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Erythrocyte microRNA sequencing reveals differential expression in relapsing-remitting multiple sclerosis

Overview of attention for article published in BMC Medical Genomics, May 2018
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  • Good Attention Score compared to outputs of the same age (69th percentile)

Mentioned by

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4 tweeters
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1 patent

Citations

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7 Dimensions

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38 Mendeley
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Title
Erythrocyte microRNA sequencing reveals differential expression in relapsing-remitting multiple sclerosis
Published in
BMC Medical Genomics, May 2018
DOI 10.1186/s12920-018-0365-7
Pubmed ID
Authors

Kira Groen, Vicki E. Maltby, Rodney A. Lea, Katherine A. Sanders, J. Lynn Fink, Rodney J. Scott, Lotti Tajouri, Jeannette Lechner-Scott

Abstract

There is a paucity of knowledge concerning erythrocytes in the aetiology of Multiple Sclerosis (MS) despite their potential to contribute to disease through impaired antioxidant capacity and altered haemorheological features. Several studies have identified an abundance of erythrocyte miRNAs and variable profiles associated with disease states, such as sickle cell disease and malaria. The aim of this study was to compare the erythrocyte miRNA profile of relapsing-remitting MS (RRMS) patients to healthy sex- and age-matched controls. Erythrocytes were purified by density-gradient centrifugation and RNA was extracted. Following library preparation, samples were run on a HiSeq4000 Illumina instrument (paired-end 100 bp sequencing). Sequenced erythrocyte miRNA profiles (9 patients and 9 controls) were analysed by DESeq2. Differentially expressed miRNAs were validated by RT-qPCR using miR-152-3p as an endogenous control and replicated in a larger cohort (20 patients and 18 controls). After logarithmic transformation, differential expression was determined by two-tailed unpaired t-tests. Logistic regression analysis was carried out and receiver operating characteristic (ROC) curves were generated to determine biomarker potential. A total of 236 erythrocyte miRNAs were identified. Of twelve differentially expressed miRNAs in RRMS two showed increased expression (adj. p < 0.05). Only modest fold-changes were evident across differentially expressed miRNAs. RT-qPCR confirmed differential expression of miR-30b-5p (0.61 fold, p < 0.05) and miR-3200-3p (0.36 fold, p < 0.01) in RRMS compared to healthy controls. Relative expression of miR-3200-5p (0.66 fold, NS p = 0.096) also approached significance. MiR-3200-5p was positively correlated with cognition measured by audio-recorded cognitive screen (r = 0.60; p < 0.01). MiR-3200-3p showed greatest biomarker potential as a single miRNA (accuracy = 75.5%, p < 0.01, sensitivity = 72.7%, specificity = 84.0%). Combining miR-3200-3p, miR-3200-5p, and miR-30b-5p into a composite biomarker increased accuracy to 83.0% (p < 0.05), sensitivity to 77.3%, and specificity to 88.0%. This is the first study to report differences in erythrocyte miRNAs in RRMS. While the role of miRNAs in erythrocytes remains to be elucidated, differential expression of erythrocyte miRNAs may be exploited as biomarkers and their potential contribution to MS pathology and cognition should be further investigated.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 38 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 18%
Student > Bachelor 6 16%
Student > Ph. D. Student 5 13%
Student > Master 5 13%
Lecturer 2 5%
Other 4 11%
Unknown 9 24%
Readers by discipline Count As %
Medicine and Dentistry 6 16%
Neuroscience 5 13%
Agricultural and Biological Sciences 5 13%
Biochemistry, Genetics and Molecular Biology 4 11%
Psychology 3 8%
Other 4 11%
Unknown 11 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 March 2021.
All research outputs
#4,733,124
of 18,917,096 outputs
Outputs from BMC Medical Genomics
#213
of 1,004 outputs
Outputs of similar age
#87,706
of 292,793 outputs
Outputs of similar age from BMC Medical Genomics
#1
of 1 outputs
Altmetric has tracked 18,917,096 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 1,004 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 292,793 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them