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Supplementary Figures A before batch correction mouse after batch correction B before batch correction rat after batch correction C before batch correction mouse rat comparison: before and after batch correction D mouse E rat Figure S1. PCA plots of rodent samples and the effects of batch correction. A,B. PCA plots of variance- stabilized transformed counts of mouse (A) and rat (B) datasets before and after batch correction. Batch correction was done internaly by Deseq2 and illustrated using the removeBatchEffect function from limma. C. Gene loadings on top PCs before batch correction for mouse (left) and rat (right). All genes - black, top 500 genes with highest loading on PC1 and PC2 are shown by blue and red dots, respectively. D,E. Gene loadings on top PCs before and after batch correction. All genes - black, top 500 PC1 genes before the correction - blue and top 500 PC1 genes after the correction - red (left). Right panel shows linear correlation of the top 500 PC2 genes before the correction and PC1 genes after the correction. A overlap between B and C, mouse B overlap between P and Br, rat C before/after batch correction, mouse D before/after batch correction, rat before after before after E mouse F rat G mouse H rat I mouse, DEGs in at least 1 dataset J rat, DEGs in at least 1 dataset Figure S2. Gene overlap and correlation between samples. A,B. Expected number of overlapping genes between mouse (A) and rat (B) datasets. The figures show the probability densities calculated by random sampling from all expressed genes (19,312 for A and 13,940 for B). Sampling sizes were equal to the respective sizes of DEG sets: 966 genes for Cobos (C) and 474 for Baskozos (B) mouse datasets (A); 5154 for Perkins (P) and 2950 for Baskozos (Br) rat datasets (B). Red lines correspond to the size of the observed overlap (p ~ 4·10-184). C,D. Effects of the correction of technical variations on the linear correlation of vst- normalized mean expression between datasets/batches or treatments (injury vs control). DEGs are marked by red on the panels with injured vs control after the correction. E,F. The linear correlation between log2fc in the expression of the common mouse (E) or rat (F) DEGs (red) and between all expressed genes (black). Red and black lables show correlation coefficients for DEGs and the whole transcriptome, respectively. G,H. The linear correlation between log2fc in the expression of the common mouse (E) or rat (F) DEGs, taking into account the spread of the log2fc between replicates. For each gene four data points were plotted, corresponding to pairwise comparison between low and upper bounds of log2fc (log2fc± lfcSE, where lfcSE is a standard error of log2fc). I,J. The linear correlation between log2fc of genes identified as DEGs in at least one dataset for mouse (I) or rat (J). A B Figure S3. Comparison of single cell and bulk expression of the common mouse neuronal DEGs. A. Mean expression changes (log2fc of transcripts per million, TPM) of 131 common mouse DEGs. For single cell dataset (red) the values were taken from [17], for all sensory neurons. For the whole DRG (black) the Cobos dataset was used, as it represents to early changes after the injury (day 7), comparable to the single cell dataset (day 3). For each gene the error bars show the spread of the expression changes (min to max). For the single cell dataset the lower and upper bounds of log2fc TPM were taken from [17], for all sensory neurons expressing the gene. For the bulk dataset error bars show min and max values of log2fc TPM, based on all replicates of the Cobos dataset. B. The differences between the mean expression changes of single cell and whole DRG datasets, calculated from A. The genes were ranked by the expression changes in single cell dataset multiplied to the differences in expression changes between single cell and the whole DRG dataset (for A) or by the differences in expression changes between single cell and the whole DRG dataset (for B). Figure S4. STRING analysis for biological interactions within anti-correlated genes between rat and mouse SNT models (Table S5). Only interacting genes, which are potentially involved in nociception are shown, with medium confidence of interactions (score 0.4). The line colour indicates the type of interaction evidence. mouse common DEGs A CC B BP rat common DEGs C CC D BP overlap betweet gene sets of top GO terms for rat/mouse common DEGs (from Fig. 2 A,B) E CC F BP overlap betweet gene sets of top GO terms for mouse common DEGs (from A,B) G CC H BP overlap betweet gene sets of top GO terms for rat common DEGs (from C,D) I CC J BP Figure S5. The enrichment analysis of the common mouse (Table S2) and rat (Table S3) DEGs. Analysis of the significantly enriched GO terms within mouse (A,B) and rat (C,D) genes using ClusterProfiler with FDR adjusted P ≤ 0.05. GO terms are shown for the cell component, (A,C) and biological processes (B,D) categories. E-J. Similarity matrices for the enriched sets of DEGs from the top GO terms shown on Fig. 2, Fig. S5A-D, showing the significance of the overlap (-log10 of P value) between each pair of gene sets. P values of the overlaps were calculated using hypergeometric distribution, with total number of drawn genes equal to 18,270 (the number