Th those from patients, but again there was no correlation with

Th those from patients, but again there was no correlation with

Th those from patients, but again there was no correlation with the positive trypanolysis result. This suggests that the miRNA profiles we observed might not be specific to trypanosomiasis alone, but could also result from other conditions.Gene Expression ProfilingTo obtain a preliminary idea of whether mRNAs that are miRNA targets were also affected by HAT, six of the patient samples were subjected to gene expression profiling, using various pools of three CATT-negative sera as controls. In total, 656 genes were found to be significantly (p,0.05) differentially regulated HIV-RT inhibitor 1 biological activity between patients and controls, but only 56 were more than 2-fold decreased and none was more than 2-fold increased (Table S1). There was no difference between stage-I and stage-II patients for any of these RNAs. Since we have no data on variations within the controls, it is not possible to tell whether any of the gene expression changes really is characteristic of HAT. Only 34 of the genes with two-fold reduced mRNA are functionally annotated, and they are involved in very diverse functions. They include RFXAP, encoding a transcriptional activator for some MHC class II genes; EFNAH4, a protein tyrosine kinase potentially involved in the regulation of erythropoiesis and nervous system gene expression; LILRB5 and LILRA4, which encode members of a leucocyte immunoglobulin-like receptor family; and CARD6, a signal transduction regulator that may affect the function of the transcription factor NF-kappaB. Further Fruquintinib supplier analysis would be required to find out whether these changes are specific to HAT.miRNA Target Prediction and Core AnalysisWe now predicted targets for the nine miRNAs that showed some difference between all patients and controls, and also for miR-195 as the next best-scoring miRNA (stage II only). More than 3,000 highly predicted or experimentally investigated putative targets were found. Next, we looked to see whether any of the genes showing changed mRNA levels could also be a possible target of the ten miRNAs. Results are shown in Table S2.Table 2. miRNAs with altered abundance in sleeping sickness.miRNA ID miR-199a-3p miR-27b miR-126* miR-98 miR-409-3p miR-4291 miR-146b-5p miR-454 miR-193b miR-195 miR-144* miR-22* miR-374c miR-338-5pLog2FC stage I 26.9 26.8 26.7 25.9 25.5 25.3 24.5 24.1 4.p-value stage I 2 E-4 2E-4 3E-4 3E-3 7E-3 7E-3 0.03 0.04 0.Log2FC stage II 26.9 26.7 26.4 26.3 25.1 26.1 24.3 24.1 4.1 25.1 24.7 25.1 24.p-value stage II 1E-5 6E-5 2E-4 6E-4 0.01 6E-4 0.02 0.04 0.05 0.01 0.01 3E-3 0.Log2FC all 26. 8 26.6 26.4 26.1 25.2 25.6 n/a 24.1 4.2 24.5 24.3 n/a n/a n/ap-value all 2E-6 2E-6 2E-6 2E-4 1E-3 2E-4 n/a 0.02 0.02 0.01 0.01 n/a n/a n/a4.0.All miRNAs that showed some alteration in at least one stage of sleeping sickness are shown. Log2 FC is Log2 of the arithmetic mean fold change in patients relative to the average value for controls. The p-values are also shown. Student’s t-tests were used to compare paired and multiple groups, with a Benjamini-Hochberg correction for false discovery. A threshold of 0.05 was set for significance. Although some miRNAs were significantly altered in only one stage, further analysis of these showed no significant difference between stage I and stage II. doi:10.1371/journal.pone.0067312.tmiRNA in Human Sleeping SicknessFigure 2. Cluster dendrogram for all samples. The samples were classified according to miRNA expression patterns, using the miRNAs in Table 1, and a dendrogram was made to show the relationships. The c.Th those from patients, but again there was no correlation with the positive trypanolysis result. This suggests that the miRNA profiles we observed might not be specific to trypanosomiasis alone, but could also result from other conditions.Gene Expression ProfilingTo obtain a preliminary idea of whether mRNAs that are miRNA targets were also affected by HAT, six of the patient samples were subjected to gene expression profiling, using various pools of three CATT-negative sera as controls. In total, 656 genes were found to be significantly (p,0.05) differentially regulated between patients and controls, but only 56 were more than 2-fold decreased and none was more than 2-fold increased (Table S1). There was no difference between stage-I and stage-II patients for any of these RNAs. Since we have no data on variations within the controls, it is not possible to tell whether any of the gene expression changes really is characteristic of HAT. Only 34 of the genes with two-fold reduced mRNA are functionally annotated, and they are involved in very diverse functions. They include RFXAP, encoding a transcriptional activator for some MHC class II genes; EFNAH4, a protein tyrosine kinase potentially involved in the regulation of erythropoiesis and nervous system gene expression; LILRB5 and LILRA4, which encode members of a leucocyte immunoglobulin-like receptor family; and CARD6, a signal transduction regulator that may affect the function of the transcription factor NF-kappaB. Further analysis would be required to find out whether these changes are specific to HAT.miRNA Target Prediction and Core AnalysisWe now predicted targets for the nine miRNAs that showed some difference between all patients and controls, and also for miR-195 as the next best-scoring miRNA (stage II only). More than 3,000 highly predicted or experimentally investigated putative targets were found. Next, we looked to see whether any of the genes showing changed mRNA levels could also be a possible target of the ten miRNAs. Results are shown in Table S2.Table 2. miRNAs with altered abundance in sleeping sickness.miRNA ID miR-199a-3p miR-27b miR-126* miR-98 miR-409-3p miR-4291 miR-146b-5p miR-454 miR-193b miR-195 miR-144* miR-22* miR-374c miR-338-5pLog2FC stage I 26.9 26.8 26.7 25.9 25.5 25.3 24.5 24.1 4.p-value stage I 2 E-4 2E-4 3E-4 3E-3 7E-3 7E-3 0.03 0.04 0.Log2FC stage II 26.9 26.7 26.4 26.3 25.1 26.1 24.3 24.1 4.1 25.1 24.7 25.1 24.p-value stage II 1E-5 6E-5 2E-4 6E-4 0.01 6E-4 0.02 0.04 0.05 0.01 0.01 3E-3 0.Log2FC all 26. 8 26.6 26.4 26.1 25.2 25.6 n/a 24.1 4.2 24.5 24.3 n/a n/a n/ap-value all 2E-6 2E-6 2E-6 2E-4 1E-3 2E-4 n/a 0.02 0.02 0.01 0.01 n/a n/a n/a4.0.All miRNAs that showed some alteration in at least one stage of sleeping sickness are shown. Log2 FC is Log2 of the arithmetic mean fold change in patients relative to the average value for controls. The p-values are also shown. Student’s t-tests were used to compare paired and multiple groups, with a Benjamini-Hochberg correction for false discovery. A threshold of 0.05 was set for significance. Although some miRNAs were significantly altered in only one stage, further analysis of these showed no significant difference between stage I and stage II. doi:10.1371/journal.pone.0067312.tmiRNA in Human Sleeping SicknessFigure 2. Cluster dendrogram for all samples. The samples were classified according to miRNA expression patterns, using the miRNAs in Table 1, and a dendrogram was made to show the relationships. The c.

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