The rheumatoid arthritis gene expression signature among women who improve or worsen during pregnancy – a pilot study

2020 ◽  
pp. jrheum.201128
Author(s):  
Amogh Pathi ◽  
Matthew Wright ◽  
Mette Kiel Smed ◽  
J. Lee Nelson ◽  
Jørn Olsen ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Disease Activity ◽  
Differential Expression ◽  
Differential Expression Analysis ◽  
Gene Expression Signature ◽  
Differentially Expressed ◽  
Expression Signature ◽  
Healthy Women ◽  
Gene Expression Signatures

Objective To assess whether gene expression signatures associated with rheumatoid arthritis (RA) before pregnancy differ between women who improve or worsen during pregnancy, and determine whether these expression signatures are altered during pregnancy when RA improves or worsens. Methods Clinical data and blood samples were collected before pregnancy (T0) and at the third trimester (T3) from 11 RA and 5 healthy women. RA disease activity was assessed using the Clinical Disease Activity Index (CDAI). At each time-point, RA-associated gene expression signatures were identified using differential expression analysis of RNA sequencing profiles between RA and healthy women. Results Of the women with RA, 6 improved by T3 (RAimproved), 3 worsened (RAworsened) and 2 were excluded. At T0, mean CDAI scores were similar in both groups (RAimproved: 11.2±9.8; RAworsened: 13.8±6.7; Wilcoxon-rank test: p=0.6). In the RAimproved group, 89 genes were differentially expressed at T0 (q<0.05 and fold-change (FC)≥2) compared to healthy women. When RA improved at T3, 65 of 89 (73%) of these no longer displayed RA-associated expression. In the RAworsened group, a largely different RA gene expression signature (429 genes) was identified at T0. When RA disease activity worsened at T3, 207 of 429 (48%) lost their differential expression, while an additional 157 genes became newly differentially expressed. Conclusion In our pilot dataset, pre-pregnancy RA expression signatures differed between women who subsequently improved or worsened during pregnancy, suggesting that inherent genomic differences perhaps influence how pregnancy impacts disease activity. Further, these RA signatures were altered during pregnancy, as disease activity changed.

scholarly journals Is gene expression among women with rheumatoid arthritis dysregulated during a postpartum flare?

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Matthew Wright ◽  
Mette K. Smed ◽  
J. Lee Nelson ◽  
Jørn Olsen ◽  
Merete L. Hetland ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Disease Activity ◽  
Activity Index ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Time Frame ◽  
Differentially Expressed ◽  
Healthy Women ◽  
Immune Related Genes

Abstract Background To evaluate our hypotheses that, when rheumatoid arthritis (RA) flares postpartum, gene expression patterns are altered compared to (a) healthy women, (b) RA women whose disease activity is low or in remission postpartum, and (c) pre-pregnancy expression profiles. Methods Twelve women with RA and five healthy women were included in this pilot study. RA disease activity and postpartum flare were assessed using the Clinical Disease Activity Index (CDAI). Total RNA from frozen whole blood was used for RNA sequencing. Differential gene expression within the same women (within-group) over time, i.e., postpartum vs. third trimester (T3) or pre-pregnancy (T0), were examined, using a significance threshold of q < 0.05 and fold-change ≥ 2. Results Nine of the women with RA experienced a flare postpartum (RAFlare), while three had low disease activity or were in remission (RANoFlare) during that time frame. Numerous immune-related genes were differentially expressed postpartum (vs. T3) during a flare. Fold-changes in expression from T3 to postpartum were mostly comparable between the RAFlare and healthy groups. At 3 months postpartum, compared to healthy women, several genes were significantly differentially expressed only among the RAFlare women, and not among the RANoFlare women. Some of these genes were among those whose “normal” expression was significantly modulated postpartum, and the postpartum expression patterns were significantly altered during the RA flare. There were also some genes that were significantly differentially expressed in RAFlare compared to both healthy and RANoFlare women, even though their expression was not significantly modulated postpartum. Furthermore, while postpartum expression profiles were similar to those at pre-pregnancy among healthy women, significant differences were found between those time points among the RAFlare women. Conclusions The large majority of gene expression changes between T3 and 3 months postpartum among RA women who flared postpartum reflected normal postpartum changes also seen among healthy women. Nonetheless, during a postpartum flare, a set of immune-related genes showed dysregulated expression compared to healthy women and women with RA whose disease activity was low or in remission during the same time frame, while other genes demonstrated significant differences in expression compared to RA pre-pregnancy levels.

