Hazard ratios (HR) for major incident post-acute COVID-19 sequelae in patients admitted to intensive care unit and matched general population controls in Estonia 2020–2021.

(RTF)</p

Baseline descriptive characteristics of patients who were hospitalized with COVID-19 and matched general population controls during 2020–2021 in Estonia.

Baseline descriptive characteristics of patients who were hospitalized with COVID-19 and matched general population controls during 2020–2021 in Estonia.</p

Kaplan-Meier curves for the comparison of the significant outcomes during 12 months comparing hospitalized COVID-19 patients (red curves) and matched general population (blue curves).

Shaded zones indicate 95% CI.</p

Hazard ratios (HR) for major incident post-acute COVID-19 sequelae in hospitalised patients and matched general population controls in Estonia 2020–2021.

(RTF)</p

Additional file 1: Figure S1. of Host genetic variation and its microbiome interactions within the Human Microbiome Project

Sequencing and variant call quality metrics. A. Percentage of contamination and chimeric reads. B. Various metrics based on variant calling. Figure S2. Distribution of genic and intergenic variants. Figure S3. Combined PCA between 1000 Genomes and HMP300. Figure S4. Correlation between high-level genetic features and microbial species in non-gut body sites. Figure S5. Correlation between high-level genetic features and microbial metabolic pathways in non-gut body sites. Figure S6. Quantile-quantile plots for association analysis between microbial species and GWAS Catalog SNVs. Figure S7. Quantile-quantile plots for association analysis between microbial metabolic pathways and GWAS Catalog SNVs. Figure S8. Putative SNV-microbial species associations. Figure S9. Putative SNV-microbial metabolic pathway associations. Table S1. Raw statistics for genetic principal component analysis. Table S2. Top SNV and microbiome association results. (PDF 1953 kb

CpGs’ distance from TSS.

<p>We measured CpGs’ distance from the transcription start site (TSS). a) Distance from TSS of all the CpGs on the methylation array. b) Distance from TSS of hypermethylated CpGs (dotted line) and distance from TSS of hypomethylated CpGs (continuous line). On the x-axis, the distance from TSS is measured in bp-s, and on the y-axis N represents the number of CpGs.</p

Survival curves of 10 differentially methylated CpG sites.

<p>We performed a survival test on each of the CpG sites. The methylation values are divided into 3 groups: low (0–0.25), medium (0.25–0.75) and high (0.75–1). As a result we found 10 CpG sites whose methylation level differs in different survival groups. The x-axis shows survival in years and the y-axis shows overall survival.</p

The concordance between microarray and qRT-PCR measurements.

<p>On the y-axis is shown average log2fold-change determined by Illumina array and qRT-PCR (8 sample pairs). Error bars indicate standard error of the mean (SEM).</p

Differential DNA methylation between NSCLC and normal lung samples.

<p>DNA methylation levels are shown for the top 100 CpG sites with the highest delta Beta values (FDR corrected) of DNA methylation between cancer tissue and normal lung tissue. Methylation Beta-values are represented as row Z-scores. A heatmap was generated using unsupervised 2D hierarchical cluster analysis. Red indicates high methylation and blue indicates low methylation relative to the row mean.</p

Design Principles of Concentration-Dependent Transcriptome Deviations in Drug-Exposed Differentiating Stem Cells

Information on design principles governing transcriptome changes upon transition from safe to hazardous drug concentrations or from tolerated to cytotoxic drug levels are important for the application of toxicogenomics data in developmental toxicology. Here, we tested the effect of eight concentrations of valproic acid (VPA; 25–1000 μM) in an assay that recapitulates the development of human embryonic stem cells to neuroectoderm. Cells were exposed to the drug during the entire differentiation process, and the number of differentially regulated genes increased continuously over the concentration range from zero to about 3000. We identified overrepresented transcription factor binding sites (TFBS) as well as superordinate cell biological processes, and we developed a gene ontology (GO) activation profiler, as well as a two-dimensional teratogenicity index. Analysis of the transcriptome data set by the above biostatistical and systems biology approaches yielded the following insights: (i) tolerated (≤25 μM), deregulated/teratogenic (150–550 μM), and cytotoxic (≥800 μM) concentrations could be differentiated. (ii) Biological signatures related to the mode of action of VPA, such as protein acetylation, developmental changes, and cell migration, emerged from the teratogenic concentrations range. (iii) Cytotoxicity was not accompanied by signatures of newly emerging canonical cell death/stress indicators, but by catabolism and decreased expression of cell cycle associated genes. (iv) Most, but not all of the GO groups and TFBS seen at the highest concentrations were already overrepresented at 350–450 μM. (v) The teratogenicity index reflected this behavior, and thus differed strongly from cytotoxicity. Our findings suggest the use of the highest noncytotoxic drug concentration for gene array toxicogenomics studies, as higher concentrations possibly yield wrong information on the mode of action, and lower drug levels result in decreased gene expression changes and thus a reduced power of the study