Predicting glucocorticoid resistance in multiple sclerosis relapse via a whole blood transcriptomic analysis

Differential gene expression in B cells and T helper cells following high-dose glucocorticoid therapy for multiple sclerosis relapse

BACKGROUND

Despite remarkable advances in the therapy of multiple sclerosis (MS), patients with MS may still experience relapses. High-dose short-term methylprednisolone (MP) remains the standard treatment in the acute management of MS relapses due to its potent anti-inflammatory and immunosuppressive properties. However, there is a lack of studies on the cell type-specific transcriptome changes that are induced by this synthetic glucocorticoid (GC). Moreover, it is not well understood why some patients do not benefit adequately from MP therapy.

METHODS

We collected peripheral blood from MS patients in relapse immediately before and after ∼3-5 days of therapy with MP at 4 study centers. CD19+ B cells and CD4+ T cells were then isolated for profiling the transcriptome with high-density arrays. The patients' improvement of neurological symptoms was evaluated after ∼2 weeks by the treating physicians. We finally analyzed the data to identify genes that were differentially expressed in response to the therapy and whose expression differed between clinical responders and non-responders.

RESULTS

After MP treatment, a total of 33 genes in B cells and 55 genes in T helper cells were significantly up- or downregulated. The gene lists overlap in 10 genes and contain genes that have already been described as GC-responsive genes in the literature on other cell types and diseases. Their differential expression points to a rapid and coordinated modulation of multiple signaling pathways that influence transcription. Genes that were previously suggested as potential prognostic biomarkers of the clinical response to MP therapy could not be confirmed in our data. However, a greater increase in the expression of genes encoding proteins with antimicrobial activity was detected in CD4+ T cells from non-responders compared to responders.

CONCLUSION

Our study delved into the cell type-specific effects of MP at the transcriptional level. The data suggest a therapy-induced ectopic expression of some genes (e.g., AZU1, ELANE and MPO), especially in non-responders. The biological consequences of this remain to be explored in greater depth. A better understanding of the molecular mechanisms underlying clinical recovery from relapses in patients with MS will help to optimize future treatment decisions.

Predicting glucocorticoid resistance in multiple sclerosis relapse via a whole blood transcriptomic analysis

AIMS

Treatment of multiple sclerosis (MS) relapses consists of short-term administration of high-dose glucocorticoids (GCs). However, over 40% of patients show an insufficient response to GC treatment. We aimed to develop a predictive model for such GC resistance.

METHODS

We performed a receiver operating characteristic (ROC) curve analysis following the transcriptomic assay of whole blood samples from stable, relapsing GC-sensitive and relapsing GC-resistant patients with MS in two different European centers.

RESULTS

We identified 12 genes being regulated during a relapse and differentially expressed between GC-sensitive and GC-resistant patients with MS. Using these genes, we defined a statistical model to predict GC resistance with an area under the curve (AUC) of the ROC analysis of 0.913. Furthermore, we observed that relapsing GC-resistant patients with MS have decreased GR, DUSP1, and TSC22D3 mRNA levels compared with relapsing GC-sensitive patients with MS. Finally, we showed that the transcriptome of relapsing GC-resistant patients with MS resembles those of stable patients with MS.

CONCLUSION

Predicting GC resistance would allow patients to benefit from prompt initiation of an alternative relapse treatment leading to increased treatment efficacy. Thus, we think our model could contribute to reducing disability development in people with MS.