scholarly journals Sex differences in the human brain transcriptome of cases with schizophrenia

2020 ◽  
Author(s):  
Gabriel E. Hoffman ◽  
Yixuan Ma ◽  
Kelsey S. Montgomery ◽  
Jaroslav Bendl ◽  
Manoj Kumar Jaiswal ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sex Differences ◽  
Differential Expression ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Age Of Onset ◽  
Differential Expression Analysis ◽  
Gene Expression Signature ◽  
Synaptic Organization ◽  
Expression Signature

AbstractWhile schizophrenia differs between males and females in age of onset, symptomatology and the course of the disease, the molecular mechanisms underlying these differences remain uncharacterized. In order to address questions about the sex-specific effects of schizophrenia, we performed a large-scale transcriptome analysis of RNA-seq data from 437 controls and 341 cases from two distinct cohorts from the CommonMind Consortium. Analysis across the cohorts identifies a reproducible gene expression signature of schizophrenia that is highly concordant with previous work. Differential expression across sex is reproducible across cohorts and identifies X- and Y-linked genes, as well as those involved in dosage compensation. Intriguingly, the sex expression signature is also enriched for genes involved in neurexin family protein binding and synaptic organization. Differential expression analysis testing a sex-by-diagnosis interaction effect did not identify any genome-wide signature after multiple testing corrections. Gene coexpression network analysis was performed to reduce dimensionality and elucidate interactions among genes. We found enrichment of co-expression modules for sex-by-diagnosis differential expression signatures, which were highly reproducible across the two cohorts and involve a number of diverse pathways, including neural nucleus development, neuron projection morphogenesis, and regulation of neural precursor cell proliferation. Overall, our results indicate that the effect size of sex differences in schizophrenia gene expression signatures is small and underscore the challenge of identifying robust sex-by-diagnosis signatures, which will require future analyses in larger cohorts.

ROR1 Negative Chronic Lymphocytic Leukemia (CLL) Have a Distinctive Gene Expression Signature and May Represent an Indolent-Disease Subtype

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 2932-2932 ◽  
Author(s):  
Emanuela M. Ghia ◽  
Laura Z. Rassenti ◽  
Liguang Chen ◽  
Bing Cui ◽  
Christopher Deboever ◽  
...  
Keyword(s):  
Gene Expression ◽  
Chronic Lymphocytic Leukemia ◽  
Transgenic Mice ◽  
Gene Expression Signature ◽  
Lymphocytic Leukemia ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Orphan Receptor ◽  
Expression Signature ◽  
Genes Encoding

