosus (SLE). Deficiencies in the number or immunosuppressive function of IL-10 producing regulatory B cells (Bregs) can cause exacerbated autoimmune inflammation. However, the exact role of Bregs in lupus pathogenesis has not been elucidated.
    Methods: We carried out gene expression analysis by scRNA-seq to characterize differences in splenic Breg subsets and molecular profiles through stages of disease progression in lupus-prone mice. Transcriptome-based changes in Bregs from mice with active disease were confirmed by phenotypic analysis.
    Results: We found that a loss of marginal zone (MZ) lineage Bregs, an increase in plasmablast/plasma cell (PB-PC) lineage Bregs, and overall increases in inflammatory gene signatures were characteristic of active disease as compared to Bregs from the pre-disease stage. However, the frequencies of both MZ Bregs and PB-PCs expressing IL-10 were significantly decreased in active-disease mice.
    Conclusion: Overall, we have identified changes to the repertoire and transcriptional landscape of Breg subsets associated with active disease that provide insights into the role of Bregs in lupus pathogenesis. These results could inform the design of Breg-targeted therapies and interventions to restore Breg suppressive function in autoimmunity.
    MeSH term(s) Animals ; Mice ; Autoimmune Diseases ; B-Lymphocytes, Regulatory ; Interleukin-10/genetics ; Interleukin-10/metabolism ; Lupus Erythematosus, Systemic/genetics ; Sequence Analysis, RNA
    Chemical Substances Interleukin-10 (130068-27-8) ; IL10 protein, mouse
    Language English
    Publishing date 2023-12-14
    Publishing country Switzerland
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ZDB-ID 2606827-8
    ISSN 1664-3224 ; 1664-3224
    ISSN (online) 1664-3224
    ISSN 1664-3224
    DOI 10.3389/fimmu.2023.1282770
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  6. Article ; Online: Deconvoluting the heterogeneity of SLE: The contribution of ancestry.

    Owen, Katherine A / Grammer, Amrie C / Lipsky, Peter E

    The Journal of allergy and clinical immunology

    2021  Volume 149, Issue 1, Page(s) 12–23

    Abstract: Systemic lupus erythematosus (SLE) is a multiorgan autoimmune disorder with a prominent genetic component. Evidence has shown that individuals of non-European ancestry experience the disease more severely, exhibiting an increased incidence of ... ...

    Abstract Systemic lupus erythematosus (SLE) is a multiorgan autoimmune disorder with a prominent genetic component. Evidence has shown that individuals of non-European ancestry experience the disease more severely, exhibiting an increased incidence of cardiovascular disease, renal involvement, and tissue damage compared with European ancestry populations. Furthermore, there seems to be variability in the response of individuals within different ancestral groups to standard medications, including cyclophosphamide, mycophenolate, rituximab, and belimumab. Although the widespread application of candidate gene, Immunochip, and genome-wide association studies has contributed to our understanding of the link between genetic variation (typically single nucleotide polymorphisms) and SLE, despite decades of research it is still unclear why ancestry remains a key determinant of poorer outcome in non-European-ancestry patients with SLE. Here, we will discuss the impact of ancestry on SLE disease burden in patients from diverse backgrounds and highlight how research efforts using novel bioinformatic and pathway-based approaches have begun to disentangle the complex genetic architecture linking ancestry to SLE susceptibility. Finally, we will illustrate how genomic and gene expression analyses can be combined to help identify novel molecular pathways and drug candidates that might uniquely impact SLE among different ancestral populations.
    MeSH term(s) Animals ; Environment ; Epigenesis, Genetic ; Genetic Predisposition to Disease ; Genomics ; Humans ; Lupus Erythematosus, Systemic/genetics ; Lupus Erythematosus, Systemic/therapy
    Language English
    Publishing date 2021-11-29
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 121011-7
    ISSN 1097-6825 ; 1085-8725 ; 0091-6749
    ISSN (online) 1097-6825 ; 1085-8725
    ISSN 0091-6749
    DOI 10.1016/j.jaci.2021.11.005
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  7. Article ; Online: Transcriptomics data: pointing the way to subclassification and personalized medicine in systemic lupus erythematosus.

    Hubbard, Erika L / Grammer, Amrie C / Lipsky, Peter E

    Current opinion in rheumatology

    2021  Volume 33, Issue 6, Page(s) 579–585

    Abstract: Purpose of review: To summarize recent studies stratifying SLE patients into subgroups based on gene expression profiling and suggest future improvements for employing transcriptomic data to foster precision medicine.: Recent findings: Bioinformatic & ...

