Additional file 1: Table S1. of Mutations in gonadotropin-releasing hormone signaling pathway in two nIHH patients with successful pregnancy outcomes

Primers for bidirectional Sanger sequencing in nIHH patients. (DOCX 15 kb

Table_1_Evaluating the effects of circulating inflammatory proteins as drivers and therapeutic targets for severe COVID-19.docx

ObjectiveThe relationships between circulating inflammatory proteins and COVID-19 have been observed in previous cohorts. However, it is not unclear which circulating inflammatory proteins may boost the risk of or protect against COVID-19.MethodsWe performed Mendelian randomization (MR) analysis using GWAS summary result of 91 circulating inflammation-related proteins (N = 14,824) to assess their causal impact on severe COVID-19. The COVID-19 phenotypes encompassed both hospitalized (N = 2,095,324) and critical COVID-19 (N = 1,086,211). Moreover, sensitivity analyses were conducted to evaluate the robustness and reliability.ResultsWe found that seven circulating inflammatory proteins confer positive causal effects on severe COVID-19. Among them, serum levels of IL-10RB, FGF-19, and CCL-2 positively contributed to both hospitalized and critical COVID-19 conditions (OR: 1.10~1.16), while the other 4 proteins conferred risk on critical COVID-19 only (OR: 1.07~1.16), including EIF4EBP1, IL-7, NTF3, and LIF. Meanwhile, five proteins exert protective effects against hospitalization and progression to critical COVID-19 (OR: 0.85~0.95), including CXCL11, CDCP1, CCL4/MIP, IFNG, and LIFR. Sensitivity analyses did not support the presence of heterogeneity in the majority of MR analyses.ConclusionsOur study revealed risk and protective inflammatory proteins for severe COVID-19, which may have vital implications for the treatment of the disease.</p

Promoter classification accuracy.

<p>Promoter classification accuracy.</p

Frequency distributions of TFBS may have different patterns around the start of transcription (position 0 on the horizontal axis).

<p>X-axis shows the distance from TSS, Y-axis reflects the frequency of motif in each window. Frequencies of ARALY493022_04 TFBS (Class 1) are plotted on the left panel, of RAP26_03 TFBS (Class 2) on the middle panel, and of MYB111_02 (Class 3) on the right panel.</p

Basic CNN architecture that was used in building promoter models implemented in the learnCNN.py program [3, 10].

<p>Basic CNN architecture that was used in building promoter models implemented in the learnCNN.py program [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0187243#pone.0187243.ref003" target="_blank">3</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0187243#pone.0187243.ref010" target="_blank">10</a>].</p

Suppression of site gain caused by nucleotide variation in promoters.

<p>Suppression of site gain caused by nucleotide variation in promoters.</p

Suppression of site loss caused by nucleotide variants in promoters.

<p>Suppression of site loss caused by nucleotide variants in promoters.</p

Methylation around transcription start site in rice in different sequence contexts.

<p>Red–CG, green—CHG, blue–CHH, where H denotes A, C or T nucleotide.</p

An example of five distinct TFBS entries in the TRANSFAC database with very similar position weight matrices (PWMs).

<p>An example of five distinct TFBS entries in the TRANSFAC database with very similar position weight matrices (PWMs).</p

Frequency of SNPs located near the TSS in rice.

<p>Frequency of SNPs located near the TSS in rice.</p