Abstract ROR1 is a type-1 tyrosine kinase-like orphan-receptor that ordinarily is expressed during embryogenesis, but that also is found on leukemia cells of patients (pts) with chronic lymphocytic leukemia (CLL). We identified patients with CLL cells that had negligible expression of ROR1, despite otherwise satisfying all standard criteria for diagnosis of CLL by iwCLL criteria. We performed next-generation-sequencing on the transcriptomes of 12 CLL cases that had negligible expression of ROR1 and 12 cases that expressed levels of ROR1 comparable to that typically observed in CLL. Eight of the 12 ROR1-negative cases expressed unmutated immunoglobulin heavy-chain variable region genes (IGHV) and 4 used mutated IGHV. Similarly, 7 of the 12 ROR1-positive cases used unmutated IGHV and 5 had mutated IGHV. We identified 3,094 genes that were differentially expressed between the ROR1-positive and ROR1-negative samples out of 14,761 protein-coding genes tested (DESeq2, BH-adjusted p < 0.05). Subnetwork analyses revealed 55 subnetworks that were differentially expressed between ROR1-positive and ROR1-negative cases. ROR1-positive CLL cells had higher-level expression of subnetworks associated with protein-kinase activation or proliferation of tumor cells, but lower-level expression of subnetworks associated with induction of apoptosis or RNA degradation and/or processing, than did ROR1-negative CLL cells. ROR1 and AKT1 were included in 7 subnetworks associated with proliferation, hematologic cancer, or inhibition of cell death. Fourteen (25%) of the 55 differentially expressed subnetworks previously were identified as being differentially expressed between ROR1-positve leukemias of ROR1xTCL1 transgenic mice and ROR1-negative leukemias of Eµ-TCL1-transgenic mice (see Widhopf et al, Proc Natl Acad Sci USA, 2014, PMC3896194). Gene-set enrichment analysis (GSEA) of genes encoding proteins involved in targeted signaling pathways in the BIOCARTA and Reactome database revealed that the ROR1+ leukemias had higher expression levels of genes encoding proteins in the AKT pathway than did the ROR1-negative cases. Immunoblot analysis revealed higher levels of activated pAKT relative to total AKT in representative cases of ROR1-positive CLL (8.8 ± 2.8, N = 7) than that detected in ROR1-negative CLL samples (1.0 ± 0.4, N = 4, P<0.01) (the ratios of pAKT/AKT were normalized to the mean ratio observed for ROR1-negative CLL samples); this is comparable to what we observed for ROR1-positive leukemias of ROR1xTCL1 mice, which had higher levels of activated AKT than the ROR1-negative leukemias of Eµ-TCL1 transgenic mice (Widhopf et al, Proc Natl Acad Sci USA, 2014, PMC3896194). Despite the small size of these two cohorts, it is noteworthy that the median time from diagnosis to initial therapy of the 12 patients with ROR1-negative CLL (9.4 years) was significantly longer than that noted for the 12 ROR1-positive CLL cases (2.5 years, (p < 0.01) used in this comparative analysis. In summary, this study describes a potentially new subtype of ROR1-negative CLL that has a distinctive gene expression signature and apparently indolent clinical course. Disclosures Kipps: Pharmacyclics Abbvie Celgene Genentech Astra Zeneca Gilead Sciences: Other: Advisor.

scholarly journals A robust gene expression signature to predict proteasome inhibitor benefit in Multiple Myeloma

2019 ◽  
Author(s):  
Joske Ubels ◽  
Pieter Sonneveld ◽  
Martin H. van Vliet ◽  
Jeroen de Ridder
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Proteasome Inhibitor ◽  
Expression Profiles ◽  
Specific Treatment ◽  
Gene Expression Signature ◽  
Hazard Ratio ◽  
Treatment Benefit ◽  
Expression Signature ◽  
Gene Expression Signatures

AbstractMany cancer drugs only benefit a subset of the patients that receive them, but are often associated with serious side effects. Predictive classification methods that can identify which patients will benefit from a specific treatment are therefore of great clinical utility. We here introduce a novel machine learning method to identify predictive gene expression signatures, based on the idea that patients who received different treatments but exhibit similar expression profiles can be used to model response to the alternative treatment. We use this method to predict proteasome inhibitor benefit in Multiple Myeloma (MM). In a dataset of 910 MM patients we identify a 14-gene expression signature that can successfully predict benefit to the proteasome inhibitor bortezomib, with a hazard ratio of 0.47 (p = 0.04) in class ‘benefit’, while in class ‘no benefit’ the hazard ratio is 0.91 (p = 0.68). Importantly, we observe a similar classification performance (HR class benefit = 0.46, p = 0.04) in an independent patient cohort which was moreover measured on a different platform, demonstrating the robustness of the signature. Moreover, we find that the genes in the discovered signature are essential, as no equivalent signature can be found when they are excluded from the analysis. Multiple genes in the signature are linked to working mechanisms of proteasome inhibitors or MM disease progression. In conclusion, our method allows for identification of gene expression signatures that can aid in treatment decisions for MM patients and provide insight into the biological mechanism behind treatment benefit.

scholarly journals Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis

2020 ◽  
Author(s):  
Dmitry Rychkov ◽  
Jessica Neely ◽  
Tomiko Oskotsky ◽  
Steven Yu ◽  
Noah Perlmutter ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Machine Learning ◽  
Feature Selection ◽  
Disease Activity ◽  
Gene Expression Data ◽  
Clinical Utility ◽  
Differentially Expressed ◽  
Healthy Controls ◽  
Expression Data

