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Jung et al. 1 SUPPLEMENTAL FIGURES 2 3 Supplemental Figure 1. Clinical relevance of natural product methyltransferases (NPMTs) in brain disorders. (A) 4 Table summarizing characteristics of 11 NPMTs using data derived from the TCGA GBM and Rembrandt datasets for 5 relative expression levels and survival. In addition, published studies of the 11 NPMTs are summarized. (B) The 1 Jung et al. 6 expression levels of 10 NPMTs in glioblastoma versus non‐tumor brain are displayed in a heatmap, ranked by 7 significance and expression levels. *, p<0.05; **, p<0.01; ***, p<0.001. 8 2 Jung et al. 9 10 Supplemental Figure 2. Anatomical distribution of methyltransferase and metabolic signatures within 11 glioblastomas. The Ivy GAP dataset was downloaded and interrogated by histological structure for NNMT, NAMPT, 12 DNMT mRNA expression and selected gene expression signatures. The results are displayed on a heatmap. The 13 sample size of each histological region as indicated on the figure. 14 3 Jung et al. 15 16 Supplemental Figure 3. Altered expression of nicotinamide and nicotinate metabolism‐related enzymes in 17 glioblastoma. (A) Heatmap (fold change of expression) of whole 25 enzymes in the KEGG nicotinate and 18 nicotinamide metabolism gene set were analyzed in indicated glioblastoma expression datasets with Oncomine. 4 Jung et al. 19 Color bar intensity indicates percentile of fold change in glioblastoma relative to normal brain. (B) Nicotinamide and 20 nicotinate and methionine salvage pathways are displayed with the relative expression levels in glioblastoma 21 specimens in the TCGA GBM dataset indicated. 22 5 Jung et al. 23 24 Supplementary Figure 4. NNMT mRNA expression is up‐regulated in cancer stem cells in NSCLC and BRCA. A and B. 25 NNMT mRNA expression levels from respective transcriptome array datasets (30, 35). 26 6 Jung et al. A B 27 28 Supplemental Figure 5. Intratumoral distribution of methyltransferase and metabolic signatures within gliomas. 29 (A, B) The expression levels of NNMT, NAMPT, and DNMTs mRNA and metabolic signatures are displayed with 30 molecular and patient characteristics from as indicated from the (A) TCGA GBMLGG RNAseq dataset grouped by 31 TCGA 2016 pan‐glioma subtypes (Codel, n = 167; G‐CIMP high, n = 236; G‐CIMP low, n = 17; PA‐like, n = 11; Others, n 32 = 39; Classical‐like, n = 69; Mesenchymal‐like, n = 98) (6) or (B) TCGA GBM U133A microarray dataset grouped by 33 TCGA 2010 GBM molecular subtypes (G‐CIMP Proneural, n = 41; Non‐G‐CIMP Proneural, n = 97; Neural, n = 84; 34 Classical, n = 145; Mesenchymal, n = 156; Non‐tumor, n = 10) (4). 35 7 Jung et al. 36 37 Supplementary Figure 6. MTAP genotype and nicotinamide metabolism in glioblastomas. (A) MTAP and CDKN2A 38 copy number status in TCGA GBM dataset (n = 281). Copy number status and clinical tracks visualized on cBioportal. 39 (B) Kaplan‐Meier survival curve of GBM patients stratified by MTAP copy number status in TCGA GBM dataset. 