of all genes used in ClusterProfiler, R-3.5.1). A B Figure S6. The enrichment analysis of the common rat DEGs (Table S3), excluding genes classified as sensory neurons - related with the mouse classification from [17]. The ClusterProfiler analysis of significantly enriched GO terms within the cell component (A) and biological processes (B) categories; FDR adjusted P ≤ 0.05. A B Figure S7. The enrichment analysis of the persistent rat DEGs, which are not present in the mouse dataset of sensory neurons from [17]. Only gene with relatively low expression changes (0.5<=|log2fc|<1) (432 genes) were analysed with ClusterProfiler within the biological processes (A) and cell component (B); FDR adjusted P ≤ 0.05. Analysis of genes with |log2fc|≥1 is shown on Fig. 4A,B. Figure S8. The pain-related PPI network of the common rat DEGs. The networks were built based on pain interactome, using the common rat DEGs, together with neighbours from the pain interactome network. The coloured arrows mark the type and direction of interactions; coloured nodes mark the expression changes in rat datasets (up/down/persistent/non-persistent/non-DEG partner), as explained in the legend. Amitriptyline − Up regulated in drug Anti−correlated src oprd1 htr1a No data scn10a scn8a scn9a Correlated grm7 s e n p2rx3 e G t a b3gat1 R drd2 htr3a nfkbia H A M H A H P 3 3 C A E C C 7 7 1 K 3 C F 5 5 E | 2 7 5 2 | | 6 2 | 9 | 1 4 2 6 4 3 5 4 h h T h | | h 6 | 1 h | | 1 | 6 1 0 1 | h 0 1 0 0 h | 0 µ 1 µ | µ M µ 1 0 M µ M M 0 M µ µ M r . The . The M Cell line . Only the cell the cell . Only correlated correlated o - drug concentration | . erve injury is shown indicates white no erve by colour; time of the exposure of the totime drug | ges duringges n pain network of Fig. 5 that are either anti 5 that arepain of Fig. either network cell linescell the the regulated signature from the treatment amitriptyline from of signature regulated - rat genes from rat genes t . column show The names regulated genes in response to drugs are shown. The comparison of the direction of the of drug shown. The comparison the direction to drugs of genes in response are regulated - Persisten with respect with respect to an up . 9 data available data for names compactness show gene names in low case, row correlated with up lines chan with the direction of expression effect Figure S Figure Amitriptyline − Down regulated in drug Anti−correlated adra2a il16 rela src nfkbia prss12 fosl1 stat6 No data cck il1rn sp1 b3gat1 drd2 oprd1 ntsr1 scn8a Correlated kcnj3 oprm1 s p2rx3 e ephb1 n scn11a e G scn10a grm7 t htr1a a vegfa R scn9a penk tac1 gria2 nppa htr3a ntrk3 aqp1 H A N P P H H A P V M H S H V H A A A H M H her her 3 C C S H C K C 5 5 3 C P A T E E A C E C C 7 4 4 7 2 C B C 3 3 1 H A P K A 1 K C C F F 5 9 9 5 9 | | E E | | P G 2 P 2 | | 5 7 5 7 6 2 2 2 | | | | 2 2 | 6 2 6 2 | 9 | 9 6 1 | | | 1 | Only the 4 4 2 6 4 2 4 4 2 2 6 6 h 4 4 3 3 5 5 | 4 h h h 4 4 | h 6 h for for ot h h T T h h h h | | 1 | h | h 6 | 2 | | 1 1 | | h | | | | 1 h h | | 0 1 1 h 1 1 | | 1 1 2 6 1 1 4 0 1 0 1 9 | 0 h | | 0 0 | 0 0 1 0 0 4 1 1 0 0 µ 1 0 0 h | h µ 0 µ 1 µ 0 0 M µ 0 µ µ µ µ µ h | µ µ | M µ M µ 1 0 M 1 M M M M µ M M | µ M µ M µ M M 1 0 0 M µ M M M 0 µ M µ µ M M M correlated or or correlated - Cell line e the legend of the of S e legend Fig.
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  • Natural Course of Congenital Hypothyroidism by Dual Oxidase 2 Mutations from the Neonatal Period Through Puberty

    Natural Course of Congenital Hypothyroidism by Dual Oxidase 2 Mutations from the Neonatal Period Through Puberty

    Y Maruo and others Clinical features of DUOX2 174:4 453–463 Clinical Study defects Natural course of congenital hypothyroidism by dual oxidase 2 mutations from the neonatal period through puberty Yoshihiro Maruo1, Keisuke Nagasaki, Katsuyuki Matsui, Yu Mimura, Asami Mori, Maki Fukami2 and Yoshihiro Takeuchi Correspondence should be addressed Department of Pediatrics, Shiga University of Medical Science, Tsukinowa, Seta, Otsu, Shiga 520-2192, Japan, to Y Maruo 1Department of Pediatrics, Niigata University, Niigata, Japan and 2Department of Molecular Endocrinology, Email National Research Institute for Child Health and Development, Tokyo, Japan [email protected] Abstract Aim: We previously reported that biallelic mutations in dual oxidase 2 (DUOX2) cause transient hypothyroidism. Since then, many cases with DUOX2 mutations have been reported. However, the clinical features and prognosis of individuals with DUOX2 defects have not been clarified. Objective: We investigated the prognosis of patients with congenital hypothyroidism (CH) due to DUOX2 mutations. Patients: Twenty-five patients were identified by a neonatal screening program and included seven familial cases. Their serum TSH values ranged from 18.9 to 734.6 mU/l. Twenty-two of the patients had low serum free thyroxine (fT4) levels (0.17–1.1 ng/dl). Twenty-four of the patients were treated with L-thyroxine. Methods: We analyzed the DUOX2, thyroid peroxidase, NaC/IK symporter, and dual oxidase maturation factor 2 genes of these 25 patients by PCR-amplified direct sequencing. An additional 11 genes were analyzed in 11 of the 25 patients using next-generation sequencing. Results: All patients had biallelic DUOX2 mutations, and seven novel alleles were detected.