    Abstract Purpose of review: To summarize recent studies stratifying SLE patients into subgroups based on gene expression profiling and suggest future improvements for employing transcriptomic data to foster precision medicine.
    Recent findings: Bioinformatic & machine learning pipelines have been employed to dissect the transcriptomic heterogeneity of lupus patients and identify more homogenous subgroups. Some examples include the use of unsupervised random forest and k-means clustering to separate adult SLE patients into seven clusters and hierarchical clustering of single-cell RNA-sequencing (scRNA-seq) of immune cells yielding four clusters in a cohort of adult SLE and pediatric SLE participants. Random forest classification of bulk RNA-seq data from sorted blood cells enabled prediction of high or low disease activity in European and Asian SLE patients. Inferred transcription factor activity stratified adult and pediatric SLE into two subgroups.
    Summary: Several different endotypes of SLE patients with differing molecular profiles have been reported but a global consensus of clinically actionable groups has not been reached. Moreover, heterogeneity between datasets, reproducibility of predictions as well as the most effective classification approach have not been resolved. Nevertheless, gene expression-based precision medicine remains an attractive option to subset lupus patients.
    MeSH term(s) Gene Expression Profiling ; Humans ; Lupus Erythematosus, Systemic/genetics ; Precision Medicine ; Reproducibility of Results ; Transcriptome
    Language English
    Publishing date 2021-08-19
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 1045317-9
    ISSN 1531-6963 ; 1040-8711
    ISSN (online) 1531-6963
    ISSN 1040-8711
    DOI 10.1097/BOR.0000000000000833
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  8. Article ; Online: Publisher Correction

    Erika L. Hubbard / Prathyusha Bachali / Kathryn M. Kingsmore / Yisha He / Michelle D. Catalina / Amrie C. Grammer / Peter E. Lipsky

    Genome Medicine, Vol 15, Iss 1, Pp 1-

    Analysis of transcriptomic features reveals molecular endotypes of SLE with clinical implications

    2023  Volume 4

    Keywords Medicine ; R ; Genetics ; QH426-470
    Language English
    Publishing date 2023-12-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Machine learning reveals distinct gene signature profiles in lesional and nonlesional regions of inflammatory skin diseases.

    Martínez, Brittany A / Shrotri, Sneha / Kingsmore, Kathryn M / Bachali, Prathyusha / Grammer, Amrie C / Lipsky, Peter E

    Science advances

    2022  Volume 8, Issue 17, Page(s) eabn4776

    Abstract: Analysis of gene expression from cutaneous lupus erythematosus, psoriasis, atopic dermatitis, and systemic sclerosis using gene set variation analysis (GSVA) revealed that lesional samples from each condition had unique features, but all four diseases ... ...

    Abstract Analysis of gene expression from cutaneous lupus erythematosus, psoriasis, atopic dermatitis, and systemic sclerosis using gene set variation analysis (GSVA) revealed that lesional samples from each condition had unique features, but all four diseases displayed common enrichment in multiple inflammatory signatures. These findings were confirmed by both classification and regression tree analysis and machine learning (ML) models. Nonlesional samples from each disease also differed from normal samples and each other by ML. Notably, the features used in classification of nonlesional disease were more distinct than their lesional counterparts, and GSVA confirmed unique features of nonlesional disease. These data show that lesional and nonlesional skin samples from inflammatory skin diseases have unique profiles of gene expression abnormalities, especially in nonlesional skin, and suggest a model in which disease-specific abnormalities in "prelesional" skin may permit environmental stimuli to trigger inflammatory responses leading to both the unique and shared manifestations of each disease.
    MeSH term(s) Dermatitis, Atopic/genetics ; Dermatitis, Atopic/metabolism ; Humans ; Machine Learning ; Psoriasis/genetics ; Psoriasis/metabolism ; Skin/metabolism
    Language English
    Publishing date 2022-04-29
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2810933-8
    ISSN 2375-2548 ; 2375-2548
    ISSN (online) 2375-2548
    ISSN 2375-2548
    DOI 10.1126/sciadv.abn4776
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  10. Article ; Online: An introduction to machine learning and analysis of its use in rheumatic diseases.

    Kingsmore, Kathryn M / Puglisi, Christopher E / Grammer, Amrie C / Lipsky, Peter E

    Nature reviews. Rheumatology

    2021  Volume 17, Issue 12, Page(s) 710–730

    Abstract: Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing ... ...

    Abstract Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing relationships in the data that were not previously appreciated. As such, the use of ML in rheumatology is increasing, and numerous studies have employed ML to classify patients with rheumatic autoimmune inflammatory diseases (RAIDs) from medical records and imaging, biometric or gene expression data. However, these studies are limited by sample size, the accuracy of sample labelling, and absence of datasets for external validation. In addition, there is potential for ML models to overfit or underfit the data and, thereby, these models might produce results that cannot be replicated in an unrelated dataset. In this Review, we introduce the basic principles of ML and discuss its current strengths and weaknesses in the classification of patients with RAIDs. Moreover, we highlight the successful analysis of the same type of input data (for example, medical records) with different algorithms, illustrating the potential plasticity of this analytical approach. Altogether, a better understanding of ML and the future application of advanced analytical techniques based on this approach, coupled with the increasing availability of biomedical data, may facilitate the development of meaningful precision medicine for patients with RAIDs.
    MeSH term(s) Humans ; Machine Learning ; Rheumatic Diseases/therapy
    Language English
    Publishing date 2021-11-02
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 2491532-4
    ISSN 1759-4804 ; 1759-4790
    ISSN (online) 1759-4804
    ISSN 1759-4790
    DOI 10.1038/s41584-021-00708-w
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