Combined genetic and transcriptome analysis of patients with SLE: distinct, targetable signatures for susceptibility and severity

Panousis, Nikolaos; Βertsias, George; Ongen, Halit; Gergianaki, Irini; Tektonidou, Maria G.; Trachana, Maria; Romano-Palumbo, Luciana; Bielser, Deborah; Howald, Cédric; Pamfil, Cristina; Fanouriakis, Antonis; Kosmara, Despoina; Repa, Argyro; Sidiropoulos, Prodromos; Dermitzakis, Emmanouil T.; Boumpas, Dimitrios T.

doi:10.1136/annrheumdis-2018-214379

Cited by 100 publications

(111 citation statements)

References 54 publications

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“…Besides, considering the high cost and complex technology of single-cell sequencing, it would be a good thing to use such a simple and effective method to study immune in ltration in periodontitis before the popularization of single-cell sequencing, if the CIBERSORT can be veri ed experimentally with high accuracy. We believe this CIBERSORT is accurate enough, after all, it is widely used and veri ed in other elds [31][32][33]. Therefore, the results of this study deserve more attention.…”

Section: Discussionmentioning

confidence: 68%

Immune Landscape of Periodontitis Unveils Alterations of Infiltrating Immunocytes and Molecular Networks-Aggregating into an Interactive Web-Tool for Periodontitis Related Immune Analysis and Visualization

Zhang

Wang

Yan

et al. 2020

Preprint

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Background: Immunity and immunocyte reaction play an essential role in periodontitis progress and we aim to investigate the underlying regulatory network of periodontitis immune alterations. Methods: CIBERSORT was used to estimated immunocyte fractions in different clinical status. Logistic regression was used to assess the immunocyte weight to periodontitis. Immune-related periodontitis subtypes were identified by the NMF algorithm. GSEA and GSVA were conducted to analyze pathway activity. Immunocytes related gene modules were identified by WGCNA. Results: Altered immunocytes in healthy-vs-periodontitis, aggressive-vs-chronic, male-vs-female and age were identified. Contributing for periodontitis of immunocytes was calculated and their correlation was also done. Two distinct immune-related periodontitis subtypes were identified and one is characterized by B cell reaction and the other is IL-6 cytokine reactions. 463 statistically significant correlations between 22 immunocytes and pathways were revealed. Immunocytes and clinical phenotypes matched their gene modules, and their functions were annotated. Last, an easy-to-use and user-friendly interactive web-tool were developed for periodontitis related immune analysis and visualization (http://118.24.100.193:3838/tool-PIA/).Conclusions: This study systematically investigated periodontitis immune atlas and glimpse the underlying mechanism of periodontitis from gene-pathway-immunocyte networks, which can not only inspire researchers but also help them in periodontitis related immune researches.

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Section: Discussionmentioning

confidence: 68%

Immune Landscape of Periodontitis Unveils Alterations of Infiltrating Immunocytes and Molecular Networks-Aggregating into an Interactive Web-Tool for Periodontitis Related Immune Analysis and Visualization

Zhang

Wang

Yan

et al. 2020

Preprint

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“…36,37 Banchereau et al 38 conducted the largest microarray study in SLE, which longitudinally monitored 158 patients with juvenile SLE and uncovered markers associated with disease activity. Recently, Panousis et al 39 examined 142 patients with SLE and 58 Table 1). (a) Principal components analysis (PCA) to visualise the variation between samples (in all genes); different symbols represent individuals in each group as shown.…”

Section: Discussionmentioning

confidence: 99%

Machine learning applied to whole‐blood RNA‐sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

Figgett

Monaghan

et al. 2019

Clin & Trans Imm

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Objectives Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus, responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the computational analysis of patients’ whole‐blood transcriptomes. Methods We applied machine learning approaches to RNA‐sequencing (RNA‐seq) data sets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta‐analysis on three recently published whole‐blood RNA‐seq data sets was carried out, and an additional similar data set of 30 patients with SLE and 29 healthy donors was incorporated in this study; a total of 161 patients with SLE and 57 healthy donors were analysed. Results Examination of SLE clusters, as opposed to unstratified SLE patients, revealed underappreciated differences in the pattern of expression of disease‐related genes relative to clinical presentation. Moreover, gene signatures correlated with flare activity were successfully identified. Conclusion Given that SLE disease heterogeneity is a key challenge hindering the design of optimal clinical trials and the adequate management of patients, our approach opens a new possible avenue addressing this limitation via a greater understanding of SLE heterogeneity in humans. Stratification of patients based on gene expression signatures may be a valuable strategy allowing the identification of separate molecular mechanisms underpinning disease in SLE. Further, this approach may have a use in understanding the variability in responsiveness to therapeutics, thereby improving the design of clinical trials and advancing personalised therapy.

