During gestation, a woman's body undergoes physiological changes that alter thyroid function. pregnant women with hypothyroidism may exhibit gestational complications, including hypertension and preeclampsia. We investigated differentially expressed genes (DEGs) in circulating RNAs from pregnant women with tSH levels just above the normal range to determine the impact of a mild elevation of TSH in pregnancy. We selected three women with healthy thyroid pregnancy (HTP), three pregnant women with gestational hypothyroidism (GHT), and three nonpregnant women (NPG) to construct transcriptome libraries. We also compared our results with data from the GEO dataset and DisGeNET. We identified 1500 DEG in GHT and 1656 DEG in HTP. From GEO dataset, we recognized 453 DEGs in trimester-specific plasma RNA, 1263 DEGs in placental tissues from healthy women, 1031 DEGs from preeclamptic uteroplacental tissues and 1657 DEGs from placental tissues from severely preeclamptic women. In this scenario, 12.26% and 12.86% genes were shared between these datasets in GHT and HTP, respectively. We stablished 62 genes in GHT DEGs related to hypertensive phenotype hallmarks. In conclusion, even in women with a mild TSH increment, we were able to detect some DEGs that could be associated with a hypertensive phenotype.Circulating Ribonucleic Acid (RNA) represents a powerful strategy, less invasive and capable of real-time tracking diseases. The circulating protein codifying transcripts (mRNA, messenger RNA) may also provide clinically useful additional information when compared to the direct protein measurement 1 . This strategy is also sensible enough to detect fetal RNA circulating in the mother's blood 2 . The use of corticotropin-releasing hormone (CRH) mRNA detection in pregnancy was also reported to be capable of working as a molecular marker for preeclampsia 3 . Nevertheless, the transcriptome analysis is efficient enough to offer much more information than the use of a single RNA. The target RNA detection strategies, like RNA-Seq, can be performed in a simple blood sample and can give us the quantitative expression of the coding and noncoding RNA from the mother, the placenta or the fetus in a unique and noninvasive way 4 .During gestation, a woman's body undergoes physiological changes that alter thyroid function and thyroid hormone metabolism. Most of these changes are related to the increased demand for thyroid hormone by the mother and the fetus, the placental degradation of the hormone and the increase in the thyroxin-binding globulin (TBG) concentration. TBG increases due to an estrogenic stimulus during pregnancy, and for this reason, free thyroxine (FT 4 ) is highly influenced by the significant increase in the thyroxine (T 4 )-bound fraction.

Gestational hypothyroidism has 5.1% prevalence and is associated with severe consequences as hypertensive complications and preeclampsia (PE). Based on the higher mortality associated with PE, the objective of this study is to identify new biomarkers useful as prognostic and therapeutically parameters. Methodology: The study was approved by the IRB Number: 665331/679727. We selected three volunteers to each group and constructed libraries of circulating RNA, for healthy pregnant women (HPW), preeclampsia (PEC) and gestational hypothyroidism (GHT). We collected blood samples for TSH and Free T4 (FT4) measurement and a PAX Gene Tube for RNA analysis. RNA extraction was performed by the PAXgene Blood RNA extraction kit (Qiagen NL, DE). NGS platform, Ion Proton System was used with Ion AmpliSeq Gene human transcriptome (Thermo Fisher Scientific Manufacturer) kit to construct the transcriptome libraries. The R software platform version 3.4.1 (R Foundation for Statistical Computing, Vienna, Austria. URL: R-project.org/) with the edgeR package 3.16.5 were used to determine the differentially expressed genes (DEG). We compared our GHT DEG with plasma transcriptome libraries from healthy pregnant women in the second and third trimesters (access number: GSE56899) available in GEO Dataset (ncbi.nlm.nih.gov/geo) using the limma package 3.26.8, p <0,05. We also used Random Forest (RF) analysis to rank the variables, Spearman Correlation, and multiclass ROC. Results: We encountered 713 DEG in the HPW with matrix metalloproteinases (MMP) upregulated, MMP8 (logFC 4.91) and MMP9 (logFC 3.44). In GEO Dataset we also found the MMP8 upregulated. We detected 195 DEG in PEC with MMP9 downregulated (logFC -2.49) and 745 DEG in GHT with MMP8 (logFC -4.06) and MMP9 (logFC -3.26) downregulated. We obtained 571 DEG in the analysis obtained by GEO Dataset and 26 genes are in common with our GHT DEG, including the MMP8 . Between the 26 in common, 88.46% of genes were downregulated in GHT and 92.30% were upregulated GEO Dataset. We found a negative correlation of MMP8 with systolic blood pressure (SBP) and diastolic blood pressure (DBP) (r: -0.313; r: -0.285), MMP9 with SBP and DBP (r: -0.349; r: -0.384), MMP8 and MMP9 with weight (r: -0.223; r: -0.209) and MMP9 with TSH (r: 0.633). From our RF model, we selected the variables SBP and DBP, FT4 and weight that is possibly modified by the difference in expression of MMP8 and MMP9 . We obtained an area under the curve of 0.97 for TSH, weight and those genes. Conclusion: Several metalloproteinases are involved in placental development, implantation and angiogenesis. We observed ...

Objective: Based on hypothetical hypothyroidism and nonthyroidal illness syndrome (NTIS) gene expression similarities, we decided to compare the patterns of expression of both as models of NTIS. The concordant profile between them may enlighten new biomarkers for NTIS challenging scenarios. Materials and methods: We used Ion Proton System next-generation sequencing to build the hypothyroidism transcriptome. We selected two databanks in GEO2 platform datasets to find the differentially expressed genes (DEGs) in adults and children with sepsis. The ROC curve was constructed to calculate the area under the curve (AUC). The AUC, chi-square, sensitivity, specificity, accuracy, kappa and likelihood were calculated. We performed Cox regression and Kaplan-Meier analyses for the survival analysis. Results: Concerning hypothyroidism DEGs, 70.42% were shared with sepsis survivors and 61.94% with sepsis nonsurvivors. Some of them were mitochondrial gene types (mitGenes), and 95 and 88 were related to sepsis survivors and nonsurvivors, respectively. BLOC1S1, ROMO1, SLIRP and TIMM8B mitGenes showed the capability to distinguish sepsis survivors and nonsurvivors. Conclusion: We matched our hypothyroidism DEGs with those in adults and children with sepsis. Additionally, we observed different patterns of hypothyroid-related genes among sepsis survivors and nonsurvivors. Finally, we demonstrated that ROMO1, SLIRP and TIMM8B could be predictive biomarkers in children's sepsis.