40 HOMDEL: homologous deletion (n = 123); WT: wild type (n =158 ). (C) Frequency of tumor specimens with 8 Jung et al. 41 homologous deletion or wild type copy number of MTAP in TCGA GBM specimens by molecular subtype. Significance 42 of difference of distribution tested with Fisher exact test on sample counts. (D) Transcriptional signature scores of 43 KEGG Methionine and Cysteine metabolism in TCGA GBM tumors with wild type or MTAP homozygous deletion. (E) 44 KEGG Methionine and Cysteine metabolism signatures analyzed by MTAP genotype and by TCGA GBM molecular 45 subtype. (F) Transcriptional signature scores of KEGG Nicotinamide and Nicotinate metabolism in TCGA GBM tumors 46 with wild type or MTAP homozygous deletion. (G) KEGG Nicotinamide and Nicotinate metabolism signatures 47 analyzed by MTAP genotype and by TCGA GBM molecular subtype. 48 9 Jung et al. 49 50 Supplemental Figure 7. NNMT RNAi does not impede in vitro viability and self‐renewal capacity in proneural GSCs. 51 (A‐H) RT‐PCR for NNMT or NAMPT knockdown efficiency in mesenchymal GSCs (T3264 and GSC20) or proneural 52 GSCs (GSC20 and T1919) transduced with lentiviral shRNA clones targeting NNMT or NAMPT compared with non‐ 53 targeting controls. For each condition, four technical replicates were analyzed. Mann‐Whitney non‐parametric test 10 Jung et al. 54 for differences in median was utilized to test for significance. (I‐L) In vitro limiting dilution assay and cell viability 55 assay of (I, J) GSC23 or (K, L) T3264 GSCs transduced with shCTRL, shNNMT.830, shNNMT.330, shNAMPT.1140, 56 shNAMPT.1183 lentivirus. Nonparametric Mann‐Whitney test was used to determine significance in differences 57 between median of shNNMT and shNAMPT treated samples. 58 11 Jung et al. 59 60 Supplemental Figure 8. Mesenchymal subtype genes correlation with NNMT and DNMT1 mRNA expression and 61 DNA methylation state. (A‐D). Representative CpG probes annotated for mesenchymal subtype genes (NOD2, 62 RUNX2, ITGAM, and CTSZ) and their DNA methylation state with significant anti‐correlation with NNMT mRNA 63 expression. (E‐H). Correlation between NNMT or DNMT1 mRNA expression and DNA methylation of CpG probes 64 annotated for respective genes. (I‐L). Correlation between respective gene mRNA expression and DNA methylation 65 state of CpG probe. 12 Jung et al. A Spearman r = -0.2481 Spearman r = -0.2442 Spearman r = -0.1873 13 13 9 p = 0.0016 p = 0.0019 p = 0.0177 12 11 11 10 9 6 mRNA (log2) mRNA 9 7 8 DNMT1 mRNA (log2) mRNA DNMT1 DNMT3A 7 5 (log2) DNMT3B mRNA 3 024 51015024 51015024 51015 NNMT mRNA (log2) NNMT mRNA (log2) NNMT mRNA (log2) B NNMT / DNMT1 Merge CCF3545-GBM 75 µm 75 µm ↑ NNMT ↓ NNMT 3D intensity ↓ DNMT1 ↑ DNMT1 66 67 Supplemental Figure 9. Negative correlation between NNMT and DNMTs in mesenchymal glioblastoma. (A) 68 Correlative analysis of NNMT was performed with indicated DNMTs in of TCGA GBM database (n = 528). Spearman 69 correlation coefficients were calculated with GraphPad Prism. (B) Representative image of patient glioblastoma 70 tissue with NNMT and DNMT1 staining. Frozen glioblastoma section was stained with anti‐NNMT, anti‐DNMT1 71 antibodies, and DAPI. Scale bars: 75 m. The 3D quantified peaks of NNMT and DNMT1 were visualized with ImageJ. 72 13 Jung et al. 73 74 Supplemental Figure 10. Kaplan‐Meier survival curves of glioblastoma patients stratified by NNMT and NAMPT 75 expression. (A‐L) Patient survival data based on expression of NNMT (A‐F) and NAMPT (G‐L) were evaluated by high 76 versus low cut‐off into different expression levels in the indicated databases using the top and bottom quartiles. 14 Jung et al. 77 78 Supplemental Figure 11. Kaplan‐Meier survival curve based on NNMT mRNA expression in non‐G‐CIMP 79 glioblastomas from the TCGA GBM microarray dataset. NNMT expression levels were divided with the exclusion of 80 G‐CIMP patients, in TCGA GBM database. P‐value represents log‐rank p‐value. 