MicroRNA expression in serum samples of sulfur mustard veterans as a diagnostic gateway to improve care

<div><p>Sulfur mustard is a vesicant chemical warfare agent, which has been used during Iraq-Iran-war. Many veterans and civilians still suffer from long-term complications of sulfur mustard exposure, especially in their lung. Although the lung lesions of these patients are similar to Chronic Obstructive Pulmonary Disease (COPD), there are some differences due to different etiology and clinical care. Less is known on the molecular mechanism of sulfur mustard patients and specific treatment options. microRNAs are master regulators of many biological pathways and proofed to be stable surrogate markers in body fluids. Based on that microRNA expression for serum samples of sulfur mustard patients were examined, to establish specific microRNA patterns as a basis for diagnostic use and insight into affected molecular pathways. Patients were categorized based on their long-term complications into three groups and microRNA serum levels were measured. The differentially regulated microRNAs and their corresponding gene targets were identified. Cell cycle arrest, ageing and TGF-beta signaling pathways showed up to be the most deregulated pathways. The candidate microRNA miR-143-3p could be validated on all individual patients. In a ROC analysis miR-143-3p turned out to be a suitable diagnostic biomarker in the mild and severe categories of patients. Further microRNAs which might own a link to the biology of the sulfur mustard patients are miR-365a-3p, miR-200a-3p, miR-663a. miR-148a-3p, which showed up only in a validation study, might be linked to the airway complications of the sulfur mustard patients. All the other candidate microRNAs do not directly link to COPD phenotype or lung complications. In summary the microRNA screening study characterizes several molecular differences in-between the clinical categories of the sulfur mustard exposure groups and established some useful microRNA biomarkers. qPCR raw data is available via the Gene Expression Omnibus <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE110797" target="_blank">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE110797</a>.</p></div

Graphical abstract.

<p>Graphical abstract.</p

Validation of miR-143-3p expression.

<p>(A) Increased expression of miR-143-3p in either 'mild' and 'severe' group. miR-143-3p expression was up-regulated in both mild and severe groups with a fold change of 3.9 and 7.0 respectively (p = 0.005 and p = 0.1*10⁻⁶). The graph shows the mean values of all validated patients. The number of patients in the individual validation is slightly different from the number of patients in the pool samples. On the y axis the fold change is denoted. The star on top of the horizontal brackets indicate a significant difference. n is giving the sample number of all individually validated patients. The standard deviation is given by the top indicators. (B) The receiver-operator characteristic curve for miR-143-3p suggests this microRNA for being a suitable biomarker. miR-143-3p is able to discriminate SMV patients from control samples by an AUC of 0.87 (p = 0.0004). The x and y axis denote the percentage values of the performance parameters 'specificity' and 'sensitivity' over the full range from 0 to 100 percent.</p

The 9 top affected cellular pathways influenced from the 29 microRNAs of the normal-severe comparison.

<p>The 9 top affected cellular pathways influenced from the 29 microRNAs of the normal-severe comparison.</p

Comparative differential microRNA analysis workflow including sampling controls.

<p>The panel (<b>A</b>) shows that two different analysis approaches were applied. On the left side the IPC and delta Ct method followed by linear model / Bayes based differential analysis was performed while on the right side a pure linear model / Bayes approach was used based on the results of the pilot analysis. The relevance of both approaches was tested by a resampling approach. The right branch was finally chosen and basis for the discussion. In panel (<b>B</b>) the results of all the alternative tests on either the normal-mild or the normal-severe comparisons are shown. The results on the right side indicate that in the normal-mild comparison 15 and in the normal-severe comparison 29 microRNAs are stable on a 5% significance level after applying the resampling control. The numbers on white background indicate the remaining candidates after the intersections were performed. The result denotes a good consistency between left and right procedure.</p

The 9 top affected cellular pathways influenced from the 15 microRNAs of the normal-mild comparison.

<p>The 9 top affected cellular pathways influenced from the 15 microRNAs of the normal-mild comparison.</p

Box plots of raw data microRNA expression values and controls.

<p>On the x axis the different measurement groups for each experiment are shown: 'control' stands for reference genes including miR-103a-3p, miR-423-5p and miR-191-5p. 'control2' denotes non-miRNA coding reference genes. 'IPC' lists the inter plate calibrators. 'targets' comprises all the measured individual microRNAs. As can be seen in this figure the distribution of the target genes is nearly consistent in all tested samples even on the raw data level. The y axis denotes the Ct values.</p

Differential microRNAs (15) of the normal-mild group.

<p>The values shown here are from the right part of the workflow in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0194530#pone.0194530.g002" target="_blank">Fig 2</a>. dCt: delta Ct, FC: fold change and sampling p: sampling p value which was finally considered.</p

Differential microRNAs (29) of the normal-severe group.

<p>The values shown here are from the right part of the workflow in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0194530#pone.0194530.g002" target="_blank">Fig 2</a>. dCt: delta Ct, FC: fold change and sampling p: sampling p value which was finally considered.</p

The 9 top affected cellular pathways influenced from the 9 microRNAs of the mild-severe intersection.

<p>The 9 top affected cellular pathways influenced from the 9 microRNAs of the mild-severe intersection.</p