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RPKM Data From GTEx TPM Data Normalization for Age, Gender and Cause of Death via linear regression Expression Analysis Using Residuals as Expression Values Across-Tissues Correlation Within-Tissues Analysis Using Random Sampling Correlation Analysis Supplementary Figure 1. Workflow of the expression pattern correlation analysis. See also dataset S3 for Python scripts used during the analysis. Supplementary Figure 2. Tissue-dependent OXPHOS gene expression. A clustered binomial heat map demonstrating similarity in the expression pattern of OXPHOS genes which are not included in the 'main OXPHOS cluster' to the expression of genes within this cluster in specific tissues (red). Dissimilarities of OXPHOS genes from the 'main OXPHOS cluster' are indicated in blue. The orange and green rectangles show the two genes groups, (COX7A1 and COX6A2) and (COX7A2, NDUFS4, NDUFC2, NDUFA11 and ATPIF1), respectively. The gray rectangles show brain tissues. *** Spearman's Rank Correlation Coefficient Correlation Rank Spearman's intra-OXPHOS OXPHOS-Genome Supplementary Figure 3. nDNA-encoded OXPHOS genes show significantly higher co-expression with mtDNA-encoded OXPHOS genes. A box-plot showing the values of the correlation coefficients between pairs of OXPHOS genes (intra-OXPHOS) and between OXPHOS genes and randomly chosen protein- coding genes (OXPHOS-genome). *** p <1E-100. Supplementary Figure 4. The correlation distribution of nDNA- and mtDNA-encoded OXPHOS genes is bi-modal. A histogram comparing the expression distribution of nDNA- encoded OXPHOS genes (green) and all other genes in the genome (blue) with medial mtDNA expression in all tissues. Median TPM Median Supplementary Figure 5. Expression of LDH subunits across human tissues. A bar-plot showing the median TPM values of the two LDH subunit-coding genes (LDHA and LDHB) across all human tissues inspected in this study. UQCRQ UQCRH (B) UQCRFS1 UQCRC1 UQCRB UQCR11 UQCR10 SDHD SDHC SDHB SDHA NDUFV3 NDUFV2 NDUFV1 NDUFS8 NDUFS7 NDUFS6 NDUFS5 NDUFS4 NDUFS3 NDUFS2 NDUFS1 NDUFC2 NDUFC1 DGF NDUFB9 ZKSCAN1 NDUFB8 (A) NDUFB7 TBP NDUFB6 TAF1 NDUFB5 SREBP1 NDUFB4 SP1 NDUFB3 SMC3 NDUFB2 SIN3AK20 NDUFB11 SIN3A NDUFB10 RUNX3 NDUFB1 REST NDUFAB1 RBBP5 NDUFA9 POLR2A NDUFA8 PHF8 NDUFA7 NRF1 NDUFA6 NFYB NDUFA5 NFYA NDUFA4 MYC NDUFA3 MXI1 NDUFA2 MAZ NDUFA13 Gene Names Gene NDUFA12 MAX Transcription Factor Names Transcription NDUFA11 MAFK NDUFA10 IRF1 NDUFA1 HDAC1 CYC1 GTF2F1 COX8C FOXP2 COX8A ELK1 COX7C ELF1 COX7B2 E2F6 COX7B E2F4 COX7A2 E2F1 COX7A1 CTCF COX6C CREB1 COX6B2 CHD2 COX6B1 CEBPB COX6A2 CCNT2 COX6A1 ATF3 COX5B COX5A ATG COX4I2 0 900 800 700 600 500 400 300 200 100 1000 COX4I1 − − − − − − − − − − ATPIF1 Bases upstream to the translation start site (TSS) ATP5O ATP5L2 ATP5L ATP5J2 ATP5J ATP5I ATP5H ATP5G3 ATP5G2 ATP5G1 ATP5F1 ATP5E ATP5D ATP5C1 ATP5B ATP5A1 ATG 0 750 500 250 1000 − − − − Bases upstream to the translation start site (TSS) Supplementary Figure 6. Graphical representation of the in vivo