Fig 1.
Participants were categorized into the four classes of normal, mild, moderate and severe based on the history of exposure to sulfur mustard, PFT and other clinical examinations. The samples of the screening study were randomly pooled into two biological replicates and measured.
Fig 2.
Comparative differential microRNA analysis workflow including sampling controls.
The panel (A) 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) 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.
Fig 3.
Box plots of raw data microRNA expression values and controls.
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.
Fig 4.
Overview on the density distributions.
The target microRNAs in the different patient`s groups show a very uniform distribution on the raw data level. The y axis denotes the normalized density, while the x axis shows the Ct values. Because of the smoothing effect of the density curves Ct values above 40 show up, which is artificial (maximum is 40).
Table 1.
Differential microRNAs (15) of the normal-mild group.
The values shown here are from the right part of the workflow in Fig 2. dCt: delta Ct, FC: fold change and sampling p: sampling p value which was finally considered.
Table 2.
Differential microRNAs (29) of the normal-severe group.
The values shown here are from the right part of the workflow in Fig 2. dCt: delta Ct, FC: fold change and sampling p: sampling p value which was finally considered.
Table 3.
The 9 top affected cellular pathways influenced from the 15 microRNAs of the normal-mild comparison.
Table 4.
The 9 top affected cellular pathways influenced from the 29 microRNAs of the normal-severe comparison.
Table 5.
The 9 top affected cellular pathways influenced from the 9 microRNAs of the mild-severe intersection.
Fig 5.
Validation of miR-143-3p expression.
(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.
Fig 6.
Graphical abstract.