15 Jung et al. 81 Supplementary Table 1. Metabolic gene expression differences between glioblastomas and non‐tumor brain 82 specimens in TCGA GBM dataset. 83 84 Supplementary Table 2. Correlation between NNMT mRNA expression and DNA methylation signatures in TCGA 85 GBM dataset 86 87 Supplementary Table 3. Primers used for C/EBP ChIP‐PCR and qMethyl‐PCR 88 16 Supplemental Table 1: Metabolic gene expression fold changes - glioblastoma vs non-tumor in TCGA GBM dataset Fold change FDR-adjusted p- Gene Description p-value GBM vs NT value A1CF APOBEC1 complementation factor -0.05396 0.590164705 0.67709077 A4GALT alpha 1,4-galactosyltransferase (globotriaosylceramide synthase) -0.50653 4.73E-06 1.70E-05 A4GNT alpha-1,4-N-acetylglucosaminyltransferase -0.01961 0.777790697 0.834872485 AACS acetoacetyl-CoA synthetase -1.74346 2.42E-29 6.16E-28 AADAC arylacetamide deacetylase (esterase) 0.09560 0.617847199 0.698600309 AANAT arylalkylamine N-acetyltransferase -0.04109 0.41280092 0.504038601 AASDHPPT aminoadipate-semialdehyde dehydrogenase-phosphopantetheinyl transferase -0.76500 3.81E-07 1.62E-06 AASS aminoadipate-semialdehyde synthase 1.37036 7.34E-11 5.11E-10 ABAT 4-aminobutyrate aminotransferase -0.41349 0.37681451 4.68E-01 ABCA1 ATP-binding cassette, sub-family A (ABC1), member 1 2.83143 2.88E-15 2.91E-14 ABCA12 ATP-binding cassette, sub-family A (ABC1), member 12 0.03119 0.764332416 0.823469954 ABCA2 ATP-binding cassette, sub-family A (ABC1), member 2 -1.17749 2.13E-12 1.73E-11 ABCA3 ATP-binding cassette, sub-family A (ABC1), member 3 -0.69196 0.000249959 6.73E-04 ABCA4 ATP-binding cassette, sub-family A (ABC1), member 4 -0.36770 0.00280619 0.00609507 ABCA5 ATP-binding cassette, sub-family A (ABC1), member 5 -0.74635 0.000812193 1.97E-03 ABCA6 ATP-binding cassette, sub-family A (ABC1), member 6 0.06202 0.598636658 0.682956412 ABCA7 ATP-binding cassette, sub-family A (ABC1), member 7 -0.20289 0.048029657 0.079321115 ABCA8 ATP-binding cassette, sub-family A (ABC1), member 8 -0.28415 0.575069646 0.662764507 ABCB1 ATP-binding cassette, sub-family B (MDR/TAP), member 1 0.04137 0.894381571 0.928640313 ABCB11 ATP-binding cassette, sub-family B (MDR/TAP), member 11 -0.28834 6.17E-12 4.77E-11 ABCB4 ATP-binding cassette, sub-family B (MDR/TAP), member 4 0.44581 0.013718913 2.56E-02 ABCB6 ATP-binding cassette, sub-family B (MDR/TAP), member 6 0.01715 0.857043577 0.898128655 ABCB7 ATP-binding cassette, sub-family B (MDR/TAP), member 7 1.07976 1.22E-16 1.40E-15 ABCB8 ATP-binding cassette, sub-family B (MDR/TAP), member 8 0.15617 0.064260539 0.10206381 ABCB9 ATP-binding cassette, sub-family B (MDR/TAP), member 9 -0.64823 9.50E-08 4.42E-07 ABCC1 ATP-binding cassette, sub-family
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  • Genome-Wide Association and Transcriptome Studies Identify Candidate Genes and Pathways for Feed Conversion Ratio in Pigs

    Genome-Wide Association and Transcriptome Studies Identify Candidate Genes and Pathways for Feed Conversion Ratio in Pigs