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“…The more fragmented expression patterns in low activity SLE genomes are suggestive of generalized perturbations in gene regulation, which could provide mechanistical explanation for the recurrent flares that tend to develop in patients who are inactive. Thus, while a desirable outcome, clinical remission may not necessarily be lacking a molecular fingerprint and the combination of the recently suggested susceptibility signature (6) with our fragmented DCE pattern may provide an interesting framework for the assessment of its stability.…”

Section: Analysis Of Dce Patterns Between Sle and Healthy Individualsmentioning

confidence: 99%

“…A recent systematic transcriptomic and genetic analysis comparing SLE patients with variable disease activity against healthy individuals, led to the definition of discrete susceptibility and severity gene signatures (6). Beyond gene expression, changes have also been observed at the epigenetic and chromatin levels, with extensive DNA hyper-hydroxymethylation in SLE T-cells (7) and altered chromatin accessibility in naive B-cells from SLE patients under flare status (8).…”

Section: Introductionmentioning

confidence: 99%

Extensive fragmentation and re-organization of gene co-expression patterns underlie the progression of Systemic Lupus Erythematosus

Panousis

Tektonidou

et al. 2020

Preprint

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Systemic Lupus Erythematosus (SLE) is the prototype of autoimmune diseases, characterized by extensive gene expression perturbations in peripheral blood immune cells. Circumstantial evidence suggests that these perturbations may be due to altered epigenetic profiles and chromatin accessibility but the relationship between transcriptional deregulation and genome organization remains largely unstudied. We developed a genomic approach that leverages patterns of gene coexpression from genome-wide transcriptome profiles in order to identify statistically robust Domains of Co-ordinated gene Expression (DCEs). By implementing this method on gene expression data from a large SLE patient cohort, we identify significant diseaseassociated alterations in gene co-regulation patterns, which also correlate with the SLE activity status. Low disease activity patient genomes are characterized by extensive fragmentation leading to DCEs of smaller size. High disease activity genomes display excessive spatial redistribution of co-expression domains with expanded and newly-appearing (emerged) DCEs. Fragmentation and redistribution of gene coexpression patterns correlate with SLE-implicated biological pathways and clinically relevant endophenotypes such as kidney involvement. Notably, genes lying at the boundaries of split DCEs of low activity genomes are enriched in the interferon and other SLE susceptibility signatures, suggesting the implication of DCE fragmentation at early disease stages. Interrogation of promoter-enhancer interactions from various immune cell subtypes shows that a significant percentage of nested connections are disrupted by a DCE split or depletion in SLE genomes. Collectively, our results underlining an important role for genome organization in shaping gene expression in SLE, could provide valuable insights into disease pathogenesis and the mechanisms underlying disease flares. Significance Although widespread gene expression changes have been reported in Systemic LupusErythematosus (SLE), attempts to link gene deregulation with genome structure have been lacking. Through a computational framework for the segmentation of gene expression data, we reveal extensive fragmentation and reorganization of gene co-regulation domains in SLE, that correlates with disease activity states. Gene co-expression domains pertaining to biological functions implicated in SLE such as the interferon pathway, are being disrupted in patients, while others associated to severe manifestations such as nephritis, emerge in previously uncorrelated regions of the genome. Our results support extensive genome re-organization underlying aberrant gene expression in SLE, which could assist in the early detection of disease flares in patients that are in remission. Graphical Abstract3

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Combined genetic and transcriptome analysis of patients with SLE: distinct, targetable signatures for susceptibility and severity

Cited by 100 publications

References 54 publications

Immune Landscape of Periodontitis Unveils Alterations of Infiltrating Immunocytes and Molecular Networks-Aggregating into an Interactive Web-Tool for Periodontitis Related Immune Analysis and Visualization

Immune Landscape of Periodontitis Unveils Alterations of Infiltrating Immunocytes and Molecular Networks-Aggregating into an Interactive Web-Tool for Periodontitis Related Immune Analysis and Visualization

Machine learning applied to whole‐blood RNA‐sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

Extensive fragmentation and re-organization of gene co-expression patterns underlie the progression of Systemic Lupus Erythematosus

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