AbstractBackground/PurposeThere is an urgent need to identify effective biomarkers for early diagnosis of rheumatoid arthritis (RA) and to accurately monitor disease activity. Here we define an RA meta-profile using publicly available cross-tissue gene expression data and apply machine learning to identify putative biomarkers, which we further validate on independent datasets.MethodsWe carried out a comprehensive search for publicly available microarray gene expression data in the NCBI Gene Expression Omnibus database for whole blood and synovial tissues from RA patients and healthy controls. The raw data from 13 synovium datasets with 284 samples and 14 blood datasets with 1,885 samples were downloaded and processed. The datasets for each tissue were merged, batch corrected and split into training and test sets. We then developed and applied a robust feature selection pipeline to identify genes dysregulated in both tissues and highly associated with RA. From the training data, we identified a set of overlapping differentially expressed genes following the condition of co-directionality. The classification performance of each gene in the resulting set was evaluated on the testing sets using the area under a receiver operating characteristic curve. Five independent datasets were used to validate and threshold the feature selected (FS) genes. Finally, we defined the RA Score, composed of the geometric mean of the selected RA Score Panel genes, and demonstrated its clinical utility.ResultsThis feature selection pipeline resulted in a set of 25 upregulated and 28 downregulated genes. To assess the robustness of these FS genes, we trained a Random Forest machine learning model with this set of 53 genes and then with the set of 33 overlapping genes differentially expressed in both tissues and tested on the validation cohorts. The model with FS genes outperformed the model with common DE genes with AUC 0.89 ± 0.04 vs 0.87 ± 0.04. The FS genes were further validated on the 5 independent datasets resulting in 10 upregulated genes, TNFAIP6, S100A8, TNFSF10, DRAM1, LY96, QPCT, KYNU, ENTPD1, CLIC1, and ATP6V0E1, which are involved in innate immune system pathways, including neutrophil degranulation and apoptosis. There were also three downregulated genes, HSP90AB1, NCL, and CIRBP, that are involved in metabolic processes and T-cell receptor regulation of apoptosis.To investigate the clinical utility of the 13 validated genes, the RA Score was developed and found to be highly correlated with the disease activity score based on the 28 examined joints (DAS28) (r = 0.33 ± 0.03, p = 7e-9) and able to distinguish osteoarthritis (OA) from RA samples (OR 0.57, 95% CI [0.34, 0.80], p = 8e-10). Moreover, the RA Score was not significantly different for rheumatoid factor (RF) positive and RF-negative RA sub-phenotypes (p = 0.9) and also distinguished polyarticular juvenile idiopathic arthritis (polyJIA) from healthy individuals in 10 independent pediatric cohorts (OR 1.15, 95% CI [1.01, 1.3], p = 2e-4) suggesting the generalizability of this score in clinical applications. The RA Score was also able to monitor the treatment effect among RA patients (t-test of treated vs untreated, p = 2e-4). Finally, we performed immunoblotting analysis of 6 proteins in unstimulated PBMC lysates from an independent cohort of 8 newly diagnosed RA patients and 7 healthy controls, where two proteins, TNFAIP6/TSG6 and HSP90AB1/HSP90, were validated and the S100A8 protein showed near significant up-regulation.ConclusionThe RA Score, consisting of 13 putative biomarkers identified through a robust feature selection procedure on public data and validated using multiple independent data sets, could be useful in the diagnosis and treatment monitoring of RA.

Synergistic Targeting of Protein Translation and Inhibition of NOTCH Signaling in T-ALL

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 3719-3719
Author(s):  
Marta Sanchez-Martin ◽  
Alberto Ambesi-Impiombato ◽  
Luyao Xu ◽  
Yue Qin ◽  
Daniel Herranz ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Notch Signaling ◽  
Cell Lines ◽  
Gene Expression Signature ◽  
Protein Translation ◽  
Withaferin A ◽  
Antileukemic Activity ◽  
Expression Signature ◽  
Gene Expression Signatures