    Miao et al. BMC Genomics (2021) 22:294 https://doi.org/10.1186/s12864-021-07570-w RESEARCH ARTICLE Open Access Genome-wide association and transcriptome studies identify candidate genes and pathways for feed conversion ratio in pigs Yuanxin Miao1,2,3, Quanshun Mei1,2, Chuanke Fu1,2, Mingxing Liao1,2,4, Yan Liu1,2, Xuewen Xu1,2, Xinyun Li1,2, Shuhong Zhao1,2 and Tao Xiang1,2* Abstract Background: The feed conversion ratio (FCR) is an important productive trait that greatly affects profits in the pig industry. Elucidating the genetic mechanisms underpinning FCR may promote more efficient improvement of FCR through artificial selection. In this study, we integrated a genome-wide association study (GWAS) with transcriptome analyses of different tissues in Yorkshire pigs (YY) with the aim of identifying key genes and signalling pathways associated with FCR. Results: A total of 61 significant single nucleotide polymorphisms (SNPs) were detected by GWAS in YY. All of these SNPs were located on porcine chromosome (SSC) 5, and the covered region was considered a quantitative trait locus (QTL) region for FCR. Some genes distributed around these significant SNPs were considered as candidates for regulating FCR, including TPH2, FAR2, IRAK3, YARS2, GRIP1, FRS2, CNOT2 and TRHDE. According to transcriptome analyses in the hypothalamus, TPH2 exhibits the potential to regulate intestinal motility through serotonergic synapse and oxytocin signalling pathways. In addition, GRIP1 may be involved in glutamatergic and GABAergic signalling pathways, which regulate FCR by affecting appetite in pigs. Moreover, GRIP1, FRS2, CNOT2,andTRHDE may regulate metabolism in various tissues through a thyroid hormone signalling pathway.
  • Genetic Landscape of Papillary Thyroid Carcinoma and Nuclear Architecture: an Overview Comparing Pediatric and Adult Populations

    Genetic Landscape of Papillary Thyroid Carcinoma and Nuclear Architecture: an Overview Comparing Pediatric and Adult Populations

    cancers Review Genetic Landscape of Papillary Thyroid Carcinoma and Nuclear Architecture: An Overview Comparing Pediatric and Adult Populations 1, 2, 2 3 Aline Rangel-Pozzo y, Luiza Sisdelli y, Maria Isabel V. Cordioli , Fernanda Vaisman , Paola Caria 4,*, Sabine Mai 1,* and Janete M. Cerutti 2 1 Cell Biology, Research Institute of Oncology and Hematology, University of Manitoba, CancerCare Manitoba, Winnipeg, MB R3E 0V9, Canada; [email protected] 2 Genetic Bases of Thyroid Tumors Laboratory, Division of Genetics, Department of Morphology and Genetics, Universidade Federal de São Paulo/EPM, São Paulo, SP 04039-032, Brazil; [email protected] (L.S.); [email protected] (M.I.V.C.); [email protected] (J.M.C.) 3 Instituto Nacional do Câncer, Rio de Janeiro, RJ 22451-000, Brazil; [email protected] 4 Department of Biomedical Sciences, University of Cagliari, 09042 Cagliari, Italy * Correspondence: [email protected] (P.C.); [email protected] (S.M.); Tel.: +1-204-787-2135 (S.M.) These authors contributed equally to this paper. y Received: 29 September 2020; Accepted: 26 October 2020; Published: 27 October 2020 Simple Summary: Papillary thyroid carcinoma (PTC) represents 80–90% of all differentiated thyroid carcinomas. PTC has a high rate of gene fusions and mutations, which can influence clinical and biological behavior in both children and adults. In this review, we focus on the comparison between pediatric and adult PTC, highlighting genetic alterations, telomere-related genomic instability and changes in nuclear organization as novel biomarkers for thyroid cancers. Abstract: Thyroid cancer is a rare malignancy in the pediatric population that is highly associated with disease aggressiveness and advanced disease stages when compared to adult population.