identification of candidate promoters in nuclear DNA-encoded OXPHOS genes. (A) A map of the upstream region of a representative OXPHOS subunit (NDUFA8) tested for TF binding in DNaseI Genomic Footprinting (DGF) sites. X-axis represent the positions in the promoter region, relative to the translation start site (TSS). DGF sites are indicated by blue lines in the upper row and below, all the listed TFs binding to the 1000 bp promoter region are shown, with their ChIP-seq peaks shown as black lines. Note that only TFs with a ChIP-seq peak overlapping a minimum of one DGF are indicated. (B) Representation of the results for all nDNA-encoded OXPHOS genes. X-axis represent the positions in the promoter region, relative to the translation start site (TSS). DGFs are shown by blue lines if they do not overlap with any identified TF-binding peak in the ChIP-seq experiments and in black and blue lines if they overlap with at least one such TF-binding peak. Supplementary Table 1. A list of OXPHOS genes analyzed in this study, following HUGO Gene Nomenclature Committee (HGNC). OXPHOS structural and assembly factors are indicated, along with their Ensemble accession numbers. Structural genes Structural genes Structural genes Structural genes Assembly Factors Name Ensemble ID Name Ensemble ID Name Ensemble ID Name Ensemble ID Name Ensemble ID MT-ND1 ENSG00000198888 NDUFB2 ENSG00000090266 UQCR10 ENSG00000184076 ATP5F1 ENSG00000116459 COX15 ENSG00000014919 MT-ND2 ENSG00000198763 NDUFB3 ENSG00000119013 UQCR11 ENSG00000127540 ATP5G1 ENSG00000159199 COX17 ENSG00000138495 MT-ND3 ENSG00000198840 NDUFB4 ENSG00000065518 NDUFA4 ENSG00000189043 ATP5G2 ENSG00000135390 COX18 ENSG00000163626 MT-ND4 ENSG00000198886 NDUFB5 ENSG00000136521 COX4I1 ENSG00000131143 ATP5G3 ENSG00000154518 COX19 ENSG00000240230 MT-ND4L ENSG00000212907 NDUFB6 ENSG00000165264 COX4I2 ENSG00000131055 ATP5H ENSG00000167863 COX20 ENSG00000203667 MT-ND5 ENSG00000198786 NDUFB7 ENSG00000099795 COX5A ENSG00000178741 ATP5I ENSG00000169020 ECSIT ENSG00000130159 MT-ND6 ENSG00000198695 NDUFB8 ENSG00000166136 COX5B ENSG00000135940 ATP5J ENSG00000154723 FOXRED1 ENSG00000110074 NDUFS1 ENSG00000023228 NDUFB9 ENSG00000147684 COX6A1 ENSG00000111775 ATP5J2 ENSG0000024146 NDUFAF1 ENSG00000137806 NDUFS2 ENSG00000158864 NDUFB10 ENSG00000140990 COX6A2 ENSG00000156885 ATP5L ENSG00000167283 NDUFAF2 ENSG00000164182 NDUFS3 ENSG00000213619 NDUFB11 ENSG00000147123 COX6B1 ENSG00000126267 ATP5L2 ENSG00000249222 NDUFAF3 ENSG00000178057 NDUFS7 ENSG00000115286 NDUFC1 ENSG00000109390 COX6B2 ENSG00000160471 ATP5O ENSG00000241837 NDUFAF4 ENSG00000123545 NDUFS8 ENSG00000110717 NDUFC2 ENSG00000151366 COX6C ENSG00000164919 MT-ATP6 ENSG00000198899 NDUFAF5 ENSG00000101247 NDUFV1 ENSG00000167792 NDUFS4 ENSG00000164258 COX7A1 ENSG00000161281 MT-ATP8 ENSG00000228253 NDUFAF6 ENSG00000156170 NDUFV2 ENSG00000178127 NDUFS5 ENSG00000168653 COX7A2 ENSG00000112695 Assembly Factors NDUFAF7 ENSG00000003509 NDUFAB1 ENSG00000004779 NDUFS6 ENSG00000145494 COX7B ENSG00000131174 Name Ensemble ID NUBPL