Abstract Oncogenic NOTCH signaling is a major driver of T-cell transformation in T-cell acute lymphoblastic leukemia (T-ALL). However, clinical studies testing the efficacy of NOTCH1 inactivation with γ-secretase inhibitors (GSIs) have shown limited antileukemic activity for these drugs as single agents. Here we used an expression-based virtual screening approach and network perturbation analyses to identify and functionally characterize new highly active antileukemic drugs synergistic with NOTCH1 inhibition in T-ALL. Gene expression profiling studies have shown a prominent gene expression signature dominated by genes involved in growth and metabolism downstream of NOTCH1 in T-ALL. Notably, loss of the PTEN tumor suppressor gene confers resistance to GSI therapy and effectively rescues the gene expression signature induced by NOTCH1 inhibition in T-ALL. We hypothesized that drugs inducing transcriptional programs overlapping with those driven by NOTCH1 inhibition and antagonizing those resulting from PTEN loss could have synergistic antileukemic effects with GSIs in PTEN wild type and PTEN null leukemia cells. To address this question we generated gene expression signatures from Pten conditional-inducible knockout NOTCH1-driven leukemias in basal condition, upon NOTCH1 inhibition by GSI treatment and upon deletion of Pten. Connectivity Map (cMAP) analysis in this series identified 17 high scoring compounds as candidate antileukemic drugs (p<0.01). Reassuringly these included two inhibitors of the mTOR/PI3K/AKT pathway (rapamycin, wortmannin), but also histone deacetylase inhibitors (vorinostat, trichostatin A and valproic acid), phenothiazine antipsychotic drugs (trifluoperazine and thioridazine), antimalarial agents (astemizole, mefloquine) and compounds with less characterized activities such as withaferin A, parthenolide and pyrvinium pamoate. Transcriptional profiling followed by pairwise gene set enrichment analysis of these compounds identified groups of drugs with highly interconnected transcriptional programs suggestive of an overlapping mechanism of action (e.g. mTOR/PI3K inhibitors, HDAC inhibitors and phenothiazines), as well as compounds with more unique expression signatures suggestive of a more distinct mode of action (e.g. withaferin A, astemizole and mefloquine). Detailed characterization of the antileukemic effects of these 17 cMAP hits alone and in combination with the GSI DBZ in a broad panel of human NOTCH1-mutated T-ALL cell lines, identified withaferin A, rapamycin, wortmannin, parthenolide and vorinostat as the most active (lethal dose 50 <0.5 µM) and GSI-synergistic (combination index <0.4) drugs in this series. Among these, withaferin A, stood out as the most cytotoxic and GSI-synergistic compound against both PTEN positive and PTEN null T-ALL cell lines. Moreover, withaferin A treatment of primary mouse NOTCH1-induced T-ALLs and primary human T-ALL xenografts demonstrated strong and GSI-synergistic antileukemic activity in vivo. To address the mechanisms mediating the antileukemic effects of withaferin A we performed a detailed analysis of the gene expression signatures induced by this drug in T-ALL lymphoblasts. These studies revealed a strong enrichment of downregulated genes involved in translation regulation in T-ALL cells upon treatment with withaferin A (p<0.001). Mechanistically, transcriptional network perturbation analysis identified the eIF2A translation initiation complex as a potential effector of the antileukemic effects of withaferin A, and withaferin A treatment induced strong dose dependent phosphorylation of eIF2S1 in position S51, a modification responsible for blocking the activity of the eIF2A complex. Consistently, polysome profiling and nascent-protein assays revealed decreased translation in T-ALL cells treated with withaferin A. In this context, expression a phosphomimetic mutant form of eIF2S1 (S51D) impaired leukemia cell viability. Moreover, expression of a non-phosphorylatable form of eIF2S1 (eIF2S1 S51A) in T-ALL cells abrogated the antileukemic effects of withaferin A.These results support a direct role of eIF2S1 phosphorylation and the inhibition of eIF2A-dependent translation as a critical mediators of the antileukemic effects of withaferin A in T-ALL and a role for the combination of GSIs and inhibitors of protein translation for the treatment of high risk T-ALL. Disclosures Califano: Therasis Inc: Employment; Cancer Genetics Inc: Consultancy; Ipsen pharmaceuticals: Consultancy; Thermo Fischer Scientific: Consultancy.

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