ENSG00000151413 NDUFA1 ENSG00000125356 NDUFV3 ENSG00000160194 COX7B2 ENSG00000170516 ACAD9 ENSG00000177646 SCO1 ENSG00000133028 NDUFA2 ENSG00000131495 SDHA ENSG00000073578 COX7C ENSG00000127184 ATPAF1 ENSG00000123472 SCO2 ENSG00000130489 NDUFA3 ENSG00000170906 SDHB ENSG00000117118 COX8A ENSG00000176340 ATPAF2 ENSG00000171953 SDHAF1 ENSG00000205138 NDUFA5 ENSG00000128609 SDHC ENSG00000143252 COX8C ENSG00000187581 BCS1L ENSG00000074582 SDHAF2 ENSG00000167985 NDUFA6 ENSG00000184983 SDHD ENSG00000204370 MT-CO1 ENSG00000198804 COA1 ENSG00000106603 SDHAF3 ENSG00000196636 NDUFA7 ENSG00000267855 CYC1 ENSG00000179091 MT-CO2 ENSG00000198712 COA3 ENSG00000183978 SDHAF4 ENSG00000154079 NDUFA8 ENSG00000119421 MT-CYB ENSG00000198727 MT-CO3 ENSG00000198938 COA4 ENSG00000181924 SURF1 ENSG00000148290 NDUFA9 ENSG00000139180 UQCRB ENSG00000156467 ATPIF1 ENSG00000130770 COA5 ENSG00000183513 TIMMDC1 ENSG00000113845 NDUFA10 ENSG00000130414 UQCRC1 ENSG00000010256 ATP5A1 ENSG00000152234 COA6 ENSG00000168275 TMEM126B ENSG00000171204 NDUFA11 ENSG00000174886 UQCRC2 ENSG00000140740 ATP5B ENSG00000110955 COA7 ENSG00000162377 UQCC1 ENSG00000101019 NDUFA12 ENSG00000184752 UQCRFS1 ENSG00000169021 ATP5C1 ENSG00000165629 COX10 ENSG00000006695 UQCC2 ENSG00000137288 NDUFA13 ENSG00000186010 UQCRH ENSG00000173660 ATP5D ENSG00000099624 COX11 ENSG00000166260 UQCC3 ENSG00000204922 NDUFB1 ENSG00000183648 UQCRQ ENSG00000164405 ATP5E ENSG00000124172 COX14 ENSG00000178449 Supplementary Table 3. Summary statistics of the expression differences between subunits within or between OXPHOS complexes. Complex MWU p-value Median Median intra-complex inter-complexes CI 1024010 0.00141 0.6171 0.5988 CII 2454 0.00687 0.56 0.4773 CIII 23912 0.07942 0.655 0.635 CIV 38126 6.21E-06 0.6957 0.6379 CV 125622 0.01499 0.6348 0.6193 Total 3724116 9.96E-07 0.6249 0.6048 Supplementary Table 4. A mathematical representation of the effect of negative expression correlation between mtDNA- and nDNA-encoded OXPHOS genes on the OXPHOS complex assembly range. M0, N0 = Initial expression values of mtDNA (M0) and nDNA (N0) genes, ΔM, ΔN = Changes in expression of mtDNA (ΔM) and nDNA (ΔN) genes. Correlation Initial ΔM ΔN Scenario Complexes Direction State Complexes Range Range (SR) (SCR) M0>N0 + + N0≤SCR≤∞ + 0≤SR≤∞ M0>N0 - - 0≤SCR≤N0 M0>N0 + - 0≤SCR≤N0 - 0≤SR<M0 M0>N0 - + 0≤SCR<M0 Supplementary Table 7. Statistically significant Spearman's rank correlation coefficient positively affecting TFs. * Candidate promoter regions. Transcription Factor nDNA OXPHOS genes' Spearman's p-value promoter coverage* (%) correlation-coefficient response (SCU) POLR2A 92.59 0.574848 <1E-6 PHF8 86.42 0.173793 <1E-6 MAZ 81.48 0.055696 0.000002 JUND 72.84 0.042717 0.000273 KDM5B 69.14 0.043484 <1E-6 CREB1 65.43 0.042579 <1E-6 TBL1XR1 55.56 0.032961 0.000203 CCNT2 53.09 0.035662 0.000034 CEBPB 50.62 0.036093 <1E-6 TCF12 49.38 0.031816 0.000188 BRCA1 40.74 0.033497 0.000016 ZBTB33 39.51 0.053593 <1E-6 FOS 35.80 0.036259 0.000022 BCLAF1 29.63 0.038006 0.000270 FOSL2 24.69 0.047823 0.000065 .
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    brain sciences Article Differential Expression of Multiple Disease-Related Protein Groups Induced by Valproic Acid in Human SH-SY5Y Neuroblastoma Cells 1,2, 1, 1 1 Tsung-Ming Hu y, Hsiang-Sheng Chung y, Lieh-Yung Ping , Shih-Hsin Hsu , Hsin-Yao Tsai 1, Shaw-Ji Chen 3,4 and Min-Chih Cheng 1,* 1 Department of Psychiatry, Yuli Branch, Taipei Veterans General Hospital, Hualien 98142, Taiwan; [email protected] (T.-M.H.); [email protected] (H.-S.C.); [email protected] (L.-Y.P.); fi[email protected] (S.-H.H.); [email protected] (H.-Y.T.) 2 Department of Future Studies and LOHAS Industry, Fo Guang University, Jiaosi, Yilan County 26247, Taiwan 3 Department of Psychiatry, Mackay Medical College, New Taipei City 25245, Taiwan; [email protected] 4 Department of Psychiatry, Taitung Mackay Memorial Hospital, Taitung County 95064, Taiwan * Correspondence: [email protected]; Tel.: +886-3888-3141 (ext. 475) These authors contributed equally to this work. y Received: 10 July 2020; Accepted: 8 August 2020; Published: 12 August 2020 Abstract: Valproic acid (VPA) is a multifunctional medication used for the treatment of epilepsy, mania associated with bipolar disorder, and migraine. The pharmacological effects of VPA involve a variety of neurotransmitter and cell signaling systems, but the molecular mechanisms underlying its clinical efficacy is to date largely unknown. In this study, we used the isobaric tags for relative and absolute quantitation shotgun proteomic analysis to screen differentially expressed proteins in VPA-treated SH-SY5Y cells. We identified changes in the expression levels of multiple proteins involved in Alzheimer’s disease, Parkinson’s disease, chromatin remodeling, controlling gene expression via the vitamin D receptor, ribosome biogenesis, ubiquitin-mediated proteolysis, and the mitochondrial oxidative phosphorylation and electron transport chain.
  • THE FUNCTIONAL SIGNIFICANCE of MITOCHONDRIAL SUPERCOMPLEXES in C. ELEGANS by WICHIT SUTHAMMARAK Submitted in Partial Fulfillment

    THE FUNCTIONAL SIGNIFICANCE of MITOCHONDRIAL SUPERCOMPLEXES in C. ELEGANS by WICHIT SUTHAMMARAK Submitted in Partial Fulfillment

    THE FUNCTIONAL SIGNIFICANCE OF MITOCHONDRIAL SUPERCOMPLEXES in C. ELEGANS by WICHIT SUTHAMMARAK Submitted in partial fulfillment of the requirements For the degree of Doctor of Philosophy Dissertation Advisor: Drs. Margaret M. Sedensky & Philip G. Morgan Department of Genetics CASE WESTERN RESERVE UNIVERSITY January, 2011 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of _____________________________________________________ candidate for the ______________________degree *. (signed)_______________________________________________ (chair of the committee) ________________________________________________ ________________________________________________ ________________________________________________ ________________________________________________ ________________________________________________ (date) _______________________ *We also certify that written approval has been obtained for any proprietary material contained therein. Dedicated to my family, my teachers and all of my beloved ones for their love and support ii ACKNOWLEDGEMENTS My advanced academic journey began 5 years ago on the opposite side of the world. I traveled to the United States from Thailand in search of a better understanding of science so that one day I can return to my homeland and apply the knowledge and experience I have gained to improve the lives of those affected by sickness and disease yet unanswered by science. Ultimately, I hoped to make the academic transition into the scholarly community by proving myself through scientific research and understanding so that I can make a meaningful contribution to both the scientific and medical communities. The following dissertation would not have been possible without the help, support, and guidance of a lot of people both near and far. I wish to thank all who have aided me in one way or another on this long yet rewarding journey. My sincerest thanks and appreciation goes to my advisors Philip Morgan and Margaret Sedensky.